Simultaneous identification of the wood types in aged cachaças and their adulterations with wood extracts using digital images and SPA-LDA

A green method for the authentication of sugarcane spirit and prediction of density and alcohol content based on near infrared spectroscopy and chemometric tools

Cachaça is a Brazilian beverage obtained from the fermentation of sugarcane juice (sugarcane spirit) and is considered one of the most consumed alcoholic beverages in the world with a strong economic impact on the northeastern Brazil, more specifically in the Brejo. This microregion produces sugarcane spirits with high quality associated to edaphoclimatic conditions. In this sense, analysis for sample authentication and quality control that uses solvent-free, environmentally friendly, rapid and non-destructive methods is advantageous for cachaça producers and production chain. Thus, in this work commercial cachaça samples using near-infrared spectroscopy (NIRS) were classified based on geographical origin using one-class classification Data-Driven in Soft Independent Modelling of Class Analogy (DD-SIMCA) and One-Class Partial Least Squares (OCPLS) and predicted quality parameters of alcohol content and density based on different chemometric algorithms. A total of 150 sugarcane spirits samples were purchased from the Brazilian retail market being 100 from Brejo and 50 from other regions of Brazil. The one-class chemometric classification model was obtained with DD-SIMCA using the Savitzky-Golay derivative with first derivative, 9-point window and 1st degree polynomial as preprocessing algorithm and sensibility was 96.70 % and specificity 100 % in the spectral range 7,290-11,726 cm^-1. Satisfactory results were obtained in the model constructs for density and the chemometric model, iSPA-PLS algorithm with baseline offset as preprocessing, obtained root mean square errors of prediction (RMSEP) of 0.0011 mg/L and Relative Error of Prediction (REP) of 0.12 %. The chemometric model for alcohol content prediction used the iSPA-PLS algorithm with Savitzky-Golay derivative with first derivative, 9-point window and 1st degree polynomial as algorithm as preprocessing obtaining RMSEP and REP of 0.69 and 1.81 % (v/v), respectively. Both models used the spectral range from 7,290-11,726 cm^-1. The results reflected the potential of vibrational spectroscopy coupled with chemometrics to build reliable models for identifying the geographical origin of cachaça samples for predicting quality parameters in cachaça samples.

A fast and low-cost approach for the discrimination of commercial aged cachaças using synchronous fluorescence spectroscopy and multivariate classification

BACKGROUND

Cachaça is the distilled beverage typical of Brazil and can be subjected to the aging process in wooden barrels. In addition to oak barrels, cachaça is also aged in barrels of different Brazilian native woods, resulting in a wide variety of its sensory characteristics. In this work, 172 cachaças aged in bálsamo, jequitibá, oak, and umburana barrels were analyzed by synchronous fluorescence spectroscopy and by the classification methods of principal component analysis and partial least squares discriminant analysis. Spectra were preprocessed by the first derivative by Savitzky-Golay smoothing, using a filter width and polynomial order determined through face-centered central composite designs. Multivariate analysis was realized using the spectra recorded at different wavelength differences, and models were compared by the classification errors in the test sets.

RESULTS

The principal component analysis applied to the synchronous fluorescence spectra presented a tendency of separation by the wood used in the aging process, and the partial least squares discriminant analysis model constructed using the fluorescence spectra recorded at a wavelength difference of 30 nm provided better performance parameters (efficiency 91-97%, sensitivity 81-100%, and specificity 91-100%).

CONCLUSION

Synchronous fluorescence spectroscopy offers a promising approach for the classification of cachaças aged in bálsamo, oak, jequitibá, and umburana barrels, and the discriminant model can be used for routine analysis as a screening method. © 2022 Society of Chemical Industry.

