Sequential (step-by-step) detection,identification and quantitation of extra virgin olive oil adulteration by chemometric treatment of chromatographic profiles

An analytical method for the sequential detection, identification and quantitation of extra virgin olive oil adulteration with four edible vegetable oils--sunflower, corn, peanut and coconut oils--is proposed. The only data required for this method are the results obtained from an analysis of the lipid fraction by gas chromatography-mass spectrometry. A total number of 566 samples (pure oils and samples of adulterated olive oil) were used to develop the chemometric models, which were designed to accomplish, step-by-step, the three aims of the method: to detect whether an olive oil sample is adulterated, to identify the type of adulterant used in the fraud, and to determine how much aldulterant is in the sample. Qualitative analysis was carried out via two chemometric approaches--soft independent modelling of class analogy (SIMCA) and K nearest neighbours (KNN)--both approaches exhibited prediction abilities that were always higher than 91% for adulterant detection and 88% for type of adulterant identification. Quantitative analysis was based on partial least squares regression (PLSR), which yielded R2 values of >0.90 for calibration and validation sets and thus made it possible to determine adulteration with excellent precision according to the Shenk criteria.     

Classification of Brazilian Coffee Using Near-Infrared Spectroscopy and Multivariate Calibration

This work describes the use of near infrared spectroscopy (NIRS) and chemometric techniques calibration for the classification of coffee samples from different lots and producers acquired in supermarkets and roasting industries in some Brazilian cities. Seventy-three samples of finely ground roasted coffee were acquired in the market and 91 samples of roasted ground Arabica beans were analyzed in the full NIR spectral range (800–2500 nm) using a diffuse reflectance accessory coupled to an MB160 Bomem spectrophotometer. Two classification models were constructed: Soft Independent Modeling Class Analogy (SIMCA) and PLS Discriminant Analysis (PLS-DA). All findings reveal that NIR spectroscopy, coupled with either SIMCA or PLS-DA multivariate models, can be a useful tool to differentiate roasted coffee grains and to replace sensory tests.     

Illuminating the flesh of bone identification – An application of near infrared spectroscopy

Near infrared (NIR) reflectance spectroscopy coupled with chemometric analysis was evaluated as a non-destructive tool to discriminate skull bone samples from different animal species. In total 70 skull bones from animals of three classes (mammalians, avian and reptiles) were scanned in the wavelength range between 950 to 1650 nm. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to analyse the NIR spectra of the skull samples. Correct classification rates of 96% and 81% were obtained for the classification of skull bone samples according to avian and mammalian classes, respectively. Overall, a 91% correct classification rate was obtained for the classification of skull samples according to the class (mammalian and avian). This study demonstrates the potential of NIR spectroscopy coupled with chemometric as data processing, as a means of a rapid, non-destructive classification technique for skull bone samples.     

Tracing the origin of extra virgin olive oils by infrared spectroscopy and chemometrics: A case study

In this paper, the potential of coupling mid- and near-infrared spectroscopic fingerprinting techniques and chemometric classification methods for the traceability of extra virgin olive oil samples from the PDO Sabina was investigated. To this purpose, two different pattern recognition algorithm representative of the discriminant (PLS-DA) and modeling (SIMCA) approach to classification were employed. Results obtained after processing the spectroscopic data by PLS-DA evidenced a rather high classification accuracy, NIR providing better predictions than MIR (as evaluated both in cross-validation and on an external test set). SIMCA confirmed these results and showed how the category models for the class Sabina can be rather sensitive and highly specific. Lastly, as samples from two harvesting years (2009 and 2010) were investigated, it was possible to evidence that the different production year can have a relevant effect on the spectroscopic fingerprint. Notwithstanding this, it was still possible to build models that are transferable from one year to another with good accuracy.     

FTIR spectroscopy coupled with multivariate classification methods to identify different powdered infant formulas adulterated with melamine and cyanuric acid

Fourier transform infrared spectroscopy (FTIR) is a nondestructive, simple, rapid, and cheap measurement technique for analysis of many multicomponent chemical systems, e.g., detection of adulterants in food samples. In this respect, this study proposes combining FTIR spectroscopy with multivariate classification methods for classification and discrimination of different samples of infant formulas adulterated by melamine or/and cyanuric acid. Different parametric and non-parametric multivariate classification methods including the linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), soft independent modeling of class analogy (SIMCA), K-nearest neighbors (KNN), and classification and regression tree (CART) approaches were used to classify the recorded FTIR data. Assessing the performance of the multivariate methods according to their sensitivity, specificity and percent of correct prediction results demonstrated that coupling FTIR spectroscopy with multivariate classification can be applied as a rapid and powerful technique to the simultaneous detection of melamine and cyanuric acid in powdered infant formulas. This combinatorial method is efficient for adulterant concentrations as low as 0.0001 w/w%.     

