Main differences between volatiles of sparkling and base wines accessed through comprehensive two dimensional gas chromatography with time-of-flight mass spectrometric detection and chemometric tools.

PubMed

Welke, Juliane Elisa; Zanus, Mauro; Lazzarotto, Marcelo; Pulgati, Fernando Hepp; Zini, Cláudia Alcaraz

2014-12-01

The main changes in the volatile profile of base wines and their corresponding sparkling wines produced by traditional method were evaluated and investigated for the first time using headspace solid-phase microextraction combined with comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry detection (GC×GC/TOFMS) and chemometric tools. Fisher ratios helped to find the 119 analytes that were responsible for the main differences between base and sparkling wines and principal component analysis explained 93.1% of the total variance related to the selected 78 compounds. It was also possible to observe five subclusters in base wines and four subclusters in sparkling wines samples through hierarchical cluster analysis, which seemed to have an organised distribution according to the regions where the wines came from. Twenty of the most important volatile compounds co-eluted with other components and separation of some of them was possible due to GC×GC/TOFMS performance. Copyright © 2014. Published by Elsevier Ltd.

Principal components of phenolics to characterize red Vinho Verde grapes: anthocyanins or non-coloured compounds?

PubMed

Dopico-García, M S; Fique, A; Guerra, L; Afonso, J M; Pereira, O; Valentão, P; Andrade, P B; Seabra, R M

2008-06-15

Phenolic profile of 10 different varieties of red "Vinho Verde" grapes (Azal Tinto, Borraçal, Brancelho, Doçal, Espadeiro, Padeiro de Basto, Pedral, Rabo de ovelha, Verdelho and Vinhão), from Minho (Portugal) were studied. Nine Flavonols, four phenolic acids, three flavan-3-ols, one stilben and eight anthocyanins were determined. Malvidin-3-O-glucoside was the most abundant anthocyanin while the main non-coloured compound was much more heterogeneous: catechin, epicatechin, myricetin-3-O-glucoside, quercetin-3-O-glucoside or syringetin-3-O-glucoside. Anthocyanin contents ranged from 42 to 97%. Principal component analysis (PCA) was applied to analyse the date and study the relations between the samples and their phenolic profiles. Anthocyanin profile proved to be a good marker to characterize the varieties even considering different origin and harvest. "Vinhão" grapes showed anthocyanins levels until twenty four times higher than the rest of the samples, with 97% of these compounds.

Broadband terahertz time-domain spectroscopy of drugs-of-abuse and the use of principal component analysis.

PubMed

Burnett, Andrew D; Fan, Wenhui; Upadhya, Prashanth C; Cunningham, John E; Hargreaves, Michael D; Munshi, Tasnim; Edwards, Howell G M; Linfield, Edmund H; Davies, A Giles

2009-08-01

Terahertz frequency time-domain spectroscopy has been used to analyse a wide range of samples containing cocaine hydrochloride, heroin and ecstasy--common drugs-of-abuse. We investigated real-world samples seized by law enforcement agencies, together with pure drugs-of-abuse, and pure drugs-of-abuse systematically adulterated in the laboratory to emulate real-world samples. In order to investigate the feasibility of automatic spectral recognition of such illicit materials by terahertz spectroscopy, principal component analysis was employed to cluster spectra of similar compounds.

Occurrence of pharmaceutically active compounds during 1-year period in wastewaters from four wastewater treatment plants in Seville (Spain).

PubMed

Santos, J L; Aparicio, I; Callejón, M; Alonso, E

2009-05-30

Several pharmaceutically active compounds have been monitored during 1-year period in influent and effluent wastewater from wastewater treatment plants (WWTPs) to evaluate their temporal evolution and removal from wastewater and to know which variables have influence in their removal rates. Pharmaceutical compounds monitored were four antiinflammatory drugs (diclofenac, ibuprofen, ketoprofen and naproxen), an antiepileptic drug (carbamazepine) and a nervous stimulant (caffeine). All of the pharmaceutically active compounds monitored, except diclofenac, were detected in influent and effluent wastewater. Mean concentrations measured in influent wastewater were 6.17, 0.48, 93.6, 1.83 and 5.41 microg/L for caffeine, carbamazepine, ibuprofen, ketoprofen and naproxen, respectively. Mean concentrations measured in effluent wastewater were 2.02, 0.56, 8.20, 0.84 and 2.10 microg/L for caffeine, carbamazepine, ibuprofen, ketoprofen and naproxen, respectively. Mean removal rates of the pharmaceuticals varied from 8.1% (carbamazepine) to 87.5% (ibuprofen). The existence of relationships between the concentrations of the pharmaceutical compounds, their removal rates, the characterization parameters of influent wastewaters and the WWTP control design parameters has been studied by means of statistical analysis (correlation and principal component analysis). With both statistical analyses, high correlations were obtained between the concentration of the pharmaceutical compounds and the characterization parameters of influent wastewaters; and between the removal rates of the pharmaceutical compounds, the removal rates of the characterization parameters of influent wastewaters and the WWTP hydraulic retention times. Principal component analysis showed the existence of two main components accounting for 76% of the total variability.

Rapid fingerprinting of spilled petroleum products using fluorescence spectroscopy coupled with parallel factor and principal component analysis.

PubMed

Mirnaghi, Fatemeh S; Soucy, Nicholas; Hollebone, Bruce P; Brown, Carl E

2018-05-19

The characterization of spilled petroleum products in an oil spill is necessary for identifying the spill source, selection of clean-up strategies, and evaluating potential environmental and ecological impacts. Existing standard methods for the chemical characterization of spilled oils are time-consuming due to the lengthy sample preparation for analysis. The main objective of this study is the development of a rapid screening method for the fingerprinting of spilled petroleum products using excitation/emission matrix (EEM) fluorescence spectroscopy, thereby delivering a preliminary evaluation of the petroleum products within hours after a spill. In addition, the developed model can be used for monitoring the changes of aromatic compositions of known spilled oils over time. This study involves establishing a fingerprinting model based on the composition of polycyclic and heterocyclic aromatic hydrocarbons (PAH and HAHs, respectively) of 130 petroleum products at different states of evaporative weathering. The screening model was developed using parallel factor analysis (PARAFAC) of a large EEM dataset. The significant fluorescing components for each sample class were determined. After which, through principal component analysis (PCA), the variation of scores of their modeled factors was discriminated based on the different classes of petroleum products. This model was then validated using gas chromatography-mass spectrometry (GC-MS) analysis. The rapid fingerprinting and the identification of unknown and new spilled oils occurs through matching the spilled product with the products of the developed model. Finally, it was shown that HAH compounds in asphaltene and resins contribute to ≥4-ring PAHs compounds in petroleum products. Copyright © 2018. Published by Elsevier Ltd.

Ripening-dependent metabolic changes in the volatiles of pineapple (Ananas comosus (L.) Merr.) fruit: II. Multivariate statistical profiling of pineapple aroma compounds based on comprehensive two-dimensional gas chromatography-mass spectrometry.

PubMed

Steingass, Christof Björn; Jutzi, Manfred; Müller, Jenny; Carle, Reinhold; Schmarr, Hans-Georg

2015-03-01

Ripening-dependent changes of pineapple volatiles were studied in a nontargeted profiling analysis. Volatiles were isolated via headspace solid phase microextraction and analyzed by comprehensive 2D gas chromatography and mass spectrometry (HS-SPME-GC×GC-qMS). Profile patterns presented in the contour plots were evaluated applying image processing techniques and subsequent multivariate statistical data analysis. Statistical methods comprised unsupervised hierarchical cluster analysis (HCA) and principal component analysis (PCA) to classify the samples. Supervised partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) regression were applied to discriminate different ripening stages and describe the development of volatiles during postharvest storage, respectively. Hereby, substantial chemical markers allowing for class separation were revealed. The workflow permitted the rapid distinction between premature green-ripe pineapples and postharvest-ripened sea-freighted fruits. Volatile profiles of fully ripe air-freighted pineapples were similar to those of green-ripe fruits postharvest ripened for 6 days after simulated sea freight export, after PCA with only two principal components. However, PCA considering also the third principal component allowed differentiation between air-freighted fruits and the four progressing postharvest maturity stages of sea-freighted pineapples.

Not-from-concentrate pilot plant 'Wonderful' cultivar pomegranate juice changes: Volatiles.

PubMed

Beaulieu, John C; Obando-Ulloa, Javier M

2017-08-15

Pilot plant ultrafiltration was used to mimic the dominant U.S. commercial pomegranate juice extraction method (hydraulic pressing whole fruit), to deliver a not-from-concentrate (NFC) juice that was high-temperature short-time pasteurized and stored at 4 and 25°C. Recovered were 46 compounds, of which 38 were routinely isolated and subjected to analysis of variance to assess these NFC juices. Herein, 18 of the 21 consensus pomegranate compounds were recovered. Ultrafiltration resulted in significant decreases for many compounds. Conversely, pasteurization resulted in compound increases. Highly significant decreases in 12 consensus compounds were observed during storage. Principal component analysis demonstrated clearly which compounds were tightly associated, and how storage samples behaved very similarly, independent of temperature. Based on these data and previous work we reported, this solid-phase microextraction (SPME) method delivered a robust 'Wonderful' volatile profile in NFC juices that is likely superior qualitatively and perhaps quantitatively to typical commercial offerings. Published by Elsevier Ltd.

The number of measurements needed to obtain high reliability for traits related to enzymatic activities and photosynthetic compounds in soybean plants infected with Phakopsora pachyrhizi.

PubMed

Oliveira, Tássia Boeno de; Azevedo Peixoto, Leonardo de; Teodoro, Paulo Eduardo; Alvarenga, Amauri Alves de; Bhering, Leonardo Lopes; Campo, Clara Beatriz Hoffmann

2018-01-01

Asian rust affects the physiology of soybean plants and causes losses in yield. Repeatability coefficients may help breeders to know how many measurements are needed to obtain a suitable reliability for a target trait. Therefore, the objectives of this study were to determine the repeatability coefficients of 14 traits in soybean plants inoculated with Phakopsora pachyrhizi and to establish the minimum number of measurements needed to predict the breeding value with high accuracy. Experiments were performed in a 3x2 factorial arrangement with three treatments and two inoculations in a random block design. Repeatability coefficients, coefficients of determination and number of measurements needed to obtain a certain reliability were estimated using ANOVA, principal component analysis based on the covariance matrix and the correlation matrix, structural analysis and mixed model. It was observed that the principal component analysis based on the covariance matrix out-performed other methods for almost all traits. Significant differences were observed for all traits except internal CO2 concentration for the treatment effects. For the measurement effects, all traits were significantly different. In addition, significant differences were found for all Treatment x Measurement interaction traits except coumestrol, chitinase and chlorophyll content. Six measurements were suitable to obtain a coefficient of determination higher than 0.7 for all traits based on principal component analysis. The information obtained from this research will help breeders and physiologists determine exactly how many measurements are needed to evaluate each trait in soybean plants infected by P. pachyrhizi with a desirable reliability.

The number of measurements needed to obtain high reliability for traits related to enzymatic activities and photosynthetic compounds in soybean plants infected with Phakopsora pachyrhizi

PubMed Central

de Oliveira, Tássia Boeno; Teodoro, Paulo Eduardo; de Alvarenga, Amauri Alves; Bhering, Leonardo Lopes; Campo, Clara Beatriz Hoffmann

2018-01-01

Asian rust affects the physiology of soybean plants and causes losses in yield. Repeatability coefficients may help breeders to know how many measurements are needed to obtain a suitable reliability for a target trait. Therefore, the objectives of this study were to determine the repeatability coefficients of 14 traits in soybean plants inoculated with Phakopsora pachyrhizi and to establish the minimum number of measurements needed to predict the breeding value with high accuracy. Experiments were performed in a 3x2 factorial arrangement with three treatments and two inoculations in a random block design. Repeatability coefficients, coefficients of determination and number of measurements needed to obtain a certain reliability were estimated using ANOVA, principal component analysis based on the covariance matrix and the correlation matrix, structural analysis and mixed model. It was observed that the principal component analysis based on the covariance matrix out-performed other methods for almost all traits. Significant differences were observed for all traits except internal CO2 concentration for the treatment effects. For the measurement effects, all traits were significantly different. In addition, significant differences were found for all Treatment x Measurement interaction traits except coumestrol, chitinase and chlorophyll content. Six measurements were suitable to obtain a coefficient of determination higher than 0.7 for all traits based on principal component analysis. The information obtained from this research will help breeders and physiologists determine exactly how many measurements are needed to evaluate each trait in soybean plants infected by P. pachyrhizi with a desirable reliability. PMID:29438380

Volatile Organic Compounds: Characteristics, distribution and sources in urban schools

NASA Astrophysics Data System (ADS)

Mishra, Nitika; Bartsch, Jennifer; Ayoko, Godwin A.; Salthammer, Tunga; Morawska, Lidia

2015-04-01

Long term exposure to organic pollutants, both inside and outside school buildings may affect children's health and influence their learning performance. Since children spend significant amount of time in school, air quality, especially in classrooms plays a key role in determining the health risks associated with exposure at schools. Within this context, the present study investigated the ambient concentrations of Volatile Organic Compounds (VOCs) in 25 primary schools in Brisbane with the aim to quantify the indoor and outdoor VOCs concentrations, identify VOCs sources and their contribution, and based on these; propose mitigation measures to reduce VOCs exposure in schools. One of the most important findings is the occurrence of indoor sources, indicated by the I/O ratio >1 in 19 schools. Principal Component Analysis with Varimax rotation was used to identify common sources of VOCs and source contribution was calculated using an Absolute Principal Component Scores technique. The result showed that outdoor 47% of VOCs were contributed by petrol vehicle exhaust but the overall cleaning products had the highest contribution of 41% indoors followed by air fresheners and art and craft activities. These findings point to the need for a range of basic precautions during the selection, use and storage of cleaning products and materials to reduce the risk from these sources.

Analyzing the flavor compounds in Chinese traditional fermented shrimp pastes by HS-SPME-GC/MS and electronic nose

NASA Astrophysics Data System (ADS)

Fan, Yan; Yin, Li'ang; Xue, Yong; Li, Zhaojie; Hou, Hu; Xue, Changhu

2017-04-01

Shrimp paste is a type of condiments with high nutritional value. However, the flavors of shrimp paste, particularly the non-uniformity flavors, have limited its application in food processing. In order to identify the characteristic flavor compounds in Chinese traditional shrimp pastes, five kinds of typical commercial products were evaluated in this study. The differences in the volatile composition of the five products were investigated. Solid phase micro-extraction method was employed to extract the volatile compounds. GC-MS and electronic nose were applied to identify the compounds, and the data were analyzed using principal component analysis (PCA). A total of 62 volatile compounds were identified, including 8 alcohols, 7 aldehydes, 3 ketones, 7 ethers, 7 acids, 3 esters, 6 hydrocarbons, 12 pyrazines, 2 phenols, and 7 other compounds. The typical volatile compounds contributing to the flavor of shrimp paste were found as follows: dimethyl disulfide, dimethyl tetrasulfide, dimethyl trisulfide, 2, 3, 5-trimethyl-6-ethyl pyrazine, ethyl-2, 5-dimethyl-pyrazine, phenol and indole. Propanoic acid, butanoic acid, furans, and 2-hydroxy-3-pentanone caused unpleasant odors, such as pungent and rancid odors. Principal component analysis showed that the content of volatile compounds varied depending on the processing conditions and shrimp species. These results indicated that the combinations of multiple analysis and identification methods could make up the limitations of a single method, enhance the accuracy of identification, and provide useful information for sensory research and product development.

Principal component analysis as a tool for library design: a case study investigating natural products, brand-name drugs, natural product-like libraries, and drug-like libraries.

PubMed

Wenderski, Todd A; Stratton, Christopher F; Bauer, Renato A; Kopp, Felix; Tan, Derek S

2015-01-01

Principal component analysis (PCA) is a useful tool in the design and planning of chemical libraries. PCA can be used to reveal differences in structural and physicochemical parameters between various classes of compounds by displaying them in a convenient graphical format. Herein, we demonstrate the use of PCA to gain insight into structural features that differentiate natural products, synthetic drugs, natural product-like libraries, and drug-like libraries, and show how the results can be used to guide library design.

Principal Component Analysis as a Tool for Library Design: A Case Study Investigating Natural Products, Brand-Name Drugs, Natural Product-Like Libraries, and Drug-Like Libraries

PubMed Central

Wenderski, Todd A.; Stratton, Christopher F.; Bauer, Renato A.; Kopp, Felix; Tan, Derek S.

2015-01-01

Principal component analysis (PCA) is a useful tool in the design and planning of chemical libraries. PCA can be used to reveal differences in structural and physicochemical parameters between various classes of compounds by displaying them in a convenient graphical format. Herein, we demonstrate the use of PCA to gain insight into structural features that differentiate natural products, synthetic drugs, natural product-like libraries, and drug-like libraries, and show how the results can be used to guide library design. PMID:25618349

Wavelet decomposition based principal component analysis for face recognition using MATLAB

NASA Astrophysics Data System (ADS)

Sharma, Mahesh Kumar; Sharma, Shashikant; Leeprechanon, Nopbhorn; Ranjan, Aashish

2016-03-01

For the realization of face recognition systems in the static as well as in the real time frame, algorithms such as principal component analysis, independent component analysis, linear discriminate analysis, neural networks and genetic algorithms are used for decades. This paper discusses an approach which is a wavelet decomposition based principal component analysis for face recognition. Principal component analysis is chosen over other algorithms due to its relative simplicity, efficiency, and robustness features. The term face recognition stands for identifying a person from his facial gestures and having resemblance with factor analysis in some sense, i.e. extraction of the principal component of an image. Principal component analysis is subjected to some drawbacks, mainly the poor discriminatory power and the large computational load in finding eigenvectors, in particular. These drawbacks can be greatly reduced by combining both wavelet transform decomposition for feature extraction and principal component analysis for pattern representation and classification together, by analyzing the facial gestures into space and time domain, where, frequency and time are used interchangeably. From the experimental results, it is envisaged that this face recognition method has made a significant percentage improvement in recognition rate as well as having a better computational efficiency.

Metabolic fingerprinting of Cannabis sativa L., cannabinoids and terpenoids for chemotaxonomic and drug standardization purposes.

PubMed

Fischedick, Justin Thomas; Hazekamp, Arno; Erkelens, Tjalling; Choi, Young Hae; Verpoorte, Rob

2010-12-01

Cannabis sativa L. is an important medicinal plant. In order to develop cannabis plant material as a medicinal product quality control and clear chemotaxonomic discrimination between varieties is a necessity. Therefore in this study 11 cannabis varieties were grown under the same environmental conditions. Chemical analysis of cannabis plant material used a gas chromatography flame ionization detection method that was validated for quantitative analysis of cannabis monoterpenoids, sesquiterpenoids, and cannabinoids. Quantitative data was analyzed using principal component analysis to determine which compounds are most important in discriminating cannabis varieties. In total 36 compounds were identified and quantified in the 11 varieties. Using principal component analysis each cannabis variety could be chemically discriminated. This methodology is useful for both chemotaxonomic discrimination of cannabis varieties and quality control of plant material. Copyright © 2010 Elsevier Ltd. All rights reserved.

Essential oil variation among natural populations of Lavandula multifida L. (Lamiaceae).

PubMed

Chograni, Hnia; Zaouali, Yosr; Rajeb, Chayma; Boussaid, Mohamed

2010-04-01

Volatiles from twelve wild Tunisian populations of Lavandula multifida L. growing in different bioclimatic zones were assessed by GC (RI) and GC/MS. Thirty-six constituents, representing 83.48% of the total oil were identified. The major components at the species level were carvacrol (31.81%), beta-bisabolene (14.89%), and acrylic acid dodecyl ester (11.43%). These volatiles, together with alpha-pinene, were also the main compounds discriminating the populations. According to these dominant compounds, one chemotype was revealed, a carvacrol/beta-bisabolene/acrylic acid dodecyl ester chemotype. However, a significant variation among the populations was observed for the majority of the constituents. A high chemical-population structure, estimated both by principal component analysis (PCA) and unweighted pair group method with averaging (UPGMA) cluster analysis based on Euclidean distances, was observed. Both methods allowed separation of the populations in three groups defined rather by minor than by major compounds. The population groups were not strictly concordant with their bioclimatic or geographic location. Conservation strategies should concern all populations, because of their low size and their high level of destruction. Populations exhibiting particular compounds other than the major ones should be protected first.

Saccharomyces cerevisiae Mixed Culture of Blackberry (Rubus ulmifolius L.) Juice: Synergism in the Aroma Compounds Production

PubMed Central

Ragazzo-Sánchez, Juan Arturo; Ortiz-Basurto, Rosa Isela; Luna-Solano, Guadalupe; Calderón-Santoyo, Montserrat

2014-01-01

Blackberry (Rubus sp.) juice was fermented using four different strains of Saccharomyces cerevisiae (Vitilevure-CM4457, Enoferm-T306, ICV-K1, and Greroche Rhona-L3574) recognized because of their use in the wine industry. A medium alcoholic graduation spirit (<6°GL) with potential to be produced at an industrial scale was obtained. Alcoholic fermentations were performed at 28°C, 200 rpm, and noncontrolled pH. The synergistic effect on the aromatic compounds production during fermentation in mixed culture was compared with those obtained by monoculture and physic mixture of spirits produced in monoculture. The aromatic composition was determined by HS-SPME-GC. The differences in aromatic profile principally rely on the proportions in aromatic compounds and not on the number of those compounds. The multivariance analysis, principal component analysis (PCA), and factorial discriminant analysis (DFA) permit to demonstrate the synergism between the strains. PMID:25506606

Metabolic Clustering Analysis as a Strategy for Compound Selection in the Drug Discovery Pipeline for Leishmaniasis.

PubMed

Armitage, Emily G; Godzien, Joanna; Peña, Imanol; López-Gonzálvez, Ángeles; Angulo, Santiago; Gradillas, Ana; Alonso-Herranz, Vanesa; Martín, Julio; Fiandor, Jose M; Barrett, Michael P; Gabarro, Raquel; Barbas, Coral

2018-05-18

A lack of viable hits, increasing resistance, and limited knowledge on mode of action is hindering drug discovery for many diseases. To optimize prioritization and accelerate the discovery process, a strategy to cluster compounds based on more than chemical structure is required. We show the power of metabolomics in comparing effects on metabolism of 28 different candidate treatments for Leishmaniasis (25 from the GSK Leishmania box, two analogues of Leishmania box series, and amphotericin B as a gold standard treatment), tested in the axenic amastigote form of Leishmania donovani. Capillary electrophoresis-mass spectrometry was applied to identify the metabolic profile of Leishmania donovani, and principal components analysis was used to cluster compounds on potential mode of action, offering a medium throughput screening approach in drug selection/prioritization. The comprehensive and sensitive nature of the data has also made detailed effects of each compound obtainable, providing a resource to assist in further mechanistic studies and prioritization of these compounds for the development of new antileishmanial drugs.

Characterization of cocoa butter and cocoa butter equivalents by bulk and molecular carbon isotope analyses: implications for vegetable fat quantification in chocolate.

PubMed

Spangenberg, J E; Dionisi, F

2001-09-01

The fatty acids from cocoa butters of different origins, varieties, and suppliers and a number of cocoa butter equivalents (Illexao 30-61, Illexao 30-71, Illexao 30-96, Choclin, Coberine, Chocosine-Illipé, Chocosine-Shea, Shokao, Akomax, Akonord, and Ertina) were investigated by bulk stable carbon isotope analysis and compound specific isotope analysis. The interpretation is based on principal component analysis combining the fatty acid concentrations and the bulk and molecular isotopic data. The scatterplot of the two first principal components allowed detection of the addition of vegetable fats to cocoa butters. Enrichment in heavy carbon isotope ((13)C) of the bulk cocoa butter and of the individual fatty acids is related to mixing with other vegetable fats and possibly to thermally or oxidatively induced degradation during processing (e.g., drying and roasting of the cocoa beans or deodorization of the pressed fat) or storage. The feasibility of the analytical approach for authenticity assessment is discussed.

An Integrated Strategy to Qualitatively Differentiate Components of Raw and Processed Viticis Fructus Based on NIR, HPLC and UPLC-MS Analysis.

PubMed

Diao, Jiayin; Xu, Can; Zheng, Huiting; He, Siyi; Wang, Shumei

2018-06-21

Viticis Fructus is a traditional Chinese herbal drug processed by various methods to achieve different clinical purposes. Thermal treatment potentially alters chemical composition, which may impact on effectiveness and toxicity. In order to interpret the constituent discrepancies of raw versus processed (stir-fried) Viticis Fructus, a multivariate detection method (NIR, HPLC, and UPLC-MS) based on metabonomics and chemometrics was developed. Firstly, synergy interval partial least squares and partial least squares-discriminant analysis were employed to screen the distinctive wavebands (4319 - 5459 cm -1 ) based on preprocessed near-infrared spectra. Then, HPLC with principal component analysis was performed to characterize the distinction. Subsequently, a total of 49 compounds were identified by UPLC-MS, among which 42 compounds were eventually characterized as having a significant change during processing via the semiquantitative volcano plot analysis. Moreover, based on the partial least squares-discriminant analysis, 16 compounds were chosen as characteristic markers that could be in close correlation with the discriminatory near-infrared wavebands. Together, all of these characterization techniques effectively discriminated raw and processed products of Viticis Fructus. In general, our work provides an integrated way of classifying Viticis Fructus, and a strategy to explore discriminatory chemical markers for other traditional Chinese herbs, thus ensuring safety and efficacy for consumers. Georg Thieme Verlag KG Stuttgart · New York.

Chemical Composition and Crystal Morphology of Epicuticular Wax in Mature Fruits of 35 Pear (Pyrus spp.) Cultivars

PubMed Central

Wu, Xiao; Yin, Hao; Shi, Zebin; Chen, Yangyang; Qi, Kaijie; Qiao, Xin; Wang, Guoming; Cao, Peng; Zhang, Shaoling

2018-01-01

An evaluation of fruit wax components will provide us with valuable information for pear breeding and enhancing fruit quality. Here, we dissected the epicuticular wax concentration, composition and structure of mature fruits from 35 pear cultivars belonging to five different species and hybrid interspecies. A total of 146 epicuticular wax compounds were detected, and the wax composition and concentration varied dramatically among species, with the highest level of 1.53 mg/cm2 in Pyrus communis and the lowest level of 0.62 mg/cm2 in Pyrus pyrifolia. Field emission scanning electron microscopy (FESEM) analysis showed amorphous structures of the epicuticular wax crystals of different pear cultivars. Cluster analysis revealed that the Pyrus bretschneideri cultivars were grouped much closer to Pyrus pyrifolia and Pyrus ussuriensis, and the Pyrus sinkiangensis cultivars were clustered into a distant group. Based on the principal component analysis (PCA), the cultivars could be divided into three groups and five groups according to seven main classes of epicuticular wax compounds and 146 wax compounds, respectively. PMID:29875784

Novel algorithm for simultaneous component detection and pseudo-molecular ion characterization in liquid chromatography-mass spectrometry.

PubMed

Zhang, Yufeng; Wang, Xiaoan; Wo, Siukwan; Ho, Hingman; Han, Quanbin; Fan, Xiaohui; Zuo, Zhong

2015-01-01

Resolving components and determining their pseudo-molecular ions (PMIs) are crucial steps in identifying complex herbal mixtures by liquid chromatography-mass spectrometry. To tackle such labor-intensive steps, we present here a novel algorithm for simultaneous detection of components and their PMIs. Our method consists of three steps: (1) obtaining a simplified dataset containing only mono-isotopic masses by removal of background noise and isotopic cluster ions based on the isotopic distribution model derived from all the reported natural compounds in dictionary of natural products; (2) stepwise resolving and removing all features of the highest abundant component from current simplified dataset and calculating PMI of each component according to an adduct-ion model, in which all non-fragment ions in a mass spectrum are considered as PMI plus one or several neutral species; (3) visual classification of detected components by principal component analysis (PCA) to exclude possible non-natural compounds (such as pharmaceutical excipients). This algorithm has been successfully applied to a standard mixture and three herbal extract/preparations. It indicated that our algorithm could detect components' features as a whole and report their PMI with an accuracy of more than 98%. Furthermore, components originated from excipients/contaminants could be easily separated from those natural components in the bi-plots of PCA. Copyright © 2014 Elsevier B.V. All rights reserved.

A litmus-type colorimetric and fluorometric volatile organic compound sensor based on inkjet-printed polydiacetylenes on paper substrates.

PubMed

Yoon, Bora; Park, In Sung; Shin, Hyora; Park, Hye Jin; Lee, Chan Woo; Kim, Jong-Man

2013-05-14

Inkjet-printed paper-based volatile organic compound (VOC) sensor strips imaged with polydiacetylenes (PDAs) are developed. A microemulsion ink containing bisurethane-substituted diacetylene (DA) monomers, 4BCMU, was inkjet printed onto paper using a conventional inkjet office printer. UV irradiation of the printed image allowed fabrication of blue-colored poly-4BCMU on the paper and the polymer was found to display colorimetric responses to VOCs. Interestingly, a blue-to-yellow color change was observed when the strip was exposed to chloroform vapor, which was accompanied by the generation of green fluorescence. The principal component analysis plot of the color and fluorescence images of the VOC-exposed polymers allowed a more precise discrimination of VOC vapors. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Quantitative determination of phenolic compounds by UHPLC-UV-MS and use of principal component analysis to differentiate chemo-types of chamomile/chrysanthemum flowerheads

USDA-ARS?s Scientific Manuscript database

A new rapid UHPLC-UV-QTOF/MS method has been developed for the simultaneous analysis of nine phenolic compounds [cis-GMCA, chlorogenic acid, trans-GMCA, quercetagetin-7-O-ß-D-glucopyranoside, luteolin-7-O-ß-D-glucoside, apigenin-7-O- ß-Dglucoside, chamaemeloside, apigenin 7-O-(6"-O-acetyl-ß-D-glucop...

Considering Horn's Parallel Analysis from a Random Matrix Theory Point of View.

PubMed

Saccenti, Edoardo; Timmerman, Marieke E

2017-03-01

Horn's parallel analysis is a widely used method for assessing the number of principal components and common factors. We discuss the theoretical foundations of parallel analysis for principal components based on a covariance matrix by making use of arguments from random matrix theory. In particular, we show that (i) for the first component, parallel analysis is an inferential method equivalent to the Tracy-Widom test, (ii) its use to test high-order eigenvalues is equivalent to the use of the joint distribution of the eigenvalues, and thus should be discouraged, and (iii) a formal test for higher-order components can be obtained based on a Tracy-Widom approximation. We illustrate the performance of the two testing procedures using simulated data generated under both a principal component model and a common factors model. For the principal component model, the Tracy-Widom test performs consistently in all conditions, while parallel analysis shows unpredictable behavior for higher-order components. For the common factor model, including major and minor factors, both procedures are heuristic approaches, with variable performance. We conclude that the Tracy-Widom procedure is preferred over parallel analysis for statistically testing the number of principal components based on a covariance matrix.

Bioactive compounds in cereal grains - occurrence, structure, technological significance and nutritional benefits - a review.

PubMed

Bartłomiej, Siurek; Justyna, Rosicka-Kaczmarek; Ewa, Nebesny

2012-12-01

This review presents current information about principal, biologically active compounds contained in grains of cereals that are most popular in Europe (wheat, rye, barley and oat). The tendency to provide consumers with safe foods, which promote their health and are based on cereal grains and/or their components with the high nutritive value, has been recently observed. The intake of protective substances contained in whole grains and their fractions contributes to a decreased risk of food-dependent diseases like the coronary heart disease and insulin-dependent diabetes. This study describes the structure, occurrence in cereal grains, technological importance and beneficial influence on human health of bioactive substances such as arabinoxylans, β-glucans, alkylresorcinols, tocols and phytosterols.

A new validation technique for estimations of body segment inertia tensors: Principal axes of inertia do matter.

PubMed

Rossi, Marcel M; Alderson, Jacqueline; El-Sallam, Amar; Dowling, James; Reinbolt, Jeffrey; Donnelly, Cyril J

2016-12-08

The aims of this study were to: (i) establish a new criterion method to validate inertia tensor estimates by setting the experimental angular velocity data of an airborne objects as ground truth against simulations run with the estimated tensors, and (ii) test the sensitivity of the simulations to changes in the inertia tensor components. A rigid steel cylinder was covered with reflective kinematic markers and projected through a calibrated motion capture volume. Simulations of the airborne motion were run with two models, using inertia tensor estimated with geometric formula or the compound pendulum technique. The deviation angles between experimental (ground truth) and simulated angular velocity vectors and the root mean squared deviation angle were computed for every simulation. Monte Carlo analyses were performed to assess the sensitivity of simulations to changes in magnitude of principal moments of inertia within ±10% and to changes in orientation of principal axes of inertia within ±10° (of the geometric-based inertia tensor). Root mean squared deviation angles ranged between 2.9° and 4.3° for the inertia tensor estimated geometrically, and between 11.7° and 15.2° for the compound pendulum values. Errors up to 10% in magnitude of principal moments of inertia yielded root mean squared deviation angles ranging between 3.2° and 6.6°, and between 5.5° and 7.9° when lumped with errors of 10° in principal axes of inertia orientation. The proposed technique can effectively validate inertia tensors from novel estimation methods of body segment inertial parameter. Principal axes of inertia orientation should not be neglected when modelling human/animal mechanics. Copyright © 2016 Elsevier Ltd. All rights reserved.

Effect of hot air drying on volatile compounds of Flammulina velutipes detected by HS-SPME-GC-MS and electronic nose.

PubMed

Yang, Wenjian; Yu, Jie; Pei, Fei; Mariga, Alfred Mugambi; Ma, Ning; Fang, Yong; Hu, Qiuhui

2016-04-01

Volatile compounds are important factors that affect the flavor quality of Flammulina velutipes, but the changes occurring during hot air drying is still unclear. To clarify the dynamic changes of flavor components during hot air drying, comprehensive flavor characterization and volatile compounds of F. velutipes were evaluated using electronic nose technology and headspace solid phase micro-extraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS), respectively. Results showed that volatile components in F. velutipes significantly changed during hot air drying according to the principal component analysis and radar fingerprint chart of electronic nose. Volatile compounds of fresh F. velutipes consisted mainly of ketones, aldehydes and alcohols, and 3-octanone was the dominant compound. Drying process could significantly decrease the relative content of ketones and promoted the generation of alcohols, acids, and esters, which became the main volatile compounds of dried F. velutipes. These may provide a theoretical basis for the formation mechanism of flavor substances in dried F. velutipes. Copyright © 2015 Elsevier Ltd. All rights reserved.

Evaluation of ice-tea quality by DART-TOF/MS.

PubMed

Rajchl, Aleš; Prchalová, Jana; Kružík, Vojtěch; Ševčík, Rudolf; Čížková, Helena

2015-11-01

DART (Direct Analysis in Real Time) coupled with Time-of-Flight Mass Spectrometry (TOF/MS) has been used for analyses of ice-teas. The article focuses on quality and authenticity of ice-teas as one of the most important tea-based products on the market. Twenty-one samples of ice-teas (black and green) were analysed. Selected compounds of ice-teas were determined: theobromine, caffeine, total phenolic compounds, total soluble solids, total amino acid concentration, preservatives and saccharides were determined. Fingerprints of DART-TOF/MS spectra were used for comprehensive assessment of the ice-tea samples. The DART-TOF/MS method was used for monitoring the following compounds: citric acid, caffeine, saccharides, artificial sweeteners (saccharin, acesulphame K), and preservatives (sorbic and benzoic acid), phosphoric acid and phenolic compounds. The measured data were subjected to a principal components analysis. The HPLC and DART-TOF/MS methods were compared in terms of determination of selected compounds (caffeine, benzoic acid, sorbic acid and saccharides) in the ice-teas. The DART-TOF/MS technique seems to be a suitable method for fast screening, testing quality and authenticity of tea-based products. Copyright © 2015 John Wiley & Sons, Ltd.

Computational Modeling-Based Discovery of Novel Classes of Anti-Inflammatory Drugs That Target Lanthionine Synthetase C-Like Protein 2

PubMed Central

Lu, Pinyi; Hontecillas, Raquel; Horne, William T.; Carbo, Adria; Viladomiu, Monica; Pedragosa, Mireia; Bevan, David R.; Lewis, Stephanie N.; Bassaganya-Riera, Josep

2012-01-01

Background Lanthionine synthetase component C-like protein 2 (LANCL2) is a member of the eukaryotic lanthionine synthetase component C-Like protein family involved in signal transduction and insulin sensitization. Recently, LANCL2 is a target for the binding and signaling of abscisic acid (ABA), a plant hormone with anti-diabetic and anti-inflammatory effects. Methodology/Principal Findings The goal of this study was to determine the role of LANCL2 as a potential therapeutic target for developing novel drugs and nutraceuticals against inflammatory diseases. Previously, we performed homology modeling to construct a three-dimensional structure of LANCL2 using the crystal structure of lanthionine synthetase component C-like protein 1 (LANCL1) as a template. Using this model, structure-based virtual screening was performed using compounds from NCI (National Cancer Institute) Diversity Set II, ChemBridge, ZINC natural products, and FDA-approved drugs databases. Several potential ligands were identified using molecular docking. In order to validate the anti-inflammatory efficacy of the top ranked compound (NSC61610) in the NCI Diversity Set II, a series of in vitro and pre-clinical efficacy studies were performed using a mouse model of dextran sodium sulfate (DSS)-induced colitis. Our findings showed that the lead compound, NSC61610, activated peroxisome proliferator-activated receptor gamma in a LANCL2- and adenylate cyclase/cAMP dependent manner in vitro and ameliorated experimental colitis by down-modulating colonic inflammatory gene expression and favoring regulatory T cell responses. Conclusions/Significance LANCL2 is a novel therapeutic target for inflammatory diseases. High-throughput, structure-based virtual screening is an effective computational-based drug design method for discovering anti-inflammatory LANCL2-based drug candidates. PMID:22509338

[Quality evaluation of Artemisiae Argyi Folium based on fingerprint analysis and quantitative analysis of multicomponents].

PubMed

Guo, Long; Jiao, Qian; Zhang, Dan; Liu, Ai-Peng; Wang, Qian; Zheng, Yu-Guang

2018-03-01

Artemisiae Argyi Folium, the dried leaves of Artemisia argyi, has been widely used in traditional Chinese and folk medicines for treatment of hemorrhage, pain, and skin itch. Phytochemical studies indicated that volatile oil, organic acid and flavonoids were the main bioactive components in Artemisiae Argyi Folium. Compared to the volatile compounds, the research of nonvolatile compounds in Artemisiae Argyi Folium are limited. In the present study, an accurate and reliable fingerprint approach was developed using HPLC for quality control of Artemisiae Argyi Folium. A total of 10 common peaks were marked，and the similarity of all the Artemisiae Argyi Folium samples was above 0.940. The established fingerprint method could be used for quality control of Artemisiae Argyi Folium. Furthermore, an HPLC method was applied for simultaneous determination of seven bioactive compounds including five organic acids and two flavonoids in Artemisiae Argyi Folium and Artemisiae Lavandulaefoliae Folium samples. Moreover, chemometrics methods such as hierarchical clustering analysis and principal component analysis were performed to compare and discriminate the Artemisiae Argyi Folium and Artemisiae Lavandulaefoliae Folium based on the quantitative data of analytes. The results indicated that simultaneous quantification of multicomponents coupled with chemometrics analysis could be a well-acceptable strategy to identify and evaluate the quality of Artemisiae Argyi Folium. Copyright© by the Chinese Pharmaceutical Association.

CLUSFAVOR 5.0: hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles

PubMed Central

Peterson, Leif E

2002-01-01

CLUSFAVOR (CLUSter and Factor Analysis with Varimax Orthogonal Rotation) 5.0 is a Windows-based computer program for hierarchical cluster and principal-component analysis of microarray-based transcriptional profiles. CLUSFAVOR 5.0 standardizes input data; sorts data according to gene-specific coefficient of variation, standard deviation, average and total expression, and Shannon entropy; performs hierarchical cluster analysis using nearest-neighbor, unweighted pair-group method using arithmetic averages (UPGMA), or furthest-neighbor joining methods, and Euclidean, correlation, or jack-knife distances; and performs principal-component analysis. PMID:12184816

Contribution of sulfur-containing compounds to the colour-inhibiting effect and improved antioxidant activity of Maillard reaction products of soybean protein hydrolysates.

PubMed

Huang, Meigui; Liu, Ping; Song, Shiqing; Zhang, Xiaoming; Hayat, Khizar; Xia, Shuqin; Jia, Chengsheng; Gu, Fenglin

2011-03-15

Light-coloured and savoury-tasting flavour enhancers are attractive to both consumers and food producers. The aim of this study was to investigate the colour-inhibiting effect of L-cysteine and thiamine during the Maillard reaction of soybean peptide and D-xylose. The correlation between volatile compounds and antioxidant activity of the corresponding products was also studied. Colour formation was markedly suppressed by cysteine. Compared with peptide/xylose (PX), the taste profile of Maillard reaction products (MRPs) derived from peptide/xylose/cysteine (PXC) and peptide/xylose/cysteine/thiamine (PXCT) was stronger, including umami, mouthfulness, continuity, meaty and overall acceptance. PXC and PXCT also exihibited distinctly higher antioxidant activity. Principal component analysis was applied to investigate the correlation between antioxidant activity and volatile compounds. Of 88 volatile compounds identified, 55 were significantly correlated with antioxidant activity by two principal components (accounting for 85.05% of the total variance). Effective colour control of the Maillard reaction by L-cysteine may allow the production of healthier (higher antioxidant activity) and tastier foods to satisfy consumers' and food producers' demands. Light-coloured products might be used as functional flavour enhancers in various food systems. Copyright © 2010 Society of Chemical Industry.

Multivariable Analysis of Gluten-Free Pasta Elaborated with Non-Conventional Flours Based on the Phenolic Profile, Antioxidant Capacity and Color.

PubMed

Camelo-Méndez, Gustavo A; Flores-Silva, Pamela C; Agama-Acevedo, Edith; Bello-Pérez, Luis A

2017-12-01

The phenolic compounds, color and antioxidant capacity of gluten-free pasta prepared with non-conventional flours such as chickpea (CHF), unripe plantain (UPF), white maize (WMF) and blue maize (BMF) were analyzed. Fifteen phenolic compounds (five anthocyanins, five hydroxybenzoic acids, three hydroxycinnamic acids, one hydroxyphenylacetic acid and one flavonol) were identified in pasta prepared with blue maize, and 10 compounds were identified for samples prepared with white maize. The principal component analysis (PCA) led to results describing 98% of the total variance establishing a clear separation for each pasta. Both the proportion (25, 50 and 75%) and type of maize flour (white and blue) affected the color parameters (L*, C ab *, h ab and ΔE* ab ) and antioxidant properties (DPPH, ABTS and FRAP methods) of samples, thus producing gluten-free products with potential health benefits intended for general consumers (including the population with celiac disease).

Multivariate analysis of chromatographic retention data as a supplementary means for grouping structurally related compounds.

PubMed

Fasoula, S; Zisi, Ch; Sampsonidis, I; Virgiliou, Ch; Theodoridis, G; Gika, H; Nikitas, P; Pappa-Louisi, A

2015-03-27

In the present study a series of 45 metabolite standards belonging to four chemically similar metabolite classes (sugars, amino acids, nucleosides and nucleobases, and amines) was subjected to LC analysis on three HILIC columns under 21 different gradient conditions with the aim to explore whether the retention properties of these analytes are determined from the chemical group they belong. Two multivariate techniques, principal component analysis (PCA) and discriminant analysis (DA), were used for statistical evaluation of the chromatographic data and extraction similarities between chemically related compounds. The total variance explained by the first two principal components of PCA was found to be about 98%, whereas both statistical analyses indicated that all analytes are successfully grouped in four clusters of chemical structure based on the retention obtained in four or at least three chromatographic runs, which, however should be performed on two different HILIC columns. Moreover, leave-one-out cross-validation of the above retention data set showed that the chemical group in which an analyte belongs can be 95.6% correctly predicted when the analyte is subjected to LC analysis under the same four or three experimental conditions as the all set of analytes was run beforehand. That, in turn, may assist with disambiguation of analyte identification in complex biological extracts. Copyright © 2015 Elsevier B.V. All rights reserved.

Development of an in Silico Model of DPPH• Free Radical Scavenging Capacity: Prediction of Antioxidant Activity of Coumarin Type Compounds.

PubMed

Goya Jorge, Elizabeth; Rayar, Anita Maria; Barigye, Stephen J; Jorge Rodríguez, María Elisa; Sylla-Iyarreta Veitía, Maité

2016-06-07

A quantitative structure-activity relationship (QSAR) study of the 2,2-diphenyl-l-picrylhydrazyl (DPPH•) radical scavenging ability of 1373 chemical compounds, using DRAGON molecular descriptors (MD) and the neural network technique, a technique based on the multilayer multilayer perceptron (MLP), was developed. The built model demonstrated a satisfactory performance for the training ( R 2 = 0.713 ) and test set ( Q ext 2 = 0.654 ) , respectively. To gain greater insight on the relevance of the MD contained in the MLP model, sensitivity and principal component analyses were performed. Moreover, structural and mechanistic interpretation was carried out to comprehend the relationship of the variables in the model with the modeled property. The constructed MLP model was employed to predict the radical scavenging ability for a group of coumarin-type compounds. Finally, in order to validate the model's predictions, an in vitro assay for one of the compounds (4-hydroxycoumarin) was performed, showing a satisfactory proximity between the experimental and predicted pIC50 values.

Effect of substituents on prediction of TLC retention of tetra-dentate Schiff bases and their Copper(II) and Nickel(II) complexes.

PubMed

Stevanović, Nikola R; Perušković, Danica S; Gašić, Uroš M; Antunović, Vesna R; Lolić, Aleksandar Đ; Baošić, Rada M

2017-03-01

The objectives of this study were to gain insights into structure-retention relationships and to propose the model to estimating their retention. Chromatographic investigation of series of 36 Schiff bases and their copper(II) and nickel(II) complexes was performed under both normal- and reverse-phase conditions. Chemical structures of the compounds were characterized by molecular descriptors which are calculated from the structure and related to the chromatographic retention parameters by multiple linear regression analysis. Effects of chelation on retention parameters of investigated compounds, under normal- and reverse-phase chromatographic conditions, were analyzed by principal component analysis, quantitative structure-retention relationship and quantitative structure-activity relationship models were developed on the basis of theoretical molecular descriptors, calculated exclusively from molecular structure, and parameters of retention and lipophilicity. Copyright © 2016 John Wiley & Sons, Ltd.

Screening and Analysis of the Marker Components in Ganoderma lucidum by HPLC and HPLC-MSn with the Aid of Chemometrics.

PubMed

Wu, Lingfang; Liang, Wenyi; Chen, Wenjing; Li, Shi; Cui, Yaping; Qi, Qi; Zhang, Lanzhen

2017-04-06

Ganoderma triterpenes (GTs) are the major secondary metabolites of Ganoderma lucidum , which is a popularly used traditional Chinese medicine for complementary cancer therapy. The present study was to establish a fingerprint evaluation system based on Similarity Analysis (SA), Cluster Analysis (CA) and Principal Component Analysis (PCA) for the identification and quality control of G. lucidum . Fifteen samples from the Chinese provinces of Hainan, Neimeng, Shangdong, Jilin, Anhui, Henan, Yunnan, Guangxi and Fujian were analyzed by HPLC-PAD and HPLC-MS n . Forty-seven compounds were detected by HPLC, of which forty-two compounds were tentatively identified by comparing their retention times and mass spectrometry data with that of reference compounds and reviewing the literature. Ganoderic acid B, 3,7,15-trihydroxy-11,23-dioxolanost-8,16-dien-26-oic acid, lucidenic acid A, ganoderic acid G, and 3,7-oxo-12-acetylganoderic acid DM were deemed to be the marker compounds to distinguish the samples with different quality according to both CA and PCA. This study provides helpful chemical information for further research on the anti-tumor activity and mechanism of action of G. lucidum . The results proved that fingerprints combined with chemometrics are a simple, rapid and effective method for the quality control of G. lucidum .

Aroma profile and sensory characteristics of a sulfur dioxide-free mulberry (Morus nigra) wine subjected to non-thermal accelerating aging techniques.

PubMed

Tchabo, William; Ma, Yongkun; Kwaw, Emmanuel; Zhang, Haining; Xiao, Lulu; Tahir, Haroon Elrasheid

2017-10-01

The present study was undertaken to assess accelerating aging effects of high pressure, ultrasound and manosonication on the aromatic profile and sensorial attributes of aged mulberry wines (AMW). A total of 166 volatile compounds were found amongst the AMW. The outcomes of the investigation were presented by means of geometric mean (GM), cluster analysis (CA), principal component analysis (PCA), partial least squares regressions (PLSR) and principal component regression (PCR). GM highlighted 24 organoleptic attributes responsible for the sensorial profile of the AMW. Moreover, CA revealed that the volatile composition of the non-thermal accelerated aged wines differs from that of the conventional aged wines. Besides, PCA discriminated the AMW on the basis of their main sensorial characteristics. Furthermore, PLSR identified 75 aroma compounds which were mainly responsible for the olfactory notes of the AMW. Finally, the overall quality of the AMW was noted to be better predicted by PLSR than PCR. Copyright © 2017 Elsevier Ltd. All rights reserved.

Source apportionment of exposures to volatile organic compounds. I. Evaluation of receptor models using simulated exposure data

NASA Astrophysics Data System (ADS)

Miller, Shelly L.; Anderson, Melissa J.; Daly, Eileen P.; Milford, Jana B.

Four receptor-oriented source apportionment models were evaluated by applying them to simulated personal exposure data for select volatile organic compounds (VOCs) that were generated by Monte Carlo sampling from known source contributions and profiles. The exposure sources modeled are environmental tobacco smoke, paint emissions, cleaning and/or pesticide products, gasoline vapors, automobile exhaust, and wastewater treatment plant emissions. The receptor models analyzed are chemical mass balance, principal component analysis/absolute principal component scores, positive matrix factorization (PMF), and graphical ratio analysis for composition estimates/source apportionment by factors with explicit restriction, incorporated in the UNMIX model. All models identified only the major contributors to total exposure concentrations. PMF extracted factor profiles that most closely represented the major sources used to generate the simulated data. None of the models were able to distinguish between sources with similar chemical profiles. Sources that contributed <5% to the average total VOC exposure were not identified.

Evaluation of solvent effect on the extraction of phenolic compounds and antioxidant capacities from the berries: application of principal component analysis.

PubMed

Boeing, Joana Schuelter; Barizão, Erica Oliveira; E Silva, Beatriz Costa; Montanher, Paula Fernandes; de Cinque Almeida, Vitor; Visentainer, Jesuí Vergilio

2014-01-01

This study evaluated the effect of the solvent on the extraction of antioxidant compounds from black mulberry (Morus nigra), blackberry (Rubus ulmifolius) and strawberry (Fragaria x ananassa). Different extracts of each berry were evaluated from the determination of total phenolic content, anthocyanin content and antioxidant capacity, and data were applied to the principal component analysis (PCA) to gain an overview of the effect of the solvent in extraction method. For all the berries analyzed, acetone/water (70/30, v/v) solvent mixture was more efficient solvent in the extracting of phenolic compounds, and methanol/water/acetic acid (70/29.5/0.5, v/v/v) showed the best values for anthocyanin content. Mixtures of ethanol/water (50/50, v/v), acetone water/acetic acid (70/29.5/0.5, v/v/v) and acetone/water (50/50, v/v) presented the highest antioxidant capacities for black mulberries, blackberries and strawberries, respectively. Antioxidants extractions are extremely affected by the solvent combination used. In addition, the obtained extracts with the organic solvent-water mixtures were distinguished from the extracts obtained with pure organic solvents, through the PCA analysis.

A SAR and QSAR study of new artemisinin compounds with antimalarial activity.

PubMed

Santos, Cleydson Breno R; Vieira, Josinete B; Lobato, Cleison C; Hage-Melim, Lorane I S; Souto, Raimundo N P; Lima, Clarissa S; Costa, Elizabeth V M; Brasil, Davi S B; Macêdo, Williams Jorge C; Carvalho, José Carlos T

2013-12-30

The Hartree-Fock method and the 6-31G** basis set were employed to calculate the molecular properties of artemisinin and 20 derivatives with antimalarial activity. Maps of molecular electrostatic potential (MEPs) and molecular docking were used to investigate the interaction between ligands and the receptor (heme). Principal component analysis and hierarchical cluster analysis were employed to select the most important descriptors related to activity. The correlation between biological activity and molecular properties was obtained using the partial least squares and principal component regression methods. The regression PLS and PCR models built in this study were also used to predict the antimalarial activity of 30 new artemisinin compounds with unknown activity. The models obtained showed not only statistical significance but also predictive ability. The significant molecular descriptors related to the compounds with antimalarial activity were the hydration energy (HE), the charge on the O11 oxygen atom (QO11), the torsion angle O1-O2-Fe-N2 (D2) and the maximum rate of R/Sanderson Electronegativity (RTe+). These variables led to a physical and structural explanation of the molecular properties that should be selected for when designing new ligands to be used as antimalarial agents.

Investigating Antibacterial Effects of Garlic (Allium sativum) Concentrate and Garlic-Derived Organosulfur Compounds on Campylobacter jejuni by Using Fourier Transform Infrared Spectroscopy, Raman Spectroscopy, and Electron Microscopy ▿ †

PubMed Central

Lu, Xiaonan; Rasco, Barbara A.; Jabal, Jamie M. F.; Aston, D. Eric; Lin, Mengshi; Konkel, Michael E.

2011-01-01

Fourier transform infrared (FT-IR) spectroscopy and Raman spectroscopy were used to study the cell injury and inactivation of Campylobacter jejuni from exposure to antioxidants from garlic. C. jejuni was treated with various concentrations of garlic concentrate and garlic-derived organosulfur compounds in growth media and saline at 4, 22, and 35°C. The antimicrobial activities of the diallyl sulfides increased with the number of sulfur atoms (diallyl sulfide < diallyl disulfide < diallyl trisulfide). FT-IR spectroscopy confirmed that organosulfur compounds are responsible for the substantial antimicrobial activity of garlic, much greater than those of garlic phenolic compounds, as indicated by changes in the spectral features of proteins, lipids, and polysaccharides in the bacterial cell membranes. Confocal Raman microscopy (532-nm-gold-particle substrate) and Raman mapping of a single bacterium confirmed the intracellular uptake of sulfur and phenolic components. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were employed to verify cell damage. Principal-component analysis (PCA), discriminant function analysis (DFA), and soft independent modeling of class analogs (SIMCA) were performed, and results were cross validated to differentiate bacteria based upon the degree of cell injury. Partial least-squares regression (PLSR) was employed to quantify and predict actual numbers of healthy and injured bacterial cells remaining following treatment. PLSR-based loading plots were investigated to further verify the changes in the cell membrane of C. jejuni treated with organosulfur compounds. We demonstrated that bacterial injury and inactivation could be accurately investigated by complementary infrared and Raman spectroscopies using a chemical-based, “whole-organism fingerprint” with the aid of chemometrics and electron microscopy. PMID:21642409

Multi-Response Extraction Optimization Based on Anti-Oxidative Activity and Quality Evaluation by Main Indicator Ingredients Coupled with Chemometric Analysis on Thymus quinquecostatus Celak.

PubMed

Chang, Yan-Li; Shen, Meng; Ren, Xue-Yang; He, Ting; Wang, Le; Fan, Shu-Sheng; Wang, Xiu-Huan; Li, Xiao; Wang, Xiao-Ping; Chen, Xiao-Yi; Sui, Hong; She, Gai-Mei

2018-04-19

Thymus quinquecostatus Celak is a species of thyme in China and it used as condiment and herbal medicine for a long time. To set up the quality evaluation of T. quinquecostatus , the response surface methodology (RSM) based on its 2,2-Diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity was introduced to optimize the extraction condition, and the main indicator components were found through an UPLC-LTQ-Orbitrap MS n method. The ethanol concentration, solid-liquid ratio, and extraction time on optimum conditions were 42.32%, 1:17.51, and 1.8 h, respectively. 35 components having 12 phenolic acids and 23 flavonoids were unambiguously or tentatively identified both positive and negative modes to employ for the comprehensive analysis in the optimum anti-oxidative part. A simple, reliable, and sensitive HPLC method was performed for the multi-component quantitative analysis of T. quinquecostatus using six characteristic and principal phenolic acids and flavonoids as reference compounds. Furthermore, the chemometrics methods (principal components analysis (PCA) and hierarchical clustering analysis (HCA)) appraised the growing areas and harvest time of this herb closely relative to the quality-controlled. This study provided full-scale qualitative and quantitative information for the quality evaluation of T. quinquecostatus , which would be a valuable reference for further study and development of this herb and related laid the foundation of further study on its pharmacological efficacy.

Age-dependent changes from allylphenol to prenylated benzoic acid production in Piper gaudichaudianum Kunth.

PubMed

Gaia, Anderson M; Yamaguchi, Lydia F; Jeffrey, Christopher S; Kato, Massuo J

2014-10-01

HPLC-DAD and principal component analysis (PCA) of the (1)H NMR spectrum of crude plant extracts showed high chemical variability among seedlings and adult organs of Piper gaudichaudianum. While gaudichaudianic acid was the major compound in the adult leaves, apiole and dillapiole were the major compounds in their seedling leaves. By the 15th month of seedling growth, the levels of apiole and dillapiole decreased and gaudichaudianic acid appeared along with two compounds, biosynthetically related to gaudichaudianic acid. Copyright © 2014 Elsevier Ltd. All rights reserved.

Multicomponent analysis of drinking water by a voltammetric electronic tongue.

PubMed

Winquist, Fredrik; Olsson, John; Eriksson, Mats

2011-01-10

A voltammetric electronic tongue is described that was used for multicomponent analysis of drinking water. Measurements were performed on drinking water from a tap and injections of the compounds NaCl, NaN(3), NaHSO(3), ascorbic acid, NaOCl and yeast suspensions could be identified by use of principal component analysis (PCA). A model based on partial least square (PLS) was developed for the simultaneously prediction of identification and concentration of the compounds NaCl, NaHSO(3) and NaOCl. By utilizing this type of non-selective sensor technique for water quality surveillance, it will be feasible to detect a plurality of events without the need of a specific sensor for each type of event. Copyright © 2010 Elsevier B.V. All rights reserved.

Photosynthetic capacity is negatively correlated with the concentration of leaf phenolic compounds across a range of different species

PubMed Central

Sumbele, Sally; Fotelli, Mariangela N.; Nikolopoulos, Dimosthenis; Tooulakou, Georgia; Liakoura, Vally; Liakopoulos, Georgios; Bresta, Panagiota; Dotsika, Elissavet; Adams, Mark A.; Karabourniotis, George

2012-01-01

Background and aims Phenolic compounds are the most commonly studied of all secondary metabolites because of their significant protective–defensive roles and their significant concentration in plant tissues. However, there has been little study on relationships between gas exchange parameters and the concentration of leaf phenolic compounds (total phenolics (TP) and condensed tannins (CT)) across a range of species. Therefore, we addressed the question: is there any correlation between photosynthetic capacity (Amax) and TP and CT across species from different ecosystems in different continents? Methodology A plethora of functional and structural parameters were measured in 49 plant species following different growth strategies from five sampling sites located in Greece and Australia. The relationships between several leaf traits were analysed by means of regression and principal component analysis. Principal results The results revealed a negative relationship between TP and CT and Amax among the different plant species, growth strategies and sampling sites, irrespective of expression (with respect to mass, area or nitrogen content). Principal component analysis showed that high concentrations of TP and CT are associated with thick, dense leaves with low nitrogen. This leaf type is characterized by low growth, Amax and transpiration rates, and is common in environments with low water and nutrient availability, high temperatures and high light intensities. Therefore, the high TP and CT in such leaves are compatible with the protective and defensive functions ascribed to them. Conclusions Our results indicate a functional integration between carbon gain and the concentration of leaf phenolic compounds that reflects the trade-off between growth and defence/protection demands, depending on the growth strategy adopted by each species. PMID:23050073

Fast, Exact Bootstrap Principal Component Analysis for p > 1 million

PubMed Central

Fisher, Aaron; Caffo, Brian; Schwartz, Brian; Zipunnikov, Vadim

2015-01-01

Many have suggested a bootstrap procedure for estimating the sampling variability of principal component analysis (PCA) results. However, when the number of measurements per subject (p) is much larger than the number of subjects (n), calculating and storing the leading principal components from each bootstrap sample can be computationally infeasible. To address this, we outline methods for fast, exact calculation of bootstrap principal components, eigenvalues, and scores. Our methods leverage the fact that all bootstrap samples occupy the same n-dimensional subspace as the original sample. As a result, all bootstrap principal components are limited to the same n-dimensional subspace and can be efficiently represented by their low dimensional coordinates in that subspace. Several uncertainty metrics can be computed solely based on the bootstrap distribution of these low dimensional coordinates, without calculating or storing the p-dimensional bootstrap components. Fast bootstrap PCA is applied to a dataset of sleep electroencephalogram recordings (p = 900, n = 392), and to a dataset of brain magnetic resonance images (MRIs) (p ≈ 3 million, n = 352). For the MRI dataset, our method allows for standard errors for the first 3 principal components based on 1000 bootstrap samples to be calculated on a standard laptop in 47 minutes, as opposed to approximately 4 days with standard methods. PMID:27616801

Secondary ion mass spectrometry imaging and multivariate data analysis reveal co-aggregation patterns of Populus trichocarpa leaf surface compounds on a micrometer scale.

PubMed

Kulkarni, Purva; Dost, Mina; Bulut, Özgül Demir; Welle, Alexander; Böcker, Sebastian; Boland, Wilhelm; Svatoš, Aleš

2018-01-01

Spatially resolved analysis of a multitude of compound classes has become feasible with the rapid advancement in mass spectrometry imaging strategies. In this study, we present a protocol that combines high lateral resolution time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging with a multivariate data analysis (MVA) approach to probe the complex leaf surface chemistry of Populus trichocarpa. Here, epicuticular waxes (EWs) found on the adaxial leaf surface of P. trichocarpa were blotted on silicon wafers and imaged using TOF-SIMS at 10 μm and 1 μm lateral resolution. Intense M +● and M -● molecular ions were clearly visible, which made it possible to resolve the individual compound classes present in EWs. Series of long-chain aliphatic saturated alcohols (C 21 -C 30 ), hydrocarbons (C 25 -C 33 ) and wax esters (WEs; C 44 -C 48 ) were clearly observed. These data correlated with the 7 Li-chelation matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis, which yielded mostly molecular adduct ions of the analyzed compounds. Subsequently, MVA was used to interrogate the TOF-SIMS dataset for identifying hidden patterns on the leaf's surface based on its chemical profile. After the application of principal component analysis (PCA), a small number of principal components (PCs) were found to be sufficient to explain maximum variance in the data. To further confirm the contributions from pure components, a five-factor multivariate curve resolution (MCR) model was applied. Two distinct patterns of small islets, here termed 'crystals', were apparent from the resulting score plots. Based on PCA and MCR results, the crystals were found to be formed by C 23 or C 29 alcohols. Other less obvious patterns observed in the PCs revealed that the adaxial leaf surface is coated with a relatively homogenous layer of alcohols, hydrocarbons and WEs. The ultra-high-resolution TOF-SIMS imaging combined with the MVA approach helped to highlight the diverse patterns underlying the leaf's surface. Currently, the methods available to analyze the surface chemistry of waxes in conjunction with the spatial information related to the distribution of compounds are limited. This study uses tools that may provide important biological insights into the composition of the wax layer, how this layer is repaired after mechanical damage or insect feeding, and which transport mechanisms are involved in deploying wax constituents to specific regions on the leaf surface. © 2017 The Authors The Plant Journal © 2017 John Wiley & Sons Ltd.

Aroma characterization based on aromatic series analysis in table grapes

PubMed Central

Wu, Yusen; Duan, Shuyan; Zhao, Liping; Gao, Zhen; Luo, Meng; Song, Shiren; Xu, Wenping; Zhang, Caixi; Ma, Chao; Wang, Shiping

2016-01-01

Aroma is an important part of quality in table grape, but the key aroma compounds and the aroma series of table grapes remains unknown. In this paper, we identified 67 aroma compounds in 20 table grape cultivars; 20 in pulp and 23 in skin were active compounds. C6 compounds were the basic background volatiles, but the aroma contents of pulp juice and skin depended mainly on the levels of esters and terpenes, respectively. Most obviously, ‘Kyoho’ grapevine series showed high contents of esters in pulp, while Muscat/floral cultivars showed abundant monoterpenes in skin. For the aroma series, table grapes were characterized mainly by herbaceous, floral, balsamic, sweet and fruity series. The simple and visualizable aroma profiles were established using aroma fingerprints based on the aromatic series. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) showed that the aroma profiles of pulp juice, skin and whole berries could be classified into 5, 3, and 5 groups, respectively. Combined with sensory evaluation, we could conclude that fatty and balsamic series were the preferred aromatic series, and the contents of their contributors (β-ionone and octanal) may be useful as indicators for the improvement of breeding and cultivation measures for table grapes. PMID:27487935

"Sweeter than a rose", at least to Triatoma phyllosoma complex males (Triatominae: Reduviidae).

PubMed

May-Concha, Irving J; Cruz-López, Leopoldo C; Rojas, Julio C; Ramsey, Janine M

2018-02-17

The Triatoma phyllosoma complex of Trypanosoma cruzi vectors (Triatominae: Reduviidae) is distributed in both Neotropical and Nearctic bioregions of Mexico. Volatile organic compounds emitted by disturbed Triatoma longipennis, Triatoma pallidipennis and Triatoma phyllosoma, and from their Brindley's and metasternal glands, were identified using solid-phase microextraction coupled with gas chromatography-mass spectrometry. Disturbed bugs and the metasternal glands from T. phyllosoma released or had significantly fewer compounds than T. longipennis and T. pallidipennis. Isobutyric acid was the most abundant compound secreted by disturbed bugs of the three species, while Brindley's glands of all species produced another four compounds: propanoic acid, isobutyric acid, pentyl butanoate, and 2-methyl hexanoic acid. Two novel compounds, both rose oxide isomers, were produced in MGs and released only by disturbed females of all three species, making this the first report in Triatominae of these monoterpenes. The principal compound in MGs of both sexes of T. longipennis and T. phyllosoma was 3-methyl-2-hexanone, while cis-rose oxide was the principal compound in T. pallidipennis females. The major components in male effluvia of T. pallidipennis were 2-decanol and 3-methyl-2-hexanone. Discriminant analysis of volatile organic compounds was significant, separating the three species and was consistent with morphological and genetic evidence for species distinctions within the complex.

UHPLC/PDA-ESI/MS analysis of the main berry and leaf flavonol glycosides from different Carpathian Hippophaë rhamnoides L. varieties.

PubMed

Pop, Raluca Maria; Socaciu, Carmen; Pintea, Adela; Buzoianu, Anca Dana; Sanders, Mark Gerardus; Gruppen, Harry; Vincken, Jean-Paul

2013-01-01

Sea buckthorn (Hippophaë rhamnoides L.) is known to be rich in many bioactive compounds (such as vitamins, phenolics, carotenoids) important for human health and nutrition. Among the phenolics, berries and leaves contain a wide range of flavonols that are good quality and authenticity biomarkers. To compare the composition of the main flavonols of Romanian sea buckthorn berry and leaf varieties and to identify the specific biomarkers that contribute to sample differentiation among varieties. Six varieties of cultivated sea buckthorn (ssp. Carpatica) berries and leaves were analysed by UHPLC/PDA-ESI/MS. Berries and leaves contained mainly isorhamnetin (I) glycosides in different ratios. Whereas I-3-neohesperidoside, I-3-glucoside, I-3-rhamnosylglucoside, I-3-sophoroside-7-rhamnoside and free isorhamnetin were predominant for berries (out of 17 compounds identified), I-3-rhamnosylglucoside, I-3-neohesperidoside, I-3-glucoside, quercetin-3-pentoside, kaempferol-3-rutinoside, and quercetin-3-glucoside were predominant in leaves (out of 19 compounds identified). Berries contained, on average, 917 mg/100 g DW flavonol glycosides. Leaves had higher content of flavonol glycosides than berries, on average 1118 mg/100 g DW. The variation of the quantitative dataset analysed using principal component analysis accounted for 91% of the total variance in the case of berries and 73% in case of leaves, demonstrating a good discrimination among samples. Based on quantitative analysis, by principal component analysis, the flavonol derivatives can be considered as biomarkers to discriminate among varieties and to recognise specifically the berry versus leaf composition. Copyright © 2013 John Wiley & Sons, Ltd.

[Interrelations between plant communities and environmental factors of wetlands and surrounding lands in mid- and lower reaches of Tarim River].

PubMed

Zhao, Ruifeng; Zhou, Huarong; Qian, Yibing; Zhang, Jianjun

2006-06-01

A total of 16 quadrants of wetlands and surrounding lands in the mid- and lower reaches of Tarim River were surveyed, and the data about the characteristics of plant communities and environmental factors were collected and counted. By using PCA (principal component analysis) ordination and regression procedure, the distribution patterns of plant communities and the relationships between the characteristics of plant community structure and environmental factors were analyzed. The results showed that the distribution of the plant communities was closely related to soil moisture, salt, and nutrient contents. The accumulative contribution rate of soil moisture and salt contents in the first principal component accounted for 35.70%, and that of soil nutrient content in the second principal component reached 25.97%. There were 4 types of habitats for the plant community distribution, i. e., fenny--light salt--medium nutrient, moist--medium salt--medium nutrient, mesophytic--medium salt--low nutrient, and medium xerophytic-heavy salt--low nutrient. Along these habitats, swamp vegetation, meadow vegetation, riparian sparse forest, halophytic desert, and salinized shrub were distributed. In the wetlands and surrounding lands of mid- and lower reaches of Tarim River, the ecological dominance of the plant communities was markedly and unitary-linearly correlated with the compound gradient of soil moisture and salt contents. The relationships between species diversity, ecological dominance, and compound gradient of soil moisture and salt contents were significantly accorded to binary-linear regression model.

Survey of whole air data from the second airborne Biomass Burning and Lightning Experiment using principal component analysis

NASA Astrophysics Data System (ADS)

Choi, Yunsoo; Elliott, Scott; Simpson, Isobel J.; Blake, Donald R.; Colman, Jonah J.; Dubey, Manvendra K.; Meinardi, Simone; Rowland, F. Sherwood; Shirai, Tomoko; Smith, Felisa A.

2003-03-01

Hydrocarbon and halocarbon measurements collected during the second airborne Biomass Burning and Lightning Experiment (BIBLE-B) were subjected to a principal component analysis (PCA), to test the capability for identifying intercorrelated compounds within a large whole air data set. The BIBLE expeditions have sought to quantify and understand the products of burning, electrical discharge, and general atmospheric chemical processes during flights arrayed along the western edge of the Pacific. Principal component analysis was found to offer a compact method for identifying the major modes of composition encountered in the regional whole air data set. Transecting the continental monsoon, urban and industrial tracers (e.g., combustion byproducts, chlorinated methanes and ethanes, xylenes, and longer chain alkanes) dominated the observed variability. Pentane enhancements reflected vehicular emissions. In general, ethyl and propyl nitrate groupings indicated oxidation under nitrogen oxide (NOx) rich conditions and hence city or lightning influences. Over the tropical ocean, methyl nitrate grouped with brominated compounds and sometimes with dimethyl sulfide and methyl iodide. Biomass burning signatures were observed during flights over the Australian continent. Strong indications of wetland anaerobics (methane) or liquefied petroleum gas leakage (propane) were conspicuous by their absence. When all flights were considered together, sources attributable to human activity emerged as the most important. We suggest that factor reductions in general and PCA in particular may soon play a vital role in the analysis of regional whole air data sets, as a complement to more familiar methods.

Synergetic Use of Principal Component Analysis Applied to Normed Physicochemical Measurements and GC × GC-MS to Reveal the Stabilization Effect of Selected Essential Oils on Heated Rapeseed Oil.

PubMed

Sghaier, Lilia; Cordella, Christophe B Y; Rutledge, Douglas N; Lefèvre, Fanny; Watiez, Mickaël; Breton, Sylvie; Sassiat, Patrick; Thiebaut, Didier; Vial, Jérôme

2017-06-01

Lipid oxidation leads to the formation of volatile compounds and very often to off-flavors. In the case of the heating of rapeseed oil, unpleasant odors, characterized as a fishy odor, are emitted. In this study, 2 different essential oils (coriander and nutmeg essential oils) were added to refined rapeseed oil as odor masking agents. The aim of this work was to determine a potential antioxidant effect of these essential oils on the thermal stability of rapeseed oil subject to heating cycles between room temperature and 180 °C. For this purpose, normed determinations of different parameters (peroxide value, anisidine value, and the content of total polar compounds, free fatty acids and tocopherols) were carried out to examine the differences between pure and degraded oil. No significant difference was observed between pure rapeseed oil and rapeseed oil with essential oils for each parameter separately. However, a stabilizing effect of the essential oils, with a higher effect for the nutmeg essential oil was highlighted by principal component analysis applied on physicochemical dataset. Moreover, the analysis of the volatile compounds performed by GC × GC showed a substantial loss of the volatile compounds of the essential oils from the first heating cycle. © 2017 Institute of Food Technologists®.

