Geographical classification of some Australian wines by discriminant analysis using HPLC with UV and chemiluminescence detection

HPLC with UV and acidified potassium permanganate chemiluminescence detection, combined with multivariate data analysis techniques, were used for the geographical classification of some Australian red (Cabernet Sauvignon) and white (Chardonnay) wines from two regions (Coonawarra and Geelong). Identification of the wine constituents prominent in the chromatography was performed by mass spectrometry. Principal components analysis and linear discriminant analysis were used to classify the wines according to region of production. Separation between regions was achieved with both detection systems and key components leading to discrimination of the wines were identified. Using two principal components, linear discriminant analysis with UV detection correctly classified 100% of the Chardonnay wines and, overall 91% of the Cabernet Sauvignon wines. With acidified potassium permanganate chemiluminescence detection, 75% of the Chardonnay wines and 94% of the Cabernet Sauvignon wines were correctly classified using two factors.     

Classification of signatures of Bovine Spongiform Encephalopathy in serum using infrared spectroscopy

Signatures of Bovine Spongiform Encephalopathy (BSE) have been identified in serum by means of "Diagnostic Pattern Recognition (DPR)". For DPR-analysis, mid-infrared spectroscopy of dried films of 641 serum samples was performed using disposable silicon sample carriers and a semi-automated DPR research system operating at room temperature. The combination of four mathematical classification approaches (principal component analysis plus linear discriminant analysis, robust linear discriminant analysis, artificial neural network, support vector machine) allowed for a reliable assignment of spectra to the class "BSE-positive" or "BSE-negative". An independent, blinded validation study was carried out on a second DPR research system at the Veterinary Laboratory Agency, Weybridge, UK. Out of 84 serum samples originating from terminally-ill, BSE-positive cattle, 78 were classified correctly. Similarly, 73 out of 76 BSE-negative samples were correctly identified by DPR such that, numerically, an accuracy of 94.4 % can be calculated. At a confidence level of 0.95 (alpha = 0.05) these results correspond to a sensitivity > 85% and a specificity > 90%. Identical class assignment by all four classifiers occurred in 75% of the cases while ambiguous results were obtained in only 8 of the 160 cases. With an area under the ROC (receiver operating charateristics) curve of 0.991, DPR may potentially supply a valuable surrogate marker for BSE even in cases in which a deliberate bias towards improved sensitivity or specificity is desired. To the best of our knowledge, DPR is the first and--up to now--only method which has demonstrated its capability of detecting BSE-related signatures in serum.     

Discrimination of almonds (Prunus dulcis) geographical origin by minerals and fatty acids profiling

Twenty-one almond samples from three different geographical origins (Sicily, Spain and California) were investigated by determining minerals and fatty acids compositions. Data were used to discriminate by chemometry almond origin by linear discriminant analysis. With respect to previous PCA profiling studies, this work provides a simpler analytical protocol for the identification of almonds geographical origin. Classification by using mineral contents data only was correct in 77% of the samples, while, by using fatty acid profiles, the percentages of samples correctly classified reached 82%. The coupling of mineral contents and fatty acid profiles lead to an increased efficiency of the classification with 87% of samples correctly classified.     

Mass spectrometry fingerprinting coupled to National Institute of Standards and Technology Mass Spectral search algorithm for pattern recognition

A new analytical strategy based on mass spectrometry fingerprinting combined with the NIST-MS search program for pattern recognition is evaluated and validated. A case study dealing with the tracing of the geographical origin of virgin olive oils (VOOs) proves the capabilities of mass spectrometry fingerprinting coupled with NIST-MS search program for classification. The volatile profiles of 220 VOOs from Liguria and other Mediterranean regions were analysed by secondary electrospray ionization-mass spectrometry (SESI-MS). MS spectra of VOOs were classified according to their origin by the freeware NIST-MS search v 2.0. The NIST classification results were compared to well-known pattern recognition techniques, such as linear discriminant analysis (LDA), partial least-squares discriminant analysis (PLS-DA), k-nearest neighbours (kNN), and counter-propagation artificial neural networks (CP-ANN). The NIST-MS search program predicted correctly 96% of the Ligurian VOOs and 92% of the non-Ligurian ones of an external independent data set; outperforming the traditional chemometric techniques (prediction abilities in the external validation achieved by kNN were 88% and 84% for the Ligurian and non-Ligurian categories respectively). This proves that the NIST-MS search software is a useful classification tool.     

