Principal component and linear discriminant analyses of free amino acids and biogenic amines in hungarian wines

Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classification on health datasets

Covariance and Hessian matrices have been analyzed separately in the literature for classification problems. However, integrating these matrices has the potential to enhance their combined power in improving classification performance. We present a novel approach that combines the eigenanalysis of a covariance matrix evaluated on a training set with a Hessian matrix evaluated on a deep learning model to achieve optimal class separability in binary classification tasks. Our approach is substantiated by formal proofs that establish its capability to maximize between-class mean distance (the concept of separation) and minimize within-class variances (the concept of compactness), which together define the two linear discriminant analysis (LDA) criteria, particularly under ideal data conditions such as isotropy around class means and dominant leading eigenvalues. By projecting data into the combined space of the most relevant eigendirections from both matrices, we achieve optimal class separability as per these LDA criteria. Empirical validation across neural and health datasets consistently supports our theoretical framework and demonstrates that our method outperforms established methods. Our method stands out by addressing both separation and compactness criteria, unlike PCA and the Hessian method, which predominantly emphasize one criterion each. This comprehensive approach captures intricate patterns and relationships, enhancing classification performance. Furthermore, through the utilization of both LDA criteria, our method outperforms LDA itself by leveraging higher-dimensional feature spaces, in accordance with Cover's theorem, which favors linear separability in higher dimensions. Additionally, our approach sheds light on complex DNN decision-making, rendering them comprehensible within a 2D space.

What the fish? Tracing the geographical origin of fish using NIR spectroscopy

Food authentication is a growing concern with rising complexities of the food supply network, with fish being an easy target of food fraud. In this regard, NIR spectroscopy has been used as an efficient tool for food authentication. This article reviews the latest research advances on NIR based fish authentication. The process from sampling/sample preparation to data analysis has been covered. Special attention was given to NIR spectra pre-processing and its unsupervised and supervised analysis. Sampling is an important aspect of traceability study and samples chosen ought to be a true representative of the population. NIR spectra acquired is often laden with overlapping bands, scattering and highly multicollinear. It needs adequate pre-processing to remove all undesirable features. The pre-processing technique can make or break a model and thus need a trial-and-error approach to find the best fit. As for spectral analysis and modelling, multicollinear nature of NIR spectra demands unsupervised analysis (PCA) to compact the features before application of supervised multivariate techniques such as LDA, PLS-DA, QDA etc. Machine learning approach of modelling has shown promising result in food authentication modelling and negates the need for unsupervised analysis before modelling.

Comparative Analysis of Amino Acid and Biogenic Amine Compositions of Fermented Grape Beverages

Amino acids and biogenic amines are important components of food and beverages. In grape-derived products such as wine and wine vinegar, they can have different origins and can influence the odor and taste of the products. Their concentration is influenced by the grape variety, vintage, and winemaking process. In our study, we carried out an LC-MS-based comparative analysis of 22 grape-derived beverages, including three different wine types and four wine vinegar samples from the Tokaj region in Hungary. The concentrations of 23 amino acids and 10 biogenic amines were examined, and the differences among the sample types were analyzed. The differences in the concentrations of some metabolites between Aszú-Furmint pairs originating from the same wineries and year provide information on the effect of botrytized grape on wine composition. Our data can provide further evidence on how the production process shapes the metabolite content of beverages and highlight the nutritional value of wine vinegar.

Benzothiadiazole Affects Grape Polyphenol Metabolism and Wine Quality in Two Greek Cultivars: Effects during Ripening Period over Two Years

Grape berries are one of the most important sources of phenolic compounds, either consumed fresh or as wine. A pioneer practice aiming to enrich grape phenolic content has been developed based on the application of biostimulants such as agrochemicals initially designed to induce resistance against plant pathogens. A field experiment was conducted in two growing seasons (2019-2020) to investigate the effect of benzothiadiazole on polyphenol biosynthesis during grape ripening in Mouhtaro (red-colored) and Savvatiano (white-colored) varieties. Grapevines were treated at the stage of veraison with 0.3 mM and 0.6 mM benzothiadiazole. The phenolic content of grapes, as well as the expression level of genes involved in the phenylpropanoid pathway were evaluated and showed an induction of genes specifically engaged in anthocyanins and stilbenoids biosynthesis. Experimental wines deriving from benzothiadiazole-treated grapes exhibited increased amounts of phenolic compounds in both varietal wines, as well as an enhancement in anthocyanin content of Mouhtaro wines. Taken together, benzothiadiazole can be utilized to induce the biosynthesis of secondary metabolites with oenological interest and to improve the quality characteristics of grapes produced under organic conditions.