Determining the adulteration of spices with Sudan I-II-II-IV dyes by UV-visible spectroscopy and multivariate classification techniques

We propose a very simple and fast method for detecting Sudan dyes (I, II, III and IV) in commercial spices, based on characterizing samples through their UV-visible spectra and using multivariate classification techniques to establish classification rules. We applied three classification techniques: K-Nearest Neighbour (KNN), Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares Discriminant Analysis (PLS-DA). A total of 27 commercial spice samples (turmeric, curry, hot paprika and mild paprika) were analysed by chromatography (HPLC-DAD) to check that they were free of Sudan dyes. These samples were then spiked with Sudan dyes (I, II, III and IV) up to a concentration of 5 mg L⁻¹. Our final data set consisted of 135 samples distributed in five classes: samples without Sudan dyes, samples spiked with Sudan I, samples spiked with Sudan II, samples spiked with Sudan III and samples spiked with Sudan IV.Classification results were good and satisfactory using the classification techniques mentioned above: 99.3%, 96.3% and 90.4% of correct classification with PLS-DA, KNN and SIMCA, respectively. It should be pointed out that with SIMCA, there are no real classification errors as no samples were assigned to the wrong class: they were just not assigned to any of the pre-defined classes.     

Discrimination of Polygoni Multiflori radix and Cynanchi Auriculati radix using ultra‐high performance liquid chromatography fingerprints and chemical pattern recognition

In this work, a strategy was proposed to discriminate Polygoni Multiflori Radix (PMR) and its adulterant (Cynanchi Auriculati Radix, CAR). Ultra‐high performance liquid chromatography (UHPLC) fingerprints were established to analyze samples containing PMR, CAR and mixtures simultaneously. Multivariate classification methods were applied to analyze the obtained UHPLC fingerprints, including principal component analysis (PCA), partial least square discriminant analysis (PLS‐DA), soft independent modeling of class analogy (SIMCA), support vector machine discriminant analysis (SVMDA) and counter‐propagation artificial neural network (CP‐ANN). A plot of PCA score showed that PMR and CAR samples belonged to separate clusters (PMR class and CAR class), and samples of mixtures were located near PMR or CAR classes. Analysis by PLS‐DA, SVMDA and CP‐ANN performed well for recognition and prediction in terms of PMR and CAR samples. Moreover, the PLS‐DA method performed best in the detection of adulterated samples, even if the adulterant was about 25%.     

Comparison of PLS1-DA, PLS2-DA and SIMCA for classification by origin of crude petroleum oils by MIR and virgin olive oils by NIR for different spectral regions

This study compares results obtained with several chemometric methods: SIMCA, PLS2-DA, PLS2-DA with SIMCA, and PLS1-DA in two infrared spectroscopic applications. The results were optimized by selecting spectral ranges containing discriminant information. In the first application, mid-infrared spectra of crude petroleum oils were classified according to their geographical origins. In the second application, near-infrared spectra of French virgin olive oils were classified in five registered designations of origins (RDOs). The PLS-DA discrimination was better than SIMCA in classification performance for both applications. In both cases, the PLS1-DA classifications give 100% good results. The encountered difficulties with SIMCA analyses were explained by the criteria of spectral variance. As a matter of fact, when the ratio between inter-spectral variance and intra-spectral variance was close to the F_c (Fisher criterion) threshold, SIMCA analysis gave poor results. The discrimination power of the variable range selection procedure was estimated from the number of correctly classified samples.     