Native fluorescence spectroscopy of blood plasma in the characterization of oral malignancy

Native fluorescence characteristics of blood plasma were studied in the visible spectral region, at two different excitation wavelengths, 405 and 420 nm, to discriminate patients with different stages of oral malignancy from healthy subjects. The fluorescence spectra of blood plasma of oral malignant subjects exhibit characteristic spectral differences with respect to normal subjects. Different ratios were calculated using the fluorescence intensity values at those emission wavelengths that give characteristic spectral features of each group of experimental subjects studied. These fluorescence intensity ratios were used as input variables for a multiple linear discriminant analysis across different groups. Leave-one out cross-validation was used to check the reliability of each discriminant analysis performed. The discriminant analysis performed across normal and oral cancerous subjects classified 94.7% of the original grouped cases and 93.7% of the cross-validated grouped cases. A classification algorithm was developed on the basis of the score of the discriminant functions (discriminant score) resulted in the analyses. The diagnostic potentiality of the present technique was also estimated in the discrimination of malignant subjects from normal and nonmalignant diseased subjects such as liver diseases. In the discriminant analysis performed across the three groups, normal, oral malignancy (including early and advanced stages) and liver diseases, 99% of the original grouped cases and 95.9% of the cross-validated grouped cases were correctly classified. Similar analysis performed across normal, early stage of oral malignancy, advanced oral malignancy and liver diseases correctly classified 94.9% of the original grouped cases and 91.8% of the cross-validated grouped cases.     

A chemometric approach to characterization of ionic liquids for gas chromatography

A chemometric study was carried out to characterize three ionic liquid types (ILs) with hexacationic imidazolium, polymeric imidazolium, and phosphonium cationic cores, using a range of contra-anions such as halogens, thiocyanate, boron anions, triflate, and bistriflimide. The solvation parameter model developed by Abraham et al., unsupervised techniques as cluster analysis (CA), and supervised techniques as linear discriminant analysis (LDA), step-LDA, quadratic discriminant analysis (QDA), and multivariate regression techniques as discriminant partial least squares (D-PLS), or multiple linear regression (MLR) were used to characterize the functionalized ILs above. CA established two main groups of phases, those with an acidic H-bond and those with basic ones. Once detected, the two natural groups, a linear and quadratic delimiters with good classification (>96 %) and prediction (>92 %) capacities were computed. The use of step-LDA technique allowed us to establish that a, b, and s solvation parameters were the most discriminant variables. These variables were used for modeling purposes, and a D-PLS and MLR models were constructed using a binary response. The explained variance of categorical variable by the model validated by cross-validation was 65 %, and 94.5 % of ILs were correctly predicted. IL characterization carried out would allow the appropriate selection of phases for gas chromatography (GC).     

Combining visible and near-infrared spectroscopy with chemometrics to trace muscles from an autochthonous breed of pig produced in Uruguay: a feasibility study

Visible (Vis) and near-infrared reflectance (NIR) spectroscopy combined with chemometrics was explored as a tool to trace muscles from autochthonous and crossbreed pigs from Uruguay. Muscles were sourced from two breeds, namely, the Pampa-Rocha (PR) and the Pampa-Rocha x Duroc (PRxD) crossbreed. Minced muscles were scanned in the Vis and NIR regions (400–2,500 nm) in a monochromator instrument in reflectance. Principal component analysis (PCA), discriminant partial least square regression (DPLS), linear discriminant analysis (LDA) based on PCA scores and soft independent modelling of class analogy (SIMCA) were used to identify the origin of the muscles based on Vis and NIR data. Full cross validation was used as validation method when classification models were developed. DPLS correctly classified 87% of PR and 78% of PRxD muscle samples. LDA calibration models correctly classified 87 and 67% of muscles as PR and PRxD, respectively. SIMCA correctly classified 100% of PR muscles. The results demonstrated the usefulness of Vis and NIR spectra combined with chemometrics as rapid method for authentication and identification of muscles according to the breed of pig.     

Forensic discrimination of photocopy and printer toners II. Discriminant analysis applied to infrared reflection-absorption spectroscopy

Copy toner samples were analyzed using reflection-absorption infrared microscopy (R-A IR). The grouping of copy toners into distinguishable classes achieved by visual comparison and computer-assisted spectral matching was compared to that achieved by multivariate discriminant analysis. For a data set containing spectra of 430 copy toners, 90% (388/430) of the spectra were initially correctly grouped into the classifications previously established by spectral matching. Three groups of samples that did not classify well contained too few samples to allow reliable classification. Samples from two other pairs of groups were similar and often misclassified. Closer examination of spectra from these groups revealed discriminating features that could be used in separate discriminant analyses to improve classification. For one pair of groups, the classification accuracy improved to 91% (81/89) and 97% (28/29), for the two groups, respectively. The other pair of groups were completely distinguishable from one another. With these additional tests, multivariate discriminant analysis correctly classified 96% of the 430 R-A IR toner spectra into the toner groups found previously by spectral matching.This is publication number 03–03 of the Laboratory Division of the Federal Bureau of Investigation. Names of commercial manufacturers are provided for identification only, and inclusion does not imply endorsement by the Federal Bureau of Investigation.     