Chemotaxonomic Study of Citrus, Poncirus and Fortunella Genotypes Based on Peel Oil Volatile Compounds - Deciphering the Genetic Origin of Mangshanyegan (Citrus nobilis Lauriro)

PubMed Central

Liu, Cuihua; Jiang, Dong; Cheng, Yunjiang; Deng, Xiuxin; Chen, Feng; Fang, Liu; Ma, Zhaocheng; Xu, Juan

2013-01-01

Volatile profiles yielded from gas chromatography-mass spectrometry (GC-MS) analysis provide abundant information not only for metabolism-related research, but also for chemotaxonomy. To study the chemotaxonomy of Mangshanyegan, its volatile profiles of fruit and leaf and those of 29 other genotypes of Citrus, Poncirus, and Fortunella were subjected to phylogenetic analyses. Results showed that 145 identified (including 64 tentatively identified) and 15 unidentified volatile compounds were detected from their peel oils. The phylogenetic analysis of peel oils based on hierarchical cluster analysis (HCA) demonstrated a good agreement with the Swingle taxonomy system, in which the three genera of Citrus, Poncirus, and Fortunella were almost completely separated. As to Citrus, HCA indicated that Citrophorum, Cephalocitrus, and Sinocitrus fell into three subgroups, respectively. Also, it revealed that Mangshanyegan contain volatile compounds similar to those from pummelo, though it is genetically believed to be a mandarin. These results were further supported by the principal component analysis of the peel oils and the HCA results of volatile profiles of leaves in the study. PMID:23516475

Performance-Based Preparation of Principals: A Framework for Improvement. A Special Report of the NASSP Consortium for the Performance-Based Preparation of Principals.

ERIC Educational Resources Information Center

National Association of Secondary School Principals, Reston, VA.

Preparation programs for principals should have excellent academic and performance based components. In examining the nature of performance based principal preparation this report finds that school administration programs must bridge the gap between conceptual learning in the classroom and the requirements of professional practice. A number of…

MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development

PubMed Central

Korkmaz, Selcuk; Zararsiz, Gokmen; Goksuluk, Dincer

2015-01-01

Virtual screening is an important step in early-phase of drug discovery process. Since there are thousands of compounds, this step should be both fast and effective in order to distinguish drug-like and nondrug-like molecules. Statistical machine learning methods are widely used in drug discovery studies for classification purpose. Here, we aim to develop a new tool, which can classify molecules as drug-like and nondrug-like based on various machine learning methods, including discriminant, tree-based, kernel-based, ensemble and other algorithms. To construct this tool, first, performances of twenty-three different machine learning algorithms are compared by ten different measures, then, ten best performing algorithms have been selected based on principal component and hierarchical cluster analysis results. Besides classification, this application has also ability to create heat map and dendrogram for visual inspection of the molecules through hierarchical cluster analysis. Moreover, users can connect the PubChem database to download molecular information and to create two-dimensional structures of compounds. This application is freely available through www.biosoft.hacettepe.edu.tr/MLViS/. PMID:25928885

Critical Factors Explaining the Leadership Performance of High-Performing Principals

ERIC Educational Resources Information Center

Hutton, Disraeli M.

2018-01-01

The study explored critical factors that explain leadership performance of high-performing principals and examined the relationship between these factors based on the ratings of school constituents in the public school system. The principal component analysis with the use of Varimax Rotation revealed that four components explain 51.1% of the…

Evaluation of 107 legumes for renewable sources of energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roth, W.B.; Carr, M.E.; Cull, I.M.

One hundred and seven species of randomly-collected Leguminosae were evaluated for their potential as energy-producing crops. Whole plants, excluding roots, were chemically analyzed, and 11 species were identified as the more promising for future considerations based on a numerical rating system developed at this Center. Of the 11 species, one contained principally rubber (polyisoprene) in the hydrocarbon fraction and 7 contained principally wax. Hydrocarbon fractions of 3 species with less than 0.4% were not examined. The oils of species with at least 3.0% oil were examined by thin layer chromatography (TLC) to determine classes of components and were given amore » saponification treatment to determine yields of unsaponifiable matter and fatty acids. The oil of one species was quantitatively analyzed for classes of compounds by TLC-flame ionization detection. Selected species with ratings greater than 10 are briefly discussed. 16 references, 1 figure, 2 tables.« less

Optimal pattern synthesis for speech recognition based on principal component analysis

NASA Astrophysics Data System (ADS)

Korsun, O. N.; Poliyev, A. V.

2018-02-01

The algorithm for building an optimal pattern for the purpose of automatic speech recognition, which increases the probability of correct recognition, is developed and presented in this work. The optimal pattern forming is based on the decomposition of an initial pattern to principal components, which enables to reduce the dimension of multi-parameter optimization problem. At the next step the training samples are introduced and the optimal estimates for principal components decomposition coefficients are obtained by a numeric parameter optimization algorithm. Finally, we consider the experiment results that show the improvement in speech recognition introduced by the proposed optimization algorithm.

Prediction of p38 map kinase inhibitory activity of 3, 4-dihydropyrido [3, 2-d] pyrimidone derivatives using an expert system based on principal component analysis and least square support vector machine

PubMed Central

Shahlaei, M.; Saghaie, L.

2014-01-01

A quantitative structure–activity relationship (QSAR) study is suggested for the prediction of biological activity (pIC50) of 3, 4-dihydropyrido [3,2-d] pyrimidone derivatives as p38 inhibitors. Modeling of the biological activities of compounds of interest as a function of molecular structures was established by means of principal component analysis (PCA) and least square support vector machine (LS-SVM) methods. The results showed that the pIC50 values calculated by LS-SVM are in good agreement with the experimental data, and the performance of the LS-SVM regression model is superior to the PCA-based model. The developed LS-SVM model was applied for the prediction of the biological activities of pyrimidone derivatives, which were not in the modeling procedure. The resulted model showed high prediction ability with root mean square error of prediction of 0.460 for LS-SVM. The study provided a novel and effective approach for predicting biological activities of 3, 4-dihydropyrido [3,2-d] pyrimidone derivatives as p38 inhibitors and disclosed that LS-SVM can be used as a powerful chemometrics tool for QSAR studies. PMID:26339262

Impact of parameter fluctuations on the performance of ethanol precipitation in production of Re Du Ning Injections, based on HPLC fingerprints and principal component analysis.

PubMed

Sun, Li-Qiong; Wang, Shu-Yao; Li, Yan-Jing; Wang, Yong-Xiang; Wang, Zhen-Zhong; Huang, Wen-Zhe; Wang, Yue-Sheng; Bi, Yu-An; Ding, Gang; Xiao, Wei

2016-01-01

The present study was designed to determine the relationships between the performance of ethanol precipitation and seven process parameters in the ethanol precipitation process of Re Du Ning Injections, including concentrate density, concentrate temperature, ethanol content, flow rate and stir rate in the addition of ethanol, precipitation time, and precipitation temperature. Under the experimental and simulated production conditions, a series of precipitated resultants were prepared by changing these variables one by one, and then examined by HPLC fingerprint analyses. Different from the traditional evaluation model based on single or a few constituents, the fingerprint data of every parameter fluctuation test was processed with Principal Component Analysis (PCA) to comprehensively assess the performance of ethanol precipitation. Our results showed that concentrate density, ethanol content, and precipitation time were the most important parameters that influence the recovery of active compounds in precipitation resultants. The present study would provide some reference for pharmaceutical scientists engaged in research on pharmaceutical process optimization and help pharmaceutical enterprises adapt a scientific and reasonable cost-effective approach to ensure the batch-to-batch quality consistency of the final products. Copyright © 2016 China Pharmaceutical University. Published by Elsevier B.V. All rights reserved.

Determination of five active compounds in Artemisia princeps and A. capillaris based on UPLC-DAD and discrimination of two species with multivariate analysis.

PubMed

Yang, Heejung; Lee, Dong Young; Jeon, Minji; Suh, Youngbae; Sung, Sang Hyun

2014-05-01

Five active compounds, chlorogenic acid, 3,5-di-O-caffeoylquinic acid, 4,5-di-O-caffeoylquinic acid, jaceosidin, and eupatilin, in Artemisia princeps (Compositae) were simultaneously determined by ultra-performance liquid chromatography connected to diode array detector. The morphological resemblance between A. princeps and A. capillaris makes it difficult to properly identify species properly. It occasionally leads to misuse or misapplication in Korean traditional medicine. In the study, the discrimination between A. princeps and A. capillaris was optimally performed by the developed validation method, which resulted in definitely a difference between two species. Also, it was developed the most reliable markers contributing to the discrimination of two species by the multivariate analysis methods, such as a principal component analysis and a partial least squares discrimination analysis.

Lipidomics study of plasma phospholipid metabolism in early type 2 diabetes rats with ancient prescription Huang-Qi-San intervention by UPLC/Q-TOF-MS and correlation coefficient.

PubMed

Wu, Xia; Zhu, Jian-Cheng; Zhang, Yu; Li, Wei-Min; Rong, Xiang-Lu; Feng, Yi-Fan

2016-08-25

Potential impact of lipid research has been increasingly realized both in disease treatment and prevention. An effective metabolomics approach based on ultra-performance liquid chromatography/quadrupole-time-of-flight mass spectrometry (UPLC/Q-TOF-MS) along with multivariate statistic analysis has been applied for investigating the dynamic change of plasma phospholipids compositions in early type 2 diabetic rats after the treatment of an ancient prescription of Chinese Medicine Huang-Qi-San. The exported UPLC/Q-TOF-MS data of plasma samples were subjected to SIMCA-P and processed by bioMark, mixOmics, Rcomdr packages with R software. A clear score plots of plasma sample groups, including normal control group (NC), model group (MC), positive medicine control group (Flu) and Huang-Qi-San group (HQS), were achieved by principal-components analysis (PCA), partial least-squares discriminant analysis (PLS-DA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). Biomarkers were screened out using student T test, principal component regression (PCR), partial least-squares regression (PLS) and important variable method (variable influence on projection, VIP). Structures of metabolites were identified and metabolic pathways were deduced by correlation coefficient. The relationship between compounds was explained by the correlation coefficient diagram, and the metabolic differences between similar compounds were illustrated. Based on KEGG database, the biological significances of identified biomarkers were described. The correlation coefficient was firstly applied to identify the structure and deduce the metabolic pathways of phospholipids metabolites, and the study provided a new methodological cue for further understanding the molecular mechanisms of metabolites in the process of regulating Huang-Qi-San for treating early type 2 diabetes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

In-vitro effects of Thymus munbyanus essential oil and thymol on human sperm motility and function.

PubMed

Chikhoune, Amirouche; Stouvenel, Laurence; Iguer-Ouada, Mokrane; Hazzit, Mohamed; Schmitt, Alain; Lorès, Patrick; Wolf, Jean Philippe; Aissat, Kamel; Auger, Jacques; Vaiman, Daniel; Touré, Aminata

2015-09-01

Traditional medicine has been used worldwide for centuries to cure or prevent disease and for male or female contraception. Only a few studies have directly investigated the effects of herbal compounds on spermatozoa. In this study, essential oil from Thymus munbyanus was extracted and its effect on human spermatozoa in vitro was analysed. Gas chromatography and Gas chromatography-mass spectrometry analyses identified 64 components, accounting for 98.9% of the composition of the oil. The principal components were thymol (52.0%), γ-terpinene (11.0%), ρ-cymene (8.5%) and carvacrol (5.2%). Freshly ejaculated spermatozoa was exposed from control individuals to various doses of the essential oil for different time periods, and recorded the vitality, the mean motility, the movement characteristics (computer-aided sperm analysis), the morphology and the ability to undergo protein hyperphosphorylation and acrosomal reaction, which constitute two markers of sperm capacitation and fertilizing ability. In vitro, both the essential oil extracted from T. munbyanus and thymol, the principal compound present in this oil, impaired human sperm motility and its capacity to undergo hyperphosphorylation and acrosome reaction. These compounds may, therefore, be of interest in the field of reproductive biology, as potential anti-spermatic agents. Copyright © 2015 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

Electronic tongue for nitro and peroxide explosive sensing.

PubMed

González-Calabuig, Andreu; Cetó, Xavier; Del Valle, Manel

2016-06-01

This work reports the application of a voltammetric electronic tongue (ET) towards the simultaneous determination of both nitro-containing and peroxide-based explosive compounds, two families that represent the vast majority of compounds employed either in commercial mixtures or in improvised explosive devices. The multielectrode array was formed by graphite, gold and platinum electrodes, which exhibited marked mix-responses towards the compounds examined; namely, 1,3,5-trinitroperhydro-1,3,5-triazine (RDX), octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX), pentaerythritol tetranitrate (PETN), 2,4,6-trinitrotoluene (TNT), N-methyl-N,2,4,6-tetranitroaniline (Tetryl) and triacetone triperoxide (TATP). Departure information was the set of voltammograms, which were first analyzed by means of principal component analysis (PCA) allowing the discrimination of the different individual compounds, while artificial neural networks (ANNs) were used for the resolution and individual quantification of some of their mixtures (total normalized root mean square error for the external test set of 0.108 and correlation of the obtained vs. expected concentrations comparison graphs r>0.929). Copyright © 2016 Elsevier B.V. All rights reserved.

Characterization of Volatile Compounds of Eleven Achillea Species from Turkey and Biological Activities of Essential Oil and Methanol Extract of A. hamzaoglui Arabacı & Budak.

PubMed

Turkmenoglu, Fatma Pinar; Agar, Osman Tuncay; Akaydin, Galip; Hayran, Mutlu; Demirci, Betul

2015-06-22

According to distribution of genus Achillea, two main centers of diversity occur in S.E. Europe and S.W. Asia. Diversified essential oil compositions from Balkan Peninsula have been numerously reported. However, report on essential oils of Achillea species growing in Turkey, which is one of the main centers of diversity, is very limited. This paper represents the chemical compositions of the essential oils obtained by hydrodistillation from the aerial parts of eleven Achillea species, identified simultaneously by gas chromatography and gas chromatography-mass spectrometry. The main components were found to be 1,8-cineole, p-cymene, viridiflorol, nonacosane, α-bisabolol, caryophyllene oxide, α-bisabolon oxide A, β-eudesmol, 15-hexadecanolide and camphor. The chemical principal component analysis based on thirty compounds identified three species groups and a subgroup, where each group constituted a chemotype. This is the first report on the chemical composition of A. hamzaoglui essential oil; as well as the antioxidant and antimicrobial evaluation of its essential oil and methanolic extract.

Effect of different drying techniques on bioactive components, fatty acid composition, and volatile profile of robusta coffee beans.

PubMed

Dong, Wenjiang; Hu, Rongsuo; Chu, Zhong; Zhao, Jianping; Tan, Lehe

2017-11-01

This study investigated the effect of different drying techniques, namely, room-temperature drying (RTD), solar drying (SD), heat-pump drying (HPD), hot-air drying (HAD), and freeze drying (FD), on bioactive components, fatty acid composition, and the volatile compound profile of robusta coffee beans. The data showed that FD was an effective method to preserve fat, organic acids, and monounsaturated fatty acids. In contrast, HAD was ideal for retaining polyunsaturated fatty acids and amino acids. Sixty-two volatile compounds were identified in the differently dried coffee beans, representing 90% of the volatile compounds. HPD of the coffee beans produced the largest number of volatiles, whereas FD resulted in the highest volatile content. A principal component analysis demonstrated a close relationship between the HPD, SD, and RTD methods whereas the FD and HAD methods were significantly different. Overall, the results provide a basis for potential application to other similar thermal sensitive materials. Copyright © 2017 Elsevier Ltd. All rights reserved.

Characterisation of volatile profiles in 50 native Peruvian chili pepper using solid phase microextraction-gas chromatography mass spectrometry (SPME-GCMS).

PubMed

Patel, Kirti; Ruiz, Candy; Calderon, Rosa; Marcelo, Mavel; Rojas, Rosario

2016-11-01

The volatiles were characterised by headspace solid phase micro extraction (HS-SPME), gas chromatography mass spectrometry (GC-FID/MS). A total of 127 compounds were identified with terpenes (including mono terpenes and sesquiterpenes - a total of 45 compounds), esters (31 compounds) and hydrocarbons (20 compounds) were the predominant volatile compounds. Principal component analysis (PCA) of the volatile compounds yielded 2 significant PC's, which together accounted for 90.3% of the total variance in the data set and the scatter plot generated between PC1 and PC2 successfully segregated the 50 chili pepper samples into 7 groups. Clusters of hydrocarbons, esters, terpenes, aldehyde and ketones formed the major determinants of the difference. Copyright © 2016 Elsevier Ltd. All rights reserved.

Changes in sparkling wine aroma during the second fermentation under CO2 pressure in sealed bottle.

PubMed

Martínez-García, Rafael; García-Martínez, Teresa; Puig-Pujol, Anna; Mauricio, Juan Carlos; Moreno, Juan

2017-12-15

High quality sparkling wine made by the traditional method requires a second alcoholic fermentation of a base wine in sealed bottles, followed by an aging time in contact with yeast lees. The CO 2 overpressure released during this second fermentation has an important effect on the yeast metabolism and therefore on the wine aroma composition. This study focuses on the changes in chemical composition and 43 aroma compounds released by yeast during this fermentation carried out under two pressure conditions. The data were subjected to statistical analysis allowing differentiating between the base wine and the wine samples taken in the middle and at the end of fermentation. The differentiation among wines obtained to the end of fermentation with or without CO 2 pressure is only achieved by a principal component analysis of 15 selected minor compounds (mainly ethyl dodecanoate, ethyl tetradecanoate, hexyl acetate, ethyl butanoate and ethyl isobutanoate). Copyright © 2017 Elsevier Ltd. All rights reserved.

A systematic study of chemogenomics of carbohydrates.

PubMed

Gu, Jiangyong; Luo, Fang; Chen, Lirong; Yuan, Gu; Xu, Xiaojie

2014-03-04

Chemogenomics focuses on the interactions between biologically active molecules and protein targets for drug discovery. Carbohydrates are the most abundant compounds in natural products. Compared with other drugs, the carbohydrate drugs show weaker side effects. Searching for multi-target carbohydrate drugs can be regarded as a solution to improve therapeutic efficacy and safety. In this work, we collected 60 344 carbohydrates from the Universal Natural Products Database (UNPD) and explored the chemical space of carbohydrates by principal component analysis. We found that there is a large quantity of potential lead compounds among carbohydrates. Then we explored the potential of carbohydrates in drug discovery by using a network-based multi-target computational approach. All carbohydrates were docked to 2389 target proteins. The most potential carbohydrates for drug discovery and their indications were predicted based on a docking score-weighted prediction model. We also explored the interactions between carbohydrates and target proteins to find the pathological networks, potential drug candidates and new indications.

Migration from printing inks in multilayer food packaging materials by GC-MS analysis and pattern recognition with chemometrics.

PubMed

Clemente, Isabel; Aznar, Margarita; Nerín, Cristina; Bosetti, Osvaldo

2016-01-01

Inks and varnishes used in food packaging multilayer materials can contain different substances that are potential migrants when packaging is in contact with food. Although printing inks are applied on the external layer, they can migrate due to set-off phenomena. In order to assess food safety, migration tests were performed from two materials sets: set A based on paper and set B based on PET; both contained inks. Migration was performed to four food simulants (EtOH 50%, isooctane, EtOH 95% and Tenax(®)) and the volatile compounds profile was analysed by GC-MS. The effect of presence/absence of inks and varnishes and also their position in the material was studied. A total of 149 volatile compounds were found in migration from set A and 156 from set B materials, some of them came from inks. Quantitative analysis and a principal component analysis were performed in order to identify patterns among sample groups.

Metabolomics Coupled with Proteomics Advancing Drug Discovery toward More Agile Development of Targeted Combination Therapies*

PubMed Central

Wang, Xijun; Zhang, Aihua; Wang, Ping; Sun, Hui; Wu, Gelin; Sun, Wenjun; Lv, Haitao; Jiao, Guozheng; Xu, Hongying; Yuan, Ye; Liu, Lian; Zou, Dixin; Wu, Zeming; Han, Ying; Yan, Guangli; Dong, Wei; Wu, Fangfang; Dong, Tianwei; Yu, Yang; Zhang, Shuxiang; Wu, Xiuhong; Tong, Xin; Meng, Xiangcai

2013-01-01

To enhance the therapeutic efficacy and reduce the adverse effects of traditional Chinese medicine, practitioners often prescribe combinations of plant species and/or minerals, called formulae. Unfortunately, the working mechanisms of most of these compounds are difficult to determine and thus remain unknown. In an attempt to address the benefits of formulae based on current biomedical approaches, we analyzed the components of Yinchenhao Tang, a classical formula that has been shown to be clinically effective for treating hepatic injury syndrome. The three principal components of Yinchenhao Tang are Artemisia annua L., Gardenia jasminoids Ellis, and Rheum Palmatum L., whose major active ingredients are 6,7-dimethylesculetin (D), geniposide (G), and rhein (R), respectively. To determine the mechanisms underlying the efficacy of this formula, we conducted a systematic analysis of the therapeutic effects of the DGR compound using immunohistochemistry, biochemistry, metabolomics, and proteomics. Here, we report that the DGR combination exerts a more robust therapeutic effect than any one or two of the three individual compounds by hitting multiple targets in a rat model of hepatic injury. Thus, DGR synergistically causes intensified dynamic changes in metabolic biomarkers, regulates molecular networks through target proteins, has a synergistic/additive effect, and activates both intrinsic and extrinsic pathways. PMID:23362329

Metabolomics coupled with proteomics advancing drug discovery toward more agile development of targeted combination therapies.

PubMed

Wang, Xijun; Zhang, Aihua; Wang, Ping; Sun, Hui; Wu, Gelin; Sun, Wenjun; Lv, Haitao; Jiao, Guozheng; Xu, Hongying; Yuan, Ye; Liu, Lian; Zou, Dixin; Wu, Zeming; Han, Ying; Yan, Guangli; Dong, Wei; Wu, Fangfang; Dong, Tianwei; Yu, Yang; Zhang, Shuxiang; Wu, Xiuhong; Tong, Xin; Meng, Xiangcai

2013-05-01

To enhance the therapeutic efficacy and reduce the adverse effects of traditional Chinese medicine, practitioners often prescribe combinations of plant species and/or minerals, called formulae. Unfortunately, the working mechanisms of most of these compounds are difficult to determine and thus remain unknown. In an attempt to address the benefits of formulae based on current biomedical approaches, we analyzed the components of Yinchenhao Tang, a classical formula that has been shown to be clinically effective for treating hepatic injury syndrome. The three principal components of Yinchenhao Tang are Artemisia annua L., Gardenia jasminoids Ellis, and Rheum Palmatum L., whose major active ingredients are 6,7-dimethylesculetin (D), geniposide (G), and rhein (R), respectively. To determine the mechanisms underlying the efficacy of this formula, we conducted a systematic analysis of the therapeutic effects of the DGR compound using immunohistochemistry, biochemistry, metabolomics, and proteomics. Here, we report that the DGR combination exerts a more robust therapeutic effect than any one or two of the three individual compounds by hitting multiple targets in a rat model of hepatic injury. Thus, DGR synergistically causes intensified dynamic changes in metabolic biomarkers, regulates molecular networks through target proteins, has a synergistic/additive effect, and activates both intrinsic and extrinsic pathways.

Characterization of yakju brewed from glutinous rice and wild-type yeast strains isolated from nuruks.

PubMed

Kim, Hye Ryun; Kim, Jae-Ho; Bae, Dong-Hoon; Ahn, Byung-Hak

2010-12-01

Korean traditional rice wines yakju and takju are generally brewed with nuruk as the source of the saccharogenic enzymes by natural fermentation. To improve the quality of Korean rice wine, the microorganisms in the nuruk need to be studied. The objective of this research was to improve the quality of Korean wine with the wild-type yeast strains isolated from the fermentation starter, nuruk. Only strain YA-6 showed high activity in 20% ethanol. Precipitation of Y89-5-3 was similar to that of very flocculent yeast (〉80%) at 75.95%. Using 18S rRNA sequencing, all 10 strains were identified as Saccharomyces cerevisiae. Volatile compounds present in yakju were analyzed by gas chromatography-mass selective detector. The principal component analysis (PCA) of the volatile compounds grouped long-chain esters on the right side of the first principal component, PC1; these compounds were found in yakju that was made with strains YA-6, Y89-5-3, Y89-5- 2, Y90-9, and Y89-1-1. On the other side of PC1 were short-chain esters; these compounds were found in wines that were brewed with strains Y183-2, Y268-3, Y54-3, Y98-4, and Y88-4. Overall, the results indicated that using different wild-type yeast strains in the fermentation process significantly affects the chemical characteristics of the glutinous rice wine.

Transformations of the chemical compositions of high molecular weight DOM along a salinity transect: Using two dimensional correlation spectroscopy and principal component analysis approaches

NASA Astrophysics Data System (ADS)

Abdulla, Hussain A. N.; Minor, Elizabeth C.; Dias, Robert F.; Hatcher, Patrick G.

2013-10-01

In a study of chemical transformations of estuarine high-molecular-weight (HMW, >1000 Da) dissolved organic matter (DOM) collected over a period of two years along a transect through the Elizabeth River/Chesapeake Bay system to the coastal Atlantic Ocean off Virginia, USA, δ13C values, N/C ratios, and principal component analysis (PCA) of the solid-state 13C NMR (nuclear magnetic resonance) spectra of HMW-DOM show an abrupt change in both its sources and chemical structural composition occurring around salinity 20. HMW-DOM in the lower salinity region had lighter isotopic values, higher aromatic and lower carbohydrate contents relative to that in the higher salinity region. These changes around a salinity of 20 are possibly due to introduction of a significant amount of new carbon (autotrophic DOM) to the transect. PC-1 loadings plot shows that spatially differing DOM components are similar to previously reported 13C NMR spectra of heteropolysaccharides (HPS) and carboxyl-rich alicyclic molecules (CRAM). Applying two dimensional correlation spectroscopy techniques to 1H NMR spectra from the same samples reveals increases in the contribution of N-acetyl amino sugars, 6-deoxy sugars, and sulfated polysaccharides to HPS components along the salinity transect, which suggests a transition from plant derived carbohydrates to marine produced carbohydrates within the HMW-DOM pool. In contrast to what has been suggested previously, our combined results from 13C NMR, 1H NMR, and FTIR indicate that CRAM consists of at least two different classes of compounds (aliphatic polycarboxyl compounds and lignin-like compounds).

A measure for objects clustering in principal component analysis biplot: A case study in inter-city buses maintenance cost data

NASA Astrophysics Data System (ADS)

Ginanjar, Irlandia; Pasaribu, Udjianna S.; Indratno, Sapto W.

2017-03-01

This article presents the application of the principal component analysis (PCA) biplot for the needs of data mining. This article aims to simplify and objectify the methods for objects clustering in PCA biplot. The novelty of this paper is to get a measure that can be used to objectify the objects clustering in PCA biplot. Orthonormal eigenvectors, which are the coefficients of a principal component model representing an association between principal components and initial variables. The existence of the association is a valid ground to objects clustering based on principal axes value, thus if m principal axes used in the PCA, then the objects can be classified into 2m clusters. The inter-city buses are clustered based on maintenance costs data by using two principal axes PCA biplot. The buses are clustered into four groups. The first group is the buses with high maintenance costs, especially for lube, and brake canvass. The second group is the buses with high maintenance costs, especially for tire, and filter. The third group is the buses with low maintenance costs, especially for lube, and brake canvass. The fourth group is buses with low maintenance costs, especially for tire, and filter.

Pressor mechanism evaluation for phytochemical compounds using in silico compound-protein interaction prediction.

PubMed

He, Min; Cao, Dong-Sheng; Liang, Yi-Zeng; Li, Ya-Ping; Liu, Ping-Le; Xu, Qing-Song; Huang, Ren-Bin

2013-10-01

In this study, a method was applied to evaluate pressor mechanisms through compound-protein interactions. Our method assumed that the compounds with different pressor mechanisms should bind to different target proteins, and thereby these mechanisms could be differentiated using compound-protein interactions. Twenty-six phytochemical components and 46 tested target proteins related to blood pressure (BP) elevation were collected. Then, in silico compound-protein interactions prediction probabilities were calculated using a random forest model, which have been implemented in a web server, and the credibility was judged using related literature and other methods. Further, a heat map was constructed, it clearly showed different prediction probabilities accompanied with hierarchical clustering analysis results. Followed by a compound-protein interaction network was depicted according to the results, we can see the connectivity layout of phytochemical components with different target proteins within the BP elevation network, which guided the hypothesis generation of poly-pharmacology. Lastly, principal components analysis (PCA) was carried out upon the prediction probabilities, and pressor targets could be divided into three large classes: neurotransmitter receptors, hormones receptors and monoamine oxidases. In addition, steroid glycosides seem to be close to the region of hormone receptors, and a weak difference existed between them. This work explored the possibility for pharmacological or toxicological mechanism classification using compound-protein interactions. Such approaches could also be used to deduce pharmacological or toxicological mechanisms for uncharacterized compounds. Copyright © 2013 Elsevier Inc. All rights reserved.

Directly Reconstructing Principal Components of Heterogeneous Particles from Cryo-EM Images

PubMed Central

Tagare, Hemant D.; Kucukelbir, Alp; Sigworth, Fred J.; Wang, Hongwei; Rao, Murali

2015-01-01

Structural heterogeneity of particles can be investigated by their three-dimensional principal components. This paper addresses the question of whether, and with what algorithm, the three-dimensional principal components can be directly recovered from cryo-EM images. The first part of the paper extends the Fourier slice theorem to covariance functions showing that the three-dimensional covariance, and hence the principal components, of a heterogeneous particle can indeed be recovered from two-dimensional cryo-EM images. The second part of the paper proposes a practical algorithm for reconstructing the principal components directly from cryo-EM images without the intermediate step of calculating covariances. This algorithm is based on maximizing the (posterior) likelihood using the Expectation-Maximization algorithm. The last part of the paper applies this algorithm to simulated data and to two real cryo-EM data sets: a data set of the 70S ribosome with and without Elongation Factor-G (EF-G), and a data set of the inluenza virus RNA dependent RNA Polymerase (RdRP). The first principal component of the 70S ribosome data set reveals the expected conformational changes of the ribosome as the EF-G binds and unbinds. The first principal component of the RdRP data set reveals a conformational change in the two dimers of the RdRP. PMID:26049077

Differentiation of tea varieties using UV-Vis spectra and pattern recognition techniques

NASA Astrophysics Data System (ADS)

Palacios-Morillo, Ana; Alcázar, Ángela.; de Pablos, Fernando; Jurado, José Marcos

2013-02-01

Tea, one of the most consumed beverages all over the world, is of great importance in the economies of a number of countries. Several methods have been developed to classify tea varieties or origins based in pattern recognition techniques applied to chemical data, such as metal profile, amino acids, catechins and volatile compounds. Some of these analytical methods become tedious and expensive to be applied in routine works. The use of UV-Vis spectral data as discriminant variables, highly influenced by the chemical composition, can be an alternative to these methods. UV-Vis spectra of methanol-water extracts of tea have been obtained in the interval 250-800 nm. Absorbances have been used as input variables. Principal component analysis was used to reduce the number of variables and several pattern recognition methods, such as linear discriminant analysis, support vector machines and artificial neural networks, have been applied in order to differentiate the most common tea varieties. A successful classification model was built by combining principal component analysis and multilayer perceptron artificial neural networks, allowing the differentiation between tea varieties. This rapid and simple methodology can be applied to solve classification problems in food industry saving economic resources.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kravtsov, A.I.

To determine the effect of geologic factors on the composition of abyssal derivates (complementing existing information on the geochemistry of volcanic gases) isotopic analysis of carbon was used to obtain physicochemical criteria of the origin of gases, independent of geologic-petrographic data. The investigations include component analysis of all the gases, particularly hydrocarbon compounds, repeatedly found in the fumarole emanations of pyroclastic streams. Volcanic carbon dioxide which is the principal component of gases of active volcanoes and hot springs in the Kuril-Kamchatka volcanic arc and of other volcanoes was investigated.

Chemometric characterization of alembic and industrial sugar cane spirits from cape verde and ceará, Brazil.

PubMed

Pereira, Regina F R; Vidal, Carla B; de Lima, Ari C A; Melo, Diego Q; Dantas, Allan N S; Lopes, Gisele S; do Nascimento, Ronaldo F; Gomes, Clerton L; da Silva, Maria Nataniela

2012-01-01

Sugar cane spirits are some of the most popular alcoholic beverages consumed in Cape Verde. The sugar cane spirit industry in Cape Verde is based mainly on archaic practices that operate without supervision and without efficient control of the production process. The objective of this work was to evaluate samples of industrial and alembic sugar cane spirits from Cape Verde and Ceará, Brazil using principal component analysis. Thirty-two samples of spirits were analyzed, twenty from regions of the islands of Cape Verde and twelve from Ceará, Brazil. Of the samples obtained from Ceará, Brazil seven are alembic and five are industrial spirits. The components analyzed in these studies included the following: volatile organic compounds (n-propanol, isobutanol, isoamylic, higher alcohols, alcoholic grade, acetaldehyde, acetic acid, acetate); copper; and sulfates.

Chemometric Characterization of Alembic and Industrial Sugar Cane Spirits from Cape Verde and Ceará, Brazil

PubMed Central

Pereira, Regina F. R.; Vidal, Carla B.; de Lima, Ari C. A.; Melo, Diego Q.; Dantas, Allan N. S.; Lopes, Gisele S.; do Nascimento, Ronaldo F.; Gomes, Clerton L.; da Silva, Maria Nataniela

2012-01-01

Sugar cane spirits are some of the most popular alcoholic beverages consumed in Cape Verde. The sugar cane spirit industry in Cape Verde is based mainly on archaic practices that operate without supervision and without efficient control of the production process. The objective of this work was to evaluate samples of industrial and alembic sugar cane spirits from Cape Verde and Ceará, Brazil using principal component analysis. Thirty-two samples of spirits were analyzed, twenty from regions of the islands of Cape Verde and twelve from Ceará, Brazil. Of the samples obtained from Ceará, Brazil seven are alembic and five are industrial spirits. The components analyzed in these studies included the following: volatile organic compounds (n-propanol, isobutanol, isoamylic, higher alcohols, alcoholic grade, acetaldehyde, acetic acid, acetate); copper; and sulfates. PMID:23227051

Identification of major phenolic compounds from Nephelium lappaceum L. and their antioxidant activities.

PubMed

Thitilertdecha, Nont; Teerawutgulrag, Aphiwat; Kilburn, Jeremy D; Rakariyatham, Nuansri

2010-03-09

Nephelium lappaceum is a tropical fruit whose peel possesses antioxidant properties. Experiments on the isolation and identification of the active constituents were conducted, and on their antioxidant activity using a lipid peroxidation inhibition assay. The methanolic extract of N. lappaceum peels exhibited strong antioxidant properties. Sephadex LH-20 chromatography was utilized in the isolation of each constituent and the antioxidant properties of each was studied. The isolated compounds were identified as ellagic acid (EA) (1), corilagin (2) and geraniin (3). These compounds accounted for 69.3% of methanolic extract, with geraniin (56.8%) as the major component, and exhibited much greater antioxidant activities than BHT in both lipid peroxidation (77-186 fold) and DPPH* (42-87 fold) assays. The results suggest that the isolated ellagitannins, as the principal components of rambutan peels, could be further utilized as both a medicine and in the food industry.

Simultaneous Analysis of Anthocyanin and Non-Anthocyanin Flavonoid in Various Tissues of Different Lotus (Nelumbo) Cultivars by HPLC-DAD-ESI-MSn

PubMed Central

Chen, Sha; Xiang, Yue; Deng, Jiao; Liu, Yanling; Li, Shaohua

2013-01-01

A validated HPLC-DAD-ESI-MSn method for the analysis of non-anthocyanin flavonoids was applied to nine different tissues of twelve lotus genotypes of Nelumbo nucifera and N. lutea, together with an optimized anthocyanin extraction and separation protocol for lotus petals. A total of five anthocyanins and twenty non-anthocyanin flavonoids was identified and quantified. Flavonoid contents and compositions varied with cultivar and tissue and were used as a basis to divide tissues into three groups characterized by kaempferol and quercetin derivatives. Influences on flower petal coloration were investigated by principal components analyses. High contents of kaempferol glycosides were detected in the petals of N. nucifera while high quercetin glycoside concentrations occurred in N. lutea. Based on these results, biosynthetic pathways leading to specific compounds in lotus tissues are deduced through metabolomic analysis of different genotypes and tissues and correlations among flavonoid compounds. PMID:23646125

Classification of 'Chemlali' accessions according to the geographical area using chemometric methods of phenolic profiles analysed by HPLC-ESI-TOF-MS.

PubMed

Taamalli, Amani; Arráez Román, David; Zarrouk, Mokhtar; Segura-Carretero, Antonio; Fernández-Gutiérrez, Alberto

2012-05-01

The present work describes a classification method of Tunisian 'Chemlali' olive oils based on their phenolic composition and geographical area. For this purpose, the data obtained by HPLC-ESI-TOF-MS from 13 samples of extra virgin olive oils, obtained from different production area throughout the country, were used for this study focusing in 23 phenolics compounds detected. The quantitative results showed a significant variability among the analysed oil samples. Factor analysis method using principal component was applied to the data in order to reduce the number of factors which explain the variability of the selected compounds. The data matrix constructed was subjected to a canonical discriminant analysis (CDA) in order to classify the oil samples. These results showed that 100% of cross-validated original group cases were correctly classified, which proves the usefulness of the selected variables. Copyright © 2011 Elsevier Ltd. All rights reserved.

Comparison of the phenolic composition of fruit juices by single step gradient HPLC analysis of multiple components versus multiple chromatographic runs optimised for individual families.

PubMed

Bremner, P D; Blacklock, C J; Paganga, G; Mullen, W; Rice-Evans, C A; Crozier, A

2000-06-01

After minimal sample preparation, two different HPLC methodologies, one based on a single gradient reversed-phase HPLC step, the other on multiple HPLC runs each optimised for specific components, were used to investigate the composition of flavonoids and phenolic acids in apple and tomato juices. The principal components in apple juice were identified as chlorogenic acid, phloridzin, caffeic acid and p-coumaric acid. Tomato juice was found to contain chlorogenic acid, caffeic acid, p-coumaric acid, naringenin and rutin. The quantitative estimates of the levels of these compounds, obtained with the two HPLC procedures, were very similar, demonstrating that either method can be used to analyse accurately the phenolic components of apple and tomato juices. Chlorogenic acid in tomato juice was the only component not fully resolved in the single run study and the multiple run analysis prior to enzyme treatment. The single run system of analysis is recommended for the initial investigation of plant phenolics and the multiple run approach for analyses where chromatographic resolution requires improvement.

Proteome comparison for discrimination between honeydew and floral honeys from botanical species Mimosa scabrella Bentham by principal component analysis.

PubMed

Azevedo, Mônia Stremel; Valentim-Neto, Pedro Alexandre; Seraglio, Siluana Katia Tischer; da Luz, Cynthia Fernandes Pinto; Arisi, Ana Carolina Maisonnave; Costa, Ana Carolina Oliveira

2017-10-01

Due to the increasing valuation and appreciation of honeydew honey in many European countries and also to existing contamination among different types of honeys, authentication is an important aspect of quality control with regard to guaranteeing the origin in terms of source (honeydew or floral) and needs to be determined. Furthermore, proteins are minor components of the honey, despite the importance of their physiological effects, and can differ according to the source of the honey. In this context, the aims of this study were to carry out protein extraction from honeydew and floral honeys and to discriminate these honeys from the same botanical species, Mimosa scabrella Bentham, through proteome comparison using two-dimensional gel electrophoresis and principal component analysis. The results showed that the proteome profile and principal component analysis can be a useful tool for discrimination between these types of honey using matched proteins (45 matched spots). Also, the proteome profile showed 160 protein spots in honeydew honey and 84 spots in the floral honey. The protein profile can be a differential characteristic of this type of honey, in view of the importance of proteins as bioactive compounds in honey. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

The classification of almonds (Prunus dulcis) by country and variety using UHPLC-HRMS-based untargeted metabolomics.

PubMed

Gil Solsona, R; Boix, C; Ibáñez, M; Sancho, J V

2018-03-01

The aim of this study was to use an untargeted UHPLC-HRMS-based metabolomics approach allowing discrimination between almonds based on their origin and variety. Samples were homogenised, extracted with ACN:H 2 O (80:20) containing 0.1% HCOOH and injected in a UHPLC-QTOF instrument in both positive and negative ionisation modes. Principal component analysis (PCA) was performed to ensure the absence of outliers. Partial least squares - discriminant analysis (PLS-DA) was employed to create and validate the models for country (with five different compounds) and variety (with 20 features), showing more than 95% accuracy. Additional samples were injected and the model was evaluated with blind samples, with more than 95% of samples being correctly classified using both models. MS/MS experiments were carried out to tentatively elucidate the highlighted marker compounds (pyranosides, peptides or amino acids, among others). This study has shown the potential of high-resolution mass spectrometry to perform and validate classification models, also providing information concerning the identification of the unexpected biomarkers which showed the highest discriminant power.

Classification of juices and fermented beverages made from unripe, ripe and senescent apples based on the aromatic profile using chemometrics.

PubMed

Braga, Cíntia Maia; Zielinski, Acácio Antonio Ferreira; Silva, Karolline Marques da; de Souza, Frederico Koch Fernandes; Pietrowski, Giovana de Arruda Moura; Couto, Marcelo; Granato, Daniel; Wosiacki, Gilvan; Nogueira, Alessandro

2013-11-15

The aim of this study was to assess differences between apple juices and fermented apple beverages elaborated with fruits from different varieties and at different ripening stages in the aroma profile by using chemometrics. Ripening influenced the aroma composition of the apple juice and fermented apple. For all varieties, senescent fruits provided more aromatic fermented apple beverages. However, no significant difference was noticed in samples made of senescent or ripe fruits of the Lisgala variety. Regarding the juices, ripe Gala apple had the highest total aroma concentration. Ethanal was the major compound identified in all the samples, with values between 11.83mg/L (unripe Lisgala juice) and 81.05mg/L (ripe Gala juice). 3-Methyl-1-butanol was the major compound identified in the fermented juices. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied and classified the juices and fermented juices based on physicochemical and aroma profile, demonstrating their applicability as tools to monitor the quality of apple-based products. Copyright © 2013 Elsevier Ltd. All rights reserved.

Characterization of volatile aroma compounds in different brewing barley cultivars.

PubMed

Dong, Liang; Hou, Yingmin; Li, Feng; Piao, Yongzhe; Zhang, Xiao; Zhang, Xiaoyu; Li, Cheng; Zhao, Changxin

2015-03-30

Beer is a popular alcoholic malt beverage resulting from fermentation of the aqueous extract of malted barley with hops. The aroma of brewing barley impacts the flavor of beer indirectly, because some flavor compounds or their precursors in beer come from the barley. The objectives of this research were to study volatile profiles and to characterize odor-active compounds of brewing barley in order to determine the variability of the aroma composition among different brewing barley cultivars. Forty-one volatiles comprising aldehydes, ketones, alcohols, organic acids, aromatic compounds and furans were identified using solid phase microextraction combined with gas chromatography/mass spectrometry, among which aldehydes, alcohols and ketones were quantitatively in greatest abundance. Quantitative measurements performed by means of solvent extraction and calculation of odor activity values revealed that acetaldehyde, 2-methylpropanal, 3-methylbutanal, 2-methylbutanal, hexanal, heptanal, octanal, nonanal, 3-methyl-1-butanol, cyclopentanol, 2,3-butanedione, 2,3-pentanedione, 2-heptanone, acetic acid, ethyl acetate, 2-pentylfuran and benzeneacetaldehyde, whose concentrations exceeded their odor thresholds, could be considered as odor-active compounds of brewing barley. Principal component analysis was employed to evaluate the differences among cultivars. The results demonstrated that the volatile profile based on the concentrations of aroma compounds enabled good differentiation of most barley cultivars. © 2014 Society of Chemical Industry.

Priority of VHS Development Based in Potential Area using Principal Component Analysis

NASA Astrophysics Data System (ADS)

Meirawan, D.; Ana, A.; Saripudin, S.

2018-02-01

The current condition of VHS is still inadequate in quality, quantity and relevance. The purpose of this research is to analyse the development of VHS based on the development of regional potential by using principal component analysis (PCA) in Bandung, Indonesia. This study used descriptive qualitative data analysis using the principle of secondary data reduction component. The method used is Principal Component Analysis (PCA) analysis with Minitab Statistics Software tool. The results of this study indicate the value of the lowest requirement is a priority of the construction of development VHS with a program of majors in accordance with the development of regional potential. Based on the PCA score found that the main priority in the development of VHS in Bandung is in Saguling, which has the lowest PCA value of 416.92 in area 1, Cihampelas with the lowest PCA value in region 2 and Padalarang with the lowest PCA value.

Radiative Transfer Modeling and Retrievals for Advanced Hyperspectral Sensors

NASA Technical Reports Server (NTRS)

Liu, Xu; Zhou, Daniel K.; Larar, Allen M.; Smith, William L., Sr.; Mango, Stephen A.

2009-01-01

A novel radiative transfer model and a physical inversion algorithm based on principal component analysis will be presented. Instead of dealing with channel radiances, the new approach fits principal component scores of these quantities. Compared to channel-based radiative transfer models, the new approach compresses radiances into a much smaller dimension making both forward modeling and inversion algorithm more efficient.

Information extraction from multivariate images

NASA Technical Reports Server (NTRS)

Park, S. K.; Kegley, K. A.; Schiess, J. R.

1986-01-01

An overview of several multivariate image processing techniques is presented, with emphasis on techniques based upon the principal component transformation (PCT). Multiimages in various formats have a multivariate pixel value, associated with each pixel location, which has been scaled and quantized into a gray level vector, and the bivariate of the extent to which two images are correlated. The PCT of a multiimage decorrelates the multiimage to reduce its dimensionality and reveal its intercomponent dependencies if some off-diagonal elements are not small, and for the purposes of display the principal component images must be postprocessed into multiimage format. The principal component analysis of a multiimage is a statistical analysis based upon the PCT whose primary application is to determine the intrinsic component dimensionality of the multiimage. Computational considerations are also discussed.

Capillary electrophoresis fingerprinting and spectrophotometric determination of antioxidant potential for classification of Mentha products.

PubMed

Roblová, Vendula; Bittová, Miroslava; Kubáň, Petr; Kubáň, Vlastimil

2016-07-01

In this work aqueous infusions from ten Mentha herbal samples (four different Mentha species and six hybrids of Mentha x piperita) and 20 different peppermint teas were screened by capillary electrophoresis with UV detection. The fingerprint separation was accomplished in a 25 mM borate background electrolyte with 10% methanol at pH 9.3. The total polyphenolic content in the extracts was determined spectrophotometrically at 765 nm by a Folin-Ciocalteu phenol assay. Total antioxidant activity was determined by scavenging of 2,2-diphenyl-1-picrylhydrazyl radical at 515 nm. The peak areas of 12 dominant peaks from CE analysis, present in all samples, and the value of total polyphenolic content and total antioxidant activity obtained by spectrophotometry was combined into a single data matrix and principal component analysis was applied. The obtained principal component analysis model resulted in distinct clusters of Mentha and peppermint tea samples distinguishing the samples according to their potential protective antioxidant effect. Principal component analysis, using a non-targeted approach with no need for compound identification, was found as a new promising tool for the screening of herbal tea products. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Airborne electromagnetic data levelling using principal component analysis based on flight line difference

NASA Astrophysics Data System (ADS)

Zhang, Qiong; Peng, Cong; Lu, Yiming; Wang, Hao; Zhu, Kaiguang

2018-04-01

A novel technique is developed to level airborne geophysical data using principal component analysis based on flight line difference. In the paper, flight line difference is introduced to enhance the features of levelling error for airborne electromagnetic (AEM) data and improve the correlation between pseudo tie lines. Thus we conduct levelling to the flight line difference data instead of to the original AEM data directly. Pseudo tie lines are selected distributively cross profile direction, avoiding the anomalous regions. Since the levelling errors of selective pseudo tie lines show high correlations, principal component analysis is applied to extract the local levelling errors by low-order principal components reconstruction. Furthermore, we can obtain the levelling errors of original AEM data through inverse difference after spatial interpolation. This levelling method does not need to fly tie lines and design the levelling fitting function. The effectiveness of this method is demonstrated by the levelling results of survey data, comparing with the results from tie-line levelling and flight-line correlation levelling.

Is Allelopathic Activity of Ipomoea murucoides Induced by Xylophage Damage?

PubMed

Flores-Palacios, Alejandro; Corona-López, Angélica María; Rios, María Yolanda; Aguilar-Guadarrama, Berenice; Toledo-Hernández, Víctor Hugo; Rodríguez-López, Verónica; Valencia-Díaz, Susana

2015-01-01

Herbivory activates the synthesis of allelochemicals that can mediate plant-plant interactions. There is an inverse relationship between the activity of xylophages and the abundance of epiphytes on Ipomoea murucoides. Xylophagy may modify the branch chemical constitution, which also affects the liberation of allelochemicals with defense and allelopathic properties. We evaluated the bark chemical content and the effect of extracts from branches subjected to treatments of exclusion, mechanical damage and the presence/absence of epiphytes, on the seed germination of the epiphyte Tillandsia recurvata. Principal component analysis showed that branches without any treatment separate from branches subjected to treatments; damaged and excluded branches had similar chemical content but we found no evidence to relate intentional damage with allelopathy; however 1-hexadecanol, a defense volatile compound correlated positively with principal component (PC) 1. The chemical constitution of branches subject to exclusion plus damage or plus epiphytes was similar among them. PC2 indicated that palmitic acid (allelopathic compound) and squalene, a triterpene that attracts herbivore enemies, correlated positively with the inhibition of seed germination of T. recurvata. Inhibition of seed germination of T. recurvata was mainly correlated with the increment of palmitic acid and this compound reached higher concentrations in excluded branches treatments. Then, it is likely that the allelopathic response of I. murucoides would increase to the damage (shade, load) that may be caused by a high load of epiphytes than to damage caused by the xylophages.

Directly reconstructing principal components of heterogeneous particles from cryo-EM images.

PubMed

Tagare, Hemant D; Kucukelbir, Alp; Sigworth, Fred J; Wang, Hongwei; Rao, Murali

2015-08-01

Structural heterogeneity of particles can be investigated by their three-dimensional principal components. This paper addresses the question of whether, and with what algorithm, the three-dimensional principal components can be directly recovered from cryo-EM images. The first part of the paper extends the Fourier slice theorem to covariance functions showing that the three-dimensional covariance, and hence the principal components, of a heterogeneous particle can indeed be recovered from two-dimensional cryo-EM images. The second part of the paper proposes a practical algorithm for reconstructing the principal components directly from cryo-EM images without the intermediate step of calculating covariances. This algorithm is based on maximizing the posterior likelihood using the Expectation-Maximization algorithm. The last part of the paper applies this algorithm to simulated data and to two real cryo-EM data sets: a data set of the 70S ribosome with and without Elongation Factor-G (EF-G), and a data set of the influenza virus RNA dependent RNA Polymerase (RdRP). The first principal component of the 70S ribosome data set reveals the expected conformational changes of the ribosome as the EF-G binds and unbinds. The first principal component of the RdRP data set reveals a conformational change in the two dimers of the RdRP. Copyright © 2015 Elsevier Inc. All rights reserved.

Principal component analysis of PiB distribution in Parkinson and Alzheimer diseases

PubMed Central

Markham, Joanne; Flores, Hubert; Hartlein, Johanna M.; Goate, Alison M.; Cairns, Nigel J.; Videen, Tom O.; Perlmutter, Joel S.

2013-01-01

Objective: To use principal component analyses (PCA) of Pittsburgh compound B (PiB) PET imaging to determine whether the pattern of in vivo β-amyloid (Aβ) in Parkinson disease (PD) with cognitive impairment is similar to the pattern found in symptomatic Alzheimer disease (AD). Methods: PiB PET scans were obtained from participants with PD with cognitive impairment (n = 53), participants with symptomatic AD (n = 35), and age-matched controls (n = 67). All were assessed using the Clinical Dementia Rating and APOE genotype was determined in 137 participants. PCA was used to 1) determine the PiB binding pattern in AD, 2) determine a possible unique PD pattern, and 3) directly compare the PiB binding patterns in PD and AD groups. Results: The first 2 principal components (PC1 and PC2) significantly separated the AD and control participants (p < 0.001). Participants with PD with cognitive impairment also were significantly different from participants with symptomatic AD on both components (p < 0.001). However, there was no difference between PD and controls on either component. Even those participants with PD with elevated mean cortical binding potentials were significantly different from participants with AD on both components. Conclusion: Using PCA, we demonstrated that participants with PD with cognitive impairment do not exhibit the same PiB binding pattern as participants with AD. These data suggest that Aβ deposition may play a different pathophysiologic role in the cognitive impairment of PD compared to that in AD. PMID:23825179

Fluorescence Intrinsic Characterization of Excitation-Emission Matrix Using Multi-Dimensional Ensemble Empirical Mode Decomposition

PubMed Central

Chang, Chi-Ying; Chang, Chia-Chi; Hsiao, Tzu-Chien

2013-01-01

Excitation-emission matrix (EEM) fluorescence spectroscopy is a noninvasive method for tissue diagnosis and has become important in clinical use. However, the intrinsic characterization of EEM fluorescence remains unclear. Photobleaching and the complexity of the chemical compounds make it difficult to distinguish individual compounds due to overlapping features. Conventional studies use principal component analysis (PCA) for EEM fluorescence analysis, and the relationship between the EEM features extracted by PCA and diseases has been examined. The spectral features of different tissue constituents are not fully separable or clearly defined. Recently, a non-stationary method called multi-dimensional ensemble empirical mode decomposition (MEEMD) was introduced; this method can extract the intrinsic oscillations on multiple spatial scales without loss of information. The aim of this study was to propose a fluorescence spectroscopy system for EEM measurements and to describe a method for extracting the intrinsic characteristics of EEM by MEEMD. The results indicate that, although PCA provides the principal factor for the spectral features associated with chemical compounds, MEEMD can provide additional intrinsic features with more reliable mapping of the chemical compounds. MEEMD has the potential to extract intrinsic fluorescence features and improve the detection of biochemical changes. PMID:24240806

Facilitating in vivo tumor localization by principal component analysis based on dynamic fluorescence molecular imaging

NASA Astrophysics Data System (ADS)

Gao, Yang; Chen, Maomao; Wu, Junyu; Zhou, Yuan; Cai, Chuangjian; Wang, Daliang; Luo, Jianwen

2017-09-01

Fluorescence molecular imaging has been used to target tumors in mice with xenograft tumors. However, tumor imaging is largely distorted by the aggregation of fluorescent probes in the liver. A principal component analysis (PCA)-based strategy was applied on the in vivo dynamic fluorescence imaging results of three mice with xenograft tumors to facilitate tumor imaging, with the help of a tumor-specific fluorescent probe. Tumor-relevant features were extracted from the original images by PCA and represented by the principal component (PC) maps. The second principal component (PC2) map represented the tumor-related features, and the first principal component (PC1) map retained the original pharmacokinetic profiles, especially of the liver. The distribution patterns of the PC2 map of the tumor-bearing mice were in good agreement with the actual tumor location. The tumor-to-liver ratio and contrast-to-noise ratio were significantly higher on the PC2 map than on the original images, thus distinguishing the tumor from its nearby fluorescence noise of liver. The results suggest that the PC2 map could serve as a bioimaging marker to facilitate in vivo tumor localization, and dynamic fluorescence molecular imaging with PCA could be a valuable tool for future studies of in vivo tumor metabolism and progression.

HPLC-PDA Combined with Chemometrics for Quantitation of Active Components and Quality Assessment of Raw and Processed Fruits of Xanthium strumarium L.

PubMed

Jiang, Hai; Yang, Liu; Xing, Xudong; Yan, Meiling; Guo, Xinyue; Yang, Bingyou; Wang, Qiuhong; Kuang, Haixue

2018-01-25

As a valuable herbal medicine, the fruits of Xanthium strumarium L. (Xanthii Fructus) have been widely used in raw and processed forms to achieve different therapeutic effects in practice. In this study, a comprehensive strategy was proposed for evaluating the active components in 30 batches of raw and processed Xanthii Fructus (RXF and PXF) samples, based on high-performance liquid chromatography coupled with photodiode array detection (HPLC-PDA). Twelve common peaks were detected and eight compounds of caffeoylquinic acids were simultaneously quantified in RXF and PXF. All the analytes were detected with satisfactory linearity (R² > 0.9991) over wide concentration ranges. Simultaneously, the chemically latent information was revealed by hierarchical cluster analysis (HCA) and principal component analysis (PCA). The results suggest that there were significant differences between RXF and PXF from different regions in terms of the content of eight caffeoylquinic acids. Potential chemical markers for XF were found during processing by chemometrics.

Authentication of virgin olive oil by a novel curve resolution approach combined with visible spectroscopy.

PubMed

Ferreiro-González, Marta; Barbero, Gerardo F; Álvarez, José A; Ruiz, Antonio; Palma, Miguel; Ayuso, Jesús

2017-04-01

Adulteration of olive oil is not only a major economic fraud but can also have major health implications for consumers. In this study, a combination of visible spectroscopy with a novel multivariate curve resolution method (CR), principal component analysis (PCA) and linear discriminant analysis (LDA) is proposed for the authentication of virgin olive oil (VOO) samples. VOOs are well-known products with the typical properties of a two-component system due to the two main groups of compounds that contribute to the visible spectra (chlorophylls and carotenoids). Application of the proposed CR method to VOO samples provided the two pure-component spectra for the aforementioned families of compounds. A correlation study of the real spectra and the resolved component spectra was carried out for different types of oil samples (n=118). LDA using the correlation coefficients as variables to discriminate samples allowed the authentication of 95% of virgin olive oil samples. Copyright © 2016 Elsevier Ltd. All rights reserved.

Synthesis, Structure-Activity Relationships (SAR) and in Silico Studies of Coumarin Derivatives with Antifungal Activity

PubMed Central

de Araújo, Rodrigo S. A.; Guerra, Felipe Q. S.; de O. Lima, Edeltrudes; de Simone, Carlos A.; Tavares, Josean F.; Scotti, Luciana; Scotti, Marcus T.; de Aquino, Thiago M.; de Moura, Ricardo O.; Mendonça, Francisco J. B.; Barbosa-Filho, José M.

2013-01-01

The increased incidence of opportunistic fungal infections, associated with greater resistance to the antifungal drugs currently in use has highlighted the need for new solutions. In this study twenty four coumarin derivatives were screened in vitro for antifungal activity against strains of Aspergillus. Some of the compounds exhibited significant antifungal activity with MICs values ranging between 16 and 32 μg/mL. The structure-activity relationships (SAR) study demonstrated that O-substitutions are essential for antifungal activity. It also showed that the presence of a short aliphatic chain and/or electron withdrawing groups (NO2 and/or acetate) favor activity. These findings were confirmed using density functional theory (DFT), when calculating the LUMO density. In Principal Component Analysis (PCA), two significant principal components (PCs) explained more than 60% of the total variance. The best Partial Least Squares Regression (PLS) model showed an r2 of 0.86 and q2cv of 0.64 corroborating the SAR observations as well as demonstrating a greater probe N1 interaction for active compounds. Descriptors generated by TIP correlogram demonstrated the importance of the molecular shape for antifungal activity. PMID:23306152

Gas chromatography/principal component similarity system for detection of E. coli and S. aureus contaminating salmon and hamburger.

PubMed

Nakai, S; Wang, Z H; Dou, J; Nakamura, S; Ogawa, M; Nakai, E; Vanderstoep, J

1999-02-01

Coho, Atlantic, Spring, and Sockeye salmon and five commercial samples of hamburger patties were analyzed by processing gas chromatography (GC) data of volatile compounds using the principal component similarity (PCS) technique. PCS scattergrams of the samples inoculated with Escherichia coli and Staphylococcus aureus followed by incubation showed the pattern-shift lines moving away from the data point for uninoculated, unincubated reference samples in different directions with increasing incubation time. When the PCS scattergrams were drawn for samples incubated overnight, the samples inoculated with the two bacterial species and the uninoculated samples appeared as three separated groups. This GC/PCS approach has the potential to ensure quality of samples by discriminating good samples from potentially spoiled samples. The latter may require further microbial assays to identify the bacteria species potentially contaminating foods.

A comparative UPLC-Q/TOF-MS-based metabolomics approach for distinguishing Zingiber officinale Roscoe of two geographical origins.

PubMed

Mais, Enos; Alolga, Raphael N; Wang, Shi-Lei; Linus, Loveth O; Yin, Xiaojin; Qi, Lian-Wen

2018-02-01

Ginger, the rhizome of Zingiber officinale Roscoe, is a popular spice used in the food, beverage and confectionary industries. In this study, we report an untargeted UPLC-Q/TOF-MS-based metabolomics approach for comprehensively discriminating between ginger from two geographical locations, Ghana in West Africa and China. Forty batches of fresh ginger from both countries were discriminated using principal component analysis and orthogonal partial least squares discrimination analysis. Sixteen differential metabolites were identified between the gingers from the two geographical locations, six of which were identified as the marker compounds responsible for the discrimination. Our study highlights the essence and predictive power of metabolomics in detecting minute differences in same varieties of plants/plant samples based on the levels and composition of their metabolites. Copyright © 2017 Elsevier Ltd. All rights reserved.

Principal Component Clustering Approach to Teaching Quality Discriminant Analysis

ERIC Educational Resources Information Center

Xian, Sidong; Xia, Haibo; Yin, Yubo; Zhai, Zhansheng; Shang, Yan

2016-01-01

Teaching quality is the lifeline of the higher education. Many universities have made some effective achievement about evaluating the teaching quality. In this paper, we establish the Students' evaluation of teaching (SET) discriminant analysis model and algorithm based on principal component clustering analysis. Additionally, we classify the SET…

Analysis of the principal component algorithm in phase-shifting interferometry.

PubMed

Vargas, J; Quiroga, J Antonio; Belenguer, T

2011-06-15

We recently presented a new asynchronous demodulation method for phase-sampling interferometry. The method is based in the principal component analysis (PCA) technique. In the former work, the PCA method was derived heuristically. In this work, we present an in-depth analysis of the PCA demodulation method.

The risk of misclassifying subjects within principal component based asset index

PubMed Central

2014-01-01

The asset index is often used as a measure of socioeconomic status in empirical research as an explanatory variable or to control confounding. Principal component analysis (PCA) is frequently used to create the asset index. We conducted a simulation study to explore how accurately the principal component based asset index reflects the study subjects’ actual poverty level, when the actual poverty level is generated by a simple factor analytic model. In the simulation study using the PC-based asset index, only 1% to 4% of subjects preserved their real position in a quintile scale of assets; between 44% to 82% of subjects were misclassified into the wrong asset quintile. If the PC-based asset index explained less than 30% of the total variance in the component variables, then we consistently observed more than 50% misclassification across quintiles of the index. The frequency of misclassification suggests that the PC-based asset index may not provide a valid measure of poverty level and should be used cautiously as a measure of socioeconomic status. PMID:24987446

Snapshot hyperspectral imaging probe with principal component analysis and confidence ellipse for classification

NASA Astrophysics Data System (ADS)

Lim, Hoong-Ta; Murukeshan, Vadakke Matham

2017-06-01

Hyperspectral imaging combines imaging and spectroscopy to provide detailed spectral information for each spatial point in the image. This gives a three-dimensional spatial-spatial-spectral datacube with hundreds of spectral images. Probe-based hyperspectral imaging systems have been developed so that they can be used in regions where conventional table-top platforms would find it difficult to access. A fiber bundle, which is made up of specially-arranged optical fibers, has recently been developed and integrated with a spectrograph-based hyperspectral imager. This forms a snapshot hyperspectral imaging probe, which is able to form a datacube using the information from each scan. Compared to the other configurations, which require sequential scanning to form a datacube, the snapshot configuration is preferred in real-time applications where motion artifacts and pixel misregistration can be minimized. Principal component analysis is a dimension-reducing technique that can be applied in hyperspectral imaging to convert the spectral information into uncorrelated variables known as principal components. A confidence ellipse can be used to define the region of each class in the principal component feature space and for classification. This paper demonstrates the use of the snapshot hyperspectral imaging probe to acquire data from samples of different colors. The spectral library of each sample was acquired and then analyzed using principal component analysis. Confidence ellipse was then applied to the principal components of each sample and used as the classification criteria. The results show that the applied analysis can be used to perform classification of the spectral data acquired using the snapshot hyperspectral imaging probe.

Assessing therapeutic relevance of biologically interesting, ampholytic substances based on their physicochemical and spectral characteristics with chemometric tools

NASA Astrophysics Data System (ADS)

Judycka, U.; Jagiello, K.; Bober, L.; Błażejowski, J.; Puzyn, T.

2018-06-01

Chemometric tools were applied to investigate the biological behaviour of ampholytic substances in relation to their physicochemical and spectral properties. Results of the Principal Component Analysis suggest that size of molecules and their electronic and spectral characteristics are the key properties required to predict therapeutic relevance of the compounds examined. These properties were used for developing the structure-activity classification model. The classification model allows assessing the therapeutic behaviour of ampholytic substances on the basis of solely values of descriptors that can be obtained computationally. Thus, the prediction is possible without necessity of carrying out time-consuming and expensive laboratory tests, which is its main advantage.

CE-TOF MS-based metabolomic profiling revealed characteristic metabolic pathways in postmortem porcine fast and slow type muscles.

PubMed

Muroya, Susumu; Oe, Mika; Nakajima, Ikuyo; Ojima, Koichi; Chikuni, Koichi

2014-12-01

To determine key compounds and metabolic pathways associated with meat quality, we profiled metabolites in postmortem porcine longissimus lumborum (LL) and vastus intermedius (VI) muscles with different aging times by global metabolomics using capillary electrophoresis-time of flight mass spectrometry. Loading analyses of the principal component analysis showed that hydrophilic amino acids and β-alanine-related compounds contributed to the muscle type positively and negatively, respectively, whereas glycolytic and ATP degradation products contributed to aging time. At 168h postmortem, LL samples were characterized by abundance of combinations of amino acids, dipeptides, and glycolytic products, whereas the VI samples were characterized by abundance of both sulfur-containing compounds and amino acids. The AMP and inosine contents in the VI were approx. 10 times higher than those in the LL at 4h postmortem, suggesting different rates of inosine 5'-monophosphate (IMP) accumulation by adenylate kinase 7 and 5'-nucleotidase, and subsequent different production levels of IMP and hypoxanthine between these two porcine muscles. Copyright © 2014 Elsevier Ltd. All rights reserved.

VOLATILE ORGANIC COMPOUND DETERMINATIONS USING SURROGATE-BASED CORRECTION FOR METHOD AND MATRIX EFFECTS

EPA Science Inventory

The principal properties related to analyte recovery in a vacuum distillate are boiling point and relative volatility. The basis for selecting compounds to measure the relationship between these properties and recovery for a vacuum distillation is presented. Surrogates are incorp...

Collection and identification of human remains volatiles by non-contact, dynamic airflow sampling and SPME-GC/MS using various sorbent materials.

PubMed

DeGreeff, Lauryn E; Furton, Kenneth G

2011-09-01

Human remains detection canines are used in locating deceased humans in diverse scenarios and environments based on odor produced during the decay process of the human body. It has been established that human remains detection canines are capable of locating human remains specifically, as opposed to living humans or animal remains, thus suggesting a difference in odor between the different sources. This work explores the collection and determination of such odors using a dynamic headspace concentration device. The airflow rate and three sorbent materials-Dukal cotton gauze, Johnson & Johnson cotton-blend gauze, and polyester material-used for odor collection were evaluated using standard compounds. It was determined that higher airflow rates and openly woven material, e.g., Dukal cotton gauze, yielded significantly less total volatile compounds due to compound breakthrough through the sorbent material. Collection from polymer- and cellulose-based materials demonstrated that the molecular backbone of the material is a factor in compound collection as well. Volatiles, including cyclic and straight-chain hydrocarbons, organic acids, sulfides, aldehydes, ketones, and alcohols, were collected from a population of 27 deceased bodies from two collection locations. The common compounds between the subjects were compared and the odor profiles were determined. These odor profiles were compared with those of animal remains and living human subjects collected in the same manner. Principal component analysis showed that the odor profiles of the three sample types were distinct.

Principal component and spatial correlation analysis of spectroscopic-imaging data in scanning probe microscopy.

PubMed

Jesse, Stephen; Kalinin, Sergei V

2009-02-25

An approach for the analysis of multi-dimensional, spectroscopic-imaging data based on principal component analysis (PCA) is explored. PCA selects and ranks relevant response components based on variance within the data. It is shown that for examples with small relative variations between spectra, the first few PCA components closely coincide with results obtained using model fitting, and this is achieved at rates approximately four orders of magnitude faster. For cases with strong response variations, PCA allows an effective approach to rapidly process, de-noise, and compress data. The prospects for PCA combined with correlation function analysis of component maps as a universal tool for data analysis and representation in microscopy are discussed.

Apiose: one of nature's witty games.

PubMed

Pičmanová, Martina; Møller, Birger Lindberg

2016-05-01

Apiose is a unique branched-chain pentose found principally in plants. It is a key component of structurally complex cell wall polysaccharides, as well as being present in a large number of naturally occurring secondary metabolites. This review provides a comprehensive overview of the current state of knowledge on the metabolism and natural occurrence of apiose, using cyanogenic glycosides and their related compounds as a case study. The biological function of apiose and of apiosylated compounds is discussed. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Wavelet based de-noising of breath air absorption spectra profiles for improved classification by principal component analysis

NASA Astrophysics Data System (ADS)

Kistenev, Yu. V.; Shapovalov, A. V.; Borisov, A. V.; Vrazhnov, D. A.; Nikolaev, V. V.; Nikiforova, O. Yu.

2015-11-01

The comparison results of different mother wavelets used for de-noising of model and experimental data which were presented by profiles of absorption spectra of exhaled air are presented. The impact of wavelets de-noising on classification quality made by principal component analysis are also discussed.

Applications of Nonlinear Principal Components Analysis to Behavioral Data.

ERIC Educational Resources Information Center

Hicks, Marilyn Maginley

1981-01-01

An empirical investigation of the statistical procedure entitled nonlinear principal components analysis was conducted on a known equation and on measurement data in order to demonstrate the procedure and examine its potential usefulness. This method was suggested by R. Gnanadesikan and based on an early paper of Karl Pearson. (Author/AL)

[Applications of three-dimensional fluorescence spectrum of dissolved organic matter to identification of red tide algae].

PubMed

Lü, Gui-Cai; Zhao, Wei-Hong; Wang, Jiang-Tao

2011-01-01

The identification techniques for 10 species of red tide algae often found in the coastal areas of China were developed by combining the three-dimensional fluorescence spectra of fluorescence dissolved organic matter (FDOM) from the cultured red tide algae with principal component analysis. Based on the results of principal component analysis, the first principal component loading spectrum of three-dimensional fluorescence spectrum was chosen as the identification characteristic spectrum for red tide algae, and the phytoplankton fluorescence characteristic spectrum band was established. Then the 10 algae species were tested using Bayesian discriminant analysis with a correct identification rate of more than 92% for Pyrrophyta on the level of species, and that of more than 75% for Bacillariophyta on the level of genus in which the correct identification rates were more than 90% for the phaeodactylum and chaetoceros. The results showed that the identification techniques for 10 species of red tide algae based on the three-dimensional fluorescence spectra of FDOM from the cultured red tide algae and principal component analysis could work well.

Stationary Wavelet-based Two-directional Two-dimensional Principal Component Analysis for EMG Signal Classification

NASA Astrophysics Data System (ADS)

Ji, Yi; Sun, Shanlin; Xie, Hong-Bo

2017-06-01

Discrete wavelet transform (WT) followed by principal component analysis (PCA) has been a powerful approach for the analysis of biomedical signals. Wavelet coefficients at various scales and channels were usually transformed into a one-dimensional array, causing issues such as the curse of dimensionality dilemma and small sample size problem. In addition, lack of time-shift invariance of WT coefficients can be modeled as noise and degrades the classifier performance. In this study, we present a stationary wavelet-based two-directional two-dimensional principal component analysis (SW2D2PCA) method for the efficient and effective extraction of essential feature information from signals. Time-invariant multi-scale matrices are constructed in the first step. The two-directional two-dimensional principal component analysis then operates on the multi-scale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify eight hand motions using 4-channel electromyographic (EMG) signals recorded in healthy subjects and amputees, which illustrates the efficiency and effectiveness of the proposed method for biomedical signal analysis.

