Studies of the liposolubility and the ecotoxicity of MC-LR degradation by-products using computational molecular modeling and in-vivo tests with Chlorella vulgaris and Daphnia magna.

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 (Fe2+ 20 mM and H2O2 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.

Feasibility study on quantification and authentication of the cassava starch content in wheat flour for bread-making using NIR spectroscopy and digital images.

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 (rpred) 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 rpred 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.

Non-destructive authentication of Gourmet ground roasted coffees using NIR spectroscopy and digital images.

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.

A fast, low-cost, sensitive, selective, and non-laborious method based on functionalized magnetic nanoparticles, magnetic solid-phase extraction, and fluorescent carbon dots for the fluorimetric determination of copper in wines without prior sample treatment.

A new fluorimetric method for copper(II) determination in wines was developed combining functionalized magnetic nanoparticles (FMNP) and fluorescent carbon dots (FCD). To produce FMNP, Fe3O4 was coated with Al2O3 forming Fe3O4@Al2O3 core-shell magnetic nanoparticles and functionalized with PAN and SDS. FCD was synthesized from pineapple juice through hydrothermal carbonization. For copper determination, aliquots of wine, the FMNP dispersion, and Britton-Robinson buffer (pH = 4.0) were mixed under stirring to allow the adsorption of copper by FMNP. Cu-FMNP complex was attracted by a niobium magnet and, after discarding the non-magnetic material, the copper(II) ions were eluted with an FCD dispersion before fluorescence quenching measurements. The proposed method presented a linear range from 0.020 to 0.100 mg L-1 (r2 = 0.9953), RSD (intraday) < 3.0%, and recovery rates from 96 to 105 %. FMNP and FCD properties permitted extraction/preconcentration/determination of copper within 1 min with an enrichment factor of nine and without prior sample treatment.

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.

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.

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

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.

Simultaneous voltammetric determination of four organic acids in fruit juices using multiway calibration.

A sensitive, fast, and inexpensive square wave voltammetric method using a cobalt phthalocyanine modified carbon paste electrode was developed for simultaneous determination of citric, lactic, malic and tartaric acids in fruit juices. To overcome the strong overlap of voltammetric signals caused by calibrated and uncalibrated constituents, multivariate curve resolution with alternating least squares (MCR-ALS) was used. Data were previous treated for correction of baseline and potential shift. The MCR-ALS calibration models were constructed and evaluated using a validation set obtained from a Taguchi design. The values predicted by the optimized MCR-ALS models were unbiased and no statistically significant difference was observed between proposed and reference methods, applying the paired t-test at a confidence level of 95%. As far as the authors know, a voltammetric method that simultaneously determines four organic acids in complex samples such as fruit juices without any previous pretreatment has not yet been reported in the literature.

Macroemulsion-based dispersive magnetic solid phase extraction for preconcentration and determination of copper(II) in gasoline.

A new method referred to as microemulsion-based Dispersive Magnetic Solid-Phase Extraction (MDM-SPE) is presented for use in the extraction and preconcentration of metal ions from complex organic matrices. MDM-SPE combines the features of magnetic nanoparticles (MNPs) and microemulsions. It was successfully applied to the extraction of copper(II) from gasoline prior to its determination by Graphite Furnace Atomic Absorption Spectrometry (GF-AAS). The material for use in MDM-SPE was obtained by first functionalizing MNPs of the type Fe3O4@Al2O3 with sodium dodecyl sulfate and the chelator 1-(2-pyridylazo)-2-naphthol (PAN) dispersed in 1-propanol. The resulting functionalized magnetic MNPs were dispersed in a microemulsion prepared from gasoline, buffer, and 1-propanol. After waiting for 5 s (during which the formation of the copper complex on the MNPs is complete), the MNPs are magnetically separated. The complex was then eluted with 2 mol L-1 HNO3, and the eluate submitted to GF-AAS. Various parameters were optimized. Copper(II) can be quantified by this method over a linear range that extends from 2.0 to 10.0 µg·L-1. Other figures of merit include (a) a 37 ng·L-1 detection limit, (b) a repeatability of 1.1%, (c) a reproducibility of 2.1%, and (d) an enrichment factor of nine. The high surface-to-volume ratio of the microemulsion containing the dispersed magnetic sorbent warrants an efficient contact for reaction between copper(II) and the complexing agent, and this results in fast (about 40 s) extraction and pre-concentration of copper(II). MDM-SPE is accurate, precise and efficient. Microemulsions do not break down, and phase separation, heating, laborious, and time-consuming sample preparation, and incorporation of impurities into the graphite furnace (which can generate inaccuracies in GF-AAS analysis) are not needed. Graphical abstract Schematic of a new method for Microemulsion-based Dispersive Magnetic Solid-Phase Extraction (MDMSPE) using functionalized magnetic nanoparticles (FMNPs). It was applied to the preconcentration of copper(II) in gasoline.

