A digital image smartphone-based approach to Slovak Tokaj wine authentication chemometric assisted.

The authentication of Slovak wines in comparison to other similar wines from various geographical regions, namely Hungary, France, Austria, and Ukraine, was conducted using the OC-PLS, DD-SIMCA, and PLS-DM models, all of them operating in rigorous way. The study involved 63 samples, of which 41 originated from Slovakia, covering diverse wine types such as varietal wines, cuvée selections (different "putnový"), and essence. To capture digital images under controlled conditions, a custom-made cardboard box with white inner surfaces was devised and equipped with a smartphone. During the training phase, sensitivities of 96%, 100%, and 96% were attained for OC-PLS, DD-SIMCA, and PLS-DM, respectively. In the subsequent stages of validation and testing for DD-SIMCA and PLS-DM, the proposed methods displayed optimal efficiency, achieving both sensitivity and specificity rates of 100%. However, such results were not achieved in the case of OC-PLS, which exhibited efficiency levels of 90% in validation and 80% in testing.

Region of interest selection in heterogeneous digital image: Wine age prediction by comprehensive two-dimensional gas chromatography.

This study integrates genetic algorithm (GA) with partial least squares regression (PLSR) and various variable selection methods to identify impactful regions of interest (ROI) in heterogeneous 2D chromatogram images for predicting wine age. As wine quality and aroma evolve over time, transitioning from youthful fruitiness to mature, complex flavors, which leads to alterations in the composition of essential aroma-contributing compounds. Chromatograms are segmented into subimages, and the GA-PLSR algorithm optimizes combinations based on grayscale, red-green-blue (RGB), and hue-saturation-value (HSV) histograms. The selected subimage histograms are further refined through interval selection, highlighting the compounds with the most significant influence on wine aging. Experimental validation involving 38 wine samples demonstrates the effectiveness of this approach. Cross-validation reduces the PLS model error from 2.8 to 2.4 years within a 10 × 10 subset, and during prediction, the error decreases from 2.5 to 2.3 years. The study presents a novel approach utilizing the selection of ROI for efficient processing of 2D chromatograms focusing on predicting wine age.

Second-order calibration high-resolution continuum source graphite furnace molecular absorption spectrometry-based determination of bromine and fluorine.

In this study, we report the simultaneous determination of bromine and fluorine using Second-Order Calibration High-Resolution Continuum Source Graphite Furnace Molecular Absorption Spectrometry (HR CS MAS). The instrumental data acquired correspond to the time versus wavelength matrix per sample that were analyzed using Parallel Factor Analysis (PARAFAC), along with Unfold and N-way Partial Least Squares combined with a post-calibration step known as Residual Bilinearization (U and N PLS/RBL). Despite the significant difference in sensitivity between bromine and fluorine, all approaches provided reasonably accurate results when predicting both analytes in synthetic mixtures within a controlled environment. The relative prediction error (REP) values for bromine were 29.8 % (PARAFAC), 23.6 % (N-PLS/RBL), and 13.1 % (U-PLS/RBL), while for fluorine, the REP values were 3.4 % (PARAFAC), 3.5 % (N-PLS/RBL), and 3.2 % (U-PLS/RBL). When applying this approach to predict unknown samples, a comparison was made between the estimated nominal concentrations of fluorine and bromine obtained using either a reference method or based on labeled values or spiked mass, and those obtained using the proposed method. It was observed that PARAFAC was unable to predict the samples accurately, whereas the REP values for the prediction of bromine and fluorine using N-PLS/RBL and U-PLS/RBL methods were 19.3 %/19.2 % and 13.6 %/13.1 %, respectively.

A non-target geographical origin screening of botrytized wines through comprehensive two-dimensional gas chromatography coupled with high-resolution mass spectrometry.