Simultaneous Determination of Amino Acids and Biogenic Amines by Liquid Chromatography Coupled to Mass Spectrometry for Assessing Wine Quality

Biogenic amines (BAs) and free amino acids (AAs) are low-molecular nitrogenous compounds occurring in a wide range of foodstuffs, found in increased amount in different fermented foods, seafood, and wines. This study deals with the development of an analytical method based on liquid chromatography with tandem mass spectrometry with precolumn derivatization with dansyl chloride for the determination of BAs and AAs in musts, wines, and sparkling wines. The resulting compositional profiles have been exploited as potential descriptors of quality and other oenological issues using chemometric methods including principal component analysis (PCA) and partial analysis of least squares-discriminants (PLS-DA). Proline is the most abundant compound, and other remarkable species are lysine, ethanolamine, tyramine, histamine, and putrescine. Fermented samples (wines and sparkling wines) are much richer in both BAs and free AAs than the initial musts. Significant differences have also been noticed in the quality, as the best products display, in general, lower levels. The dissimilarities in the content of the analytes between the two grape varieties studied (pinot noir and xarel·lo) and those dealing with quality aspects have made it possible to establish a tree to classify the samples based on these two features with excellent classification rates.

Comprehensive metabolomics analysis of key taste components in different varieties of table grapes

Grapes are one of the world's largest fruit crops, which are rich in nutrients and taste. Summer Black, Gui Fei, Kyoho Grape, Giant Rose, Shine Muscat, and Rosario Bianco are the six most popular table grapes in Wuxi city, Jiangsu province. Owing to the lack of comprehensive investigations of metabolites in table grapes, the metabolic causes of differences in their taste are unknown. In this study, metabolites of six table grapes were profiled using ultra-high-performance liquid chromatography-Q-Exactive Orbitrap tandem mass spectrometry combined with a multivariate analysis. Orthogonal partial least squares discriminant analysis clearly discriminated among the metabolites of these varieties. Metabolic pathway analysis revealed that carbohydrate and amino acid metabolisms was highly conserved among these varieties. Our results suggest that the taste differences in the six table grape varieties can be explained by variations in composition and abundance of carbohydrates, organic acids, amino acids, and polyphenols. This study provides comprehensive insights into the underlying metabolic causes of taste variation in table grapes. This article is protected by copyright. All rights reserved.

Quantification of Biogenic Amines in 35 Korean Cottage Industry Traditional Gochujang (Fermented Red Pepper Paste) Products

Traditional gochujang is well known for its distinguished flavor and taste. However, the safety of cottage industry gochujang products is uncertain, particularly, in terms of biogenic amine (BA) content which is not yet documented. The present study aimed to determine the level of BAs present in 35 traditional gochujang products nationwide. All gochujang products had considerable amounts of total BAs ranging from 52.95 mg/kg to 176.24 mg/kg. Individually, histamine and tyramine were either not detected or detected up to 16.94 mg/kg and 2.15–52.34 mg/kg, respectively. In all the tested gochujang products, putrescine, spermidine, and spermine were detected in the range of 7.60–56.72 mg/kg, 14.96–36.93 mg/kg, and 4.68–16.31 mg/kg, respectively. A total of 22 and 19 gochujang products had less than 1 mg/kg of cadaverine and histamine, respectively. The findings indicate that all the gochujang products tested herein had BA levels below the suggested toxicity limits recommended by the various regulatory authorities, which reveal that they are safe for human consumption.

