Distribution of organochlorine, organophosphates, carbamate, thiocarbamate, pyrethroids, and strobilurins in surface sediments of the Rio de Ondas watershed by GC-MS.

The determination of some pesticides in surface sediments can provide important information about their distribution in the water column. This work aimed to determine the distribution of the classes of pesticides along the Ondas River's hydrographic basin (ORHB), in eighteen different points, during the dry and rainy periods. The pesticides were extracted from the sediment samples by solid-liquid extraction and then analyzed using a gas chromatograph coupled to mass spectrometry. After the development and validation of the method, nineteen pesticides from the group of organochlorine, organophosphates, carbamate and thiocarbamate, pyrethroids, and strobilurins were quantified in at least one point in the two collection periods, with accuracy varying between 86 and 126%. The average concentrations were 0.020 ng g-1 (carbofuran) to 249.123 ng g-1 (dimethoate) and 0.029 ng g-1 (carbofuran and sulfotep) to 533.522 ng g-1 in the dry and rainy periods, respectively. The results showed a wide distribution of pesticide residues in the ORHB, with higher levels for dimethoate, phenitrothion, and malathion, which may be related to their agricultural use in the region. In Brazil, it does not have specific legislation for maximum permitted values of pesticides in sediment, allowing for inappropriate or prohibited use and, consequently, affecting water quality.

Pesticides in surface water of the Ondas river watershed, western Bahia, Brazil: Spatial-seasonal distribution and risk assessment.

This study investigated the occurrence and seasonal distribution of different classes of pesticides in surface waters of the Ondas River Watershed, as well as potential risks to the aquatic health and human water consumption in the western region of Bahia state, Brazil. Two gas chromatography-mass spectrometry analytical methods were applied to monitor 34 pesticides in water samples collected during both the dry and rainy seasons at 17 sites. Upon individual analysis, only γ-HCH, methoxychlor, demeton-S, methyl parathion, fenitrothion, chlorpyrifos, and azoxystrobin exhibited statistically significant differences between seasons. During rainy season, concentration medians of residues were higher for γ-HCH (74.7 ng L-1), methoxychlor (25.1 ng L-1), and azoxystrobin (47.2 ng L-1), potentially linked to historical contamination or illegal use. Conversely, pesticides like methyl parathion, fenitrothion, and chlorpyrifos, belonging to the organophosphate class, showed higher concentration medians in the dry period, measuring 75.1, 5.50, and 10.8 ng L-1, respectively, probably due to region crop activities. The risk quotient (RQ) assessment for aquatic life indicated that 59.0% of the samples in the dry season and 76.0% in the rainy season had RQ values greater than one, signifying a critical scenario for species conservation. Regarding human consumption, elevated risks were observed for heptachlor in both sampling periods and for azoxystrobin during the rainy season, surpassing RQ levels above 1, indicating danger in untreated water ingestion. Additionally, 24.0% and 53.0% of the samples in the dry and rainy seasons, respectively, contained at least one pesticide exceeding the EU resolution limit (100 ng L-1). Therefore, considering this information, implementing mitigation measures to avoid the river's contamination becomes imperative.

An overview on the Brazilian Coffea canephora scenario and the current chemometrics-based spectroscopic research.

