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Computational molecular modelling, mass spectrometry and in-vivo tests with Chlorella vulgaris (C. vulgaris) and Daphnia magna (D. magna) were used to investigate the liposolubility and ecotoxicity of MC-LR degradation by-products generated after oxidation by OH • radicals in Fenton process. Exposure of MC-LR (5 µg.L -1 ) to the most severe oxidation conditions (Fe 2+ 20 mM and H 2 O 2 60 mM) resulted in a reduction in the toxin concentration of 96% (0.16 µg.L -1 ), however, with the formation of many by-products. The by-product of m/z 445 was the most resistant to degradation and retained a toxic structure of diene bonds present in the Adda amino acid. Computational modeling revealed that m/z 445 (tPSA = 132.88 Ų; KOW = 2.02) is more fat-soluble than MC-LR (tPSA = 340.64 Ų; KOW = 0.68), evidencing an easier transport process of this by-product. Given this, toxicity tests using C. vulgaris and D. magna indicated greater toxicity of the by-product m/z 445 compared to MC-LR. When the conversion of MC-LR to by-products was 77%, the growth inhibition of C. vulgaris and the D. magna immobility were, respectively, 6.14 and 0%, with 96% conversion; growth inhibition and the immobility were both 100%  for both species., (Copyright © 2022 Elsevier B.V. All rights reserved.)

This works proposed a feasibility study on NIR spectroscopy and chemometrics-assisted color histogram-based analytical systems (CACHAS) to determine and authenticate the cassava starch content in wheat flour. Prediction results of partial least squares (PLS) achieved coefficient of correlation (r pred ) of 0.977 and root mean square error of prediction (RMSEP) of 1.826 mg kg -1 for the certified additive-free wheat flour, while r pred of 0.995 and RMSEP of 1.004 mg kg -1 were obtained for the commercial wheat flour containing chemical additives. Additionally, Data-Driven Soft Independent Modelling of Class Analogy (dd-SIMCA) presented similar predictive ability using NIR and CACHAS for the certified wheat flour, authenticating all target samples, besides correctly recognizing samples that could represent a fraud. No satisfactory results were obtained for the commercial wheat flour. Therefore, NIR spectroscopy is more useful to offer definitive quantitative and qualitative analysis, while CACHAS can only provide an alternative preliminary analysis., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The growing demand for excellent-quality coffees allied with their symbolic aestheticization that add value to the products favor the adulteration practices and consequently economic losses. So, this work proposes the suitability of NIR spectroscopy and Digital Images (from CACHAS) coupled with one-class classification methods for the non-destructive authentication of Gourmet ground roasted coffees. For this, Gourmet coffees (n = 44) were discriminated from Traditional (n = 36) and Superior (n = 10) by directly analyzing their powder without any sample preparation. Then, OC-PLS and dd-SIMCA were used to construct the models. dd-SIMCA using offset correction for NIR and RGB histogram for CACHAS achieved the best results, correctly recognizing all the 90 samples in both the training and test sets. Therefore, the proposed methodologies can be useful for both the consumers and regulatory agencies because it confirms the elevated standards of excellence of Brazilian specialty coffees, preventing fraudulent labeling, besides following the Principles of Green Analytical Chemistry., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

