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A comprehensive data set of ecstasy samples containing MDMA (N-methyl-3,4-methylenedioxyamphetamine) and MDA (3,4-methylenedioxyamphetamine) seized by the Brazilian Federal Police was characterized using spectral data obtained by a compact, low-cost, near-infrared Fourier-transform based spectrophotometer. Qualitative and quantitative characterization was accomplished using soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA) classification, discriminating partial least square (PLS-DA), and regression models based on partial least square (PLS). By applying chemometric analysis, a protocol can be proposed for the in-field screening of seized ecstasy samples. The validation led to an efficiency superior to 96 % for ecstasy classification and estimating total actives, MDMA, and MDA content in the samples with a root mean square error of validation of 4.4, 4.2, and 2.7 % (m/m), respectively. The feasibility and drawbacks of the NIR technology applied to ecstasy characterization and the compromise between false positives and false negatives rate achieved by the classification models are discussed and a new approach to improve the classification robustness was proposed considering the forensic context.

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New Brazilian Canephora coffees (Conilon and Robusta) of high added value from specific origins have been protected by geographical indication to guarantee their origin and quality. Recently, benchtop near-infrared (NIR) spectroscopy combined with chemometrics has demonstrated its usefulness to discriminate them. It was the first study, however, and therefore the possibility exists to develop a new portable NIR method for this purpose. This work assessed a miniaturized NIR as a cheaper spectrometer to discriminate and authenticate new Brazilian Canephora coffees with certified geographical origins and to differentiate them from specialty Arabica. Discriminant chemometric and class modeling techniques have been applied and have obtained good predictive ability on external test sets. In addition, models with similar classification purpose were compared with those obtained in previous research carried out with benchtop NIR for the same samples, obtaining comparable results. In this context, the portable method was used as a laboratory technique and has the advantage of being cheaper than benchtop NIR spectrometer. Furthermore, it brings a high possibility to be implemented in small coffee cooperatives, industries or control agencies in the future that do not have high economic resources.

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The conventional intensive system produces cheap and safe chicken eggs, but exposes the animals to stress due to overcrowding on farms. This work compared the 1HNMR lipidic profile of chicken eggs produced in conventional and free-range systems. Sample preparation consisted of a single-step extraction and centrifugation, and the 1H NMR experimental time was just 3 min per sample. Eggs from free-range chickens had higher concentrations of ω-3 and ω-6 polyunsaturated fatty acids. The ratio between the signals at δ2.85 and 4.14 from bis-allylic polyunsaturated fatty acids and glycerol moiety, respectively, was able to correctly classify 93.8 % of the samples. These results were similar to those of PLS-DA, used for comparative purposes. Therefore, the proposed method could be easily used to assist quality control and fraud prevention in the egg industry. Free-range eggs had higher concentrations of cholesterol but, as they are smaller, similar amounts to conventional ones.

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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.

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The present work was proposal the potential evaluation of Fourier-Transform Mid-Infrared (FT-MIR) associated with chemometric approach in green beans, in order to discriminate the origin of special Arabica coffees in a single state that has heterogeneous environments. Partial Least Squares Discriminant Analysis (PLS-DA) model presented as result: 3 latent variables, R 2 X (cum) = 0.892, R 2 Y (cum) = 0.659; Q 2 Y (cum) = 0.494, RMSEP = 0.182387, p-value CV-Anova = 0.009, 100% of both sensitivity and specificity and the prediction classification obtained was: 100, 83.33, 100, 83.33% for class 1, class 2, class 3 and class 4, respectively. These results can be considered adequate for the proposed hypothesis. The obtained results that the regions have markers such as trigonelline, chlorogenic and fatty acids, sensitive to absorption in the mid-infrared and that are able to determine the origin of green coffee beans of Arabica. Thus, the FT-MIR associated with chemometrics has the potential to employ speed, modernity and cost reduction in the certification of origin of coffees.

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Touriga Nacional is a well-adapted Portuguese grape variety in São Francisco River Valley (northeastern Brazil). Nevertheless, it has only been indicated to short-term consumption because of the lack of chemical stability, which is attributed to low grape acidity and incomplete phenolic maturity. Therefore, we used Ultra-Performance Liquid Chromatography coupled High-resolution Mass Spectrometry, Nuclear Magnetic Resonance and chemometrics (PCA and PLS-DA) to evaluate the grape maturity and maceration time on chemical composition of wines from two harvest seasons. Moreover, we investigated how these experimental factors could affect their chemical stability. Grapes maturity showed to be the main effect. Overall, phenolic acids and short-chain organic acids were found to be at higher levels in wines produced with unripe grapes from February and shorter maceration time (p < 0.05). Proanthocyanidins and other flavonoids were increased in wines macerated for longer time using overripe grapes harvested in July. Furthermore, stable wines were made from overripe grapes, which contained more galacturonic acid.