基于液体阵列味觉仿生传感器鉴别白酒香型的新方法

通过模拟哺乳动物的味觉系统, 建立了交叉响应的液体阵列传感器, 为鉴别白酒香型提供了新方法. 选用7种染料和1种卟啉化合物作为传感单元, 构建液体阵列传感器, 集合8个传感单元的光谱响应信号构成分析物的指纹图谱, 达到识别的目的. 使用96孔板酶标仪采集响应数据, 结合主成分分析(PCA)、分层聚类分析(HCA)和判别分析(LDA)等模式识别方法进行数据处理, 对9种具有代表性的不同香型白酒样品进行了鉴别分析. PCA结果表明, 该方法对于白酒的检测主要基于酒体微量成分, 其中酸类物质对识别的贡献最大(贡献率达54.3%), 芳香类物质贡献率为18.6%; 同时, 仅用63.4%的数据信息量即可对白酒香型进行区分. HCA结果表明, 平行样均正确归类, 各白酒之间的相似程度在聚类图上得到体现. LDA结果表明, 该阵列对于9种白酒样品香型识别的准确率达到100%.     

Direct coupling of a gas-liquid separator to an ion mobility spectrometer for the classification of different white wines using chemometrics tools

The potential of a vanguard technique as is the ion mobility spectrometry with ultraviolet ionization (UV-IMS) coupled to a continuous flow system (CFS) have been demonstrated in this work using a gas phase separator (GPS). This vanguard system (CFS-GPS-UV-IMS) has been used for the analysis of different types of white wines to obtain a characteristic profile for each type of wine and their posterior classification using different chemometric tools. Precision of the method was 3.1% expressed as relative standard deviation. A deep chemometric study was carried out for the classification of the four types of wines selected. The best classification performance was obtained by first reducing the data dimensionality by principal component analysis (PCA) followed by linear discriminant analysis (LDA) and finally using a k-nearest neighbour (kNN) classifier. The classification rate in an independent validation set was 92.0% classification rate value with confidence interval [89.0%, 95.0%] at 95% confidence level.The same white wines analyzed using CFS-GPS-UV-IMS were analyzed using gas chromatography with a flame detector (GC-FID) as conventional technique. The chromatographic method used for the determination of superior alcohols in wine samples shown in the Regulation CEE 1238/1992 was selected to carry out the analysis of the same samples set and later the classification using appropriate chemometrics tools. In this case, strategies PCA-LDA and kNN classifier were also used for the correct classification of the wine samples. This combination showed similar results to the ones obtained with the proposed method.     

Multivariate statistical analysis of tobacco of different origin, grade and variety according to polyphenols and organic acids

The aim of this work was to determine the concentration of polyphenols, organic acids in tobacco of different areas, grades and varieties by ultra-performance liquid chromatography tandem mass spectrometry (UPLC/MS/MS) and to achieve statistical classification by principal component analysis (PCA) and linear discriminant analysis (LDA). The obtained results revealed that tobacco of different varieties can be correctly classified according to the contents of polyphenols or organic acid. The results of PCA showed that different grades and geographic regions cannot completely be discriminated using polyphenols or organic acid as independent variables. However, there were marked differences in special class from the same type or grade tobacco. At the same time, the results of LDA also showed that the samples were correctly classified at 100% for different varieties of tobacco, but only 55.3% and 60% for different grades and areas, respectively. These results demonstrated that the composition of polyphenols and organic acids can be used as the useful variables to characterize the type and the special class or grade of tobacco.     

Comparative study of artificial neural network and multivariate methods to classify Spanish DO rose wines

Classical multivariate analysis techniques such as factor analysis and stepwise linear discriminant analysis and artificial neural networks method (ANN) have been applied to the classification of Spanish denomination of origin (DO) rose wines according to their geographical origin. Seventy commercial rose wines from four different Spanish DO (Ribera del Duero, Rioja, Valdepeñas and La Mancha) and two successive vintages were studied. Nineteen different variables were measured in these wines. The stepwise linear discriminant analyses (SLDA) model selected 10 variables obtaining a global percentage of correct classification of 98.8% and of global prediction of 97.3%. The ANN model selected seven variables, five of which were also selected by the SLDA model, and it gave a 100% of correct classification for training and prediction. So, both models can be considered satisfactory and acceptable, being the selected variables useful to classify and differentiate these wines by their origin. Furthermore, the casual index analysis gave information that can be easily explained from an enological point of view.     