Screening Brazilian C gasoline quality: application of the SIMCA chemometric method to gas chromatographic data

A total of 2400 samples of commercial Brazilian C gasoline were collected over a 6-month period from different gas stations in the S?o Paulo state, Brazil, and analysed with respect to 12 physicochemical parameters according to regulation 309 of the Brazilian Government Petroleum, Natural Gas and Biofuels Agency (ANP). The percentages (v/v) of hydrocarbons (olefins, aromatics and saturated) were also determined. Hierarchical cluster analysis (HCA) was employed to select 150 representative samples that exhibited least similarity on the basis of their physicochemical parameters and hydrocarbon compositions. The chromatographic profiles of the selected samples were measured by gas chromatography with flame ionisation detection and analysed using soft independent modelling of class analogy (SIMCA) method in order to create a classification scheme to identify conform gasolines according to ANP 309 regulation. Following the optimisation of the SIMCA algorithm, it was possible to classify correctly 96% of the commercial gasoline samples present in the training set of 100. In order to check the quality of the model, an external group of 50 gasoline samples (the prediction set) were analysed and the developed SIMCA model classified 94% of these correctly. The developed chemometric method is recommended for screening commercial gasoline quality and detection of potential adulteration.     

NMR and chemometrics in tracing European olive oils: The case study of Ligurian samples

An NMR and chemometric analytical approach to classify extra virgin olive oils according to their geographical origin was developed within the European TRACE project (FP6-2003-FOOD-2-A, contract number: 0060942). Olive oils (896 samples) of three consecutive harvesting years (2005, 2006, and 2007) coming from Mediterranean areas were analyzed by ¹H NMR spectroscopy. Olive oil samples from Liguria, an Italian region, were chosen as a case study and PLS-DA and SIMCA modeling analyses were used to build up statistical models both to discriminate between Ligurian and non-Ligurian olive oils and to define the Ligurian olive oil class to confirm the declared provenience.     

Preliminary study on application of mid infrared spectroscopy for the evaluation of the virgin olive oil "freshness"

The freshness of virgin olive oils (VOO) from typical cultivars of Garda regions was evaluated by attenuated total reflectance (ATR) and Fourier transform infrared (FTIR) spectroscopy, in combination with multivariate analysis. The olive oil freshness decreased during storage mainly because of oxidation processes. In this research, 91 virgin olive oils were packaged in glass bottles and stored either in the light or in the dark at room temperature for different periods. The oils were analysed, before and after storage, using both chemical methods and spectroscopic technique.Classification strategies investigated were partial least square discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and soft independent modelling of class analogy (SIMCA).The results show that ATR-MIR spectroscopy is an interesting technique compared with traditional chemical index in classifying olive oil samples stored in different conditions. In fact, the FTIR PCA results allowed a better discrimination among fresh and oxidized oils, than samples separation obtained by PCA applied to chemical data. Moreover, the results obtained by the different classification techniques (PLS-DA, LDA, SIMCA) evidenced the ability of FTIR spectra to evaluate the olive oil freshness. FTIR spectroscopy results are in agreement with classical methods. The spectroscopic technique could be applied for the prediction of VOOs freshness giving information related to chemical modifications. The great advantages of this technique, compared to chemical analysis, are related to rapidity, non-destructive characteristics and low cost per sample. In conclusion, ATR-MIR represents a reliable, cheap and fast classification tool able to assess the freshness of virgin olive oils.     

Screening Brazilian Commercial Gasoline Quality by ASTM D6733 GC and Pattern-Recognition Multivariate SIMCA Chemometric Analysis

The combination of ASTM D6733 gas chromatographic fingerprinting data with pattern-recognition multivariate soft independent modeling of class analogy (SIMCA) chemometric analysis provides an original and alternative approach to screening Brazilian commercial gasoline quality in a monitoring program for quality control of automotive fuels. SIMCA was performed on chromatographic fingerprints to classify the quality of the gasoline samples. Using SIMCA, it was possible to correctly classify 94.0% of commercial gasoline samples, which is considered acceptable. The method is recommended for quality-control monitoring. Quality control and police laboratories could employ this method for rapid monitoring.     

Analysis of sexual assault evidence: statistical classification of condoms by ambient mass spectrometry

Desorption electrospray ionization mass spectrometry (DESI‐MS) and easy ambient sonic‐spray ionization mass spectrometry (EASI‐MS) are employed here in the forensic analysis of chemical compounds found in condoms and relative traces, and their analytical performances are compared. Statistical analysis of data obtained from mass spectra only was applied in order to obtain classification rules for distinguishing ten types of condoms. In particular, two supervised chemometric techniques [linear discriminant analysis (LDA) and soft independent modeling of class analogy (SIMCA)] were carried out on absolute and relative intensity values to test the performances of statistical models in terms of predictive capacity. The achieved classification of samples was excellent because of the high prediction percentages of the method used both for DESI and EASI mass spectrometry analyses, confirming these two as potential ambient ionization techniques for forensic analyses in case of sexual assault crimes. EASI‐MS showed 99% prediction ability for LDA using relative data and 100% prediction ability for SIMCA using both absolute and relative ones, while DESI showed 94% prediction ability for both LDA and SIMCA. The absence of any sample preparation technique gives advantages in terms of sample preservation and reduced contamination, allowing successive analyses to be performed on the same sample by other techniques. Copyright © 2015 John Wiley & Sons, Ltd.     