An ultrasonic reactor (UR) was developed and coupled to a digital movie-based flow-batch analyzer (DM-FBA) for the ultrasonic-assisted extraction (UAE) and fast determination of catalase and lipase activities in bovine and poultry livers. The lab-made UR mainly consisted of a borosilicate glass container and a piezoelectric disc. The DM-FBA mainly consisted of a webcam, an ultrasonic actuator controller, a peristaltic pump, six solenoid valves, a valve driver, a mixing chamber, a magnetic stirrer, an Arduino Mega 2560, and a personal computer. This setup, named UR-DM-FBA, was controlled by custom software. Ultrasound (US) frequency, US power, sonication time, and concentration of extraction agent were optimized using the Taguchi method. Experiments at silent conditions (mechanical stirring at 1500 rpm) were carried out to evaluate extraction efficiency. Optimized parameters for the UAE of catalase were US frequency of 30 kHz, 2.0 mL of Triton X-100, sonication time of 270 s, and US power of 10.8 W. For the UAE of lipase, the optimized parameters were US frequency of 20 kHz, 0.30 mL of triethanolamine, sonication time of 270 s, and US power of 18 W. Catalase and lipase activities obtained with the UR were, on average, 1.9 × 10 3 % and 2.0 × 10 3 % higher than those obtained at silent conditions, respectively, which indicates that that the lab-made UR was capable of extracting these enzymes more efficiently. Determinations using the UR-DM-FBA were highly accurate (relative error ranging from -1.98% to 1.96% for bovine catalase, -0.65% to 0.76% for bovine lipase, -2.03 to 2.08% for poultry catalase, and -0.55% to 0.64% for poultry lipase) and precise (overall coefficient of variation <0.02% for bovine and poultry catalase and <0.2% for bovine and poultry lipase). Results obtained with the proposed system and reference methods were in good agreement according to the paired t-test (95% confidence level). High sampling rates (>69 h -1 ) and low sample/reagent consumption (<1.6 mL) were also obtained. Due to the highly efficient UAE, the proposed system can be applied for fast and accurate quantification of lipase and catalase in biological samples with low waste generation., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

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., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Cachaça is a sugarcane-derived alcoholic spirit exclusively produced in Brazil. It can be aged in barrels made from different types of wood, similar to other distilled beverages. The choice of wood type promotes different effects on color, flavor, aroma and consequently the price of cachaça, favoring fraudulent activities. This paper proposes the simultaneous identification of different wood types in aged cachaças and their adulterations with wood extracts using a digital-image based methodology employing color histograms obtained from digital images associated with pattern recognition methods, without any sample preparation step. Linear Discriminant Analysis, coupled with Successive Projections Algorithm for variable selection (SPA-LDA), obtained the best results, reaching accuracy, sensitivity, and specificity rates higher than 90.0% in the test set. This can be a rapid and reliable tool to prevent fraudulent labeling; ensuring that what is on the label reflects the quality of aged cachaças, affording security to consumers and regulatory agencies., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

This paper proposes a new variable selection method for nonlinear multivariate calibration, combining the Successive Projections Algorithm for interval selection (iSPA) with the Kernel Partial Least Squares (Kernel-PLS) modelling technique. The proposed iSPA-Kernel-PLS algorithm is employed in a case study involving a Vis-NIR spectrometric dataset with complex nonlinear features. The analytical problem consists of determining Brix and sucrose content in samples from a sugar production system, on the basis of transflectance spectra. As compared to full-spectrum Kernel-PLS, the iSPA-Kernel-PLS models involve a smaller number of variables and display statistically significant superiority in terms of accuracy and/or bias in the predictions., (Published by Elsevier B.V.)

Determining fat content in hamburgers is very important to minimize or control the negative effects of fat on human health, effects such as cardiovascular diseases and obesity, which are caused by the high consumption of saturated fatty acids and cholesterol. This study proposed an alternative analytical method based on Near Infrared Spectroscopy (NIR) and Successive Projections Algorithm for interval selection in Partial Least Squares regression (iSPA-PLS) for fat content determination in commercial chicken hamburgers. For this, 70 hamburger samples with a fat content ranging from 14.27 to 32.12mgkg -1 were prepared based on the upper limit recommended by the Argentinean Food Codex, which is 20% (ww -1 ). NIR spectra were then recorded and then preprocessed by applying different approaches: base line correction, SNV, MSC, and Savitzky-Golay smoothing. For comparison, full-spectrum PLS and the Interval PLS are also used. The best performance for the prediction set was obtained for the first derivative Savitzky-Golay smoothing with a second-order polynomial and window size of 19 points, achieving a coefficient of correlation of 0.94, RMSEP of 1.59mgkg -1 , REP of 7.69% and RPD of 3.02. The proposed methodology represents an excellent alternative to the conventional Soxhlet extraction method, since waste generation is avoided, yet without the use of either chemical reagents or solvents, which follows the primary principles of Green Chemistry. The new method was successfully applied to chicken hamburger analysis, and the results agreed with those with reference values at a 95% confidence level, making it very attractive for routine analysis., (Copyright © 2017 Elsevier B.V. All rights reserved.)