Vis-NIR spectrometric determination of Brix and sucrose in sugar production samples using kernel partial least squares with interval selection based on the successive projections algorithm.

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.

Determination of fat content in chicken hamburgers using NIR spectroscopy and the Successive Projections Algorithm for interval selection in PLS regression (iSPA-PLS).

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.

Fluorescent fingerprints of edible oils and biodiesel by means total synchronous fluorescence and Tucker3 modeling.

The present work proposes the use of total synchronous fluorescence spectroscopy (TSFS) as a discrimination methodology for fluorescent compounds in edible oils, which are preserved after the transesterification processes in the biodiesel production. In the same way, a similar study is presented to identify fluorophores that do not change in expired vegetal oils, to associate physicochemical parameters to fluorescent measures, as contribution to a fingerprint for increasing the chemical knowledge of these products. The fluorescent fingerprints were obtained by Tucker3 decomposition of a three-way array of the total synchronous fluorescence matrices. This chemometric method presents the ability for modeling non-bilinear data, as Total Synchronous Fluorescence Spectra data, and consists in the decomposition of the three way data arrays (samples×Δλ×λ excitation), into four new data matrices: A (scores), B (profile in Δλ mode), C (profile in spectra mode) and G (relationships between A, B and C). In this study, 50 samples of oil from soybean, corn and sunflower seeds before and after its expiration time, as well as 50 biodiesel samples obtained by transesterification of the same oils were measured by TSFS. This study represents an immediate application of chemical fingerprint for the discrimination of non-expired and expired edible oils and biodiesel. This method does not require the use of reagents or laborious procedures for the chemical characterization of samples.

Using UV-Vis spectroscopy for simultaneous geographical and varietal classification of tea infusions simulating a home-made tea cup.

In this work we proposed a method to verify the differentiating characteristics of simple tea infusions prepared in boiling water alone (simulating a home-made tea cup), which represents the final product as ingested by the consumers. For this purpose we used UV-Vis spectroscopy and variable selection through the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA) for simultaneous classification of the teas according to their variety and geographic origin. For comparison, KNN, CART, SIMCA, PLS-DA and PCA-LDA were also used. SPA-LDA and PCA-LDA provided significantly better results for tea classification of the five studied classes (Argentinean green tea; Brazilian green tea; Argentinean black tea; Brazilian black tea; and Sri Lankan black tea). The proposed methodology provides a simpler, faster and more affordable classification of simple tea infusions, and can be used as an alternative approach to traditional tea quality evaluation as made by skilful tasters, which is evidently partial and cannot assess geographic origins.

Handling time misalignment and rank deficiency in liquid chromatography by multivariate curve resolution: Quantitation of five biogenic amines in fish.