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Although cachaça and rum are distilled beverages obtained from the same raw material, they present differences in their chemical compositions. In this study, synchronous fluorescence spectroscopy was used combined with supervised classification models based on the partial least squares discriminant analysis to develop a rapid and low-cost model for discriminating between 50 cachaça and 40 rum samples. Partial least squares discriminant analysis models were constructed using synchronous fluorescence spectra recorded at wavelength differences of 10-100 nm. Initially, spectra were preprocessed by the first derivative with the Savitzky-Golay smoothing, and filter width and polynomial order were selected through face-centered central composite designs. For the construction and validation models, the spectra data were split into two datasets: the training and the test sets containing 60 (C, n = 33; R, n = 27) and 30 (C, n = 17; R, n = 13) samples, respectively. The best discrimination was achieved using fluorescence spectra recorded at wavelength difference 10 nm, allowing the discrimination of cachaça and rum with a classification efficiency of 98%. These results indicate that synchronous fluorescence spectroscopy offers a promising approach for the authentication of cachaças and rums.

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The consequences of a hit-and-run car crash are significant and may include serious injuries to the victims, health system overload and even victim's death. The vehicle and driver identification are often challenging for local law enforcement. The aim of this study was to develop a methodology to discriminate between automotive paint samples according to the make of the vehicle and its color shade. 143 white samples (collected at traffic accident scenes) were analyzed in situ by Fourier transform infrared spectroscopy with attenuated total reflectance (ATR-FTIR) and coupled microscopy. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were performed for data analysis. The samples were split into three groups: calibration set, validation set and external test set. The figures of merit were calculated to assess the quality of the model. Sensitivity, specificity, and efficiency rates were, respectively, 98,9%, 98.4% and 98.6%, for the calibration set. For the validation group, the classification accuracy was 100%. Correct classification rates for the internal validation set and external test set were 100% and 79.1% respectively. The technique is clean, fast, relatively low-cost, and non-destructive. Damaged regions of the samples were avoided by using the attached microscope. Limiting the age of the samples to a maximum of 10 years was enough to avoid misclassifications due to the natural degradation and weathering of the sample. Since the external test group is formed by underrepresented classes, its correct classification rate (79.1%) can be potentially improved at any time, by including and analyzing more samples.

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Two artisanal fermentation processes for Criollo cocoa beans with different turning start times (24 h and 48 h) were studied. The aromatic profile of cocoa turned every 24 h (B1) displayed volatile compounds associated with fermented, bready, and fruity aromas. When cocoa beans were fermented with a different turning technique with a start time of 48 h (B2), they provided volatile compounds mainly associated with descriptors of floral, woody, sweet, fruity and chocolate aromas. The turning start time of 48 h stimulated a microbial profile dominated by yeast such as Hanseniaspora opuntiae, Pichia manshurica, and Meyerozyma carpophila, favoring the production of several key aroma markers associated with cocoa bean fermentation quality, such as phenylethyl acetate, 2-phenylacetaldehyde, 3-methylbutanal, 2-phenylethyl alcohol, 2,3-butanedione, 3-methylbutanoic acid, and 2-methylpropanoic acid, while an immediate turning start time (24 h) favored an aerobic environment that stimulated the rapid growth of Acetobacter pasteurianus, Bacillus subtilis and a higher biodiversity of lactic acid bacteria (LAB) (e.g., Lactobacillus plantarum and Pediococcus acidilactici), which increased the production of ethyl acetate and 3-hydroxy-2-butanone. Volatile compound generation and microbial populations were evaluated and analyzed by multivariate analysis (principal component analysis and partial least squares discriminant analysis) to find correlations and significant differences. This study shows that the method of turning Criollo cacao beans can lead to the formation of desirable aromatic compounds.