Rapid quantification of acid value in frying oil using iron tetraphenylporphyrin fluorescent sensor coupled with density functional theory and multivariate analysis

Metalloporphyrin-based fluorescent sensor was developed for the acid value in frying oil. The electronic and structural performances of iron tetraphenylporphyrin (FeTPP) were theoretically investigated using time-dependent density functional theory (TD-DFT) and DFT at the B3LYP/LANL2DZ level. The quantified FeTPP-based fluorescent sensor results revealed its excellent performance in discriminating different analytes. In the present work, the acid value of palm olein was determined after every single frying cycle. A total of 10 frying cycles were conducted each day for 10 consecutive days. The FeTPP-based fluorescent sensor was used to quantify the acid value and the results were compared with the chemical data obtained by conventional titration method. The synchronous fluorescence spectrum for each sample was recorded. Parallel factor analysis (PARAFAC) was used to decompose the three-dimensional spectrum data. Then, the support vector regression (SVR), partial least squares (PLS), and back-propagation artificial neural network (BP-ANN) methods were applied to build the regression models. After the comparison of the constructed models, the SVR models exhibited the highest correlation coefficients among all models, with 0.9748 and 0.9276 for the training and test set, respectively. The findings suggested the potential of FeTPP-based fluorescent sensor in rapid monitoring of the used frying oil quality and perhaps also in other foods with higher oil content.

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.

Kurtosis-based projection pursuit analysis to evaluate South American rapadura

Rapadura is an artisanal candy obtained from concentrated sugarcane juice. In this study, a differentiation between South American rapadura producers has been tried using a Kurtosis-based projection pursuit analysis (kPPA) concerning essential minerals, acrylamide, moisture contents, pH, and color. These parameters revealed significant inter- and intra-country differences. Based on the employed measurements, a multivariate exploration with kPPA extracted information from rapadura even though it is a very artisanal product and was effective in separating classes, especially Brazilian and Ecuadorian rapadura, where principal component analysis failed. Moreover, ellipse confidence regions showed significant differences between non-organic and organic rapadura from Colombia and Peru in granulated form. From a chemometric point of view, the application of kPPA can be used in cases when other metrics (as based on the variance) fail and can be useful in the exploratory analysis of complex multivariate chemical data.

Assessment of dairy products stability by physicochemical and spectroscopic analyses and digital images

Abstract The oxidative action of chemical substances present in dairy products may contribute to the darkening of the product. Product color is one of the first factors to be considered by the consumer for acceptance or rejection. In the food industry, the color parameter is measured using colorimeters and spectrophotometers; nevertheless, the use of digital images for colorimetric tests has been surveyed in the food area. Therefore, the present work aimed at investigating for 45 days the chemical, physicochemical and colorimetric alterations of creamy dairy dessert with white chocolate flavor and strawberry-flavored yogurt. These alterations were monitored by the analysis of the parameters pH, acidity, soluble solids content, in addition to spectroscopy in the middle-infrared region and digital images. The data collected were processed in a computational environment applying chemometric tools. As result, it was verified that there were alterations in the parameters evaluated; nonetheless, the acidity of the dairy dessert remained constant during the storage period. From the Principal Component Analysis (PCA) using the color variables, it was observed that the samples were grouped and separated by type and storage time in agreement with the visually observed colorimetric modifications.

Use of color based chromatographic images obtained from comprehensive two-dimensional gas chromatography in authentication analyses

Comprehensive two-dimensional gas chromatography (GC×GC) has been an important technique used to acquire as much information as possible from a wide variety of samples. Qualitative contour plots analysis provides useful information and in daily use it ends up being handled as images of the volatile organic compounds by analysts. Cachaça samples are used in this paper to showcase the use of two-dimensional chromatographic images as the main source for authentication purposes through one-class classifiers, such as data-driven soft independent modeling of class analogy (DD-SIMCA). The proposed workflow summarizes this fast and easy process, which can be used to certify a specific brand in comparison to other brands, as well as to authenticate if samples have been adulterated. Lower quality cachaças, non-aged cachaças and cachaças aged in different wooden barrels were tested as adulterants. Chromatographic images allowed for the distinction of all brands and nearly every adulteration tested. Sensitivity was estimated at 100% for all models and specificity ranged from 96% to 100%. Different approaches were used, alternating from working with whole-sized images to working with smaller resized versions of those images. Resized chromatographic images could be potentially useful to easily compensate for slight chromatographic misalignments, allowing for faster calculations and the use of simpler software. Reductions to 50% and 25% of the original size were tested and the results did not greatly differ from whole images model. As such, 2D chromatographic images have been found to be an interesting form of evaluating a product's authenticity.

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.