One of the most effective methods for gaining insight into the composition of trace-level volatile organic characteristics of wine products is through the use of a comprehensive two-dimensional gas chromatography-high resolution mass spectrometry (GC × GC-HRMS) technique. The vast amount of data generated by this method, however, can often be overwhelming requiring exhaustive and time-consuming analysis to identify significant statistical characteristics. The use of advanced chemometric software can achieve the same or even higher efficiency. This study aimed to identify differences based on geographical locations by analyzing the volatile organic compounds in the composition of botrytized wines from Slovakia, Hungary, France, and Austria. The volatile organic compounds were extracted by solid-phase microextraction and analyzed using GC × GC-HRMS. The data obtained from the analysis underwent Fisher-ratio (F-ratio) tile-based analysis to identify statistically significant differences. Principal component analysis demonstrated a significant distinction between wine samples based on geographical location, using only 10 statistically significant features with the highest F-ratio. In the samples, the following compounds were analyzed: methyl-octadecanoate, 2-cyanophenyl-ß-phenylpropionate, α-ionone, n-octanoic acid, 1,2-dihydro-1,1,6-trimethyl-naphthalene, methyl-hexadecanoate, ethyl-pentadecanoate, ethyl-decanoate, and γ-nonalactone. These, all play an important role in cluster pattern observed on principal component analysis results. Additionally, hierarchical cluster analysis confirmed this.

An improved successive projections algorithm version to variable selection in multiple linear regression.

The aim of the successive projections algorithm (SPA) is to enhance the accuracy of multiple linear regressions (MLR) by minimizing the impact of collinearity effects in the calibration data set. Combining SPA with MLR as a variable selection approach has resulted in the SPA-MLR method, which has been reported in literature to produce models with good prediction ability compared to conventional full-spectrum models obtained with partial-least-squares (PLS) in some cases. This paper proposes the addition of a filter step to the current version of the SPA algorithm to reduce the number of uninformative variables before the projection phase and assist the algorithm in selecting the best variables on subsequent steps. The proposed fSPA-MLR algorithm is evaluated in two case studies involving the near-infrared spectrometric analysis of pharmaceutical tablet and diesel/biodiesel mixture samples. Compared to PLS, the fSPA-MLR models demonstrate similar or better performance. Moreover, the fSPA-MLR models outperform the original SPA-MLR in both cross-validation and external prediction. The fSPA-MLR models deliver superior results regardless of the pre-processing algorithm tested, including first-derivative Savitzky-Golay (SG) and Standard Normal Variate (SNV), or even in raw spectra data.

Analytical and chemometric strategies for elucidation of yerba mate composition.

Yerba mate, a popular plant consumed mainly as an infusion, possesses nutritional and medicinal properties attributed to its secondary metabolites. This study aimed to develop strategies to elucidate the phenolic composition of yerba mate samples from Brazil, Argentina, Uruguay, and Paraguay. Optimization of ultrasonic-assisted extraction (UAE) was performed, and the extracted compounds were characterized using ultra-high-pressure liquid chromatography coupled with quadrupole/time-of-flight mass spectrometry (UHPLC-QTOF-MS), molecular fluorescence and high-pressure liquid chromatography with diode-array detection (HPLC-DAD). Chemometric analysis, including parallel factor analysis (PARAFAC) and principal component analysis (PCA) explored metabolite profiles and identify patterns. PARAFAC modelling of the molecular fluorescence results revealed higher pigment content in Brazilian samples, while other countries' samples exhibited higher phenolic content. PCA modeling of HPLC-DAD results indicated that cultivated yerba mate contained higher chlorogenic acids levels, and samples from Argentina, Paraguay, and Uruguay exhibited higher concentrations of chlorogenic acids and flavonoids.

Spatial-temporal occurrence of contaminants of emerging concern in urban rivers in southern Brazil.