[A study of Boletus bicolor from different areas using Fourier transform infrared spectrometry].

PubMed

Zhou, Zai-Jin; Liu, Gang; Ren, Xian-Pei

2010-04-01

It is hard to differentiate the same species of wild growing mushrooms from different areas by macromorphological features. In this paper, Fourier transform infrared (FTIR) spectroscopy combined with principal component analysis was used to identify 58 samples of boletus bicolor from five different areas. Based on the fingerprint infrared spectrum of boletus bicolor samples, principal component analysis was conducted on 58 boletus bicolor spectra in the range of 1 350-750 cm(-1) using the statistical software SPSS 13.0. According to the result, the accumulated contributing ratio of the first three principal components accounts for 88.87%. They included almost all the information of samples. The two-dimensional projection plot using first and second principal component is a satisfactory clustering effect for the classification and discrimination of boletus bicolor. All boletus bicolor samples were divided into five groups with a classification accuracy of 98.3%. The study demonstrated that wild growing boletus bicolor at species level from different areas can be identified by FTIR spectra combined with principal components analysis.

Experimental and computational prediction of glass transition temperature of drugs.

PubMed

Alzghoul, Ahmad; Alhalaweh, Amjad; Mahlin, Denny; Bergström, Christel A S

2014-12-22

Glass transition temperature (Tg) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between Tg and melting temperature (Tm) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of Tg were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on Tm predicted Tg with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict Tg of drug-like molecules with high accuracy were developed. If Tm is available, a simple linear regression can be used to predict Tg. However, the results also suggest that support vector regression and calculated molecular descriptors can predict Tg with equal accuracy, already before compound synthesis.

The influence of phytochemical composition and resulting sensory attributes on preference for salad rocket (Eruca sativa) accessions by consumers of varying TAS2R38 diplotype.

PubMed

Bell, Luke; Methven, Lisa; Wagstaff, Carol

2017-05-01

Seven accessions of Eruca sativa ("salad rocket") were subjected to a randomised consumer assessment. Liking of appearance and taste attributes were analysed, as well as perceptions of bitterness, hotness, pepperiness and sweetness. Consumers were genotyped for TAS2R38 status to determine if liking is influenced by perception of bitter compounds such as glucosinolates (GSLs) and isothiocyanates (ITCs). Responses were combined with previously published data relating to phytochemical content and sensory data in Principal Component Analysis to determine compounds influencing liking/perceptions. Hotness, not bitterness, is the main attribute on which consumers base their liking of rocket. Some consumers rejected rocket based on GSL/ITC concentrations, whereas some preferred hotness. Bitter perception did not significantly influence liking of accessions, despite PAV/PAV 'supertasters' scoring higher for this attribute. High sugar-GSL/ITC ratios significantly reduce perceptions of hotness and bitterness for some consumers. Importantly the GSL glucoraphanin does not impart significant influence on liking or perception traits. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Analysis and improvement measures of flight delay in China

NASA Astrophysics Data System (ADS)

Zang, Yuhang

2017-03-01

Firstly, this paper establishes the principal component regression model to analyze the data quantitatively, based on principal component analysis to get the three principal component factors of flight delays. Then the least square method is used to analyze the factors and obtained the regression equation expression by substitution, and then found that the main reason for flight delays is airlines, followed by weather and traffic. Aiming at the above problems, this paper improves the controllable aspects of traffic flow control. For reasons of traffic flow control, an adaptive genetic queuing model is established for the runway terminal area. This paper, establish optimization method that fifteen planes landed simultaneously on the three runway based on Beijing capital international airport, comparing the results with the existing FCFS algorithm, the superiority of the model is proved.

Evaluation of Low-Voltage Distribution Network Index Based on Improved Principal Component Analysis

NASA Astrophysics Data System (ADS)

Fan, Hanlu; Gao, Suzhou; Fan, Wenjie; Zhong, Yinfeng; Zhu, Lei

2018-01-01

In order to evaluate the development level of the low-voltage distribution network objectively and scientifically, chromatography analysis method is utilized to construct evaluation index model of low-voltage distribution network. Based on the analysis of principal component and the characteristic of logarithmic distribution of the index data, a logarithmic centralization method is adopted to improve the principal component analysis algorithm. The algorithm can decorrelate and reduce the dimensions of the evaluation model and the comprehensive score has a better dispersion degree. The clustering method is adopted to analyse the comprehensive score because the comprehensive score of the courts is concentrated. Then the stratification evaluation of the courts is realized. An example is given to verify the objectivity and scientificity of the evaluation method.

Comprehensive analysis of commercial willow bark extracts by new technology platform: combined use of metabolomics, high-performance liquid chromatography-solid-phase extraction-nuclear magnetic resonance spectroscopy and high-resolution radical scavenging assay.

PubMed

Agnolet, Sara; Wiese, Stefanie; Verpoorte, Robert; Staerk, Dan

2012-11-02

Here, proof-of-concept of a new analytical platform used for the comprehensive analysis of a small set of commercial willow bark products is presented, and compared with a traditional standardization solely based on analysis of salicin and salicin derivatives. The platform combines principal component analysis (PCA) of two chemical fingerprints, i.e., HPLC and (1)H NMR data, and a pharmacological fingerprint, i.e., high-resolution 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonate) radical cation (ABTS(+)) reduction profile, with targeted identification of constituents of interest by hyphenated HPLC-solid-phase extraction-tube transfer NMR, i.e., HPLC-SPE-ttNMR. Score plots from PCA of HPLC and (1)H NMR fingerprints showed the same distinct grouping of preparations formulated as capsules of Salix alba bark and separation of S. alba cortex. Loading plots revealed this to be due to high amount of salicin in capsules and ampelopsin, taxifolin, 7-O-methyltaxifolin-3'-O-glucoside, and 7-O-methyltaxifolin in S. alba cortex, respectively. PCA of high-resolution radical scavenging profiles revealed clear separation of preparations along principal component 1 due to the major radical scavengers (+)-catechin and ampelopsin. The new analytical platform allowed identification of 16 compounds in commercial willow bark extracts, and identification of ampelopsin, taxifolin, 7-O-methyltaxifolin-3'-O-glucoside, and 7-O-methyltaxifolin in S. alba bark extract is reported for the first time. The detection of the novel compound, ethyl 1-hydroxy-6-oxocyclohex-2-enecarboxylate, is also described. Copyright © 2012 Elsevier B.V. All rights reserved.

Is Allelopathic Activity of Ipomoea murucoides Induced by Xylophage Damage?

PubMed Central

Flores-Palacios, Alejandro; Corona-López, Angélica María; Rios, María Yolanda; Aguilar-Guadarrama, Berenice; Toledo-Hernández, Víctor Hugo; Rodríguez-López, Verónica; Valencia-Díaz, Susana

2015-01-01

Herbivory activates the synthesis of allelochemicals that can mediate plant-plant interactions. There is an inverse relationship between the activity of xylophages and the abundance of epiphytes on Ipomoea murucoides. Xylophagy may modify the branch chemical constitution, which also affects the liberation of allelochemicals with defense and allelopathic properties. We evaluated the bark chemical content and the effect of extracts from branches subjected to treatments of exclusion, mechanical damage and the presence/absence of epiphytes, on the seed germination of the epiphyte Tillandsia recurvata. Principal component analysis showed that branches without any treatment separate from branches subjected to treatments; damaged and excluded branches had similar chemical content but we found no evidence to relate intentional damage with allelopathy; however 1-hexadecanol, a defense volatile compound correlated positively with principal component (PC) 1. The chemical constitution of branches subject to exclusion plus damage or plus epiphytes was similar among them. PC2 indicated that palmitic acid (allelopathic compound) and squalene, a triterpene that attracts herbivore enemies, correlated positively with the inhibition of seed germination of T. recurvata. Inhibition of seed germination of T. recurvata was mainly correlated with the increment of palmitic acid and this compound reached higher concentrations in excluded branches treatments. Then, it is likely that the allelopathic response of I. murucoides would increase to the damage (shade, load) that may be caused by a high load of epiphytes than to damage caused by the xylophages. PMID:26625350

Discrimination of red and white rice bran from Indonesia using HPLC fingerprint analysis combined with chemometrics.

PubMed

Sabir, Aryani; Rafi, Mohamad; Darusman, Latifah K

2017-04-15

HPLC fingerprint analysis combined with chemometrics was developed to discriminate between the red and the white rice bran grown in Indonesia. The major component in rice bran is γ-oryzanol which consisted of 4 main compounds, namely cycloartenol ferulate, cyclobranol ferulate, campesterol ferulate and β-sitosterol ferulate. Separation of these four compounds along with other compounds was performed using C18 and methanol-acetonitrile with gradient elution system. By using these intensity variations, principal component and discriminant analysis were performed to discriminate the two samples. Discriminant analysis was successfully discriminated the red from the white rice bran with predictive ability of the model showed a satisfactory classification for the test samples. The results of this study indicated that the developed method was suitable as quality control method for rice bran in terms of identification and discrimination of the red and the white rice bran. Copyright © 2016 Elsevier Ltd. All rights reserved.

Migration of styrene and ethylbenzene from virgin and recycled expanded polystyrene containers and discrimination of these two kinds of polystyrene by principal component analysis.

PubMed

Lin, Qin-Bao; Song, Xue-Chao; Fang, Hong; Wu, Yu-Mei; Wang, Zhi-Wei

2017-01-01

The migration of styrene and ethylbenzene from virgin and recycled expanded polystyrene (EPS) containers into isooctane was investigated using gas chromatography-mass spectrometry (GC-MS). EPS containers were in two-sided contact with isooctane at temperatures of 25 and 40°C. It was shown that recycled EPS gave greater migration ratios compared with virgin EPS, which indicated that styrene and ethylbenzene migrated more easily from recycled EPS. In addition, an analytical method to distinguish between virgin and recycled EPS containers was established by GC-MS followed by principal component analysis (PCA). The relative peak area of the identified compounds was used as input data for PCA. Distinct separation between virgin and recycled EPS was achieved on a score plot. Extension of this method to other plastics may be of great interest for recycled plastics identification.

Typification of cider brandy on the basis of cider used in its manufacture.

PubMed

Rodríguez Madrera, Roberto; Mangas Alonso, Juan J

2005-04-20

A study of typification of cider brandies on the basis of the origin of the raw material used in their manufacture was conducted using chemometric techniques (principal component analysis, linear discriminant analysis, and Bayesian analysis) together with their composition in volatile compounds, as analyzed by gas chromatography with flame ionization to detect the major volatiles and by mass spectrometric to detect the minor ones. Significant principal components computed by a double cross-validation procedure allowed the structure of the database to be visualized as a function of the raw material, that is, cider made from fresh apple juice versus cider made from apple juice concentrate. Feasible and robust discriminant rules were computed and validated by a cross-validation procedure that allowed the authors to classify fresh and concentrate cider brandies, obtaining classification hits of >92%. The most discriminating variables for typifying cider brandies according to their raw material were 1-butanol and ethyl hexanoate.

Untargeted MS-based small metabolite identification from the plant leaves and stems of Impatiens balsamina.

PubMed

Chua, Lee Suan

2016-09-01

The identification of plant metabolites is very important for the understanding of plant physiology including plant growth, development and defense mechanism, particularly for herbal medicinal plants. The metabolite profile could possibly be used for future drug discovery since the pharmacological activities of the indigenous herbs have been proven for centuries. An untargeted mass spectrometric approach was used to identify metabolites from the leaves and stems of Impatiens balsamina using LC-DAD-MS/MS. The putative compounds are mostly from the groups of phenolic, organic and amino acids which are essential for plant growth and as intermediates for other compounds. Alanine appeared to be the main amino acid in the plant because many alanine derived metabolites were detected. There are also several secondary metabolites from the groups of benzopyrones, benzofuranones, naphthoquinones, alkaloids and flavonoids. The widely reported bioactive components such as kaempferol, quercetin and their glycosylated, lawsone and its derivatives were detected in this study. The results also revealed that aqueous methanol could extract flavonoids better than water, and mostly, flavonoids were detected from the leaf samples. The score plots of component analysis show that there is a minor variance in the metabolite profiles of water and aqueous methanolic extracts with 21.5 and 30.5% of the total variance for the first principal component at the positive and negative ion modes, respectively. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Mini-DIAL system measurements coupled with multivariate data analysis to identify TIC and TIM simulants: preliminary absorption database analysis.

NASA Astrophysics Data System (ADS)

Gaudio, P.; Malizia, A.; Gelfusa, M.; Martinelli, E.; Di Natale, C.; Poggi, L. A.; Bellecci, C.

2017-01-01

Nowadays Toxic Industrial Components (TICs) and Toxic Industrial Materials (TIMs) are one of the most dangerous and diffuse vehicle of contamination in urban and industrial areas. The academic world together with the industrial and military one are working on innovative solutions to monitor the diffusion in atmosphere of such pollutants. In this phase the most common commercial sensors are based on “point detection” technology but it is clear that such instruments cannot satisfy the needs of the smart cities. The new challenge is developing stand-off systems to continuously monitor the atmosphere. Quantum Electronics and Plasma Physics (QEP) research group has a long experience in laser system development and has built two demonstrators based on DIAL (Differential Absorption of Light) technology could be able to identify chemical agents in atmosphere. In this work the authors will present one of those DIAL system, the miniaturized one, together with the preliminary results of an experimental campaign conducted on TICs and TIMs simulants in cell with aim of use the absorption database for the further atmospheric an analysis using the same DIAL system. The experimental results are analysed with standard multivariate data analysis technique as Principal Component Analysis (PCA) to develop a classification model aimed at identifying organic chemical compound in atmosphere. The preliminary results of absorption coefficients of some chemical compound are shown together pre PCA analysis.

Binding affinity toward human prion protein of some anti-prion compounds - Assessment based on QSAR modeling, molecular docking and non-parametric ranking.

PubMed

Kovačević, Strahinja; Karadžić, Milica; Podunavac-Kuzmanović, Sanja; Jevrić, Lidija

2018-01-01

The present study is based on the quantitative structure-activity relationship (QSAR) analysis of binding affinity toward human prion protein (huPrP C ) of quinacrine, pyridine dicarbonitrile, diphenylthiazole and diphenyloxazole analogs applying different linear and non-linear chemometric regression techniques, including univariate linear regression, multiple linear regression, partial least squares regression and artificial neural networks. The QSAR analysis distinguished molecular lipophilicity as an important factor that contributes to the binding affinity. Principal component analysis was used in order to reveal similarities or dissimilarities among the studied compounds. The analysis of in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) parameters was conducted. The ranking of the studied analogs on the basis of their ADMET parameters was done applying the sum of ranking differences, as a relatively new chemometric method. The main aim of the study was to reveal the most important molecular features whose changes lead to the changes in the binding affinities of the studied compounds. Another point of view on the binding affinity of the most promising analogs was established by application of molecular docking analysis. The results of the molecular docking were proven to be in agreement with the experimental outcome. Copyright © 2017 Elsevier B.V. All rights reserved.

Chemical composition of the Lippia origanoides essential oils and their antigenotoxicity against bleomycin-induced DNA damage.

PubMed

Vicuña, Gloria Carolina; Stashenko, Elena E; Fuentes, Jorge Luis

2010-07-01

The present work evaluated the chemical composition of the essential oils (EO) obtained from Lippia origanoides and their DNA protective effect against bleomycin-induced genotoxicity. L. origanoides EO chemical composition was determined by gas chromatography-mass spectrometry (GC-MS). The major compounds of the L. origanoides EOs were thymol (34-58%) and carvacrol (26%). The antigenotoxic effects of the EOs, major compounds and standard compound (epigallocatechin gallate) were assayed in co-incubation procedures using the SOS chromotest in Escherichia coli. Both EOs and their major compounds protected bacterial cells against bleomycin-induced genotoxicity indicating that these two compounds were principally responsible for the antigenotoxicity detected in the oils. Thymol and carvacrol antigenotoxicity was lower than those observed with epigallocatechin gallate. The results were discussed in relation to the chemopreventive potential of L. origanoides EOs and their major components, carvacrol and thymol. Copyright 2009 Elsevier B.V. All rights reserved.

Exploring functional data analysis and wavelet principal component analysis on ecstasy (MDMA) wastewater data.

PubMed

Salvatore, Stefania; Bramness, Jørgen G; Røislien, Jo

2016-07-12

Wastewater-based epidemiology (WBE) is a novel approach in drug use epidemiology which aims to monitor the extent of use of various drugs in a community. In this study, we investigate functional principal component analysis (FPCA) as a tool for analysing WBE data and compare it to traditional principal component analysis (PCA) and to wavelet principal component analysis (WPCA) which is more flexible temporally. We analysed temporal wastewater data from 42 European cities collected daily over one week in March 2013. The main temporal features of ecstasy (MDMA) were extracted using FPCA using both Fourier and B-spline basis functions with three different smoothing parameters, along with PCA and WPCA with different mother wavelets and shrinkage rules. The stability of FPCA was explored through bootstrapping and analysis of sensitivity to missing data. The first three principal components (PCs), functional principal components (FPCs) and wavelet principal components (WPCs) explained 87.5-99.6 % of the temporal variation between cities, depending on the choice of basis and smoothing. The extracted temporal features from PCA, FPCA and WPCA were consistent. FPCA using Fourier basis and common-optimal smoothing was the most stable and least sensitive to missing data. FPCA is a flexible and analytically tractable method for analysing temporal changes in wastewater data, and is robust to missing data. WPCA did not reveal any rapid temporal changes in the data not captured by FPCA. Overall the results suggest FPCA with Fourier basis functions and common-optimal smoothing parameter as the most accurate approach when analysing WBE data.

Consumer palatability scores and volatile beef flavor compounds of five USDA quality grades and four muscles.

PubMed

Legako, J F; Brooks, J C; O'Quinn, T G; Hagan, T D J; Polkinghorne, R; Farmer, L J; Miller, M F

2015-02-01

Proximate data, consumer palatability scores and volatile compounds were investigated for four beef muscles (Longissimus lumborum, Psoas major, Semimembranosus and Gluteus medius) and five USDA quality grades(Prime, Upper 2/3 Choice, Low Choice, Select, and Standard). Quality grade did not directly affect consumer scores or volatiles but interactions (P < 0.05) between muscle and grade were determined. Consumer scores and volatiles differed (P < 0.05) between muscles. Consumers scored Psoas major highest for tenderness, juiciness, flavor liking and overall liking, followed by Longissimus lumborum, Gluteus medius, and Semimembranosus (P < 0.05). Principal component analysis revealed clustering of compound classes, formed by related mechanisms. Volatile n-aldehydes were inversely related to percent fat. Increases in lipid oxidation compounds were associated with Gluteus medius and Semimembranosus, while greater quantities of sulfur-containing compounds were associated with Psoas major. Relationships between palatability scores and volatile compound classes suggest that differences in the pattern of volatile compounds may play a valuable role in explaining consumer liking.

Alkylation of organic aromatic compounds

DOEpatents

Smith, L.A. Jr.

1989-07-18

Aromatic compounds are alkylated in a catalytic distillation, wherein the catalyst structure also serves as a distillation component by contacting the aromatic compound with a C[sub 2] to C[sub 10] olefin in the catalyst bed under 0.25 to 50 atmospheres of pressure and at temperatures in the range of 80 C to 500 C, using as the catalyst a mole sieve characterized as acidic or an acidic cation exchange resin. For example, ethyl benzene is produced by feeding ethylene below the catalyst bed while benzene is conveniently added through the reflux in molar excess to that required to react with ethylene, thereby reacting substantially all of the ethylene and recovering benzene as the principal overhead and ethyl benzene in the bottoms. 1 fig.

Alkylation of organic aromatic compounds

DOEpatents

Smith, Jr., Lawrence A.; Arganbright, Robert P.; Hearn, Dennis

1994-01-01

Aromatic compounds are alkylated in a catalytic distillation, wherein the catalyst structure also serves as a distillation component by contacting the aromatic compound with a C.sub.2 to C.sub.10 olefin in the catalyst bed under 0.25 to 50 atmospheres of pressure and at temperatures in the range of 80.degree. C. to 500.degree. C., using as the catalyst a mole sieve characterized as acidic or an acidic cation exchange resin. For example, ethyl benzene is produced by feeding ethylene below the catalyst bed while benzene is conveniently added through the reflux in molar excess to that required to react with ethylene, thereby reacting substantially all of the ethylene and recovering benzene as the principal overhead and ethyl benzene in the bottoms.

Alkylation of organic aromatic compounds

DOEpatents

Smith, Jr., Lawrence A.

1989-01-01

Aromatic compounds are alkylated in a catalytic distillation, wherein the catalyst structure also serves as a distillation component by contacting the aromatic compound with a C.sub.2 to C.sub.10 olefin in the catalyst bed under 0.25 to 50 atmospheres of pressure and at temperatures in the range of 80.degree. C. to 500.degree. C., using as the catalyst a mole sieve characterized as acidic or an acidic cation exchange resin. For example, ethyl benzene is produced by feeding ethylene below the catalyst bed while benzene is conveniently added through the reflux in molar excess to that required to react with ethylene, thereby reacting substantially all of the ethylene and recovering benzene as the principal overhead and ethyl benzene in the bottoms.

Alkylation of organic aromatic compounds

DOEpatents

Smith, L.A. Jr.; Arganbright, R.P.; Hearn, D.

1994-06-14

Aromatic compounds are alkylated in a catalytic distillation, wherein the catalyst structure also serves as a distillation component by contacting the aromatic compound with a C[sub 2] to C[sub 10] olefin in the catalyst bed under 0.25 to 50 atmospheres of pressure and at temperatures in the range of 80 C to 500 C, using as the catalyst a molecular sieve characterized as acidic or an acidic cation exchange resin. For example, ethyl benzene is produced by feeding ethylene below the catalyst bed while benzene is conveniently added through the reflux in molar excess to that required to react with ethylene, thereby reacting substantially all of the ethylene and recovering benzene as the principal overhead and ethyl benzene in the bottoms. 1 fig.

Changes in volatile composition and sensory properties of Iranian ultrafiltered white cheese as affected by blends of Rhizomucor miehei protease or camel chymosin.

PubMed

Soltani, M; Sahingil, D; Gokce, Y; Hayaloglu, A A

2016-10-01

The effect of using various combinations of Rhizomucor miehei protease and camel chymosin (100:0, 75:25, 50:50, 25:75, and 0:100, respectively) on volatile composition and sensory scores in Iranian ultrafiltered white cheese was studied during 90d of ripening. A solid-phase microextraction-gas chromatography-mass spectrometric method was used for determining the volatile compounds of the cheeses. Forty compounds including esters (12), acids (6), ketones (9), alcohols (3), and miscellaneous compounds (10) were identified. The main classes of volatile components in the cheeses are esters, miscellaneous compounds, and ketones. The type and concentration of the coagulants influenced both volatile composition and sensory scores of the cheeses. Principal component analysis separated the cheeses based on the use of 2 coagulants in various combinations and ripening time. The cheeses produced using higher concentrations of R. miehei were separately located on the plot compared with the cheeses produced using higher concentrations of camel chymosin. Sensory evaluation of the cheeses showed that, in general, the cheeses produced using higher concentrations of camel chymosin received higher body and texture and odor and flavor scores than the cheese produced using higher concentrations of R. miehei. In conclusion, 2 combinations of R. miehei and camel chymosin (75:25 and 25:75, respectively) can be successfully used for the production of Iranian ultrafiltered white cheese, considering the results of volatile composition and sensory analysis. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

[The principal components analysis--method to classify the statistical variables with applications in medicine].

PubMed

Dascălu, Cristina Gena; Antohe, Magda Ecaterina

2009-01-01

Based on the eigenvalues and the eigenvectors analysis, the principal component analysis has the purpose to identify the subspace of the main components from a set of parameters, which are enough to characterize the whole set of parameters. Interpreting the data for analysis as a cloud of points, we find through geometrical transformations the directions where the cloud's dispersion is maximal--the lines that pass through the cloud's center of weight and have a maximal density of points around them (by defining an appropriate criteria function and its minimization. This method can be successfully used in order to simplify the statistical analysis on questionnaires--because it helps us to select from a set of items only the most relevant ones, which cover the variations of the whole set of data. For instance, in the presented sample we started from a questionnaire with 28 items and, applying the principal component analysis we identified 7 principal components--or main items--fact that simplifies significantly the further data statistical analysis.

Principal Component-Based Radiative Transfer Model (PCRTM) for Hyperspectral Sensors. Part I; Theoretical Concept

NASA Technical Reports Server (NTRS)

Liu, Xu; Smith, William L.; Zhou, Daniel K.; Larar, Allen

2005-01-01

Modern infrared satellite sensors such as Atmospheric Infrared Sounder (AIRS), Cosmic Ray Isotope Spectrometer (CrIS), Thermal Emission Spectrometer (TES), Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) and Infrared Atmospheric Sounding Interferometer (IASI) are capable of providing high spatial and spectral resolution infrared spectra. To fully exploit the vast amount of spectral information from these instruments, super fast radiative transfer models are needed. This paper presents a novel radiative transfer model based on principal component analysis. Instead of predicting channel radiance or transmittance spectra directly, the Principal Component-based Radiative Transfer Model (PCRTM) predicts the Principal Component (PC) scores of these quantities. This prediction ability leads to significant savings in computational time. The parameterization of the PCRTM model is derived from properties of PC scores and instrument line shape functions. The PCRTM is very accurate and flexible. Due to its high speed and compressed spectral information format, it has great potential for super fast one-dimensional physical retrievals and for Numerical Weather Prediction (NWP) large volume radiance data assimilation applications. The model has been successfully developed for the National Polar-orbiting Operational Environmental Satellite System Airborne Sounder Testbed - Interferometer (NAST-I) and AIRS instruments. The PCRTM model performs monochromatic radiative transfer calculations and is able to include multiple scattering calculations to account for clouds and aerosols.

Performance evaluation of PCA-based spike sorting algorithms.

PubMed

Adamos, Dimitrios A; Kosmidis, Efstratios K; Theophilidis, George

2008-09-01

Deciphering the electrical activity of individual neurons from multi-unit noisy recordings is critical for understanding complex neural systems. A widely used spike sorting algorithm is being evaluated for single-electrode nerve trunk recordings. The algorithm is based on principal component analysis (PCA) for spike feature extraction. In the neuroscience literature it is generally assumed that the use of the first two or most commonly three principal components is sufficient. We estimate the optimum PCA-based feature space by evaluating the algorithm's performance on simulated series of action potentials. A number of modifications are made to the open source nev2lkit software to enable systematic investigation of the parameter space. We introduce a new metric to define clustering error considering over-clustering more favorable than under-clustering as proposed by experimentalists for our data. Both the program patch and the metric are available online. Correlated and white Gaussian noise processes are superimposed to account for biological and artificial jitter in the recordings. We report that the employment of more than three principal components is in general beneficial for all noise cases considered. Finally, we apply our results to experimental data and verify that the sorting process with four principal components is in agreement with a panel of electrophysiology experts.

Photosynthetic capacity is negatively correlated with the concentration of leaf phenolic compounds across a range of different species.

PubMed

Sumbele, Sally; Fotelli, Mariangela N; Nikolopoulos, Dimosthenis; Tooulakou, Georgia; Liakoura, Vally; Liakopoulos, Georgios; Bresta, Panagiota; Dotsika, Elissavet; Adams, Mark A; Karabourniotis, George

2012-01-01

Phenolic compounds are the most commonly studied of all secondary metabolites because of their significant protective-defensive roles and their significant concentration in plant tissues. However, there has been little study on relationships between gas exchange parameters and the concentration of leaf phenolic compounds (total phenolics (TP) and condensed tannins (CT)) across a range of species. Therefore, we addressed the question: is there any correlation between photosynthetic capacity (A(max)) and TP and CT across species from different ecosystems in different continents? A plethora of functional and structural parameters were measured in 49 plant species following different growth strategies from five sampling sites located in Greece and Australia. The relationships between several leaf traits were analysed by means of regression and principal component analysis. The results revealed a negative relationship between TP and CT and A(max) among the different plant species, growth strategies and sampling sites, irrespective of expression (with respect to mass, area or nitrogen content). Principal component analysis showed that high concentrations of TP and CT are associated with thick, dense leaves with low nitrogen. This leaf type is characterized by low growth, A(max) and transpiration rates, and is common in environments with low water and nutrient availability, high temperatures and high light intensities. Therefore, the high TP and CT in such leaves are compatible with the protective and defensive functions ascribed to them. Our results indicate a functional integration between carbon gain and the concentration of leaf phenolic compounds that reflects the trade-off between growth and defence/protection demands, depending on the growth strategy adopted by each species.

Source Characterization of Volatile Organic Compounds Affecting the Air Quality in a Coastal Urban Area of South Texas

PubMed Central

Sanchez, Marciano; Karnae, Saritha; John, Kuruvilla

2008-01-01

Selected Volatile Organic Compounds (VOC) emitted from various anthropogenic sources including industries and motor vehicles act as primary precursors of ozone, while some VOC are classified as air toxic compounds. Significantly large VOC emission sources impact the air quality in Corpus Christi, Texas. This urban area is located in a semi-arid region of South Texas and is home to several large petrochemical refineries and industrial facilities along a busy ship-channel. The Texas Commission on Environmental Quality has setup two continuous ambient monitoring stations (CAMS 633 and 634) along the ship channel to monitor VOC concentrations in the urban atmosphere. The hourly concentrations of 46 VOC compounds were acquired from TCEQ for a comprehensive source apportionment study. The primary objective of this study was to identify and quantify the sources affecting the ambient air quality within this urban airshed. Principal Component Analysis/Absolute Principal Component Scores (PCA/APCS) was applied to the dataset. PCA identified five possible sources accounting for 69% of the total variance affecting the VOC levels measured at CAMS 633 and six possible sources affecting CAMS 634 accounting for 75% of the total variance. APCS identified natural gas emissions to be the major source contributor at CAMS 633 and it accounted for 70% of the measured VOC concentrations. The other major sources identified at CAMS 633 included flare emissions (12%), fugitive gasoline emissions (9%), refinery operations (7%), and vehicle exhaust (2%). At CAMS 634, natural gas sources were identified as the major source category contributing to 31% of the observed VOC. The other sources affecting this site included: refinery operations (24%), flare emissions (22%), secondary industrial processes (12%), fugitive gasoline emissions (8%) and vehicle exhaust (3%). PMID:19139530

Research on Air Quality Evaluation based on Principal Component Analysis

NASA Astrophysics Data System (ADS)

Wang, Xing; Wang, Zilin; Guo, Min; Chen, Wei; Zhang, Huan

2018-01-01

Economic growth has led to environmental capacity decline and the deterioration of air quality. Air quality evaluation as a fundamental of environmental monitoring and air pollution control has become increasingly important. Based on the principal component analysis (PCA), this paper evaluates the air quality of a large city in Beijing-Tianjin-Hebei Area in recent 10 years and identifies influencing factors, in order to provide reference to air quality management and air pollution control.

Discrimination of Swiss cheese from 5 different factories by high impact volatile organic compound profiles determined by odor activity value using selected ion flow tube mass spectrometry and odor threshold.

PubMed

Taylor, Kaitlyn; Wick, Cheryl; Castada, Hardy; Kent, Kyle; Harper, W James

2013-10-01

Swiss cheese contains more than 200 volatile organic compounds (VOCs). Gas chromatography-mass spectrometry has been utilized for the analysis of volatile compounds in food products; however, it is not sensitive enough to measure VOCs directly in the headspace of a food at low concentrations. Selected ion flow tube mass spectrometry (SIFT-MS) provides a basis for determining the concentrations of VOCs in the head space of the sample in real time at low concentration levels of parts per billion/trillion by volume. Of the Swiss cheese VOCs, relatively few have a major impact on flavor quality. VOCs with odor activity values (OAVs) (concentration/odor threshold) greater than one are considered high-impact flavor compounds. The objective of this study was to utilize SIFT-MS concentrations in conjunction with odor threshold values to determine OAVs thereby identifying high-impact VOCs to use for differentiating Swiss cheese from five factories and identify the factory variability. Seventeen high-impact VOCs were identified for Swiss cheese based on an OAV greater than one in at least 1 of the 5 Swiss cheese factories. Of these, 2,3-butanedione was the only compound with significantly different OAVs in all factories; however, cheese from any pair of factories had multiple statistically different compounds based on OAV. Principal component analysis using soft independent modeling of class analogy statistical differentiation plots, with all of the OAVs, showed differentiation between the 5 factories. Overall, Swiss cheese from different factories was determined to have different OAV profiles utilizing SIFT-MS to determine OAVs of high impact compounds. © 2013 Institute of Food Technologists®

Estimating persistence of brominated and chlorinated organic pollutants in air, water, soil, and sediments with the QSPR-based classification scheme.

PubMed

Puzyn, T; Haranczyk, M; Suzuki, N; Sakurai, T

2011-02-01

We have estimated degradation half-lives of both brominated and chlorinated dibenzo-p-dioxins (PBDDs and PCDDs), furans (PBDFs and PCDFs), biphenyls (PBBs and PCBs), naphthalenes (PBNs and PCNs), diphenyl ethers (PBDEs and PCDEs) as well as selected unsubstituted polycyclic aromatic hydrocarbons (PAHs) in air, surface water, surface soil, and sediments (in total of 1,431 compounds in four compartments). Next, we compared the persistence between chloro- (relatively well-studied) and bromo- (less studied) analogs. The predictions have been performed based on the quantitative structure-property relationship (QSPR) scheme with use of k-nearest neighbors (kNN) classifier and the semi-quantitative system of persistence classes. The classification models utilized principal components derived from the principal component analysis of a set of 24 constitutional and quantum mechanical descriptors as input variables. Accuracies of classification (based on an external validation) were 86, 85, 87, and 75% for air, surface water, surface soil, and sediments, respectively. The persistence of all chlorinated species increased with increasing halogenation degree. In the case of brominated organic pollutants (Br-OPs), the trend was the same for air and sediments. However, we noticed that the opposite trend for persistence in surface water and soil. The results suggest that, due to high photoreactivity of C-Br chemical bonds, photolytic processes occurring in surface water and soil are able to play significant role in transforming and removing Br-OPs from these compartments. This contribution is the first attempt of classifying together Br-OPs and Cl-OPs according to their persistence, in particular, environmental compartments.

Lingzhi or Reishi Medicinal Mushroom, Ganoderma lucidum (Agaricomycetes), Inhibits Candida Biofilms: A Metabolomic Approach.

PubMed

Bhardwaj, Anuja; Gupta, Payal; Kumar, Navin; Mishra, Jigni; Kumar, Ajai; Rakhee, Rajput; Misra, Kshipra

2017-01-01

This article presents a comparative gas chromatography (GC)-mass spectrometry (MS)-based metabolomic analysis of mycelia and fruiting bodies of the medicinal mushroom Ganoderma lucidum. Three aqueous extracts-mycelia, fruiting bodies, and a mixture of them-and their sequential fractions (methanolic and ethyl acetate), prepared using an accelerated solvent extractor, were characterized by GC-MS to determine volatile organic compounds and by high-performance thin-layer chromatography to quantify ascorbic acid, a potent antioxidant. In addition, these extracts and fractions were assessed against Candida albicans and C. glabrata biofilms via the XTT reduction assay, and their antioxidant potential was evaluated. Application of chemometrics (hierarchical cluster analysis and principal component analysis) to GC data revealed variability in volatile organic compound profiles among G. lucidum extracts and fractions. The mycelial aqueous extract demonstrated higher anti-Candida activity and ascorbic acid content among all the extracts and fractions. Thus, this study illustrates the preventive effect of G. lucidum against C. albicans and C. glabrata biofilms along with its nutritional value.

Differentiation of wines according to grape variety and geographical origin based on volatiles profiling using SPME-MS and SPME-GC/MS methods.

PubMed

Ziółkowska, Angelika; Wąsowicz, Erwin; Jeleń, Henryk H

2016-12-15

Among methods to detect wine adulteration, profiling volatiles is one with a great potential regarding robustness, analysis time and abundance of information for subsequent data treatment. Volatile fraction fingerprinting by solid-phase microextraction with direct analysis by mass spectrometry without compounds separation (SPME-MS) was used for differentiation of white as well as red wines. The aim was to differentiate between varieties used for wine production and to also differentiate wines by country of origin. The results obtained were compared to SPME-GC/MS analysis in which compounds were resolved by gas chromatography. For both approaches the same type of statistical procedure was used to compare samples: principal component analysis (PCA) followed by linear discriminant analysis (LDA). White wines (38) and red wines (41) representing different grape varieties and various regions of origin were analysed. SPME-MS proved to be advantageous in use due to better discrimination and higher sample throughput. Copyright © 2016 Elsevier Ltd. All rights reserved.

Inferring sources of polycyclic aromatic hydrocarbons (PAHs) in sediments from the western Taiwan Strait through end-member mixing analysis.

PubMed

Li, Tao; Sun, Guihua; Ma, Shengzhong; Liang, Kai; Yang, Chupeng; Li, Bo; Luo, Weidong

2016-11-15

Concentration, spatial distribution, composition and sources of polycyclic aromatic hydrocarbons (PAHs) were investigated based on measurements of 16 PAH compounds in surface sediments of the western Taiwan Strait. Total PAH concentrations ranged from 2.41 to 218.54ngg -1 . Cluster analysis identified three site clusters representing the northern, central and southern regions. Sedimentary PAHs mainly originated from a mixture of pyrolytic and petrogenic in the north, from pyrolytic in the central, and from petrogenic in the south. An end-member mixing model was performed using PAH compound data to estimate mixing proportions for unknown end-members (i.e., extreme-value sample points) proposed by principal component analysis (PCA). The results showed that the analyzed samples can be expressed as mixtures of three end-members, and the mixing of different end-members was strongly related to the transport pathway controlled by two currents, which alternately prevail in the Taiwan Strait during different seasons. Copyright © 2016. Published by Elsevier Ltd.

Chemical diversity of the essential oils of twenty populations of Tanacetum polycephalum Sch. Bip. from Iran.

PubMed

Mojarrad, Mehran; Hosseini Sarghein, Siavash; Sonboli, Ali

2018-05-16

Chemical diversity of the essential oils of twenty wild populations of Tanacetum polycephalum Sch. Bip., was investigated. The aerial parts of T. polycephalum were collected at full flowering stage from West Azerbaijan Province of Iran, air-dried; hydrodistilled to produce essential oils. The essential oils were analyzed by GC-FID and GC-MS. A total of forty compounds were identified accounting for 96.4-99.9% of the total oils. The most principal compounds were cis-thujone (0-82.3%), trans-thujone (0-79.8%), camphor (1.3-75.0%), 1,8-cineole (4.5-43.3%), borneol (1.0-36.2%) and bornyl acetate (0-26.8%). Hierarchical cluster analysis based on the percentages (>0.5%) of the essential oils components was carried out to determine the chemical diversity among the populations studied. The cluster analysis resulted in the identification of four main chemotypes namely: 'camphor + 1,8-cineole', 'mixed', 'cis-thujone' and 'trans-thujone'.

Characteristic fingerprint based on gingerol derivative analysis for discrimination of ginger (Zingiber officinale) according to geographical origin using HPLC-DAD combined with chemometrics.

PubMed

Yudthavorasit, Soparat; Wongravee, Kanet; Leepipatpiboon, Natchanun

2014-09-01

Chromatographic fingerprints of gingers from five different ginger-producing countries (China, India, Malaysia, Thailand and Vietnam) were newly established to discriminate the origin of ginger. The pungent bioactive principles of ginger, gingerols and six other gingerol-related compounds were determined and identified. Their variations in HPLC profiles create the characteristic pattern of each origin by employing similarity analysis, hierarchical cluster analysis (HCA), principal component analysis (PCA) and linear discriminant analysis (LDA). As results, the ginger profiles tended to be grouped and separated on the basis of the geographical closeness of the countries of origin. An effective mathematical model with high predictive ability was obtained and chemical markers for each origin were also identified as the characteristic active compounds to differentiate the ginger origin. The proposed method is useful for quality control of ginger in case of origin labelling and to assess food authenticity issues. Copyright © 2014 Elsevier Ltd. All rights reserved.

Application of mass spectrometry based electronic nose and chemometrics for fingerprinting radiation treatment

NASA Astrophysics Data System (ADS)

Gupta, Sumit; Variyar, Prasad S.; Sharma, Arun

2015-01-01

Volatile compounds were isolated from apples and grapes employing solid phase micro extraction (SPME) and subsequently analyzed by GC/MS equipped with a transfer line without stationary phase. Single peak obtained was integrated to obtain total mass spectrum of the volatile fraction of samples. A data matrix having relative abundance of all mass-to-charge ratios was subjected to principal component analysis (PCA) and linear discriminant analysis (LDA) to identify radiation treatment. PCA results suggested that there is sufficient variability between control and irradiated samples to build classification models based on supervised techniques. LDA successfully aided in segregating control from irradiated samples at all doses (0.1, 0.25, 0.5, 1.0, 1.5, 2.0 kGy). SPME-MS with chemometrics was successfully demonstrated as simple screening method for radiation treatment.

Principal component reconstruction (PCR) for cine CBCT with motion learning from 2D fluoroscopy.

PubMed

Gao, Hao; Zhang, Yawei; Ren, Lei; Yin, Fang-Fang

2018-01-01

This work aims to generate cine CT images (i.e., 4D images with high-temporal resolution) based on a novel principal component reconstruction (PCR) technique with motion learning from 2D fluoroscopic training images. In the proposed PCR method, the matrix factorization is utilized as an explicit low-rank regularization of 4D images that are represented as a product of spatial principal components and temporal motion coefficients. The key hypothesis of PCR is that temporal coefficients from 4D images can be reasonably approximated by temporal coefficients learned from 2D fluoroscopic training projections. For this purpose, we can acquire fluoroscopic training projections for a few breathing periods at fixed gantry angles that are free from geometric distortion due to gantry rotation, that is, fluoroscopy-based motion learning. Such training projections can provide an effective characterization of the breathing motion. The temporal coefficients can be extracted from these training projections and used as priors for PCR, even though principal components from training projections are certainly not the same for these 4D images to be reconstructed. For this purpose, training data are synchronized with reconstruction data using identical real-time breathing position intervals for projection binning. In terms of image reconstruction, with a priori temporal coefficients, the data fidelity for PCR changes from nonlinear to linear, and consequently, the PCR method is robust and can be solved efficiently. PCR is formulated as a convex optimization problem with the sum of linear data fidelity with respect to spatial principal components and spatiotemporal total variation regularization imposed on 4D image phases. The solution algorithm of PCR is developed based on alternating direction method of multipliers. The implementation is fully parallelized on GPU with NVIDIA CUDA toolbox and each reconstruction takes about a few minutes. The proposed PCR method is validated and compared with a state-of-art method, that is, PICCS, using both simulation and experimental data with the on-board cone-beam CT setting. The results demonstrated the feasibility of PCR for cine CBCT and significantly improved reconstruction quality of PCR from PICCS for cine CBCT. With a priori estimated temporal motion coefficients using fluoroscopic training projections, the PCR method can accurately reconstruct spatial principal components, and then generate cine CT images as a product of temporal motion coefficients and spatial principal components. © 2017 American Association of Physicists in Medicine.

Three dimensional empirical mode decomposition analysis apparatus, method and article manufacture

NASA Technical Reports Server (NTRS)

Gloersen, Per (Inventor)

2004-01-01

An apparatus and method of analysis for three-dimensional (3D) physical phenomena. The physical phenomena may include any varying 3D phenomena such as time varying polar ice flows. A repesentation of the 3D phenomena is passed through a Hilbert transform to convert the data into complex form. A spatial variable is separated from the complex representation by producing a time based covariance matrix. The temporal parts of the principal components are produced by applying Singular Value Decomposition (SVD). Based on the rapidity with which the eigenvalues decay, the first 3-10 complex principal components (CPC) are selected for Empirical Mode Decomposition into intrinsic modes. The intrinsic modes produced are filtered in order to reconstruct the spatial part of the CPC. Finally, a filtered time series may be reconstructed from the first 3-10 filtered complex principal components.

Research on distributed heterogeneous data PCA algorithm based on cloud platform

NASA Astrophysics Data System (ADS)

Zhang, Jin; Huang, Gang

2018-05-01

Principal component analysis (PCA) of heterogeneous data sets can solve the problem that centralized data scalability is limited. In order to reduce the generation of intermediate data and error components of distributed heterogeneous data sets, a principal component analysis algorithm based on heterogeneous data sets under cloud platform is proposed. The algorithm performs eigenvalue processing by using Householder tridiagonalization and QR factorization to calculate the error component of the heterogeneous database associated with the public key to obtain the intermediate data set and the lost information. Experiments on distributed DBM heterogeneous datasets show that the model method has the feasibility and reliability in terms of execution time and accuracy.

Study of the aroma formation and transformation during the manufacturing process of oolong tea by solid-phase micro-extraction and gas chromatography-mass spectrometry combined with chemometrics.

PubMed

Ma, Chengying; Li, Junxing; Chen, Wei; Wang, Wenwen; Qi, Dandan; Pang, Shi; Miao, Aiqing

2018-06-01

Oolong tea is a typical semi-fermented tea and is famous for its unique aroma. The aim of this study was to compare the volatile compounds during manufacturing process to reveal the formation of aroma. In this paper, a method was developed based on head-space solid phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS) combined with chemometrics to assess volatile profiles during manufacturing process (fresh leaves, sun-withered leaves, rocked leaves and leaves after de-enzyming). A total of 24 aroma compounds showing significant differences during manufacturing process were identified. Subsequently, according to these aroma compounds, principal component analysis and hierarchical cluster analysis showed that the four samples were clearly distinguished from each other, which suggested that the 24 identified volatile compounds can represent the changes of volatile compounds during the four steps. Additionally, sun-withering, rocking and de-enzyming can influence the variations of volatile compounds in different degree, and we found the changes of volatile compounds in withering step were less than other two manufacturing process, indicating that the characteristic volatile compounds of oolong tea might be mainly formed in rocking stage by biological reactions and de-enzyming stage through thermal chemical transformations rather than withering stage. This study suggested that HS-SPME/GC-MS combined with chemometrics methods is accurate, sensitive, fast and ideal for rapid routine analysis of the aroma compounds changes in oolong teas during manufacturing processing. Copyright © 2018 Elsevier Ltd. All rights reserved.

Compounds from Silicones Alter Enzyme Activity in Curing Barnacle Glue and Model Enzymes

PubMed Central

Rittschof, Daniel; Orihuela, Beatriz; Harder, Tilmann; Stafslien, Shane; Chisholm, Bret; Dickinson, Gary H.

2011-01-01

Background Attachment strength of fouling organisms on silicone coatings is low. We hypothesized that low attachment strength on silicones is, in part, due to the interaction of surface available components with natural glues. Components could alter curing of glues through bulk changes or specifically through altered enzyme activity. Methodology/Principal Findings GC-MS analysis of silicone coatings showed surface-available siloxanes when the coatings were gently rubbed with a cotton swab for 15 seconds or given a 30 second rinse with methanol. Mixtures of compounds were found on 2 commercial and 8 model silicone coatings. The hypothesis that silicone components alter glue curing enzymes was tested with curing barnacle glue and with commercial enzymes. In our model, barnacle glue curing involves trypsin-like serine protease(s), which activate enzymes and structural proteins, and a transglutaminase which cross-links glue proteins. Transglutaminase activity was significantly altered upon exposure of curing glue from individual barnacles to silicone eluates. Activity of purified trypsin and, to a greater extent, transglutaminase was significantly altered by relevant concentrations of silicone polymer constituents. Conclusions/Significance Surface-associated silicone compounds can disrupt glue curing and alter enzyme properties. Altered curing of natural glues has potential in fouling management. PMID:21379573

Cannabinoids and cancer: pros and cons of an antitumour strategy

PubMed Central

Bifulco, Maurizio; Laezza, Chiara; Pisanti, Simona; Gazzerro, Patrizia

2006-01-01

In the last two decades, research has dramatically increased the knowledge of cannabinoids biology and pharmacology. In mammals, compounds with properties similar to active components of Cannabis sativa, the so called ‘endocannabinoids', have been shown to modulate key cell-signalling pathways involved in cancer cell growth, invasion and metastasis. To date, cannabinoids have been licensed for clinical use as palliative treatment of chemotherapy, but increased evidences showed direct antiproliferative actions of cannabinoid agonists on several tumour cells in vitro and in animal models. In this article, we will review the principal molecular pathways modulated by cannabinoids on cancer and summarize pros and cons evidence on the possible future use of endocannabinoid-based drugs in cancer therapy. PMID:16501583

Metabolite profiling of Clinacanthus nutans leaves extracts obtained from different drying methods by 1H NMR-based metabolomics

NASA Astrophysics Data System (ADS)

Hashim, Noor Haslinda Noor; Latip, Jalifah; Khatib, Alfi

2016-11-01

The metabolites of Clinacanthus nutans leaves extracts and their dependence on drying process were systematically characterized using 1H nuclear magnetic resonance spectroscopy (NMR) multivariate data analysis. Principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) were able to distinguish the leaves extracts obtained from different drying methods. The identified metabolites were carbohydrates, amino acid, flavonoids and sulfur glucoside compounds. The major metabolites responsible for the separation in PLS-DA loading plots were lupeol, cycloclinacosides, betulin, cerebrosides and choline. The results showed that the combination of 1H NMR spectroscopy and multivariate data analyses could act as an efficient technique to understand the C. nutans composition and its variation.

A multifactor approach to forecasting Romanian gross domestic product (GDP) in the short run.

PubMed

Armeanu, Daniel; Andrei, Jean Vasile; Lache, Leonard; Panait, Mirela

2017-01-01

The purpose of this paper is to investigate the application of a generalized dynamic factor model (GDFM) based on dynamic principal components analysis to forecasting short-term economic growth in Romania. We have used a generalized principal components approach to estimate a dynamic model based on a dataset comprising 86 economic and non-economic variables that are linked to economic output. The model exploits the dynamic correlations between these variables and uses three common components that account for roughly 72% of the information contained in the original space. We show that it is possible to generate reliable forecasts of quarterly real gross domestic product (GDP) using just the common components while also assessing the contribution of the individual variables to the dynamics of real GDP. In order to assess the relative performance of the GDFM to standard models based on principal components analysis, we have also estimated two Stock-Watson (SW) models that were used to perform the same out-of-sample forecasts as the GDFM. The results indicate significantly better performance of the GDFM compared with the competing SW models, which empirically confirms our expectations that the GDFM produces more accurate forecasts when dealing with large datasets.

A multifactor approach to forecasting Romanian gross domestic product (GDP) in the short run

PubMed Central

Armeanu, Daniel; Lache, Leonard; Panait, Mirela

2017-01-01

The purpose of this paper is to investigate the application of a generalized dynamic factor model (GDFM) based on dynamic principal components analysis to forecasting short-term economic growth in Romania. We have used a generalized principal components approach to estimate a dynamic model based on a dataset comprising 86 economic and non-economic variables that are linked to economic output. The model exploits the dynamic correlations between these variables and uses three common components that account for roughly 72% of the information contained in the original space. We show that it is possible to generate reliable forecasts of quarterly real gross domestic product (GDP) using just the common components while also assessing the contribution of the individual variables to the dynamics of real GDP. In order to assess the relative performance of the GDFM to standard models based on principal components analysis, we have also estimated two Stock-Watson (SW) models that were used to perform the same out-of-sample forecasts as the GDFM. The results indicate significantly better performance of the GDFM compared with the competing SW models, which empirically confirms our expectations that the GDFM produces more accurate forecasts when dealing with large datasets. PMID:28742100

HT-FRTC: a fast radiative transfer code using kernel regression

NASA Astrophysics Data System (ADS)

Thelen, Jean-Claude; Havemann, Stephan; Lewis, Warren

2016-09-01

The HT-FRTC is a principal component based fast radiative transfer code that can be used across the electromagnetic spectrum from the microwave through to the ultraviolet to calculate transmittance, radiance and flux spectra. The principal components cover the spectrum at a very high spectral resolution, which allows very fast line-by-line, hyperspectral and broadband simulations for satellite-based, airborne and ground-based sensors. The principal components are derived during a code training phase from line-by-line simulations for a diverse set of atmosphere and surface conditions. The derived principal components are sensor independent, i.e. no extra training is required to include additional sensors. During the training phase we also derive the predictors which are required by the fast radiative transfer code to determine the principal component scores from the monochromatic radiances (or fluxes, transmittances). These predictors are calculated for each training profile at a small number of frequencies, which are selected by a k-means cluster algorithm during the training phase. Until recently the predictors were calculated using a linear regression. However, during a recent rewrite of the code the linear regression was replaced by a Gaussian Process (GP) regression which resulted in a significant increase in accuracy when compared to the linear regression. The HT-FRTC has been trained with a large variety of gases, surface properties and scatterers. Rayleigh scattering as well as scattering by frozen/liquid clouds, hydrometeors and aerosols have all been included. The scattering phase function can be fully accounted for by an integrated line-by-line version of the Edwards-Slingo spherical harmonics radiation code or approximately by a modification to the extinction (Chou scaling).

A stable systemic risk ranking in China's banking sector: Based on principal component analysis

NASA Astrophysics Data System (ADS)

Fang, Libing; Xiao, Binqing; Yu, Honghai; You, Qixing

2018-02-01

In this paper, we compare five popular systemic risk rankings, and apply principal component analysis (PCA) model to provide a stable systemic risk ranking for the Chinese banking sector. Our empirical results indicate that five methods suggest vastly different systemic risk rankings for the same bank, while the combined systemic risk measure based on PCA provides a reliable ranking. Furthermore, according to factor loadings of the first component, PCA combined ranking is mainly based on fundamentals instead of market price data. We clearly find that price-based rankings are not as practical a method as fundamentals-based ones. This PCA combined ranking directly shows systemic risk contributions of each bank for banking supervision purpose and reminds banks to prevent and cope with the financial crisis in advance.

Selection of quantum chemical descriptors by chemometric methods in the study of antioxidant activity of flavonoid compounds

NASA Astrophysics Data System (ADS)

Weber, K. C.; Honório, K. M.; da Silva, S. L.; Mercadante, R.; da Silva, A. B. F.

In the present study, the aim was to select electronic properties responsible for free radical scavenging ability of a set of 25 flavonoid compounds employing chemometric methods. Electronic parameters were calculated using the AM1 semiempirical method, and chemometric methods (principal component analysis, hierarchical cluster analysis, and k-nearest neighbor) were used with the aim to build models able to find relationships between electronic features and the antioxidant activity presented by the compounds studied. According to these models, four electronic variables can be considered important to discriminate more and less antioxidant flavonoid compounds: polarizability (α), charge at carbon 3 (QC3), total charge at substituent 5 (QS5), and total charge at substituent 3' (QS3'). The features found as being responsible for the antioxidant activity of the flavonoid compounds studied are consistent with previous results found in the literature. The results obtained can also bring improvements in the search for better antioxidant flavonoid compounds.

Flavoromics approach in monitoring changes in volatile compounds of virgin rapeseed oil caused by seed roasting.

PubMed

Gracka, Anna; Jeleń, Henryk H; Majcher, Małgorzata; Siger, Aleksander; Kaczmarek, Anna

2016-01-08

Two varieties of rapeseed (one high oleic - containing 76% of oleic acid, and the other - containing 62% of oleic acid) were used to produce virgin (pressed) oil. The rapeseeds were roasted at different temperature/time combinations (at 140-180°C, and for 5-15min); subsequently, oil was pressed from the roasted seeds. The roasting improved the flavour and contributed to a substantial increase in the amount of a potent antioxidant-canolol. The changes in volatile compounds related to roasting conditions were monitored using comprehensive gas chromatography-mass spectrometry (GC×GC-ToFMS), and the key odorants for the non-roasted and roasted seeds oils were determined by gas chromatography-olfactometry (GC-O). The most important compounds determining the flavour of oils obtained from the roasted seeds were dimethyl sulphide, dimethyltrisulfide, 2,3-diethyl-5-methylpyrazine, 2,3-butenedione, octanal, 3-isopropyl-2-methoxypyrazine and phenylacetaldehyde. For the oils obtained from the non-roasted seeds, the dominant compounds were dimethylsulfide, hexanal and octanal. Based on GC×GC-ToFMS and principal component analysis (PCA) of the data, several compounds were identified that were associated with roasting at the highest temperatures regardless of the rapeseed variety: these were, among others, methyl ketones (2-hexanone, 2-heptanone and 2-octanone). Copyright © 2015 Elsevier B.V. All rights reserved.

Quantitative structure-activity relationship of the curcumin-related compounds using various regression methods

NASA Astrophysics Data System (ADS)

Khazaei, Ardeshir; Sarmasti, Negin; Seyf, Jaber Yousefi

2016-03-01

Quantitative structure activity relationship were used to study a series of curcumin-related compounds with inhibitory effect on prostate cancer PC-3 cells, pancreas cancer Panc-1 cells, and colon cancer HT-29 cells. Sphere exclusion method was used to split data set in two categories of train and test set. Multiple linear regression, principal component regression and partial least squares were used as the regression methods. In other hand, to investigate the effect of feature selection methods, stepwise, Genetic algorithm, and simulated annealing were used. In two cases (PC-3 cells and Panc-1 cells), the best models were generated by a combination of multiple linear regression and stepwise (PC-3 cells: r2 = 0.86, q2 = 0.82, pred_r2 = 0.93, and r2m (test) = 0.43, Panc-1 cells: r2 = 0.85, q2 = 0.80, pred_r2 = 0.71, and r2m (test) = 0.68). For the HT-29 cells, principal component regression with stepwise (r2 = 0.69, q2 = 0.62, pred_r2 = 0.54, and r2m (test) = 0.41) is the best method. The QSAR study reveals descriptors which have crucial role in the inhibitory property of curcumin-like compounds. 6ChainCount, T_C_C_1, and T_O_O_7 are the most important descriptors that have the greatest effect. With a specific end goal to design and optimization of novel efficient curcumin-related compounds it is useful to introduce heteroatoms such as nitrogen, oxygen, and sulfur atoms in the chemical structure (reduce the contribution of T_C_C_1 descriptor) and increase the contribution of 6ChainCount and T_O_O_7 descriptors. Models can be useful in the better design of some novel curcumin-related compounds that can be used in the treatment of prostate, pancreas, and colon cancers.

Phenolic contents and antioxidant activities of major Australian red wines throughout the winemaking process.

PubMed

Ginjom, Irine R; D'Arcy, Bruce R; Caffin, Nola A; Gidley, Michael J

2010-09-22

Three Australian red wine types (Shiraz, Cabernet Sauvignon, and Merlot) were analyzed for antioxidant activity and a range of phenolic component contents using various spectral methods. More than half of the total phenolic compounds were tannins, whereas monomeric anthocyanins and flavonols were present in much lesser amounts (<10%). The evolution of phenolic contents and the respective antioxidant activities in wine samples from all stages of winemaking showed progressive changes toward those of commercial wines. The antioxidant activity of the wines in DPPH and ABTS assays was positively correlated with total phenolic contents and tannins. Comparisons of the three wine varieties based on their individual phenolic component groups and antioxidant activities showed limited differences between the different varieties. However, when all of the variables were combined in a principal component analysis, variety differentiation was observed. The three varieties of red wines all contained similar and high concentrations of antioxidants despite differences in grape variety/maturity and winemaking process, suggesting that related health benefits would accrue from all of the red wines studied.

Implementation of an integrating sphere for the enhancement of noninvasive glucose detection using quantum cascade laser spectroscopy

NASA Astrophysics Data System (ADS)

Werth, Alexandra; Liakat, Sabbir; Dong, Anqi; Woods, Callie M.; Gmachl, Claire F.

2018-05-01

An integrating sphere is used to enhance the collection of backscattered light in a noninvasive glucose sensor based on quantum cascade laser spectroscopy. The sphere enhances signal stability by roughly an order of magnitude, allowing us to use a thermoelectrically (TE) cooled detector while maintaining comparable glucose prediction accuracy levels. Using a smaller TE-cooled detector reduces form factor, creating a mobile sensor. Principal component analysis has predicted principal components of spectra taken from human subjects that closely match the absorption peaks of glucose. These principal components are used as regressors in a linear regression algorithm to make glucose concentration predictions, over 75% of which are clinically accurate.

Authentication of commercial spices based on the similarities between gas chromatographic fingerprints.

PubMed

Matsushita, Takaya; Zhao, Jing Jing; Igura, Noriyuki; Shimoda, Mitsuya

2018-06-01

A simple and solvent-free method was developed for the authentication of commercial spices. The similarities between gas chromatographic fingerprints were measured using similarity indices and multivariate data analyses, as morphological differentiation between dried powders and small spice particles was challenging. The volatile compounds present in 11 spices (i.e. allspice, anise, black pepper, caraway, clove, coriander, cumin, dill, fennel, star anise, and white pepper) were extracted by headspace solid-phase microextraction, and analysed by gas chromatography-mass spectrometry. The largest 10 peaks were selected from each total ion chromatogram, and a total of 65 volatiles were tentatively identified. The similarity indices (i.e. the congruence coefficients) were calculated using the data matrices of the identified compound relative peak areas to differentiate between two sets of fingerprints. Where pairs of similar fingerprints produced high congruence coefficients (>0.80), distinctive volatile markers were employed to distinguish between these samples. In addition, hierarchical cluster analysis and principal component analysis were performed to visualise the similarity among fingerprints, and the analysed spices were grouped and characterised according to their distinctive major components. This method is suitable for screening unknown spices, and can therefore be employed to evaluate the quality and authenticity of various spices. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Mass Spectrometry Imaging of Drug Related Crystal-Like Structures in Formalin-Fixed Frozen and Paraffin-Embedded Rabbit Kidney Tissue Sections

NASA Astrophysics Data System (ADS)

Bruinen, Anne L.; van Oevelen, Cateau; Eijkel, Gert B.; Van Heerden, Marjolein; Cuyckens, Filip; Heeren, Ron M. A.

2016-01-01

A multimodal mass spectrometry imaging (MSI) based approach was used to characterize the molecular content of crystal-like structures in a frozen and paraffin embedded piece of a formalin-fixed rabbit kidney. Matrix assisted laser desorption/ionization time-of-flight (MALDI-TOF) imaging and desorption electrospray ionization (DESI) mass spectrometry imaging were combined to analyze the frozen and paraffin embedded sample without further preparation steps to remove the paraffin. The investigated rabbit kidney was part of a study on a drug compound in development, in which severe renal toxicity was observed in dosed rabbits. Histological examination of the kidney showed tubular degeneration with precipitation of crystal-like structures in the cortex, which were assumed to cause the renal toxicity. The MS imaging approach was used to find out whether the crystal-like structures were composed of the drug compound, metabolites, or an endogenous compound as a reaction to the drug administration. The generated MALDI-MSI data were analyzed using principal component analysis. In combination with the MS/MS results, this way of data processing demonstrates that the crystal structures were mainly composed of metabolites and relatively little parent drug.

Proline-Based Carbamates as Cholinesterase Inhibitors.

PubMed

Pizova, Hana; Havelkova, Marketa; Stepankova, Sarka; Bak, Andrzej; Kauerova, Tereza; Kozik, Violetta; Oravec, Michal; Imramovsky, Ales; Kollar, Peter; Bobal, Pavel; Jampilek, Josef

2017-11-14

Series of twenty-five benzyl (2S)-2-(arylcarbamoyl)pyrrolidine-1-carboxylates was prepared and completely characterized. All the compounds were tested for their in vitro ability to inhibit acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), and the selectivity of compounds to individual cholinesterases was determined. Screening of the cytotoxicity of all the compounds was performed using a human monocytic leukaemia THP-1 cell line, and the compounds demonstrated insignificant toxicity. All the compounds showed rather moderate inhibitory effect against AChE; benzyl (2 S )-2-[(2-chlorophenyl)carbamoyl]pyrrolidine-1-carboxylate (IC 50 = 46.35 μM) was the most potent agent. On the other hand, benzyl (2 S )-2-[(4-bromophenyl)-] and benzyl (2 S )-2-[(2-bromophenyl)carbamoyl]pyrrolidine-1-carboxylates expressed anti-BChE activity (IC 50 = 28.21 and 27.38 μM, respectively) comparable with that of rivastigmine. The ortho -brominated compound as well as benzyl (2 S )-2-[(2-hydroxyphenyl)carbamoyl]pyrrolidine-1-carboxylate demonstrated greater selectivity to BChE. The in silico characterization of the structure-inhibitory potency for the set of proline-based carbamates considering electronic, steric and lipophilic properties was provided using comparative molecular surface analysis (CoMSA) and principal component analysis (PCA). Moreover, the systematic space inspection with splitting data into the training/test subset was performed to monitor the statistical estimators performance in the effort to map the probability-guided pharmacophore pattern. The comprehensive screening of the AChE/BChE profile revealed potentially relevant structural and physicochemical features that might be essential for mapping of the carbamates inhibition efficiency indicating qualitative variations exerted on the reaction site by the substituent in the 3'-/4'-position of the phenyl ring. In addition, the investigation was completed by a molecular docking study of recombinant human AChE.

Controlling the release of wood extractives into water bodies by selecting suitable eucalyptus species

NASA Astrophysics Data System (ADS)

Kilulya, K. F.; Msagati, T. A. M.; Mamba, B. B.; Ngila, J. C.; Bush, T.

Pulping industries are increasing worldwide as a result of the increase in the demand for pulp for cellulose derivatives and paper manufacturing. Due to the activities involved in pulping processes, different chemicals from raw materials (wood) and bleaching agents are released in pulp-mill effluent streams discharged into the environment and find their way into water bodies. Large quantities of water and chemicals used in pulping result in large amounts of wastewater with high concentrations of extractives such as unsaturated fatty acids, which are known to be toxic, and plant sterols which affect the development, growth and reproduction of aquatic organisms. This study was aimed at assessing the composition of extractives in two eucalyptus species used for pulp production in South Africa, in order to identify the suitable species with regard to extractive content. Samples from two eucalyptus plant species (Eucalyptus grandis and Eucalyptus dunnii) were collected from three sites and analysed for extractives by first extracting with water, followed by Soxhlet extraction using acetone. Compounds were identified and quantified using gas chromatography-mass spectrometry (GC-MS). Major classes of extractives identified were fatty acids (mainly hexadecanoic acid, 9,12-octadecadienoic, 9-octadecenoic and octadecanoic acids) and sterols (mainly β-sitosterol and stigmastanol). E. dunnii was found to contain higher amounts of the compounds compared to those found in E. grandis in all sampled sites. Principal component analysis (PCA) was performed and explained 92.9% of the total variation using three principal components. It was revealed that the percentage of fatty acids, which has a negative influence on both principal components 2 and 3, was responsible for the difference between the species. E. grandis, which was found to contain low amounts of extractives, was therefore found suitable for pulping with regard to minimal water usage and environment pollution.

Odor Profile of Different Varieties of Extra-Virgin Olive Oil During Deep Frying Using an Electronic Nose and SPME-GC-FID

NASA Astrophysics Data System (ADS)

Messina, Valeria; Biolatto, Andrea; Sancho, Ana; Descalzo, Adriana; Grigioni, Gabriela; de Reca, Noemí Walsöe

2011-09-01

The aim of the performed work was to evaluate with an electronic nose changes in odor profile of Arauco and Arbequina varieties of extra-virgin olive oil during deep-frying. Changes in odor were analyzed using an electronic nose composed of 16 sensors. Volatile compounds were analyzed by SPME-GC-FID. Principal Component Analysis was applied for electronic results. Arauco variety showed the highest response for sensors. Statistical analysis for volatile compounds indicated a significant (P<0.001) interaction between variety and time of frying processes. Arauco variety showed the highest production of volatile compounds at 60 min of deep frying. The two varieties presented distinct patterns of volatile products, being clearly identified with the electronic nose.

Alkylation of organic aromatic compounds

DOEpatents

Smith, L.A. Jr.; Arganbright, R.P.; Hearn, D.

1993-09-07

Aromatic compounds are alkylated in a catalytic distillation, wherein the catalyst structure also serves as a distillation component by contacting the aromatic compound with a C[sub 2] to C[sub 10] olefin in the catalyst bed under 0.25 to 50 atmospheres of pressure and at temperatures in the range of 80 C to 500 C, using as the catalyst a molecular sieve characterized as acidic or an acidic cation exchange resin. For example, ethyl benzene is produced by feeding ethylene to about the mid point of the catalyst bed while benzene is conveniently added through the reflux in molar excess to that required to react with ethylene, thereby reacting substantially all of the ethylene and recovering benzene as the principal overhead and ethyl benzene in the bottoms. 1 figures.

Alkylation of organic aromatic compounds

DOEpatents

Smith, Jr., Lawrence A.; Arganbright, Robert P.; Hearn, Dennis

1993-01-01

Aromatic compounds are alkylated in a catalytic distillation, wherein the catalyst structure also serves as a distillation component by contacting the aromatic compound with a C.sub.2 to C.sub.10 olefin in the catalyst bed under 0.25 to 50 atmospheres of pressure and at temperatures in the range of 80.degree. C. to 500.degree. C., using as the catalyst a mole sieve characterized as acidic or an acidic cation exchange resin. For example, ethyl benzene is produced by feeding ethylene to about the mid point of the catalyst bed while benzene is conveniently added through the reflux in molar excess to that required to react with ethylene, thereby reacting substantially all of the ethylene and recovering benzene as the principal overhead and ethyl benzene in the bottoms.

Pixel-level multisensor image fusion based on matrix completion and robust principal component analysis

NASA Astrophysics Data System (ADS)

Wang, Zhuozheng; Deller, J. R.; Fleet, Blair D.

2016-01-01

Acquired digital images are often corrupted by a lack of camera focus, faulty illumination, or missing data. An algorithm is presented for fusion of multiple corrupted images of a scene using the lifting wavelet transform. The method employs adaptive fusion arithmetic based on matrix completion and self-adaptive regional variance estimation. Characteristics of the wavelet coefficients are used to adaptively select fusion rules. Robust principal component analysis is applied to low-frequency image components, and regional variance estimation is applied to high-frequency components. Experiments reveal that the method is effective for multifocus, visible-light, and infrared image fusion. Compared with traditional algorithms, the new algorithm not only increases the amount of preserved information and clarity but also improves robustness.

Rapid fingerprinting of white wine oxidizable fraction and classification of white wines using disposable screen printed sensors and derivative voltammetry.

PubMed

Ugliano, Maurizio

2016-12-01

This work describes the application of disposable screen printed carbon paste sensors for the analysis of the main white wine oxidizable compounds as well as for the rapid fingerprinting and classification of white wines from different grape varieties. The response of individual white wine antioxidants such as flavanols, flavanol derivatives, phenolic acids, SO2 and ascorbic acid was first assessed in model wine. Analysis of commercial white wines gave voltammograms featuring two unresolved anodic waves corresponding to the oxidation of different compounds, mostly phenolic antioxidants. Calculation of the first order derivative of measured current vs. applied potential allowed resolving these two waves, highlighting the occurrence of several electrode processes corresponding to the oxidation of individual wine components. Through the application of Principal Component Analysis (PCA), derivative voltammograms were used to discriminate among wines of different varieties. Copyright © 2016 Elsevier Ltd. All rights reserved.

Modified neural networks for rapid recovery of tokamak plasma parameters for real time control

NASA Astrophysics Data System (ADS)

Sengupta, A.; Ranjan, P.

2002-07-01

Two modified neural network techniques are used for the identification of the equilibrium plasma parameters of the Superconducting Steady State Tokamak I from external magnetic measurements. This is expected to ultimately assist in a real time plasma control. As different from the conventional network structure where a single network with the optimum number of processing elements calculates the outputs, a multinetwork system connected in parallel does the calculations here in one of the methods. This network is called the double neural network. The accuracy of the recovered parameters is clearly more than the conventional network. The other type of neural network used here is based on the statistical function parametrization combined with a neural network. The principal component transformation removes linear dependences from the measurements and a dimensional reduction process reduces the dimensionality of the input space. This reduced and transformed input set, rather than the entire set, is fed into the neural network input. This is known as the principal component transformation-based neural network. The accuracy of the recovered parameters in the latter type of modified network is found to be a further improvement over the accuracy of the double neural network. This result differs from that obtained in an earlier work where the double neural network showed better performance. The conventional network and the function parametrization methods have also been used for comparison. The conventional network has been used for an optimization of the set of magnetic diagnostics. The effective set of sensors, as assessed by this network, are compared with the principal component based network. Fault tolerance of the neural networks has been tested. The double neural network showed the maximum resistance to faults in the diagnostics, while the principal component based network performed poorly. Finally the processing times of the methods have been compared. The double network and the principal component network involve the minimum computation time, although the conventional network also performs well enough to be used in real time.