Simultaneous determination of the tobacco smoke uptake parameters nicotine,cotinine and thiocyanate in urine,saliva and hair,using gas chromatography-mass spectrometry for characterisation of smoking status of recently exposed subjects

A method using gas chromatography (GC)-mass spectrometry (MS) for the simultaneous determination of the smoke uptake parameters thiocyanate, nicotine and cotinine in human tissues is reported. Nicotine, cotinine and thiocyanate, in combination with a phase-transfer catalyst, were extracted from urine, saliva and hair into dichloromethane (DCM). Thiocyanate was alkylated in the DCM-layer to form a pentafluorobenzyl derivative. The biochemical markers in DCM were directly injected into the GC system and separated on a DB-1MS column using a 9.4 min temperature program. The method was validated in urine and saliva between the limits of quantitation (1.0-15 microg ml(-1) thiocyanate, 0.010-3.0 microg ml(-1) nicotine and cotinine in urine, 0.010-1.0 microg ml(-1) nicotine and cotinine in saliva). The calibration curves were found to be linear (r > 0.996), the within- and between-day accuracy's were 83-120%, the repeatability coefficients of variation were 3-20% and the limits of detection were 0.060 ng ml(-1) thiocyanate and 0.60 ng ml(-1) nicotine and cotinine. The results of the analysis of the biomarkers in the urine of 44 volunteers were used to develop a predictive model for smoking status, using discriminant analysis. The classification model correctly classified 93.2% of cross-validated grouped cases. Saliva samples were used to confirm the results of the classification method.     

QuBiLs-MAS method in early drug discovery and rational drug identification of antifungal agents

The QuBiLs-MAS approach is used for the in silico modelling of the antifungal activity of organic molecules. To this effect, non-stochastic (NS) and simple-stochastic (SS) atom-based quadratic indices are used to codify chemical information for a comprehensive dataset of 2478 compounds having a great structural variability, with 1087 of them being antifungal agents, covering the broadest antifungal mechanisms of action known so far. The NS and SS index-based antifungal activity classification models obtained using linear discriminant analysis (LDA) yield correct classification percentages of 90.73% and 92.47%, respectively, for the training set. Additionally, these models are able to correctly classify 92.16% and 87.56% of 706 compounds in an external test set. A comparison of the statistical parameters of the QuBiLs-MAS LDA-based models with those for models reported in the literature reveals comparable to superior performance, although the latter were built over much smaller and less diverse datasets, representing fewer mechanisms of action. It may therefore be inferred that the QuBiLs-MAS method constitutes a valuable tool useful in the design and/or selection of new and broad spectrum agents against life-threatening fungal infections.     

Tomocomd-Cardd, a novel approach for computer-aided ‘ rational’ drug design: I. Theoretical and experimental assessment of a promising method for computational screening and in silico design of new anthelmintic compounds

Summary In this work, the TOMOCOMD-CARDD approach has been applied to estimate the anthelmintic activity. Total and local (both atom and atom-type) quadratic indices and linear discriminant analysis were used to obtain a quantitative model that discriminates between anthelmintic and non-anthelmintic drug-like compounds. The obtained model correctly classified 90.37% of compounds in the training set. External validation processes to assess the robustness and predictive power of the obtained model were carried out. The QSAR model correctly classified 88.18% of compounds in this external prediction set. A second model was performed to outline some conclusions about the possible modes of action of anthelmintic drugs. This model permits the correct classification of 94.52% of compounds in the training set, and 80.00% of good global classification in the external prediction set. After that, the developed model was used in virtual in silicoscreening and several compounds from the Merck Index, Negwers handbook and Goodman and Gilman were identified by models as anthelmintic. Finally, the experimental assay of one organic chemical (G-1) by an in vivo test coincides fairly well (100) with model predictions. These results suggest that the proposed method will be a good tool for studying the biological properties of drug candidates during the early state of the drug-development process.     