Biogenic amines (BAs) are used for identifying spoilage in food. The most common are tryptamine (TRY), 2-phenylethylamine (PHE), putrescine (PUT), cadaverine (CAD) and histamine (HIS). Due to lack of chromophores, chemical derivatization with dansyl was employed to analyze these BAs using high performance liquid chromatography with a diode array detector (HPLC-DAD). However, the derivatization reaction occurs with any primary or secondary amine, leading to co-elution of analytes and interferents with identical spectral profiles, and thus causing rank deficiency. When the spectral profile is the same and peak misalignment is present on the chromatographic runs, it is not possible to handle the data only with Multivariate Curve Resolution and Alternative Least Square (MCR-ALS), by augmenting the time, or the spectral mode. A way to circumvent this drawback is to receive information from another detector that leads to a selective profile for the analyte. To overcome both problems, (tri-linearity break in time, and spectral mode), this paper proposes a new analytical methodology for fast quantitation of these BAs in fish with HPLC-DAD by using the icoshift algorithm for temporal misalignment correction before MCR-ALS spectral mode augmented treatment. Limits of detection, relative errors of prediction (REP) and average recoveries, ranging from 0.14 to 0.50 µg mL(-1), 3.5-8.8% and 88.08%-99.68%, respectively. These are outstanding results obtained, reaching quantification limits for the five BAs much lower than those established by the Food and Agriculture Organization of the United Nations and World Health Organization (FAO/WHO), and the European Food Safety Authority (EFSA), all without any pre-concentration steps. The concentrations of BAs in fish samples ranged from 7.82 to 29.41 µg g(-1), 8.68-25.95 µg g(-1), 4.76-28.54 µg g(-1), 5.18-39.95 µg g(-1) and 1.45-52.62 µg g(-1) for TRY, PHE, PUT, CAD, and HIS, respectively. In addition, the proposed method spends less than 4 min in an isocratic run, consuming less solvent in accordance with the principles of green analytical chemistry.

Calibration transfer employing univariate correction and robust regression.

This paper proposes a new method for calibration transfer, which was specifically designed to work with isolated variables, rather than the full spectrum or spectral windows. For this purpose, a univariate procedure is initially employed to correct the spectral measurements of the secondary instrument, given a set of transfer samples. A robust regression technique is then used to obtain a model with low sensitivity with respect to the univariate correction residuals. The proposed method is employed in two case studies involving near infrared spectrometric determination of specific mass, research octane number and naphthenes in gasoline, and moisture and oil in corn. In both cases, better calibration transfer results were obtained in comparison with piecewise direct standardization (PDS). The proposed method should be of a particular value for use with application-targeted instruments that monitor only a small set of spectral variables.

Modeling nonbilinear total synchronous fluorescence data matrices with a novel adapted partial least squares method.

A new residual modeling algorithm for nonbilinear data is presented, namely unfolded partial least squares with interference modeling of non bilinear data by multivariate curve resolution by alternating least squares (U-PLS/IMNB/MCR-ALS). Nonbilinearity represents a challenging data structure problem to achieve analyte quantitation from second-order data in the presence of uncalibrated components. Total synchronous fluorescence spectroscopy (TSFS) generates matrices which constitute a typical example of this kind of data. Although the nonbilinear profile of the interferent can be achieved by modeling TSFS data with unfolded partial least squares with residual bilinearization (U-PLS/RBL), an extremely large number of RBL factors has to be considered. Simulated data show that the new model can conveniently handle the studied analytical problem with better performance than PARAFAC, U-PLS/RBL and MCR-ALS, the latter modeling the unfolded data. Besides, one example involving TSFS real matrices illustrates the ability of the new method to handle experimental data, which consists in the determination of ciprofloxacin in the presence of norfloxacin as interferent in water samples.

Binary classification of chalcone derivatives with LDA or KNN based on their antileishmanial activity and molecular descriptors selected using the Successive Projections Algorithm feature-selection technique.