The widespread use and misuse of antibiotics and pesticides has been linked with several risks to the environment and human health. In the present report, the results of the monitoring of 64 pharmaceuticals and 134 pesticides occurrence in an urban river in Southern Brazil are presented and discussed. Sampling campaigns have covered the period 2016-2018. The identification and determination of the analytes were achieved by high-resolution mass spectrometry. The data were analyzed using chemometric tools to obtain spatial-temporal models. Toxicological evaluation was achieved using acute toxicity (zebrafish standardized protocol), and determination of risk quotient. Within the 198 analytes included in the targeted analysis method for surface water, 33 were identified in an urban river during 2 years of monitoring, being 20 pharmaceuticals and 13 pesticides. Using high-resolution mass spectrometry, a suspect screening approach was established in an un-target analysis. The evaluation was carried out using a data bank built from consumption data of drugs and pesticides, in the metropolitan region of Porto Alegre - RS and their respective metabolites. The suspect screening analysis done with a data bank with more than 1450 compounds results in 27 suspect findings. The target analysis results showed a continuous prevalence of non-steroidal anti-inflammatories, analgesics, antipyretics, beta-blockers, corticoids, and antibiotics. Regarding the pesticides, the main classes were fungicides, especially those from triazol and strobilurin classes.

Discrimination of whole grape juice using fluorescence spectroscopy data with linear discriminant analysis coupled to genetic and ant colony optimisation algorithms.

In this study, a new approach was developed for classifying grape juices produced in Brazil using unfolded excitation-emission matrix (EEM) fluorescence spectroscopy and chemometrics, with respect to the agricultural production system, namely the conventional or organic agricultural one. Linear discriminant analysis (LDA) coupled to ant colony optimisation (ACO) and the genetic algorithm (GA) were used to select a more effective subset of variables to discriminate grape juice samples. The best results demonstrated highly efficient classification of grape juice samples according to a conventional or organic production process with an accuracy rate of up to 97% for the models and 94% in the prediction of these classes for samples external to the model. The models showed high selectivity and sensitivity with a rate of up to 100% for the training and test datasets, in addition to determining the most significant variables that explain the separation of classes. The proposed method proves to be viable, as it is fast and requires minimal sample preparation, allowing quality control in the food industry.

Enhanced multi-stir bar sorptive extraction for wine analysis: Alteration in headspace mode.

Multi-stir bar sorptive extraction (multi-SBSE) represents a viable alternative for recent trends in sample preparation based on a combination of extraction techniques. In this case, increased amount of sorbent and its extended polarity range could advance quality of experimental data obtained in foodomics or metabolomics investigations. With this in mind, it was developed multi-SBSE procedure suitable for authentication of botrytized wine produced in different countries of Tokaj wine region. A design of headspace mode of multi-SBSE was modified to provide additional agitation of a stir bar. An expanded profile of wine samples was obtained with the application of EG-Silicone and PDMS coated stir bars in headspace and direction immersion mode, respectively. Multivariate optimization based on central composite design was selected to determine the influence of various experimental parameters, including extraction temperature enhancing headspace extraction. In our case, proper description of the optimization results required application of a third-order polynomial model, which highlighted dominant influence of salt addition for extraction in both modes. Due to a large number of extracted compounds, comprehensive two-dimensional gas chromatography (GC × GC) was used for assessment of wine samples. Such approach allows reveleaing the presence of sulphur containing compounds, diols, ketone derivatives and methoxybenzenes linking a specific geographical origin. At the same time, the results obtained for compounds common for all the samples were processed with principal component analysis (PCA). Considerable progress for discrimination of the botrytized wines was mainly achieved with combined data from EG-Silicone and PDMS extraction.

Optimization of enantiomer separation in flow-modulated comprehensive two-dimensional gas chromatography by response surface methodology coupled to artificial neural networks: Wine analysis case study.