A novel strategy to verification of adulteration in alcoholic beverages based on Schlieren effect measurements and chemometric techniques

A novel strategy to evaluation of adulteration in alcoholic beverages based on the measurement of the Schlieren effect using an automated FIA system with photometric detection is proposed. The assay is based on the Schlieren effect produced when beverage samples are injected in a single-line FIA system that uses water as carrier stream and a light-emitting diode-phototransistor photometer controlled by microcomputer as detector. The flow system presents limited mixing conditions which make possible to create gradients of refractive index (Schlieren effect) in the injected sample zone. These gradients are reproducible, characteristic of each alcoholic beverage and undergo specific modifications when adulterations with water or ethanol are imposed. Schlieren effect data of brandies, cachaças, rums, whiskies and vodkas were treated by SIMCA to elaborate class models applied in the evaluation of alcoholic beverages adulteration. Samples of the original matrix of each sort of beverages were adulterated in laboratory by adding water, methanol and ethanol in levels of 5% and 10% (v/v). These samples were used as test set to validate SIMCA class models. The verification of authenticity using Schlieren effect measurements presented good results making possible to identify 100% of the beverages samples adulterated in laboratory and 93% of the actual adulterated alcoholic beverages with confidence levels of 95%. As principal advantage, the automated system does not use reagents to carry out the analysis.     

Chemometric characterization of 5th century A.D. amphora-producing centres in the Mediterranean

The application of two chemometric techniques for data structure analysis (Ward's hierarchical clustering and Principal Components Analysis) has permitted the characterization of 5th century A.D. amphora-producing centres in the Mediterranean on the basis of X-ray fluorescence data on 66 amphora fragments. The chemometric study complements and reinforces the conclusions drawn from typological and archaeological analysis. Nineteen samples of unknown origin have been classified (using the SIMCA method) into the eight proposed classes of amphora production.     

Classification of binary mass spectra of toxic compounds with an inductive expert system and comparison with SIMCA class modeling

The performance of an inexpensive, inductive rule-building expert shell system, based on the ID3 algorithm, was compared to that of SIMCA class modeling in classifying the binary mass spectra of 78 toxic and related compounds. The compressed mass spectra consisted of 17 masses chosen by using information theory. The expert rules verified the six main classes and two subclasses found with SIMCA class modeling. These classes were: all benzenes and all alkanes/ alkenes (alka(e)nes); nonhalobenzenes, chlorobenzenes, bromoalka(e)nes, and chloroalka(e)nes; and mono-, dichloroalka(e)nes and polychloroalka(e)nes. Training set classification accuracies obtained with the expert system were 93–100% as opposed to 62–98% for SIMCA. For 73 compounds, the expert rules gave a classification accuracy of 97–100% vs. 79–96% for SIMCA. Predictive accuracy for the four main classes was 78%. In general, fewer masses were involved with the rules than with the SIMCA models, and the rules are normally optimized with regard to minimum number of steps in the rule, not minimum number of variables. The expert rules work best with closed sets of objects where all possibilities can be included in the training sets. The expert rules represent planes partitioning the multidimensional measurement space (hypercube) into a subvolume nearest the SIMCA cylinders for an appropriate class. Overall, the performance of the expert system was very good.     