This review explores the historical, botanical, sensory, and quality aspects of Coffea canephora, with a focus on Brazil's rise as a producer of specialty canephora coffees in the Amazon region, Espírito Santo, and Bahia. Brazil has gained global recognition through the first geographical indications for canephora: Matas de Rondônia for robusta amazônico coffee and Espírito Santo for conilon coffee. Despite this, comprehensive insights into how variety, terroir, environmental conditions, and cultivation practices influence the chemical and sensory attributes of Brazilian canephora remain underdeveloped compared to well-studied arabica coffee. Producers and researchers are working to elevate canephora coffees to higher market levels, despite technological, production, and perception challenges stemming from its historical reputation for poor quality. Ensuring the sustainability of Amazonian canephora coffee without deforestation is particularly challenging due to the need to verify practices across numerous small-scale farms. There is also a critical need for standardized production and tasting protocols for Brazilian canephora, leveraging local expertise and professional cuppers to ensure consistent quality and reliable sustainability claims. Significant opportunities exist in valuing the production chain of geographically unique canephora coffees, which could increase specialty exports, enhance economic prospects for local farmers, and support Amazon preservation. Recognizing and marketing these coffees as premium products with unique flavor profiles can boost their global appeal. Another challenge lies in establishing new specialty standards for soluble coffee from specialty canephora to meet consumer demands for convenience without compromising taste or ethical standards. In such a scenario, several analytical methods have been suggested to identify high-quality variants, combating their stigmatization. The potential of spectroscopy techniques and chemometrics-based data science is highlighted in confirming coffee quality, authenticity, traceability, and geographical origin, enhancing model interpretation and predictive accuracy through synergistic and complementary information. Non-targeted spectroscopic analyses, providing comprehensive spectral fingerprints, are contrasted with targeted analyses. Overall, this review offers valuable insights for the coffee scientific community, exporters, importers, roasters, and consumers in recognizing the potential of Brazilian canephora coffees.

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.

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.

Spatiotemporal evaluation of organochlorine pesticide residues in bottom sediments of the Rio de Ondas hydrographic basin, western Bahia, Brazil.

The Rio de Ondas Hydrographic Basin (ROHB), Bahia state, Brazil, is located in a region with abundant water resources and is highly impacted by intense agricultural activity. In such a scenario, the use of organochlorine pesticides can represent a potential risk to the aquatic environments, due to their persistence, high bioaccumulation capacity, and high toxicity. Thus, organochlorine pesticide residues in bottom sediment samples from rivers on eighteen sites distributed along the ROHB in the dry and rainy periods were analyzed by gas chromatography coupled with mass spectrometry. The validated method showed no matrix effect, recoveries ranging from 82% (ß-HCB) to 118% (DDD), limits of detection between 0.003 ng g-1 (α-HCH) and 0.011 ng g-1 (DDT), limits of quantification of 0.010 ng g-1 (α-HCH) to 0.036 ng g-1 (DDT), repeatability with the highest relative standard deviation of 0.97% (α-hexachlorocyclohexane at 2.000 ng g-1), and inter-day precision ranging from 10% (aldrin at 0.050 ng g-1 and 0.600 ng g-1 and α-endosulfan at 0.600 ng g-1) to 25% (ß-endosulfan at 0.050 ng g-1). Although most compounds were banned since 1985, it was observed that their residues were widely distributed in the ROHB, with the total concentrations varying from 3.242 ng g-1 (P02) to 12.052 ng g-1 (P17) and from 0.313 ng g-1 (P14) to 30.861 ng g-1 (P13) in the dry and rainy periods, respectively, which may be related to historical contamination and/or prohibited use. Moreover, the spatiotemporal variation showed the highest concentrations of organochlorine pesticide residues in the rainy season, coinciding with the planting period.

Honey authentication in terms of its adulteration with sugar syrups using UV-Vis spectroscopy and one-class classifiers.

This work proposes the use of UV-Vis spectroscopy and one-class classifiers to authenticate honey in terms of their individual and simultaneous adulterations with corn syrup, agave syrup, and sugarcane molasses. Then, spectra of aqueous authentic (n = 73) and adulterated (n = 162) honey samples were recorded. Before the construction of OC-PLS and DD-SIMCA models, different pre-processing techniques were used to removed baseline shifts. The best result obtained by DD-SIMCA using offset correction, correctly classifying all the samples in the test set. Therefore, the proposed methodology can be used as a promising tool to authenticate honey and prevent fraudulent labeling, affording security to consumers and providing an alternative to regulatory agencies. Moreover, it avoids laborious sample preparation and additional operational costs, since the analytical information is acquired using a routine instrumental technique, without the need for any sample preparation step, other than dilution of the samples in water alone.

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.

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.

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 ±â€¯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.

Qualitative and quantitative analysis based on digital images to determine the adulteration of ketchup samples with Sudan I dye.