Tailored ß-Cyclodextrin Blocks the Translocation Pores of Binary Exotoxins from C. Botulinum and C. Perfringens and Protects Cells from Intoxication

PubMed Central

Nestorovich, Ekaterina M.; Karginov, Vladimir A.; Popoff, Michel R.; Bezrukov, Sergey M.; Barth, Holger

2011-01-01

Background Clostridium botulinum C2 toxin and Clostridium perfringens iota toxin are binary exotoxins, which ADP-ribosylate actin in the cytosol of mammalian cells and thereby destroy the cytoskeleton. C2 and iota toxin consists of two individual proteins, an enzymatic active (A-) component and a separate receptor binding and translocation (B-) component. The latter forms a complex with the A-component on the surface of target cells and after receptor-mediated endocytosis, it mediates the translocation of the A-component from acidified endosomal vesicles into the cytosol. To this end, the B-components form heptameric pores in endosomal membranes, which serve as translocation channels for the A-components. Methodology/Principal Findings Here we demonstrate that a 7-fold symmetrical positively charged ß-cyclodextrin derivative, per-6-S-(3-aminomethyl)benzylthio-ß-cyclodextrin, protects cultured cells from intoxication with C2 and iota toxins in a concentration-dependent manner starting at low micromolar concentrations. We discovered that the compound inhibited the pH-dependent membrane translocation of the A-components of both toxins in intact cells. Consistently, the compound strongly blocked transmembrane channels formed by the B-components of C2 and iota toxin in planar lipid bilayers in vitro. With C2 toxin, we consecutively ruled out all other possible inhibitory mechanisms showing that the compound did not interfere with the binding of the toxin to the cells or with the enzyme activity of the A-component. Conclusions/Significance The described ß-cyclodextrin derivative was previously identified as one of the most potent inhibitors of the binary lethal toxin of Bacillus anthracis both in vitro and in vivo, implying that it might represent a broad-spectrum inhibitor of binary pore-forming exotoxins from pathogenic bacteria. PMID:21887348

Changes in Volatile Compounds of Chinese Luzhou-Flavor Liquor during the Fermentation and Distillation Process.

PubMed

Ding, Xiaofei; Wu, Chongde; Huang, Jun; Zhou, Rongqing

2015-11-01

The aim of this study was to investigate the dynamic of volatile compounds in the Zaopei during the fermentation and distillation process by headspace solid-phase microextraction-gas chromatography mass spectrometry (HS-SPME-GCMS). Physicochemical properties analysis of Zaopei (fermented grains [FG], fermented grains mixed with sorghum [FGS], streamed grains [SG], and streamed grains mixed with Daqu [SGD]) showed distinct changes. A total number of 66 volatile compounds in the Zaopei were identified, in which butanoic acid, hexanoic acid, ethyl hexanoate, ethyl lactate, ethyl octanoate, hexyl hexanoate, ethyl hydrocinnamate, ethyl oleate, ethyl hexadecanoate, and ethyl linoleate were considered to be the dominant compounds due to their high concentrations. FG had the highest volatile compounds (112.43 mg/kg), which significantly decreased by 17.05% in the FGS, 67.12% in the SG, and 73.75% in the SGD. Furthermore, about 61.49% of volatile compounds of FGS were evaporated into raw liquor, whereas head, heart, and tail liquor accounted for 29.84%, 39.49%, and 30.67%, respectively. Each volatile class generally presented a decreasing trend, except for furans. Especially, the percentage of esters was 55.51% to 67.41% in the Zaopei, and reached 92.60% to 97.67% in the raw liquor. Principal component analysis based ordination of volatile compounds data segregated FGS and SGD samples. In addition, radar diagrams of the odor activity values suggested that intense flavor of fruit was weakened most from FG to SGD. The dynamic of volatile compounds in the Zaopei during the fermentation and distillation process was tested by SPME-GCMS. The result of this study demonstrated that both volatile compounds of Zaopei and thermal reaction during distillation simply determined the unique feature of raw liquor. This study was conducted based on the real products from liquor manufactory, so it is practicable that the method can be used in an industry setting. © 2015 Institute of Food Technologists®

Discrimination of honeys using colorimetric sensor arrays, sensory analysis and gas chromatography techniques.

PubMed

Tahir, Haroon Elrasheid; Xiaobo, Zou; Xiaowei, Huang; Jiyong, Shi; Mariod, Abdalbasit Adam

2016-09-01

Aroma profiles of six honey varieties of different botanical origins were investigated using colorimetric sensor array, gas chromatography-mass spectrometry (GC-MS) and descriptive sensory analysis. Fifty-eight aroma compounds were identified, including 2 norisoprenoids, 5 hydrocarbons, 4 terpenes, 6 phenols, 7 ketones, 9 acids, 12 aldehydes and 13 alcohols. Twenty abundant or active compounds were chosen as key compounds to characterize honey aroma. Discrimination of the honeys was subsequently implemented using multivariate analysis, including hierarchical clustering analysis (HCA) and principal component analysis (PCA). Honeys of the same botanical origin were grouped together in the PCA score plot and HCA dendrogram. SPME-GC/MS and colorimetric sensor array were able to discriminate the honeys effectively with the advantages of being rapid, simple and low-cost. Moreover, partial least squares regression (PLSR) was applied to indicate the relationship between sensory descriptors and aroma compounds. Copyright © 2016 Elsevier Ltd. All rights reserved.

Effect of immobilized Lactobacillus casei on the evolution of flavor compounds in probiotic dry-fermented sausages during ripening.

PubMed

Sidira, Marianthi; Kandylis, Panagiotis; Kanellaki, Maria; Kourkoutas, Yiannis

2015-02-01

The effect of immobilized Lactobacillus casei ATCC 393 on wheat grains on the generation of volatile compounds in probiotic dry-fermented sausages during ripening was investigated. For comparison reasons, sausages containing free L. casei cells or no starter culture were also included in the study. Samples were collected after 1, 28 and 45days of ripening and subjected to SPME GC/MS analysis. Both the probiotic culture and the ripening process affected significantly the concentration of all volatile compounds. The significantly highest content of total volatiles, esters, alcohols and miscellaneous compounds was observed in sausages containing the highest amount of immobilized culture (300g/kg of stuffing mixture) ripened for 45days. Principal component analysis of the semi-quantitative data revealed that primarily the concentration of the immobilized probiotic culture affected the volatile composition. Copyright © 2014 Elsevier Ltd. All rights reserved.

Experimental Researches on the Durability Indicators and the Physiological Comfort of Fabrics using the Principal Component Analysis (PCA) Method

NASA Astrophysics Data System (ADS)

Hristian, L.; Ostafe, M. M.; Manea, L. R.; Apostol, L. L.

2017-06-01

The work pursued the distribution of combed wool fabrics destined to manufacturing of external articles of clothing in terms of the values of durability and physiological comfort indices, using the mathematical model of Principal Component Analysis (PCA). Principal Components Analysis (PCA) applied in this study is a descriptive method of the multivariate analysis/multi-dimensional data, and aims to reduce, under control, the number of variables (columns) of the matrix data as much as possible to two or three. Therefore, based on the information about each group/assortment of fabrics, it is desired that, instead of nine inter-correlated variables, to have only two or three new variables called components. The PCA target is to extract the smallest number of components which recover the most of the total information contained in the initial data.

Quantitative structure-activity relationship study of P2X7 receptor inhibitors using combination of principal component analysis and artificial intelligence methods.

PubMed

Ahmadi, Mehdi; Shahlaei, Mohsen

2015-01-01

P2X7 antagonist activity for a set of 49 molecules of the P2X7 receptor antagonists, derivatives of purine, was modeled with the aid of chemometric and artificial intelligence techniques. The activity of these compounds was estimated by means of combination of principal component analysis (PCA), as a well-known data reduction method, genetic algorithm (GA), as a variable selection technique, and artificial neural network (ANN), as a non-linear modeling method. First, a linear regression, combined with PCA, (principal component regression) was operated to model the structure-activity relationships, and afterwards a combination of PCA and ANN algorithm was employed to accurately predict the biological activity of the P2X7 antagonist. PCA preserves as much of the information as possible contained in the original data set. Seven most important PC's to the studied activity were selected as the inputs of ANN box by an efficient variable selection method, GA. The best computational neural network model was a fully-connected, feed-forward model with 7-7-1 architecture. The developed ANN model was fully evaluated by different validation techniques, including internal and external validation, and chemical applicability domain. All validations showed that the constructed quantitative structure-activity relationship model suggested is robust and satisfactory.

Quantitative structure–activity relationship study of P2X7 receptor inhibitors using combination of principal component analysis and artificial intelligence methods

PubMed Central

Ahmadi, Mehdi; Shahlaei, Mohsen

2015-01-01

P2X7 antagonist activity for a set of 49 molecules of the P2X7 receptor antagonists, derivatives of purine, was modeled with the aid of chemometric and artificial intelligence techniques. The activity of these compounds was estimated by means of combination of principal component analysis (PCA), as a well-known data reduction method, genetic algorithm (GA), as a variable selection technique, and artificial neural network (ANN), as a non-linear modeling method. First, a linear regression, combined with PCA, (principal component regression) was operated to model the structure–activity relationships, and afterwards a combination of PCA and ANN algorithm was employed to accurately predict the biological activity of the P2X7 antagonist. PCA preserves as much of the information as possible contained in the original data set. Seven most important PC's to the studied activity were selected as the inputs of ANN box by an efficient variable selection method, GA. The best computational neural network model was a fully-connected, feed-forward model with 7−7−1 architecture. The developed ANN model was fully evaluated by different validation techniques, including internal and external validation, and chemical applicability domain. All validations showed that the constructed quantitative structure–activity relationship model suggested is robust and satisfactory. PMID:26600858

High hydrostatic pressure treatments enhance volatile components of pre-germinated brown rice revealed by aromatic fingerprinting based on HS-SPME/GC-MS and chemometric methods.

PubMed

Xia, Qiang; Mei, Jun; Yu, Wenjuan; Li, Yunfei

2017-01-01

Germination favors to significantly enhance functional components and health attributes of whole-grain brown rice (BR), but the production of germinated BR (GBR) compromises the typical rice flavor perception due to soaking process. Simultaneously, high hydrostatic pressure (HHP) is considered as an effective processing technique to enhance micronutrients utilization efficiency of GBR and improve products flavor, but no information about the effects of HHP treatments on volatile fingerprinting of GBR has been reported. Therefore, the objective of this work was to apply HHP to improve the flavor and odor of GBR grains by exploring HHP-induced changes in aroma compounds. GBR grains were obtained by incubating at 37°C for 36h, and subsequently subjected to HHP treatments at pressures 100, 300 and 500MPa for 15min, using 0.1MPa as control. Headspace solid-phase micro extraction coupled to gas chromatography mass spectrometry was used to characterize process-induced shifts of volatile organic compounds fingerprinting, followed by multivariate analysis. Our results confirmed the significant reduction of total volatile fractions derived from germination process. Contrarily, the following HHP treatments greatly enhanced the flavor components of GBR, particularly characteristic odorants including aldehydes, ketones, and alcohols. Principal component analysis further indicated the different influence of germination and high pressure on the changes in volatile components. Partial least square-discrimination analysis suggested that 4-vinylguaiacol was closely linked to germination, whereas E,E-2,4-decadienal, E-2-hexenal, E,E-2,4-heptadienal and benzyl alcohol could be considered as volatile biomarkers of high pressure. Copyright © 2016 Elsevier Ltd. All rights reserved.

In vitro antifungal activity of the tea tree (Melaleuca alternifolia) essential oil and its major components against plant pathogens.

PubMed

Terzi, V; Morcia, C; Faccioli, P; Valè, G; Tacconi, G; Malnati, M

2007-06-01

The aim of this study was to examine the effect of Melaleuca alternifolia essential oil (TTO) and its principal components on four cereal-pathogenic fungi. The antimycotic properties of TTO and of terpinen-4-ol, gamma-terpinen and 1,8-cineole (eucalyptol) were evaluated in vitro on Fusarium graminearum, Fusarium culmorum and Pyrenophora graminea. Moreover, barley leaves infected with Blumeria graminis were treated with whole TTO. All the tested fungi were susceptible to TTO and its components. TTO exerted a wide spectrum of antimycotic activity. Single TTO purified components were more active than the whole oil in reducing in vitro growth of fungal mycelium and, among the tested compounds, terpinen-4-ol was the most effective. TTO and its components can be considered potential alternative natural fungicides.

An integrtated approach to the use of Landsat TM data for gold exploration in west central Nevada

NASA Technical Reports Server (NTRS)

Mouat, D. A.; Myers, J. S.; Miller, N. L.

1987-01-01

This paper represents an integration of several Landsat TM image processing techniques with other data to discriminate the lithologies and associated areas of hydrothermal alteration in the vicinity of the Paradise Peak gold mine in west central Nevada. A microprocessor-based image processing system and an IDIMS system were used to analyze data from a 512 X 512 window of a Landsat-5 TM scene collected on June 30, 1984. Image processing techniques included simple band composites, band ratio composites, principal components composites, and baseline-based composites. These techniques were chosen based on their ability to discriminate the spectral characteristics of the products of hydrothermal alteration as well as of the associated regional lithologies. The simple band composite, ratio composite, two principal components composites, and the baseline-based composites separately can define the principal areas of alteration. Combined, they provide a very powerful exploration tool.

Compression map, functional groups and fossilization: A chemometric approach (Pennsylvanian neuropteroid foliage, Canada)

USGS Publications Warehouse

D'Angelo, J. A.; Zodrow, E.L.; Mastalerz, Maria

2012-01-01

Nearly all of the spectrochemical studies involving Carboniferous foliage of seed-ferns are based on a limited number of pinnules, mainly compressions. In contrast, in this paper we illustrate working with a larger pinnate segment, i.e., a 22-cm long neuropteroid specimen, compression-preserved with cuticle, the compression map. The objective is to study preservation variability on a larger scale, where observation of transparency/opacity of constituent pinnules is used as a first approximation for assessing the degree of pinnule coalification/fossilization. Spectrochemical methods by Fourier transform infrared spectrometry furnish semi-quantitative data for principal component analysis.The compression map shows a high degree of preservation variability, which ranges from comparatively more coalified pinnules to less coalified pinnules that resemble fossilized-cuticles, noting that the pinnule midveins are preserved more like fossilized-cuticles. A general overall trend of coalified pinnules towards fossilized-cuticles, i.e., variable chemistry, is inferred from the semi-quantitative FTIR data as higher contents of aromatic compounds occur in the visually more opaque upper location of the compression map. The latter also shows a higher condensation of the aromatic nuclei along with some variation in both ring size and degree of aromatic substitution. From principal component analysis we infer correspondence between transparency/opacity observation and chemical information which correlate with varying degree to fossilization/coalification among pinnules. ?? 2011 Elsevier B.V.

Taste characteristics based quantitative and qualitative evaluation of ginseng adulteration.

PubMed

Cui, Shaoqing; Yang, Liangcheng; Wang, Jun; Wang, Xinlei

2015-05-01

Adulteration of American ginseng with Asian ginseng is common and has caused much damage to customers. Panel evaluation is commonly used to determine their differences, but it is subjective. Chemical instruments are used to identify critical compounds but they are time-consuming and expensive. Therefore, a fast, accurate and convenient method is required. A taste sensing system, combining both advantages of the above two technologies, provides a novel potential technology for determining ginseng adulteration. The aim is to build appropriate models to distinguish and predict ginseng adulteration by using taste characteristics. It was found that ginsenoside contents decreased linearly (R(2) = 0.92) with mixed ratios. A bioplot of principal component analysis showed a good performance in classing samples with the first two principal components reaching 89.7%, and it was noted that it was the bitterness, astringency, aftertaste of bitterness and astringency, and saltiness leading the successful determination. After factor screening, bitterness, astringency, aftertaste of bitterness and saltiness were employed to build latent models. Tastes of bitterness, astringency and aftertaste bitterness were demonstrated to be most effective in predicting adulteration ratio, mean while, bitterness and aftertaste bitterness turned out to be most effective in ginsenoside content prediction. Taste characteristics of adulterated ginsengs, considered as taste fingerprint, can provide novel guidance for determining the adulteration of American and Asian ginseng. © 2014 Society of Chemical Industry.

Chemical Composition of Juniperus communis L. Cone Essential Oil and Its Variability among Wild Populations in Kosovo.

PubMed

Hajdari, Avni; Mustafa, Behxhet; Nebija, Dashnor; Miftari, Elheme; Quave, Cassandra L; Novak, Johannes

2015-11-01

Ripe cones of Juniperus communis L. (Cupressaceae) were collected from five wild populations in Kosovo, with the aim of investigating the chemical composition and natural variation of essential oils between and within wild populations. Ripe cones were collected, air dried, crushed, and the essential oils obtained by hydrodistillation. The essential-oil constituents were identified by GC-FID and GC/MS analyses. The yield of essential oil differed depending on the population origins and ranged from 0.4 to 3.8% (v/w, based on the dry weight). In total, 42 compounds were identified in the essential oils of all populations. The principal components of the cone-essential oils were α-pinene, followed by β-myrcene, sabinene, and D-limonene. Taking into consideration the yield and chemical composition, the essential oil originating from various collection sites in Kosovo fulfilled the minimum requirements for J. communis essential oils of the European Pharmacopoeia. Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used to determine the influence of the geographical variations on the essential-oil composition. These statistical analyses suggested that the clustering of populations was not related to their geographic location, but rather appeared to be linked to local selective forces acting on the chemotype diversity. Copyright © 2015 Verlag Helvetica Chimica Acta AG, Zürich.

Separating strain from composition in unit cell parameter maps obtained from aberration corrected high resolution transmission electron microscopy imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schulz, T.; Remmele, T.; Korytov, M.

2014-01-21

Based on the evaluation of lattice parameter maps in aberration corrected high resolution transmission electron microscopy images, we propose a simple method that allows quantifying the composition and disorder of a semiconductor alloy at the unit cell scale with high accuracy. This is realized by considering, next to the out-of-plane, also the in-plane lattice parameter component allowing to separate the chemical composition from the strain field. Considering only the out-of-plane lattice parameter component not only yields large deviations from the true local alloy content but also carries the risk of identifying false ordering phenomena like formations of chains or platelets.more » Our method is demonstrated on image simulations of relaxed supercells, as well as on experimental images of an In{sub 0.20}Ga{sub 0.80}N quantum well. Principally, our approach is applicable to all epitaxially strained compounds in the form of quantum wells, free standing islands, quantum dots, or wires.« less

Mosquito repellent activity of essential oils of aromatic plants growing in Argentina.

PubMed

Gillij, Y G; Gleiser, R M; Zygadlo, J A

2008-05-01

Mosquitoes are important vectors of diseases and nuisance pests. Repellents minimize contact with mosquitoes. Repellents based on essential oils (EO) are being developed as an alternative to DEET (N,N-diethyl-m-methylbenzamide), an effective compound that has disadvantages including toxic reactions, and damage to plastic and synthetic fabric. This work evaluated the repellency against Aedes aegypti of EO from aromatic plants that grow in Argentina: Acantholippia seriphioides, Achyrocline satureioides, Aloysia citriodora, Anemia tomentosa, Baccharis spartioides, Chenopodium ambrosioides, Eucalyptus saligna, Hyptis mutabilis, Minthostachys mollis, Rosmarinus officinalis, Tagetes minuta and Tagetes pusilla. Most EO were effective. Variations depending on geographic origin of the plant were detected. At a 90% EO concentration, A. satureoides and T. pusilla were the least repellent. At concentrations of 12.5% B. spartioides, R. officinalis and A. citriodora showed the longest repellency times. Comparisons of the principal components of each EO suggest that limonene and camphor were the main components responsible for the repellent effects.

Essential Oils Composition and Antimicrobial Activity of Six Conifers Harvested in Lebanon.

PubMed

Fahed, Layal; Khoury, Madona; Stien, Didier; Ouaini, Naïm; Eparvier, Véronique; El Beyrouthy, Marc

2017-02-01

The chemical composition and antimicrobial activity of the essential oils (EOs) of six conifers harvested in Lebanon, Abies cilicica, Cupressus sempervirens, Juniperus excelsa, Juniperus oxycedrus, Cedrus libani and Cupressus macrocarpa gold crest, were investigated. The EOs were obtained by hydrodistillation using a Clevenger-type apparatus and characterized by GC and GC/MS analyses. A principal components analysis based on Pearson correlation between essential oils chemical analyses was also conducted. The minimum inhibitory concentrations (MICs) of these essentials oils were determined against a range of bacteria and fungi responsible for cutaneous infections in human, using the broth microdilution technique. The EOs showed the most interesting bioactivity on the dermatophytes species (MIC values 32 - 64 μg/ml). Each of the major compounds of C. macrocarpa as well as an artificial reconstructed EO were tested on Trichophyton rubrum showing a contribution of the minor components to the overall activity. © 2017 Wiley-VHCA AG, Zurich, Switzerland.

Fouling analysis of membrane bioreactor treating antibiotic production wastewater at different hydraulic retention times.

PubMed

Yu, Dawei; Chen, Yutao; Wei, Yuansong; Wang, Jianxing; Wang, Yawei; Li, Kun

2017-04-01

Membrane fouling, including foulants and factors, was investigated during hydraulic retention time (HRT) optimization of a membrane bioreactor (MBR) that treated wastewater from the production of antibiotics. The results showed that HRT played an important role in membrane fouling. Trans-membrane pressure (TMP), membrane flux, and resistance were stable at -6 kPa, 76 L m -2  h -1  bar -1 , and 4.5 × 10 12  m -1 when HRT was at 60, 48, and 36 h, respectively. Using Fourier transform infrared spectroscopy, foulants were identified as carbohydrates and proteins, which correlated with effluent organic matter and effluent chemical oxygen demand (COD) compounds. Therefore, membrane fouling trends would benefit from low supernatant COD (378 mg L -1 ) and a low membrane removal rate (26 %) at a HRT of 36 h. Serious membrane fouling at 72 and 24 h was related to soluble microbial products and extracellular polymeric substances in mixed liquor, respectively. Based on the TMP decrease and flux recovery after physical and chemical cleaning, irremovable fouling aggravation was related to extracellular polymeric substances' increase and soluble microbial products' decrease. According to changes in the specific oxygen uptake rate (SOUR) and mixed liquor suspended solids (MLSSs) during HRT optimization in this study, antibiotic production wastewater largely inhibited MLSS growth, which only increased from 4.5 to 5.0 g L -1 when HRT was decreased from 72 to 24 h, but did not limit sludge activity. The results of a principal component analysis highlighted both proteins and carbohydrates in extracellular polymeric substances as the primary foulants. Membrane fouling associated with the first principal component was positively related to extracellular polymeric substances and negatively related to soluble microbial products. Principal component 2 was primarily related to proteins in the influent. Additional membrane fouling factors included biomass characteristics, operational conditions, and feed characteristics.

Reformulation of Traditional Chamomile Oil: Quality Controls and Fingerprint Presentation Based on Cluster Analysis of Attenuated Total Reflectance–Infrared Spectral Data

PubMed Central

Sakhteman, Amirhossein; Faridi, Pouya; Daneshamouz, Saeid; Akbarizadeh, Amin Reza; Borhani-Haghighi, Afshin; Mohagheghzadeh, Abdolali

2017-01-01

Herbal oils have been widely used in Iran as medicinal compounds dating back to thousands of years in Iran. Chamomile oil is widely used as an example of traditional oil. We remade chamomile oils and tried to modify it with current knowledge and facilities. Six types of oil (traditional and modified) were prepared. Microbial limit tests and physicochemical tests were performed on them. Also, principal component analysis, hierarchical cluster analysis, and partial least squares discriminant analysis were done on the spectral data of attenuated total reflectance–infrared in order to obtain insight based on classification pattern of the samples. The results show that we can use modified versions of the chamomile oils (modified Clevenger-type apparatus method and microwave method) with the same content of traditional ones and with less microbial contaminations and better physicochemical properties. PMID:28585466

Reformulation of Traditional Chamomile Oil: Quality Controls and Fingerprint Presentation Based on Cluster Analysis of Attenuated Total Reflectance-Infrared Spectral Data.

PubMed

Zargaran, Arman; Sakhteman, Amirhossein; Faridi, Pouya; Daneshamouz, Saeid; Akbarizadeh, Amin Reza; Borhani-Haghighi, Afshin; Mohagheghzadeh, Abdolali

2017-10-01

Herbal oils have been widely used in Iran as medicinal compounds dating back to thousands of years in Iran. Chamomile oil is widely used as an example of traditional oil. We remade chamomile oils and tried to modify it with current knowledge and facilities. Six types of oil (traditional and modified) were prepared. Microbial limit tests and physicochemical tests were performed on them. Also, principal component analysis, hierarchical cluster analysis, and partial least squares discriminant analysis were done on the spectral data of attenuated total reflectance-infrared in order to obtain insight based on classification pattern of the samples. The results show that we can use modified versions of the chamomile oils (modified Clevenger-type apparatus method and microwave method) with the same content of traditional ones and with less microbial contaminations and better physicochemical properties.

Exploring the Saccharomyces cerevisiae Volatile Metabolome: Indigenous versus Commercial Strains

PubMed Central

Alves, Zélia; Melo, André; Figueiredo, Ana Raquel; Coimbra, Manuel A.; Gomes, Ana C.; Rocha, Sílvia M.

2015-01-01

Winemaking is a highly industrialized process and a number of commercial Saccharomyces cerevisiae strains are used around the world, neglecting the diversity of native yeast strains that are responsible for the production of wines peculiar flavours. The aim of this study was to in-depth establish the S. cerevisiae volatile metabolome and to assess inter-strains variability. To fulfill this objective, two indigenous strains (BT2652 and BT2453 isolated from spontaneous fermentation of grapes collected in Bairrada Appellation, Portugal) and two commercial strains (CSc1 and CSc2) S. cerevisiae were analysed using a methodology based on advanced multidimensional gas chromatography (HS-SPME/GC×GC-ToFMS) tandem with multivariate analysis. A total of 257 volatile metabolites were identified, distributed over the chemical families of acetals, acids, alcohols, aldehydes, ketones, terpenic compounds, esters, ethers, furan-type compounds, hydrocarbons, pyrans, pyrazines and S-compounds. Some of these families are related with metabolic pathways of amino acid, carbohydrate and fatty acid metabolism as well as mono and sesquiterpenic biosynthesis. Principal Component Analysis (PCA) was used with a dataset comprising all variables (257 volatile components), and a distinction was observed between commercial and indigenous strains, which suggests inter-strains variability. In a second step, a subset containing esters and terpenic compounds (C10 and C15), metabolites of particular relevance to wine aroma, was also analysed using PCA. The terpenic and ester profiles express the strains variability and their potential contribution to the wine aromas, specially the BT2453, which produced the higher terpenic content. This research contributes to understand the metabolic diversity of indigenous wine microflora versus commercial strains and achieved knowledge that may be further exploited to produce wines with peculiar aroma properties. PMID:26600152

Geochemical differentiation processes for arc magma of the Sengan volcanic cluster, Northeastern Japan, constrained from principal component analysis

NASA Astrophysics Data System (ADS)

Ueki, Kenta; Iwamori, Hikaru

2017-10-01

In this study, with a view of understanding the structure of high-dimensional geochemical data and discussing the chemical processes at work in the evolution of arc magmas, we employed principal component analysis (PCA) to evaluate the compositional variations of volcanic rocks from the Sengan volcanic cluster of the Northeastern Japan Arc. We analyzed the trace element compositions of various arc volcanic rocks, sampled from 17 different volcanoes in a volcanic cluster. The PCA results demonstrated that the first three principal components accounted for 86% of the geochemical variation in the magma of the Sengan region. Based on the relationships between the principal components and the major elements, the mass-balance relationships with respect to the contributions of minerals, the composition of plagioclase phenocrysts, geothermal gradient, and seismic velocity structure in the crust, the first, the second, and the third principal components appear to represent magma mixing, crystallizations of olivine/pyroxene, and crystallizations of plagioclase, respectively. These represented 59%, 20%, and 6%, respectively, of the variance in the entire compositional range, indicating that magma mixing accounted for the largest variance in the geochemical variation of the arc magma. Our result indicated that crustal processes dominate the geochemical variation of magma in the Sengan volcanic cluster.

Metabolic changes in different developmental stages of Vanilla planifolia pods.

PubMed

Palama, Tony Lionel; Khatib, Alfi; Choi, Young Hae; Payet, Bertrand; Fock, Isabelle; Verpoorte, Robert; Kodja, Hippolyte

2009-09-09

The metabolomic analysis of developing Vanilla planifolia green pods (between 3 and 8 months after pollination) was carried out by nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis. Multivariate data analysis of the (1)H NMR spectra, such as principal component analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA), showed a trend of separation of those samples based on the metabolites present in the methanol/water (1:1) extract. Older pods had a higher content of glucovanillin, vanillin, p-hydroxybenzaldehyde glucoside, p-hydroxybenzaldehyde, and sucrose, while younger pods had more bis[4-(beta-D-glucopyranosyloxy)-benzyl]-2-isopropyltartrate (glucoside A), bis[4-(beta-D-glucopyranosyloxy)-benzyl]-2-(2-butyl)tartrate (glucoside B), glucose, malic acid, and homocitric acid. A liquid chromatography-mass spectrometry (LC-MS) analysis targeted at phenolic compound content was also performed on the developing pods and confirmed the NMR results. Ratios of aglycones/glucosides were estimated and thus allowed for detection of more minor metabolites in the green vanilla pods. Quantification of compounds based on both LC-MS and NMR analyses showed that free vanillin can reach 24% of the total vanillin content after 8 months of development in the vanilla green pods.

Acaricidal Activity of Eugenol Based Compounds against Scabies Mites

PubMed Central

Pasay, Cielo; Mounsey, Kate; Stevenson, Graeme; Davis, Rohan; Arlian, Larry; Morgan, Marjorie; Vyszenski-Moher, DiAnn; Andrews, Kathy; McCarthy, James

2010-01-01

Backgound Human scabies is a debilitating skin disease caused by the “itch mite” Sarcoptes scabiei. Ordinary scabies is commonly treated with topical creams such as permethrin, while crusted scabies is treated with topical creams in combination with oral ivermectin. Recent reports of acaricide tolerance in scabies endemic communities in Northern Australia have prompted efforts to better understand resistance mechanisms and to identify potential new acaricides. In this study, we screened three essential oils and four pure compounds based on eugenol for acaricidal properties. Methodology/Principal Findings Contact bioassays were performed using live permethrin-sensitive S. scabiei var suis mites harvested from pigs and permethrin-resistant S. scabiei var canis mites harvested from rabbits. Results of bioassays showed that clove oil was highly toxic against scabies mites. Nutmeg oil had moderate toxicity and ylang ylang oil was the least toxic. Eugenol, a major component of clove oil and its analogues –acetyleugenol and isoeugenol, demonstrated levels of toxicity comparable to benzyl benzoate, the positive control acaricide, killing mites within an hour of contact. Conclusions The acaricidal properties demonstrated by eugenol and its analogues show promise as leads for future development of alternative topical acaricides to treat scabies. PMID:20711455

Discrimination of a chestnut-oak forest unit for geologic mapping by means of a principal component enhancement of Landsat multispectral scanner data.

USGS Publications Warehouse

Krohn, M.D.; Milton, N.M.; Segal, D.; Enland, A.

1981-01-01

A principal component image enhancement has been effective in applying Landsat data to geologic mapping in a heavily forested area of E Virginia. The image enhancement procedure consists of a principal component transformation, a histogram normalization, and the inverse principal componnet transformation. The enhancement preserves the independence of the principal components, yet produces a more readily interpretable image than does a single principal component transformation. -from Authors

Chemometric evaluation of the volatile profile of probiotic melon and probiotic cashew juice.

PubMed

de Godoy Alves Filho, Elenilson; Rodrigues, Tigressa Helena Soares; Fernandes, Fabiano André Narciso; Pereira, Ana Lucia Fernandes; Narain, Narendra; de Brito, Edy Sousa; Rodrigues, Sueli

2017-09-01

The aim of this study was to evaluate the influence of the lactic acid fermentation on volatile compounds of melon and cashew apple juices. The effect of the fermentation processing on the volatile profile of probiotic juices was assessed by HS-SPME/GC-MS coupled to chemometrics with 67.9% and 81.0% of the variance in the first principal component for melon and cashew juices, respectively. The Lactobacillus casei fermentation imparted a reduction of ethyl butanoate, ethyl-2-methylbutirate, and ethyl hexanoate for melon juice; and of ethyl acetate, ethyl-2-methyl butanoate, ethyl crotonate, ethyl isovalerate, benzaldehyde, and ethyl hexanoate for cashew juice. Measurements of the stability of these compounds and the formation of the component 3-methyl-2-butenyl in melon juice may be used as a volatile marker to follow the juice fermentation. These findings suggested that even though it is not a dairy product the lactic acid fermentation of fruits developed a volatile profile combining the fruit and lactic acid fermentation volatiles with mildly formation or degradation of aroma compounds. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dittrichia graveolens (L.) Greuter Essential Oil: Chemical Composition, Multivariate Analysis, and Antimicrobial Activity.

PubMed

Mitic, Violeta; Stankov Jovanovic, Vesna; Ilic, Marija; Jovanovic, Olga; Djordjevic, Aleksandra; Stojanovic, Gordana

2016-01-01

The chemical composition and in vitro antimicrobial activities of Dittrichia graveolens (L.) Greuter essential oil was studied. Moreover, using agglomerative hierarchical cluster (AHC) and principal component analyses (PCA), the interrelationships of the D. graveolens essential-oil profiles characterized so far (including the sample from this study) were investigated. To evaluate the chemical composition of the essential oil, GC-FID and GC/MS analyses were performed. Altogether, 54 compounds were identified, accounting for 92.9% of the total oil composition. The D. graveolens oil belongs to the monoterpenoid chemotype, with monoterpenoids comprising 87.4% of the totally identified compounds. The major components were borneol (43.6%) and bornyl acetate (38.3%). Multivariate analysis showed that the compounds borneol and bornyl acetate exerted the greatest influence on the spatial differences in the composition of the reported oils. The antimicrobial activity against five bacterial and one fungal strain was determined using a disk-diffusion assay. The studied essential oil was active only against Gram-positive bacteria. Copyright © 2016 Verlag Helvetica Chimica Acta AG, Zürich.

The Middle Management Paradox of the Urban High School Assistant Principal: Making It Happen

ERIC Educational Resources Information Center

Jubilee, Sabriya Kaleen

2013-01-01

Scholars of transformational leadership literature assert that school-based management teams are a vital component in transforming schools. Many of these works focus heavily on the roles of principals and teachers, ignoring the contribution of Assistant Principals (APs). More attention is now being given to the unique role that Assistant…

Synthesis of Novel Aza-aromatic Curcuminoids with Improved Biological Activities towards Various Cancer Cell Lines.

PubMed

Theppawong, Atiruj; Van de Walle, Tim; Grootaert, Charlotte; Bultinck, Margot; Desmet, Tom; Van Camp, John; D'hooghe, Matthias

2018-05-01

Curcumin, a natural compound extracted from the rhizomes of Curcuma longa , displays pronounced anticancer properties but lacks good bioavailability and stability. In a previous study, we initiated structure modification of the curcumin scaffold by imination of the labile β-diketone moiety to produce novel β-enaminone derivatives. These compounds showed promising properties for elaborate follow-up studies. In this work, we focused on another class of nitrogen-containing curcuminoids with a similar objective: to address the bioavailability and stability issues and to improve the biological activity of curcumin. This paper thus reports on the synthesis of new pyridine-, indole-, and pyrrole-based curcumin analogues (aza-aromatic curcuminoids) and discusses their water solubility, antioxidant activity, and antiproliferative properties. In addition, multivariate statistics, including hierarchical clustering analysis and principal component analysis, were performed on a broad set of nitrogen-containing curcuminoids. Compared to their respective mother structures, that is, curcumin and bisdemethoxycurcumin, all compounds, and especially the pyridin-3-yl β-enaminone analogues, showed better water solubility profiles. Interestingly, the pyridine-, indole-, and pyrrole-based curcumin derivatives demonstrated improved biological effects in terms of mitochondrial activity impairment and protein content, in addition to comparable or decreased antioxidant properties. Overall, the biologically active N -alkyl β-enaminone aza-aromatic curcuminoids were shown to offer a desirable balance between good solubility and significant bioactivity.

Selection of representative emerging micropollutants for drinking water treatment studies: a systematic approach.

PubMed

Jin, Xiaohui; Peldszus, Sigrid

2012-01-01

Micropollutants remain of concern in drinking water, and there is a broad interest in the ability of different treatment processes to remove these compounds. To gain a better understanding of treatment effectiveness for structurally diverse compounds and to be cost effective, it is necessary to select a small set of representative micropollutants for experimental studies. Unlike other approaches to-date, in this research micropollutants were systematically selected based solely on their physico-chemical and structural properties that are important in individual water treatment processes. This was accomplished by linking underlying principles of treatment processes such as coagulation/flocculation, oxidation, activated carbon adsorption, and membrane filtration to compound characteristics and corresponding molecular descriptors. A systematic statistical approach not commonly used in water treatment was then applied to a compound pool of 182 micropollutants (identified from the literature) and their relevant calculated molecular descriptors. Principal component analysis (PCA) was used to summarize the information residing in this large dataset. D-optimal onion design was then applied to the PCA results to select structurally representative compounds that could be used in experimental treatment studies. To demonstrate the applicability and flexibility of this selection approach, two sets of 22 representative micropollutants are presented. Compounds in the first set are representative when studying a range of water treatment processes (coagulation/flocculation, oxidation, activated carbon adsorption, and membrane filtration), whereas the second set shows representative compounds for ozonation and advanced oxidation studies. Overall, selected micropollutants in both lists are structurally diverse, have wide-ranging physico-chemical properties and cover a large spectrum of applications. The systematic compound selection approach presented here can also be adjusted to fit individual research needs with respect to type of micropollutants, treatment processes and number of compounds selected. Copyright © 2011 Elsevier B.V. All rights reserved.

Development of new VOC exposure metrics and their relationship to ''Sick Building Syndrome'' symptoms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ten Brinke, JoAnn

1995-08-01

Volatile organic compounds (VOCs) are suspected to contribute significantly to ''Sick Building Syndrome'' (SBS), a complex of subchronic symptoms that occurs during and in general decreases away from occupancy of the building in question. A new approach takes into account individual VOC potencies, as well as the highly correlated nature of the complex VOC mixtures found indoors. The new VOC metrics are statistically significant predictors of symptom outcomes from the California Healthy Buildings Study data. Multivariate logistic regression analyses were used to test the hypothesis that a summary measure of the VOC mixture, other risk factors, and covariates for eachmore » worker will lead to better prediction of symptom outcome. VOC metrics based on animal irritancy measures and principal component analysis had the most influence in the prediction of eye, dermal, and nasal symptoms. After adjustment, a water-based paints and solvents source was found to be associated with dermal and eye irritation. The more typical VOC exposure metrics used in prior analyses were not useful in symptom prediction in the adjusted model (total VOC (TVOC), or sum of individually identified VOCs (ΣVOC i)). Also not useful were three other VOC metrics that took into account potency, but did not adjust for the highly correlated nature of the data set, or the presence of VOCs that were not measured. High TVOC values (2--7 mg m -3) due to the presence of liquid-process photocopiers observed in several study spaces significantly influenced symptoms. Analyses without the high TVOC values reduced, but did not eliminate the ability of the VOC exposure metric based on irritancy and principal component analysis to explain symptom outcome.« less

Fast principal component analysis for stacking seismic data

NASA Astrophysics Data System (ADS)

Wu, Juan; Bai, Min

2018-04-01

Stacking seismic data plays an indispensable role in many steps of the seismic data processing and imaging workflow. Optimal stacking of seismic data can help mitigate seismic noise and enhance the principal components to a great extent. Traditional average-based seismic stacking methods cannot obtain optimal performance when the ambient noise is extremely strong. We propose a principal component analysis (PCA) algorithm for stacking seismic data without being sensitive to noise level. Considering the computational bottleneck of the classic PCA algorithm in processing massive seismic data, we propose an efficient PCA algorithm to make the proposed method readily applicable for industrial applications. Two numerically designed examples and one real seismic data are used to demonstrate the performance of the presented method.

Principal component regression analysis with SPSS.

PubMed

Liu, R X; Kuang, J; Gong, Q; Hou, X L

2003-06-01

The paper introduces all indices of multicollinearity diagnoses, the basic principle of principal component regression and determination of 'best' equation method. The paper uses an example to describe how to do principal component regression analysis with SPSS 10.0: including all calculating processes of the principal component regression and all operations of linear regression, factor analysis, descriptives, compute variable and bivariate correlations procedures in SPSS 10.0. The principal component regression analysis can be used to overcome disturbance of the multicollinearity. The simplified, speeded up and accurate statistical effect is reached through the principal component regression analysis with SPSS.

Development of a SPME-GC-MS method for the determination of volatile compounds in Shanxi aged vinegar and its analytical characterization by aroma wheel.

PubMed

Zhu, Hong; Zhu, Jie; Wang, Lili; Li, Zaigui

2016-01-01

A solid-phase microextraction followed by gas chromatography-mass spectrometry method was developed to determine the volatile compounds in Shanxi aged vinegar. The optimal extraction conditions were: 50 °C for 20 min with a PDMS/DVB fiber. This analytical method was validated and showed satisfactory repeatability (0.5 %

Architectural measures of the cancellous bone of the mandibular condyle identified by principal components analysis.

PubMed

Giesen, E B W; Ding, M; Dalstra, M; van Eijden, T M G J

2003-09-01

As several morphological parameters of cancellous bone express more or less the same architectural measure, we applied principal components analysis to group these measures and correlated these to the mechanical properties. Cylindrical specimens (n = 24) were obtained in different orientations from embalmed mandibular condyles; the angle of the first principal direction and the axis of the specimen, expressing the orientation of the trabeculae, ranged from 10 degrees to 87 degrees. Morphological parameters were determined by a method based on Archimedes' principle and by micro-CT scanning, and the mechanical properties were obtained by mechanical testing. The principal components analysis was used to obtain a set of independent components to describe the morphology. This set was entered into linear regression analyses for explaining the variance in mechanical properties. The principal components analysis revealed four components: amount of bone, number of trabeculae, trabecular orientation, and miscellaneous. They accounted for about 90% of the variance in the morphological variables. The component loadings indicated that a higher amount of bone was primarily associated with more plate-like trabeculae, and not with more or thicker trabeculae. The trabecular orientation was most determinative (about 50%) in explaining stiffness, strength, and failure energy. The amount of bone was second most determinative and increased the explained variance to about 72%. These results suggest that trabecular orientation and amount of bone are important in explaining the anisotropic mechanical properties of the cancellous bone of the mandibular condyle.

Potential use of hydrocarbons for aging Lucilia sericata blowfly larvae to establish the postmortem interval.

PubMed

Moore, Hannah E; Adam, Craig D; Drijfhout, Falko P

2013-03-01

Previous studies on Diptera have shown the potential for the use of cuticular hydrocarbons' analysis in the determination of larval age and hence the postmortem interval (PMI) for an associated cadaver. In this work, hydrocarbon compounds, extracted daily until pupation from the cuticle of the blowfly Lucilia sericata (Diptera: Calliphoridae), have been analyzed using gas chromatography-mass spectrometry (GC-MS). The results show distinguishing features within the hydrocarbon profile over the period of the larvae life cycle, with significant chemical changes occurring from the younger larvae to the postfeeding larvae. Further interpretation of the chromatograms using principal component analysis revealed a strong correlation between the magnitudes of particular principal components and time. This outcome suggests that, under the conditions of this study, the cuticular hydrocarbons evolve in a systematic fashion with time, thus supporting the potential for GC-MS analysis as a tool for establishing PMI where such a species is present. © 2012 American Academy of Forensic Sciences.

Characterization of organic and conventional sweet basil leaves using chromatographic and flow-injection mass spectrometric (FIMS) fingerprints combined with principal component analysis

PubMed Central

Lu, Yingjian; Gao, Boyan; Chen, Pei; Charles, Denys; Yu, Liangli (Lucy)

2014-01-01

Sweet basil, Ocimum basilicum., is one of the most important and wildly used spices and has been shown to have antioxidant, antibacterial, and anti-diarrheal activities. In this study, high performance liquid chromatographic (HPLC) and flow-injection mass spectrometric (FIMS) fingerprinting techniques were used to differentiate organic and conventional sweet basil leaf samples. Principal component analysis (PCA) of the fingerprints indicated that both HPLC and FIMS fingerprints could effectively detect the chemical differences in the organic and conventional sweet basil leaf samples. This study suggested that the organic basil sample contained greater concentrations of almost all the major compounds than its conventional counterpart on a per same botanical weight basis. The FIMS method was able to rapidly differentiate the organic and conventional sweet basil leaf samples (1 min analysis time), whereas the HPLC fingerprints provided more information about the chemical composition of the basil samples with a longer analytical time. PMID:24518341

Characterisation of organic and conventional sweet basil leaves using chromatographic and flow-injection mass spectrometric (FIMS) fingerprints combined with principal component analysis.

PubMed

Lu, Yingjian; Gao, Boyan; Chen, Pei; Charles, Denys; Yu, Liangli Lucy

2014-07-01

Sweet basil, Ocimum basilicum, is one of the most important and wildly used spices and has been shown to have antioxidant, antibacterial, and anti-diarrheal activities. In this study, high performance liquid chromatographic (HPLC) and flow-injection mass spectrometric (FIMS) fingerprinting techniques were used to differentiate organic and conventional sweet basil leaf samples. Principal component analysis (PCA) of the fingerprints indicated that both HPLC and FIMS fingerprints could effectively detect the chemical differences in the organic and conventional sweet basil leaf samples. This study suggested that the organic basil sample contained greater concentrations of almost all the major compounds than its conventional counterpart on a per same botanical weight basis. The FIMS method was able to rapidly differentiate the organic and conventional sweet basil leaf samples (1min analysis time), whereas the HPLC fingerprints provided more information about the chemical composition of the basil samples with a longer analytical time. Copyright © 2014 Elsevier Ltd. All rights reserved.

Principal component analysis (PCA) of volatile terpene compounds dataset emitted by genetically modified sweet orange fruits and juices in which a D-limonene synthase was either up- or down-regulated vs. empty vector controls.

PubMed

Rodríguez, Ana; Peris, Josep E; Redondo, Ana; Shimada, Takehiko; Peña, Leandro

2016-12-01

We have categorized the dataset from content and emission of terpene volatiles of peel and juice in both Navelina and Pineapple sweet orange cultivars in which D-limonene was either up- (S), down-regulated (AS) or non-altered (EV; control) ("Impact of D-limonene synthase up- or down-regulation on sweet orange fruit and juice odor perception"(A. Rodríguez, J.E. Peris, A. Redondo, T. Shimada, E. Costell, I. Carbonell, C. Rojas, L. Peña, (2016)) [1]). Data from volatile identification and quantification by HS-SPME and GC-MS were classified by Principal Component Analysis (PCA) individually or as chemical groups. AS juice was characterized by the higher influence of the oxygen fraction, and S juice by the major influence of ethyl esters. S juices emitted less linalool compared to AS and EV juices.

Exploring sources of biogenic secondary organic aerosol compounds using chemical analysis and the FLEXPART model

NASA Astrophysics Data System (ADS)

Martinsson, Johan; Monteil, Guillaume; Sporre, Moa K.; Kaldal Hansen, Anne Maria; Kristensson, Adam; Eriksson Stenström, Kristina; Swietlicki, Erik; Glasius, Marianne

2017-09-01

Molecular tracers in secondary organic aerosols (SOAs) can provide information on origin of SOA, as well as regional scale processes involved in their formation. In this study 9 carboxylic acids, 11 organosulfates (OSs) and 2 nitrooxy organosulfates (NOSs) were determined in daily aerosol particle filter samples from Vavihill measurement station in southern Sweden during June and July 2012. Several of the observed compounds are photo-oxidation products from biogenic volatile organic compounds (BVOCs). Highest average mass concentrations were observed for carboxylic acids derived from fatty acids and monoterpenes (12. 3 ± 15. 6 and 13. 8 ± 11. 6 ng m-3, respectively). The FLEXPART model was used to link nine specific surface types to single measured compounds. It was found that the surface category sea and ocean was dominating the air mass exposure (56 %) but contributed to low mass concentration of observed chemical compounds. A principal component (PC) analysis identified four components, where the one with highest explanatory power (49 %) displayed clear impact of coniferous forest on measured mass concentration of a majority of the compounds. The three remaining PCs were more difficult to interpret, although azelaic, suberic, and pimelic acid were closely related to each other but not to any clear surface category. Hence, future studies should aim to deduce the biogenic sources and surface category of these compounds. This study bridges micro-level chemical speciation to air mass surface exposure at the macro level.

Online monitoring of coffee roasting by proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS): towards a real-time process control for a consistent roast profile.

PubMed

Wieland, Flurin; Gloess, Alexia N; Keller, Marco; Wetzel, Andreas; Schenker, Stefan; Yeretzian, Chahan

2012-03-01

A real-time automated process control tool for coffee roasting is presented to consistently and accurately achieve a targeted roast degree. It is based on the online monitoring of volatile organic compounds (VOC) in the off-gas of a drum roaster by proton transfer reaction time-of-flight mass spectrometry at a high time (1 Hz) and mass resolution (5,500 m/Δm at full width at half-maximum) and high sensitivity (better than parts per billion by volume). Forty-two roasting experiments were performed with the drum roaster being operated either on a low, medium or high hot-air inlet temperature (= energy input) and the coffee (Arabica from Antigua, Guatemala) being roasted to low, medium or dark roast degrees. A principal component analysis (PCA) discriminated, for each one of the three hot-air inlet temperatures, the roast degree with a resolution of better than ±1 Colorette. The 3D space of the three first principal components was defined based on 23 mass spectral profiles of VOCs and their roast degree at the end point of roasting. This provided a very detailed picture of the evolution of the roasting process and allowed establishment of a predictive model that projects the online-monitored VOC profile of the roaster off-gas in real time onto the PCA space defined by the calibration process and, ultimately, to control the coffee roasting process so as to achieve a target roast degree and a consistent roasting.

Separation process using pervaporation and dephlegmation

DOEpatents

Vane, Leland M.; Mairal, Anurag P.; Ng, Alvin; Alvarez, Franklin R.; Baker, Richard W.

2004-06-29

A process for treating liquids containing organic compounds and water. The process includes a pervaporation step in conjunction with a dephlegmation step to treat at least a portion of the permeate vapor from the pervaporation step. The process yields a membrane residue stream, a stream enriched in the more volatile component (usually the organic) as the overhead stream from the dephlegmator and a condensate stream enriched in the less volatile component (usually the water) as a bottoms stream from the dephlegmator. Any of these may be the principal product of the process. The membrane separation step may also be performed in the vapor phase, or by membrane distillation.

Essential oils and chemical diversity of southeast European populations of Salvia officinalis L.

PubMed

Cvetkovikj, Ivana; Stefkov, Gjoshe; Karapandzova, Marija; Kulevanova, Svetlana; Satović, Zlatko

2015-07-01

The essential oils of 25 populations of Dalmatian sage (Salvia officinalis L.) from nine Balkan countries, including 17 indigenous populations (representing almost the entire native distribution area) and eight non-indigenous (cultivated or naturalized) populations were analyzed. Their essential-oil yield ranged from 0.25 to 3.48%. Within the total of 80 detected compounds, ten (β-pinene, 1,8-cineole, cis-thujone, trans-thujone, camphor, borneol, trans-caryophyllene, α-humulene, viridiflorol, and manool) represented 42.60 to 85.70% of the components in the analyzed essential oils. Strong positive correlations were observed between the contents of trans-caryophyllene and α-humulene, α-humulene and viridiflorol, and viridiflorol and manool. Principal component analysis (PCA) on the basis of the contents of the ten main compounds showed that four principal components had an eigenvalue greater than 1 and explained 79.87% of the total variation. Performing cluster analysis (CA), the sage populations could be grouped into four distinct chemotypes (A-D). The essential oils of 14 out of the 25 populations of Dalmatian sage belonged to Chemotype A and were rich in cis-thujone and camphor, with low contents of trans-thujone. The correlation between the essential-oil composition and geographic variables of the indigenous populations was not significant; hence, the similarities in the essential-oil profile among populations could not be explained by the physical proximity of the populations. Additionally, the southeastern populations tended to have higher EO yields than the northwestern ones. Copyright © 2015 Verlag Helvetica Chimica Acta AG, Zürich.

Pepper seed variety identification based on visible/near-infrared spectral technology

NASA Astrophysics Data System (ADS)

Li, Cuiling; Wang, Xiu; Meng, Zhijun; Fan, Pengfei; Cai, Jichen

2016-11-01

Pepper is a kind of important fruit vegetable, with the expansion of pepper hybrid planting area, detection of pepper seed purity is especially important. This research used visible/near infrared (VIS/NIR) spectral technology to detect the variety of single pepper seed, and chose hybrid pepper seeds "Zhuo Jiao NO.3", "Zhuo Jiao NO.4" and "Zhuo Jiao NO.5" as research sample. VIS/NIR spectral data of 80 "Zhuo Jiao NO.3", 80 "Zhuo Jiao NO.4" and 80 "Zhuo Jiao NO.5" pepper seeds were collected, and the original spectral data was pretreated with standard normal variable (SNV) transform, first derivative (FD), and Savitzky-Golay (SG) convolution smoothing methods. Principal component analysis (PCA) method was adopted to reduce the dimension of the spectral data and extract principal components, according to the distribution of the first principal component (PC1) along with the second principal component(PC2) in the twodimensional plane, similarly, the distribution of PC1 coupled with the third principal component(PC3), and the distribution of PC2 combined with PC3, distribution areas of three varieties of pepper seeds were divided in each twodimensional plane, and the discriminant accuracy of PCA was tested through observing the distribution area of samples' principal components in validation set. This study combined PCA and linear discriminant analysis (LDA) to identify single pepper seed varieties, results showed that with the FD preprocessing method, the discriminant accuracy of pepper seed varieties was 98% for validation set, it concludes that using VIS/NIR spectral technology is feasible for identification of single pepper seed varieties.

Chemical compositions and antimicrobial and antioxidant activities of the essential oils from Magnolia grandiflora, Chrysactinia mexicana, and Schinus molle found in northeast Mexico.

PubMed

Guerra-Boone, Laura; Alvarez-Román, Rocío; Salazar-Aranda, Ricardo; Torres-Cirio, Anabel; Rivas-Galindo, Verónica Mayela; Waksman de Torres, Noemí; González González, Gloria María; Pérez-López, Luis Alejandro

2013-01-01

The essential oils from Magnolia grandiflora and Chrysactinia mexicana leaves, and from Schinus molle leaves and fruit, were characterized by gas chromatography/flame-ionization detection and gas chromatography/mass spectrometry. Twenty-eight compounds from M. grandiflora leaves were identified (representing 93.6% of the total area of the gas chromatogram), with the major component being bornyl acetate (20.9%). Colorless and yellow oils were obtained from the C. mexicana leaves with 18 (86.7%) and 11 (100%) compounds identified, respectively. In both fractions, the principal component was sylvestrene (36.8% and 41.1%, respectively). The essential oils ofS. molle leaves and fruit were each separated into colorless and yellow fractions, in which 14 (98.2) and 20 (99.8%) compounds were identified. The main component was alpha-phellandrene in all fractions (between 32.8% and 45.0%). The M. grandiflora oil displayed antifungal activity against five dermatophyte strains. The oils from S. molle and M. grandiflora leaves had antimicrobial activity against Staphylococcus aureus and Streptococcus pyogenes, which cause skin infections that potentially may lead to sepsis. However, the antioxidant activities of all oils were small (half maximal effective concentration values >250 microg/mL).

A comparative study of three tissue-cultured Dendrobium species and their wild correspondences by headspace gas chromatography-mass spectrometry combined with chemometric methods.

PubMed

Chen, Nai-Dong; You, Tao; Li, Jun; Bai, Li-Tao; Hao, Jing-Wen; Xu, Xiao-Yuan

2016-10-01

Plant tissue culture technique is widely used in the conservation and utilization of rare and endangered medicinal plants and it is crucial for tissue culture stocks to obtain the ability to produce similar bioactive components as their wild correspondences. In this paper, a headspace gas chromatography-mass spectrometry method combined with chemometric methods was applied to analyze and evaluate the volatile compounds in tissue-cultured and wild Dendrobium huoshanense Cheng and Tang, Dendrobium officinale Kimura et Migo and Dendrobium moniliforme (Linn.) Sw. In total, 63 volatile compounds were separated, with 53 being identified from the three Dendrobium spp. Different provenances of Dendrobiums had characteristic chemicals and showed remarkable quantity discrepancy of common compositions. The similarity evaluation disclosed that the accumulation of volatile compounds in Dendrobium samples might be affected by their provenance. Principal component analysis showed that the first three components explained 85.9% of data variance, demonstrating a good discrimination between samples. Gas chromatography-mass spectrometry techniques, combined with chemometrics, might be an effective strategy for identifying the species and their provenance, especially in the assessment of tissue-cultured Dendrobium quality for use in raw herbal medicines. Copyright © 2016. Published by Elsevier B.V.

New drugs from ancient natural foods. Oleocanthal, the natural occurring spicy compound of olive oil: a brief history.

PubMed

Scotece, Morena; Conde, Javier; Abella, Vanessa; Lopez, Veronica; Pino, Jesús; Lago, Francisca; Smith, Amos B; Gómez-Reino, Juan J; Gualillo, Oreste

2015-04-01

Extra-virgin olive oil (EVOO), a principal component of the Mediterranean diet (Med diet), is one of the most ancient known foods and has long been associated with health benefits. Many phenolic compounds extracted from Olea europea L. have attracted attention since their discovery. Among these phenolic constituents, oleocanthal has recently emerged as a potential therapeutic molecule for different diseases, showing relevant pharmacological properties in various pathogenic processes, including inflammation, cancers and neurodegenerative diseases. Here, we discuss and summarize the most recent pharmacological evidence for the medical relevance of oleocanthal, focusing our attention on its anti-inflammatory and chemotherapeutic roles. Copyright © 2014 Elsevier Ltd. All rights reserved.

Skin lipids of the striped plateau lizard (Sceloporus virgatus) correlate with female receptivity and reproductive quality alongside visual ornaments.

PubMed

Goldberg, Jay K; Wallace, Alisa K; Weiss, Stacey L

2017-09-14

Sex pheromones can perform a variety of functions ranging from revealing the location of suitable mates to being honest signals of mate quality, and they are used in the mate selection process by many species of reptile. In this study, we determined whether the skin lipids of female striped plateau lizards (Sceloporus virgatus) can predict the reproductive quality of females, thereby having the potential to serve as pheromones. Using gas chromatography/mass spectrometry, we identified 17 compounds present in skin lipids of female lizards. Using principal component analysis to compare the skin lipid profile of receptive and non-receptive females, we determined that an uncharacterized compound may allow for chemical identification of receptive mates. We also compared extracted principal components to measures of female fitness and reproductive qualities and found that the level of two 18 carbon fatty acids present in a female's skin lipids may indicate her clutch size. Finally, we compared the information content of the skin lipids to that of female-specific color ornaments to assess whether chemical and visual cues transmit different information or not. We found that the chroma of a female's orange throat patch is also related to her clutch size, suggesting that chemical signals may reinforce the information communicated by visual ornamentation in this species which would support the "backup signals" hypothesis for multiple signals.

Skin lipids of the striped plateau lizard ( Sceloporus virgatus) correlate with female receptivity and reproductive quality alongside visual ornaments

NASA Astrophysics Data System (ADS)

Goldberg, Jay K.; Wallace, Alisa K.; Weiss, Stacey L.

2017-10-01

Sex pheromones can perform a variety of functions ranging from revealing the location of suitable mates to being honest signals of mate quality, and they are used in the mate selection process by many species of reptile. In this study, we determined whether the skin lipids of female striped plateau lizards ( Sceloporus virgatus) can predict the reproductive quality of females, thereby having the potential to serve as pheromones. Using gas chromatography/mass spectrometry, we identified 17 compounds present in skin lipids of female lizards. Using principal component analysis to compare the skin lipid profile of receptive and non-receptive females, we determined that an uncharacterized compound may allow for chemical identification of receptive mates. We also compared extracted principal components to measures of female fitness and reproductive qualities and found that the level of two 18 carbon fatty acids present in a female's skin lipids may indicate her clutch size. Finally, we compared the information content of the skin lipids to that of female-specific color ornaments to assess whether chemical and visual cues transmit different information or not. We found that the chroma of a female's orange throat patch is also related to her clutch size, suggesting that chemical signals may reinforce the information communicated by visual ornamentation in this species which would support the "backup signals" hypothesis for multiple signals.

A stock market forecasting model combining two-directional two-dimensional principal component analysis and radial basis function neural network.

PubMed

Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J

2015-01-01

In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron.

A Stock Market Forecasting Model Combining Two-Directional Two-Dimensional Principal Component Analysis and Radial Basis Function Neural Network

PubMed Central

Guo, Zhiqiang; Wang, Huaiqing; Yang, Jie; Miller, David J.

2015-01-01

In this paper, we propose and implement a hybrid model combining two-directional two-dimensional principal component analysis ((2D)2PCA) and a Radial Basis Function Neural Network (RBFNN) to forecast stock market behavior. First, 36 stock market technical variables are selected as the input features, and a sliding window is used to obtain the input data of the model. Next, (2D)2PCA is utilized to reduce the dimension of the data and extract its intrinsic features. Finally, an RBFNN accepts the data processed by (2D)2PCA to forecast the next day's stock price or movement. The proposed model is used on the Shanghai stock market index, and the experiments show that the model achieves a good level of fitness. The proposed model is then compared with one that uses the traditional dimension reduction method principal component analysis (PCA) and independent component analysis (ICA). The empirical results show that the proposed model outperforms the PCA-based model, as well as alternative models based on ICA and on the multilayer perceptron. PMID:25849483

Application of principal component analysis to ecodiversity assessment of postglacial landscape (on the example of Debnica Kaszubska commune, Middle Pomerania)

NASA Astrophysics Data System (ADS)

Wojciechowski, Adam

2017-04-01

In order to assess ecodiversity understood as a comprehensive natural landscape factor (Jedicke 2001), it is necessary to apply research methods which recognize the environment in a holistic way. Principal component analysis may be considered as one of such methods as it allows to distinguish the main factors determining landscape diversity on the one hand, and enables to discover regularities shaping the relationships between various elements of the environment under study on the other hand. The procedure adopted to assess ecodiversity with the use of principal component analysis involves: a) determining and selecting appropriate factors of the assessed environment qualities (hypsometric, geological, hydrographic, plant, and others); b) calculating the absolute value of individual qualities for the basic areas under analysis (e.g. river length, forest area, altitude differences, etc.); c) principal components analysis and obtaining factor maps (maps of selected components); d) generating a resultant, detailed map and isolating several classes of ecodiversity. An assessment of ecodiversity with the use of principal component analysis was conducted in the test area of 299,67 km2 in Debnica Kaszubska commune. The whole commune is situated in the Weichselian glaciation area of high hypsometric and morphological diversity as well as high geo- and biodiversity. The analysis was based on topographical maps of the commune area in scale 1:25000 and maps of forest habitats. Consequently, nine factors reflecting basic environment elements were calculated: maximum height (m), minimum height (m), average height (m), the length of watercourses (km), the area of water reservoirs (m2), total forest area (ha), coniferous forests habitats area (ha), deciduous forest habitats area (ha), alder habitats area (ha). The values for individual factors were analysed for 358 grid cells of 1 km2. Based on the principal components analysis, four major factors affecting commune ecodiversity were distinguished: hypsometric component (PC1), deciduous forest habitats component (PC2), river valleys and alder habitats component (PC3), and lakes component (PC4). The distinguished factors characterise natural qualities of postglacial area and reflect well the role of the four most important groups of environment components in shaping ecodiversity of the area under study. The map of ecodiversity of Debnica Kaszubska commune was created on the basis of the first four principal component scores and then five classes of diversity were isolated: very low, low, average, high and very high. As a result of the assessment, five commune regions of very high ecodiversity were separated. These regions are also very attractive for tourists and valuable in terms of their rich nature which include protected areas such as Slupia Valley Landscape Park. The suggested method of ecodiversity assessment with the use of principal component analysis may constitute an alternative methodological proposition to other research methods used so far. Literature Jedicke E., 2001. Biodiversität, Geodiversität, Ökodiversität. Kriterien zur Analyse der Landschaftsstruktur - ein konzeptioneller Diskussionsbeitrag. Naturschutz und Landschaftsplanung, 33(2/3), 59-68.

Nutritional Evaluation of Non-Conventional Vegetables in Brazil.

PubMed

Silva, Luis Felipe Lima E; Souza, Douglas C DE; Resende, Luciane V; Nassur, Rita DE Cássia M R; Samartini, Carolina Q; Gonçalves, Wilson M

2018-01-01

The objective of this study was to characterize the nutritional compounds of interest present in vegetables known as non-conventional, in Brazil. The following evaluations were carried out: antioxidant activity, phenolic compounds, vitamin C, calories, carbohydrates, humidity, lipids, proteins, fiber, acidity and quantification of minerals (P, K, Ca, Mg, S, Cu, Fe, Mn, Zn and B). The species studied were Amaranthus hybridus L., Amaranthus viridis L., Basella alba L., Eryngium campestre L., Hibiscus sabdariffa L., Lactuca canadensis L., Rumex acetosa L., Stachys byzantina K. Koch, Tropaeolum majus L. and Xanthosoma sagittifolium L. Representative samples of plant structures of interest were harvested from each species suitable for human consumption such as leaves, flowers and flower buds. The results were submitted to multivariate analysis - principal components analysis (PCA). All the species present nutritional compounds of interest in different levels among the evaluated structures.

Correlation between the pattern volatiles and the overall aroma of wild edible mushrooms.

PubMed

de Pinho, P Guedes; Ribeiro, Bárbara; Gonçalves, Rui F; Baptista, Paula; Valentão, Patrícia; Seabra, Rosa M; Andrade, Paula B

2008-03-12

Volatile and semivolatile components of 11 wild edible mushrooms, Suillus bellini, Suillus luteus, Suillus granulatus, Tricholomopsis rutilans, Hygrophorus agathosmus, Amanita rubescens, Russula cyanoxantha, Boletus edulis, Tricholoma equestre, Fistulina hepatica, and Cantharellus cibarius, were determined by headspace solid-phase microextraction (HS-SPME) and by liquid extraction combined with gas chromatography-mass spectrometry (GC-MS). Fifty volatiles and nonvolatiles components were formally identified and 13 others were tentatively identified. Using sensorial analysis, the descriptors "mushroomlike", "farm-feed", "floral", "honeylike", "hay-herb", and "nutty" were obtained. A correlation between sensory descriptors and volatiles was observed by applying multivariate analysis (principal component analysis and agglomerative hierarchic cluster analysis) to the sensorial and chemical data. The studied edible mushrooms can be divided in three groups. One of them is rich in C8 derivatives, such as 3-octanol, 1-octen-3-ol, trans-2-octen-1-ol, 3-octanone, and 1-octen-3-one; another one is rich in terpenic volatile compounds; and the last one is rich in methional. The presence and contents of these compounds give a considerable contribution to the sensory characteristics of the analyzed species.

An Efficient Data Compression Model Based on Spatial Clustering and Principal Component Analysis in Wireless Sensor Networks.

PubMed

Yin, Yihang; Liu, Fengzheng; Zhou, Xiang; Li, Quanzhong

2015-08-07

Wireless sensor networks (WSNs) have been widely used to monitor the environment, and sensors in WSNs are usually power constrained. Because inner-node communication consumes most of the power, efficient data compression schemes are needed to reduce the data transmission to prolong the lifetime of WSNs. In this paper, we propose an efficient data compression model to aggregate data, which is based on spatial clustering and principal component analysis (PCA). First, sensors with a strong temporal-spatial correlation are grouped into one cluster for further processing with a novel similarity measure metric. Next, sensor data in one cluster are aggregated in the cluster head sensor node, and an efficient adaptive strategy is proposed for the selection of the cluster head to conserve energy. Finally, the proposed model applies principal component analysis with an error bound guarantee to compress the data and retain the definite variance at the same time. Computer simulations show that the proposed model can greatly reduce communication and obtain a lower mean square error than other PCA-based algorithms.

The use of principal component and cluster analysis to differentiate banana peel flours based on their starch and dietary fibre components.

PubMed

Ramli, Saifullah; Ismail, Noryati; Alkarkhi, Abbas Fadhl Mubarek; Easa, Azhar Mat

2010-08-01

Banana peel flour (BPF) prepared from green or ripe Cavendish and Dream banana fruits were assessed for their total starch (TS), digestible starch (DS), resistant starch (RS), total dietary fibre (TDF), soluble dietary fibre (SDF) and insoluble dietary fibre (IDF). Principal component analysis (PCA) identified that only 1 component was responsible for 93.74% of the total variance in the starch and dietary fibre components that differentiated ripe and green banana flours. Cluster analysis (CA) applied to similar data obtained two statistically significant clusters (green and ripe bananas) to indicate difference in behaviours according to the stages of ripeness based on starch and dietary fibre components. We concluded that the starch and dietary fibre components could be used to discriminate between flours prepared from peels obtained from fruits of different ripeness. The results were also suggestive of the potential of green and ripe BPF as functional ingredients in food.

The Use of Principal Component and Cluster Analysis to Differentiate Banana Peel Flours Based on Their Starch and Dietary Fibre Components

PubMed Central

Ramli, Saifullah; Ismail, Noryati; Alkarkhi, Abbas Fadhl Mubarek; Easa, Azhar Mat

2010-01-01

Banana peel flour (BPF) prepared from green or ripe Cavendish and Dream banana fruits were assessed for their total starch (TS), digestible starch (DS), resistant starch (RS), total dietary fibre (TDF), soluble dietary fibre (SDF) and insoluble dietary fibre (IDF). Principal component analysis (PCA) identified that only 1 component was responsible for 93.74% of the total variance in the starch and dietary fibre components that differentiated ripe and green banana flours. Cluster analysis (CA) applied to similar data obtained two statistically significant clusters (green and ripe bananas) to indicate difference in behaviours according to the stages of ripeness based on starch and dietary fibre components. We concluded that the starch and dietary fibre components could be used to discriminate between flours prepared from peels obtained from fruits of different ripeness. The results were also suggestive of the potential of green and ripe BPF as functional ingredients in food. PMID:24575193

Spectral decomposition of asteroid Itokawa based on principal component analysis

NASA Astrophysics Data System (ADS)

Koga, Sumire C.; Sugita, Seiji; Kamata, Shunichi; Ishiguro, Masateru; Hiroi, Takahiro; Tatsumi, Eri; Sasaki, Sho

2018-01-01

The heliocentric stratification of asteroid spectral types may hold important information on the early evolution of the Solar System. Asteroid spectral taxonomy is based largely on principal component analysis. However, how the surface properties of asteroids, such as the composition and age, are projected in the principal-component (PC) space is not understood well. We decompose multi-band disk-resolved visible spectra of the Itokawa surface with principal component analysis (PCA) in comparison with main-belt asteroids. The obtained distribution of Itokawa spectra projected in the PC space of main-belt asteroids follows a linear trend linking the Q-type and S-type regions and is consistent with the results of space-weathering experiments on ordinary chondrites and olivine, suggesting that this trend may be a space-weathering-induced spectral evolution track for S-type asteroids. Comparison with space-weathering experiments also yield a short average surface age (< a few million years) for Itokawa, consistent with the cosmic-ray-exposure time of returned samples from Itokawa. The Itokawa PC score distribution exhibits asymmetry along the evolution track, strongly suggesting that space weathering has begun saturated on this young asteroid. The freshest spectrum found on Itokawa exhibits a clear sign for space weathering, indicating again that space weathering occurs very rapidly on this body. We also conducted PCA on Itokawa spectra alone and compared the results with space-weathering experiments. The obtained results indicate that the first principal component of Itokawa surface spectra is consistent with spectral change due to space weathering and that the spatial variation in the degree of space weathering is very large (a factor of three in surface age), which would strongly suggest the presence of strong regional/local resurfacing process(es) on this small asteroid.

VOC source identification from personal and residential indoor, outdoor and workplace microenvironment samples in EXPOLIS-Helsinki, Finland

NASA Astrophysics Data System (ADS)

Edwards, Rufus D.; Jurvelin, J.; Koistinen, K.; Saarela, K.; Jantunen, M.