Nutritional evaluation of whole soybean curd made from different soybean materials based on amino acid profiles

Objectives

Amino acid profiles of whole soybean curds (WSCs) and conventional soybean curds (CSCs) were comparatively studied to 1., evaluate their nutritional value and 2., study the suitable soybean material to fabricate WSCs.

Materials and Methods

Nine types of soybean materials were selected to make WSCs and CSCs. Protein digestibility-corrected amino acid score (PDCAAS) and principal component analysis were selected to perform the comparative analysis of amino acid profiles of the products.

Results

Total contents of amino acids in WSCs and CSCs were 41.24–54.87 mg/g and 27.77–36.82 mg/g. Content ranges of essential amino acids were 13.59–19.38 mg/g and 8.46–11.76 mg/g for WSCs and CSCs. The essential amino acid pattern of WSCs was basically close to amino acid pattern spectrum requirements except valine and sulfur amino acids. As a limiting amino acid, methionine represented less than 0.4 for PDCAAS in all soybean curds except WSCs made from relay-cropping Nandou12 and Nandou25.

Conclusions

The overall quality of amino acids in WSCs was higher than that in CSCs, indicating that WSCs indeed exhibited higher nutritional value than CSCs from the viewpoint of amino acid profile. WSCs with the high nutritional value could be made using Nandou25 and relay-cropping Nandou12 as materials.

Amino acids and biogenic amines as food quality factors

The importance of amino acids and biogenic amines is widely recognised in various fields, particularly in the fields of food science and nutrition. This mini-review contains a summary of my main research field that centres on aspects of Food Quality and Food Safety, with a particular emphasis on amino acids and biogenic amines. It also gives an overview of the recent developments on the related areas.

Spectroscopic and chemometric evaluation of cling films used for wrapping of foodstuff and illicit drugs

Thin films of various polymers are currently used for wrapping various food products and illicit drugs worldwide. In thin films, polyvinyl chloride is the third most widely used polymer after the polyethylene and polypropylene. These films usually contain harmful plasticizers such as adipates, phthalates, and citrates along with other additives. Plasticizers adversely affect the human health and therefore, their migration in foodstuff must be monitored carefully. By considering all these facts, this study summarizes the utilities of non-destructive ATR-FTIR spectroscopy in the identification of base polymer as well as plasticizers in various cling films and application of multivariate analysis in the identification and classification of the cling films to their respective groups. In the present research, a considerable transfer of plasticizers from the wrapping material is observed. A spectral library is developed for all 22 cling film samples for further comparison of the unknown sample. The obtained results are promising, especially for classification purposes. The multivariate method significantly provides 100% of cross-validation classification along with 99.13% discrimination. In this study, we have developed a method to compare or link a cling film which somebody uses and found that there is a transfer of plasticizers and other additives into the food and if the manufacturing company disown that this specific cling film does not belong to our company. In such type of cases, this method could link the suspected cling film to their manufacturers with precision and accurateness. The chronic effect of plasticizers on human health can also be linked to the type of cling film the accused was using consistently.

Mixed Starter Culture Regulates Biogenic Amines Formation via Decarboxylation and Transamination during Chinese Rice Wine Fermentation

The utilization of amine-negative starter based on an understanding of nitrogen metabolism is a useful method for controlling biogenic amine (BA) in Chinese rice wine (CRW) fermentation. The contribution of brewing materials to protein degradation was analyzed; wheat Qu protein had no effect, and yeast autolysis generated 10% amino nitrogen. Milling degree of rice was strongly correlated with BAs formation ( R² = 0.99). Subsequently, Lactobacillus plantarum and Staphylococcus xylosus were coinoculated as amine-negative starter at an optimized ratio of 1:2. Coinoculation induced a significant reduction in total BAs (43.7%, 44.5 mg L^-1), putrescine (43.0%, 20.4 mg L^-1), tyramine (42.8%, 14.3 mg L^-1), and histamine (42.6%, 3.5 mg L^-1) content. Notably, BAs degradation ability of Staphylococcus xylosus was stronger than the suppression effect of Lactobacillus plantarum, and higher lactic acid bacteria (LAB) amount has a positive correlation with lower BAs content. Overall, mixed strains exerted a synergistic effect in lowering BAs accumulation via decarboxylation and transamination.