Characterization of Galician (N.W. Spain) quality brand potatoes: a comparison study of several pattern recognition techniques

Authenticity is an important food quality criterion and rapid methods to guarantee it are widely demanded by food producers, processors, consumers and regulatory bodies. The objective of this work was to develop a classification system in order to confirm the authenticity of Galician potatoes with a Certified Brand of Origin and Quality (CBOQ) 'Denominación Específica: Patata de Galicia' and to differentiate them from other potatoes that did not have this CBOQ. Ten selected metals were determined by atomic spectroscopy in 102 potato samples which were divided into two categories: CBOQ and non-CBOQ potatoes. Multivariate chemometric techniques, such as cluster analysis and principal component analysis, were applied to perform a preliminary study of the data structure. Four supervised pattern recognition procedures [including linear discriminant analysis (LDA), K-nearest neighbours (KNN), soft independent modelling of class analogy (SIMCA) and multilayer feed-forward neural networks (MLF-ANN)] were used to classify samples into the two categories considered on the basis of the chemical data. Results for LDA, KNN and MLF-ANN are acceptable for the non-CBOQ class, whereas SIMCA showed better recognition and prediction abilities for the CBOQ class. A more sophisticated neural network approach performed by the combination of the self-organizing with adaptive neighbourhood network (SOAN) and MLF network was employed to optimize the classification. Using this combined method, excellent performance in terms of classification and prediction abilities was obtained for the two categories with a success rate ranging from 98 to 100%. The metal profiles provided sufficient information to enable classification rules to be developed for identifying potatoes according to their origin brand based on SOAN-MLF neural networks.     

Classification of biodiesel using NIR spectrometry and multivariate techniques

This article describes the classification of biodiesel samples using NIR spectroscopy and chemometric techniques. A total of 108 spectra of biodiesel samples were taken (being three samples each of four types of oil, cottonseed, sunflower, soybean and canola), from nine manufacturers. The measurements for each of the three samples were in the spectral region between 12,500 and 4000 cm⁻¹. The data were preprocessed by selecting a spectral range of 5000-4500 cm⁻¹, and then a Savitzky-Golay second-order polynomial was used with 21 data points to obtain second derivative spectra. Characterization of the biodiesel was done using chemometric models based on hierarchical cluster analysis (HCA), principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) elaborated for each group of biodiesel samples (cotton, sunflower, soybean and canola). For the HCA and PCA, the formation of clusters for each group of biodiesel was observed, and SIMCA models were built using 18 spectral measurements for each type of biodiesel (training set), and nine spectral measurements to construct a classification set (except for the canola oil which used eight spectra). The SIMCA classifications obtained 100% accurate identifications. Using this strategy, it was feasible to classify biodiesel quickly and nondestructively without the need for various analytical determinations.     

Flow injection mass spectroscopic fingerprinting and multivariate analysis for differentiation of three Panax species

This study describes the use of spectral fingerprints acquired by flow injection(FI)-MS and multivariate analysis to differentiate three Panax species: P. ginseng, P. quinquefolius, and P. notoginseng. Data were acquired using both high resolution and unit resolution MS, and were processed using principal component analysis (PCA), soft independent modeling of class analogy (SIMCA), partial least squares-discriminant analysis (PLS-DA), and a fuzzy rule-building expert system (FuRES). Both high and unit resolution MS allowed discrimination among the three Panax species. PLS-DA and FuRES provided classification with 100% accuracy while SIMCA provided classification accuracies of 77 and 88% by high- and low-resolution MS, respectively. The method does not quantify any of the sample components. With FI-MS, the analysis time was less than 2 min.     

Data-handling strategies for metabonomic studies: example of the UHPLC-ESI/ToF urinary signature of tetrahydrocannabinol in humans

Metabonomics has become a very valuable tool and many research fields rely on results coming out from this combination of analytical techniques, chemometric strategies, and biological interpretation. Moreover, the matrices are more and more complex and the implications of the results are often of major importance. In this context, the need for pertinent validation strategies comes naturally. The choice of the appropriate chemometric method remains nevertheless a difficult task due to particularities such as: the number of measured variables, the complexity of the matrix and the purposes of the study. Consequently, this paper presents a detailed metabonomic study on human urine with a special emphasis on the importance of assessing the data's quality. It also describes, step by step, the statistical tools currently used and offers a critical view on some of their limits. In this work, 29 urine samples among which 15 samples obtained from tetrahydrocannabinol (delta-9-tetrahydrocannabinol)-consuming athletes, 5 samples provided by volunteers, and 9 samples obtained from athletes were submitted to untargeted analysis by means of ultra high-pressure liquid chromatography–electrospray ionization–time-of-flight mass spectrometry. Next, the quality of the obtained data was assessed and the results were compared to those found in databases. Then, unsupervised (principal component analysis (PCA)) and supervised (ANOVA/PCA, partial least-square–discriminant analysis (PLS-DA), orthogonal PLS-DA) univariate and multivariate statistical methods were applied.