Principal component analyses (varimax rotation) were used to identify common sources of 30 target volatile organic compounds (VOCs) in residential outdoor, residential indoor and workplace microenvironment and personal 48-h exposure samples, as a component of the EXPOLIS-Helsinki study. Variability in VOC concentrations in residential outdoor microenvironments was dominated by compounds associated with long-range transport of pollutants, followed by traffic emissions, emissions from trees and product emissions. Variability in VOC concentrations in environmental tobacco smoke (ETS) free residential indoor environments was dominated by compounds associated with indoor cleaning products, followed by compounds associated with traffic emissions, long-range transport of pollutants and product emissions. Median indoor/outdoor ratios for compounds typically associated with traffic emissions and long-range transport of pollutants exceeded 1, in some cases quite considerably, indicating substantial indoor source contributions. Changes in the median indoor/outdoor ratios during different seasons reflected different seasonal ventilation patterns as increased ventilation led to dilution of those VOC compounds in the indoor environment that had indoor sources. Variability in workplace VOC concentrations was dominated by compounds associated with traffic emissions followed by product emissions, long-range transport and air fresheners. Variability in VOC concentrations in ETS free personal exposure samples was dominated by compounds associated with traffic emissions, followed by long-range transport, cleaning products and product emissions. VOC sources in personal exposure samples reflected the times spent in different microenvironments, and personal exposure samples were not adequately represented by any one microenvironment, demonstrating the need for personal exposure sampling.

[The application of the multidimensional statistical methods in the evaluation of the influence of atmospheric pollution on the population's health].

PubMed

Surzhikov, V D; Surzhikov, D V

2014-01-01

The search and measurement of causal relationships between exposure to air pollution and health state of the population is based on the system analysis and risk assessment to improve the quality of research. With this purpose there is applied the modern statistical analysis with the use of criteria of independence, principal component analysis and discriminate function analysis. As a result of analysis out of all atmospheric pollutants there were separated four main components: for diseases of the circulatory system main principal component is implied with concentrations of suspended solids, nitrogen dioxide, carbon monoxide, hydrogen fluoride, for the respiratory diseases the main c principal component is closely associated with suspended solids, sulfur dioxide and nitrogen dioxide, charcoal black. The discriminant function was shown to be used as a measure of the level of air pollution.

Visualizing excipient composition and homogeneity of Compound Liquorice Tablets by near-infrared chemical imaging

NASA Astrophysics Data System (ADS)

Wu, Zhisheng; Tao, Ou; Cheng, Wei; Yu, Lu; Shi, Xinyuan; Qiao, Yanjiang

2012-02-01

This study demonstrated that near-infrared chemical imaging (NIR-CI) was a promising technology for visualizing the spatial distribution and homogeneity of Compound Liquorice Tablets. The starch distribution (indirectly, plant extraction) could be spatially determined using basic analysis of correlation between analytes (BACRA) method. The correlation coefficients between starch spectrum and spectrum of each sample were greater than 0.95. Depending on the accurate determination of starch distribution, a method to determine homogeneous distribution was proposed by histogram graph. The result demonstrated that starch distribution in sample 3 was relatively heterogeneous according to four statistical parameters. Furthermore, the agglomerates domain in each tablet was detected using score image layers of principal component analysis (PCA) method. Finally, a novel method named Standard Deviation of Macropixel Texture (SDMT) was introduced to detect agglomerates and heterogeneity based on binary image. Every binary image was divided into different sizes length of macropixel and the number of zero values in each macropixel was counted to calculate standard deviation. Additionally, a curve fitting graph was plotted on the relationship between standard deviation and the size length of macropixel. The result demonstrated the inter-tablet heterogeneity of both starch and total compounds distribution, simultaneously, the similarity of starch distribution and the inconsistency of total compounds distribution among intra-tablet were signified according to the value of slope and intercept parameters in the curve.

Non-targeted metabolite profiling highlights the potential of strawberry leaves as a resource for specific bioactive compounds.

PubMed

Kårlund, Anna; Hanhineva, Kati; Lehtonen, Marko; McDougall, Gordon J; Stewart, Derek; Karjalainen, Reijo O

2017-05-01

The non-edible parts of horticultural crops, such as leaves, contain substantial amounts of valuable bioactive compounds which are currently only little exploited. For example, strawberry (Fragaria × ananassa) leaves may be a promising bioresource for diverse health-related applications. However, product standardization sets a real challenge, especially when the leaf material comes from varying cultivars. The first step towards better quality control of berry fruit leaf-based ingredients and supplements is to understand metabolites present and their stability in different plant cultivars, so this study surveyed the distribution of potentially bioactive strawberry leaf metabolites in six different strawberry cultivars. Non-targeted metabolite profiling analysis using LC/qTOF-ESI-MS with data processing via principal component analysis and k-means clustering analysis was utilized to examine differences and commonalities between the leaf metabolite profiles. Quercetin and kaempferol derivatives were the dominant flavonol groups in strawberry leaves. Previously described and novel caffeic and chlorogenic acid derivatives were among the major phenolic acids. In addition, ellagitannins were one of the distinguishing compound classes in strawberry leaves. In general, strawberry leaves also contained high levels of octadecatrienoic acid derivatives, precursors of valuable odour compounds. The specific bioactive compounds found in the leaves of different strawberry cultivars offer the potential for the selection of optimized leaf materials for added-value food and non-food applications. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Principal Component 2-D Long Short-Term Memory for Font Recognition on Single Chinese Characters.

PubMed

Tao, Dapeng; Lin, Xu; Jin, Lianwen; Li, Xuelong

2016-03-01

Chinese character font recognition (CCFR) has received increasing attention as the intelligent applications based on optical character recognition becomes popular. However, traditional CCFR systems do not handle noisy data effectively. By analyzing in detail the basic strokes of Chinese characters, we propose that font recognition on a single Chinese character is a sequence classification problem, which can be effectively solved by recurrent neural networks. For robust CCFR, we integrate a principal component convolution layer with the 2-D long short-term memory (2DLSTM) and develop principal component 2DLSTM (PC-2DLSTM) algorithm. PC-2DLSTM considers two aspects: 1) the principal component layer convolution operation helps remove the noise and get a rational and complete font information and 2) simultaneously, 2DLSTM deals with the long-range contextual processing along scan directions that can contribute to capture the contrast between character trajectory and background. Experiments using the frequently used CCFR dataset suggest the effectiveness of PC-2DLSTM compared with other state-of-the-art font recognition methods.

[Determination and principal component analysis of mineral elements based on ICP-OES in Nitraria roborowskii fruits from different regions].

PubMed

Yuan, Yuan-Yuan; Zhou, Yu-Bi; Sun, Jing; Deng, Juan; Bai, Ying; Wang, Jie; Lu, Xue-Feng

2017-06-01

The content of elements in fifteen different regions of Nitraria roborowskii samples were determined by inductively coupled plasma-atomic emission spectrometry(ICP-OES), and its elemental characteristics were analyzed by principal component analysis. The results indicated that 18 mineral elements were detected in N. roborowskii of which V cannot be detected. In addition, contents of Na, K and Ca showed high concentration. Ti showed maximum content variance, while K is minimum. Four principal components were gained from the original data. The cumulative variance contribution rate is 81.542% and the variance contribution of the first principal component was 44.997%, indicating that Cr, Fe, P and Ca were the characteristic elements of N. roborowskii.Thus, the established method was simple, precise and can be used for determination of mineral elements in N.roborowskii Kom. fruits. The elemental distribution characteristics among N.roborowskii fruits are related to geographical origins which were clearly revealed by PCA. All the results will provide good basis for comprehensive utilization of N.roborowskii. Copyright© by the Chinese Pharmaceutical Association.

[Studies on the brand traceability of milk powder based on NIR spectroscopy technology].

PubMed

Guan, Xiao; Gu, Fang-Qing; Liu, Jing; Yang, Yong-Jian

2013-10-01

Brand traceability of several different kinds of milk powder was studied by combining near infrared spectroscopy diffuse reflectance mode with soft independent modeling of class analogy (SIMCA) in the present paper. The near infrared spectrum of 138 samples, including 54 Guangming milk powder samples, 43 Netherlands samples, and 33 Nestle samples and 8 Yili samples, were collected. After pretreatment of full spectrum data variables in training set, principal component analysis was performed, and the contribution rate of the cumulative variance of the first three principal components was about 99.07%. Milk powder principal component regression model based on SIMCA was established, and used to classify the milk powder samples in prediction sets. The results showed that the recognition rate of Guangming milk powder, Netherlands milk powder and Nestle milk powder was 78%, 75% and 100%, the rejection rate was 100%, 87%, and 88%, respectively. Therefore, the near infrared spectroscopy combined with SIMCA model can classify milk powder with high accuracy, and is a promising identification method of milk powder variety.

Purpurolic acid: A new natural alkaloid from Claviceps purpurea (Fr.) Tul.

PubMed

Roberts, Andrew; Beaumont, Claire; Manzarpour, Azita; Mantle, Peter

2016-01-01

A novel secondary metabolite from the sclerotia of Claviceps purpurea (Fr.) Tul. is described; the structure is based on (1)H and (13)C NMR spectroscopy and electrospray mass spectrometry. It has an elemental composition C10H16N2O7 and is comprised mainly of proline and alanine moieties, although without peptide linkage. Notably, these amino-acids are also components of the cyclic tripeptide side chain of several classic ergoline alkaloids. Designated as purpurolic acid, the new compound is the principal free amino-acid in ergot and its natural abundance exceeds that of the ergoline alkaloids with which it accumulates in parallel during parasitic development. In contrast, it does not accumulate in the fungus in axenic culture, even when ergotamine is synthesised. The extent to which the compound is a metabolite of other ergot fungi worldwide is unknown. Biological activity and metabolic significance also remain unknown, but purpurolic acid could become a biomarker for detection of ergot contamination in agricultural products of temperate latitudes. Copyright © 2015 The British Mycological Society. Published by Elsevier Ltd. All rights reserved.

Potential of genetically engineered hybrid poplar for pyrolytic production of bio-based phenolic compounds.

PubMed

Toraman, Hilal E; Vanholme, Ruben; Borén, Eleonora; Vanwonterghem, Yumi; Djokic, Marko R; Yildiz, Guray; Ronsse, Frederik; Prins, Wolter; Boerjan, Wout; Van Geem, Kevin M; Marin, Guy B

2016-05-01

Wild-type and two genetically engineered hybrid poplar lines were pyrolyzed in a micro-pyrolysis (Py-GC/MS) and a bench scale setup for fast and intermediate pyrolysis studies. Principal component analysis showed that the pyrolysis vapors obtained by micro-pyrolysis from wood of caffeic acid O-methyltransferase (COMT) and caffeoyl-CoA O-methyltransferase (CCoAOMT) down-regulated poplar trees differed significantly from the pyrolysis vapors obtained from non-transgenic control trees. Both fast micro-pyrolysis and intermediate pyrolysis of transgenic hybrid poplars showed that down-regulation of COMT can enhance the relative yield of guaiacyl lignin-derived products, while the relative yield of syringyl lignin-derived products was up to a factor 3 lower. This study indicates that lignin engineering via genetic modifications of genes involved in the phenylpropanoid and monolignol biosynthetic pathways can help to steer the pyrolytic production of guaiacyl and syringyl lignin-derived phenolic compounds such as guaiacol, 4-methylguaiacol, 4-ethylguaiacol, 4-vinylguaiacol, syringol, 4-vinylsyringol, and syringaldehyde present in the bio-oil. Copyright © 2016 Elsevier Ltd. All rights reserved.

Role of the cultivar in choosing Clementine fruits with a high level of health-promoting compounds.

PubMed

Milella, Luigi; Caruso, Marisa; Galgano, Fernanda; Favati, Fabio; Padula, Maria Carmela; Martelli, Giuseppe

2011-05-25

Thirteen cultivars and two hybrids of Clementine fruits (Citrus clementina Hort. Ex. Tan) cultivated in Italy were characterized according to pH, titratable acidity, total soluble solids, total polyphenols, carotenoids, vitamin C, hesperidin, rutin, narirutin and naringin and radical scavenging activity. The presence of rutin in Clementine fruit juice is reported for the first time here. The results indicated that all chemical parameters statistically differentiated each cultivar (P < 0.001). In particular, principal component analysis showed a clear discrimination of five cultivars from all the other varieties based on vitamin C and total polyphenols for the Caffin cultivar, which showed also the highest antioxidant activity; narirutin for the Etna hybrid cultivar; hesperidin, rutin and total soluble solids for the SRA 89 cultivar; and naringin, hesperidin and rutin for the Esbal cultivar. Moreover, the Mandalate hybrid cultivar showed the lowest antioxidant activity as well as vitamin C and total polyphenols content, while titratable acidity and naringin level were the highest. The antioxidant activity assessed in all the fruits was closely correlated with vitamin C and total polyphenols content, rather than with the flavonoid compounds.

Assessing prescription drug abuse using functional principal component analysis (FPCA) of wastewater data.

PubMed

Salvatore, Stefania; Røislien, Jo; Baz-Lomba, Jose A; Bramness, Jørgen G

2017-03-01

Wastewater-based epidemiology is an alternative method for estimating the collective drug use in a community. We applied functional data analysis, a statistical framework developed for analysing curve data, to investigate weekly temporal patterns in wastewater measurements of three prescription drugs with known abuse potential: methadone, oxazepam and methylphenidate, comparing them to positive and negative control drugs. Sewage samples were collected in February 2014 from a wastewater treatment plant in Oslo, Norway. The weekly pattern of each drug was extracted by fitting of generalized additive models, using trigonometric functions to model the cyclic behaviour. From the weekly component, the main temporal features were then extracted using functional principal component analysis. Results are presented through the functional principal components (FPCs) and corresponding FPC scores. Clinically, the most important weekly feature of the wastewater-based epidemiology data was the second FPC, representing the difference between average midweek level and a peak during the weekend, representing possible recreational use of a drug in the weekend. Estimated scores on this FPC indicated recreational use of methylphenidate, with a high weekend peak, but not for methadone and oxazepam. The functional principal component analysis uncovered clinically important temporal features of the weekly patterns of the use of prescription drugs detected from wastewater analysis. This may be used as a post-marketing surveillance method to monitor prescription drugs with abuse potential. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Minimum number of measurements for evaluating soursop (Annona muricata L.) yield.

PubMed

Sánchez, C F B; Teodoro, P E; Londoño, S; Silva, L A; Peixoto, L A; Bhering, L L

2017-05-31

Repeatability studies on fruit species are of great importance to identify the minimum number of measurements necessary to accurately select superior genotypes. This study aimed to identify the most efficient method to estimate the repeatability coefficient (r) and predict the minimum number of measurements needed for a more accurate evaluation of soursop (Annona muricata L.) genotypes based on fruit yield. Sixteen measurements of fruit yield from 71 soursop genotypes were carried out between 2000 and 2016. In order to estimate r with the best accuracy, four procedures were used: analysis of variance, principal component analysis based on the correlation matrix, principal component analysis based on the phenotypic variance and covariance matrix, and structural analysis based on the correlation matrix. The minimum number of measurements needed to predict the actual value of individuals was estimated. Principal component analysis using the phenotypic variance and covariance matrix provided the most accurate estimates of both r and the number of measurements required for accurate evaluation of fruit yield in soursop. Our results indicate that selection of soursop genotypes with high fruit yield can be performed based on the third and fourth measurements in the early years and/or based on the eighth and ninth measurements at more advanced stages.

In situ FTIR and flash pyrolysis/GC-MS characterization of Protosalvinia (Upper Devonian, Kentucky, USA): Implications for maceral classification

USGS Publications Warehouse

Mastalerz, Maria; Hower, J.C.; Carmo, A.

1998-01-01

Protosalvinia from Devonian rocks in Kentucky has been analyzed using petrographic and in situ FTIR and flash pyrolysis/GC-MS techniques in order to discuss its origin and placement in organic matter classification. In reflected light, Protosalvinia resembles cutinite in shape, color and reflectance, whereas in fluorescent mode it reveals yellow-green fluorescence, reminiscent of alginite. Alkylbenzenes, alkylnaphthalenes, and n-alkanes are the principal compounds in the pyrolyzates, whereas alkylphenols and n-alk-l-enes are present in minor concentrations. FTIR results show that aliphatic bands (both in stretching and bending modes) are prominent. Protosalvinia also reveals well developed aromatic bands in the out-of-plane region. Such a mixture of aliphatic and aromatic components is not known in documented organic matter types of either marine or terrestrial origin. It is suggested that Protosalvinia might belong to rare marine organisms that yield aromatic pyrolyzates. Based on morphological features and optical properties Protosalvinia should be classified as a maceral of the liptinite group. It does not, however, fit precisely within any of the established categories of the liptinite macerals.Protosalvinia from Devonian rocks in Kentucky has been analyzed using petrographic and in situ FTIR and flash pyrolysis/GC-MS techniques in order to discuss its origin and placement in organic matter classification. In reflected light, Protosalvinia resembles cutinite in shape, color and reflectance, whereas in fluorescent mode it reveals yellow-green fluorescence, reminiscent of alginite. Alkylbenzenes, alkylnaphthalenes, and n-alkanes are the principal compounds in the pyrolyzates, whereas alkylphenols and n-alk-l-enes are present in minor concentrations. FTIR results show that aliphatic bands (both in stretching and bending modes) are prominent. Protosalvinia also reveals well developed aromatic bands in the out-of-plane region. Such a mixture of aliphatic and aromatic components is not known in documented organic matter types of either marine or terrestrial origin. It is suggested that Protosalvinia might belong to rare marine organisms that yield aromatic pyrolyzates. Based on morphological features and optical properties Protosalvinia should be classified as a maceral of the liptinite group. It does not, however, fit precisely within any of the established categories of the liptinite macerals.

An efficient classification method based on principal component and sparse representation.

PubMed

Zhai, Lin; Fu, Shujun; Zhang, Caiming; Liu, Yunxian; Wang, Lu; Liu, Guohua; Yang, Mingqiang

2016-01-01

As an important application in optical imaging, palmprint recognition is interfered by many unfavorable factors. An effective fusion of blockwise bi-directional two-dimensional principal component analysis and grouping sparse classification is presented. The dimension reduction and normalizing are implemented by the blockwise bi-directional two-dimensional principal component analysis for palmprint images to extract feature matrixes, which are assembled into an overcomplete dictionary in sparse classification. A subspace orthogonal matching pursuit algorithm is designed to solve the grouping sparse representation. Finally, the classification result is gained by comparing the residual between testing and reconstructed images. Experiments are carried out on a palmprint database, and the results show that this method has better robustness against position and illumination changes of palmprint images, and can get higher rate of palmprint recognition.

Polyhedral gamut representation of natural objects based on spectral reflectance database and its application

NASA Astrophysics Data System (ADS)

Haneishi, Hideaki; Sakuda, Yasunori; Honda, Toshio

2002-06-01

Spectral reflectance of most reflective objects such as natural objects and color hardcopy is relatively smooth and can be approximated by several numbers of principal components with high accuracy. Though the subspace spanned by those principal components represents a space in which reflective objects can exist, it dos not provide the bound in which the samples distribute. In this paper we propose to represent the gamut of reflective objects in more distinct form, i.e., as a polyhedron in the subspace spanned by several principal components. Concept of the polyhedral gamut representation and its application to calculation of metamer ensemble are described. Color-mismatch volume caused by different illuminant and/or observer for a metamer ensemble is also calculated and compared with theoretical one.

Principal Component Analysis Based Measure of Structural Holes

NASA Astrophysics Data System (ADS)

Deng, Shiguo; Zhang, Wenqing; Yang, Huijie

2013-02-01

Based upon principal component analysis, a new measure called compressibility coefficient is proposed to evaluate structural holes in networks. This measure incorporates a new effect from identical patterns in networks. It is found that compressibility coefficient for Watts-Strogatz small-world networks increases monotonically with the rewiring probability and saturates to that for the corresponding shuffled networks. While compressibility coefficient for extended Barabasi-Albert scale-free networks decreases monotonically with the preferential effect and is significantly large compared with that for corresponding shuffled networks. This measure is helpful in diverse research fields to evaluate global efficiency of networks.

Radar fall detection using principal component analysis

NASA Astrophysics Data System (ADS)

Jokanovic, Branka; Amin, Moeness; Ahmad, Fauzia; Boashash, Boualem

2016-05-01

Falls are a major cause of fatal and nonfatal injuries in people aged 65 years and older. Radar has the potential to become one of the leading technologies for fall detection, thereby enabling the elderly to live independently. Existing techniques for fall detection using radar are based on manual feature extraction and require significant parameter tuning in order to provide successful detections. In this paper, we employ principal component analysis for fall detection, wherein eigen images of observed motions are employed for classification. Using real data, we demonstrate that the PCA based technique provides performance improvement over the conventional feature extraction methods.

Time series analysis of collective motions in proteins

NASA Astrophysics Data System (ADS)

Alakent, Burak; Doruker, Pemra; ćamurdan, Mehmet C.

2004-01-01

The dynamics of α-amylase inhibitor tendamistat around its native state is investigated using time series analysis of the principal components of the Cα atomic displacements obtained from molecular dynamics trajectories. Collective motion along a principal component is modeled as a homogeneous nonstationary process, which is the result of the damped oscillations in local minima superimposed on a random walk. The motion in local minima is described by a stationary autoregressive moving average model, consisting of the frequency, damping factor, moving average parameters and random shock terms. Frequencies for the first 50 principal components are found to be in the 3-25 cm-1 range, which are well correlated with the principal component indices and also with atomistic normal mode analysis results. Damping factors, though their correlation is less pronounced, decrease as principal component indices increase, indicating that low frequency motions are less affected by friction. The existence of a positive moving average parameter indicates that the stochastic force term is likely to disturb the mode in opposite directions for two successive sampling times, showing the modes tendency to stay close to minimum. All these four parameters affect the mean square fluctuations of a principal mode within a single minimum. The inter-minima transitions are described by a random walk model, which is driven by a random shock term considerably smaller than that for the intra-minimum motion. The principal modes are classified into three subspaces based on their dynamics: essential, semiconstrained, and constrained, at least in partial consistency with previous studies. The Gaussian-type distributions of the intermediate modes, called "semiconstrained" modes, are explained by asserting that this random walk behavior is not completely free but between energy barriers.

Quality evaluation of Houttuynia cordata Thunb. by high performance liquid chromatography with photodiode-array detection (HPLC-DAD).

PubMed

Yang, Zhan-nan; Sun, Yi-ming; Luo, Shi-qiong; Chen, Jin-wu; Chen, Jin-wu; Yu, Zheng-wen; Sun, Min

2014-03-01

A new, validated method, developed for the simultaneous determination of 16 phenolics (chlorogenic acid, scopoletin, vitexin, rutin, afzelin, isoquercitrin, narirutin, kaempferitrin, quercitrin, quercetin, kaempferol, chrysosplenol D, vitexicarpin, 5-hydroxy-3,3',4',7-tetramethoxy flavonoids, 5-hydroxy-3,4',6,7-tetramethoxy flavonoids and kaempferol-3,7,4'-trimethyl ether) in Houttuynia cordata Thunb. was successfully applied to 35 batches of samples collected from different regions or at different times and their total antioxidant activities (TAAs) were investigated. The aim was to develop a quality control method to simultaneously determine the major active components in H. cordata. The HPLC-DAD method was performed using a reverse-phase C18 column with a gradient elution system (acetonitrile-methanol-water) and simultaneous detection at 345 nm. Linear behaviors of method for all the analytes were observed with linear regression relationship (r(2)>0.999) at the concentration ranges investigated. The recoveries of the 16 phenolics ranged from 98.93% to 101.26%. The samples analyzed were differentiated and classified based on the contents of the 16 characteristic compounds and the TAA using hierarchical clustering analysis (HCA) and principal component analysis (PCA). The results analyzed showed that similar chemical profiles and TAAs were divided into the same group. There was some evidence that active compounds, although they varied significantly, may possess uniform anti-oxidant activities and have potentially synergistic effects.

Simultaneous qualitative and quantitative analysis of flavonoids and alkaloids from the leaves of Nelumbo nucifera Gaertn. using high-performance liquid chromatography with quadrupole time-of-flight mass spectrometry.

PubMed

Guo, Yujie; Chen, Xi; Qi, Jin; Yu, Boyang

2016-07-01

A reliable method, combining qualitative analysis by high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry and quantitative assessment by high-performance liquid chromatography with photodiode array detection, has been developed to simultaneously analyze flavonoids and alkaloids in lotus leaf extracts. In the qualitative analysis, a total of 30 compounds, including 12 flavonoids, 16 alkaloids, and two proanthocyanidins, were identified. The fragmentation behaviors of four types of flavone glycoside and three types of alkaloid are summarized. The mass spectra of four representative components, quercetin 3-O-glucuronide, norcoclaurine, nuciferine, and neferine, are shown to illustrate their fragmentation pathways. Five pairs of isomers were detected and three of them were distinguished by comparing the elution order with reference substances and the mass spectrometry data with reported data. In the quantitative analysis, 30 lotus leaf samples from different regions were analyzed to investigate the proportion of eight representative compounds. Quercetin 3-O-glucuronide was found to be the predominant constituent of lotus leaf extracts. For further discrimination among the samples, hierarchical cluster analysis, and principal component analysis, based on the areas of the eight quantitative peaks, were carried out. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Nonlinear Model for Gene-Based Gene-Environment Interaction.

PubMed

Sa, Jian; Liu, Xu; He, Tao; Liu, Guifen; Cui, Yuehua

2016-06-04

A vast amount of literature has confirmed the role of gene-environment (G×E) interaction in the etiology of complex human diseases. Traditional methods are predominantly focused on the analysis of interaction between a single nucleotide polymorphism (SNP) and an environmental variable. Given that genes are the functional units, it is crucial to understand how gene effects (rather than single SNP effects) are influenced by an environmental variable to affect disease risk. Motivated by the increasing awareness of the power of gene-based association analysis over single variant based approach, in this work, we proposed a sparse principle component regression (sPCR) model to understand the gene-based G×E interaction effect on complex disease. We first extracted the sparse principal components for SNPs in a gene, then the effect of each principal component was modeled by a varying-coefficient (VC) model. The model can jointly model variants in a gene in which their effects are nonlinearly influenced by an environmental variable. In addition, the varying-coefficient sPCR (VC-sPCR) model has nice interpretation property since the sparsity on the principal component loadings can tell the relative importance of the corresponding SNPs in each component. We applied our method to a human birth weight dataset in Thai population. We analyzed 12,005 genes across 22 chromosomes and found one significant interaction effect using the Bonferroni correction method and one suggestive interaction. The model performance was further evaluated through simulation studies. Our model provides a system approach to evaluate gene-based G×E interaction.

Boron neutron capture therapy of malignant brain tumors at the Brookhaven Medical Research Reactor

DOE Office of Scientific and Technical Information (OSTI.GOV)

Joel, D.D.; Coderre, J.A.; Chanana, A.D.

1996-12-31

Boron neutron capture therapy (BNCT) is a bimodal form of radiation therapy for cancer. The first component of this treatment is the preferential localization of the stable isotope {sup 10}B in tumor cells by targeting with boronated compounds. The tumor and surrounding tissue is then irradiated with a neutron beam resulting in thermal neutron/{sup 10}B reactions ({sup 10}B(n,{alpha}){sup 7}Li) resulting in the production of localized high LET radiation from alpha and {sup 7}Li particles. These products of the neutron capture reaction are very damaging to cells, but of short range so that the majority of the ionizing energy released ismore » microscopically confined to the vicinity of the boron-containing compound. In principal it should be possible with BNCT to selectively destroy small nests or even single cancer cells located within normal tissue. It follows that the major improvements in this form of radiation therapy are going to come largely from the development of boron compounds with greater tumor selectivity, although there will certainly be advances made in neutron beam quality as well as the possible development of alternative sources of neutron beams, particularly accelerator-based epithermal neutron beams.« less

Metabolic Profiling of Hoodia, Chamomile, Terminalia Species and Evaluation of Commercial Preparations Using Ultrahigh-Performance Liquid Chromatography Quadrupole-Time-of-Flight Mass Spectrometry.

PubMed

Avula, Bharathi; Wang, Yan-Hong; Isaac, Giorgis; Yuk, Jimmy; Wrona, Mark; Yu, Kate; Khan, Ikhlas A

2017-11-01

Ultrahigh-performance liquid chromatography quadrupole-time-of-flight mass spectrometry (UHPLC-QToF-MS) profiling was used for the identification of marker compounds and generation of metabolic patterns that could be interrogated using chemometric modeling software. UHPLC-QToF-MS was used to generate comprehensive fingerprints of three botanicals ( Hoodia, Terminalia , and chamomile), each having different classes of compounds. Detection of a broad range of ions was carried out in full scan mode in both positive and negative modes over the range m/z 100-1700 using high-resolution mass spectrometry. Multivariate statistical analysis was used to extract relevant chemical information from the data to easily differentiate between Terminalia species, chamomile varieties, and quality control of Hoodia products. Using nontargeted analysis, identification of 37 compounds contributed to the differences between Terminalia species, 26 flavonoids were identified to show the differences between German and Roman chamomile, and 43 pregnane glycosides were identified from Hoodia gordonii samples. The UHPLC-QToF-MS-based chemical fingerprinting with principal component analysis was able to correctly distinguish botanicals and their commercial products. This work can be used as a basis to assure the quality of botanicals and commercial products. Georg Thieme Verlag KG Stuttgart · New York.

Use of Natural Products as Chemical Library for Drug Discovery and Network Pharmacology

PubMed Central

Gu, Jiangyong; Gui, Yuanshen; Chen, Lirong; Yuan, Gu; Lu, Hui-Zhe; Xu, Xiaojie

2013-01-01

Background Natural products have been an important source of lead compounds for drug discovery. How to find and evaluate bioactive natural products is critical to the achievement of drug/lead discovery from natural products. Methodology We collected 19,7201 natural products structures, reported biological activities and virtual screening results. Principal component analysis was employed to explore the chemical space, and we found that there was a large portion of overlap between natural products and FDA-approved drugs in the chemical space, which indicated that natural products had large quantity of potential lead compounds. We also explored the network properties of natural product-target networks and found that polypharmacology was greatly enriched to those compounds with large degree and high betweenness centrality. In order to make up for a lack of experimental data, high throughput virtual screening was employed. All natural products were docked to 332 target proteins of FDA-approved drugs. The most potential natural products for drug discovery and their indications were predicted based on a docking score-weighted prediction model. Conclusions Analysis of molecular descriptors, distribution in chemical space and biological activities of natural products was conducted in this article. Natural products have vast chemical diversity, good drug-like properties and can interact with multiple cellular target proteins. PMID:23638153

Quantitative determination of multi markers in five varieties of Withania somnifera using ultra-high performance liquid chromatography with hybrid triple quadrupole linear ion trap mass spectrometer combined with multivariate analysis: Application to pharmaceutical dosage forms.

PubMed

Chandra, Preeti; Kannujia, Rekha; Saxena, Ankita; Srivastava, Mukesh; Bahadur, Lal; Pal, Mahesh; Singh, Bhim Pratap; Kumar Ojha, Sanjeev; Kumar, Brijesh

2016-09-10

An ultra-high performance liquid chromatography electrospray ionization tandem mass spectrometry method has been developed and validated for simultaneous quantification of six major bioactive compounds in five varieties of Withania somnifera in various plant parts (leaf, stem and root). The analysis was accomplished on Waters ACQUITY UPLC BEH C18 column with linear gradient elution of water/formic acid (0.1%) and acetonitrile at a flow rate of 0.3mLmin(-1). The proposed method was validated with acceptable linearity (r(2), 0.9989-0.9998), precision (RSD, 0.16-2.01%), stability (RSD, 1.04-1.62%) and recovery (RSD ≤2.45%), under optimum conditions. The method was also successfully applied for the simultaneous determination of six marker compounds in twenty-six marketed formulations. Hierarchical cluster analysis and principal component analysis were applied to discriminate these twenty-six batches based on characteristics of the bioactive compounds. The results indicated that this method is advance, rapid, sensitive and suitable to reveal the quality of Withania somnifera and also capable of performing quality evaluation of polyherbal formulations having similar markers/raw herbs. Copyright © 2016 Elsevier B.V. All rights reserved.

Evaluating the Predictivity of Virtual Screening for Abl Kinase Inhibitors to Hinder Drug Resistance

PubMed Central

Gani, Osman A B S M; Narayanan, Dilip; Engh, Richard A

2013-01-01

Virtual screening methods are now widely used in early stages of drug discovery, aiming to rank potential inhibitors. However, any practical ligand set (of active or inactive compounds) chosen for deriving new virtual screening approaches cannot fully represent all relevant chemical space for potential new compounds. In this study, we have taken a retrospective approach to evaluate virtual screening methods for the leukemia target kinase ABL1 and its drug-resistant mutant ABL1-T315I. ‘Dual active’ inhibitors against both targets were grouped together with inactive ligands chosen from different decoy sets and tested with virtual screening approaches with and without explicit use of target structures (docking). We show how various scoring functions and choice of inactive ligand sets influence overall and early enrichment of the libraries. Although ligand-based methods, for example principal component analyses of chemical properties, can distinguish some decoy sets from active compounds, the addition of target structural information via docking improves enrichment, and explicit consideration of multiple target conformations (i.e. types I and II) achieves best enrichment of active versus inactive ligands, even without assuming knowledge of the binding mode. We believe that this study can be extended to other therapeutically important kinases in prospective virtual screening studies. PMID:23746052

On the Fallibility of Principal Components in Research

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.; Li, Tenglong

2017-01-01

The measurement error in principal components extracted from a set of fallible measures is discussed and evaluated. It is shown that as long as one or more measures in a given set of observed variables contains error of measurement, so also does any principal component obtained from the set. The error variance in any principal component is shown…

Seven Things a Principal Should Know about School Finance.

ERIC Educational Resources Information Center

Sharp, William L.

1994-01-01

Secondary school principals should understand school finance basics, including property tax components (tax base, assessment practice, and tax rate); allowable tax reductions and exemptions; common arguments against the property tax; cost and valuation per pupil formulas; educational equity arguments; state foundation programs; and various types…

From cow to cheese: genetic parameters of the flavour fingerprint of cheese investigated by direct-injection mass spectrometry (PTR-ToF-MS).

PubMed

Bergamaschi, Matteo; Cecchinato, Alessio; Biasioli, Franco; Gasperi, Flavia; Martin, Bruno; Bittante, Giovanni

2016-11-16

Volatile organic compounds determine important quality traits in cheese. The aim of this work was to infer genetic parameters of the profile of volatile compounds in cheese as revealed by direct-injection mass spectrometry of the headspace gas from model cheeses that were produced from milk samples from individual cows. A total of 1075 model cheeses were produced using raw whole-milk samples that were collected from individual Brown Swiss cows. Single spectrometry peaks and a combination of these peaks obtained by principal component analysis (PCA) were analysed. Using a Bayesian approach, we estimated genetic parameters for 240 individual spectrometry peaks and for the first ten principal components (PC) extracted from them. Our results show that there is some genetic variability in the volatile compound fingerprint of these model cheeses. Most peaks were characterized by a substantial heritability and for about one quarter of the peaks, heritability (up to 21.6%) was higher than that of the best PC. Intra-herd heritability of the PC ranged from 3.6 to 10.2% and was similar to heritabilities estimated for milk fat, specific fatty acids, somatic cell count and some coagulation parameters in the same population. We also calculated phenotypic correlations between PC (around zero as expected), the corresponding genetic correlations (from -0.79 to 0.86) and correlations between herds and sampling-processing dates (from -0.88 to 0.66), which confirmed that there is a relationship between cheese flavour and the dairy system in which cows are reared. This work reveals the existence of a link between the cow's genetic background and the profile of volatile compounds in cheese. Analysis of the relationships between the volatile organic compound (VOC) content and the sensory characteristics of cheese as perceived by the consumer, and of the genetic basis of these relationships could generate new knowledge that would open up the possibility of controlling and improving the sensory properties of cheese through genetic selection of cows. More detailed investigations are necessary to connect VOC with the sensory properties of cheese and gain a better understanding of the significance of these new phenotypes.

A new methodology based on functional principal component analysis to study postural stability post-stroke.

PubMed

Sánchez-Sánchez, M Luz; Belda-Lois, Juan-Manuel; Mena-Del Horno, Silvia; Viosca-Herrero, Enrique; Igual-Camacho, Celedonia; Gisbert-Morant, Beatriz

2018-05-05

A major goal in stroke rehabilitation is the establishment of more effective physical therapy techniques to recover postural stability. Functional Principal Component Analysis provides greater insight into recovery trends. However, when missing values exist, obtaining functional data presents some difficulties. The purpose of this study was to reveal an alternative technique for obtaining the Functional Principal Components without requiring the conversion to functional data beforehand and to investigate this methodology to determine the effect of specific physical therapy techniques in balance recovery trends in elderly subjects with hemiplegia post-stroke. A randomized controlled pilot trial was developed. Thirty inpatients post-stroke were included. Control and target groups were treated with the same conventional physical therapy protocol based on functional criteria, but specific techniques were added to the target group depending on the subjects' functional level. Postural stability during standing was quantified by posturography. The assessments were performed once a month from the moment the participants were able to stand up to six months post-stroke. The target group showed a significant improvement in postural control recovery trend six months after stroke that was not present in the control group. Some of the assessed parameters revealed significant differences between treatment groups (P < 0.05). The proposed methodology allows Functional Principal Component Analysis to be performed when data is scarce. Moreover, it allowed the dynamics of recovery of two different treatment groups to be determined, showing that the techniques added in the target group increased postural stability compared to the base protocol. Copyright © 2018 Elsevier Ltd. All rights reserved.

Analysis of Phenolic Compounds and Antioxidant Activity in Wild Blackberry Fruits

PubMed Central

Oszmiański, Jan; Nowicka, Paulina; Teleszko, Mirosława; Wojdyło, Aneta; Cebulak, Tomasz; Oklejewicz, Krzysztof

2015-01-01

Twenty three different wild blackberry fruit samples were assessed regarding their phenolic profiles and contents (by LC/MS quadrupole time-of-flight (QTOF) and antioxidant activity (ferric reducing ability of plasma (FRAP) and 2,2-azinobis (3-ethyl-benzothiazoline-6-sulfonic acid) (ABTS)) by two different extraction methods. Thirty four phenolic compounds were detected (8 anthocyanins, 15 flavonols, 3 hydroxycinnamic acids, 6 ellagic acid derivatives and 2 flavones). In samples, where pressurized liquid extraction (PLE) was used for extraction, a greater increase in yields of phenolic compounds was observed, especially in ellagic acid derivatives (max. 59%), flavonols (max. 44%) and anthocyanins (max. 29%), than after extraction by the ultrasonic technique extraction (UAE) method. The content of phenolic compounds was significantly correlated with the antioxidant activity of the analyzed samples. Principal component analysis (PCA) revealed that the PLE method was more suitable for the quantitative extraction of flavonols, while the UAE method was for hydroxycinnamic acids. PMID:26132562

Characterisation of metabolic profile of banana genotypes, aiming at biofortified Musa spp. cultivars.

PubMed

Borges, Cristine Vanz; Amorim, Vanusia Batista de Oliveira; Ramlov, Fernanda; Ledo, Carlos Alberto da Silva; Donato, Marcela; Maraschin, Marcelo; Amorim, Edson Perito

2014-02-15

The banana is an important, widely consumed fruit, especially in areas of rampant undernutrition. Twenty-nine samples were analysed, including 9 diploids, 13 triploids and 7 tetraploids, in the Active Germplasm Bank, at Embrapa Cassava & Fruits, to evaluate the bioactive compounds. The results of this study reveal the presence of a diversity of bioactive compounds, e.g., catechins; they are phenolic compounds with high antioxidant potential and antitumour activity. In addition, accessions with appreciable amounts of pVACs were identified, especially compared with the main cultivars that are currently marketed. The ATR-FTIR, combined with principal components analysis, identified accessions with distinct metabolic profiles in the fingerprint regions of compounds important for human health. Likewise, starch fraction characterisation allowed discrimination of accessions according to their physical, chemical, and functional properties. The results of this study demonstrate that the banana has functional characteristics endowing it with the potential to promote human health. Copyright © 2013 Elsevier Ltd. All rights reserved.

Chemometric analysis for the evaluation of phenolic patterns in olive leaves from six cultivars at different growth stages.

PubMed

Talhaoui, Nassima; Gómez-Caravaca, Ana María; Roldán, Cristina; León, Lorenzo; De la Rosa, Raúl; Fernández-Gutiérrez, Alberto; Segura-Carretero, Antonio

2015-02-18

Leaves from six important olive cultivars grown under the same agronomic conditions were collected at four different times from June to December and analyzed by high performance liquid chromatography-diode array detector-time-of-flight-mass spectrometry (HPLC-DAD-TOF-MS). Twenty-eight phenolic compounds were identified and quantified. No qualitative differences were detected among leaves. However, for all cultivars, total concentrations of phenolic compounds decreased from June to August, then increased from October on, and reached higher levels again in December. Principal component analysis provided a clear separation of the phenolic content in leaves for different sampling times and cultivars. Hence, the availability of phenolic compounds depends on both the season and the cultivar. June and December seem to be good times to collect leaves as a source of phenolic compounds. December coincides with the harvest period of olives in the Andalusian region. Thus, in December olive leaves could be valorized efficiently as olive byproducts.

A chemometrics as a powerful tool in the elucidation of the role of metals in the biosynthesis of volatile organic compounds in Hungarian thyme samples.

PubMed

Arsenijević, Jelena; Marković, Jelena; Soštarić, Ivan; Ražić, Slavica

2013-10-01

The volatile fraction of the leaves of Thymus pannonicus All. (Lamiaceae) was analyzed by headspace extraction followed by GC-FID and GC-MS analysis. The different headspace profiles were recognized, with citral and with monoterpene hydrocarbons as dominant compounds. In addition, the determination of Cr, Co, Ni, Mo, Cu, Zn, Mn, Fe, Mg, Ca, K and Na was conducted by spectroscopic techniques (FAAS, GFAAS and ICP-OES). In order to evaluate the relationship between volatile organic compounds and metals, a chemometrics approach was applied. The data obtained by analysis of the headspace and elemental content were subjected to correlation analysis, factor analysis, principal component analysis and cluster analysis. A number of significant correlations of metals with plant volatiles were found. Correlation of Zn with citral, Mn with oxygenated monoterpenes and Mg with β-bourbonene, could be explained by involvement of metals in the biosynthesis of volatile organic compounds. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Effect of cultivar and variety on phenolic compounds and antioxidant activity of cherry wine.

PubMed

Xiao, Zuobing; Fang, Lingling; Niu, Yunwei; Yu, Haiyan

2015-11-01

To compare the influence of cultivar and variety on the phenolic compounds and antioxidant activity (AA) of cherry wines, total phenolic (TP), total flavonoid (TF), total anthocyanin (TA), total tannin (TT), five individual phenolic acids, and AA were determined. An ultra-performance liquid chromatography tandem mass spectrometry (HPLC-DAD/ESI-MS) method was developed for the determination of gallic acid (GAE), p-hydroxybenzoic acid (PHB), chlorogenic acid (CHL), vanillic acid (VAN), and caffeic acid (CAF). A principal component analysis (PCA) and a cluster analysis (CA) were used to analyze differences related to cultivar and variety. The TP, TF, TA, TT, and AA of samples sourced from the Shandong province of China were higher than those from the Jiangsu province. The PCA and CA results showed that phenolic compounds in cherry wines were closely related to cultivar and variety and that cultivar had more influence on the phenolic compounds of cherry wines than variety. Copyright © 2015 Elsevier Ltd. All rights reserved.

Effect of outside air ventilation rate on VOC concentrations and emissions in a call center

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hodgson, A.T.; Faulkner, D.; Sullivan, D.P.

2002-01-01

A study of the relationship between outside air ventilation rate and concentrations of VOCs generated indoors was conducted in a call center. Ventilation rates were manipulated in the building's four air handling units (AHUs). Concentrations of VOCs in the AHU returns were measured on 7 days during a 13-week period. Indoor minus outdoor concentrations and emission factors were calculated. The emission factor data was subjected to principal component analysis to identify groups of co-varying compounds based on source type. One vector represented emissions of solvents from cleaning products. Another vector identified occupant sources. Direct relationships between ventilation rate and concentrationsmore » were not observed for most of the abundant VOCs. This result emphasizes the importance of source control measures for limiting VOC concentrations in buildings.« less

Chromatographic analysis with different detectors in the chemical characterisation and dereplication of African propolis.

PubMed

Zhang, Tong; Omar, Ruwida; Siheri, Weam; Al Mutairi, Sultan; Clements, Carol; Fearnley, James; Edrada-Ebel, RuAngelie; Watson, David

2014-03-01

Propolis or bee glue has very diverse composition and is potentially a source of biologically active compounds. Comprehensive chemical profiling was performed on 22 African propolis samples collected from the sub-Saharan region of Africa by using various hyphenated analytical techniques including Liquid Chromatography (LC)-UltraViolet Detection (UV)-Evaporative Light Scattering Detection (ELSD), LC-High Resolution Mass Spectrometry (HRMS), Gas Chromatography (GC)-MS and LC-Diode Array Detector (DAD)-HRMS/MS. The diversity of the composition of these African propolis samples could be observed by heat mapping the LC-UV and ELSD data. The characteristic chemical components were uncovered by applying Principal Component Analysis (PCA) to the LC-HRMS data and a preliminary dereplication was carried out by searching their accurate masses in the Dictionary of Natural Products (DNP). A further identification was achieved by comparing their GC-MS or LC-DAD-HRMS/MS spectra with previously published data. Generally no clear geographic delineation was observed in the classification of these African propolis samples. Triterpenoids were found as the major chemical components in more than half of the propolis samples analysed in this study and some others were classified as temperate and Eastern Mediterranean type of propolis. Based on the comparative chemical profiling and dereplication studies one uncommon propolis from southern Nigeria stood out from others by presenting prenylated isoflavonoids, which indicated that it was more like Brazilian red propolis, and more significantly a high abundance of stilbenoid compounds which could be novel in propolis. Copyright © 2013 Elsevier B.V. All rights reserved.

Principal component analysis for the comparison of metabolic profiles from human rectal cancer biopsies and colorectal xenografts using high-resolution magic angle spinning 1H magnetic resonance spectroscopy

PubMed Central

Seierstad, Therese; Røe, Kathrine; Sitter, Beathe; Halgunset, Jostein; Flatmark, Kjersti; Ree, Anne H; Olsen, Dag Rune; Gribbestad, Ingrid S; Bathen, Tone F

2008-01-01

Background This study was conducted in order to elucidate metabolic differences between human rectal cancer biopsies and colorectal HT29, HCT116 and SW620 xenografts by using high-resolution magnetic angle spinning (MAS) magnetic resonance spectroscopy (MRS) and for determination of the most appropriate human rectal xenograft model for preclinical MR spectroscopy studies. A further aim was to investigate metabolic changes following irradiation of HT29 xenografts. Methods HR MAS MRS of tissue samples from xenografts and rectal biopsies were obtained with a Bruker Avance DRX600 spectrometer and analyzed using principal component analysis (PCA) and partial least square (PLS) regression analysis. Results and conclusion HR MAS MRS enabled assignment of 27 metabolites. Score plots from PCA of spin-echo and single-pulse spectra revealed separate clusters of the different xenografts and rectal biopsies, reflecting underlying differences in metabolite composition. The loading profile indicated that clustering was mainly based on differences in relative amounts of lipids, lactate and choline-containing compounds, with HT29 exhibiting the metabolic profile most similar to human rectal cancers tissue. Due to high necrotic fractions in the HT29 xenografts, radiation-induced changes were not detected when comparing spectra from untreated and irradiated HT29 xenografts. However, PLS calibration relating spectral data to the necrotic fraction revealed a significant correlation, indicating that necrotic fraction can be assessed from the MR spectra. PMID:18439252

Chemical Composition of Cacti Wood and Comparison with the Wood of Other Taxonomic Groups.

PubMed

Maceda, Agustín; Soto-Hernández, Marcos; Peña-Valdivia, Cecilia B; Terrazas, Teresa

2018-04-01

The aims of this study were to determine the wood chemical composition of 25 species of Cactaceae and to relate the composition to their anatomical diversity. The hypothesis was that wood chemical components differ in relationship to their wood features. The results showed significant differences in wood chemical compounds across species and genera (P < 0.05). Pereskia had the highest percentage of lignin, whereas species of Coryphantha had the lowest; extractive compounds in water were highest for Echinocereus, Mammillaria, and Opuntia. Principal component analysis showed that lignin proportion separated the fibrous, dimorphic, and non-fibrous groups; additionally, the differences within each type of wood occurred because of the lignification of the vascular tissue and the type of wall thickening. Compared with other groups of species, the Cactaceae species with fibrous and dimorphic wood had a higher lignin percentage than did gymnosperms and Acer species. Lignin may confer special rigidity to tracheary elements to withstand desiccation without damage during adverse climatic conditions. © 2018 Wiley-VHCA AG, Zurich, Switzerland.

Boiling points of halogenated aliphatic compounds: a quantitative structure-property relationship for prediction and validation.

PubMed

Oberg, Tomas

2004-01-01

Halogenated aliphatic compounds have many technical uses, but substances within this group are also ubiquitous environmental pollutants that can affect the ozone layer and contribute to global warming. The establishment of quantitative structure-property relationships is of interest not only to fill in gaps in the available database but also to validate experimental data already acquired. The three-dimensional structures of 240 compounds were modeled with molecular mechanics prior to the generation of empirical descriptors. Two bilinear projection methods, principal component analysis (PCA) and partial-least-squares regression (PLSR), were used to identify outliers. PLSR was subsequently used to build a multivariate calibration model by extracting the latent variables that describe most of the covariation between the molecular structure and the boiling point. Boiling points were also estimated with an extension of the group contribution method of Stein and Brown.

Development of an Integrated Metabolomic Profiling Approach for Infectious Diseases Research

PubMed Central

Lv, Haitao; Hung, Chia S.; Chaturvedi, Kaveri S.; Hooton, Thomas M.; Henderson, Jeffrey P.

2013-01-01

Metabolomic profiling offers direct insights into the chemical environment and metabolic pathway activities at sites of human disease. During infection, this environment may receive important contributions from both host and pathogen. Here we apply untargeted metabolomics approach to identify compounds associated with an E. coli urinary tract infection population. Correlative and structural data from minimally processed samples were obtained using an optimized LC-MS platform capable of resolving ∼2300 molecular features. Principal components analysis readily distinguished patient groups and multiple supervised chemometric analyses resolved robust metabolomic shifts between groups. These analyses revealed nine compounds whose provisional structures suggest candidate infection-associated endocrine, catabolic, and lipid pathways. Several of these metabolite signatures may derive from microbial processing of host metabolites. Overall, this study highlights the ability of metabolomic approaches to directly identify compounds encountered by, and produced from, bacterial pathogens within human hosts. PMID:21922104

Influence of Protein-Phenolic Complex on the Antioxidant Capacity of Flaxseed (Linum usitatissimum L.) Products.

PubMed

Guimarães Drummond E Silva, Fernanda; Miralles, Beatriz; Hernández-Ledesma, Blanca; Amigo, Lourdes; Iglesias, Amadeu Hoshi; Reyes Reyes, Felix Guillermo; Netto, Flavia Maria

2017-02-01

The impact of the naturally present phenolic compounds and/or proteins on the antioxidant capacity of flaxseed products (phenolic fraction, protein concentrates, and hydrolysates) before and after simulated gastrointestinal digestion was studied. For that, whole and phenolic reduced products were assessed. Four glycosylated phenolic compounds (secoisolariciresinol and ferulic, p-coumaric, and caffeic acids) were identified in flaxseed products. Phenolic fraction exerts the highest antioxidant capacity that increased by alkaline hydrolysis and by simulated gastrointestinal digestion. The action of Alcalase and digestive enzymes resulted in an increase of the antioxidant capacity of whole and phenolic reduced products. Principal component analysis showed that proteinaceous samples act as antioxidant is by H + transfer, while those samples containing phenolic compounds exert their effects by both electron donation and H + transfer mechanisms. Protein/peptide-phenolic complexation, confirmed by fluorescence spectra, exerted a positive effect on the antioxidant capacity, mainly in protein concentrates.

The impact of hybridization on the volatile and sensorial profile of Ocimum basilicum L.

PubMed

da Costa, Andréa Santos; Arrigoni-Blank, Maria de Fátima; da Silva, Maria Aparecida Azevedo Pereira; Alves, Mércia Freitas; Santos, Darlisson de Alexandria; Alves, Péricles Barreto; Blank, Arie Fitzgerald

2014-01-01

The aim of the present study was to investigate the volatile and sensorial profile of basil (Ocimum basilicum L.) by quantitative descriptive analysis (QDA) of the essential oil of three hybrids ("Cinnamon" × "Maria Bonita," "Sweet Dani" × "Cinnamon," and "Sweet Dani" × "Maria Bonita"). Twelve descriptive terms were developed by a selected panel that also generated the definition of each term and the reference samples. The data were subjected to ANOVA, Tukey's test, and principal component analysis. The hybrid "Cinnamon" × "Maria Bonita" exhibited a stronger global aroma that was less citric than the other samples. Hybridization favored the generation of novel compounds in the essential oil of the hybrid "Sweet Dani" × "Maria Bonita," such as canfora and (E)-caryophyllene; (E)-caryophyllene also was a novel compound in the hybrid "Sweet Dani" × "Cinnamon"; this compound was not present in the essential oils of the parents.

Measurement of Scenic Spots Sustainable Capacity Based on PCA-Entropy TOPSIS: A Case Study from 30 Provinces, China

PubMed Central

Liang, Xuedong; Liu, Canmian; Li, Zhi

2017-01-01

In connection with the sustainable development of scenic spots, this paper, with consideration of resource conditions, economic benefits, auxiliary industry scale and ecological environment, establishes a comprehensive measurement model of the sustainable capacity of scenic spots; optimizes the index system by principal components analysis to extract principal components; assigns the weight of principal components by entropy method; analyzes the sustainable capacity of scenic spots in each province of China comprehensively in combination with TOPSIS method and finally puts forward suggestions aid decision-making. According to the study, this method provides an effective reference for the study of the sustainable development of scenic spots and is very significant for considering the sustainable development of scenic spots and auxiliary industries to establish specific and scientific countermeasures for improvement. PMID:29271947

Measurement of Scenic Spots Sustainable Capacity Based on PCA-Entropy TOPSIS: A Case Study from 30 Provinces, China.

PubMed

Liang, Xuedong; Liu, Canmian; Li, Zhi

2017-12-22

In connection with the sustainable development of scenic spots, this paper, with consideration of resource conditions, economic benefits, auxiliary industry scale and ecological environment, establishes a comprehensive measurement model of the sustainable capacity of scenic spots; optimizes the index system by principal components analysis to extract principal components; assigns the weight of principal components by entropy method; analyzes the sustainable capacity of scenic spots in each province of China comprehensively in combination with TOPSIS method and finally puts forward suggestions aid decision-making. According to the study, this method provides an effective reference for the study of the sustainable development of scenic spots and is very significant for considering the sustainable development of scenic spots and auxiliary industries to establish specific and scientific countermeasures for improvement.

From measurements to metrics: PCA-based indicators of cyber anomaly

NASA Astrophysics Data System (ADS)

Ahmed, Farid; Johnson, Tommy; Tsui, Sonia

2012-06-01

We present a framework of the application of Principal Component Analysis (PCA) to automatically obtain meaningful metrics from intrusion detection measurements. In particular, we report the progress made in applying PCA to analyze the behavioral measurements of malware and provide some preliminary results in selecting dominant attributes from an arbitrary number of malware attributes. The results will be useful in formulating an optimal detection threshold in the principal component space, which can both validate and augment existing malware classifiers.

Reconstruction Error and Principal Component Based Anomaly Detection in Hyperspectral Imagery

DTIC Science & Technology

2014-03-27

2003), and (Jackson D. A., 1993). In 1933, Hotelling ( Hotelling , 1933), who coined the term ‘principal components,’ surmised that there was a...goodness of fit and multivariate quality control with the statistic Qi = (Xi(1×p) − X̂i(1×p) )(Xi(1×p) − X̂i(1×p) ) T (20) where, under the...sparsely targeted scenes through SNR or other methods. 5) Customize sorting and histogram construction methods in Multiple PCA to avoid redundancy

Spectral Comparison and Stability of Red Regions on Jupiter

NASA Technical Reports Server (NTRS)

Simon, A. A.; Carlson, R. W.; Sanchez-Lavega, A.

2013-01-01

A study of absolute color on Jupiter from Hubble Space Telescope imaging data shows that the Great Red Spot (GRS) is not the reddest region of the planet. Rather, a transient red cyclone visible in 1995 and the North Equatorial Belt both show redder spectra than the GRS (i.e., more absorption at blue and green wavelengths). This cyclone is unique among vortices in that it is intensely colored yet low altitude, unlike the GRS. Temporal analysis shows that the darkest regions of the NEB are relative constant in color from 1995 to 2008, while the slope of the GRS core may vary slightly. Principal component analysis shows several spectral components are needed, in agreement with past work, and further highlights the differences between regions. These color differences may be indicative of the same chromophore(s) under different conditions, such as mixing with white clouds, longer UV irradiation at higher altitude, and thermal processing, or may indicate abundance variations in colored compounds. A single compound does not fit the spectrum of any region well and mixes of multiple compounds including NH4SH, photolyzed NH3, hydrocarbons, and possibly P4, are likely needed to fully match each spectrum.

Principal Component and Linkage Analysis of Cardiovascular Risk Traits in the Norfolk Isolate

PubMed Central

Cox, Hannah C.; Bellis, Claire; Lea, Rod A.; Quinlan, Sharon; Hughes, Roger; Dyer, Thomas; Charlesworth, Jac; Blangero, John; Griffiths, Lyn R.

2009-01-01

Objective(s) An individual's risk of developing cardiovascular disease (CVD) is influenced by genetic factors. This study focussed on mapping genetic loci for CVD-risk traits in a unique population isolate derived from Norfolk Island. Methods This investigation focussed on 377 individuals descended from the population founders. Principal component analysis was used to extract orthogonal components from 11 cardiovascular risk traits. Multipoint variance component methods were used to assess genome-wide linkage using SOLAR to the derived factors. A total of 285 of the 377 related individuals were informative for linkage analysis. Results A total of 4 principal components accounting for 83% of the total variance were derived. Principal component 1 was loaded with body size indicators; principal component 2 with body size, cholesterol and triglyceride levels; principal component 3 with the blood pressures; and principal component 4 with LDL-cholesterol and total cholesterol levels. Suggestive evidence of linkage for principal component 2 (h2 = 0.35) was observed on chromosome 5q35 (LOD = 1.85; p = 0.0008). While peak regions on chromosome 10p11.2 (LOD = 1.27; p = 0.005) and 12q13 (LOD = 1.63; p = 0.003) were observed to segregate with principal components 1 (h2 = 0.33) and 4 (h2 = 0.42), respectively. Conclusion(s): This study investigated a number of CVD risk traits in a unique isolated population. Findings support the clustering of CVD risk traits and provide interesting evidence of a region on chromosome 5q35 segregating with weight, waist circumference, HDL-c and total triglyceride levels. PMID:19339786

Composition, distribution, and potential toxicity of organochlorine mixtures in bed sediments of streams

USGS Publications Warehouse

Phillips, Patrick J.; Nowell, Lisa H.; Gilliom, Robert J.; Nakagaki, Naomi; Murray, Karen; VanAlstyne, Carolyn

2010-01-01

Mixtures of organochlorine compounds have the potential for additive or interactive toxicity to organisms exposed in the stream. This study uses a variety of methods to identify mixtures and a modified concentration-addition approach to estimate their potential toxicity at 845 stream sites across the United States sampled between 1992 and 2001 for organochlorine pesticides and polychlorinated biphenyls (PCBs) in bed sediment. Principal-component (PC) analysis identified five PCs that account for 77% of the total variance in 14 organochlorine compounds in the original dataset. The five PCs represent: (1) chlordane-related compounds and dieldrin; (2) p,p′-DDT and its degradates; (3) o,p′-DDT and its degradates; (4) the pesticide degradates oxychlordane and heptachlor epoxide; and (5) PCBs. The PC analysis grouped compounds that have similar chemical structure (such as parent compound and degradate), common origin (in the same technical pesticide mixture), and(or) similar relation of concentrations to land use. For example, the highest concentrations of chlordane compounds and dieldrin occurred at urban sites, reflecting past use of parent pesticides for termite control. Two approaches to characterizing mixtures—PC-based mixtures and unique mixtures—were applied to all 299 samples with a detection of two or more organochlorine compounds. PC-based mixtures are defined by the presence (in the sample) of one or more compounds associated with that PC. Unique mixtures are defined as a specific combination of two or more compounds detected in a sample, regardless of how many other compounds were also detected in that sample. The simplest PC-based mixtures (containing compounds from 1 or 2 PCs) commonly occurred in a variety of land use settings. Complex mixtures (containing compounds from 3 or more PCs) were most common in samples from urban and mixed/urban sites, especially in the Northeast, reflecting high concentrations of multiple chlordane, dieldrin, DDT-related compounds, and(or) PCBs. The most commonly occurring unique mixture (p,p′-DDE, p,p′-DDD) occurred in both simple and complex PC-based mixtures, and at both urban and agricultural sites. Mean Probable Effect Concentration Quotients (PEC-Q) values, which estimate the potential toxicity of organochlorine contaminant mixtures, were highest for complex mixtures. Mean PEC-Q values were highest for urban sites in the Northeast, followed by mixed/urban sites in the Northeast and agricultural sites in cotton growing areas. These results demonstrate that the PEC-Q approach can be used in combination with PC-based and unique mixture analyses to relate potential aquatic toxicity of contaminant mixtures to mixture complexity, land use, and other surrogates for contaminant sources.

Optical character recognition based on nonredundant correlation measurements.

PubMed

Braunecker, B; Hauck, R; Lohmann, A W

1979-08-15

The essence of character recognition is a comparison between the unknown character and a set of reference patterns. Usually, these reference patterns are all possible characters themselves, the whole alphabet in the case of letter characters. Obviously, N analog measurements are highly redundant, since only K = log(2)N binary decisions are enough to identify one out of N characters. Therefore, we devised K reference patterns accordingly. These patterns, called principal components, are found by digital image processing, but used in an optical analog computer. We will explain the concept of principal components, and we will describe experiments with several optical character recognition systems, based on this concept.

Characterization of the Key Aroma Compounds in Proso Millet Wine Using Headspace Solid-Phase Microextraction and Gas Chromatography-Mass Spectrometry.

PubMed

Liu, Jingke; Zhao, Wei; Li, Shaohui; Zhang, Aixia; Zhang, Yuzong; Liu, Songyan

2018-02-20

The volatile compounds in proso millet wine were extracted by headspace solid-phase microextraction (85 μm polyacrylate (PA), 100 μm polydimethylsiloxane (PDMS), 75 μm Carboxen (CAR)/PDMS, and 50/30 μm divinylbenzene (DVB)/CAR/PDMS fibers), and analyzed using gas chromatography-mass spectrometry; the odor characteristics and intensities were analyzed by the odor activity value (OAV). Different sample preparation factors were used to optimize this method: sample amount, extraction time, extraction temperature, and content of NaCl. A total of 64 volatile compounds were identified from the wine sample, including 14 esters, seven alcohols, five aldehydes, five ketones, 12 benzene derivatives, 12 hydrocarbons, two terpenes, three phenols, two acids, and two heterocycles. Ethyl benzeneacetate, phenylethyl alcohol, and benzaldehyde were the main volatile compounds found in the samples. According to their OAVs, 14 volatile compounds were determined to be odor-active compounds (OAV > 1), and benzaldehyde, benzeneacetaldehyde, 1-methyl-naphthalene, 2-methyl-naphthalene, and biphenyl were the prominent odor-active compounds (OAV > 50), having a high OAV. Principal component analysis (PCA) showed the difference of distribution of the 64 volatile compounds and 14 odor-active compounds with four solid-phase microextraction (SPME) fibers.

Confocal Raman imaging for cancer cell classification

NASA Astrophysics Data System (ADS)

Mathieu, Evelien; Van Dorpe, Pol; Stakenborg, Tim; Liu, Chengxun; Lagae, Liesbet

2014-05-01

We propose confocal Raman imaging as a label-free single cell characterization method that can be used as an alternative for conventional cell identification techniques that typically require labels, long incubation times and complex sample preparation. In this study it is investigated whether cancer and blood cells can be distinguished based on their Raman spectra. 2D Raman scans are recorded of 114 single cells, i.e. 60 breast (MCF-7), 5 cervix (HeLa) and 39 prostate (LNCaP) cancer cells and 10 monocytes (from healthy donors). For each cell an average spectrum is calculated and principal component analysis is performed on all average cell spectra. The main features of these principal components indicate that the information for cell identification based on Raman spectra mainly comes from the fatty acid composition in the cell. Based on the second and third principal component, blood cells could be distinguished from cancer cells; and prostate cancer cells could be distinguished from breast and cervix cancer cells. However, it was not possible to distinguish breast and cervix cancer cells. The results obtained in this study, demonstrate the potential of confocal Raman imaging for cell type classification and identification purposes.

How Many Separable Sources? Model Selection In Independent Components Analysis

PubMed Central

Woods, Roger P.; Hansen, Lars Kai; Strother, Stephen

2015-01-01

Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis/Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though computationally intensive alternative for model selection. Application of the algorithm is illustrated using Fisher's iris data set and Howells' craniometric data set. Mixed ICA/PCA is of potential interest in any field of scientific investigation where the authenticity of blindly separated non-Gaussian sources might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian. PMID:25811988

Variability in chemical composition of Vitis vinifera cv Mencía from different geographic areas and vintages in Ribeira Sacra (NW Spain).

PubMed

Vilanova, M; Rodríguez, I; Canosa, P; Otero, I; Gamero, E; Moreno, D; Talaverano, I; Valdés, E

2015-02-15

A chemical study was conducted from 2009 to 2012 to examine spatial and seasonal variability of red Vitis vinifera Mencía located in different geographic areas (Amandi, Chantada, Quiroga-Bibei, Ribeiras do Sil and Ribeiras do Miño) from NW Spain. Mencía samples were analysed for phenolic, (flavan-3-ols, flavonols, anthocyanins, acids and resveratrol), nitrogen (TAC, TAN, YAN and TAS) and volatiles compounds (alcohols, C6 compounds, ethyl esters, terpenes, aldehydes, acids, lactones, volatile phenols and carbonyl compounds) by GC-MS and HPLC. Results showed that the composition of Mencía cultivar was more affected by the vintage than the geographic area. The amino acid composition was less affected by both geographic origin and vintage, showing more varietal stability. Application of Principal Component Analysis (PCA) to experimental data showed a good separation of Mencía grape according to geographical origin and vintages. PCA also showed high correlations between the ripening ratio and C6 compounds, resveratrol and carbonyl compounds. Copyright © 2014 Elsevier Ltd. All rights reserved.

Chemometric studies on potential larvicidal compounds against Aedes aegypti.

PubMed

Scotti, Luciana; Scotti, Marcus Tullius; Silva, Viviane Barros; Santos, Sandra Regina Lima; Cavalcanti, Sócrates C H; Mendonça, Francisco J B

2014-03-01

The mosquito Aedes aegypti (Diptera, Culicidae) is the vector of yellow and dengue fever. In this study, chemometric tools, such as, Principal Component Analysis (PCA), Consensus PCA (CPCA), and Partial Least Squares Regression (PLS), were applied to a set of fifty five active compounds against Ae. aegypti larvae, which includes terpenes, cyclic alcohols, phenolic compounds, and their synthetic derivatives. The calculations were performed using the VolSurf+ program. CPCA analysis suggests that the higher weight blocks of descriptors were SIZE/SHAPE, DRY, and H2O. The PCA was generated with 48 descriptors selected from the previous blocks. The scores plot showed good separation between more and less potent compounds. The first two PCs accounted for over 60% of the data variance. The best model obtained in PLS, after validation leave-one-out, exhibited q(2) = 0.679 and r(2) = 0.714. External prediction model was R(2) = 0.623. The independent variables having a hydrophobic profile were strongly correlated to the biological data. The interaction maps generated with the GRID force field showed that the most active compounds exhibit more interaction with the DRY probe.

Systematic chemical analysis approach reveals superior antioxidant capacity via the synergistic effect of flavonoid compounds in red vegetative tissues

NASA Astrophysics Data System (ADS)

Qin, Xiaoxiao; Lu, Yanfen; Peng, Zhen; Fan, Shuangxi; Yao, Yuncong

2018-02-01

The flavonoid system comprises an abundance of compounds with multiple functions; however, their potential synergism in antioxidant function remains unclear. We established an approach using ever-red (RL) and ever-green leaves (GL) of crabapple cultivars during their development to determine interrelationships among flavonoid compounds. RL scored significantly better than GL in terms of the type, composition, and diversity of flavonoids than GL. Principal component analysis predicted flavonoids in RL to have positive interaction effects, and the total antioxidant capacity was significantly higher than the sum of antioxidant capacities of the individual compounds. This synergy was verified by the high antioxidant capacity in rat serum after feeding on red leaves. Our findings suggest that the synergistic effect is a result of the high transcription levels regulated by McMYBs in RL. In summary, individual flavonoids cooperate in a flavonoid system, thus producing a synergistic antioxidant effect, and the approach used herein can provide insights into the roles of flavonoids and other compounds in future studies.

Systematic Chemical Analysis Approach Reveals Superior Antioxidant Capacity via the Synergistic Effect of Flavonoid Compounds in Red Vegetative Tissues

PubMed Central

Qin, Xiaoxiao; Lu, Yanfen; Peng, Zhen; Fan, Shuangxi; Yao, Yuncong

2018-01-01

The flavonoid system comprises an abundance of compounds with multiple functions; however, their potential synergism in antioxidant function remains unclear. We established an approach using ever-red (RL) and ever-green leaves (GL) of crabapple cultivars during their development to determine interrelationships among flavonoid compounds. RL scored significantly better than GL in terms of the type, composition, and diversity of flavonoids than GL. Principal component analysis predicted flavonoids in RL to have positive interaction effects, and the total antioxidant capacity was significantly higher than the sum of antioxidant capacities of the individual compounds. This synergy was verified by the high antioxidant capacity in rat serum after feeding on red leaves. Our findings suggest that the synergistic effect is a result of the high transcription levels regulated by McMYBs in RL. In summary, individual flavonoids cooperate in a flavonoid system, thus producing a synergistic antioxidant effect, and the approach used herein can provide insights into the roles of flavonoids and other compounds in future studies. PMID:29468147

QSAR modeling of flotation collectors using principal components extracted from topological indices.

PubMed

Natarajan, R; Nirdosh, Inderjit; Basak, Subhash C; Mills, Denise R

2002-01-01

Several topological indices were calculated for substituted-cupferrons that were tested as collectors for the froth flotation of uranium. The principal component analysis (PCA) was used for data reduction. Seven principal components (PC) were found to account for 98.6% of the variance among the computed indices. The principal components thus extracted were used in stepwise regression analyses to construct regression models for the prediction of separation efficiencies (Es) of the collectors. A two-parameter model with a correlation coefficient of 0.889 and a three-parameter model with a correlation coefficient of 0.913 were formed. PCs were found to be better than partition coefficient to form regression equations, and inclusion of an electronic parameter such as Hammett sigma or quantum mechanically derived electronic charges on the chelating atoms did not improve the correlation coefficient significantly. The method was extended to model the separation efficiencies of mercaptobenzothiazoles (MBT) and aminothiophenols (ATP) used in the flotation of lead and zinc ores, respectively. Five principal components were found to explain 99% of the data variability in each series. A three-parameter equation with correlation coefficient of 0.985 and a two-parameter equation with correlation coefficient of 0.926 were obtained for MBT and ATP, respectively. The amenability of separation efficiencies of chelating collectors to QSAR modeling using PCs based on topological indices might lead to the selection of collectors for synthesis and testing from a virtual database.

Quali-quantitative characterization of the volatile constituents in Cordia verbenacea D.C. essential oil exploiting advanced chromatographic approaches and nuclear magnetic resonance analysis.