Merging vibrational spectroscopic data for wine classification according to the geographic origin

The wine making procedure is no longer a secret and it is nowadays well described and repeated around the world. Nevertheless, wines present unique features, strongly associated with their geographic origin. Classification systems were developed to catalogue wines according to the provenance, and are currently established by official authorities in order to ensure wine authenticity. The use of near-infrared (NIR), mid-infrared (MIR) and Raman spectroscopy for tracing the origin of wine samples, has been reported with different levels of success. This work evaluated and compared the performance of these techniques, as well as their joint use, in terms of geographic origin classification. NIR, MIR and Raman spectra of wine samples belonging to four Portuguese wine regions (Vinhos Verdes, Lisboa, Açores and Távora-Varosa) were analyzed by partial least squares discriminant analysis (PLS-DA). Results revealed the better suitability of MIR spectroscopy (87.7% of correct predictions) over NIR (60.4%) and Raman (60.8%). The joint use of spectral sets did not improve the predictive ability of the models. The best models were achieved by combining MIR and NIR spectra resulting in 86.7% of correct predictions. Multiblock partial least squares (MB-PLS) models were developed to further explore the combination of spectral data. Although these models did not improve the percentage of correct predictions, they demonstrated the higher contribution of MIR spectroscopic data, in the development of the models.

Geographical Origin Traceability of Red Wines Based on Chemometric Classification via Organic Acid Profiles

A preliminary study on the chemometric classification of red wines produced from different grape varieties and geographical origins was performed based on their chromatographic profiles of organic acids. Tartaric, malic, citric, lactic, acetic, and succinic acids in wines were detected via high performance liquid chromatography (HPLC). Employing multivariate statistical methods including principal component analysis (PCA) and linear discriminant analysis (LDA), pattern recognition models were built for the classification of the investigated wines regarding the grape varieties and geographical origins. The PCA clearly grouped the wines according to variety, and the LDA further offered 100% classification ability toward geographical identification of the wines and the leave-one-out cross-validated assignments were 100%, 86.7%, and 100% correct for Cabernet Sauvignon, Merlot, and Pinot Noir wines, respectively. The results reveal the potential of using chromatographic profiles of organic acid as the characteristic indices for chemometric classification of red wines.

Multivariate analysis of the volatile components in tobacco based on infrared-assisted extraction coupled to headspace solid-phase microextraction and gas chromatography-mass spectrometry

A novel infrared-assisted extraction coupled to headspace solid-phase microextraction followed by gas chromatography with mass spectrometry method has been developed for the rapid determination of the volatile components in tobacco. The optimal extraction conditions for maximizing the extraction efficiency were as follows: 65 μm polydimethylsiloxane-divinylbenzene fiber, extraction time of 20 min, infrared power of 175 W, and distance between the infrared lamp and the headspace vial of 2 cm. Under the optimum conditions, 50 components were found to exist in all ten tobacco samples from different geographical origins. Compared with conventional water-bath heating and nonheating extraction methods, the extraction efficiency of infrared-assisted extraction was greatly improved. Furthermore, multivariate analysis including principal component analysis, hierarchical cluster analysis, and similarity analysis were performed to evaluate the chemical information of these samples and divided them into three classifications, including rich, moderate, and fresh flavors. The above-mentioned classification results were consistent with the sensory evaluation, which was pivotal and meaningful for tobacco discrimination. As a simple, fast, cost-effective, and highly efficient method, the infrared-assisted extraction coupled to headspace solid-phase microextraction technique is powerful and promising for distinguishing the geographical origins of the tobacco samples coupled to suitable chemometrics.