Online Color Classification System of Solid Wood Flooring Based on Characteristic Features

Solid wood flooring has good esthetic properties and is an excellent material for interior decoration. To meet the artistic effects of specific interior decoration requirements, the color of solid wood flooring needs to be coordinated. Thus, the color of the produced solid wood flooring needs to be sorted to meet the individual needs of customers. In this work, machine vision, deep learning methods, and ensemble learning methods are introduced to reduce the cost of manual sorting and improve production efficiency. The color CCD camera was used to collect 108 solid wood floors of three color grades provided by the company and obtained 108 18,000 × 2048 pixel wood images. A total of 432 images were obtained after data expansion. Deep learning methods, such as VGG16, DenseNet121, and XGBoost, were compared. After using XGBoost to filter the features, the accuracy of solid wood flooring color classification was 97.22%, the training model time was 5.27 s, the average test time for each picture was 51 ms, and a good result was achieved.

A new highly selective colorimetric Schiff base chemosensor for determining the copper content in artisanal cachaças

This work demonstrated the feasibility of applying the Schiff base 5-bromo-2-salicyl-beta-alanine as a colorimetric chemosensor for the spectrophotometric quantification of the copper content in artisanal cachaças. For this, the experimental conditions were optimized to obtain an efficient, sensitive, reversible, and highly selective chemosensor to Cu²⁺ ions. The complex stoichiometry was 1:1, with a formation constant of 5.82 × 10² L mol^-1 and molar absorptivity of 5.82 × 10³ mol L^-1 cm^-1. Then, a spectrophotometric analytical method was developed and validated according to the Brazilian legislation. The linearity of the analytical curve was demonstrated by ANOVA, at a confidence level of 95%. The limits of detection and quantification were 0.0659 and 0.200 mg L^-1, respectively. The coefficients of variation for both the intra- and inter-day precisions were lower than 3.83%, and the accuracy presented a mean recovery of 100.55 ± 2.87%. The absence of a matrix effect was confirmed by the standard addition method, and the copper content in three artisanal cachaças from different geographical origins was estimated as lower than 2.93 mg L^-1. This result was in accordance with the Brazilian legislation but reinforces the need to carry out stricter quality control to achieve exportation standards. Therefore, the proposed method can be considered a simple, selective, linear, precise, and accurate tool that involves only a simple complexation reaction through the addition of the chemosensor solution in a buffered medium. As a consequence, the simplicity, practicality, rapidity, and low cost of synthesis of the proposed Schiff base chemosensor are highlighted.

Digital image-based tracing of geographic origin, winemaker, and grape type for red wine authentication

This work proposes the development of a simple, fast, and inexpensive methodology based on color histograms (obtained from digital images), and supervised pattern recognition techniques to classify red wines produced in the São Francisco Valley (SFV) region to trace geographic origin, winemaker, and grape variety. PCA-LDA coupled with HSI histograms correctly differentiated all of the SFV samples from the other geographic regions in the test set; SPA-LDA selecting just 10 variables in the Grayscale + HSI histogram achieved 100% accuracy in the test set when classifying three different SFV winemakers. Regarding the three grape varieties, SPA-LDA selected 15 variables in the RGB histogram to obtain the best result, misclassifying only 2 samples in the test set. Pairwise grape variety classification was also performed with only 1 misclassification. Besides following the principles of Green Chemistry, the proposed methodology is a suitable analytical tool; for tracing origins, grape type, and even (SFV) winemakers.

Multistage Signals Based on Cyclic Chemiluminescence for Decoding Complex Samples

Identification of complex samples presents a difficult challenge for modern analytical techniques, and the differentiation among closely similar mixtures often remains indeterminate. In this article, we designed a simplified cyclic chemiluminescence (CCL) system that is able to measure multistage signals in a single sample injection. The system was used to investigate the CCL reactions of the binary, ternary, and multicomponent mixtures. Results showed that each mixture has a unique exponential decay equation (EDE) with a constant decay coefficient (k-value) to describe the change law of its multistage signals. Further studies found that different brands of liquor, beer, toner, and baby powder have different k-values, and the same brand of the commodities between different batches have the same k-values, which allows facile identification of these complex samples. We then used different catalysts to design digital codes of the k-value for further improving the identifying ability of CCL. Moreover, the multistage signals are like fingerprints and could be used for linear discriminate analysis, which provides another complementary approach for identification of complex samples. Finally, we demonstrated that CCL shows potential applications in certification and quality assurance according to the change of the k-values of the sample. This work demonstrates that excellent discrimination ability of CCL for the identification of complex samples and provides a promising technology for quality assurance.