PubMed

Sciarrone, Danilo; Giuffrida, Daniele; Rotondo, Archimede; Micalizzi, Giuseppe; Zoccali, Mariosimone; Pantò, Sebastiano; Donato, Paola; Rodrigues-das-Dores, Rosana Goncalves; Mondello, Luigi

2017-11-17

Cordia verbenacea D.C. (Boraginaceae, Varronia curassavica Jacq. synonym) is a medicinal plant, native from Brazil, especially the leaves are used in folk medicine. The aim of this study was to extend the characterization of the volatile fraction of the essential oil obtained from this plant, by using GC-FID, GC-MS, and chiral GC. Moreover, to further clarify the composition of the volatile fraction, preparative multidimensional-GC (prep-MDGC) was used to collect unknown compounds, followed by NMR characterization. Specifically, the chemical characterization, both qualitative and quantitative, of the volatile fraction of the essential oil obtained from Cordia verbenacea cultivated in the Minas Gerais area (central area of Brazil) was investigated for the first time. The principal components from a quantitative point of view were α-pinene (25.32%; 24.48g/100g) and α-santalene (17.90%; 17.30g/100g), belonging to the terpenes family. Chiral-GC data are reported for the enantiomeric distribution of 7 different components. Last, to obtain the complete characterization of the essential oil constituents, prep-MDGC analysis was used to attain the isolation of two compounds, not present in the principal MS databases, which were unambiguously identified by NMR investigation as (E)-α-santalal and (E)-α-bergamotenal, reported for the first time in Cordia verbenacea essential oil. Copyright © 2017 Elsevier B.V. All rights reserved.

Bioconcentration of haloxyfop-methyl in bluegill (Lepomis macrochirus Rafinesque)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Murphy, P.G.; Lutenske, N.E.

1990-01-01

Bluegill (Lepomis macrochirus Rafinesque) were exposed to a {sup 14}C haloxyfop-methyl (methyl 2-(4-((3-chloro-5-(trifluoromethyl)-2-pyridinyl)oxy)phenoxy)propanoate) concentration averaging 0.29 {mu}g/L under flow-through conditions for 28 days. At the end of 28 days, the fish were transferred to clean water for a 4-day flow-through clearance period. Bluegill were found to rapidly absorb the ester from water which was then biotransformed at an extremely fast rate within the fish, such that essentially no haloxyfop-methyl was detected in the fish. The estimated bioconcentration factor for haloxyfop-methyl in whole fish was <17, based upon the detection limit for the ester in fish (0.005 {mu}g/g) and the averagemore » concentration of haloxyfop-methyl in exposure water (0.29 {mu}g/L). The principal component of the {sup 14}C residue within whole fish was haloxyfop acid (2-(4-((3-chloro-5-(trifluoromethyl)-2-pyridinyl)oxy)phenoxy)propanoic acid) which accounted for an average of about 60% of the total radioactivity. The high rate of biotransformation of the parent compound within the fish demonstrates the importance of basing the bioconcentration factor upon the actual concentration of parent material within the organism rather than the total radioactive residue levels for bioconcentration studies with radiolabeled compounds.« less

Tailoring gas sensor arrays via the design of short peptides sequences as binding elements.

PubMed

Mascini, Marcello; Pizzoni, Daniel; Perez, German; Chiarappa, Emilio; Di Natale, Corrado; Pittia, Paola; Compagnone, Dario

2017-07-15

A semi-combinatorial virtual approach was used to prepare peptide-based gas sensors with binding properties towards five different chemical classes (alcohols, aldehydes, esters, hydrocarbons and ketones). Molecular docking simulations were conducted for a complete tripeptide library (8000 elements) versus 58 volatile compounds belonging to those five chemical classes. By maximizing the differences between chemical classes, a subset of 120 tripeptides was extracted and used as scaffolds for generating a combinatorial library of 7912 tetrapeptides. This library was processed in an analogous way to the former. Five tetrapeptides (IHRI, KSDS, LGFD, TGKF and WHVS) were chosen depending on their virtual affinity and cross-reactivity for the experimental step. The five peptides were covalently bound to gold nanoparticles by adding a terminal cysteine to each tetrapeptide and deposited onto 20MHz quartz crystal microbalances to construct the gas sensors. The behavior of peptides after this chemical modification was simulated at the pH range used in the immobilization step. ΔF signals analyzed by principal component analysis matched the virtually screened data. The array was able to clearly discriminate the 13 volatile compounds tested based on their hydrophobicity and hydrophilicity molecules as well as the molecular weight. Copyright © 2016 Elsevier B.V. All rights reserved.

NMR-based metabolomics study of the biochemical relationship between sugarcane callus tissues and their respective nutrient culture media

PubMed Central

Mahmud, Iqbal; Thapaliya, Monica; Boroujerdi, Arezue; Chowdhury, Kamal

2014-01-01

The culture of sugarcane leaf explant onto culture induction medium triggers the stimulation of cell metabolism into both embryogenic and non-embryogenic callus tissues. Previous analyses demonstrated that embryogenic and nonembryogenic callus tissues have distinct metabolic profiles. This study is the follow-up to understand the biochemical relationship between the nutrient media and callus tissues using one-dimensional (1D 1H) and two-dimensional (2D 1H–13C) NMR spectroscopy followed by principal component analysis (PCA). 1D 1H spectral comparisons of fresh unspent media (FM), embryogenic callus media (ECM), non-embryogenic callus media (NECM), embryogenic callus (EC), and non-embryogenic callus (NEC), showed different metabolic relationships between callus tissues and media. Based on metabolite fold change analysis, significantly changing sugar compounds such as glucose, fructose, sucrose, and maltose were maintained in large quantities by EC only. Significantly different amino acid compounds such as valine, leucine, alanine, threonine, asparagine, and glutamine and different organic acid derivatives such as lactate, 2-hydroxyisobutyrate, 4-aminobutyrate, malonate, and choline were present in EC, NEC, and NECM, which indicates that EC maintained these nutrients, while NEC either maintained or secreted the metabolites. These media and callus-specific results suggest that EC and NEC utilize and/or secrete media nutrients differently. PMID:25012359

Discrimination of gender-, speed-, and shoe-dependent movement patterns in runners using full-body kinematics.

PubMed

Maurer, Christian; Federolf, Peter; von Tscharner, Vinzenz; Stirling, Lisa; Nigg, Benno M

2012-05-01

Changes in gait kinematics have often been analyzed using pattern recognition methods such as principal component analysis (PCA). It is usually just the first few principal components that are analyzed, because they describe the main variability within a dataset and thus represent the main movement patterns. However, while subtle changes in gait pattern (for instance, due to different footwear) may not change main movement patterns, they may affect movements represented by higher principal components. This study was designed to test two hypotheses: (1) speed and gender differences can be observed in the first principal components, and (2) small interventions such as changing footwear change the gait characteristics of higher principal components. Kinematic changes due to different running conditions (speed - 3.1m/s and 4.9 m/s, gender, and footwear - control shoe and adidas MicroBounce shoe) were investigated by applying PCA and support vector machine (SVM) to a full-body reflective marker setup. Differences in speed changed the basic movement pattern, as was reflected by a change in the time-dependent coefficient derived from the first principal. Gender was differentiated by using the time-dependent coefficient derived from intermediate principal components. (Intermediate principal components are characterized by limb rotations of the thigh and shank.) Different shoe conditions were identified in higher principal components. This study showed that different interventions can be analyzed using a full-body kinematic approach. Within the well-defined vector space spanned by the data of all subjects, higher principal components should also be considered because these components show the differences that result from small interventions such as footwear changes. Crown Copyright © 2012. Published by Elsevier B.V. All rights reserved.

Multivariate relationships between groundwater chemistry and toxicity in an urban aquifer.

PubMed

Dewhurst, Rachel E; Wells, N Claire; Crane, Mark; Callaghan, Amanda; Connon, Richard; Mather, John D

2003-11-01

Multivariate statistical methods were used to investigate the causes of toxicity and controls on groundwater chemistry from 274 boreholes in an urban area (London) of the United Kingdom. The groundwater was alkaline to neutral, and chemistry was dominated by calcium, sodium, and sulfate. Contaminants included fuels, solvents, and organic compounds derived from landfill material. The presence of organic material in the aquifer caused decreases in dissolved oxygen, sulfate and nitrate concentrations, and increases in ferrous iron and ammoniacal nitrogen concentrations. Pearson correlations between toxicity results and the concentration of individual analytes indicated that concentrations of ammoniacal nitrogen, dissolved oxygen, ferrous iron, and hydrocarbons were important where present. However, principal component and regression analysis suggested no significant correlation between toxicity and chemistry over the whole area. Multidimensional scaling was used to investigate differences in sites caused by historical use, landfill gas status, or position within the sample area. Significant differences were observed between sites with different historical land use and those with different gas status. Examination of the principal component matrix revealed that these differences are related to changes in the importance of reduced chemical species.

Principal Components of Thermography analyses of the Silk Tomb, Petra (Jordan)

NASA Astrophysics Data System (ADS)

Gomez-Heras, Miguel; Alvarez de Buergo, Monica; Fort, Rafael

2015-04-01

This communication presents the results of an active thermography survey of the Silk Tomb, which belongs to the Royal Tombs compound in the archaeological city of Petra in Jordan. The Silk Tomb is carved in the variegated Palaeozoic Umm Ishrin sandstone and it is heavily backweathered due to surface runoff from the top of the cliff where it is carved. Moreover, the name "Silk Tomb" was given because of the colourful display of the variegated sandstone due to backweathering. A series of infrared images were taken as the façade was heated by sunlight to perform a Principal Component of Thermography analyses with IR view 1.7.5 software. This was related to indirect moisture measurements (percentage of Wood Moisture Equivalent) taken across the façade, by means of a Protimeter portable moisture meter. Results show how moisture retention is deeply controlled by lithological differences across the façade. Research funded by Geomateriales 2 S2013/MIT-2914 and CEI Moncloa (UPM, UCM, CSIC) through a PICATA contract and the equipment from RedLAbPAt Network

Principal Component Relaxation Mode Analysis of an All-Atom Molecular Dynamics Simulation of Human Lysozyme

NASA Astrophysics Data System (ADS)

Nagai, Toshiki; Mitsutake, Ayori; Takano, Hiroshi

2013-02-01

A new relaxation mode analysis method, which is referred to as the principal component relaxation mode analysis method, has been proposed to handle a large number of degrees of freedom of protein systems. In this method, principal component analysis is carried out first and then relaxation mode analysis is applied to a small number of principal components with large fluctuations. To reduce the contribution of fast relaxation modes in these principal components efficiently, we have also proposed a relaxation mode analysis method using multiple evolution times. The principal component relaxation mode analysis method using two evolution times has been applied to an all-atom molecular dynamics simulation of human lysozyme in aqueous solution. Slow relaxation modes and corresponding relaxation times have been appropriately estimated, demonstrating that the method is applicable to protein systems.

Principal component analysis and neurocomputing-based models for total ozone concentration over different urban regions of India

NASA Astrophysics Data System (ADS)

Chattopadhyay, Goutami; Chattopadhyay, Surajit; Chakraborthy, Parthasarathi

2012-07-01

The present study deals with daily total ozone concentration time series over four metro cities of India namely Kolkata, Mumbai, Chennai, and New Delhi in the multivariate environment. Using the Kaiser-Meyer-Olkin measure, it is established that the data set under consideration are suitable for principal component analysis. Subsequently, by introducing rotated component matrix for the principal components, the predictors suitable for generating artificial neural network (ANN) for daily total ozone prediction are identified. The multicollinearity is removed in this way. Models of ANN in the form of multilayer perceptron trained through backpropagation learning are generated for all of the study zones, and the model outcomes are assessed statistically. Measuring various statistics like Pearson correlation coefficients, Willmott's indices, percentage errors of prediction, and mean absolute errors, it is observed that for Mumbai and Kolkata the proposed ANN model generates very good predictions. The results are supported by the linearly distributed coordinates in the scatterplots.

Principal component analysis of indocyanine green fluorescence dynamics for diagnosis of vascular diseases

NASA Astrophysics Data System (ADS)

Seo, Jihye; An, Yuri; Lee, Jungsul; Choi, Chulhee

2015-03-01

Indocyanine green (ICG), a near-infrared fluorophore, has been used in visualization of vascular structure and non-invasive diagnosis of vascular disease. Although many imaging techniques have been developed, there are still limitations in diagnosis of vascular diseases. We have recently developed a minimally invasive diagnostics system based on ICG fluorescence imaging for sensitive detection of vascular insufficiency. In this study, we used principal component analysis (PCA) to examine ICG spatiotemporal profile and to obtain pathophysiological information from ICG dynamics. Here we demonstrated that principal components of ICG dynamics in both feet showed significant differences between normal control and diabetic patients with vascula complications. We extracted the PCA time courses of the first three components and found distinct pattern in diabetic patient. We propose that PCA of ICG dynamics reveal better classification performance compared to fluorescence intensity analysis. We anticipate that specific feature of spatiotemporal ICG dynamics can be useful in diagnosis of various vascular diseases.

Identification of Imitation Cheese and Imitation Ice Cream Based on Vegetable Fat Using NMR Spectroscopy and Chemometrics

PubMed Central

Monakhova, Yulia B.; Godelmann, Rolf; Andlauer, Claudia; Kuballa, Thomas; Lachenmeier, Dirk W.

2013-01-01

Vegetable oils and fats may be used as cheap substitutes for milk fat to manufacture imitation cheese or imitation ice cream. In this study, 400 MHz nuclear magnetic resonance (NMR) spectroscopy of the fat fraction of the products was used in the context of food surveillance to validate the labeling of milk-based products. For sample preparation, the fat was extracted using an automated Weibull-Stoldt methodology. Using principal component analysis (PCA), imitation products can be easily detected. In both cheese and ice cream, a differentiation according to the type of raw material (milk fat and vegetable fat) was possible. The loadings plot shows that imitation products were distinguishable by differences in their fatty acid ratios. Furthermore, a differentiation of several types of cheese (Edamer, Gouda, Emmentaler, and Feta) was possible. Quantitative data regarding the composition of the investigated products can also be predicted from the same spectra using partial least squares (PLS) regression. The models obtained for 13 compounds in cheese (R 2 0.75–0.95) and 17 compounds in ice cream (R 2 0.83–0.99) (e.g., fatty acids and esters) were suitable for a screening analysis. NMR spectroscopy was judged as suitable for the routine analysis of dairy products based on milk or on vegetable fat substitutes. PMID:26904597

Interpretable functional principal component analysis.

PubMed

Lin, Zhenhua; Wang, Liangliang; Cao, Jiguo

2016-09-01

Functional principal component analysis (FPCA) is a popular approach to explore major sources of variation in a sample of random curves. These major sources of variation are represented by functional principal components (FPCs). The intervals where the values of FPCs are significant are interpreted as where sample curves have major variations. However, these intervals are often hard for naïve users to identify, because of the vague definition of "significant values". In this article, we develop a novel penalty-based method to derive FPCs that are only nonzero precisely in the intervals where the values of FPCs are significant, whence the derived FPCs possess better interpretability than the FPCs derived from existing methods. To compute the proposed FPCs, we devise an efficient algorithm based on projection deflation techniques. We show that the proposed interpretable FPCs are strongly consistent and asymptotically normal under mild conditions. Simulation studies confirm that with a competitive performance in explaining variations of sample curves, the proposed FPCs are more interpretable than the traditional counterparts. This advantage is demonstrated by analyzing two real datasets, namely, electroencephalography data and Canadian weather data. © 2015, The International Biometric Society.

Finger crease pattern recognition using Legendre moments and principal component analysis

NASA Astrophysics Data System (ADS)

Luo, Rongfang; Lin, Tusheng

2007-03-01

The finger joint lines defined as finger creases and its distribution can identify a person. In this paper, we propose a new finger crease pattern recognition method based on Legendre moments and principal component analysis (PCA). After obtaining the region of interest (ROI) for each finger image in the pre-processing stage, Legendre moments under Radon transform are applied to construct a moment feature matrix from the ROI, which greatly decreases the dimensionality of ROI and can represent principal components of the finger creases quite well. Then, an approach to finger crease pattern recognition is designed based on Karhunen-Loeve (K-L) transform. The method applies PCA to a moment feature matrix rather than the original image matrix to achieve the feature vector. The proposed method has been tested on a database of 824 images from 103 individuals using the nearest neighbor classifier. The accuracy up to 98.584% has been obtained when using 4 samples per class for training. The experimental results demonstrate that our proposed approach is feasible and effective in biometrics.

Corrected confidence bands for functional data using principal components.

PubMed

Goldsmith, J; Greven, S; Crainiceanu, C

2013-03-01

Functional principal components (FPC) analysis is widely used to decompose and express functional observations. Curve estimates implicitly condition on basis functions and other quantities derived from FPC decompositions; however these objects are unknown in practice. In this article, we propose a method for obtaining correct curve estimates by accounting for uncertainty in FPC decompositions. Additionally, pointwise and simultaneous confidence intervals that account for both model- and decomposition-based variability are constructed. Standard mixed model representations of functional expansions are used to construct curve estimates and variances conditional on a specific decomposition. Iterated expectation and variance formulas combine model-based conditional estimates across the distribution of decompositions. A bootstrap procedure is implemented to understand the uncertainty in principal component decomposition quantities. Our method compares favorably to competing approaches in simulation studies that include both densely and sparsely observed functions. We apply our method to sparse observations of CD4 cell counts and to dense white-matter tract profiles. Code for the analyses and simulations is publicly available, and our method is implemented in the R package refund on CRAN. Copyright © 2013, The International Biometric Society.

Corrected Confidence Bands for Functional Data Using Principal Components

PubMed Central

Goldsmith, J.; Greven, S.; Crainiceanu, C.

2014-01-01

Functional principal components (FPC) analysis is widely used to decompose and express functional observations. Curve estimates implicitly condition on basis functions and other quantities derived from FPC decompositions; however these objects are unknown in practice. In this article, we propose a method for obtaining correct curve estimates by accounting for uncertainty in FPC decompositions. Additionally, pointwise and simultaneous confidence intervals that account for both model- and decomposition-based variability are constructed. Standard mixed model representations of functional expansions are used to construct curve estimates and variances conditional on a specific decomposition. Iterated expectation and variance formulas combine model-based conditional estimates across the distribution of decompositions. A bootstrap procedure is implemented to understand the uncertainty in principal component decomposition quantities. Our method compares favorably to competing approaches in simulation studies that include both densely and sparsely observed functions. We apply our method to sparse observations of CD4 cell counts and to dense white-matter tract profiles. Code for the analyses and simulations is publicly available, and our method is implemented in the R package refund on CRAN. PMID:23003003

Experimental Investigation of Principal Residual Stress and Fatigue Performance for Turned Nickel-Based Superalloy Inconel 718.

PubMed

Hua, Yang; Liu, Zhanqiang

2018-05-24

Residual stresses of turned Inconel 718 surface along its axial and circumferential directions affect the fatigue performance of machined components. However, it has not been clear that the axial and circumferential directions are the principle residual stress direction. The direction of the maximum principal residual stress is crucial for the machined component service life. The present work aims to focuses on determining the direction and magnitude of principal residual stress and investigating its influence on fatigue performance of turned Inconel 718. The turning experimental results show that the principal residual stress magnitude is much higher than surface residual stress. In addition, both the principal residual stress and surface residual stress increase significantly as the feed rate increases. The fatigue test results show that the direction of the maximum principal residual stress increased by 7.4%, while the fatigue life decreased by 39.4%. The maximum principal residual stress magnitude diminished by 17.9%, whereas the fatigue life increased by 83.6%. The maximum principal residual stress has a preponderant influence on fatigue performance as compared to the surface residual stress. The maximum principal residual stress can be considered as a prime indicator for evaluation of the residual stress influence on fatigue performance of turned Inconel 718.

The dimensionality of stellar chemical space using spectra from the Apache Point Observatory Galactic Evolution Experiment

NASA Astrophysics Data System (ADS)

Price-Jones, Natalie; Bovy, Jo

2018-03-01

Chemical tagging of stars based on their similar compositions can offer new insights about the star formation and dynamical history of the Milky Way. We investigate the feasibility of identifying groups of stars in chemical space by forgoing the use of model derived abundances in favour of direct analysis of spectra. This facilitates the propagation of measurement uncertainties and does not pre-suppose knowledge of which elements are important for distinguishing stars in chemical space. We use ˜16 000 red giant and red clump H-band spectra from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) and perform polynomial fits to remove trends not due to abundance-ratio variations. Using expectation maximized principal component analysis, we find principal components with high signal in the wavelength regions most important for distinguishing between stars. Different subsamples of red giant and red clump stars are all consistent with needing about 10 principal components to accurately model the spectra above the level of the measurement uncertainties. The dimensionality of stellar chemical space that can be investigated in the H band is therefore ≲10. For APOGEE observations with typical signal-to-noise ratios of 100, the number of chemical space cells within which stars cannot be distinguished is approximately 1010±2 × (5 ± 2)n - 10 with n the number of principal components. This high dimensionality and the fine-grained sampling of chemical space are a promising first step towards chemical tagging based on spectra alone.

Functional principal component analysis of glomerular filtration rate curves after kidney transplant.

PubMed

Dong, Jianghu J; Wang, Liangliang; Gill, Jagbir; Cao, Jiguo

2017-01-01

This article is motivated by some longitudinal clinical data of kidney transplant recipients, where kidney function progression is recorded as the estimated glomerular filtration rates at multiple time points post kidney transplantation. We propose to use the functional principal component analysis method to explore the major source of variations of glomerular filtration rate curves. We find that the estimated functional principal component scores can be used to cluster glomerular filtration rate curves. Ordering functional principal component scores can detect abnormal glomerular filtration rate curves. Finally, functional principal component analysis can effectively estimate missing glomerular filtration rate values and predict future glomerular filtration rate values.

A Principle Component Analysis of Galaxy Properties from a Large, Gas-Selected Sample

DOE PAGES

Chang, Yu-Yen; Chao, Rikon; Wang, Wei-Hao; ...

2012-01-01

Disney emore » t al. (2008) have found a striking correlation among global parameters of H i -selected galaxies and concluded that this is in conflict with the CDM model. Considering the importance of the issue, we reinvestigate the problem using the principal component analysis on a fivefold larger sample and additional near-infrared data. We use databases from the Arecibo Legacy Fast Arecibo L -band Feed Array Survey for the gas properties, the Sloan Digital Sky Survey for the optical properties, and the Two Micron All Sky Survey for the near-infrared properties. We confirm that the parameters are indeed correlated where a single physical parameter can explain 83% of the variations. When color ( g - i ) is included, the first component still dominates but it develops a second principal component. In addition, the near-infrared color ( i - J ) shows an obvious second principal component that might provide evidence of the complex old star formation. Based on our data, we suggest that it is premature to pronounce the failure of the CDM model and it motivates more theoretical work.« less

Principal component analysis of dynamic fluorescence images for diagnosis of diabetic vasculopathy

NASA Astrophysics Data System (ADS)

Seo, Jihye; An, Yuri; Lee, Jungsul; Ku, Taeyun; Kang, Yujung; Ahn, Chulwoo; Choi, Chulhee

2016-04-01

Indocyanine green (ICG) fluorescence imaging has been clinically used for noninvasive visualizations of vascular structures. We have previously developed a diagnostic system based on dynamic ICG fluorescence imaging for sensitive detection of vascular disorders. However, because high-dimensional raw data were used, the analysis of the ICG dynamics proved difficult. We used principal component analysis (PCA) in this study to extract important elements without significant loss of information. We examined ICG spatiotemporal profiles and identified critical features related to vascular disorders. PCA time courses of the first three components showed a distinct pattern in diabetic patients. Among the major components, the second principal component (PC2) represented arterial-like features. The explained variance of PC2 in diabetic patients was significantly lower than in normal controls. To visualize the spatial pattern of PCs, pixels were mapped with red, green, and blue channels. The PC2 score showed an inverse pattern between normal controls and diabetic patients. We propose that PC2 can be used as a representative bioimaging marker for the screening of vascular diseases. It may also be useful in simple extractions of arterial-like features.

Origin Discrimination of Osmanthus fragrans var. thunbergii Flowers using GC-MS and UPLC-PDA Combined with Multivariable Analysis Methods.

PubMed

Zhou, Fei; Zhao, Yajing; Peng, Jiyu; Jiang, Yirong; Li, Maiquan; Jiang, Yuan; Lu, Baiyi

2017-07-01

Osmanthus fragrans flowers are used as folk medicine and additives for teas, beverages and foods. The metabolites of O. fragrans flowers from different geographical origins were inconsistent in some extent. Chromatography and mass spectrometry combined with multivariable analysis methods provides an approach for discriminating the origin of O. fragrans flowers. To discriminate the Osmanthus fragrans var. thunbergii flowers from different origins with the identified metabolites. GC-MS and UPLC-PDA were conducted to analyse the metabolites in O. fragrans var. thunbergii flowers (in total 150 samples). Principal component analysis (PCA), soft independent modelling of class analogy analysis (SIMCA) and random forest (RF) analysis were applied to group the GC-MS and UPLC-PDA data. GC-MS identified 32 compounds common to all samples while UPLC-PDA/QTOF-MS identified 16 common compounds. PCA of the UPLC-PDA data generated a better clustering than PCA of the GC-MS data. Ten metabolites (six from GC-MS and four from UPLC-PDA) were selected as effective compounds for discrimination by PCA loadings. SIMCA and RF analysis were used to build classification models, and the RF model, based on the four effective compounds (caffeic acid derivative, acteoside, ligustroside and compound 15), yielded better results with the classification rate of 100% in the calibration set and 97.8% in the prediction set. GC-MS and UPLC-PDA combined with multivariable analysis methods can discriminate the origin of Osmanthus fragrans var. thunbergii flowers. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Oral LD50 toxicity modeling and prediction of per- and polyfluorinated chemicals on rat and mouse.

PubMed

Bhhatarai, Barun; Gramatica, Paola

2011-05-01

Quantitative structure-activity relationship (QSAR) analyses were performed using the LD(50) oral toxicity data of per- and polyfluorinated chemicals (PFCs) on rodents: rat and mouse. PFCs are studied under the EU project CADASTER which uses the available experimental data for prediction and prioritization of toxic chemicals for risk assessment by using the in silico tools. The methodology presented here applies chemometrical analysis on the existing experimental data and predicts the toxicity of new compounds. QSAR analyses were performed on the available 58 mouse and 50 rat LD(50) oral data using multiple linear regression (MLR) based on theoretical molecular descriptors selected by genetic algorithm (GA). Training and prediction sets were prepared a priori from available experimental datasets in terms of structure and response. These sets were used to derive statistically robust and predictive (both internally and externally) models. The structural applicability domain (AD) of the models were verified on 376 per- and polyfluorinated chemicals including those in REACH preregistration list. The rat and mouse endpoints were predicted by each model for the studied compounds, and finally 30 compounds, all perfluorinated, were prioritized as most important for experimental toxicity analysis under the project. In addition, cumulative study on compounds within the AD of all four models, including two earlier published models on LC(50) rodent analysis was studied and the cumulative toxicity trend was observed using principal component analysis (PCA). The similarities and the differences observed in terms of descriptors and chemical/mechanistic meaning encoded by descriptors to prioritize the most toxic compounds are highlighted.

A new strategy for statistical analysis-based fingerprint establishment: Application to quality assessment of Semen sojae praeparatum.

PubMed

Guo, Hui; Zhang, Zhen; Yao, Yuan; Liu, Jialin; Chang, Ruirui; Liu, Zhao; Hao, Hongyuan; Huang, Taohong; Wen, Jun; Zhou, Tingting

2018-08-30

Semen sojae praeparatum with homology of medicine and food is a famous traditional Chinese medicine. A simple and effective quality fingerprint analysis, coupled with chemometrics methods, was developed for quality assessment of Semen sojae praeparatum. First, similarity analysis (SA) and hierarchical clusting analysis (HCA) were applied to select the qualitative markers, which obviously influence the quality of Semen sojae praeparatum. 21 chemicals were selected and characterized by high resolution ion trap/time-of-flight mass spectrometry (LC-IT-TOF-MS). Subsequently, principal components analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were conducted to select the quantitative markers of Semen sojae praeparatum samples from different origins. Moreover, 11 compounds with statistical significance were determined quantitatively, which provided an accurate and informative data for quality evaluation. This study proposes a new strategy for "statistic analysis-based fingerprint establishment", which would be a valuable reference for further study. Copyright © 2018 Elsevier Ltd. All rights reserved.

Problems in the fingerprints based polycyclic aromatic hydrocarbons source apportionment analysis and a practical solution.

PubMed

Zou, Yonghong; Wang, Lixia; Christensen, Erik R

2015-10-01

This work intended to explain the challenges of the fingerprints based source apportionment method for polycyclic aromatic hydrocarbons (PAH) in the aquatic environment, and to illustrate a practical and robust solution. The PAH data detected in the sediment cores from the Illinois River provide the basis of this study. Principal component analysis (PCA) separates PAH compounds into two groups reflecting their possible airborne transport patterns; but it is not able to suggest specific sources. Not all positive matrix factorization (PMF) determined sources are distinguishable due to the variability of source fingerprints. However, they constitute useful suggestions for inputs for a Bayesian chemical mass balance (CMB) analysis. The Bayesian CMB analysis takes into account the measurement errors as well as the variations of source fingerprints, and provides a credible source apportionment. Major PAH sources for Illinois River sediments are traffic (35%), coke oven (24%), coal combustion (18%), and wood combustion (14%). Copyright © 2015. Published by Elsevier Ltd.

Strategies to select yeast starters cultures for production of flavor compounds in cachaça fermentations.

PubMed

de Souza, Anderson Proust Gonçalves; Vicente, Maristela de Araújo; Klein, Raphael Contelli; Fietto, Luciano Gomes; Coutrim, Maurício Xavier; de Cássia Franco Afonso, Robson José; Araújo, Leandro Dias; da Silva, Paulo Henrique Alves; Bouillet, Leoneide Erica Maduro; Castro, Ieso Miranda; Brandão, Rogelio Lopes

2012-02-01

In this work, we have used classical genetics techniques to find improved starter strains to produce cachaça with superior sensorial quality. Our strategy included the selection of yeast strains resistant to 5,5',5″-trifluor-D: ,L: -leucine (TLF) and cerulenin, since these strains produce higher levels of higher alcohols and esters than parental strains. However, no clear relationship was observed when levels of flavoring compounds were compared with the levels expression of the genes (BAT1, BAT2, ATF2, EEB1 genes) involved with the biosynthesis of flavoring compounds. Furthermore, we determined the stability of phenotypes considered as the best indicators of the quality of the cachaça for a parental strain and its segregants. By applying the principal component analysis, a cluster of segregants, showing a high number of characteristics similar to the parental strain, was recognized. One segregant, that was resistant to TLF and cerulenin, also showed growth stability after six consecutive replications on plates containing high concentrations of sugar and ethanol. "Cachaça" produced at laboratory scale using a parental strain and this segregant showed a higher level of flavoring compounds. Both strains predominated in an open fermentative process through seven cycles, as was shown by mitochondrial restriction fragment length polymorphisms analysis. Based on the physical chemical composition of the obtained products, the results demonstrate the usefulness of the developed strategies for the selection of yeast strains to be used as starters in "cachaça" production.

Fingerprint analysis and quality consistency evaluation of flavonoid compounds for fermented Guava leaf by combining high-performance liquid chromatography time-of-flight electrospray ionization mass spectrometry and chemometric methods.

PubMed

Wang, Lu; Tian, Xiaofei; Wei, Wenhao; Chen, Gong; Wu, Zhenqiang

2016-10-01

Guava leaves are used in traditional herbal teas as antidiabetic therapies. Flavonoids are the main active of Guava leaves and have many physiological functions. However, the flavonoid compositions and activities of Guava leaves could change due to microbial fermentation. A high-performance liquid chromatography time-of-flight electrospray ionization mass spectrometry method was applied to identify the varieties of the flavonoids in Guava leaves before and after fermentation. High-performance liquid chromatography, hierarchical cluster analysis and principal component analysis were used to quantitatively determine the changes in flavonoid compositions and evaluate the consistency and quality of Guava leaves. Monascus anka Saccharomyces cerevisiae fermented Guava leaves contained 2.32- and 4.06-fold more total flavonoids and quercetin, respectively, than natural Guava leaves. The flavonoid compounds of the natural Guava leaves had similarities ranging from 0.837 to 0.927. The flavonoid compounds from the Monascus anka S. cerevisiae fermented Guava leaves had similarities higher than 0.993. This indicated that the quality consistency of the fermented Guava leaves was better than that of the natural Guava leaves. High-performance liquid chromatography fingerprinting and chemometric analysis are promising methods for evaluating the degree of fermentation of Guava leaves based on quality consistency, which could be used in assessing flavonoid compounds for the production of fermented Guava leaves. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

An Analysis of Communication as a Key Component in Leadership with Diverse School Populations

ERIC Educational Resources Information Center

Cagle, Jo E.; Wiley, Sandy T.

2012-01-01

"Effective Communication as an Essential Element of Leaders with Diverse School Populations" describes a problem based learning project focused on issues related to practices used by principals to address diverse school populations. The team found that communication between principals and diverse populations in schools was a challenge.…

The Relationship between Principal Leadership Effectiveness and School Performance in South Carolina High Schools

ERIC Educational Resources Information Center

Lempesis, Costa

2009-01-01

A critical component for successful schools is effective leadership. In the 1980's the concept of leadership emerged and the rules changed for school principals (Lashway, 2002). Previously, administrators were primarily evaluated based upon their abilities in managing school facilities and operations efficiently. Academics became the new focus.…

The Relation between Factor Score Estimates, Image Scores, and Principal Component Scores

ERIC Educational Resources Information Center

Velicer, Wayne F.

1976-01-01

Investigates the relation between factor score estimates, principal component scores, and image scores. The three methods compared are maximum likelihood factor analysis, principal component analysis, and a variant of rescaled image analysis. (RC)

The Butterflies of Principal Components: A Case of Ultrafine-Grained Polyphase Units

NASA Astrophysics Data System (ADS)

Rietmeijer, F. J. M.

1996-03-01

Dusts in the accretion regions of chondritic interplanetary dust particles [IDPs] consisted of three principal components: carbonaceous units [CUs], carbon-bearing chondritic units [GUs] and carbon-free silicate units [PUs]. Among others, differences among chondritic IDP morphologies and variable bulk C/Si ratios reflect variable mixtures of principal components. The spherical shapes of the initially amorphous principal components remain visible in many chondritic porous IDPs but fusion was documented for CUs, GUs and PUs. The PUs occur as coarse- and ultrafine-grained units that include so called GEMS. Spherical principal components preserved in an IDP as recognisable textural units have unique proporties with important implications for their petrological evolution from pre-accretion processing to protoplanet alteration and dynamic pyrometamorphism. Throughout their lifetime the units behaved as closed-systems without chemical exchange with other units. This behaviour is reflected in their mineralogies while the bulk compositions of principal components define the environments wherein they were formed.

Feature extraction and selection from volatile compounds for analytical classification of Chinese red wines from different varieties.

PubMed

Zhang, Jian; Li, Li; Gao, Nianfa; Wang, Depei; Gao, Qiang; Jiang, Shengping

2010-03-10

This work was undertaken to evaluate whether it is possible to determine the variety of a Chinese wine on the basis of its volatile compounds, and to investigate if discrimination models could be developed with the experimental wines that could be used for the commercial ones. A headspace solid-phase microextraction gas chromatographic (HS-SPME-GC) procedure was used to determine the volatile compounds and a blind analysis based on Ac/Ais (peak area of volatile compound/peak area of internal standard) was carried out for statistical purposes. One way analysis of variance (ANOVA), principal component analysis (PCA) and stepwise linear discriminant analysis (SLDA) were used to process data and to develop discriminant models. Only 11 peaks enabled to differentiate and classify the experimental wines. SLDA allowed 100% recognition ability for three grape varieties, 100% prediction ability for Cabernet Sauvignon and Cabernet Gernischt wines, but only 92.31% for Merlot wines. A more valid and robust way was to use the PCA scores to do the discriminant analysis. When we performed SLDA this way, 100% recognition ability and 100% prediction ability were obtained. At last, 11 peaks which selected by SLDA from raw analysis set had been identified. When we demonstrated the models using commercial wines, the models showed 100% recognition ability for the wines collected directly from winery and without ageing, but only 65% for the others. Therefore, the varietal factor was currently discredited as a differentiating parameter for commercial wines in China. Nevertheless, this method could be applied as a screening tool and as a complement to other methods for grape base liquors which do not need ageing and blending procedures. 2010 Elsevier B.V. All rights reserved.

Bioassay-guided purification and identification of antimicrobial components in Chinese green tea extract.

PubMed

Si, Weiduo; Gong, Joshua; Tsao, Rong; Kalab, Milosh; Yang, Raymond; Yin, Yulong

2006-09-01

The Chinese green tea extract was found to strongly inhibit the growth of major food-borne pathogens, Escherichia coli O157:H7, Salmonella Typhimurium DT104, Listeria monocytogenes, Staphylococcus aureus, and a diarrhoea food-poisoning pathogen Bacillus cereus, by 44-100% with the highest activity found against S. aureus and lowest against E. coli O157:H7. A bioassay-guided fractionation technique was used for identifying the principal active component. A simple and efficient reversed-phase high-speed counter-current chromatography (HSCCC) method was developed for the separation and purification of four bioactive polyphenol compounds, epicatechin gallate (ECG), epigallocatechin gallate (EGCG), epicatechin (EC), and caffeine (CN). The structures of these polyphenols were confirmed with mass spectrometry. Among the four compounds, ECG and EGCG were the most active, particularly EGCG against S. aureus. EGCG had the lowest MIC90 values against S. aureus (MSSA) (58 mg/L) and its methicilin-resistant S. aureus (MRSA) (37 mg/L). Scanning electron microscopy (SEM) studies showed that these two compounds altered bacterial cell morphology, which might have resulted from disturbed cell division. This study demonstrated a direct link between the antimicrobial activity of tea and its specific polyphenolic compositions. The activity of tea polyphenols, particularly EGCG on antibiotics-resistant strains of S. aureus, suggests that these compounds are potential natural alternatives for the control of bovine mastitis and food poisoning caused by S. aureus.

Oenology: red wine procyanidins and vascular health.

PubMed

Corder, R; Mullen, W; Khan, N Q; Marks, S C; Wood, E G; Carrier, M J; Crozier, A

2006-11-30

Regular, moderate consumption of red wine is linked to a reduced risk of coronary heart disease and to lower overall mortality, but the relative contribution of wine's alcohol and polyphenol components to these effects is unclear. Here we identify procyanidins as the principal vasoactive polyphenols in red wine and show that they are present at higher concentrations in wines from areas of southwestern France and Sardinia, where traditional production methods ensure that these compounds are efficiently extracted during vinification. These regions also happen to be associated with increased longevity in the population.

Multivariate analyses of salt stress and metabolite sensing in auto- and heterotroph Chenopodium cell suspensions.

PubMed

Wongchai, C; Chaidee, A; Pfeiffer, W

2012-01-01

Global warming increases plant salt stress via evaporation after irrigation, but how plant cells sense salt stress remains unknown. Here, we searched for correlation-based targets of salt stress sensing in Chenopodium rubrum cell suspension cultures. We proposed a linkage between the sensing of salt stress and the sensing of distinct metabolites. Consequently, we analysed various extracellular pH signals in autotroph and heterotroph cell suspensions. Our search included signals after 52 treatments: salt and osmotic stress, ion channel inhibitors (amiloride, quinidine), salt-sensing modulators (proline), amino acids, carboxylic acids and regulators (salicylic acid, 2,4-dichlorphenoxyacetic acid). Multivariate analyses revealed hirarchical clusters of signals and five principal components of extracellular proton flux. The principal component correlated with salt stress was an antagonism of γ-aminobutyric and salicylic acid, confirming involvement of acid-sensing ion channels (ASICs) in salt stress sensing. Proline, short non-substituted mono-carboxylic acids (C2-C6), lactic acid and amiloride characterised the four uncorrelated principal components of proton flux. The proline-associated principal component included an antagonism of 2,4-dichlorphenoxyacetic acid and a set of amino acids (hydrophobic, polar, acidic, basic). The five principal components captured 100% of variance of extracellular proton flux. Thus, a bias-free, functional high-throughput screening was established to extract new clusters of response elements and potential signalling pathways, and to serve as a core for quantitative meta-analysis in plant biology. The eigenvectors reorient research, associating proline with development instead of salt stress, and the proof of existence of multiple components of proton flux can help to resolve controversy about the acid growth theory. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.

Data-driven signal-resolving approaches of infrared spectra to explore the macroscopic and microscopic spatial distribution of organic and inorganic compounds in plant.

PubMed

Chen, Jian-bo; Sun, Su-qin; Zhou, Qun

2015-07-01

The nondestructive and label-free infrared (IR) spectroscopy is a direct tool to characterize the spatial distribution of organic and inorganic compounds in plant. Since plant samples are usually complex mixtures, signal-resolving methods are necessary to find the spectral features of compounds of interest in the signal-overlapped IR spectra. In this research, two approaches using existing data-driven signal-resolving methods are proposed to interpret the IR spectra of plant samples. If the number of spectra is small, "tri-step identification" can enhance the spectral resolution to separate and identify the overlapped bands. First, the envelope bands of the original spectrum are interpreted according to the spectra-structure correlations. Then the spectrum is differentiated to resolve the underlying peaks in each envelope band. Finally, two-dimensional correlation spectroscopy is used to enhance the spectral resolution further. For a large number of spectra, "tri-step decomposition" can resolve the spectra by multivariate methods to obtain the structural and semi-quantitative information about the chemical components. Principal component analysis is used first to explore the existing signal types without any prior knowledge. Then the spectra are decomposed by self-modeling curve resolution methods to estimate the spectra and contents of significant chemical components. At last, targeted methods such as partial least squares target can explore the content profiles of specific components sensitively. As an example, the macroscopic and microscopic distribution of eugenol and calcium oxalate in the bud of clove is studied.

[Discrimination of types of polyacrylamide based on near infrared spectroscopy coupled with least square support vector machine].

PubMed

Zhang, Hong-Guang; Yang, Qin-Min; Lu, Jian-Gang

2014-04-01

In this paper, a novel discriminant methodology based on near infrared spectroscopic analysis technique and least square support vector machine was proposed for rapid and nondestructive discrimination of different types of Polyacrylamide. The diffuse reflectance spectra of samples of Non-ionic Polyacrylamide, Anionic Polyacrylamide and Cationic Polyacrylamide were measured. Then principal component analysis method was applied to reduce the dimension of the spectral data and extract of the principal compnents. The first three principal components were used for cluster analysis of the three different types of Polyacrylamide. Then those principal components were also used as inputs of least square support vector machine model. The optimization of the parameters and the number of principal components used as inputs of least square support vector machine model was performed through cross validation based on grid search. 60 samples of each type of Polyacrylamide were collected. Thus a total of 180 samples were obtained. 135 samples, 45 samples for each type of Polyacrylamide, were randomly split into a training set to build calibration model and the rest 45 samples were used as test set to evaluate the performance of the developed model. In addition, 5 Cationic Polyacrylamide samples and 5 Anionic Polyacrylamide samples adulterated with different proportion of Non-ionic Polyacrylamide were also prepared to show the feasibilty of the proposed method to discriminate the adulterated Polyacrylamide samples. The prediction error threshold for each type of Polyacrylamide was determined by F statistical significance test method based on the prediction error of the training set of corresponding type of Polyacrylamide in cross validation. The discrimination accuracy of the built model was 100% for prediction of the test set. The prediction of the model for the 10 mixing samples was also presented, and all mixing samples were accurately discriminated as adulterated samples. The overall results demonstrate that the discrimination method proposed in the present paper can rapidly and nondestructively discriminate the different types of Polyacrylamide and the adulterated Polyacrylamide samples, and offered a new approach to discriminate the types of Polyacrylamide.

The influence of iliotibial band syndrome history on running biomechanics examined via principal components analysis.

PubMed

Foch, Eric; Milner, Clare E

2014-01-03

Iliotibial band syndrome (ITBS) is a common knee overuse injury among female runners. Atypical discrete trunk and lower extremity biomechanics during running may be associated with the etiology of ITBS. Examining discrete data points limits the interpretation of a waveform to a single value. Characterizing entire kinematic and kinetic waveforms may provide additional insight into biomechanical factors associated with ITBS. Therefore, the purpose of this cross-sectional investigation was to determine whether female runners with previous ITBS exhibited differences in kinematics and kinetics compared to controls using a principal components analysis (PCA) approach. Forty participants comprised two groups: previous ITBS and controls. Principal component scores were retained for the first three principal components and were analyzed using independent t-tests. The retained principal components accounted for 93-99% of the total variance within each waveform. Runners with previous ITBS exhibited low principal component one scores for frontal plane hip angle. Principal component one accounted for the overall magnitude in hip adduction which indicated that runners with previous ITBS assumed less hip adduction throughout stance. No differences in the remaining retained principal component scores for the waveforms were detected among groups. A smaller hip adduction angle throughout the stance phase of running may be a compensatory strategy to limit iliotibial band strain. This running strategy may have persisted after ITBS symptoms subsided. © 2013 Published by Elsevier Ltd.

Synthesis, lipophilicity and antimicrobial activity evaluation of some new thiazolyl-oxadiazolines

PubMed Central

STOICA, CRISTINA IOANA; IONUȚ, IOANA; PÎRNĂU, ADRIAN; POP, CARMEN; ROTAR, ANCUȚA; VLASE, LAURIAN; ONIGA, SMARANDA; ONIGA, OVIDIU

2015-01-01

Background and aims Synthesis of new potential antimicrobial agents and evaluation of their lipophilicity. Methods Ten new thiazolyl-oxadiazoline derivatives were synthesized and their structures were validated by 1H-NMR and mass spectrometry. The lipophilicity of the compounds was evaluated using the principal component analysis (PCA) method. The necessary data for applying this method were obtained by reverse-phase thin-layer chromatography (RP-TLC). The antimicrobial activities were tested in vitro against four bacterial strains and one fungal strain. Results The lipophilicity varied with the structure but could not be correlated with the antimicrobial activity, since this was modest. Conclusions We have synthesized ten new heterocyclic compounds. After their physical and chemical characterization, we determined their lipophilicity and screened their antimicrobial activity. PMID:26733751

Simultaneous qualitative and quantitative evaluation of Ilex kudingcha C. J. tseng by using UPLC and UHPLC-qTOF-MS/MS.

PubMed

Zhou, Jie; Yi, Huan; Zhao, Zhong-Xiang; Shang, Xue-Ying; Zhu, Ming-Juan; Kuang, Guo-Jun; Zhu, Chen-Chen; Zhang, Lei

2018-06-05

In this study, a systematic method was established for the holistic quality control of Ilex kudingcha C. J. Tseng, a popular functional drink for adjuvant treatment of diabetes, hypertension, obesity and hyperlipidemia. Both qualitative and quantitative analyses were conducted. For qualitative analysis, an ultra high performance liquid chromatography (UHPLC) coupled with an electrospray ionization quadrupole time-of-flight mass spectrometry (ESI-qTOF-MS) method was established for rapid separation and structural identification of the constituents in Ilex kudingcha. Samples were separated on an ACQUITY UPLC HSS T3C 18 column (2.1 mm × 100 mm, 1.8 μm) by gradient elution using 0.1% (v/v) formic acid (solvent A) and acetonitrile (solvent B) as mobile phases at a flow rate of 0.25 mL min -1 . The chromatographic profiling of Ilex kudingcha by UHPLC-qTOF-MS/MS resulted in the characterization of 53 compounds, comprising 18 compounds that were unambiguously identified by comparison with reference standards. For quantitative analysis, 18 major compounds from 15 batches of Ilex kudingcha samples were simultaneously detected by UPLC-DAD at wavelengths of 210 nm, 260 nm, and 326 nm. The method was validated with respect to precision, linearity, repeatability, stability, accuracy, and so on. The contents of the 18 target compounds were applied for hierarchical clustering analysis (HCA) and principal component analysis (PCA) to differentiate between the samples. The results of HCA and PCA were consistent with each other. Sample No. 1 differed significantly based on HCA and PCA, and the differentiating components were confirmed to originate from different batches of samples. Phenolic acids and triterpenes were found to be the main ingredients in Ilex kudingcha. This strategy was effective and straightforward, and provided a potential approach for holistic quality control of Ilex kudingcha. Copyright © 2018. Published by Elsevier B.V.

Changes in tissue nitrite concentration in the crop of the turkey poult in response to Salmonella typhimurium challenge.

PubMed

Thaxton, J P; Cutler, S A; Griffith, R; Scanes, C G

2006-06-01

The present study examines whether Salmonella typhimurium colonization of the crop of young turkeys influences nitrite concentration in the component tissues of the crop. Nitric oxide (NO) is the principal compound in biological samples that is converted into nitrites and NO is known to be a component of the early host response to infection. Challenge with S. typhimurium increased the concentration of nitrite in the crop wall of 3-wk-old turkey poults. The magnitude of the response was reduced at 8 h and absent at 48 h after challenge. As would be expected, S. typhimurium concentrations in the crop were markedly increased following challenge, and were nondetectable in control poults.

Method of Real-Time Principal-Component Analysis

NASA Technical Reports Server (NTRS)

Duong, Tuan; Duong, Vu

2005-01-01

Dominant-element-based gradient descent and dynamic initial learning rate (DOGEDYN) is a method of sequential principal-component analysis (PCA) that is well suited for such applications as data compression and extraction of features from sets of data. In comparison with a prior method of gradient-descent-based sequential PCA, this method offers a greater rate of learning convergence. Like the prior method, DOGEDYN can be implemented in software. However, the main advantage of DOGEDYN over the prior method lies in the facts that it requires less computation and can be implemented in simpler hardware. It should be possible to implement DOGEDYN in compact, low-power, very-large-scale integrated (VLSI) circuitry that could process data in real time.

Iris recognition based on robust principal component analysis

NASA Astrophysics Data System (ADS)

Karn, Pradeep; He, Xiao Hai; Yang, Shuai; Wu, Xiao Hong

2014-11-01

Iris images acquired under different conditions often suffer from blur, occlusion due to eyelids and eyelashes, specular reflection, and other artifacts. Existing iris recognition systems do not perform well on these types of images. To overcome these problems, we propose an iris recognition method based on robust principal component analysis. The proposed method decomposes all training images into a low-rank matrix and a sparse error matrix, where the low-rank matrix is used for feature extraction. The sparsity concentration index approach is then applied to validate the recognition result. Experimental results using CASIA V4 and IIT Delhi V1iris image databases showed that the proposed method achieved competitive performances in both recognition accuracy and computational efficiency.

Leaf Volatile Compounds and Associated Gene Expression during Short-Term Nitrogen Deficient Treatments in Cucumis Seedlings

PubMed Central

Deng, Jie; Yu, Hong-Jun; Li, Yun-Yun; Zhang, Xiao-Meng; Liu, Peng; Li, Qiang; Jiang, Wei-Jie

2016-01-01

Nitrogen (N) is an important macronutrient for plant growth and development, but the regulatory mechanism of volatile compounds in response to N deficiency is not well understood, especially in cucumber, which consumes excessive N during growth. In this study, the major volatile compounds from cucumber leaves subjected to N deficiency were analyzed by GC-MS. A total of 24 volatile components were identified including 15 aldehydes, two ketones, two alkenes, and five other volatile compounds in 9930 leaves. Principal component analysis using volatile compounds from cucumber leaves provided good separation between N-sufficient and N-deficient treatments. The main volatiles in cucumber leaves were found to be C6 and C9 aldehydes, especially (E)-2-hexanal and (E,Z)-2,6-nonadienal. (E)-2-hexanal belonged to the C6 aldehyde and was the most abundant compound, whereas (E,Z)-2,6-nonadienal was the chief component of C9 aldehydes. During N-deficient treatment, short-chain volatile content was significantly improved at 5 day, other volatiles displayed significant reduction or no significantly changes in all sampling points. Improvement of short-chain volatiles was confirmed in the six other inbred lines at 5 day after N-deficient treatments. The expression analysis of 12 cucumber LOX genes and two HPL genes revealed that CsLOX19, CsLOX20, and CsLOX22 had common up-regulated expression patterns in response to N-deficient stress in most inbred lines; meanwhile, most sample points of CsHPL1 also had significant up-regulated expression patterns. This research focused on the relationship between volatiles in cucumber and different nitrogen environments to provide valuable insight into the effect of cultivation and management of the quality of cucumber and contributes to further research on volatile metabolism in cucumber. PMID:27827841

Microorganisms detection on substrates using QCL spectroscopy

NASA Astrophysics Data System (ADS)

Padilla-Jiménez, Amira C.; Ortiz-Rivera, William; Castro-Suarez, John R.; Ríos-Velázquez, Carlos; Vázquez-Ayala, Iris; Hernández-Rivera, Samuel P.

2013-05-01

Recent investigations have focused on the improvement of rapid and accurate methods to develop spectroscopic markers of compounds constituting microorganisms that are considered biological threats. Quantum cascade lasers (QCL) systems have revolutionized many areas of research and development in defense and security applications, including his area of research. Infrared spectroscopy detection based on QCL was employed to acquire mid infrared (MIR) spectral signatures of Bacillus thuringiensis (Bt), Escherichia coli (Ec) and Staphylococcus epidermidis (Se), which were used as biological agent simulants of biothreats. The experiments were carried out in reflection mode on various substrates such as cardboard, glass, travel baggage, wood and stainless steel. Chemometrics statistical routines such as principal component analysis (PCA) regression and partial least squares-discriminant analysis (PLS-DA) were applied to the recorded MIR spectra. The results show that the infrared vibrational techniques investigated are useful for classification/detection of the target microorganisms on the types of substrates studied.

A quality function deployment framework for the service quality of health information websites.

PubMed

Chang, Hyejung; Kim, Dohoon

2010-03-01

This research was conducted to identify both the users' service requirements on health information websites (HIWs) and the key functional elements for running HIWs. With the quality function deployment framework, the derived service attributes (SAs) are mapped into the suppliers' functional characteristics (FCs) to derive the most critical FCs for the users' satisfaction. Using the survey data from 228 respondents, the SAs, FCs and their relationships were analyzed using various multivariate statistical methods such as principal component factor analysis, discriminant analysis, correlation analysis, etc. Simple and compound FC priorities were derived by matrix calculation. Nine factors of SAs and five key features of FCs were identified, and these served as the basis for the house of quality model. Based on the compound FC priorities, the functional elements pertaining to security and privacy, and usage support should receive top priority in the course of enhancing HIWs. The quality function deployment framework can improve the FCs of the HIWs in an effective, structured manner, and it can also be utilized for critical success factors together with their strategic implications for enhancing the service quality of HIWs. Therefore, website managers could efficiently improve website operations by considering this study's results.

Chemical Profiling of the Essential Oils of Syzygium aqueum, Syzygium samarangense and Eugenia uniflora and Their Discrimination Using Chemometric Analysis.

PubMed

Sobeh, Mansour; Braun, Markus Santhosh; Krstin, Sonja; Youssef, Fadia S; Ashour, Mohamed L; Wink, Michael

2016-11-01

The essential oil compositions of the leaves of three related Myrtaceae species, namely Syzygium aqueum, Syzygium samarangense and Eugenia uniflora, were investigated using GLC/MS and GLC/FID. Altogether, 125 compounds were identified: α-Selinene (13.85%), β-caryophyllene (12.72%) and β-selinene constitute the most abundant constituents in S. aqueum. Germacrene D (21.62%) represents the major compound in S. samarangense whereas in E. uniflora, spathulenol (15.80%) represents the predominant component. Multivariate chemometric analyses were used to discriminate the essential oils using hierarchical cluster analysis (HCA) and principal component analysis (PCA) based on the chromatographic results. The antimicrobial activity of the popularly used E. uniflora essential oil was assessed using broth microdilution method against six Gram-positive, three Gram-negative bacteria and two fungi. The oil showed moderate antimicrobial activity against Bacillus licheniformis exhibiting MIC and MMC of 0.63 mg/ml. The cytotoxic activity of E. uniflora essential oil was investigated against Trypanosoma brucei brucei (T. b. brucei) and MCF-7 cancer cell line using MTT assay. It showed moderate activity against MCF-7 cells with an IC 50 value of 76.40 μg/ml. On the other hand, T. brucei was highly susceptible to E. uniflora essential oil with IC 50 of 11.20 μg/ml, and a selectivity index of 6.82. © 2016 Wiley-VHCA AG, Zurich, Switzerland.

PLANT-BASED REMEDIATION OF MTBE AND OTHER COMPOUNDS IN GASOLINE. (R825549C062)

EPA Science Inventory

The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Concl...

Using Curriculum-Based Measurement to Improve Achievement

ERIC Educational Resources Information Center

Clarke, Suzanne

2009-01-01

Response to intervention (RTI) is on the radar screen of most principals these days--finding out what it is, how it can improve teaching and learning, and what needs to be done to implement it effectively. One critical component of RTI that will require particular attention from principals is student progress monitoring, which is required in every…

Variations in chemical fingerprints and major flavonoid contents from the leaves of thirty‐one accessions of Hibiscus sabdariffa L.

PubMed Central

Wang, Jin; Cao, Xianshuang; Ferchaud, Vanessa; Jiang, Hao; Tang, Feng; Chin, Kit L.

2015-01-01

Abstract The leaves of Hibiscus sabdariffa L. have been used as traditional folk medicines for treating high blood pressure and fever. There are many accessions of H. sabdariffa L. throughout the world. To assess the chemical variations of 31 different accessions of H. sabdariffa L., fingerprinting analysis and quantitation of major flavonoids were performed by high‐performance liquid chromatography (HPLC). The HPLC method was validated for linearity, sensitivity, precision, repeatability and accuracy. A quadrupole‐time‐of‐flight mass spectrometry (Q‐TOF‐MS) was applied for the characterization of major compounds. A total of 9 compounds were identified, including 6 flavonoids and 3 phenolic acids. In the fingerprint analysis, similarity analysis (SA) and principal component analysis (PCA) were used to differentiate the 31 accessions of H. sabdariffa L. Based on the results of PCA and SA, the samples No. 15 and 19 appeared much different from the main group. The total content of five flavonoids varied greatly among different accessions, ranging from 3.35 to 23.30 mg/g. Rutin was found to be the dominant compound and the content of rutin could contribute to chemical variations among different accessions. This study was helpful to understand the chemical variations between different accessions of H. sabdariffa L., which could be used for quality control. © 2015 The Authors Biomedical Chromatography Published by John Wiley & Sons Ltd. PMID:26394363

Chemical indices and methods of multivariate statistics as a tool for odor classification.

PubMed

Mahlke, Ingo T; Thiesen, Peter H; Niemeyer, Bernd

2007-04-01

Industrial and agricultural off-gas streams are comprised of numerous volatile compounds, many of which have substantially different odorous properties. State-of-the-art waste-gas treatment includes the characterization of these molecules and is directed at, if possible, either the avoidance of such odorants during processing or the use of existing standardized air purification techniques like bioscrubbing or afterburning, which however, often show low efficiency under ecological and economical regards. Selective odor separation from the off-gas streams could ease many of these disadvantages but is not yet widely applicable. Thus, the aim of this paper is to identify possible model substances in selective odor separation research from 155 volatile molecules mainly originating from livestock facilities, fat refineries, and cocoa and coffee production by knowledge-based methods. All compounds are examined with regard to their structure and information-content using topological and information-theoretical indices. Resulting data are fitted in an observation matrix, and similarities between the substances are computed. Principal component analysis and k-means cluster analysis are conducted showing that clustering of indices data can depict odor information correlating well to molecular composition and molecular shape. Quantitative molecule describtion along with the application of such statistical means therefore provide a good classification tool of malodorant structure properties with no thermodynamic data needed. The approximate look-alike shape of odorous compounds within the clusters suggests a fair choice of possible model molecules.

Statistical classification of hydrogeologic regions in the fractured rock area of Maryland and parts of the District of Columbia, Virginia, West Virginia, Pennsylvania, and Delaware

USGS Publications Warehouse

Fleming, Brandon J.; LaMotte, Andrew E.; Sekellick, Andrew J.

2013-01-01

Hydrogeologic regions in the fractured rock area of Maryland were classified using geographic information system tools with principal components and cluster analyses. A study area consisting of the 8-digit Hydrologic Unit Code (HUC) watersheds with rivers that flow through the fractured rock area of Maryland and bounded by the Fall Line was further subdivided into 21,431 catchments from the National Hydrography Dataset Plus. The catchments were then used as a common hydrologic unit to compile relevant climatic, topographic, and geologic variables. A principal components analysis was performed on 10 input variables, and 4 principal components that accounted for 83 percent of the variability in the original data were identified. A subsequent cluster analysis grouped the catchments based on four principal component scores into six hydrogeologic regions. Two crystalline rock hydrogeologic regions, including large parts of the Washington, D.C. and Baltimore metropolitan regions that represent over 50 percent of the fractured rock area of Maryland, are distinguished by differences in recharge, Precipitation minus Potential Evapotranspiration, sand content in soils, and groundwater contributions to streams. This classification system will provide a georeferenced digital hydrogeologic framework for future investigations of groundwater availability in the fractured rock area of Maryland.

Application of Hyperspectral Imaging and Chemometric Calibrations for Variety Discrimination of Maize Seeds

PubMed Central

Zhang, Xiaolei; Liu, Fei; He, Yong; Li, Xiaoli

2012-01-01

Hyperspectral imaging in the visible and near infrared (VIS-NIR) region was used to develop a novel method for discriminating different varieties of commodity maize seeds. Firstly, hyperspectral images of 330 samples of six varieties of maize seeds were acquired using a hyperspectral imaging system in the 380–1,030 nm wavelength range. Secondly, principal component analysis (PCA) and kernel principal component analysis (KPCA) were used to explore the internal structure of the spectral data. Thirdly, three optimal wavelengths (523, 579 and 863 nm) were selected by implementing PCA directly on each image. Then four textural variables including contrast, homogeneity, energy and correlation were extracted from gray level co-occurrence matrix (GLCM) of each monochromatic image based on the optimal wavelengths. Finally, several models for maize seeds identification were established by least squares-support vector machine (LS-SVM) and back propagation neural network (BPNN) using four different combinations of principal components (PCs), kernel principal components (KPCs) and textural features as input variables, respectively. The recognition accuracy achieved in the PCA-GLCM-LS-SVM model (98.89%) was the most satisfactory one. We conclude that hyperspectral imaging combined with texture analysis can be implemented for fast classification of different varieties of maize seeds. PMID:23235456

Spectral discrimination of bleached and healthy submerged corals based on principal components analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Holden, H.; LeDrew, E.

1997-06-01

Remote discrimination of substrate types in relatively shallow coastal waters has been limited by the spatial and spectral resolution of available sensors. An additional limiting factor is the strong attenuating influence of the water column over the substrate. As a result, there have been limited attempts to map submerged ecosystems such as coral reefs based on spectral characteristics. Both healthy and bleached corals were measured at depth with a hand-held spectroradiometer, and their spectra compared. Two separate principal components analyses (PCA) were performed on two sets of spectral data. The PCA revealed that there is indeed a spectral difference basedmore » on health. In the first data set, the first component (healthy coral) explains 46.82%, while the second component (bleached coral) explains 46.35% of the variance. In the second data set, the first component (bleached coral) explained 46.99%; the second component (healthy coral) explained 36.55%; and the third component (healthy coral) explained 15.44 % of the total variance in the original data. These results are encouraging with respect to using an airborne spectroradiometer to identify areas of bleached corals thus enabling accurate monitoring over time.« less

Quality Evaluation and Chemical Markers Screening of Salvia miltiorrhiza Bge. (Danshen) Based on HPLC Fingerprints and HPLC-MSn Coupled with Chemometrics.

PubMed

Liang, Wenyi; Chen, Wenjing; Wu, Lingfang; Li, Shi; Qi, Qi; Cui, Yaping; Liang, Linjin; Ye, Ting; Zhang, Lanzhen

2017-03-17

Danshen, the dried root of Salvia miltiorrhiza Bge., is a widely used commercially available herbal drug, and unstable quality of different samples is a current issue. This study focused on a comprehensive and systematic method combining fingerprints and chemical identification with chemometrics for discrimination and quality assessment of Danshen samples. Twenty-five samples were analyzed by HPLC-PAD and HPLC-MS n . Forty-nine components were identified and characteristic fragmentation regularities were summarized for further interpretation of bioactive components. Chemometric analysis was employed to differentiate samples and clarify the quality differences of Danshen including hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis. Consistent results were that the samples were divided into three categories which reflected the difference in quality of Danshen samples. By analyzing the reasons for sample classification, it was revealed that the processing method had a more obvious impact on sample classification than the geographical origin, it induced the different content of bioactive compounds and finally lead to different qualities. Cryptotanshinone, trijuganone B, and 15,16-dihydrotanshinone I were screened out as markers to distinguish samples by different processing methods. The developed strategy could provide a reference for evaluation and discrimination of other traditional herbal medicines.

Supercritical Fluid Chromatography of Drugs: Parallel Factor Analysis for Column Testing in a Wide Range of Operational Conditions

PubMed Central

Al-Degs, Yahya; Andri, Bertyl; Thiébaut, Didier; Vial, Jérôme

2017-01-01

Retention mechanisms involved in supercritical fluid chromatography (SFC) are influenced by interdependent parameters (temperature, pressure, chemistry of the mobile phase, and nature of the stationary phase), a complexity which makes the selection of a proper stationary phase for a given separation a challenging step. For the first time in SFC studies, Parallel Factor Analysis (PARAFAC) was employed to evaluate the chromatographic behavior of eight different stationary phases in a wide range of chromatographic conditions (temperature, pressure, and gradient elution composition). Design of Experiment was used to optimize experiments involving 14 pharmaceutical compounds present in biological and/or environmental samples and with dissimilar physicochemical properties. The results showed the superiority of PARAFAC for the analysis of the three-way (column × drug × condition) data array over unfolding the multiway array to matrices and performing several classical principal component analyses. Thanks to the PARAFAC components, similarity in columns' function, chromatographic trend of drugs, and correlation between separation conditions could be simply depicted: columns were grouped according to their H-bonding forces, while gradient composition was dominating for condition classification. Also, the number of drugs could be efficiently reduced for columns classification as some of them exhibited a similar behavior, as shown by hierarchical clustering based on PARAFAC components. PMID:28695040

Methods of Implementation of Evidence-Based Stroke Care in Europe: European Implementation Score Collaboration.

PubMed

Di Carlo, Antonio; Pezzella, Francesca Romana; Fraser, Alec; Bovis, Francesca; Baeza, Juan; McKevitt, Chris; Boaz, Annette; Heuschmann, Peter; Wolfe, Charles D A; Inzitari, Domenico

2015-08-01

Differences in stroke care and outcomes reported in Europe may reflect different degrees of implementation of evidence-based interventions. We evaluated strategies for implementing research evidence into stroke care in 10 European countries. A questionnaire was developed and administered through face-to-face interviews with key informants. Implementation strategies were investigated considering 3 levels (macro, meso, and micro, eg, policy, organization, patients/professionals) identified by the framing analysis, and different settings (primary, hospital, and specialist) of stroke care. Similarities and differences among countries were evaluated using the categorical principal components analysis. Implementation methods reported by ≥7 countries included nonmandatory policies, public financial incentives, continuing professional education, distribution of educational material, educational meetings and campaigns, guidelines, opinion leaders', and stroke patients associations' activities. Audits were present in 6 countries at national level; national and regional regulations in 4 countries. Private financial incentives, reminders, and educational outreach visits were reported only in 2 countries. At national level, the first principal component of categorical principal components analysis separated England, France, Scotland, and Sweden, all with positive object scores, from the other countries. Belgium and Lithuania obtained the lowest scores. At regional level, England, France, Germany, Italy, and Sweden had positive scores in the first principal component, whereas Belgium, Lithuania, Poland, and Scotland showed negative scores. Spain was in an intermediate position. We developed a novel method to assess different domains of implementation in stroke care. Clear variations were observed among European countries. The new tool may be used elsewhere for future contributions. © 2015 American Heart Association, Inc.

Characterization of Type Ia Supernova Light Curves Using Principal Component Analysis of Sparse Functional Data

NASA Astrophysics Data System (ADS)

He, Shiyuan; Wang, Lifan; Huang, Jianhua Z.

2018-04-01

With growing data from ongoing and future supernova surveys, it is possible to empirically quantify the shapes of SNIa light curves in more detail, and to quantitatively relate the shape parameters with the intrinsic properties of SNIa. Building such relationships is critical in controlling systematic errors associated with supernova cosmology. Based on a collection of well-observed SNIa samples accumulated in the past years, we construct an empirical SNIa light curve model using a statistical method called the functional principal component analysis (FPCA) for sparse and irregularly sampled functional data. Using this method, the entire light curve of an SNIa is represented by a linear combination of principal component functions, and the SNIa is represented by a few numbers called “principal component scores.” These scores are used to establish relations between light curve shapes and physical quantities such as intrinsic color, interstellar dust reddening, spectral line strength, and spectral classes. These relations allow for descriptions of some critical physical quantities based purely on light curve shape parameters. Our study shows that some important spectral feature information is being encoded in the broad band light curves; for instance, we find that the light curve shapes are correlated with the velocity and velocity gradient of the Si II λ6355 line. This is important for supernova surveys (e.g., LSST and WFIRST). Moreover, the FPCA light curve model is used to construct the entire light curve shape, which in turn is used in a functional linear form to adjust intrinsic luminosity when fitting distance models.

Bearing monitoring

NASA Astrophysics Data System (ADS)

Xu, Roger; Stevenson, Mark W.; Kwan, Chi-Man; Haynes, Leonard S.

2001-07-01

At Ford Motor Company, thrust bearing in drill motors is often damaged by metal chips. Since the vibration frequency is several Hz only, it is very difficult to use accelerometers to pick up the vibration signals. Under the support of Ford and NASA, we propose to use a piezo film as a sensor to pick up the slow vibrations of the bearing. Then a neural net based fault detection algorithm is applied to differentiate normal bearing from bad bearing. The first step involves a Fast Fourier Transform which essentially extracts the significant frequency components in the sensor. Then Principal Component Analysis is used to further reduce the dimension of the frequency components by extracting the principal features inside the frequency components. The features can then be used to indicate the status of bearing. Experimental results are very encouraging.

Squalenes, phytanes and other isoprenoids as major neutral lipids of methanogenic and thermoacidophilic 'archaebacteria'

NASA Technical Reports Server (NTRS)

Tornabene, T. G.; Langworthy, T. A.; Holzer, G.; Oro, J.

1979-01-01

The neutral lipids from nine species of methanogenic bacteria (five methanobacilli, two methanococci, a methanospirillum and a methanosarcina) and two thermoacidophilic bacteria (Thermo-plasma and Sulfolobus) have been analyzed. The neutral lipids were found to comprise a wide range (C14 to C30) of polyisoprenyl hydrocarbons with varying degrees of saturation. The principal components represented the three major isoprenoid series (C20 phytanyl, C25 pentaisoprenyl, and C30 squalenyl), in contrast with the neutral lipids of extreme halophiles, which consist predominantly of C2O (phytanyl, geranylgeraniol), C30 (squalenes), C40 (carotenes) and C50 (bacterioruberins compounds), as reported by Kates (1978). These results, which indicate strong general similarities between genetically diverse organisms, support the classification of these organisms in a separate phylogenetic group. The occurrence of similar isoprenoid compounds in petroleum and ancient sediments and the fact that the methanogens, halophiles and thermoacidophiles live in conditions presumed to have prevailed in archaen times suggest that the isoprenoid compounds in petroleum compounds and sediment may have been directly synthesized by organisms of this type

First Approach to the Analytical Characterization of
Barrel-Aged Grape Marc Distillates Using Phenolic Compounds and Colour Parameters

PubMed Central

Rodríguez-Solana, Raquel; Salgado, José Manuel; Domínguez, José Manuel

2014-01-01

Summary Phenolic compounds (benzoic and cinnamic acid derivatives) were determined by high-performance liquid chromatography with multiple wavelength detector (HPLC- -MWD) in grape marc distillates aged in Quercus petraea, Quercus robur and Quercus alba wooden barrels. In addition to colour indices and evaluable polyphenols, all samples were described by sensorial analysis. There were significant differences in the mean concentrations of the majority of phenolic compounds among the samples. Gallic and benzoic acids were the most abundant and samples aged in Q. robur from Galicia (NW of Spain) had the highest concentration of most of the determined phenols. Grape marc distillates aged in Q. robur obtained the highest values of all sensorial attributes, whereas samples aged in Q. petraea and Q. alba obtained similar scores. Principal component analysis accounted for 88.32% of total variance, showing a good separation of aged distillates in terms of phenolic compounds and colour characteristics, according to the species and origin of the oak wood used in the ageing process. PMID:27904312

Discrimination of Schisandrae Chinensis Fructus and Schisandrae Sphenantherae Fructus based on fingerprint profiles of hydrophilic components by high-performance liquid chromatography with ultraviolet detection.