Insights into the Biogenic Amine Metabolic Landscape during Industrial Semidry Chinese Rice Wine Fermentation

Inspired by concerns about food safety, the metabolic landscape of biogenic amines (BAs) was elucidated during industrial semidry Chinese rice wine fermentation. The main fermentation process represented the largest contribution to BA formation, which corresponded to 69.1% (54.3 mg/L). Principal component analysis revealed that total acid and ethanol were strongly correlated with BAs, indicating that BA formation favored acidic and stressful conditions. Other than putrescine (PUT), spermidine (SPD), and spermine (SPM), 5 BAs exhibited strong relationships with the precursor amino acids (R² > 0.85). PUT was mainly decarboxylated from arginine (89.6%) whereas SPD (100%) and SPM (83.1%) were obtained from ornithine. Interestingly, some SPD could convert back to PUT (24.3%). All 8 BAs showed good relationships with lactic acid bacteria (LAB) (R² around 0.75). Moreover, among the five main LAB genera, Lactobacillus had a positive correlation with BA formation.

Improved Discrimination for Brassica Vegetables Treated with Agricultural Fertilizers Using a Combined Chemometric Approach

Multielement and stable isotope (δ(13)C, δ(15)N, δ(2)H, δ(18)O, (207)Pb/(206)Pb, and (208)Pb/(206)Pb) analyses were combined to provide a new chemometric approach to improve the discrimination between organic and conventional Brassica vegetable production. Different combinations of organic and conventional fertilizer treatments were used to demonstrate this authentication approach using Brassica chinensis planted in experimental test pots. Stable isotope analyses (δ(15)N and δ(13)C) of B. chinensis using elemental analyzer-isotope ratio mass spectrometry easily distinguished organic and chemical fertilizer treatments. However, for low-level application fertilizer treatments, this dual isotope approach became indistinguishable over time. Using a chemometric approach (combined isotope and elemental approach), organic and chemical fertilizer mixes and low-level applications of synthetic and organic fertilizers were detectable in B. chinensis and their associated soils, improving the detection limit beyond the capacity of individual isotopes or elemental characterization. LDA shows strong promise as an improved method to discriminate genuine organic Brassica vegetables from produce treated with chemical fertilizers and could be used as a robust test for organic produce authentication.

Classification of foodborne pathogens using near infrared (NIR) laser scatter imaging system with multivariate calibration

An optical sensor system, namely NIR laser scatter imaging system, was developed for rapid and noninvasive classification of foodborne pathogens. This developed system was used for images acquisition. The current study is focused on exploring the potential of this system combined with multivariate calibrations in classifying three categories of popular bacteria. Initially, normalization and Zernike moments extraction were performed, and the resultant translation, scale and rotation invariances were applied as the characteristic variables for subsequent discriminant analysis. Both linear (LDA, KNN and PLSDA) and nonlinear (BPANN, SVM and OSELM) pattern recognition methods were employed comparatively for modeling, and optimized by cross validation. Experimental results showed that the performances of nonlinear tools were superior to those of linear tools, especially for OSELM model with 95% discrimination rate in the prediction set. The overall results showed that it is extremely feasible for rapid and noninvasive classifying foodborne pathogens using this developed system combined with appropriate multivariate calibration.

Amino Acids and Biogenic Amines Evolution during theEstufagemof Fortified Wines

The current study was focused on the impact of accelerated ageing (heating step) on the amino acid and biogenic amine profiles of fortified wines. In this sense, three Madeira wines from two commonly used grape varieties (one red and the other white) were analysed during the heating, at standard (45°C, 3 months) and overheating (70°C, 1 month) conditions, following a precolumn derivatization procedure using iodoacetic acid,o-phthaldialdehyde, and 2-mercaptoethanol, carried out in the injection loop prior to RP-HPLC-FLD detection. Eighteen amino acids were identified, with arginine being the most abundant. An important decrease of the amino acid levels was detected during the standard heating (up to 30%), enhanced up to 61% by the temperature increase. Cysteine, histidine, and asparagine revealed the greatest decreases at 45°C. Conversely, some amino acids, such as asparagine, slightly increased. Four biogenic amines were identified but always in trace amounts. Finally, it was observed that the accelerated ageing did not favour the biogenic amine development. The results also indicate that the heating process promotes the amino acid transformation into new ageing products.