Chalcones are naturally occurring aromatic ketones, which consist of an α-, ß-unsaturated carbonyl system joining two aryl rings. These compounds are reported to exhibit several pharmacological activities, including antiparasitic, antibacterial, antifungal, anticancer, immunomodulatory, nitric oxide inhibition and anti-inflammatory effects. In the present work, a Quantitative Structure-Activity Relationship (QSAR) study is carried out to classify chalcone derivatives with respect to their antileishmanial activity (active/inactive) on the basis of molecular descriptors. For this purpose, two techniques to select descriptors are employed, the Successive Projections Algorithm (SPA) and the Genetic Algorithm (GA). The selected descriptors are initially employed to build Linear Discriminant Analysis (LDA) models. An additional investigation is then carried out to determine whether the results can be improved by using a non-parametric classification technique (One Nearest Neighbour, 1NN). In a case study involving 100 chalcone derivatives, the 1NN models were found to provide better rates of correct classification than LDA, both in the training and test sets. The best result was achieved by a SPA-1NN model with six molecular descriptors, which provided correct classification rates of 97% and 84% for the training and test sets, respectively.

Eco-friendly sonoluminescent determination of free glycerol in biodiesel samples.

This paper proposes a flow-batch methodology for the determination of free glycerol in biodiesel that is notably eco-friendly, since non-chemical reagents are used. Deionized water (the solvent) was used alone for glycerol (sample) extractions from the biodiesel. The same water was used to generate water-cavitation sonoluminescence signals, which were modulated by the quenching effect associated with the amount of extracted glycerol. The necessarily reproducible signal generation was achieved by using a simple and inexpensive piezoelectric device. A linear response was observed for glycerol within the 0.001-100 mg/L range, equivalent to 0.004-400 mg/kg free glycerol in biodiesel. The lowest measurable concentration of free glycerol was estimated at 1.0 µg/L. The selectivity of the proposed method was confirmed by comparing the shape and retention of both real and calibration samples to standard solution chromatograms, presenting no peaks other than glycerol. All samples (after extraction) are greatly diluted; this minimizes (toward non-detectability) potential interference effects. The methodology was successfully applied to biodiesel analysis at a high sampling rate, with neither reagent nor solvent (other than water), and with minimum waste generation. The results agreed with the reference method (ASTM D6584-07), at a 95% confidence level.

A flow-batch analyzer using a low cost aquarium pump for classification of citrus juice with respect to brand.

This paper proposes a novel flow-batch analyzer (FBA), which employs a compact, low-cost aquarium air pump as an alternative to a peristaltic pump. The feasibility of using this simple propulsion device is demonstrated in a case study involving the classification of citrus juice samples with respect to brand. For this purpose, UV-vis spectra and SIMCA (soft independent modelling of class analogies), PLS-DA (Partial Least Squares for Discriminant Analysis) and SPA-LDA (Linear Discriminant Analysis with variables selected by the Successive Projections Algorithm) are employed. Good classification results were obtained, thus indicating that the proposed FBA system is a viable alternative to the use of more costly peristaltic pumps. In addition, the smaller size and weight of the aquarium pump are useful features for the construction of portable FBAs to be deployed in field applications.

Screening analysis of beer ageing using near infrared spectroscopy and the Successive Projections Algorithm for variable selection.

This work proposes a method for monitoring the ageing of beer using near-infrared (NIR) spectroscopy and chemometrics classification tools. For this purpose, the Successive Projections Algorithm (SPA) is used to select spectral variables for construction of Linear Discriminant Analysis (LDA) classification models. A total of 83 alcoholic and non-alcoholic beer samples packaged in bottles and cans were examined. To simulate a long storage period, some of the samples were stored in an oven at 40°C, in the dark, during intervals of 10 and 20 days. The NIR spectrum of these samples in the range 12,500-5405 cm(-1) was then compared against those of the fresh samples. The results of a Principal Component Analysis (PCA) indicated that the alcoholic beer samples could be clearly discriminated with respect to ageing stage (fresh, 10-day or 20-day forced ageing). However, such discrimination was not apparent for the non-alcoholic samples. These findings were corroborated by a classification study using Soft Independent Modelling of Class Analogy (SIMCA). In contrast, the use of SPA-LDA provided good results for both types of beer (only one misclassified sample) by using a single wavenumber in each case, namely 5550 cm(-1) for non-alcoholic samples and 7228 cm(-1) for alcoholic samples.