In spite of extensive applications of flow modulated comprehensive two-dimensional gas chromatography (FM-GG × GC) in different research areas, its application in the field of chiral separation is very limited. From a practical point of view, the establishment of experimental parameters for enantiomer separations is possibly more demanding in this case. Since the carrier gas flows in both dimensions, it affects not only the separation parameters, but also the fill/flush volumes of the modulator and its working efficiency. In this context, a multivariate design of experiment was applied to find the optimum experimental parameters of a reversed fill/flush (RFF) modulator for enantiomer separation of organic compounds present in botrytized wine samples. The results were described both with response surface methodology and artificial neural networks (ANN). The enantiomeric composition of chiral compounds present in the botrytized wines was used to identify their geographical origin, by principal component analysis (PCA). In addition, the developed one-class partial least squares (OC-PLS) model enabled recognition of the wine samples from the Tokaj wine region with 93% effectiveness in the presence of other samples.

Handling multiblock data in wine authenticity by sequentially orthogonalized one class partial least squares.

New approach to deal with food authentication by modelling methods based on data recorded from different sources is proposed and called OC-PLS, combines an orthogonalization step between the different data sets to eliminate redundant information followed by definition of an acceptance area for a target class by OC-PLS. The proposed method was evaluated in two case studies. The first study used a controlled scenario with simulated data. In the second case study, the approach was applied using UV-VIS and IR data, in order to differentiate Slovak Tokaj Selection wines of high quality from other lower market value wines from the Slovak Tokaj wine region. In both cases, better results were reached than when individual blocks of data were achieved. The proposed method proved to be effective in properly exploring common and distinct information in each data block. The best compromise between sensitivity and selectivity in the prediction step was achieved.

Variable selection in the chemometric treatment of food data: A tutorial review.

Food analysis covers aspects of quality and detection of possible frauds to ensure the integrity of the food. The arsenal of analytical instruments available for food analysis is broad and allows the generation of a large volume of information per sample. But this instrumental information may not yet give the desired answer; it must be processed to provide a final answer for decision making. The possibility of discarding non-informative and/or redundant signals can lead to models of better accuracy, robustness, and chemical interpretability, in line with the principle of parsimony. Thus, in this tutorial review, we cover aspects of variable selection in food analysis, including definitions, theoretical aspects of variable selection, and case studies showing the advantages of variable selection-based models concerning the use of a wide range of non-informative and redundant instrumental information in the analysis of food matrices.

Homogeneity and stability assessment of a candidate to pumpkin seed flour reference material by means of computer vision based chemometrics assisted approach.

This study proposes a preliminary assessment of the homogeneity and stability through digital image acquisition of a candidate for mechanically processed pumpkin seed meal reference material, exploring the concepts of homogeneity curve and the analysis of texture characteristics by Continuous-Level Moving Block through Robust Principal Component Analysis. This innovative methodology allowed us to examine the percentage of homogeneity in a set of samples, revealing an average of 41% with only one outlier in relation to the entire sample, indicating low homogeneity. In the stability study carried out after storing samples for 12 months at different temperatures, 83% of the samples were considered regular and 17% were outlier, which means that most of them were considered stable. Therefore, this methodology is useful for screening samples for homogeneity, by textural analysis, and detected non-homogeneity can be corrected in advance for quantification by standard protocols.

Multivariate optimization of dual-sorbent dynamic headspace extraction of volatiles in wine analysis.

The main critical point of newly developed miniaturized sample preparation techniques is a limited extraction capacity. Dynamic headspace extraction offers increased volume of sorbent which is commonly used in environmental analysis. Application of two sorbents (Carbopack B/Carbopack X and Tenax® TA) at different extraction temperatures allows enhancing a range of volatile organic compounds available for analysis. Such approach was applied in our research for quantification of volatile organic compounds in botrytized wines with gas chromatography. The central composite design was included to analysis simultaneous effects of incubation time, incubation temperature, purge volume and purge flow. In attempt to properly assess results, the data evaluation involved Pareto charts, surface response methodology and principal component analysis. Multivariate experimental design revealed statistical significance of purge volume and quadratic terms of incubation time and temperature, for response of volatiles. The quantification method with 0.2-2.0 µg/L LOD and 0.5-5.0 µg/L LOQ values, was developed under simultaneously optimized experimental conditions such as a 54 °C incubation temperature, a 20.18 min incubation time, a 344.3 mL purge volume and a 16.0 mL/min purge flow. The increased levels of linalool oxide, ethyl phenylacetate, γ-hexalactone and α-terpineol were observed in the samples, that correlated with botrytized wine technology.