Comparison and chemometric analysis of the phenolic compounds and organic acids composition of chinese wines

Characteristics of 106 wines from 5 major grape varieties and 3 typical geographic regions in China were investigated by means of profiling of organic acids and phenolic compounds analysis. An ultra-performance liquid chromatography method was developed and thus, large number of samples could be determined in a quick and reliable way. The results showed that different origins and varieties were characteristic of various profiles of organic acid and phenolic compounds. In order to investigate possible correlation between organic acids and phenolic compounds content and grape variety and/or geographical origin, analysis of variance and linear discriminant analysis (LDA) were conducted. A satisfactory LDA result for red wines according to geographic origin was obtained, in which the correct classification was 100% and the leave-one-out validation accuracy was 90%. The corresponding results of white wines were 91% and 86%, respectively. When LDA was processed, according to grape varieties, the proportionality of successfully classified wines was 96%, while the leave-one-out validation accuracy was 94%. The organic acids and phenolic compounds profiles were useful in the classification of Chinese wines according to grape variety and geographic origin.

Qualitative and quantitative prediction of volatile compounds from initial amino acid profiles in Korean rice wine (makgeolli) model

In Korean rice wine (makgeolli) model, we tried to develop a prediction model capable of eliciting a quantitative relationship between initial amino acids in makgeolli mash and major aromatic compounds, such as fusel alcohols, their acetate esters, and ethyl esters of fatty acids, in makgeolli brewed. Mass-spectrometry-based electronic nose (MS-EN) was used to qualitatively discriminate between makgeollis made from makgeolli mashes with different amino acid compositions. Following this measurement, headspace solid-phase microextraction coupled to gas chromatography-mass spectrometry (GC-MS) combined with partial least-squares regression (PLSR) method was employed to quantitatively correlate amino acid composition of makgeolli mash with major aromatic compounds evolved during makgeolli fermentation. In qualitative prediction with MS-EN analysis, the makgeollis were well discriminated according to the volatile compounds derived from amino acids of makgeolli mash. Twenty-seven ion fragments with mass-to-charge ratio (m/z) of 55 to 98 amu were responsible for the discrimination. In GC-MS combined with PLSR method, a quantitative approach between the initial amino acids of makgeolli mash and the fusel compounds of makgeolli demonstrated that coefficient of determination (R(2)) of most of the fusel compounds ranged from 0.77 to 0.94 in good correlation, except for 2-phenylethanol (R(2) = 0.21), whereas R(2) for ethyl esters of MCFAs including ethyl caproate, ethyl caprylate, and ethyl caprate was 0.17 to 0.40 in poor correlation.

PRACTICAL APPLICATION

The amino acids have been known to affect the aroma in alcoholic beverages. In this study, we demonstrated that an electronic nose qualitatively differentiated Korean rice wines (makgeollis) by their volatile compounds evolved from amino acids with rapidity and reproducibility and successively, a quantitative correlation with acceptable R2 between amino acids and fusel compounds could be established via HS-SPME GC-MS combined with partial least-squares regression. Our approach for predicting the quantities of volatile compounds in the finished product from initial condition of fermentation will give an insight to food researchers to modify and optimize the qualities of the corresponding products.

Sensory evaluation and electronic tongue for sensing flavored mineral water taste attributes

In this article a trained sensory panel evaluated 6 flavored mineral water samples. The samples consisted of 3 different brands, each with 2 flavors (pear-lemon grass and josta berry). The applied sensory method was profile analysis. Our aim was to analyze the sensory profiles and to investigate the similarities between the sensitivity of the trained human panel and an electronic tongue device. Another objective was to demonstrate the possibilities for the prediction of sensory attributes from electronic tongue measurements using a multivariate statistical method (Partial Least Squares regression [PLS]). The results showed that the products manufactured under different brand name but with the same aromas had very similar sensory profiles. The panel performance evaluation showed that it is appropriate (discrimination ability, repeatability, and panel consensus) to compare the panel's results with the results of the electronic tongue. The samples can be discriminated by the electronic tongue and an accurate classification model can be built. Principal Component Analysis BiPlot diagrams showed that Brand A and B were similar because the manufacturers use the same aroma brands for their products. It can be concluded that Brand C was quite different compared to the other samples independently of the aroma content. Based on the electronic tongue results good prediction models can be obtained with high correlation coefficient (r(2) > 0.81) and low prediction error (RMSEP < 13.71 on the scale of the sensory evaluation from 0 to 100).