CE Profiling of Organic Acids in Distilled Alcohol Beverages Using Pattern Recognition Analysis

The objective of this work has been to assess the potential of capillary isotachophoretic organic acids profiling using multivariate statistical methods to classify brandy samples and wine distillate samples. The leading electrolyte was 10 mmol L⁻¹ hydrochloric acid including 0.1% methylhydroxylethylcellulose adjusted with β-alanine to pH 2.9. The terminating electrolyte was 5 mmol L⁻¹ acetic acid. Principal component analysis, cluster analysis, and linear discriminant analysis were used for the classification of beverages. The results show that for the 12 acids analysed, 98.57% of the total variance is extracted by the six principal components (PC). After performing backward linear discriminant analysis, a classification function was obtained containing four variables: formic (PC2-loadings: 0.989), lactic (PC1-loadings: 0.886), malic (PC1-loadings: 0.989) and oxalic (PC2-loadings: 0.777) acids, which provide 100.0% correct classification of brandies and wine distillates.

     

Vinegar classification based on feature extraction and selection from headspace solid-phase microextraction/gas chromatography volatile analyses: a feasibility study

Headspace solid-phase microextraction (HS-SPME) coupled with gas chromatography (GC) and multivariate data analysis were applied to classify different vinegar types (white and red, balsamic, sherry and cider vinegars) on the basis of their volatile composition. The collected chromatographic signals were analysed using the stepwise linear discriminant analysis (SLDA) method, thus simultaneously performing feature selection and classification. Several options, more or less restrictive according to the final number of considered categories, were explored in order to identify the one that afforded highest discrimination ability. The simplicity and effectiveness of the classification methodology proposed in the present study (all the samples were correctly classified and predicted by cross-validation) are promising and encourage the feasibility of using a similar strategy to evaluate the quality and origin of vinegar samples in a reliable, fast, reproducible and cost-efficient way in routine applications. The high quality results obtained were even more remarkable considering the reduced number of discriminant variables finally selected by the stepwise procedure. The use of only 14 peaks enabled differentiation between cider, balsamic, sherry and wine vinegars, whereas only 3 variables were selected to discriminate between red (RW) and white wine (WW) vinegars. The subsequent identification by gas chromatography-mass spectrometry (GC-MS) of the volatile compounds associated with the discriminant peaks selected in the classification process served to interpret their chemical significance.     

Elemental Chemometrics as Tools to Depict Stalked Barnacle (Pollicipes pollicipes) Harvest Locations and Food Safety

The stalked barnacle Pollicipes pollicipes is an abundant species on the very exposed rocky shore habitats of the Spanish and Portuguese coasts, constituting also an important economical resource, as a seafood item with high commercial value. Twenty-four elements were measured by untargeted total reflection X-ray fluorescence spectroscopy (TXRF) in the edible peduncle of stalked barnacles sampled in six sites along the Portuguese western coast, comprising a total of 90 individuals. The elemental profile of 90 individuals originated from several geographical sites (N = 15 per site), were analysed using several chemometric multivariate approaches (variable in importance partial least square discriminant analysis (VIP-PLS-DA), stepwise linear discriminant analysis (S-LDA), linear discriminant analysis (LDA), random forests (RF) and canonical analysis of principal components (CAP)), to evaluate the ability of each approach to trace the geographical origin of the animals collected. As a suspension feeder, this species introduces a high degree of background noise, leading to a comparatively lower classification of the chemometric approaches based on the complete elemental profile of the peduncle (canonical analysis of principal components and linear discriminant analysis). The application of variable selection approaches such as the VIP-PLS-DA and S-LDA significantly increased the classification accuracy (77.8% and 84.4%, respectively) of the samples according to their harvesting area, while reducing the number of elements needed for this classification, and thus the background noise. Moreover, the selected elements are similar to those selected by other random and non-random approaches, reinforcing the reliability of this selection. This untargeted analytical procedure also allowed to depict the degree of risk, in terms of human consumption of these animals, highlighting the geographical areas where these delicacies presented lower values for critical elements compared to the standard thresholds for human consumption.     

Recognition of benzene, toluene and xylene using TGS array integrated with linear and non-linear classifier

Three volatile organic compounds (VOCs): benzene, toluene and xylene were measured with an array of six Taguchi gas sensors in the air with variable humidity content. The recognition of single compounds was performed, based on measurement results. The principal component analysis (PCA) pointed at humidity as the main classification factor in the measurement data set. The linear discriminant analysis (LDA) was applied to overcome this drawback and enforce classification with respect to benzene, toluene or xylene. It was shown that discriminant function analysis (DFA), which is an LDA method allowed for 100% success rate in test samples recognition of benzene. It did not allow for accurate recognition of test samples of toluene or xylene. Following, the non-linear classifier, radial basis function neural network (RBFNN) was applied. A specific configuration of input ‘s was found, which provided for successful recognition of each single compound: benzene, toluene or xylene in air with variable humidity content.