VOCs profile of Colletotrichum spp. as a potential tool for quality control of açaí pulp.

Colletotrichum has been identified as responsible for the "dried fruit" disease in açaí (Euterpe oleracea). Besides concern for açaí pulp quality control, the characterization of Colletotrichum has been difficult, which has motivated the search for chemical markers in the Volatile Organic Compounds (VOCs) profile for use as a tool in the identification of açaí pulp contaminated by the fungus. Extracted VOCs by Headspace - Solid Phase Micro Extraction (HS-SPME) were identified through gas chromatography mass spectrometry (GC-MS). From GC-MS analyzes 26 VOCs were identified, with a predominance of the terpenoids. Chemometrically, menthol and menthone were assigned as potential markers of the genus. The analysis of VOCs in açaí pulps contaminated by Colletotrichum under different cultivation conditions enabled the detection of menthone. This result illustrated the selectivity of the culture medium and the potential of this tool for use in the quality control of açaí pulp.

Pharmaceuticals, pesticides and metals/metalloids in Lake Guaíba in Southern Brazil: Spatial and temporal evaluation and a chemometrics approach.

Compiling and reporting data related to the presence of pharmaceuticals and pesticides are crucial means of assessing the risk those chemicals pose to human health and environment. Data sets from different sources were combined using a data fusion approach to produce a spatial and temporal variation of contaminants presents in water from Lake Guaíba (29°55'-30°24' S; 51°01'-51°20' W). Lake Guaíba is a 496 km2 water body situated in the geological depression of Rio Grande do Sul State, Brazil; that is fed by several rivers from the metropolitan area, the 5th largest metro area in Brazil, with approximately 5 million inhabitants. Analytical methodology to quantify pharmaceuticals and pesticides by LC-QTOF-MS and GC-MS/MS was validated for 41 pharmaceutical and 62 pesticides. Furthermore, 27 chemical elements were analyzed by ICP-MS, and physical chemical parameters were determined using established methodologies. All validation parameters were in accordance with the National Institute of Metrology, Standardization, and Industrial Quality. Thirty-five water samples were analyzed from January to August 2019, and 15 pharmaceuticals and 25 pesticides were present in concentrations ranging from 6.00 ng L-1 to 580.00 ng L-1. Twenty-seven elements were analyzed during the same period, and 18 were present in concentrations ranging from 0.2 µg L-1 to 7060 µg L-1. Samples were tagged according to the points and months of collection to identify temporal and spatial patterns. The main findings show that the compounds are distributed throughout the studied area without an apparent regular pattern, suggesting that events in a specific point affect the entire ecosystem. Conversely, temporal variations were well defined, as samples were grouped according to the climatic conditions of the months of collection. Considering the calculated quotient risks, atrazine, cyproconazole, diuron, and simazine showed the highest risk levels for algae; acetaminophen, diclofenac, and ibuprofen showed the highest risk levels for aquatics invertebrates.

Scores selection via Fisher's discriminant power in PCA-LDA to improve the classification of food data.