Analysis of amino acid composition in proteins of animal tissues and foods as pre-column o-phthaldialdehyde derivatives by HPLC with fluorescence detection

Studies of protein nutrition and biochemistry require reliable methods for analysis of amino acid (AA) composition in polypeptides of animal tissues and foods. Proteins are hydrolyzed by 6M HCl (110°C for 24h), 4.2M NaOH (105°C for 20 h), or proteases. Analytical techniques that require high-performance liquid chromatography (HPLC) include pre-column derivatization with 4-chloro-7-nitrobenzofurazan, 9-fluorenyl methylchloroformate, phenylisothiocyanate, naphthalene-2,3-dicarboxaldehyde, 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate, and o-phthaldialdehyde (OPA). OPA reacts with primary AA (except cysteine or cystine) in the presence of 2-mercaptoethanol or 3-mercaptopropionic acid to form a highly fluorescent adduct. OPA also reacts with 4-amino-1-butanol and 4-aminobutane-1,3-diol produced from oxidation of proline and 4-hydroxyproline, respectively, in the presence of chloramine-T plus sodium borohydride at 60°C, or with S-carboxymethyl-cysteine formed from cysteine and iodoacetic acid at 25°C. Fluorescence of OPA derivatives is monitored at excitation and emission wavelengths of 340 and 455 nm, respectively. Detection limits are 50 fmol for AA. This technique offers the following advantages: simple procedures for preparation of samples, reagents, and mobile-phase solutions; rapid pre-column formation of OPA-AA derivatives and their efficient separation at room temperature (e.g., 20-25°C); high sensitivity of detection; easy automation on the HPLC apparatus; few interfering side reactions; a stable chromatography baseline for accurate integration of peak areas; and rapid regeneration of guard and analytical columns. Thus, the OPA method provides a useful tool to determine AA composition in proteins of animal tissues (e.g., skeletal muscle, liver, intestine, placenta, brain, and body homogenates) and foods (e.g., milk, corn grain, meat, and soybean meal).

Identification of Jiangxi wines by three-dimensional fluorescence fingerprints

A new assay of identifying wines was developed based on fingerprints of three-dimensional fluorescence spectra, and 30 samples from different manufacturers were analyzed. The techniques of principal component analysis (PCA) and hierarchical cluster analysis (HCA) were used to differentiate and evaluate the character parameters of wines' three-dimensional fluorescence spectra. At the same time, the back-propagation network (BPN) was applied to predict the attribution of unknown samples. The results of PCA and HCA showed that there was definite different information among the wine samples from different manufacturers. It was promising that the method could be applied to distinguish wine samples produced by different manufacturers. The proposed method could provide the criterion for the quality control of wines.

Application of consensus scoring and principal component analysis for virtual screening against β-secretase (BACE-1)

Background

In order to identify novel chemical classes of β-secretase (BACE-1) inhibitors, an alternative scoring protocol, Principal Component Analysis (PCA), was proposed to summarize most of the information from the original scoring functions and re-rank the results from the virtual screening against BACE-1.

Method

Given a training set (50 BACE-1 inhibitors and 9950 inactive diverse compounds), three rank-based virtual screening methods, individual scoring, conventional consensus scoring and PCA, were judged by the hit number in the top 1% of the ranked list. The docking poses were generated by Surflex, five scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, and PMF_Score) were used for pose extraction. For each pose group, twelve scoring functions (Surflex_Score, D_Score, G_Score, ChemScore, PMF_Score, LigScore1, LigScore2, PLP1, PLP2, jain, Ludi_1, and Ludi_2) were used for the pose rank. For a test set, 113,228 chemical compounds (Sigma-Aldrich® corporate chemical directory) were docked by Surflex, then ranked by the same three ranking methods motioned above to select the potential active compounds for experimental test.