PubMed

Oshima, Ryusei; Kotani, Akira; Kuroda, Minpei; Yamamoto, Kazuhiro; Mimaki, Yoshihiro; Hakamata, Hideki

2018-03-01

High-performance liquid chromatography with ultraviolet detection (HPLC-UV) using 20 mM phosphate mobile phase and an octadecylsilyl column (Triart C18, 150 × 3.0 mm i.d., 3 μm) has been developed for the analysis of hydrophilic compounds in the water extract of Schisandrae Fructus samples. The present HPLC-UV method permits the accurate and precise determination of malic, citric, and protocatechuic acids in the Japanese Pharmacopoeia (JP) Schisandrae Fructus, Schisandrae Chinensis Fructus and Schisandrae Sphenantherae Fructus. The JP Schisandrae Fructus studied contains 27.98 mg/g malic, 107.08 mg/g citric, and 0.42 mg/g protocatechuic acids, with a relative standard deviation (RSD) of repeatability of <0.9% (n = 6). The content of malic acids in Schisandrae Chinensis Fructus is approximately ten times that in Schisandrae Sphenantherae Fructus. To examine whether the HPLC-UV method is applicable to the fingerprint-based discrimination of Schisandrae Fructus samples obtained from Chinese markets, principal component analysis (PCA) was performed using the determined contents of organic acids and the ratio of six characteristic unknown peaks derived from hydrophilic components to internal standard peak areas. On the score plots, Schisandrae Chinensis Fructus and Schisandrae Sphenantherae Fructus samples are clearly discriminated. Therefore, the HPLC-UV method for the analysis of hydrophilic components coupled with PCA has been shown to be practical and useful in the quality control of Schisandrae Fructus.

Multivariate analysis of FTIR and ion chromatographic data for the quality control of tequila.

PubMed

Lachenmeier, Dirk W; Richling, Elke; López, Mercedes G; Frank, Willi; Schreier, Peter

2005-03-23

Principal component analysis (PCA) was applied to the chromatographic and spectroscopic data of authentic Mexican tequilas (n = 14) and commercially available samples purchased in Mexico and Germany (n = 24). The scores scatter plot of the first two principal components (PC) of the anions chloride, nitrate, sulfate, acetate, and oxalate accounting for 78% of the variability allowed a classification between tequilas bottled in Mexico and overseas; however, no discrimination between tequila categories was possible. Mexican products had a significantly (p = 0.0014) lower inorganic anion concentration (range = 1.5-5.1 mg/L; mean = 2.5 mg/L) than the products bottled in the importing countries (range = 3.3-62.6 mg/L; mean = 26.3 mg/L). FTIR allowed a rapid screening of density and ethanol as well as the volatile compounds methanol, ethyl acetate, propanol-1, isobutanol, and 2-/3-methyl-1-butanol using partial least-squares regression (precisions = 5.3-29.3%). Using PCA of the volatile compounds, a differentiation between tequila derived from "100% agave" (Agave tequilana Weber var. azul, Agavaceae) and tequila produced with other fermentable sugars ("mixed"tequila) was possible. The first two PCs describe 89% of the total variability of the data. Methanol and isobutanol influenced the variability in PC1, which led to discrimination. The concentrations of methanol and isobutanol were significantly higher (methanol, p = 0.004; isobutanol, p = 0.005) in the 100% agave (methanol, 297.9 +/- 49.5; isobutanol, 251.3 +/- 34.9) than in the mixed tequilas (methanol, 197.8 +/- 118.5; isobutanol, 151.4 +/- 52.8).

Substantial equivalence analysis in fruits from three Theobroma species through chemical composition and protein profiling.

PubMed

Pérez-Mora, Walter; Jorrin-Novo, Jesús V; Melgarejo, Luz Marina

2018-02-01

Substantial equivalence studies were performed in three Theobroma spp., cacao, bicolor and grandiflorum through chemical composition analysis and protein profiling of fruit (pulp juice and seeds). Principal component analysis of sugar, organic acid, and phenol content in pulp juice revealed equivalence among the three species, with differences in some of the compounds that may result in different organoleptic properties. Proteins were extracted from seeds and pulp juice, resolved by two dimensional electrophoresis and major spots subjected to mass spectrometry analysis and identification. The protein profile, as revealed by principal component analysis, was variable among the three species in both seed and pulp, with qualitative and quantitative differences in some of protein species. The functional grouping of the identified proteins correlated with the biological role of each organ. Some of the identified proteins are of interest, being minimally discussed, including vicilin, a protease inhibitor, and a flavonol synthase/flavanone 3-hydroxylase. Theobroma grandiflorum and Theobroma bicolor are endemic Amazonian plants that are poorly traded at the local level. As close relatives of Theobroma cacao, they may provide a good alternative for human consumption and industrial purposes. In this regard, we performed equivalence studies by conducting a comparative biochemical and proteomics analysis of the fruit, pulp juice and seeds of these three species. The results indicated equivalent chemical compositions and variable protein profiles with some differences in the content of the specific compounds or protein species that may result in variable organoleptic properties between the species and can be exploited for traceability purposes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Application of principal component analysis to distinguish patients with schizophrenia from healthy controls based on fractional anisotropy measurements.

PubMed

Caprihan, A; Pearlson, G D; Calhoun, V D

2008-08-15

Principal component analysis (PCA) is often used to reduce the dimension of data before applying more sophisticated data analysis methods such as non-linear classification algorithms or independent component analysis. This practice is based on selecting components corresponding to the largest eigenvalues. If the ultimate goal is separation of data in two groups, then these set of components need not have the most discriminatory power. We measured the distance between two such populations using Mahalanobis distance and chose the eigenvectors to maximize it, a modified PCA method, which we call the discriminant PCA (DPCA). DPCA was applied to diffusion tensor-based fractional anisotropy images to distinguish age-matched schizophrenia subjects from healthy controls. The performance of the proposed method was evaluated by the one-leave-out method. We show that for this fractional anisotropy data set, the classification error with 60 components was close to the minimum error and that the Mahalanobis distance was twice as large with DPCA, than with PCA. Finally, by masking the discriminant function with the white matter tracts of the Johns Hopkins University atlas, we identified left superior longitudinal fasciculus as the tract which gave the least classification error. In addition, with six optimally chosen tracts the classification error was zero.

Support vector machine based classification of fast Fourier transform spectroscopy of proteins

NASA Astrophysics Data System (ADS)

Lazarevic, Aleksandar; Pokrajac, Dragoljub; Marcano, Aristides; Melikechi, Noureddine

2009-02-01

Fast Fourier transform spectroscopy has proved to be a powerful method for study of the secondary structure of proteins since peak positions and their relative amplitude are affected by the number of hydrogen bridges that sustain this secondary structure. However, to our best knowledge, the method has not been used yet for identification of proteins within a complex matrix like a blood sample. The principal reason is the apparent similarity of protein infrared spectra with actual differences usually masked by the solvent contribution and other interactions. In this paper, we propose a novel machine learning based method that uses protein spectra for classification and identification of such proteins within a given sample. The proposed method uses principal component analysis (PCA) to identify most important linear combinations of original spectral components and then employs support vector machine (SVM) classification model applied on such identified combinations to categorize proteins into one of given groups. Our experiments have been performed on the set of four different proteins, namely: Bovine Serum Albumin, Leptin, Insulin-like Growth Factor 2 and Osteopontin. Our proposed method of applying principal component analysis along with support vector machines exhibits excellent classification accuracy when identifying proteins using their infrared spectra.

Chemical composition and sensory profile of pomelo (Citrus grandis (L.) Osbeck) juice.

PubMed

Cheong, Mun Wai; Liu, Shao Quan; Zhou, Weibiao; Curran, Philip; Yu, Bin

2012-12-15

Two cultivars (Citrus grandis (L.) Osbeck PO 51 and PO 52) of Malaysian pomelo juices were studied by examining their physicochemical properties (i.e. pH, °Brix and titratable acidity), volatile and non-volatile components (sugars and organic acids). Using solvent extraction and headspace solid-phase microextraction, 49 and 65 volatile compounds were identified by gas chromatography-mass spectrometer/flame ionisation detector, respectively. Compared to pink pomelo juice (cultivar PO 52), white pomelo juice (cultivar PO 51) contained lower amount of total volatiles but higher terpenoids. Descriptive sensory evaluation indicated that white pomelo juice was milder in taste especially acidity. Furthermore, principal component analysis and partial least square regression revealed a strong correlation in pomelo juices between their chemical components and some flavour attributes (i.e. acidic, fresh, peely and sweet). Hence, this research enabled a deeper insight into the flavour of this unique citrus fruit. Copyright © 2012 Elsevier Ltd. All rights reserved.

Characterization of volatile profile from ten different varieties of Chinese jujubes by HS-SPME/GC-MS coupled with E-nose.

PubMed

Chen, Qinqin; Song, Jianxin; Bi, Jinfeng; Meng, Xianjun; Wu, Xinye

2018-03-01

Volatile profile of ten different varieties of fresh jujubes was characterized by HS-SPME/GC-MS (headspace solid phase micro-extraction combined with gas chromatography-mass spectrometry) and E-nose (electronic nose). GC-MS results showed that a total of 51 aroma compounds were identified in jujubes, hexanoic acid, hexanal, (E)-2-hexenal, (Z)-2-heptenal, benzaldehyde and (E)-2-nonenal were the main aroma components with contributions that over 70%. Differentiation of jujube varieties was conducted by cluster analysis of GC-MS data and principal component analysis & linear discriminant analysis of E-nose data. Both results showed that jujubes could be mainly divided into two groups: group A (JZ, PDDZ, JSXZ and LWZZ) and group B (BZ, YZ, MZ, XZ and DZ). There were significant differences in contents of alcohols, acids and aromatic compounds between group A and B. GC-MS coupled with E-nose could be a fast and accurate method to identify the general flavor difference in different varieties of jujubes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Evaluation of 107 legumes for renewable source of energy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Roth, W.B.; Carr, M.E.; Cull, I.M.

One hundred and seven species of randomly-collected Leguminosae were evaluated for their potential as energy-producing crops. Whole plants, excluding roots, were chemically analyzed, and 11 species were identified as the more promising for future considerations based on a numerical rating system developed at this Center. Botanical, fiber, and protein characteristics of the more promising species that had rating of less than 11 were considered excellent. Other characteristics, including contents of oil (1.7-3.2%; dry, ash-free, sample basis), polyphenol (5.4-16.5%), and hydrocarbon (0.3-0.6% for 10 species and 2.6% for one), were generally lower than those of promising species in other families previouslymore » analyzed. Of the 11 species, one contained principally rubber (polyisoprene) in the hydrocarbon fraction and 7 contained principally wax. Hydrocarbon fractions of 3 species with less than 0.4% were not examined. The oils of species with at least 3.0% oil were examined by thin layer chromatography (TLC) to determine classes of components and were given a saponification treatment to determine yields of unsaponifiable matter and fatty acids. The oil of one species was quantitatively analyzed for classes of compounds by TLC-flame ionization detection. Selected species with ratings greater than 10 are briefly discussed.« less

Nonlinear Principal Components Analysis: Introduction and Application

ERIC Educational Resources Information Center

Linting, Marielle; Meulman, Jacqueline J.; Groenen, Patrick J. F.; van der Koojj, Anita J.

2007-01-01

The authors provide a didactic treatment of nonlinear (categorical) principal components analysis (PCA). This method is the nonlinear equivalent of standard PCA and reduces the observed variables to a number of uncorrelated principal components. The most important advantages of nonlinear over linear PCA are that it incorporates nominal and ordinal…

Selective principal component regression analysis of fluorescence hyperspectral image to assess aflatoxin contamination in corn

USDA-ARS?s Scientific Manuscript database

Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression co...

Similarities between principal components of protein dynamics and random diffusion

NASA Astrophysics Data System (ADS)

Hess, Berk

2000-12-01

Principal component analysis, also called essential dynamics, is a powerful tool for finding global, correlated motions in atomic simulations of macromolecules. It has become an established technique for analyzing molecular dynamics simulations of proteins. The first few principal components of simulations of large proteins often resemble cosines. We derive the principal components for high-dimensional random diffusion, which are almost perfect cosines. This resemblance between protein simulations and noise implies that for many proteins the time scales of current simulations are too short to obtain convergence of collective motions.

Minimum number of measurements for evaluating Bertholletia excelsa.

PubMed

Baldoni, A B; Tonini, H; Tardin, F D; Botelho, S C C; Teodoro, P E

2017-09-27

Repeatability studies on fruit species are of great importance to identify the minimum number of measurements necessary to accurately select superior genotypes. This study aimed to identify the most efficient method to estimate the repeatability coefficient (r) and predict the minimum number of measurements needed for a more accurate evaluation of Brazil nut tree (Bertholletia excelsa) genotypes based on fruit yield. For this, we assessed the number of fruits and dry mass of seeds of 75 Brazil nut genotypes, from native forest, located in the municipality of Itaúba, MT, for 5 years. To better estimate r, four procedures were used: analysis of variance (ANOVA), principal component analysis based on the correlation matrix (CPCOR), principal component analysis based on the phenotypic variance and covariance matrix (CPCOV), and structural analysis based on the correlation matrix (mean r - AECOR). There was a significant effect of genotypes and measurements, which reveals the need to study the minimum number of measurements for selecting superior Brazil nut genotypes for a production increase. Estimates of r by ANOVA were lower than those observed with the principal component methodology and close to AECOR. The CPCOV methodology provided the highest estimate of r, which resulted in a lower number of measurements needed to identify superior Brazil nut genotypes for the number of fruits and dry mass of seeds. Based on this methodology, three measurements are necessary to predict the true value of the Brazil nut genotypes with a minimum accuracy of 85%.

Early forest fire detection using principal component analysis of infrared video

NASA Astrophysics Data System (ADS)

Saghri, John A.; Radjabi, Ryan; Jacobs, John T.

2011-09-01

A land-based early forest fire detection scheme which exploits the infrared (IR) temporal signature of fire plume is described. Unlike common land-based and/or satellite-based techniques which rely on measurement and discrimination of fire plume directly from its infrared and/or visible reflectance imagery, this scheme is based on exploitation of fire plume temporal signature, i.e., temperature fluctuations over the observation period. The method is simple and relatively inexpensive to implement. The false alarm rate is expected to be lower that of the existing methods. Land-based infrared (IR) cameras are installed in a step-stare-mode configuration in potential fire-prone areas. The sequence of IR video frames from each camera is digitally processed to determine if there is a fire within camera's field of view (FOV). The process involves applying a principal component transformation (PCT) to each nonoverlapping sequence of video frames from the camera to produce a corresponding sequence of temporally-uncorrelated principal component (PC) images. Since pixels that form a fire plume exhibit statistically similar temporal variation (i.e., have a unique temporal signature), PCT conveniently renders the footprint/trace of the fire plume in low-order PC images. The PC image which best reveals the trace of the fire plume is then selected and spatially filtered via simple threshold and median filter operations to remove the background clutter, such as traces of moving tree branches due to wind.

Financing Continuing Education in Mental Health.

ERIC Educational Resources Information Center

Southern Regional Education Board, Atlanta, GA.

Based on a study of the component parts of the mental health continuing education system, this publication presents guidelines for the following fiscal functions: determining funding needs, obtaining funds, budgeting funds, expending funds, and cost accounting. In addition to considering these components, the guidelines explore principal issues in…

Reconstruction of organochlorine compound inputs in the Tagus Prodelta.

PubMed

Mil-Homens, Mário; Vicente, Maria; Grimalt, Joan O; Micaelo, Cristina; Abrantes, Fátima

2016-01-01

Twenty century time-resolved variability of riverine deposits of polychlorobiphenyls (PCBs), DDTs, hexachlorocyclohexanes (HCHs) and hexachlorobenzene (HCB) was studied in three (210)Pb dated sediment cores collected in a depositional shelf area adjacent to the Tagus estuary (the Tagus Prodelta). The geographic and temporal distribution patterns were consistent with discharge of these organochlorine compounds (OCs) in the area associated with the Tagus mouth. Their concentrations were not correlated with the sedimentary total organic carbon. The PCB down-core profiles were dominated by CB138 and CB153 (hexa-CBs) congeners followed by CB180 (hepta-CBs). Principal Component Analysis of the congener distributions of these compounds did not define temporal down-core trends. The ratios of DDT metabolites (p,p'-DDE/p,p'-DDT) were consistent with recent DDT inputs into the environment and/or earlier applications and long-term residence in soils/sediments until these were eroded and remobilized. Copyright © 2015 Elsevier B.V. All rights reserved.

New PLS analysis approach to wine volatile compounds characterization by near infrared spectroscopy (NIR).

PubMed

Genisheva, Z; Quintelas, C; Mesquita, D P; Ferreira, E C; Oliveira, J M; Amaral, A L

2018-04-25

This work aims to explore the potential of near infrared (NIR) spectroscopy to quantify volatile compounds in Vinho Verde wines, commonly determined by gas chromatography. For this purpose, 105 Vinho Verde wine samples were analyzed using Fourier transform near infrared (FT-NIR) transmission spectroscopy in the range of 5435 cm -1 to 6357 cm -1 . Boxplot and principal components analysis (PCA) were performed for clusters identification and outliers removal. A partial least square (PLS) regression was then applied to develop the calibration models, by a new iterative approach. The predictive ability of the models was confirmed by an external validation procedure with an independent sample set. The obtained results could be considered as quite good with coefficients of determination (R 2 ) varying from 0.94 to 0.97. The current methodology, using NIR spectroscopy and chemometrics, can be seen as a promising rapid tool to determine volatile compounds in Vinho Verde wines. Copyright © 2017 Elsevier Ltd. All rights reserved.

The Impact of Hybridization on the Volatile and Sensorial Profile of Ocimum basilicum L.

PubMed Central

da Costa, Andréa Santos; Arrigoni-Blank, Maria de Fátima; da Silva, Maria Aparecida Azevedo Pereira; Alves, Mércia Freitas; Santos, Darlisson de Alexandria; Alves, Péricles Barreto; Blank, Arie Fitzgerald

2014-01-01

The aim of the present study was to investigate the volatile and sensorial profile of basil (Ocimum basilicum L.) by quantitative descriptive analysis (QDA) of the essential oil of three hybrids (“Cinnamon” × “Maria Bonita,” “Sweet Dani” × “Cinnamon,” and “Sweet Dani” × “Maria Bonita”). Twelve descriptive terms were developed by a selected panel that also generated the definition of each term and the reference samples. The data were subjected to ANOVA, Tukey's test, and principal component analysis. The hybrid “Cinnamon” × “Maria Bonita” exhibited a stronger global aroma that was less citric than the other samples. Hybridization favored the generation of novel compounds in the essential oil of the hybrid “Sweet Dani” × “Maria Bonita,” such as canfora and (E)-caryophyllene; (E)-caryophyllene also was a novel compound in the hybrid “Sweet Dani” × “Cinnamon”; this compound was not present in the essential oils of the parents. PMID:24558334

GCMS investigation of volatile compounds in green coffee affected by potato taste defect and the Antestia bug.

PubMed

Jackels, Susan C; Marshall, Eric E; Omaiye, Angelica G; Gianan, Robert L; Lee, Fabrice T; Jackels, Charles F

2014-10-22

Potato taste defect (PTD) is a flavor defect in East African coffee associated with Antestiopsis orbitalis feeding and 3-isopropyl-2-methoxypyrazine (IPMP) in the coffee. To elucidate the manifestation of PTD, surface and interior volatile compounds of PTD and non-PTD green coffees were sampled by headspace solid phase microextraction and analyzed by gas chromatography mass spectrometry. Principal component analysis of the chromatographic data revealed a profile of surface volatiles distinguishing PTD from non-PTD coffees dominated by tridecane, dodecane, and tetradecane. While not detected in surface volatiles, IPMP was found in interior volatiles of PTD coffee. Desiccated antestia bugs were analyzed by GCMS, revealing that the three most prevalent volatiles were tridecane, dodecane, and tetradecane, as was found in the surface profile PTD coffee. Coffee having visible insect damage exhibited both a PTD surface volatile profile and IPMP in interior volatiles, supporting the hypothesis linking antestia bug feeding activity with PTD profile compounds on the surface and IPMP in the interior of the beans.

Liquid chromatographic/electrospray ionization tandem mass spectrometric study of polyphenolic composition of four cultivars of Fragaria vesca L. berries and their comparative evaluation.

PubMed

Del Bubba, Massimo; Checchini, Leonardo; Chiuminatto, Ugo; Doumett, Saer; Fibbi, Donatella; Giordani, Edgardo

2012-09-01

High-performance liquid chromatography coupled with ion spray mass spectrometry in the tandem mode with both negative and positive ionization was used for investigating a variety of polyphenolic compounds in four genotypes of Fragaria vesca berries. About 60 phenolic compounds belonging to the compound classes of phenolic acids, ellagitannins, ellagic acid derivatives, flavonols, monomeric and oligomeric flavanols, dihydrochalcones and anthocyanins were reported, providing for the first time a quite complete picture of polyphenolic composition of F. vesca berries. Some of the polyphenols herein investigated, such as a tris-galloyl-hexahydroxydiphenoyl-hexose, two castalagin/vescalagin-like isomers and peonidin-malonylglucoside, were described for the first time. Principal component analysis applied on original HPLC-MS/MS data, acquired in multiple reaction monitoring mode, successfully discriminated the four investigated cultivars on the basis of their polyphenolic composition, highlighting the fundamental role of mass spectrometry for food characterization. Copyright © 2012 John Wiley & Sons, Ltd.

Enzymatic hybridization of α-lipoic acid with bioactive compounds in ionic solvents.

PubMed

Papadopoulou, Athena A; Katsoura, Maria H; Chatzikonstantinou, Alexandra; Kyriakou, Eleni; Polydera, Angeliki C; Tzakos, Andreas G; Stamatis, Haralambos

2013-05-01

The lipase-catalyzed molecular hybridization of α-lipoic acid (LA) with bioactive compounds pyridoxine, tyrosol and tyramine was performed in ionic solvents and deep eutectic solvents. The biocatalytic reactions were catalyzed by Candida antarctica lipase B immobilized onto various functionalized multi-walled carbon nanotubes (f-CNTs-CaLB), as well as by commercial Novozym 435. The use of f-CNTs-CaLB leads, in most cases, to higher conversion yields as compared to Novozym 435. The nature and ion composition of ionic solvents affect the performance of the biocatalytic process. The highest conversion yield was observed in (mtoa)NTf2. The high enzyme stability and the relatively low solubility of substrates in specific media account for the improved biocatalytic synthesis of molecular hybrids of LA. Principal component analysis was used to screen for potential lipoxygenase inhibitors. In vitro studies showed that the synthesized compounds exhibit up to 10-fold increased inhibitory activity on lipoxygenase mediated lipid peroxidation as compared to parent molecules. Copyright © 2013 Elsevier Ltd. All rights reserved.

Profiling of Phenolic Compounds and Antioxidant Activity of 12 Cruciferous Vegetables.

PubMed

Li, Zhifeng; Lee, Hui Wen; Liang, Xu; Liang, Dong; Wang, Qi; Huang, Dejian; Ong, Choon Nam

2018-05-10

The phenolic profiles of 12 cruciferous vegetables (pakchoi, choysum, Chinese cabbage, kailan, Brussels sprout, cabbage, cauliflower, broccoli, rocket salad, red cherry radish, daikon radish, and watercress) were studied with UHPLC-MS/MS. Antioxidant activity and total phenolic content (TPC) were also evaluated. A total of 74 phenolic compounds were identified, including 16 hydroxycinnamic acids and derivatives, and 58 flavonoids and derivatives. The main flavonoids identified were glycosylated quercetin, kaempferol and isorhamnetin, and the main hydroxycinnamic acids were ferulic, sinapic, caffeic and p -coumaric acids. Principal component analysis (PCA) revealed that the distribution of phenolic compounds in different genera of cruciferous vegetables was in accordance with their conventional taxonomy. The DPPH, ORAC and TPC values ranged from 1.11 to 9.54 µmoles Trolox equivalent/g FW, 5.34 to 32.92 µmoles Trolox equivalent/g FW, and 0.16 to 1.93 mg gallic acid equivalent/g FW respectively. Spearman’s correlation showed significant ( p < 0.05) positive correlations between TPC, flavonoids and antioxidant activity.

Fault Identification Based on Nlpca in Complex Electrical Engineering

NASA Astrophysics Data System (ADS)

Zhang, Yagang; Wang, Zengping; Zhang, Jinfang

2012-07-01

The fault is inevitable in any complex systems engineering. Electric power system is essentially a typically nonlinear system. It is also one of the most complex artificial systems in this world. In our researches, based on the real-time measurements of phasor measurement unit, under the influence of white Gaussian noise (suppose the standard deviation is 0.01, and the mean error is 0), we used mainly nonlinear principal component analysis theory (NLPCA) to resolve fault identification problem in complex electrical engineering. The simulation results show that the fault in complex electrical engineering is usually corresponding to the variable with the maximum absolute value coefficient in the first principal component. These researches will have significant theoretical value and engineering practical significance.

Searching for the main anti-bacterial components in artificial Calculus bovis using UPLC and microcalorimetry coupled with multi-linear regression analysis.

PubMed

Zang, Qing-Ce; Wang, Jia-Bo; Kong, Wei-Jun; Jin, Cheng; Ma, Zhi-Jie; Chen, Jing; Gong, Qian-Feng; Xiao, Xiao-He

2011-12-01

The fingerprints of artificial Calculus bovis extracts from different solvents were established by ultra-performance liquid chromatography (UPLC) and the anti-bacterial activities of artificial C. bovis extracts on Staphylococcus aureus (S. aureus) growth were studied by microcalorimetry. The UPLC fingerprints were evaluated using hierarchical clustering analysis. Some quantitative parameters obtained from the thermogenic curves of S. aureus growth affected by artificial C. bovis extracts were analyzed using principal component analysis. The spectrum-effect relationships between UPLC fingerprints and anti-bacterial activities were investigated using multi-linear regression analysis. The results showed that peak 1 (taurocholate sodium), peak 3 (unknown compound), peak 4 (cholic acid), and peak 6 (chenodeoxycholic acid) are more significant than the other peaks with the standard parameter estimate 0.453, -0.166, 0.749, 0.025, respectively. So, compounds cholic acid, taurocholate sodium, and chenodeoxycholic acid might be the major anti-bacterial components in artificial C. bovis. Altogether, this work provides a general model of the combination of UPLC chromatography and anti-bacterial effect to study the spectrum-effect relationships of artificial C. bovis extracts, which can be used to discover the main anti-bacterial components in artificial C. bovis or other Chinese herbal medicines with anti-bacterial effects. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Identification of Human Semiochemicals Attractive to the Major Vectors of Onchocerciasis

PubMed Central

Young, Ryan M.; Burkett-Cadena, Nathan D.; McGaha, Tommy W.; Rodriguez-Perez, Mario A.; Toé, Laurent D.; Adeleke, Monsuru A.; Sanfo, Moussa; Soungalo, Traore; Katholi, Charles R.; Noblet, Raymond; Fadamiro, Henry; Torres-Estrada, Jose L.; Salinas-Carmona, Mario C.; Baker, Bill; Unnasch, Thomas R.; Cupp, Eddie W.

2015-01-01

Background Entomological indicators are considered key metrics to document the interruption of transmission of Onchocerca volvulus, the etiological agent of human onchocerciasis. Human landing collection is the standard employed for collection of the vectors for this parasite. Recent studies reported the development of traps that have the potential for replacing humans for surveillance of O. volvulus in the vector population. However, the key chemical components of human odor that are attractive to vector black flies have not been identified. Methodology/Principal Findings Human sweat compounds were analyzed using GC-MS analysis and compounds common to three individuals identified. These common compounds, with others previously identified as attractive to other hematophagous arthropods were evaluated for their ability to stimulate and attract the major onchocerciasis vectors in Africa (Simulium damnosum sensu lato) and Latin America (Simulium ochraceum s. l.) using electroantennography and a Y tube binary choice assay. Medium chain length carboxylic acids and aldehydes were neurostimulatory for S. damnosum s.l. while S. ochraceum s.l. was stimulated by short chain aliphatic alcohols and aldehydes. Both species were attracted to ammonium bicarbonate and acetophenone. The compounds were shown to be attractive to the relevant vector species in field studies, when incorporated into a formulation that permitted a continuous release of the compound over time and used in concert with previously developed trap platforms. Conclusions/Significance The identification of compounds attractive to the major vectors of O. volvulus will permit the development of optimized traps. Such traps may replace the use of human vector collectors for monitoring the effectiveness of onchocerciasis elimination programs and could find use as a contributing component in an integrated vector control/drug program aimed at eliminating river blindness in Africa. PMID:25569240

Neutron absorbing room temperature vulcanizable silicone rubber compositions

DOEpatents

Zoch, Harold L.

1979-11-27

A neutron absorbing composition comprising a one-component room temperature vulcanizable silicone rubber composition or a two-component room temperature vulcanizable silicone rubber composition in which the composition contains from 25 to 300 parts by weight based on the base silanol or vinyl containing diorganopolysiloxane polymer of a boron compound or boron powder as the neutron absorbing ingredient. An especially useful boron compound in this application is boron carbide.

Efficient principal component analysis for multivariate 3D voxel-based mapping of brain functional imaging data sets as applied to FDG-PET and normal aging.

PubMed

Zuendorf, Gerhard; Kerrouche, Nacer; Herholz, Karl; Baron, Jean-Claude

2003-01-01

Principal component analysis (PCA) is a well-known technique for reduction of dimensionality of functional imaging data. PCA can be looked at as the projection of the original images onto a new orthogonal coordinate system with lower dimensions. The new axes explain the variance in the images in decreasing order of importance, showing correlations between brain regions. We used an efficient, stable and analytical method to work out the PCA of Positron Emission Tomography (PET) images of 74 normal subjects using [(18)F]fluoro-2-deoxy-D-glucose (FDG) as a tracer. Principal components (PCs) and their relation to age effects were investigated. Correlations between the projections of the images on the new axes and the age of the subjects were carried out. The first two PCs could be identified as being the only PCs significantly correlated to age. The first principal component, which explained 10% of the data set variance, was reduced only in subjects of age 55 or older and was related to loss of signal in and adjacent to ventricles and basal cisterns, reflecting expected age-related brain atrophy with enlarging CSF spaces. The second principal component, which accounted for 8% of the total variance, had high loadings from prefrontal, posterior parietal and posterior cingulate cortices and showed the strongest correlation with age (r = -0.56), entirely consistent with previously documented age-related declines in brain glucose utilization. Thus, our method showed that the effect of aging on brain metabolism has at least two independent dimensions. This method should have widespread applications in multivariate analysis of brain functional images. Copyright 2002 Wiley-Liss, Inc.

Evaluation of the separation characteristics of application-specific (volatile organic compounds) open-tubular columns for gas chromatography.

PubMed

Poole, Colin F; Qian, Jing; Kiridena, Waruna; Dekay, Colleen; Koziol, Wladyslaw W

2006-11-17

The solvation parameter model is used to characterize the separation characteristics of two application-specific open-tubular columns (Rtx-Volatiles and Rtx-VGC) and a general purpose column for the separation of volatile organic compounds (DB-WAXetr) at five equally spaced temperatures over the range 60-140 degrees C. System constant differences and retention factor correlation plots are then used to determine selectivity differences between the above columns and their closest neighbors in a large database of system constants and retention factors for forty-four open-tubular columns. The Rtx-Volatiles column is shown to have separation characteristics predicted for a poly(dimethyldiphenylsiloxane) stationary phase containing about 16% diphenylsiloxane monomer. The Rtx-VGC column has separation properties similar to the poly(cyanopropylphenyldimethylsiloxane) stationary phase containing 14% cyanopropylphenylsiloxane monomer DB-1701 for non-polar and dipolar/polarizable compounds but significantly different characteristics for the separation of hydrogen-bond acids. For all practical purposes the DB-WAXetr column is shown to be selectivity equivalent to poly(ethylene glycol) columns prepared using different chemistries for bonding and immobilizing the stationary phase. Principal component analysis and cluster analysis are then used to classify the system constants for the above columns and a sub-database of eleven open-tubular columns (DB-1, HP-5, DB-VRX, Rtx-20, DB-35, Rtx-50, Rtx-65, DB-1301, DB-1701, DB-200, and DB-624) commonly used for the separation of volatile organic compounds. A rationale basis for column selection based on differences in intermolecular interactions is presented as an aid to method development for the separation of volatile organic compounds.

Estimation of Theaflavins (TF) and Thearubigins (TR) Ratio in Black Tea Liquor Using Electronic Vision System

NASA Astrophysics Data System (ADS)

Akuli, Amitava; Pal, Abhra; Ghosh, Arunangshu; Bhattacharyya, Nabarun; Bandhopadhyya, Rajib; Tamuly, Pradip; Gogoi, Nagen

2011-09-01

Quality of black tea is generally assessed using organoleptic tests by professional tea tasters. They determine the quality of black tea based on its appearance (in dry condition and during liquor formation), aroma and taste. Variation in the above parameters is actually contributed by a number of chemical compounds like, Theaflavins (TF), Thearubigins (TR), Caffeine, Linalool, Geraniol etc. Among the above, TF and TR are the most important chemical compounds, which actually contribute to the formation of taste, colour and brightness in tea liquor. Estimation of TF and TR in black tea is generally done using a spectrophotometer instrument. But, the analysis technique undergoes a rigorous and time consuming effort for sample preparation; also the operation of costly spectrophotometer requires expert manpower. To overcome above problems an Electronic Vision System based on digital image processing technique has been developed. The system is faster, low cost, repeatable and can estimate the amount of TF and TR ratio for black tea liquor with accuracy. The data analysis is done using Principal Component Analysis (PCA), Multiple Linear Regression (MLR) and Multiple Discriminate Analysis (MDA). A correlation has been established between colour of tea liquor images and TF, TR ratio. This paper describes the newly developed E-Vision system, experimental methods, data analysis algorithms and finally, the performance of the E-Vision System as compared to the results of traditional spectrophotometer.

Principal component analysis on a torus: Theory and application to protein dynamics.

PubMed

Sittel, Florian; Filk, Thomas; Stock, Gerhard

2017-12-28

A dimensionality reduction method for high-dimensional circular data is developed, which is based on a principal component analysis (PCA) of data points on a torus. Adopting a geometrical view of PCA, various distance measures on a torus are introduced and the associated problem of projecting data onto the principal subspaces is discussed. The main idea is that the (periodicity-induced) projection error can be minimized by transforming the data such that the maximal gap of the sampling is shifted to the periodic boundary. In a second step, the covariance matrix and its eigendecomposition can be computed in a standard manner. Adopting molecular dynamics simulations of two well-established biomolecular systems (Aib 9 and villin headpiece), the potential of the method to analyze the dynamics of backbone dihedral angles is demonstrated. The new approach allows for a robust and well-defined construction of metastable states and provides low-dimensional reaction coordinates that accurately describe the free energy landscape. Moreover, it offers a direct interpretation of covariances and principal components in terms of the angular variables. Apart from its application to PCA, the method of maximal gap shifting is general and can be applied to any other dimensionality reduction method for circular data.

Principal component analysis on a torus: Theory and application to protein dynamics

NASA Astrophysics Data System (ADS)

Sittel, Florian; Filk, Thomas; Stock, Gerhard

2017-12-01

A dimensionality reduction method for high-dimensional circular data is developed, which is based on a principal component analysis (PCA) of data points on a torus. Adopting a geometrical view of PCA, various distance measures on a torus are introduced and the associated problem of projecting data onto the principal subspaces is discussed. The main idea is that the (periodicity-induced) projection error can be minimized by transforming the data such that the maximal gap of the sampling is shifted to the periodic boundary. In a second step, the covariance matrix and its eigendecomposition can be computed in a standard manner. Adopting molecular dynamics simulations of two well-established biomolecular systems (Aib9 and villin headpiece), the potential of the method to analyze the dynamics of backbone dihedral angles is demonstrated. The new approach allows for a robust and well-defined construction of metastable states and provides low-dimensional reaction coordinates that accurately describe the free energy landscape. Moreover, it offers a direct interpretation of covariances and principal components in terms of the angular variables. Apart from its application to PCA, the method of maximal gap shifting is general and can be applied to any other dimensionality reduction method for circular data.

Study on nondestructive discrimination of genuine and counterfeit wild ginsengs using NIRS

NASA Astrophysics Data System (ADS)

Lu, Q.; Fan, Y.; Peng, Z.; Ding, H.; Gao, H.

2012-07-01

A new approach for the nondestructive discrimination between genuine wild ginsengs and the counterfeit ones by near infrared spectroscopy (NIRS) was developed. Both discriminant analysis and back propagation artificial neural network (BP-ANN) were applied to the model establishment for discrimination. Optimal modeling wavelengths were determined based on the anomalous spectral information of counterfeit samples. Through principal component analysis (PCA) of various wild ginseng samples, genuine and counterfeit, the cumulative percentages of variance of the principal components were obtained, serving as a reference for principal component (PC) factor determination. Discriminant analysis achieved an identification ratio of 88.46%. With sample' truth values as its outputs, a three-layer BP-ANN model was built, which yielded a higher discrimination accuracy of 100%. The overall results sufficiently demonstrate that NIRS combined with BP-ANN classification algorithm performs better on ginseng discrimination than discriminant analysis, and can be used as a rapid and nondestructive method for the detection of counterfeit wild ginsengs in food and pharmaceutical industry.

A Fast and Sensitive New Satellite SO2 Retrieval Algorithm based on Principal Component Analysis: Application to the Ozone Monitoring Instrument

NASA Technical Reports Server (NTRS)

Li, Can; Joiner, Joanna; Krotkov, A.; Bhartia, Pawan K.

2013-01-01

We describe a new algorithm to retrieve SO2 from satellite-measured hyperspectral radiances. We employ the principal component analysis technique in regions with no significant SO2 to capture radiance variability caused by both physical processes (e.g., Rayleigh and Raman scattering and ozone absorption) and measurement artifacts. We use the resulting principal components and SO2 Jacobians calculated with a radiative transfer model to directly estimate SO2 vertical column density in one step. Application to the Ozone Monitoring Instrument (OMI) radiance spectra in 310.5-340 nm demonstrates that this approach can greatly reduce biases in the operational OMI product and decrease the noise by a factor of 2, providing greater sensitivity to anthropogenic emissions. The new algorithm is fast, eliminates the need for instrument-specific radiance correction schemes, and can be easily adapted to other sensors. These attributes make it a promising technique for producing longterm, consistent SO2 records for air quality and climate research.

Image restoration for three-dimensional fluorescence microscopy using an orthonormal basis for efficient representation of depth-variant point-spread functions

PubMed Central

Patwary, Nurmohammed; Preza, Chrysanthe

2015-01-01

A depth-variant (DV) image restoration algorithm for wide field fluorescence microscopy, using an orthonormal basis decomposition of DV point-spread functions (PSFs), is investigated in this study. The efficient PSF representation is based on a previously developed principal component analysis (PCA), which is computationally intensive. We present an approach developed to reduce the number of DV PSFs required for the PCA computation, thereby making the PCA-based approach computationally tractable for thick samples. Restoration results from both synthetic and experimental images show consistency and that the proposed algorithm addresses efficiently depth-induced aberration using a small number of principal components. Comparison of the PCA-based algorithm with a previously-developed strata-based DV restoration algorithm demonstrates that the proposed method improves performance by 50% in terms of accuracy and simultaneously reduces the processing time by 64% using comparable computational resources. PMID:26504634

Approximation-based common principal component for feature extraction in multi-class brain-computer interfaces.

PubMed

Hoang, Tuan; Tran, Dat; Huang, Xu

2013-01-01

Common Spatial Pattern (CSP) is a state-of-the-art method for feature extraction in Brain-Computer Interface (BCI) systems. However it is designed for 2-class BCI classification problems. Current extensions of this method to multiple classes based on subspace union and covariance matrix similarity do not provide a high performance. This paper presents a new approach to solving multi-class BCI classification problems by forming a subspace resembled from original subspaces and the proposed method for this approach is called Approximation-based Common Principal Component (ACPC). We perform experiments on Dataset 2a used in BCI Competition IV to evaluate the proposed method. This dataset was designed for motor imagery classification with 4 classes. Preliminary experiments show that the proposed ACPC feature extraction method when combining with Support Vector Machines outperforms CSP-based feature extraction methods on the experimental dataset.

Analysis of Floral Volatile Components and Antioxidant Activity of Different Varieties of Chrysanthemum morifolium.

PubMed

Yang, Lu; Cheng, Ping; Wang, Jin-Hui; Li, Hong

2017-10-23

This study investigated the volatile flavor compounds and antioxidant properties of the essential oil of chrysanthemums that was extracted from the fresh flowers of 10 taxa of Chrysanthemum morifolium from three species; namely Dendranthema morifolium (Ramat.) Yellow, Dendranthema morifolium (Ramat.) Red, Dendranthema morifolium (Ramat.) Pink, Dendranthema morifolium (Ramat.) White, Pericallis hybrid Blue, Pericallis hybrid Pink, Pericallis hybrid Purple, Bellis perennis Pink, Bellis perennis Yellow, and Bellis perennis White. The antioxidant capacity of the essential oil was assayed by spectrophotometric analysis. The volatile flavor compounds from the fresh flowers were collected using dynamic headspace collection, analyzed using auto thermal desorber-gas chromatography/mass spectrometry, and identified with quantification using the external standard method. The antioxidant activities of Chrysanthemum morifolium were evaluated by DPPH and FRAP assays, and the results showed that the antioxidant activity of each sample was not the same. The different varieties of fresh Chrysanthemum morifolium flowers were distinguished and classified by fingerprint similarity evaluation, principle component analysis (PCA), and cluster analysis. The results showed that the floral volatile component profiles were significantly different among the different Chrysanthemum morifolium varieties. A total of 36 volatile flavor compounds were identified with eight functional groups: hydrocarbons, terpenoids, aromatic compounds, alcohols, ketones, ethers, aldehydes, and esters. Moreover, the variability among Chrysanthemum morifolium in basis to the data, and the first three principal components (PC1, PC2, and PC3) accounted for 96.509% of the total variance (55.802%, 30.599%, and 10.108%, respectively). PCA indicated that there were marked differences among Chrysanthemum morifolium varieties. The cluster analysis confirmed the results of the PCA analysis. In conclusion, the results of this study provide a basis for breeding Chrysanthemum cultivars with desirable floral scents, and they further support the view that some plants are promising sources of natural antioxidants.

Feature selection for neural network based defect classification of ceramic components using high frequency ultrasound.

PubMed

Kesharaju, Manasa; Nagarajah, Romesh

2015-09-01

The motivation for this research stems from a need for providing a non-destructive testing method capable of detecting and locating any defects and microstructural variations within armour ceramic components before issuing them to the soldiers who rely on them for their survival. The development of an automated ultrasonic inspection based classification system would make possible the checking of each ceramic component and immediately alert the operator about the presence of defects. Generally, in many classification problems a choice of features or dimensionality reduction is significant and simultaneously very difficult, as a substantial computational effort is required to evaluate possible feature subsets. In this research, a combination of artificial neural networks and genetic algorithms are used to optimize the feature subset used in classification of various defects in reaction-sintered silicon carbide ceramic components. Initially wavelet based feature extraction is implemented from the region of interest. An Artificial Neural Network classifier is employed to evaluate the performance of these features. Genetic Algorithm based feature selection is performed. Principal Component Analysis is a popular technique used for feature selection and is compared with the genetic algorithm based technique in terms of classification accuracy and selection of optimal number of features. The experimental results confirm that features identified by Principal Component Analysis lead to improved performance in terms of classification percentage with 96% than Genetic algorithm with 94%. Copyright © 2015 Elsevier B.V. All rights reserved.

Chemometrics-based Approach in Analysis of Arnicae flos

PubMed Central

Zheleva-Dimitrova, Dimitrina Zh.; Balabanova, Vessela; Gevrenova, Reneta; Doichinova, Irini; Vitkova, Antonina

2015-01-01

Introduction: Arnica montana flowers have a long history as herbal medicines for external use on injuries and rheumatic complaints. Objective: To investigate Arnicae flos of cultivated accessions from Bulgaria, Poland, Germany, Finland, and Pharmacy store for phenolic derivatives and sesquiterpene lactones (STLs). Materials and Methods: Samples of Arnica from nine origins were prepared by ultrasound-assisted extraction with 80% methanol for phenolic compounds analysis. Subsequent reverse-phase high-performance liquid chromatography (HPLC) separation of the analytes was performed using gradient elution and ultraviolet detection at 280 and 310 nm (phenolic acids), and 360 nm (flavonoids). Total STLs were determined in chloroform extracts by solid-phase extraction-HPLC at 225 nm. The HPLC generated chromatographic data were analyzed using principal component analysis (PCA) and hierarchical clustering (HC). Results: The highest total amount of phenolic acids was found in the sample from Botanical Garden at Joensuu University, Finland (2.36 mg/g dw). Astragalin, isoquercitrin, and isorhamnetin 3-glucoside were the main flavonol glycosides being present up to 3.37 mg/g (astragalin). Three well-defined clusters were distinguished by PCA and HC. Cluster C1 comprised of the German and Finnish accessions characterized by the highest content of flavonols. Cluster C2 included the Bulgarian and Polish samples presenting a low content of flavonoids. Cluster C3 consisted only of one sample from a pharmacy store. Conclusion: A validated HPLC method for simultaneous determination of phenolic acids, flavonoid glycosides, and aglycones in A. montana flowers was developed. The PCA loading plot showed that quercetin, kaempferol, and isorhamnetin can be used to distinguish different Arnica accessions. SUMMARY A principal component analysis (PCA) on 13 phenolic compounds and total amount of sesquiterpene lactones in Arnicae flos collection tended to cluster the studied 9 accessions into three main groups. The profiles obtained demonstrated that the samples from Germany and Finland are characterized by greater amounts of phenolic derivatives than the Bulgarian and Polish ones. The PCA loading plot showed that quercetin, kaemferol and isorhamnetin can be used to distinguish different arnica accessions. PMID:27013791

Principal component analysis on molecular descriptors as an alternative point of view in the search of new Hsp90 inhibitors.

PubMed

Lauria, Antonino; Ippolito, Mario; Almerico, Anna Maria

2009-10-01

Inhibiting a protein that regulates multiple signal transduction pathways in cancer cells is an attractive goal for cancer therapy. Heat shock protein 90 (Hsp90) is one of the most promising molecular targets for such an approach. In fact, Hsp90 is a ubiquitous molecular chaperone protein that is involved in folding, activating and assembling of many key mediators of signal transduction, cellular growth, differentiation, stress-response and apoptothic pathways. With the aim to analyze which molecular descriptors have the higher importance in the binding interactions of these classes, we first performed molecular docking experiments on the 187 Hsp90 inhibitors included in the BindingDB, a public database of measured binding affinities. Further, for each frozen conformation obtained from the docking, a set of 250 molecular descriptors was calculated, and the resulting Structure/Descriptors matrix was submitted to Principal Component Analysis. From the factor scores it emerged a good clusterization among similar compounds both in terms of structural class and activity spectrum, while examination of the loadings of the first two factors also allowed to study the classes of descriptors which mainly contribute to each one.

An Introductory Application of Principal Components to Cricket Data

ERIC Educational Resources Information Center

Manage, Ananda B. W.; Scariano, Stephen M.

2013-01-01

Principal Component Analysis is widely used in applied multivariate data analysis, and this article shows how to motivate student interest in this topic using cricket sports data. Here, principal component analysis is successfully used to rank the cricket batsmen and bowlers who played in the 2012 Indian Premier League (IPL) competition. In…

Least Principal Components Analysis (LPCA): An Alternative to Regression Analysis.

ERIC Educational Resources Information Center

Olson, Jeffery E.

Often, all of the variables in a model are latent, random, or subject to measurement error, or there is not an obvious dependent variable. When any of these conditions exist, an appropriate method for estimating the linear relationships among the variables is Least Principal Components Analysis. Least Principal Components are robust, consistent,…

Identifying apple surface defects using principal components analysis and artifical neural networks

USDA-ARS?s Scientific Manuscript database

Artificial neural networks and principal components were used to detect surface defects on apples in near-infrared images. Neural networks were trained and tested on sets of principal components derived from columns of pixels from images of apples acquired at two wavelengths (740 nm and 950 nm). I...

Characterization of drug authenticity using thin-layer chromatography imaging with a mobile phone.

PubMed

Yu, Hojeong; Le, Huy M; Kaale, Eliangiringa; Long, Kenneth D; Layloff, Thomas; Lumetta, Steven S; Cunningham, Brian T

2016-06-05

Thin-layer chromatography (TLC) has a myriad of separation applications in chemistry, biology, and pharmacology due to its simplicity and low cost. While benchtop laboratory sample application and detection systems for TLC provide accurate quantitation of TLC spot positions and densities, there are many applications where inexpensive and portable instruments would greatly expand the applicability of the technology. In this work, we demonstrate identity verification and concentration determination of pharmaceutical compounds via TLC using a custom 3D-printed cradle that interfaces with an ordinary mobile phone. The cradle holds the mobile phone's internal, rear-facing camera in a fixed position relative to a UV lamp and a TLC plate that includes a phosphor in the stationary phase. Analysis of photographs thus reveals the locations and intensities of principal spots of UV--absorbing drugs. Automated image analysis software determines the center location and density of dark spots, which, using integrated calibration spots of known drug compounds and concentrations, can be used to determine if a drug has been diluted or substituted. Two independent image processing approaches have been developed that may be selected based upon the processing capabilities of the smartphone. Each approach is able to discern 5% drug concentration differences. Using single-component solutions of nevirapine, amodiaquine, and paracetamol that have been manually applied, the mobile phone-based detection instrument provides measurements that are equivalent to those obtained with a commercially available lab-based desktop TLC densitometer. Copyright © 2016 Elsevier B.V. All rights reserved.

Chemical profiling of guarana seeds (Paullinia cupana) from different geographical origins using UPLC-QTOF-MS combined with chemometrics.

PubMed

da Silva, Givaldo Souza; Canuto, Kirley Marques; Ribeiro, Paulo Riceli Vasconcelos; de Brito, Edy Sousa; Nascimento, Madson Moreira; Zocolo, Guilherme Julião; Coutinho, Janclei Pereira; de Jesus, Raildo Mota

2017-12-01

Paullinia cupana, commonly known as guarana, is an Amazonian fruit whose seeds are used to produce the powdered guarana, which is rich in caffeine and consumed for its stimulating activity. The metabolic profile of guarana from the two largest producing regions was investigated using UPLC-MS combined with multivariate statistical analysis. The principal component analysis (PCA) showed significant differences between samples produced in the states of Bahia and Amazonas. The metabolites responsible for the differentiation were identified by orthogonal partial least squares discriminant analysis (OPLS-DA). Fourteen phenolic compounds were characterized in guarana powder samples, and catechin, epicatechin, B-type procyanidin dimer, A-type procyanidin trimer and A-type procyanidin dimer were the main compounds responsible for the geographical variation of the samples. Copyright © 2017. Published by Elsevier Ltd.

Development of methodology for identification the nature of the polyphenolic extracts by FTIR associated with multivariate analysis

NASA Astrophysics Data System (ADS)

Grasel, Fábio dos Santos; Ferrão, Marco Flôres; Wolf, Carlos Rodolfo

2016-01-01

Tannins are polyphenolic compounds of complex structures formed by secondary metabolism in several plants. These polyphenolic compounds have different applications, such as drugs, anti-corrosion agents, flocculants, and tanning agents. This study analyses six different type of polyphenolic extracts by Fourier transform infrared spectroscopy (FTIR) combined with multivariate analysis. Through both principal component analysis (PCA) and hierarchical cluster analysis (HCA), we observed well-defined separation between condensed (quebracho and black wattle) and hydrolysable (valonea, chestnut, myrobalan, and tara) tannins. For hydrolysable tannins, it was also possible to observe the formation of two different subgroups between samples of chestnut and valonea and between samples of tara and myrobalan. Among all samples analysed, the chestnut and valonea showed the greatest similarity, indicating that these extracts contain equivalent chemical compositions and structure and, therefore, similar properties.

Volatile compounds formation in alcoholic fermentation from grapes collected at 2 maturation stages: influence of nitrogen compounds and grape variety.

PubMed

Martínez-Gil, Ana M; Garde-Cerdán, Teresa; Lorenzo, Cándida; Lara, José Félix; Pardo, Francisco; Salinas, M Rosario

2012-01-01

The aim of this work was to study the influence of nitrogen compounds on the formation of volatile compounds during the alcoholic fermentation carried out with 4 nonaromatic grape varieties collected at 2 different maturation stages. To do this, Monastrell, Merlot, Syrah, and Petit Verdot grapes were collected 1 wk before harvest and at harvest. Then, the musts were inoculated with the same Saccharomyces cerevisiae yeast strain and were fermented in the same winemaking conditions. Amino acids that showed the highest and the lowest concentration in the must were the same, regardless of the grape variety and maturation stage. Moreover, the consumption of amino acids during the fermentation increased with their concentration in the must. The formation of volatile compounds was not nitrogen composition dependent. However, the concentration of amino acids in the must from grapes collected 1 wk before harvest can be used as a parameter to estimate the concentration of esters in wines from grapes collected at harvest and therefore to have more information to know the grape oenological capacity. Application of principal components analysis (PCA) confirmed the possibility to estimate the concentration of esters in the wines with the concentration of nitrogen compounds in the must. © 2011 Institute of Food Technologists®

Building Component Maintenance and Repair Data Base: Heating, Ventilating, and Air Conditioning (HVAC) Systems

DTIC Science & Technology

1991-05-01

Building Component Maintenance and Repair Data Base: Heating, Ventilating, and Air Conditioning (HVAC) Systems by Edgar S. Neely Robert D. Neathammer...Repair Data Base: Heating, Ventilating, and Air Conditioning (HVAC) Systems RDTE dated 1980EIMB 1984 - 1989 6. AUTHOR(S) Edgar S. Neely, Robert D...Laboratory (USACERL). The Principal Investigators were Dr. Edgar Neely and Mr. Robert Neathammer (USACERL-FS). The primary contractor for much of the

Finding Planets in K2: A New Method of Cleaning the Data

NASA Astrophysics Data System (ADS)

Currie, Miles; Mullally, Fergal; Thompson, Susan E.

2017-01-01

We present a new method of removing systematic flux variations from K2 light curves by employing a pixel-level principal component analysis (PCA). This method decomposes the light curves into its principal components (eigenvectors), each with an associated eigenvalue, the value of which is correlated to how much influence the basis vector has on the shape of the light curve. This method assumes that the most influential basis vectors will correspond to the unwanted systematic variations in the light curve produced by K2’s constant motion. We correct the raw light curve by automatically fitting and removing the strongest principal components. The strongest principal components generally correspond to the flux variations that result from the motion of the star in the field of view. Our primary method of calculating the strongest principal components to correct for in the raw light curve estimates the noise by measuring the scatter in the light curve after using an algorithm for Savitsy-Golay detrending, which computes the combined photometric precision value (SG-CDPP value) used in classic Kepler. We calculate this value after correcting the raw light curve for each element in a list of cumulative sums of principal components so that we have as many noise estimate values as there are principal components. We then take the derivative of the list of SG-CDPP values and take the number of principal components that correlates to the point at which the derivative effectively goes to zero. This is the optimal number of principal components to exclude from the refitting of the light curve. We find that a pixel-level PCA is sufficient for cleaning unwanted systematic and natural noise from K2’s light curves. We present preliminary results and a basic comparison to other methods of reducing the noise from the flux variations.

(2R,5S)-Theaspirane Identified as the Kairomone for the Banana Weevil, Cosmopolites sordidus, from Attractive Senesced Leaves of the Host Banana, Musa spp.

PubMed

Abagale, Samson A; Woodcock, Christine M; Hooper, Antony M; Caulfield, John C; Withall, David; Chamberlain, Keith; Acquaah, Samuel O; Van Emden, Helmut; Braimah, Haruna; Pickett, John A; Birkett, Michael A

2018-04-12

The principal active component produced by highly attractive senesced host banana leaves, Musa spp., for the banana weevil, Cosmopolites sordidus, is shown by coupled gas chromatography-electroantennography (GC-EAG), coupled GC-mass spectrometry (GC-MS), chemical synthesis and coupled enantioselective (chiral) GC-EAG to be (2R,5S)-theaspirane. In laboratory behaviour tests, the synthetic compound is as attractive as natural host leaf material and presents a new opportunity for pest control. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Increasing the Coverage of Medicinal Chemistry-Relevant Space in Commercial Fragments Screening

PubMed Central

2014-01-01

Analyzing the chemical space coverage in commercial fragment screening collections revealed the overlap between bioactive medicinal chemistry substructures and rule-of-three compliant fragments is only ∼25%. We recommend including these fragments in fragment screening libraries to maximize confidence in discovering hit matter within known bioactive chemical space, while incorporation of nonoverlapping substructures could offer novel hits in screening libraries. Using principal component analysis, polar and three-dimensional substructures display a higher-than-average enrichment of bioactive compounds, indicating increasing representation of these substructures may be beneficial in fragment screening. PMID:24405118

Characterization of Leaf Extracts of Schinus terebinthifolius Raddi by GC-MS and Chemometric Analysis

PubMed Central

Carneiro, Fabíola B.; Lopes, Pablo Q.; Ramalho, Ricardo C.; Scotti, Marcus T.; Santos, Sócrates G.; Soares, Luiz A. L.

2017-01-01

Background: Schinus terebinthifolius Raddi belongs to Anacardiacea family and is widely known as “aroeira.” This species originates from South America, and its extracts are used in folk medicine due to its therapeutic properties, which include antimicrobial, anti-inflammatory, and antipyretic effects. The complexity and variability of the chemical constitution of the herbal raw material establishes the quality of the respective herbal medicine products. Objective: Thus, the purpose of this study was to investigate the variability of the volatile compounds from leaves of S. terebinthifolius. Materials and Methods: The samples were collected from different states of the Northeast region of Brazil and analyzed with a gas chromatograph coupled to a mass spectrometer (GC-MS). The collected data were analyzed using multivariate data analysis. Results: The samples’ chromatograms, obtained by GC-MS, showed similar chemical profiles in a number of peaks, but some differences were observed in the intensity of these analytical markers. The chromatographic fingerprints obtained by GC-MS were suitable for discrimination of the samples; these results along with a statistical treatment (principal component analysis [PCA]) were used as a tool for comparative analysis between the different samples of S. terebinthifolius. Conclusion: The experimental data show that the PCA used in this study clustered the samples into groups with similar chemical profiles, which builds an appropriate approach to evaluate the similarity in the phytochemical pattern found in the different leaf samples. SUMMARY The leave extracts of Schinus terebinthifolius were obtained by turbo-extractionThe extracts were partitioned with hexane and analyzed by GC-MSThe chromatographic data were analyzed using the principal component analysis (PCA)The PCA plots showed the main compounds (phellandrene, limonene, and carene), which were used to group the samples from a different geographical location in accordance to their chemical similarity. Abbreviations used: AL: Alagoas, BA: Bahia, CE: Ceará, CPETEC: Center for Weather Forecasting and Climate Studies, GC-MS: Gas chromatograph coupled to a mass spectrometer, MA: Maranhão, MVA: Multivariate data analysis, PB: Paraíba, PC1: Direction that describes the maximum variance of the original data, PC2: Maximum direction variance of the data in the subspace orthogonal to PC1, PCA: Principal component analysis, PE: Pernambuco, PI: Piauí, RN: Rio Grande do Norte, SE: Sergipe. PMID:29142431

Chemical variability of Xylopia quintasii Engl. & Diels leaf oil from Côte d'Ivoire.

PubMed

Yapi, Thierry Acafou; Boti, Jean Brice; Tonzibo, Zanahi Félix; Ahibo, Coffy Antoine; Bighelli, Ange; Casanova, Joseph; Tomi, Félix

2014-02-01

The chemical composition of 42 essential-oil samples isolated from the leaves of Xylopia quintasii harvested in three Ivoirian forests was investigated by GC-FID, including the determination of retention indices (RIs), and by (13) C-NMR analyses. In total, 36 components accounting for 91.9-92.6% of the oil composition were identified. The content of the main components varied drastically from sample to sample: (E)-β-caryophyllene (0.9-56.9%), (Z)-β-ocimene (0.3-54.6%), β-pinene (0.8-27.9%), α-pinene (0.1-22.8%), and furanoguaia-1,4-diene (0.0-17.6%). The 42 oil compositions were submitted to hierarchical cluster and principal components analysis, which allowed the distinction of three groups within the oil samples. The composition of the oils of the major group (22 samples) was dominated by (E)-β-caryophyllene. The oils of the second group (12 samples) contained β-pinene and α-pinene as the principal compounds, while the oils of the third group (8 samples) were dominated by (Z)-β-ocimene, germacrene D, (E)-β-ocimene, and furanoguaia-1,4-diene. The oil samples of Group I and II came from clay-soil forests, while the oil samples belonging to Group III were isolated from leaves harvested in a sandy-soil forest. Copyright © 2014 Verlag Helvetica Chimica Acta AG, Zürich.

40 CFR 60.2998 - What are the principal components of the model rule?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 6 2010-07-01 2010-07-01 false What are the principal components of... December 9, 2004 Model Rule-Use of Model Rule § 60.2998 What are the principal components of the model rule... management plan. (c) Operator training and qualification. (d) Emission limitations and operating limits. (e...

40 CFR 60.2570 - What are the principal components of the model rule?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 6 2010-07-01 2010-07-01 false What are the principal components of... Construction On or Before November 30, 1999 Use of Model Rule § 60.2570 What are the principal components of... (k) of this section. (a) Increments of progress toward compliance. (b) Waste management plan. (c...

Establishing ¹H nuclear magnetic resonance based metabonomics fingerprinting profile for spinal cord injury: a pilot study.

PubMed

Jiang, Hua; Peng, Jin; Zhou, Zhi-yuan; Duan, Yu; Chen, Wei; Cai, Bin; Yang, Hao; Zhang, Wei

2010-09-01

Spinal cord injury (SCI) is a complex trauma that consists of multiple pathological mechanisms involving cytotoxic, oxidation stress and immune-endocrine. This study aimed to establish plasma metabonomics fingerprinting atlas for SCI using (1)H nuclear magnetic resonance (NMR) based metabonomics methodology and principal component analysis techniques. Nine Sprague-Dawley (SD) male rats were randomly divided into SCI, normal and sham-operation control groups. Plasma samples were collected for (1)H NMR spectroscopy 3 days after operation. The NMR data were analyzed using principal component analysis technique with Matlab software. Metabonomics analysis was able to distinguish the three groups (SCI, normal control, sham-operation). The fingerprinting atlas indicated that, compared with those without SCI, the SCI group demonstrated the following characteristics with regard to second principal component: it is made up of fatty acids, myc-inositol, arginine, very low-density lipoprotein (VLDL), low-density lipoprotein (LDL), triglyceride (TG), glucose, and 3-methyl-histamine. The data indicated that SCI results in several significant changes in plasma metabolism early on and that a metabonomics approach based on (1)H NMR spectroscopy can provide a metabolic profile comprising several metabolite classes and allow for relative quantification of such changes. The results also provided support for further development and application of metabonomics technologies for studying SCI and for the utilization of multivariate models for classifying the extent of trauma within an individual.

Determination of the Characteristics and Classification of Near-Infrared Spectra of Patchouli Oil (Pogostemon Cablin Benth.) from Different Origin

NASA Astrophysics Data System (ADS)

Diego, M. C. R.; Purwanto, Y. A.; Sutrisno; Budiastra, I. W.

2018-05-01

Research related to the non-destructive method of near-infrared (NIR) spectroscopy in aromatic oil is still in development in Indonesia. The objectives of the study were to determine the characteristics of the near-infrared spectra of patchouli oil and classify it based on its origin. The samples were selected from seven different places in Indonesia (Bogor and Garut from West Java, Aceh, and Jambi from Sumatra and Konawe, Masamba and Kolaka from Sulawesi Island). The spectral data of patchouli oil was obtained by FT-NIR spectrometer at the wavelength of 1000-2500 nm, and after that, the samples were subjected to composition analysis using Gas Chromatography-Mass Spectrometry. The transmittance and absorbance spectra were analyzed and then principal component analysis (PCA) was carried out. Discriminant analysis (DA) of the principal component was developed to classify patchouli oil based on its origin. The result shows that the data of both spectra (transmittance and absorbance spectra) by the PC analysis give a similar result for discriminating the seven types of patchouli oil due to their distribution and behavior. The DA of the three principal component in both data processed spectra could classify patchouli oil accurately. This result exposed that NIR spectroscopy can be successfully used as a correct method to classify patchouli oil based on its origin.

Essential Oil Composition of Pinus peuce Griseb. Needles and Twigs from Two National Parks of Kosovo.

PubMed

Hajdari, Avni; Mustafa, Behxhet; Nebija, Dashnor; Selimi, Hyrmete; Veselaj, Zeqir; Breznica, Pranvera; Quave, Cassandra Leah; Novak, Johannes

The principal aim of this study was to analyze the chemical composition and qualitative and quantitative variability of essential oils obtained from seven naturally grown populations of the Pinus peuce Grisebach, Pinaceae in Kosovo. Plant materials were collected from three populations in the Sharri National Park and from four other populations in the Bjeshkët e Nemuna National Park, in Kosovo. Essential oils were obtained by steam distillation and analyzed by GC-FID (Gas Chromatography-Flame Ionization Detection) and GC-MS (Gas Chromatography-Mass Spectrometry). The results showed that the yield of essential oils (v/w dry weight) varied depending on the origin of population and the plant organs and ranged from 0.7 to 3.3%. In total, 51 compounds were identified. The main compounds were α-pinene (needles: 21.6-34.9%; twigs: 11.0-24%), β-phellandrene (needles: 4.1-27.7; twigs: 29.0-49.8%), and β-pinene (needles: 10.0-16.1; twigs: 6.9-20.7%). HCA (Hierarchical Cluster Analysis) and PCA (Principal Component Analyses) were used to assess geographical variations in essential oil composition. Statistical analysis showed that the analyzed populations are grouped in three main clusters which seem to reflect microclimatic conditions on the chemical composition of the essential oils.

Free energy landscape of a biomolecule in dihedral principal component space: sampling convergence and correspondence between structures and minima.

PubMed

Maisuradze, Gia G; Leitner, David M

2007-05-15

Dihedral principal component analysis (dPCA) has recently been developed and shown to display complex features of the free energy landscape of a biomolecule that may be absent in the free energy landscape plotted in principal component space due to mixing of internal and overall rotational motion that can occur in principal component analysis (PCA) [Mu et al., Proteins: Struct Funct Bioinfo 2005;58:45-52]. Another difficulty in the implementation of PCA is sampling convergence, which we address here for both dPCA and PCA using a tetrapeptide as an example. We find that for both methods the sampling convergence can be reached over a similar time. Minima in the free energy landscape in the space of the two largest dihedral principal components often correspond to unique structures, though we also find some distinct minima to correspond to the same structure. 2007 Wiley-Liss, Inc.

Efficient three-dimensional resist profile-driven source mask optimization optical proximity correction based on Abbe-principal component analysis and Sylvester equation

NASA Astrophysics Data System (ADS)

Lin, Pei-Chun; Yu, Chun-Chang; Chen, Charlie Chung-Ping

2015-01-01

As one of the critical stages of a very large scale integration fabrication process, postexposure bake (PEB) plays a crucial role in determining the final three-dimensional (3-D) profiles and lessening the standing wave effects. However, the full 3-D chemically amplified resist simulation is not widely adopted during the postlayout optimization due to the long run-time and huge memory usage. An efficient simulation method is proposed to simulate the PEB while considering standing wave effects and resolution enhancement techniques, such as source mask optimization and subresolution assist features based on the Sylvester equation and Abbe-principal component analysis method. Simulation results show that our algorithm is 20× faster than the conventional Gaussian convolution method.

Principal Workload: Components, Determinants and Coping Strategies in an Era of Standardization and Accountability

ERIC Educational Resources Information Center

Oplatka, Izhar

2017-01-01

Purpose: In order to fill the gap in theoretical and empirical knowledge about the characteristics of principal workload, the purpose of this paper is to explore the components of principal workload as well as its determinants and the coping strategies commonly used by principals to face this personal state. Design/methodology/approach:…

Resveratrols in Grape Berry Skins and Leaves in Vitis Germplasm

PubMed Central

Wang, Lijun; Xu, Man; Liu, Chunyan; Wang, Junfang; Xi, Huifen; Wu, Benhong; Loescher, Wayne; Duan, Wei; Fan, Peige; Li, Shaohua

2013-01-01

Background Resveratrol is an important stilbene that benefits human health. However, it is only distributed in a few species including grape and is very expensive. At present, grape has been an important source resveratrol. However, the details are scarce on resveratrol distribution in different Vitis species or cultivars. Methodology/Principal Finding The composition and content of resveratrols were investigated by HPLC for assessing genotypic variation in berry skins and leaves of 75 grape cultivars, belonging to 3 species and 7 interspecific hybrids. Trans-resveratrol, cis-piceid and trans-piceid were detected in berry skins and leaves, but cis-resveratrol was not. Resveratrol content largely varied with genetic background as well as usage. In most cultivars, total resveratrol including the above three compounds was higher in berry skins than leaves. In berry skins of most cultivars and leaves of almost all cultivars, cis-piceid was the most abundant resveratrol; trans-resveratrol and trans-piceid were minor components. Some specific cultivars were found with extremely high levels of trans-resveratrol, cis- piceid, trans-piceid or total resveratrols in berry skins or leaves. In skins and leaves, rootstock cultivars had a higher content of total resveratrols, and the cultivated European type cultivars and their hybrids with V. labrusca had relatively low totals. There were no significant correlations of the amounts of total resveratrols or any individual resveratrol between berry skins and leaves. All 75 cultivars can be divided into four groups based on the composition of resveratrols and their concentration by principal component analysis. Conclusion Resveratrol content of grape berries and leaves varied largely with their genetic background and usage. Rootstock cultivars had a higher content of total resveratrols than the other germplasm. Total resveratrols were lower in leaves than berry skins in most cultivars. Cis-piceid was the most abundant resveratrol in most cultivars, and trans-res and trans-pd were minor components. PMID:23637874

1H nuclear magnetic resonance-based metabolomic characterization of wines by grape varieties and production areas.

PubMed

Son, Hong-Seok; Kim, Ki Myong; van den Berg, Frans; Hwang, Geum-Sook; Park, Won-Mok; Lee, Cherl-Ho; Hong, Young-Shick

2008-09-10

(1)H NMR spectroscopy was used to investigate the metabolic differences in wines produced from different grape varieties and different regions. A significant separation among wines from Campbell Early, Cabernet Sauvignon, and Shiraz grapes was observed using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The metabolites contributing to the separation were assigned to be 2,3-butanediol, lactate, acetate, proline, succinate, malate, glycerol, tartarate, glucose, and phenolic compounds by PCA and PLS-DA loading plots. Wines produced from Cabernet Sauvignon grapes harvested in the continental areas of Australia, France, and California were also separated. PLS-DA loading plots revealed that the level of proline in Californian Cabernet Sauvignon wines was higher than that in Australian and French Cabernet Sauvignon, Australian Shiraz, and Korean Campbell Early wines, showing that the chemical composition of the grape berries varies with the variety and growing area. This study highlights the applicability of NMR-based metabolomics with multivariate statistical data sets in determining wine quality and product origin.

GLS-Finder: A Platform for Fast Profiling of Glucosinolates in Brassica Vegetables.

PubMed

Sun, Jianghao; Zhang, Mengliang; Chen, Pei

2016-06-01

Mass spectrometry combined with related tandem techniques has become the most popular method for plant secondary metabolite characterization. We introduce a new strategy based on in-database searching, mass fragmentation behavior study, formula predicting for fast profiling of glucosinolates, a class of important compounds in brassica vegetables. A MATLAB script-based expert system computer program, "GLS-Finder", was developed. It is capable of qualitative and semi-quantitative analyses of glucosinolates in samples using data generated by ultrahigh-performance liquid chromatography-high-resolution accurate mass with multi-stage mass fragmentation (UHPLC-HRAM/MS(n)). A suite of bioinformatic tools was integrated into the "GLS-Finder" to perform raw data deconvolution, peak alignment, glucosinolate putative assignments, semi-quantitation, and unsupervised principal component analysis (PCA). GLS-Finder was successfully applied to identify intact glucosinolates in 49 commonly consumed Brassica vegetable samples in the United States. It is believed that this work introduces a new way of fast data processing and interpretation for qualitative and quantitative analyses of glucosinolates, where great efficacy was improved in comparison to identification manually.

Liver DCE-MRI Registration in Manifold Space Based on Robust Principal Component Analysis.

PubMed

Feng, Qianjin; Zhou, Yujia; Li, Xueli; Mei, Yingjie; Lu, Zhentai; Zhang, Yu; Feng, Yanqiu; Liu, Yaqin; Yang, Wei; Chen, Wufan

2016-09-29

A technical challenge in the registration of dynamic contrast-enhanced magnetic resonance (DCE-MR) imaging in the liver is intensity variations caused by contrast agents. Such variations lead to the failure of the traditional intensity-based registration method. To address this problem, a manifold-based registration framework for liver DCE-MR time series is proposed. We assume that liver DCE-MR time series are located on a low-dimensional manifold and determine intrinsic similarities between frames. Based on the obtained manifold, the large deformation of two dissimilar images can be decomposed into a series of small deformations between adjacent images on the manifold through gradual deformation of each frame to the template image along the geodesic path. Furthermore, manifold construction is important in automating the selection of the template image, which is an approximation of the geodesic mean. Robust principal component analysis is performed to separate motion components from intensity changes induced by contrast agents; the components caused by motion are used to guide registration in eliminating the effect of contrast enhancement. Visual inspection and quantitative assessment are further performed on clinical dataset registration. Experiments show that the proposed method effectively reduces movements while preserving the topology of contrast-enhancing structures and provides improved registration performance.