Sudan I is a synthetic-azo dye commonly used to adulterate foods to increase sensory appearance. However, it is banned due to its carcinogenic, mutagenic and genotoxic properties, which represent a serious risk to human health. Thus, this paper proposes a feasibility study to identify and quantify Sudan I dye in ketchup samples using colour histograms (obtained from digital images) and multivariate analysis. The successive projections algorithm coupled with linear discriminant analysis (SPA-LDA) classified correctly all samples, while the partial least squares coupled with SPA for interval selection (iSPA-PLS) quantified adequately the adulterant, attaining values of RMSEP of 11.64 mg kg-1, R2 of 0.96, RPD of 5.28, REP of 13.63% and LOD of 39.45 mg kg-1. Therefore, the proposed methodology provides a simple, fast, inexpensive, promising analytical tool for the screening of both the quality and safety of ketchup samples. As a consequence, it can help to protect the consumer's health.

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.

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.

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.

Digital image-based classification of biodiesel.

This work proposes a simple, rapid, inexpensive, and non-destructive methodology based on digital images and pattern recognition techniques for classification of biodiesel according to oil type (cottonseed, sunflower, corn, or soybean). For this, differing color histograms in RGB (extracted from digital images), HSI, Grayscale channels, and their combinations were used as analytical information, which was then statistically evaluated using Soft Independent Modeling by Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and variable selection using the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA). Despite good performances by the SIMCA and PLS-DA classification models, SPA-LDA provided better results (up to 95% for all approaches) in terms of accuracy, sensitivity, and specificity for both the training and test sets. The variables selected Successive Projections Algorithm clearly contained the information necessary for biodiesel type classification. This is important since a product may exhibit different properties, depending on the feedstock used. Such variations directly influence the quality, and consequently the price. Moreover, intrinsic advantages such as quick analysis, requiring no reagents, and a noteworthy reduction (the avoidance of chemical characterization) of waste generation, all contribute towards the primary objective of green chemistry.

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.

Flow injection photometric determination of NaCl, KCl and glucose in injectable drugs exploiting Schlieren signals.

A flow injection photometric system that exploits Schlieren signals for analytical measurement is described. The system was designed to be used as a new strategy for determining the contents of sodium chloride, potassium chloride and glucose, each respectively in injectable drugs. The proposed methodology was based on the difference between the refractive indices of the sample zone and of the carrier stream. With this perspective, a lab-made photometer based on LED-phototransistor technology was employed as a detection system to investigate the different analytical profiles related to the Schlieren effect in low flow rate conditions. The parameters of the flow system, such as flow-rate, optical path length, and sampling loop, were adjusted in order to obtain suitable Schlieren profiles for the measurements. Data evaluation was performed with the application of partial least squares regression (PLS-1). The obtained results demonstrated the predictive ability of the constructed PLS models, and the predicted concentration values were in agreement with the reference values, with a 95% confidence level.

Digital image-based flame emission spectrometry.

A digital image-based flame emission spectrometric (DIB-FES) method for the quantitative chemical analysis is proposed here for the first time. The DIB-FES method employs a webcam to capture the digital images which are associated to a radiation emitted by the analyte into an air-butane flame. Since the detection by webcam is based on the RGB (red-green-blue) colour system, a novel mathematical model was developed in order to build DIB-FES analytical curves and estimate figures of merit for the proposed method. In this approach, each image is retrieved in the three R, G and B individual components and their values were used to define a position vector in RGB three-dimensional space. The norm of this vector is then adopted as the RGB-based value (analytical response) and it has revealed to be linearly related to the analyte concentration. The feasibility of the DIB-FES method is illustrated in three applications involving the determination of lithium, sodium and calcium in anti-depressive drug, physiological serum and water, respectively. In comparison with the traditional flame emission spectrometry (trad-FES), no statistic difference has been observed between the results by applying the paired t-test at the 95% confidence level. However, the DIB-FES method has offered the largest sensitivities and precision, as well as the smallest limits of detection and quantification for the three analytes. These advantageous characteristics are attributed to the trivariate nature of the detection by webcam.