This paper proposes an adaptation of the Fisher's discriminability criterion (named here as discriminant power, DP) for choosing principal components (obtained from Principal Component Analysis, PCA), which will be used to construct supervised Linear Discriminant Analysis (LDA) models for solving classification problems of food data. The proposed PCA-DP-LDA algorithm was then applied to (i) simulated data, (ii) classify soybean oils with respect to expiration date, and (iii) identify cachaça adulteration with wood extracts that simulated aging. For comparison, PCA-DP-LDA was evaluated against conventional PCA-LDA (based on explained variance) and Partial Least Squares-Discriminant Analysis (PLS-DA). Among them, PCA-DP-LDA achieved the most parsimonious and interpretable results, with similar or better classification performance. Therefore, the new algorithm can be considered a good alternative to the already well-established discriminant methods, being potentially applied where the discriminability of the principal components may not follow the same behavior of the explained variance.

Geographical origin authentication of southern Brazilian red wines by means of EEM-pH four-way data modelling coupled with one class classification approach.

EEM data recorded at different pH values was exploited by MCR-ALS in order to determine qualitative information about Brazilian red wines. In addition, the geographical traceability of wines produced in the Serra Gaúcha (Rio Grande do Sul) was carried out by DD-SIMCA considering 53 samples from the target class and 20 from other producing regions. The fluorescence signal corresponds to 9 EEMs recorded at different pH (3-11), generating four-way data. By MCR-ALS decomposition, eight factors were retrieved and related to typical chemical compounds found in red wine. In addition, the EEM pH data was used to build a one-class classification model, considering that MCR scores and all samples of the target class were properly recognised as belonging to the target class, with maximal sensitivity equal to 1. Samples of the non-target class were also adequately rejected by the model, and the specificity was found to be 0.97.

Slovak Tokaj wines classification with respect to geographical origin by means of one class approaches.

Tokaj wines could be produced only in so called Tokaj/Tokay wine region that falls within two countries Slovakia and Hungary. Thus, wines bearing Tokaj appellation must be produced only in Hungary and Slovakia by traditional process. Unfortunately, some counterfeit wines from neighbour region in Ukraine could be found in market. The aim of this work is to explore a simple UV-VIS spectrum to recognise true Tokaj/Tokay wines from counterfeits and try to differentiate wines based on their country of origin. This type of question can be duly answered using one class classification approach. Two different approaches, Data Driven Soft Independent Modelling of Class Analogy - DD-SIMCA and One-Class Partial Least Squares - OC-PLS were tested and evaluated for this purpose. In both cases, rigorous way models were built and optimized using only samples of the target class. A set of external samples containing samples from target class and non-target were used to validate the models ability to recognize Slovak samples and reject non-Slovak samples. Model based on DD-SIMCA showed better performance (97% correct rating) compared to OC-PLS models (80% correct rating). Comparing both approaches in terms of sensitivity and specificity, both exhibit high sensitivity (low false negative rate: DD-SIMCA 95% and OC-PLS 100%), however the OC-PLS based model showed low specificity (40%) while DD-SIMCA showed high specificity (100%) rejecting all samples out of Slovak origin. Therefore, the results found in this study show that it is possible to successfully combine UV-VIS spectra and DD-SIMCA models to discriminate Tokaj wine samples of Slovak origin from others. Equally important is environmentally friendly (fast, simple, absence of solvents) classification method in line with green chemistry.

A fast and inexpensive approach to characterize Slovak Tokaj selection wines using infrared spectroscopy and chemometrics.

The Tokaj Selection wines (hungarian equivalent "Aszú") are typical noble sweet wines produced in Tokaj wine region that falls within two countries, Slovakia and Hungary. Taking into account the economic importance and uniqueness of these wines, in this work, a new, fast and inexpensive method that combines infrared spectroscopy and multivariate models for characterization Slovak Tokaj Selection wines was developed. At first, sample authentication via one class models (dd-SIMCA) considered Slovak Tokaj Selection wines as target class. The non-target sample was considered to be only a Tokaj sample of Slovak origin. The resulting model was able to properly recognize samples of the target class with high sensitivity and specificity. Subsequently, the putna index was predicted via PLS models. RMSEP equals 0.44; REPpred of 9.6 and R2 0.95 was found in prediction step.