Analysing Normal and Partial Glossectomee Tongues Using Ultrasound

ERIC Educational Resources Information Center

Bressmann, Tim; Uy, Catherine; Irish, Jonathan C.

2005-01-01

The present study aimed at identifying underlying parameters that govern the shape of the tongue. A functional topography of the tongue surface was developed based on three-dimensional ultrasound scans of sustained speech sounds in ten normal subjects. A principal component analysis extracted three components that explained 89.2% of the variance…

ASCS online fault detection and isolation based on an improved MPCA

NASA Astrophysics Data System (ADS)

Peng, Jianxin; Liu, Haiou; Hu, Yuhui; Xi, Junqiang; Chen, Huiyan

2014-09-01

Multi-way principal component analysis (MPCA) has received considerable attention and been widely used in process monitoring. A traditional MPCA algorithm unfolds multiple batches of historical data into a two-dimensional matrix and cut the matrix along the time axis to form subspaces. However, low efficiency of subspaces and difficult fault isolation are the common disadvantages for the principal component model. This paper presents a new subspace construction method based on kernel density estimation function that can effectively reduce the storage amount of the subspace information. The MPCA model and the knowledge base are built based on the new subspace. Then, fault detection and isolation with the squared prediction error (SPE) statistic and the Hotelling ( T 2) statistic are also realized in process monitoring. When a fault occurs, fault isolation based on the SPE statistic is achieved by residual contribution analysis of different variables. For fault isolation of subspace based on the T 2 statistic, the relationship between the statistic indicator and state variables is constructed, and the constraint conditions are presented to check the validity of fault isolation. Then, to improve the robustness of fault isolation to unexpected disturbances, the statistic method is adopted to set the relation between single subspace and multiple subspaces to increase the corrective rate of fault isolation. Finally fault detection and isolation based on the improved MPCA is used to monitor the automatic shift control system (ASCS) to prove the correctness and effectiveness of the algorithm. The research proposes a new subspace construction method to reduce the required storage capacity and to prove the robustness of the principal component model, and sets the relationship between the state variables and fault detection indicators for fault isolation.

Using multi-scale entropy and principal component analysis to monitor gears degradation via the motor current signature analysis

NASA Astrophysics Data System (ADS)

Aouabdi, Salim; Taibi, Mahmoud; Bouras, Slimane; Boutasseta, Nadir

2017-06-01

This paper describes an approach for identifying localized gear tooth defects, such as pitting, using phase currents measured from an induction machine driving the gearbox. A new tool of anomaly detection based on multi-scale entropy (MSE) algorithm SampEn which allows correlations in signals to be identified over multiple time scales. The motor current signature analysis (MCSA) in conjunction with principal component analysis (PCA) and the comparison of observed values with those predicted from a model built using nominally healthy data. The Simulation results show that the proposed method is able to detect gear tooth pitting in current signals.

Superpixel-based spectral classification for the detection of head and neck cancer with hyperspectral imaging

NASA Astrophysics Data System (ADS)

Chung, Hyunkoo; Lu, Guolan; Tian, Zhiqiang; Wang, Dongsheng; Chen, Zhuo Georgia; Fei, Baowei

2016-03-01

Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications. HSI acquires two dimensional images at various wavelengths. The combination of both spectral and spatial information provides quantitative information for cancer detection and diagnosis. This paper proposes using superpixels, principal component analysis (PCA), and support vector machine (SVM) to distinguish regions of tumor from healthy tissue. The classification method uses 2 principal components decomposed from hyperspectral images and obtains an average sensitivity of 93% and an average specificity of 85% for 11 mice. The hyperspectral imaging technology and classification method can have various applications in cancer research and management.

Evidence of tampering in watermark identification

NASA Astrophysics Data System (ADS)

McLauchlan, Lifford; Mehrübeoglu, Mehrübe

2009-08-01

In this work, watermarks are embedded in digital images in the discrete wavelet transform (DWT) domain. Principal component analysis (PCA) is performed on the DWT coefficients. Next higher order statistics based on the principal components and the eigenvalues are determined for different sets of images. Feature sets are analyzed for different types of attacks in m dimensional space. The results demonstrate the separability of the features for the tampered digital copies. Different feature sets are studied to determine more effective tamper evident feature sets. The digital forensics, the probable manipulation(s) or modification(s) performed on the digital information can be identified using the described technique.

Identification and classification of upper limb motions using PCA.

PubMed

Veer, Karan; Vig, Renu

2018-03-28

This paper describes the utility of principal component analysis (PCA) in classifying upper limb signals. PCA is a powerful tool for analyzing data of high dimension. Here, two different input strategies were explored. The first method uses upper arm dual-position-based myoelectric signal acquisition and the other solely uses PCA for classifying surface electromyogram (SEMG) signals. SEMG data from the biceps and the triceps brachii muscles and four independent muscle activities of the upper arm were measured in seven subjects (total dataset=56). The datasets used for the analysis are rotated by class-specific principal component matrices to decorrelate the measured data prior to feature extraction.

Genetic diversity analysis of fruit characteristics of hawthorn germplasm.

PubMed

Su, K; Guo, Y S; Wang, G; Zhao, Y H; Dong, W X

2015-12-07

One hundred and six accessions of hawthorn intraspecific resources, from the National Germplasm Repository at Shenyang, were subjected to genetic diversity and principal component analysis based on evaluation data of 15 fruit traits. Results showed that the genetic diversity of hawthorn fruit traits varied. Among the 15 traits, the fruit shape variable coefficient had the most obvious evaluation, followed by fruit surface state, dot color, taste, weight of single fruit, sepal posture, peduncle form, and metula traits. These are the primary traits by which hawthorn could be classified in the future. The principal component demonstrated that these traits are the most influential factors of hawthorn fruit characteristics.

Dimensionality reduction for the quantitative evaluation of a smartphone-based Timed Up and Go test.

PubMed

Palmerini, Luca; Mellone, Sabato; Rocchi, Laura; Chiari, Lorenzo

2011-01-01

The Timed Up and Go is a clinical test to assess mobility in the elderly and in Parkinson's disease. Lately instrumented versions of the test are being considered, where inertial sensors assess motion. To improve the pervasiveness, ease of use, and cost, we consider a smartphone's accelerometer as the measurement system. Several parameters (usually highly correlated) can be computed from the signals recorded during the test. To avoid redundancy and obtain the features that are most sensitive to the locomotor performance, a dimensionality reduction was performed through principal component analysis (PCA). Forty-nine healthy subjects of different ages were tested. PCA was performed to extract new features (principal components) which are not redundant combinations of the original parameters and account for most of the data variability. They can be useful for exploratory analysis and outlier detection. Then, a reduced set of the original parameters was selected through correlation analysis with the principal components. This set could be recommended for studies based on healthy adults. The proposed procedure could be used as a first-level feature selection in classification studies (i.e. healthy-Parkinson's disease, fallers-non fallers) and could allow, in the future, a complete system for movement analysis to be incorporated in a smartphone.

Factors affecting medication adherence in community-managed patients with hypertension based on the principal component analysis: evidence from Xinjiang, China.

PubMed

Zhang, Yuji; Li, Xiaoju; Mao, Lu; Zhang, Mei; Li, Ke; Zheng, Yinxia; Cui, Wangfei; Yin, Hongpo; He, Yanli; Jing, Mingxia

2018-01-01

The analysis of factors affecting the nonadherence to antihypertensive medications is important in the control of blood pressure among patients with hypertension. The purpose of this study was to assess the relationship between factors and medication adherence in Xinjiang community-managed patients with hypertension based on the principal component analysis. A total of 1,916 community-managed patients with hypertension, selected randomly through a multi-stage sampling, participated in the survey. Self-designed questionnaires were used to classify the participants as either adherent or nonadherent to their medication regimen. A principal component analysis was used in order to eliminate the correlation between factors. Factors related to nonadherence were analyzed by using a χ 2 -test and a binary logistic regression model. This study extracted nine common factors, with a cumulative variance contribution rate of 63.6%. Further analysis revealed that the following variables were significantly related to nonadherence: severity of disease, community management, diabetes, and taking traditional medications. Community management plays an important role in improving the patients' medication-taking behavior. Regular medication regimen instruction and better community management services through community-level have the potential to reduce nonadherence. Mild hypertensive patients should be monitored by community health care providers.

New molecular descriptors based on local properties at the molecular surface and a boiling-point model derived from them.

PubMed

Ehresmann, Bernd; de Groot, Marcel J; Alex, Alexander; Clark, Timothy

2004-01-01

New molecular descriptors based on statistical descriptions of the local ionization potential, local electron affinity, and the local polarizability at the surface of the molecule are proposed. The significance of these descriptors has been tested by calculating them for the Maybridge database in addition to our set of 26 descriptors reported previously. The new descriptors show little correlation with those already in use. Furthermore, the principal components of the extended set of descriptors for the Maybridge data show that especially the descriptors based on the local electron affinity extend the variance in our set of descriptors, which we have previously shown to be relevant to physical properties. The first nine principal components are shown to be most significant. As an example of the usefulness of the new descriptors, we have set up a QSPR model for boiling points using both the old and new descriptors.

Incremental Principal Component Analysis Based Outlier Detection Methods for Spatiotemporal Data Streams

NASA Astrophysics Data System (ADS)

Bhushan, A.; Sharker, M. H.; Karimi, H. A.

2015-07-01

In this paper, we address outliers in spatiotemporal data streams obtained from sensors placed across geographically distributed locations. Outliers may appear in such sensor data due to various reasons such as instrumental error and environmental change. Real-time detection of these outliers is essential to prevent propagation of errors in subsequent analyses and results. Incremental Principal Component Analysis (IPCA) is one possible approach for detecting outliers in such type of spatiotemporal data streams. IPCA has been widely used in many real-time applications such as credit card fraud detection, pattern recognition, and image analysis. However, the suitability of applying IPCA for outlier detection in spatiotemporal data streams is unknown and needs to be investigated. To fill this research gap, this paper contributes by presenting two new IPCA-based outlier detection methods and performing a comparative analysis with the existing IPCA-based outlier detection methods to assess their suitability for spatiotemporal sensor data streams.

PCA-LBG-based algorithms for VQ codebook generation

NASA Astrophysics Data System (ADS)

Tsai, Jinn-Tsong; Yang, Po-Yuan

2015-04-01

Vector quantisation (VQ) codebooks are generated by combining principal component analysis (PCA) algorithms with Linde-Buzo-Gray (LBG) algorithms. All training vectors are grouped according to the projected values of the principal components. The PCA-LBG-based algorithms include (1) PCA-LBG-Median, which selects the median vector of each group, (2) PCA-LBG-Centroid, which adopts the centroid vector of each group, and (3) PCA-LBG-Random, which randomly selects a vector of each group. The LBG algorithm finds a codebook based on the better vectors sent to an initial codebook by the PCA. The PCA performs an orthogonal transformation to convert a set of potentially correlated variables into a set of variables that are not linearly correlated. Because the orthogonal transformation efficiently distinguishes test image vectors, the proposed PCA-LBG-based algorithm is expected to outperform conventional algorithms in designing VQ codebooks. The experimental results confirm that the proposed PCA-LBG-based algorithms indeed obtain better results compared to existing methods reported in the literature.

Randomized subspace-based robust principal component analysis for hyperspectral anomaly detection

NASA Astrophysics Data System (ADS)

Sun, Weiwei; Yang, Gang; Li, Jialin; Zhang, Dianfa

2018-01-01

A randomized subspace-based robust principal component analysis (RSRPCA) method for anomaly detection in hyperspectral imagery (HSI) is proposed. The RSRPCA combines advantages of randomized column subspace and robust principal component analysis (RPCA). It assumes that the background has low-rank properties, and the anomalies are sparse and do not lie in the column subspace of the background. First, RSRPCA implements random sampling to sketch the original HSI dataset from columns and to construct a randomized column subspace of the background. Structured random projections are also adopted to sketch the HSI dataset from rows. Sketching from columns and rows could greatly reduce the computational requirements of RSRPCA. Second, the RSRPCA adopts the columnwise RPCA (CWRPCA) to eliminate negative effects of sampled anomaly pixels and that purifies the previous randomized column subspace by removing sampled anomaly columns. The CWRPCA decomposes the submatrix of the HSI data into a low-rank matrix (i.e., background component), a noisy matrix (i.e., noise component), and a sparse anomaly matrix (i.e., anomaly component) with only a small proportion of nonzero columns. The algorithm of inexact augmented Lagrange multiplier is utilized to optimize the CWRPCA problem and estimate the sparse matrix. Nonzero columns of the sparse anomaly matrix point to sampled anomaly columns in the submatrix. Third, all the pixels are projected onto the complemental subspace of the purified randomized column subspace of the background and the anomaly pixels in the original HSI data are finally exactly located. Several experiments on three real hyperspectral images are carefully designed to investigate the detection performance of RSRPCA, and the results are compared with four state-of-the-art methods. Experimental results show that the proposed RSRPCA outperforms four comparison methods both in detection performance and in computational time.

Preliminary Results Of PCA On MRO CRISM Multispectral Images

NASA Astrophysics Data System (ADS)

Klassen, David R.; Smith, M. D.

2008-09-01

Mars Reconnaissance Orbiter arrived at Mars in March 2006 and by September had achieved its science-phase orbit with the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) beginning its visible to near-infrared (VIS/NIR) spectral imaging shortly thereafter. One of the goals of CRISM is to fill in the spatial gaps between the various targeted observations, eventually mapping the entire surface. Due to the large volume of data this would create, the instrument works in a reduced spectral sampling mode creating "multispectral” images. From this data we can create image cubes using 70 wavelengths from 0.410 to 3.504 µm. We present here a preliminary analysis of these multispectral mode data products using the technique of Principal Components Analysis. Previous work with ground-based images has shown that over an entire visible hemisphere, there are only three to four meaningful components out of 32-105 wavelengths over 1.5-4.1 µm. The first two of these components are fairly consistent over all time intervals from day-to-day and season-to-season. [1-4] The preliminary work on the CRISM images cubes implies similar results_three to four significant principal components that are fairly consistent over time. We will show these components and a rough linear mixture modeling based on in-data spectral endmembers derived from the extrema of the principal components [5]. References: [1] Klassen, D. R. and Bell III, J. F. (2001) BAAS 33, 1069. [2] Klassen, D. R. and Bell III, J. F. (2003) BAAS, 35, 936. [3] Klassen, D. R., Wark, T. J., Cugliotta, C. G. (2005) BAAS, 37, 693. [4] Klassen, D. R. and Bell III, J. F. (2007) in preparation. [5] Klassen, D. R. and Bell III, J. F. (2000) BAAS, 32, 1105.

Lippia origanoides chemotype differentiation based on essential oil GC-MS and principal component analysis.

PubMed

Stashenko, Elena E; Martínez, Jairo R; Ruíz, Carlos A; Arias, Ginna; Durán, Camilo; Salgar, William; Cala, Mónica

2010-01-01

Chromatographic (GC/flame ionization detection, GC/MS) and statistical analyses were applied to the study of essential oils and extracts obtained from flowers, leaves, and stems of Lippia origanoides plants, growing wild in different Colombian regions. Retention indices, mass spectra, and standard substances were used in the identification of 139 substances detected in these essential oils and extracts. Principal component analysis allowed L. origanoides classification into three chemotypes, characterized according to their essential oil major components. Alpha- and beta-phellandrenes, p-cymene, and limonene distinguished chemotype A; carvacrol and thymol were the distinctive major components of chemotypes B and C, respectively. Pinocembrin (5,7-dihydroxyflavanone) was found in L. origanoides chemotype A supercritical fluid (CO(2)) extract at a concentration of 0.83+/-0.03 mg/g of dry plant material, which makes this plant an interesting source of an important bioactive flavanone with diverse potential applications in cosmetic, food, and pharmaceutical products.

Effects of cumulative illness severity on hippocampal gray matter volume in major depression: a voxel-based morphometry study.

PubMed

Zaremba, Dario; Enneking, Verena; Meinert, Susanne; Förster, Katharina; Bürger, Christian; Dohm, Katharina; Grotegerd, Dominik; Redlich, Ronny; Dietsche, Bruno; Krug, Axel; Kircher, Tilo; Kugel, Harald; Heindel, Walter; Baune, Bernhard T; Arolt, Volker; Dannlowski, Udo

2018-02-08

Patients with major depression show reduced hippocampal volume compared to healthy controls. However, the contribution of patients' cumulative illness severity to hippocampal volume has rarely been investigated. It was the aim of our study to find a composite score of cumulative illness severity that is associated with hippocampal volume in depression. We estimated hippocampal gray matter volume using 3-tesla brain magnetic resonance imaging in 213 inpatients with acute major depression according to DSM-IV criteria (employing the SCID interview) and 213 healthy controls. Patients' cumulative illness severity was ascertained by six clinical variables via structured clinical interviews. A principal component analysis was conducted to identify components reflecting cumulative illness severity. Regression analyses and a voxel-based morphometry approach were used to investigate the influence of patients' individual component scores on hippocampal volume. Principal component analysis yielded two main components of cumulative illness severity: Hospitalization and Duration of Illness. While the component Hospitalization incorporated information from the intensity of inpatient treatment, the component Duration of Illness was based on the duration and frequency of illness episodes. We could demonstrate a significant inverse association of patients' Hospitalization component scores with bilateral hippocampal gray matter volume. This relationship was not found for Duration of Illness component scores. Variables associated with patients' history of psychiatric hospitalization seem to be accurate predictors of hippocampal volume in major depression and reliable estimators of patients' cumulative illness severity. Future studies should pay attention to these measures when investigating hippocampal volume changes in major depression.

Ion channeling study of defects in compound crystals using Monte Carlo simulations

NASA Astrophysics Data System (ADS)

Turos, A.; Jozwik, P.; Nowicki, L.; Sathish, N.

2014-08-01

Ion channeling is a well-established technique for determination of structural properties of crystalline materials. Defect depth profiles have been usually determined basing on the two-beam model developed by Bøgh (1968) [1]. As long as the main research interest was focused on single element crystals it was considered as sufficiently accurate. New challenge emerged with growing technological importance of compound single crystals and epitaxial heterostructures. Overlap of partial spectra due to different sublattices and formation of complicated defect structures makes the two beam method hardly applicable. The solution is provided by Monte Carlo computer simulations. Our paper reviews principal aspects of this approach and the recent developments in the McChasy simulation code. The latter made it possible to distinguish between randomly displaced atoms (RDA) and extended defects (dislocations, loops, etc.). Hence, complex defect structures can be characterized by the relative content of these two components. The next refinement of the code consists of detailed parameterization of dislocations and dislocation loops. Defect profiles for variety of compound crystals (GaN, ZnO, SrTiO3) have been measured and evaluated using the McChasy code. Damage accumulation curves for RDA and extended defects revealed non monotonous defect buildup with some characteristic steps. Transition to each stage is governed by the different driving force. As shown by the complementary high resolution XRD measurements lattice strain plays here the crucial role and can be correlated with the concentration of extended defects.

Crossfit analysis: a novel method to characterize the dynamics of induced plant responses.

PubMed

Jansen, Jeroen J; van Dam, Nicole M; Hoefsloot, Huub C J; Smilde, Age K

2009-12-16

Many plant species show induced responses that protect them against exogenous attacks. These responses involve the production of many different bioactive compounds. Plant species belonging to the Brassicaceae family produce defensive glucosinolates, which may greatly influence their favorable nutritional properties for humans. Each responding compound may have its own dynamic profile and metabolic relationships with other compounds. The chemical background of the induced response is therefore highly complex and may therefore not reveal all the properties of the response in any single model. This study therefore aims to describe the dynamics of the glucosinolate response, measured at three time points after induction in a feral Brassica, by a three-faceted approach, based on Principal Component Analysis. First the large-scale aspects of the response are described in a 'global model' and then each time-point in the experiment is individually described in 'local models' that focus on phenomena that occur at specific moments in time. Although each local model describes the variation among the plants at one time-point as well as possible, the response dynamics are lost. Therefore a novel method called the 'Crossfit' is described that links the local models of different time-points to each other. Each element of the described analysis approach reveals different aspects of the response. The crossfit shows that smaller dynamic changes may occur in the response that are overlooked by global models, as illustrated by the analysis of a metabolic profiling dataset of the same samples.

Crossfit analysis: a novel method to characterize the dynamics of induced plant responses

PubMed Central

2009-01-01

Background Many plant species show induced responses that protect them against exogenous attacks. These responses involve the production of many different bioactive compounds. Plant species belonging to the Brassicaceae family produce defensive glucosinolates, which may greatly influence their favorable nutritional properties for humans. Each responding compound may have its own dynamic profile and metabolic relationships with other compounds. The chemical background of the induced response is therefore highly complex and may therefore not reveal all the properties of the response in any single model. Results This study therefore aims to describe the dynamics of the glucosinolate response, measured at three time points after induction in a feral Brassica, by a three-faceted approach, based on Principal Component Analysis. First the large-scale aspects of the response are described in a 'global model' and then each time-point in the experiment is individually described in 'local models' that focus on phenomena that occur at specific moments in time. Although each local model describes the variation among the plants at one time-point as well as possible, the response dynamics are lost. Therefore a novel method called the 'Crossfit' is described that links the local models of different time-points to each other. Conclusions Each element of the described analysis approach reveals different aspects of the response. The crossfit shows that smaller dynamic changes may occur in the response that are overlooked by global models, as illustrated by the analysis of a metabolic profiling dataset of the same samples. PMID:20015363

Profiling and classification of French propolis by combined multivariate data analysis of planar chromatograms and scanning direct analysis in real time mass spectra.

PubMed

Chasset, Thibaut; Häbe, Tim T; Ristivojevic, Petar; Morlock, Gertrud E

2016-09-23

Quality control of propolis is challenging, as it is a complex natural mixture of compounds, and thus, very difficult to analyze and standardize. Shown on the example of 30 French propolis samples, a strategy for an improved quality control was demonstrated in which high-performance thin-layer chromatography (HPTLC) fingerprints were evaluated in combination with selected mass signals obtained by desorption-based scanning mass spectrometry (MS). The French propolis sample extracts were separated by a newly developed reversed phase (RP)-HPTLC method. The fingerprints obtained by two different detection modes, i.e. after (1) derivatization and fluorescence detection (FLD) at UV 366nm and (2) scanning direct analysis in real time (DART)-MS, were analyzed by multivariate data analysis. Thus, RP-HPTLC-FLD and RP-HPTLC-DART-MS fingerprints were explored and the best classification was obtained using both methods in combination with pattern recognition techniques, such as principal component analysis. All investigated French propolis samples were divided in two types and characteristic patterns were observed. Phenolic compounds such as caffeic acid, p-coumaric acid, chrysin, pinobanksin, pinobanksin-3-acetate, galangin, kaempferol, tectochrysin and pinocembrin were identified as characteristic marker compounds of French propolis samples. This study expanded the research on the European poplar type of propolis and confirmed the presence of two botanically different types of propolis, known as the blue and orange types. Copyright © 2016 Elsevier B.V. All rights reserved.

Variations in chemical fingerprints and major flavonoid contents from the leaves of thirty-one accessions of Hibiscus sabdariffa L.

PubMed

Wang, Jin; Cao, Xianshuang; Ferchaud, Vanessa; Qi, Yadong; Jiang, Hao; Tang, Feng; Yue, Yongde; Chin, Kit L

2016-06-01

The leaves of Hibiscus sabdariffa L. have been used as traditional folk medicines for treating high blood pressure and fever. There are many accessions of H. sabdariffa L. throughout the world. To assess the chemical variations of 31 different accessions of H. sabdariffa L., fingerprinting analysis and quantitation of major flavonoids were performed by high-performance liquid chromatography (HPLC). The HPLC method was validated for linearity, sensitivity, precision, repeatability and accuracy. A quadrupole-time-of-flight mass spectrometry (Q-TOF-MS) was applied for the characterization of major compounds. A total of 9 compounds were identified, including 6 flavonoids and 3 phenolic acids. In the fingerprint analysis, similarity analysis (SA) and principal component analysis (PCA) were used to differentiate the 31 accessions of H. sabdariffa L. Based on the results of PCA and SA, the samples No. 15 and 19 appeared much different from the main group. The total content of five flavonoids varied greatly among different accessions, ranging from 3.35 to 23.30 mg/g. Rutin was found to be the dominant compound and the content of rutin could contribute to chemical variations among different accessions. This study was helpful to understand the chemical variations between different accessions of H. sabdariffa L., which could be used for quality control. © 2015 The Authors Biomedical Chromatography Published by John Wiley & Sons Ltd. © 2015 The Authors Biomedical Chromatography Published by John Wiley & Sons Ltd.

Compound Structure-Independent Activity Prediction in High-Dimensional Target Space.

PubMed

Balfer, Jenny; Hu, Ye; Bajorath, Jürgen

2014-08-01

Profiling of compound libraries against arrays of targets has become an important approach in pharmaceutical research. The prediction of multi-target compound activities also represents an attractive task for machine learning with potential for drug discovery applications. Herein, we have explored activity prediction in high-dimensional target space. Different types of models were derived to predict multi-target activities. The models included naïve Bayesian (NB) and support vector machine (SVM) classifiers based upon compound structure information and NB models derived on the basis of activity profiles, without considering compound structure. Because the latter approach can be applied to incomplete training data and principally depends on the feature independence assumption, SVM modeling was not applicable in this case. Furthermore, iterative hybrid NB models making use of both activity profiles and compound structure information were built. In high-dimensional target space, NB models utilizing activity profile data were found to yield more accurate activity predictions than structure-based NB and SVM models or hybrid models. An in-depth analysis of activity profile-based models revealed the presence of correlation effects across different targets and rationalized prediction accuracy. Taken together, the results indicate that activity profile information can be effectively used to predict the activity of test compounds against novel targets. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A two-step ultra-high-performance liquid chromatography-quadrupole/time of flight mass spectrometry with mass defect filtering method for rapid identification of analogues from known components of different chemical structure types in Fructus Gardeniae-Fructus Forsythiae herb pair extract and in rat's blood.

PubMed

Zhou, Wei; Shan, Jinjun; Meng, Minxin

2018-08-17

Fructus Gardeniae-Fructus Forsythiae herb pair is an herbal formula used extensively to treat inflammation and fever, but few systematic identification studies of the bioactive components have been reported. Herein, the unknown analogues in the first-step screening were rapidly identified from representative compounds in different structure types (geniposide as iridoid type, crocetin as crocetin type, jasminoside B as monocyclic monoterpene type, oleanolic acid as saponin type, 3-caffeoylquinic acid as organic acid type, forsythoside A as phenylethanoid type, phillyrin as lignan type and quercetin 3-rutinoside as flavonoid type) by UPLC-Q-Tof/MS combined with mass defect filtering (MDF), and further confirmed with reference standards and published literatures. Similarly, in the second step, other unknown components were rapidly discovered from the compounds identified in the first step by MDF. Using the two-step screening method, a total of 58 components were characterized in Fructus Gardeniae-Fructus Forsythiae (FG-FF) decoction. In rat's blood, 36 compounds in extract and 16 metabolites were unambiguously or tentatively identified. Besides, we found the principal metabolites were glucuronide conjugates, with the glucuronide conjugates of caffeic acid, quercetin and kaempferol confirmed as caffeic acid 3-glucuronide, quercetin 3-glucuronide and kaempferol 3-glucuronide by reference standards, respectively. Additionally, most of them bound more strongly to human serum albumin than their respective prototypes, predicted by Molecular Docking and Simulation, indicating that they had lower blood clearance in vivo and possibly more contribution to pharmacological effects. This study developed a novel two-step screening method in addressing how to comprehensively screen components in herbal medicine by UPLC-Q-Tof/MS with MDF. Copyright © 2018 Elsevier B.V. All rights reserved.

HS-GC/MS volatile profile of different varieties of garlic and their behavior under heating.

PubMed

Molina-Calle, María; Priego-Capote, Feliciano; de Castro, María D Luque

2016-05-01

Garlic is one of the most used seasonings in the world whose beneficial health effects, mainly ascribed to organosulfur compounds, are shared with the rest of the Allium family. The fact that many of these compounds are volatile makes the evaluation of the volatile profile of garlic interesting. For this purpose, three garlic varieties-White, Purple, and Chinese-cultivated in the South of Spain were analyzed by a method based on a headspace (HS) device coupled to a gas chromatograph and mass detector (HS-GC/MS). The main temperatures in the HS were optimized to achieve the highest concentration of volatiles. A total number of 45 volatiles were tentatively identified (among them 17 were identified for the first time in garlic); then, all were classified, also for the first time, and their relative concentration in three garlic varieties was used to evaluate differences among them and to study their profiles according to the heating time. Chinese garlic was found to be the richest variety in sulfur volatiles, while the three varieties presented a similar trend under preset heating times allowing differentiation between varieties and heating time using principal component analysis. Graphical Abstract HS-GC/MS analysis of the volatile profile of garlic.

A Quality Function Deployment Framework for the Service Quality of Health Information Websites

PubMed Central

Kim, Dohoon

2010-01-01

Objectives This research was conducted to identify both the users' service requirements on health information websites (HIWs) and the key functional elements for running HIWs. With the quality function deployment framework, the derived service attributes (SAs) are mapped into the suppliers' functional characteristics (FCs) to derive the most critical FCs for the users' satisfaction. Methods Using the survey data from 228 respondents, the SAs, FCs and their relationships were analyzed using various multivariate statistical methods such as principal component factor analysis, discriminant analysis, correlation analysis, etc. Simple and compound FC priorities were derived by matrix calculation. Results Nine factors of SAs and five key features of FCs were identified, and these served as the basis for the house of quality model. Based on the compound FC priorities, the functional elements pertaining to security and privacy, and usage support should receive top priority in the course of enhancing HIWs. Conclusions The quality function deployment framework can improve the FCs of the HIWs in an effective, structured manner, and it can also be utilized for critical success factors together with their strategic implications for enhancing the service quality of HIWs. Therefore, website managers could efficiently improve website operations by considering this study's results. PMID:21818418

Direct infusion MS-based lipid profiling reveals the pharmacological effects of compound K-reinforced ginsenosides in high-fat diet induced obese mice.

PubMed

Shon, Jong Cheol; Shin, Hwa-Soo; Seo, Yong Ki; Yoon, Young-Ran; Shin, Heungsop; Liu, Kwang-Hyeon

2015-03-25

The serum lipid metabolites of lean and obese mice fed normal or high-fat diets were analyzed via direct infusion nanoelectrospray-ion trap mass spectrometry followed by multivariate analysis. In addition, lipidomic biomarkers responsible for the pharmacological effects of compound K-reinforced ginsenosides (CK), thus the CK fraction, were evaluated in mice fed high-fat diets. The obese and lean groups were clearly discriminated upon principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) score plot, and the major metabolites contributing to such discrimination were triglycerides (TGs), cholesteryl esters (CEs), phosphatidylcholines (PCs), and lysophosphatidylcholines (LPCs). TGs with high total carbon number (>50) and low total carbon number (<50) were negatively and positively associated with high-fat diet induced obesity in mice, respectively. When the CK fraction was fed to obese mice that consumed a high-fat diet, the levels of certain lipids including LPCs and CEs became similar to those of mice fed a normal diet. Such metabolic markers can be used to better understand obesity and related diseases induced by a hyperlipidic diet. Furthermore, changes in the levels of such metabolites can be employed to assess the risk of obesity and the therapeutic effects of obesity management.

Optimisation of solid-phase microextraction combined with gas chromatography-mass spectrometry based methodology to establish the global volatile signature in pulp and skin of Vitis vinifera L. grape varieties.

PubMed

Perestrelo, Rosa; Barros, António S; Rocha, Sílvia M; Câmara, José S

2011-09-15

The volatiles (VOCs) and semi-volatile organic compounds (SVOCs) responsible for aroma are mainly present in skin of grape varieties. Thus, the present investigation is directed towards the optimisation of a solvent free methodology based on headspace-solid-phase microextraction (HS-SPME) combined with gas chromatography-quadrupole mass spectrometry (GC-qMS) in order to establish the global volatile composition in pulp and skin of Bual and Bastardo Vitis vinifera L. varieties. A deep study on the extraction-influencing parameters was performed, and the best results, expressed as GC peak area, number of identified compounds and reproducibility, were obtained using 4 g of sample homogenised in 5 mL of ultra-pure Milli-Q water in a 20 mL glass vial with addition of 2g of sodium chloride (NaCl). A divinylbenzene/carboxen/polydimethylsiloxane fibre was selected for extraction at 60°C for 45 min under continuous stirring at 800 rpm. More than 100 VOCs and SVOCs, including 27 monoterpenoids, 27 sesquiterpenoids, 21 carbonyl compounds, 17 alcohols (from which 2 aromatics), 10 C(13) norisoprenoids and 5 acids were identified. The results showed that, for both grape varieties, the levels and number of volatiles in skin were considerably higher than those observed in pulp. According to the data obtained by principal component analysis (PCA), the establishment of the global volatile signature of grape and the relationship between different part of grapes-pulp and skin, may be an useful tool to winemaker decision to define the vinification procedures that improves the organoleptic characteristics of the corresponding wines and consequently contributed to an economic valorization and consumer acceptance. Copyright © 2011 Elsevier B.V. All rights reserved.

Occurrence and transport of pesticides and alkylphenols in water samples along the Ebro River Basin

NASA Astrophysics Data System (ADS)

Navarro, Alícia; Tauler, Romà; Lacorte, Sílvia; Barceló, Damià

2010-03-01

SummaryWe report the temporal and geographical variations of a set of 30 pesticides (including triazines, organophosphorus and acetanilides) and industrial compounds in surface waters along the Ebro River during the period 2004-2006. Using descriptive statistics we found that the compounds with industrial origin (tributylphosphate, octylphenol and nonylphenol) appeared in over 60% of the samples analyzed and at very high concentrations, while pesticides had a point source origin in the Ebro delta area and overall low-levels, between 0.005 and 2.575 μg L -1. Correlations among pollutants and their distributions were studied using Principal Component Analysis (PCA), a multivariate exploratory data analysis technique which permitted us to discern between agricultural and industrial source contamination. Over a 3 years period a seasonal trend revealed highest concentrations of pesticides over the spring-summer period following pesticide application.

Multivariate statistical analysis of the polyphenolic constituents in kiwifruit juices to trace fruit varieties and geographical origins.

PubMed

Guo, Jing; Yuan, Yahong; Dou, Pei; Yue, Tianli

2017-10-01

Fifty-one kiwifruit juice samples of seven kiwifruit varieties from five regions in China were analyzed to determine their polyphenols contents and to trace fruit varieties and geographical origins by multivariate statistical analysis. Twenty-one polyphenols belonging to four compound classes were determined by ultra-high-performance liquid chromatography coupled with ultra-high-resolution TOF mass spectrometry. (-)-Epicatechin, (+)-catechin, procyanidin B1 and caffeic acid derivatives were the predominant phenolic compounds in the juices. Principal component analysis (PCA) allowed a clear separation of the juices according to kiwifruit varieties. Stepwise linear discriminant analysis (SLDA) yielded satisfactory categorization of samples, provided 100% success rate according to kiwifruit varieties and 92.2% success rate according to geographical origins. The result showed that polyphenolic profiles of kiwifruit juices contain enough information to trace fruit varieties and geographical origins. Copyright © 2017 Elsevier Ltd. All rights reserved.

Development of methodology for identification the nature of the polyphenolic extracts by FTIR associated with multivariate analysis.

PubMed

Grasel, Fábio dos Santos; Ferrão, Marco Flôres; Wolf, Carlos Rodolfo

2016-01-15

Tannins are polyphenolic compounds of complex structures formed by secondary metabolism in several plants. These polyphenolic compounds have different applications, such as drugs, anti-corrosion agents, flocculants, and tanning agents. This study analyses six different type of polyphenolic extracts by Fourier transform infrared spectroscopy (FTIR) combined with multivariate analysis. Through both principal component analysis (PCA) and hierarchical cluster analysis (HCA), we observed well-defined separation between condensed (quebracho and black wattle) and hydrolysable (valonea, chestnut, myrobalan, and tara) tannins. For hydrolysable tannins, it was also possible to observe the formation of two different subgroups between samples of chestnut and valonea and between samples of tara and myrobalan. Among all samples analysed, the chestnut and valonea showed the greatest similarity, indicating that these extracts contain equivalent chemical compositions and structure and, therefore, similar properties. Copyright © 2015 Elsevier B.V. All rights reserved.

Characteristic aroma components of rennet casein.

PubMed

Karagül-Yüceer, Yonca; Vlahovich, Katrina N; Drake, MaryAnne; Cadwallader, Keith R

2003-11-05

Rennet casein, produced by enzymatic (rennet) precipitation of casein from pasteurized skim milk, is used in both industrial (technical) and food applications. The flavor of rennet casein powder is an important quality parameter; however, the product often contains an odor described as like that of animal/wet dog. Two commercial rennet casein powders were evaluated to determine the compounds responsible for the typical odor. Aroma extracts were prepared by high-vacuum distillation of direct solvent (ether) extracts and analyzed by gas chromatography-olfactometry (GCO), aroma extract dilution analysis (AEDA), and GC-mass spectrometry (MS). Odorants detected by GCO were typical of those previously reported in skim milk powders and consisted mainly of short-chain volatile acids, phenolic compounds, lactones, and furanones. Results of AEDA indicated o-aminoacetophenone to be a potent odorant; however, sensory descriptive sensory analysis of model aroma systems revealed that the typical odor of rennet casein was principally caused by hexanoic acid, indole, guaiacol, and p-cresol.

Fingerprinting Breast Cancer vs. Normal Mammary Cells by Mass Spectrometric Analysis of Volatiles

NASA Astrophysics Data System (ADS)

He, Jingjing; Sinues, Pablo Martinez-Lozano; Hollmén, Maija; Li, Xue; Detmar, Michael; Zenobi, Renato

2014-06-01

There is increasing interest in the development of noninvasive diagnostic methods for early cancer detection, to improve the survival rate and quality of life of cancer patients. Identification of volatile metabolic compounds may provide an approach for noninvasive early diagnosis of malignant diseases. Here we analyzed the volatile metabolic signature of human breast cancer cell lines versus normal human mammary cells. Volatile compounds in the headspace of conditioned culture medium were directly fingerprinted by secondary electrospray ionization-mass spectrometry. The mass spectra were subsequently treated statistically to identify discriminating features between normal vs. cancerous cell types. We were able to classify different samples by using feature selection followed by principal component analysis (PCA). Additionally, high-resolution mass spectrometry allowed us to propose their chemical structures for some of the most discriminating molecules. We conclude that cancerous cells can release a characteristic odor whose constituents may be used as disease markers.

Principal components analysis of an evaluation of the hemiplegic subject based on the Bobath approach.

PubMed

Corriveau, H; Arsenault, A B; Dutil, E; Lepage, Y

1992-01-01

An evaluation based on the Bobath approach to treatment has previously been developed and partially validated. The purpose of the present study was to verify the content validity of this evaluation with the use of a statistical approach known as principal components analysis. Thirty-eight hemiplegic subjects participated in the study. Analysis of the scores on each of six parameters (sensorium, active movements, muscle tone, reflex activity, postural reactions, and pain) was evaluated on three occasions across a 2-month period. Each time this produced three factors that contained 70% of the variation in the data set. The first component mainly reflected variations in mobility, the second mainly variations in muscle tone, and the third mainly variations in sensorium and pain. The results of such exploratory analysis highlight the fact that some of the parameters are not only important but also interrelated. These results seem to partially support the conceptual framework substantiating the Bobath approach to treatment.

Comparison of the Trace Elements and Active Components of Lonicera japonica flos and Lonicera flos Using ICP-MS and HPLC-PDA.

PubMed

Zhao, Yueran; Dou, Deqiang; Guo, Yueqiu; Qi, Yue; Li, Jun; Jia, Dong

2018-06-01

Thirteen trace elements and active constituents of 40 batches of Lonicera japonica flos and Lonicera flos were comparatively studied using inductively coupled plasma mass-spectrometry (ICP-MS) and high-performance liquid chromatography-photodiode array (HPLC-PDA). The trace elements were 24 Mg, 52 Cr, 55 Mn, 57 Fe, 60 Ni, 63 Cu, 66 Zn, 75 As, 82 Se, 98 Mo, 114 Cd, 202 Hg, and 208 Pb, and the active compounds were chlorogenic acid, 3,5-O-dicaffeoylquinc acid, 4,5-O-dicaffeoylquinc acid, luteolin-7-O-glucoside, and 4-O-caffeoylquinic acid. The data of 18 variables were statistically processed using principal component analysis (PCA) and discriminate analysis (DA) to classify L. japonica flos and L. flos. The validated method was developed to divide the 40 samples into two groups based on the PCA in terms of 18 variables. Furthermore, the species of Lonicera was better discriminated by using DA with 12 variables. These results suggest that the method and statistical analysis of the contents of trace elements and chemical components can classify the L. japonica flos and L. flos using 12 variables, such as 3,5-O-dicaffeoylquincacid, luteolin-7-O-glucoside, Cd, Mn, Hg, Pb, Ni, 4-O-caffeoyl-quinic acid, 4,5-O-dicaffeoylquinc acid, Fe, Mg, and Cr.

Fluorescent polymer sensor array for detection and discrimination of explosives in water.

PubMed

Woodka, Marc D; Schnee, Vincent P; Polcha, Michael P

2010-12-01

A fluorescent polymer sensor array (FPSA) was made from commercially available fluorescent polymers coated onto glass beads and was tested to assess the ability of the array to discriminate between different analytes in aqueous solution. The array was challenged with exposures to 17 different analytes, including the explosives trinitrotoluene (TNT), tetryl, and RDX, various explosive-related compounds (ERCs), and nonexplosive electron-withdrawing compounds (EWCs). The array exhibited a natural selectivity toward EWCs, while the non-electron-withdrawing explosive 1,3,5-trinitroperhydro-1,3,5-triazine (RDX) produced no response. Response signatures were visualized by principal component analysis (PCA), and classified by linear discriminant analysis (LDA). RDX produced the same response signature as the sampled blanks and was classified accordingly. The array exhibited excellent discrimination toward all other compounds, with the exception of the isomers of nitrotoluene and aminodinitrotoluene. Of particular note was the ability of the array to discriminate between the three isomers of dinitrobenzene. The natural selectivity of the FPSA toward EWCs, plus the ability of the FPSA to discriminate between different EWCs, could be used to design a sensor with a low false alarm rate and an excellent ability to discriminate between explosives and explosive-related compounds.

Quantification of selected aroma-active compounds in strawberries by headspace solid-phase microextraction gas chromatography and correlation with sensory descriptive analysis.

PubMed

Jetti, R R; Yang, E; Kurnianta, A; Finn, C; Qian, M C

2007-09-01

Selected aroma-active compounds in strawberries were quantified using headspace solid-phase microextraction and gas chromatography. Ten strawberry cultivars grown in California and Oregon were studied. The standard curves were built in a synthetic matrix and quantification was achieved using multiple internal standards. Odor activity values (OAVs) of the aroma compounds were calculated to understand their contribution to the overall aroma. Although the concentrations of the aroma compounds varied depending on the cultivars, in general, ethyl butanoate, mesifurane, ethyl hexanoate, ethyl 3-methylbutanoate, hexyl acetate, and gamma-dodecalactone had the highest OAVs. Descriptive sensory analysis was performed by a trained panel of 10 members. A PCA plot was built to understand the aroma contribution of principal components. The chemical results were compared with sensory data. The OAV of esters correlated well with the floral, pineapple, and banana notes. The green notes did not correlate with the concentration or OAVs of aldehydes or C6 alcohols. It is assumed that the higher amounts of green, sulfur, musty, and waxy notes in some cultivars were due to the lack of fruity notes.

Gas Chromatography Analysis with Olfactometric Detection (GC-O) as a Useful Methodology for Chemical Characterization of Odorous Compounds

PubMed Central

Brattoli, Magda; Cisternino, Ezia; Dambruoso, Paolo Rosario; de Gennaro, Gianluigi; Giungato, Pasquale; Mazzone, Antonio; Palmisani, Jolanda; Tutino, Maria

2013-01-01

The gas chromatography-olfactometry (GC-O) technique couples traditional gas chromatographic analysis with sensory detection in order to study complex mixtures of odorous substances and to identify odor active compounds. The GC-O technique is already widely used for the evaluation of food aromas and its application in environmental fields is increasing, thus moving the odor emission assessment from the solely olfactometric evaluations to the characterization of the volatile components responsible for odor nuisance. The aim of this paper is to describe the state of the art of gas chromatography-olfactometry methodology, considering the different approaches regarding the operational conditions and the different methods for evaluating the olfactometric detection of odor compounds. The potentials of GC-O are described highlighting the improvements in this methodology relative to other conventional approaches used for odor detection, such as sensoristic, sensorial and the traditional gas chromatographic methods. The paper also provides an examination of the different fields of application of the GC-O, principally related to fragrances and food aromas, odor nuisance produced by anthropic activities and odorous compounds emitted by materials and medical applications. PMID:24316571

Characterization of French and Spanish dry-cured hams: influence of the volatiles from the muscles and the subcutaneous fat quantified by SPME-GC.

PubMed

Sánchez-Peña, Carolina M; Luna, Guadalupe; García-González, Diego L; Aparicio, Ramón

2005-04-01

The influence of the volatile compounds on the characterization of Spanish and French dry-cured hams was studied. Thirty volatiles were quantified in each one of four locations (biceps femoris, semimembranosus and semitendinosus muscles and subcutaneous fat) of 29 dry-cured hams by solid-phase microextraction gas-chromatography (SPME-GC). The Brown-Forsythe univariate test allowed determination of the volatiles that individually could characterize (p<0.05) the samples by their geographical origin (France, Spain) and breed type (Iberian, white). Stepwise linear discriminant procedure, under very strict conditions (F-to-Enter for a F-distribution>0.95), then selected the most remarkable volatile compounds. Four compounds from the subcutaneous fat (methyl benzene and octanol) and the semitendinosus muscle (2-butanone and 2-octanone) allowed 100% correct classifications by geographic origin. On the other hand, only two compounds from the subcutaneous fat (octanol) and the biceps femoris muscle (3-methyl 1-butanol) correctly classified all the samples by the breed type. The ability of these variables to classify the samples was checked by the unsupervised procedure of principal components.

Capturing multidimensionality in stroke aphasia: mapping principal behavioural components to neural structures

PubMed Central

Butler, Rebecca A.

2014-01-01

Stroke aphasia is a multidimensional disorder in which patient profiles reflect variation along multiple behavioural continua. We present a novel approach to separating the principal aspects of chronic aphasic performance and isolating their neural bases. Principal components analysis was used to extract core factors underlying performance of 31 participants with chronic stroke aphasia on a large, detailed battery of behavioural assessments. The rotated principle components analysis revealed three key factors, which we labelled as phonology, semantic and executive/cognition on the basis of the common elements in the tests that loaded most strongly on each component. The phonology factor explained the most variance, followed by the semantic factor and then the executive-cognition factor. The use of principle components analysis rendered participants’ scores on these three factors orthogonal and therefore ideal for use as simultaneous continuous predictors in a voxel-based correlational methodology analysis of high resolution structural scans. Phonological processing ability was uniquely related to left posterior perisylvian regions including Heschl’s gyrus, posterior middle and superior temporal gyri and superior temporal sulcus, as well as the white matter underlying the posterior superior temporal gyrus. The semantic factor was uniquely related to left anterior middle temporal gyrus and the underlying temporal stem. The executive-cognition factor was not correlated selectively with the structural integrity of any particular region, as might be expected in light of the widely-distributed and multi-functional nature of the regions that support executive functions. The identified phonological and semantic areas align well with those highlighted by other methodologies such as functional neuroimaging and neurostimulation. The use of principle components analysis allowed us to characterize the neural bases of participants’ behavioural performance more robustly and selectively than the use of raw assessment scores or diagnostic classifications because principle components analysis extracts statistically unique, orthogonal behavioural components of interest. As such, in addition to improving our understanding of lesion–symptom mapping in stroke aphasia, the same approach could be used to clarify brain–behaviour relationships in other neurological disorders. PMID:25348632

[Analysis of variation of monoterpene glycosides and polyhydroxy compounds in paeoniae radix alba during preliminary processing].

PubMed

Xu, Yuan; Liu, Pei; Yan, Hui; Qian, Da-Wei; Duan, Jin-Ao

2014-05-01

To investigate variation of monoterpene glycosides and polyhydroxy compounds in Paeoniae Radix Alba dried by different processing methods. The crude drugs were processed sequentially as washed, removed the head, tail, fine roots and dried. The samples were divided into eight groups by whether peeled and decocted or not. Each group was dried by 35, 45, 60, 80,100, 120 degrees C, sun-dried and shade-dried. HPLC-PDA method was adopted to determine the content of monoterpene glycosides compounds (paeoniflorin alibiflorin, oxypaeoniflorin and benzoylpaeoniflorin), polyhydroxy compounds (catechin and gallic acid) and benzoic acid. Chromatographic conditions: Phecad C18 column (250 mm x 4.6 mm, 5 microm). A principal component analysis (PCA) method was used subsequently to get data processed. The retained content of seven constituents decreased in those peeled crude drug, and after cooked, monoterpene glycosides and polyhydroxy compounds increased while the benzoic acid decreased. It was believed that rele- vant enzymes were inactivated while being cooked so that drying temperature showed little influence on the biotransformation. Contents of effective ingredients in Paeoniae Radix Alba are influenced by drying processing. The preferable method shows to be that crude drug should be cooked before being peeled and dried. As a matter of processing convtence, it is suggested to be peeled and sliced before being dried.

The Influence Function of Principal Component Analysis by Self-Organizing Rule.

PubMed

Higuchi; Eguchi

1998-07-28

This article is concerned with a neural network approach to principal component analysis (PCA). An algorithm for PCA by the self-organizing rule has been proposed and its robustness observed through the simulation study by Xu and Yuille (1995). In this article, the robustness of the algorithm against outliers is investigated by using the theory of influence function. The influence function of the principal component vector is given in an explicit form. Through this expression, the method is shown to be robust against any directions orthogonal to the principal component vector. In addition, a statistic generated by the self-organizing rule is proposed to assess the influence of data in PCA.

Classifying Facial Actions

PubMed Central

Donato, Gianluca; Bartlett, Marian Stewart; Hager, Joseph C.; Ekman, Paul; Sejnowski, Terrence J.

2010-01-01

The Facial Action Coding System (FACS) [23] is an objective method for quantifying facial movement in terms of component actions. This system is widely used in behavioral investigations of emotion, cognitive processes, and social interaction. The coding is presently performed by highly trained human experts. This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. These techniques include analysis of facial motion through estimation of optical flow; holistic spatial analysis, such as principal component analysis, independent component analysis, local feature analysis, and linear discriminant analysis; and methods based on the outputs of local filters, such as Gabor wavelet representations and local principal components. Performance of these systems is compared to naive and expert human subjects. Best performances were obtained using the Gabor wavelet representation and the independent component representation, both of which achieved 96 percent accuracy for classifying 12 facial actions of the upper and lower face. The results provide converging evidence for the importance of using local filters, high spatial frequencies, and statistical independence for classifying facial actions. PMID:21188284

Pattern recognition and genetic algorithms for discrimination of orange juices and reduction of significant components from headspace solid-phase microextraction.

PubMed

Rinaldi, Maurizio; Gindro, Roberto; Barbeni, Massimo; Allegrone, Gianna

2009-01-01

Orange (Citrus sinensis L.) juice comprises a complex mixture of volatile components that are difficult to identify and quantify. Classification and discrimination of the varieties on the basis of the volatile composition could help to guarantee the quality of a juice and to detect possible adulteration of the product. To provide information on the amounts of volatile constituents in fresh-squeezed juices from four orange cultivars and to establish suitable discrimination rules to differentiate orange juices using new chemometric approaches. Fresh juices of four orange cultivars were analysed by headspace solid-phase microextraction (HS-SPME) coupled with GC-MS. Principal component analysis, linear discriminant analysis and heuristic methods, such as neural networks, allowed clustering of the data from HS-SPME analysis while genetic algorithms addressed the problem of data reduction. To check the quality of the results the chemometric techniques were also evaluated on a sample. Thirty volatile compounds were identified by HS-SPME and GC-MS analyses and their relative amounts calculated. Differences in composition of orange juice volatile components were observed. The chosen orange cultivars could be discriminated using neural networks, genetic relocation algorithms and linear discriminant analysis. Genetic algorithms applied to the data were also able to detect the most significant compounds. SPME is a useful technique to investigate orange juice volatile composition and a flexible chemometric approach is able to correctly separate the juices.

Chemical composition and allelopathic potential of essential oils obtained from Acacia cyanophylla Lindl. Cultivated in Tunisia.

PubMed

El Ayeb-Zakhama, Asma; Sakka-Rouis, Lamia; Bergaoui, Afifa; Flamini, Guido; Ben Jannet, Hichem; Harzallah-Skhiri, Fethia

2015-04-01

Acacia cyanophylla Lindl. (Fabaceae), synonym Acacia saligna (Labill.) H. L.Wendl., native to West Australia and naturalized in North Africa and South Europe, was introduced in Tunisia for rangeland rehabilitation, particularly in the semiarid zones. In addition, this evergreen tree represents a potential forage resource, particularly during periods of drought. A. cyanophylla is abundant in Tunisia and some other Mediterranean countries. The chemical composition of the essential oils obtained by hydrodistillation from different plant parts, viz., roots, stems, phyllodes, flowers, and pods (fully mature fruits without seeds), was characterized for the first time here. According to GC-FID and GC/MS analyses, the principal compound in the phyllode and flower oils was dodecanoic acid (4), representing 22.8 and 66.5% of the total oil, respectively. Phenylethyl salicylate (8; 34.9%), heptyl valerate (3; 17.3%), and nonadecane (36%) were the main compounds in the root, stem, and pod oils, respectively. The phyllode and flower oils were very similar, containing almost the same compounds. Nevertheless, the phyllode oil differed from the flower oil for its higher contents of hexahydrofarnesyl acetone (6), linalool (1), pentadecanal, α-terpineol, and benzyl benzoate (5) and its lower content of 4. Principal component and hierarchical cluster analyses separated the five essential oils into four groups, each characterized by its main constituents. Furthermore, the allelopathic activity of each oil was evaluated using lettuce (Lactuca sativa L.) as a plant model. The phyllode, flower, and pod oils exhibited a strong allelopathic activity against lettuce. Copyright © 2015 Verlag Helvetica Chimica Acta AG, Zürich.

Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds.

PubMed

Gao, Mengxuan; Igata, Hideyoshi; Takeuchi, Aoi; Sato, Kaoru; Ikegaya, Yuji

2017-02-01

Various biological factors have been implicated in convulsive seizures, involving side effects of drugs. For the preclinical safety assessment of drug development, it is difficult to predict seizure-inducing side effects. Here, we introduced a machine learning-based in vitro system designed to detect seizure-inducing side effects. We recorded local field potentials from the CA1 alveus in acute mouse neocortico-hippocampal slices, while 14 drugs were bath-perfused at 5 different concentrations each. For each experimental condition, we collected seizure-like neuronal activity and merged their waveforms as one graphic image, which was further converted into a feature vector using Caffe, an open framework for deep learning. In the space of the first two principal components, the support vector machine completely separated the vectors (i.e., doses of individual drugs) that induced seizure-like events and identified diphenhydramine, enoxacin, strychnine and theophylline as "seizure-inducing" drugs, which indeed were reported to induce seizures in clinical situations. Thus, this artificial intelligence-based classification may provide a new platform to detect the seizure-inducing side effects of preclinical drugs. Copyright © 2017 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

Hyperspectral Image Denoising Using a Nonlocal Spectral Spatial Principal Component Analysis

NASA Astrophysics Data System (ADS)

Li, D.; Xu, L.; Peng, J.; Ma, J.

2018-04-01

Hyperspectral images (HSIs) denoising is a critical research area in image processing duo to its importance in improving the quality of HSIs, which has a negative impact on object detection and classification and so on. In this paper, we develop a noise reduction method based on principal component analysis (PCA) for hyperspectral imagery, which is dependent on the assumption that the noise can be removed by selecting the leading principal components. The main contribution of paper is to introduce the spectral spatial structure and nonlocal similarity of the HSIs into the PCA denoising model. PCA with spectral spatial structure can exploit spectral correlation and spatial correlation of HSI by using 3D blocks instead of 2D patches. Nonlocal similarity means the similarity between the referenced pixel and other pixels in nonlocal area, where Mahalanobis distance algorithm is used to estimate the spatial spectral similarity by calculating the distance in 3D blocks. The proposed method is tested on both simulated and real hyperspectral images, the results demonstrate that the proposed method is superior to several other popular methods in HSI denoising.

Discrimination of healthy and osteoarthritic articular cartilage by Fourier transform infrared imaging and Fisher’s discriminant analysis

PubMed Central

Mao, Zhi-Hua; Yin, Jian-Hua; Zhang, Xue-Xi; Wang, Xiao; Xia, Yang

2016-01-01

Fourier transform infrared spectroscopic imaging (FTIRI) technique can be used to obtain the quantitative information of content and spatial distribution of principal components in cartilage by combining with chemometrics methods. In this study, FTIRI combining with principal component analysis (PCA) and Fisher’s discriminant analysis (FDA) was applied to identify the healthy and osteoarthritic (OA) articular cartilage samples. Ten 10-μm thick sections of canine cartilages were imaged at 6.25μm/pixel in FTIRI. The infrared spectra extracted from the FTIR images were imported into SPSS software for PCA and FDA. Based on the PCA result of 2 principal components, the healthy and OA cartilage samples were effectively discriminated by the FDA with high accuracy of 94% for the initial samples (training set) and cross validation, as well as 86.67% for the prediction group. The study showed that cartilage degeneration became gradually weak with the increase of the depth. FTIRI combined with chemometrics may become an effective method for distinguishing healthy and OA cartilages in future. PMID:26977354

Dimensionality Reduction Through Classifier Ensembles

NASA Technical Reports Server (NTRS)

Oza, Nikunj C.; Tumer, Kagan; Norwig, Peter (Technical Monitor)

1999-01-01

In data mining, one often needs to analyze datasets with a very large number of attributes. Performing machine learning directly on such data sets is often impractical because of extensive run times, excessive complexity of the fitted model (often leading to overfitting), and the well-known "curse of dimensionality." In practice, to avoid such problems, feature selection and/or extraction are often used to reduce data dimensionality prior to the learning step. However, existing feature selection/extraction algorithms either evaluate features by their effectiveness across the entire data set or simply disregard class information altogether (e.g., principal component analysis). Furthermore, feature extraction algorithms such as principal components analysis create new features that are often meaningless to human users. In this article, we present input decimation, a method that provides "feature subsets" that are selected for their ability to discriminate among the classes. These features are subsequently used in ensembles of classifiers, yielding results superior to single classifiers, ensembles that use the full set of features, and ensembles based on principal component analysis on both real and synthetic datasets.

[Discrimination of varieties of brake fluid using visual-near infrared spectra].

PubMed

Jiang, Lu-lu; Tan, Li-hong; Qiu, Zheng-jun; Lu, Jiang-feng; He, Yong

2008-06-01

A new method was developed to fast discriminate brands of brake fluid by means of visual-near infrared spectroscopy. Five different brands of brake fluid were analyzed using a handheld near infrared spectrograph, manufactured by ASD Company, and 60 samples were gotten from each brand of brake fluid. The samples data were pretreated using average smoothing and standard normal variable method, and then analyzed using principal component analysis (PCA). A 2-dimensional plot was drawn based on the first and the second principal components, and the plot indicated that the clustering characteristic of different brake fluid is distinct. The foregoing 6 principal components were taken as input variable, and the band of brake fluid as output variable to build the discriminate model by stepwise discriminant analysis method. Two hundred twenty five samples selected randomly were used to create the model, and the rest 75 samples to verify the model. The result showed that the distinguishing rate was 94.67%, indicating that the method proposed in this paper has good performance in classification and discrimination. It provides a new way to fast discriminate different brands of brake fluid.

Identifying overarching excipient properties towards an in-depth understanding of process and product performance for continuous twin-screw wet granulation.

PubMed

Willecke, N; Szepes, A; Wunderlich, M; Remon, J P; Vervaet, C; De Beer, T

2017-04-30

The overall objective of this work is to understand how excipient characteristics influence the process and product performance for a continuous twin-screw wet granulation process. The knowledge gained through this study is intended to be used for a Quality by Design (QbD)-based formulation design approach and formulation optimization. A total of 9 preferred fillers and 9 preferred binders were selected for this study. The selected fillers and binders were extensively characterized regarding their physico-chemical and solid state properties using 21 material characterization techniques. Subsequently, principal component analysis (PCA) was performed on the data sets of filler and binder characteristics in order to reduce the variety of single characteristics to a limited number of overarching properties. Four principal components (PC) explained 98.4% of the overall variability in the fillers data set, while three principal components explained 93.4% of the overall variability in the data set of binders. Both PCA models allowed in-depth evaluation of similarities and differences in the excipient properties. Copyright © 2017. Published by Elsevier B.V.

Use of principal-component, correlation, and stepwise multiple-regression analyses to investigate selected physical and hydraulic properties of carbonate-rock aquifers

USGS Publications Warehouse

Brown, C. Erwin

1993-01-01

Correlation analysis in conjunction with principal-component and multiple-regression analyses were applied to laboratory chemical and petrographic data to assess the usefulness of these techniques in evaluating selected physical and hydraulic properties of carbonate-rock aquifers in central Pennsylvania. Correlation and principal-component analyses were used to establish relations and associations among variables, to determine dimensions of property variation of samples, and to filter the variables containing similar information. Principal-component and correlation analyses showed that porosity is related to other measured variables and that permeability is most related to porosity and grain size. Four principal components are found to be significant in explaining the variance of data. Stepwise multiple-regression analysis was used to see how well the measured variables could predict porosity and (or) permeability for this suite of rocks. The variation in permeability and porosity is not totally predicted by the other variables, but the regression is significant at the 5% significance level. ?? 1993.

Application of time series analysis on molecular dynamics simulations of proteins: a study of different conformational spaces by principal component analysis.

PubMed

Alakent, Burak; Doruker, Pemra; Camurdan, Mehmet C

2004-09-08

Time series analysis is applied on the collective coordinates obtained from principal component analysis of independent molecular dynamics simulations of alpha-amylase inhibitor tendamistat and immunity protein of colicin E7 based on the Calpha coordinates history. Even though the principal component directions obtained for each run are considerably different, the dynamics information obtained from these runs are surprisingly similar in terms of time series models and parameters. There are two main differences in the dynamics of the two proteins: the higher density of low frequencies and the larger step sizes for the interminima motions of colicin E7 than those of alpha-amylase inhibitor, which may be attributed to the higher number of residues of colicin E7 and/or the structural differences of the two proteins. The cumulative density function of the low frequencies in each run conforms to the expectations from the normal mode analysis. When different runs of alpha-amylase inhibitor are projected on the same set of eigenvectors, it is found that principal components obtained from a certain conformational region of a protein has a moderate explanation power in other conformational regions and the local minima are similar to a certain extent, while the height of the energy barriers in between the minima significantly change. As a final remark, time series analysis tools are further exploited in this study with the motive of explaining the equilibrium fluctuations of proteins. Copyright 2004 American Institute of Physics

Application of time series analysis on molecular dynamics simulations of proteins: A study of different conformational spaces by principal component analysis

NASA Astrophysics Data System (ADS)

Alakent, Burak; Doruker, Pemra; Camurdan, Mehmet C.

2004-09-01

Time series analysis is applied on the collective coordinates obtained from principal component analysis of independent molecular dynamics simulations of α-amylase inhibitor tendamistat and immunity protein of colicin E7 based on the Cα coordinates history. Even though the principal component directions obtained for each run are considerably different, the dynamics information obtained from these runs are surprisingly similar in terms of time series models and parameters. There are two main differences in the dynamics of the two proteins: the higher density of low frequencies and the larger step sizes for the interminima motions of colicin E7 than those of α-amylase inhibitor, which may be attributed to the higher number of residues of colicin E7 and/or the structural differences of the two proteins. The cumulative density function of the low frequencies in each run conforms to the expectations from the normal mode analysis. When different runs of α-amylase inhibitor are projected on the same set of eigenvectors, it is found that principal components obtained from a certain conformational region of a protein has a moderate explanation power in other conformational regions and the local minima are similar to a certain extent, while the height of the energy barriers in between the minima significantly change. As a final remark, time series analysis tools are further exploited in this study with the motive of explaining the equilibrium fluctuations of proteins.

RP-HPLC method using 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate incorporated with normalization technique in principal component analysis to differentiate the bovine, porcine and fish gelatins.

PubMed

Azilawati, M I; Hashim, D M; Jamilah, B; Amin, I

2015-04-01

The amino acid compositions of bovine, porcine and fish gelatin were determined by amino acid analysis using 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate as derivatization reagent. Sixteen amino acids were identified with similar spectral chromatograms. Data pre-treatment via centering and transformation of data by normalization were performed to provide data that are more suitable for analysis and easier to be interpreted. Principal component analysis (PCA) transformed the original data matrix into a number of principal components (PCs). Three principal components (PCs) described 96.5% of the total variance, and 2 PCs (91%) explained the highest variances. The PCA model demonstrated the relationships among amino acids in the correlation loadings plot to the group of gelatins in the scores plot. Fish gelatin was correlated to threonine, serine and methionine on the positive side of PC1; bovine gelatin was correlated to the non-polar side chains amino acids that were proline, hydroxyproline, leucine, isoleucine and valine on the negative side of PC1 and porcine gelatin was correlated to the polar side chains amino acids that were aspartate, glutamic acid, lysine and tyrosine on the negative side of PC2. Verification on the database using 12 samples from commercial products gelatin-based had confirmed the grouping patterns and the variables correlations. Therefore, this quantitative method is very useful as a screening method to determine gelatin from various sources. Copyright © 2014 Elsevier Ltd. All rights reserved.

Comparison of common components analysis with principal components analysis and independent components analysis: Application to SPME-GC-MS volatolomic signatures.

PubMed

Bouhlel, Jihéne; Jouan-Rimbaud Bouveresse, Delphine; Abouelkaram, Said; Baéza, Elisabeth; Jondreville, Catherine; Travel, Angélique; Ratel, Jérémy; Engel, Erwan; Rutledge, Douglas N

2018-02-01

The aim of this work is to compare a novel exploratory chemometrics method, Common Components Analysis (CCA), with Principal Components Analysis (PCA) and Independent Components Analysis (ICA). CCA consists in adapting the multi-block statistical method known as Common Components and Specific Weights Analysis (CCSWA or ComDim) by applying it to a single data matrix, with one variable per block. As an application, the three methods were applied to SPME-GC-MS volatolomic signatures of livers in an attempt to reveal volatile organic compounds (VOCs) markers of chicken exposure to different types of micropollutants. An application of CCA to the initial SPME-GC-MS data revealed a drift in the sample Scores along CC2, as a function of injection order, probably resulting from time-related evolution in the instrument. This drift was eliminated by orthogonalization of the data set with respect to CC2, and the resulting data are used as the orthogonalized data input into each of the three methods. Since the first step in CCA is to norm-scale all the variables, preliminary data scaling has no effect on the results, so that CCA was applied only to orthogonalized SPME-GC-MS data, while, PCA and ICA were applied to the "orthogonalized", "orthogonalized and Pareto-scaled", and "orthogonalized and autoscaled" data. The comparison showed that PCA results were highly dependent on the scaling of variables, contrary to ICA where the data scaling did not have a strong influence. Nevertheless, for both PCA and ICA the clearest separations of exposed groups were obtained after autoscaling of variables. The main part of this work was to compare the CCA results using the orthogonalized data with those obtained with PCA and ICA applied to orthogonalized and autoscaled variables. The clearest separations of exposed chicken groups were obtained by CCA. CCA Loadings also clearly identified the variables contributing most to the Common Components giving separations. The PCA Loadings did not highlight the most influencing variables for each separation, whereas the ICA Loadings highlighted the same variables as did CCA. This study shows the potential of CCA for the extraction of pertinent information from a data matrix, using a procedure based on an original optimisation criterion, to produce results that are complementary, and in some cases may be superior, to those of PCA and ICA. Copyright © 2017 Elsevier B.V. All rights reserved.

Genetic algorithm applied to the selection of factors in principal component-artificial neural networks: application to QSAR study of calcium channel antagonist activity of 1,4-dihydropyridines (nifedipine analogous).

PubMed

Hemmateenejad, Bahram; Akhond, Morteza; Miri, Ramin; Shamsipur, Mojtaba

2003-01-01

A QSAR algorithm, principal component-genetic algorithm-artificial neural network (PC-GA-ANN), has been applied to a set of newly synthesized calcium channel blockers, which are of special interest because of their role in cardiac diseases. A data set of 124 1,4-dihydropyridines bearing different ester substituents at the C-3 and C-5 positions of the dihydropyridine ring and nitroimidazolyl, phenylimidazolyl, and methylsulfonylimidazolyl groups at the C-4 position with known Ca(2+) channel binding affinities was employed in this study. Ten different sets of descriptors (837 descriptors) were calculated for each molecule. The principal component analysis was used to compress the descriptor groups into principal components. The most significant descriptors of each set were selected and used as input for the ANN. The genetic algorithm (GA) was used for the selection of the best set of extracted principal components. A feed forward artificial neural network with a back-propagation of error algorithm was used to process the nonlinear relationship between the selected principal components and biological activity of the dihydropyridines. A comparison between PC-GA-ANN and routine PC-ANN shows that the first model yields better prediction ability.

Comparative analysis of the volatile composition of honeys from Brazilian stingless bees by static headspace GC-MS.

PubMed

de Lima Morais da Silva, Patricia; de Lima, Liliane Schier; Caetano, Ísis Kaminski; Torres, Yohandra Reyes

2017-12-01

The volatile composition of honeys produced by eight species of stingless bees collected in three municipalities in the state of Paraná (Brazil) was compared by combining static headspace GC-MS and chemometrics methods. Forty-four compounds were identified using NIST library and linear retention index relative to n-alkanes (C 8 -C 40 ). Linalool derivatives were the most abundant peaks in most honeys regardless geographical or entomological origin. However, Principal Component Analysis discriminated honeys from different geographical origins considering their distinctive minor volatile components. Honey samples from Guaraqueçaba were characterized by the presence of hotrienol while those from Cambará showed epoxylinalol, benzaldehyde and TDN as minor discriminating compounds. Punctual species such as Borá showed similar fingerprints regardless geographical origin, with ethyl octanoate and ethyl decanoate as characteristic intense chromatographic peaks, which may suggest a specialized behavior for nectar collection. Discriminant Analysis allowed correct geographic discrimination of most honeys produced in the three spots tested. We concluded that volatile profile of stingless bee honeys can be used to attest authenticity related to regional origin of honeys. Copyright © 2017. Published by Elsevier Ltd.

Nuclear norm-based 2-DPCA for extracting features from images.

PubMed

Zhang, Fanlong; Yang, Jian; Qian, Jianjun; Xu, Yong

2015-10-01

The 2-D principal component analysis (2-DPCA) is a widely used method for image feature extraction. However, it can be equivalently implemented via image-row-based principal component analysis. This paper presents a structured 2-D method called nuclear norm-based 2-DPCA (N-2-DPCA), which uses a nuclear norm-based reconstruction error criterion. The nuclear norm is a matrix norm, which can provide a structured 2-D characterization for the reconstruction error image. The reconstruction error criterion is minimized by converting the nuclear norm-based optimization problem into a series of F-norm-based optimization problems. In addition, N-2-DPCA is extended to a bilateral projection-based N-2-DPCA (N-B2-DPCA). The virtue of N-B2-DPCA over N-2-DPCA is that an image can be represented with fewer coefficients. N-2-DPCA and N-B2-DPCA are applied to face recognition and reconstruction and evaluated using the Extended Yale B, CMU PIE, FRGC, and AR databases. Experimental results demonstrate the effectiveness of the proposed methods.

Creating the Climate for Humanistic Change in the Elementary School with Principal as Change Agent

ERIC Educational Resources Information Center

Heichberger, Robert L.

1975-01-01

It is suggested that three necessary components prerequisite to educational change are: dynamic leadership, a philosophical base, and a positive environment. The purpose of this paper is to discuss these components and indicate why and how they can be made available in a given elementary school situation. (Editor/RK)