Results

For the training set, the PCA approach yielded consistently superior rankings compared to conventional consensus scoring and single scoring. For the test set, the top 20 compounds according to conventional consensus scoring were experimentally tested, no inhibitor was found. Then, we relied on PCA scoring protocol to test another different top 20 compounds and two low micromolar inhibitors (S450588 and 276065) were emerged through the BACE-1 fluorescence resonance energy transfer (FRET) assay.

Conclusion

The PCA method extends the conventional consensus scoring in a quantitative statistical manner and would appear to have considerable potential for chemical screening applications.

Discrimination between Shiraz wines from different Australian regions: the role of spectroscopy and chemometrics

This study reports the use of UV-visible (UV-vis), near-infrared (NIR), and midinfrared (MIR) spectroscopy combined with chemometrics to discriminate among Shiraz wines produced in five Australian regions. In total, 98 commercial Shiraz samples (vintage 2006) were analyzed using UV-vis, NIR, and MIR wavelength regions. Spectral data were interpreted using principal component analysis (PCA), linear discriminant analysis (LDA), and soft independent model of class analogy (SIMCA) to classify the wine samples according to region. The results indicated that wine samples from Western Australia and Coonawarra can be separated from the other wines based on their MIR spectra. Classification results based on MIR spectra also indicated that LDA achieved 73% overall correct classification, while SIMCA 95.3%. This study demonstrated that IR spectroscopy combined with chemometric methods can be a useful tool for wine region discrimination.

Multivariate classification of rice wines according to ageing time and brand based on amino acid profiles

Discrimination of Chinese rice wines according to ageing time and brand using amino acid profiles was presented in this study. Free amino acids (16) in 98 rice wines were simultaneously determined using high-performance liquid chromatograph-diode array detection (HPLC-DAD). Then the experimental data was subjected to multivariate analysis. Principal component analysis (PCA) was employed to differentiate samples from various ageing times (3, 9, 11 and 15months) and brands ("pagoda", "kuaijishan", and "guyuelongshan"). Partial least square discriminant analysis (PLS-DA) and full (leave-one-out) cross-validation were used to develop classification models. The overall correct classification rate for different ageing times and brands was 99.7% and 94.9%, respectively. The proposed method shows an effective strategy for the detection of mislabelling of rice wines.

Chemometric interpretation of heavy metal patterns in soils worldwide

Principal component analysis (PCA) was applied on data sets containing levels of six heavy metals (Pb, Cu, Zn, Cd, Ni, Cr) in soils from different parts of the world in order to investigate the information captured in the global heavy metal patterns. Data used in this study consisted of the heavy metal contents determined in 23 soil samples from and around the Novi Sad city area in the Vojvodina Province, northern part of Serbia, together with those from the city of Banja Luka, the second largest city in Bosnia and Herzegovina, and the ones reported previously in the relevant literature in order to evaluate heavy metal distribution pattern in soils of different land-use types, as well as spatial and temporal differences in the patterns. The chemometric analysis was applied on the following input data sets: the overall set with all data gathered in this study containing 264 samples, and two sub sets obtained after dividing the overall set in accordance to the soil metal index, SMI, calculated here, i.e. the set of unpolluted soils having SMIs<100%, and the set of polluted soils with SMIs>100%. Additionally, univariate descriptive statistics and the Spearman's non-parametric rank correlation coefficients were calculated for these three sets. A Box-Cox transformation was used as a data pretreatment before the statistical methods applied. According to the results, it was seen that anthropogenic and background sources had different impact on the data variability in the case of polluted and unpolluted soils. The sample discrimination regarding the land-use types was more evident for the unpolluted soils than for the polluted ones. Using linear discriminant analysis, content of Cu was determined as a variable with a major discriminant capacity. The correct classification of 73.3% was achieved for predefined land-use types. Classification of the samples in accordance to the pollution level expressed as SMI was necessary in order to avoid the "masking" effect of the polluted soil patterns over the non-polluted ones.