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This paper presents the combination of wooden-tip electrospray ionization mass spectrometry (WTESI-MS) and multivariate pattern recognition methods (principal component analysis, PCA and partial least squares discriminant analysis, PLS-DA) for the rapid and reliable discrimination, via chemical fingerprints, of garlic origin. A total of 312 garlic samples grown in different countries (Brazil, China, Argentina, Spain, and Chile) were studied. The methodology was based on a direct sampling approach, which relies on loading the sample by penetrating the garlic cloves with a pre-wetted wooden tip, followed by direct prompt analysis by WTESI-MS. Thus, no sample preparation is needed, which prevents the degradation of important metabolites and increases the analytical throughput. Parameters that affects the WTESI were optimized and the best performance in terms of signal stability and intensity was achieved using the positive ion mode. Most of the ions in WTESI mass spectra were assigned to amino acids, sugars, organosulfur compounds, and lipids. The discriminative model showed good performance (accuracy rates between 81.9% and 98.6%) and enabled identifying diagnostic ions for garlic samples from different origins. The differentiation and classification of garlic origin is of major importance as this food flavoring product is widely consumed, with worldwide trade representing billions of dollars every year, and is very often the subject of fraud.

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Cocoa butter provides desirable sensory properties to chocolates; however, the exposure of chocolate to temperature variations during transportation and/or storage can lead to changes in the polymorphic form of butter, with the appearance of a dull-white film on the chocolate surface, known as fat bloom. This study investigated the use of a portable NIR spectrometer combined with chemometric tools to discriminate milk chocolate, white chocolate, 40% cocoa chocolate, and 70% cocoa chocolate samples, which were subjected to temperature abuse for 6 hours. The PCA allowed separating the samples into three classes: control at 20 °C, chocolate subjected to 35 °C, and chocolate subjected to 40 °C, for each type of chocolate studied. The PLS-DA models provided sensibility, specificity, and accuracy values in the range of 80 to 100%, and allowed identifying the wavelengths associated with the different chocolates that most impacted the construction of the models.

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The genus Sargassum is well represented by benthic and pelagic species, some of which form massive aggregates that can travel long distances due to the force of the ocean currents. Although they constitute an essential habitat for fish and invertebrate species, large accumulations of Sargassum in coastal areas generate several economic, environmental, and health impacts. It is important to recognize the species forming these aggregates, and identify the metabolites they produce, allowing for its exploitation, and therefore, better management practices. NMR metabolic profiling is a technique that can discriminate samples while detecting their unique or differential chemical features, and has been successfully used in the study and classification of several algal species. The present investigation studied the metabolic profiling of Sargassum species found on strandings at Puerto Morelos (Quintana Roo) east coast of the Mexican Caribbean. PCA of the 1 H-NMR profiles corresponding to S. natans, S. natans (morphotype VIII), S. fluitans, and a benthic Sargassum buxifolium allowed the discrimination of samples amongst them. Furthermore, discrimination between the two forms of S. natans was also possible. The PCA loading plot revealed that glutamine and glutamate have the highest influence in the clustering of the benthic Sargassum, while a high abundance of lactate, Myo-inositol, and trimethylamine is a unique feature from the S. natans morphotype VIII. Additional PLS-DA models showed that a heat-drying process improved the extraction of metabolites. Maceration and microwave-assisted extraction with water-ethanol led to similar profiles and thus any of them could be used in future investigations.

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Being a product with a high market value, olive oil undergoes adulterations. Therefore, studies that make the verification of the authenticity of olive oil more efficient are necessary. The aim of this study was to develop a robust model using FT-NIR and PLS-DA to discriminate extra virgin olive oil samples and build individual models to differentiate adulterated extra virgin olive oil samples. The best PLS-DA-OPS classification model for olive oils showed specificity (Spe) and accuracy (Acc) values higher than 99.7% and 99.9%. For the classification of adulterants, PLS-DA-OPS models presented values of Spe at 96.0% and Acc above 95.5% for varieties. For the blend, the best PLS-DA-GA model presented Acc and Spe values greater than 98.2% and 98.8%. Reliable and robust models have been built, allowing differentiation from seven adulterants to genuine extra virgin olive oils.

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Egletes viscosa is a Brazilian medicinal herb consumed as flower bud tea due to its gastroprotective properties. This plant possesses two essential oil-based chemical varieties: trans-pinocarveyl acetate-rich chemotype A and cis-isopinocarveyl acetate- rich chemotype B. Therefore, we developed two simple, fast and reliable methods for discrimination of E. viscosa chemotypes using NIR and 1H qNMR spectroscopies combined with the chemometrics tools (iPLS and PLS-DA). Both methods showed high sensitivity, precision and specificity in the cross-validation tests. The NIR method has the advantages of being non-destructive and analyzable by portable devices, enabling its application for field and industrial evaluations. Meanwhile, the 1H qNMR method allows the quantification of the bioactive components ternatin, tanabalin, and centipedic acid. These aforementioned compounds were found higher in the chemotype A. Accordingly, our methods showed to be complimentary approaches for authenticity and/or quality control of E. viscosa-derived raw materials and herbal products.

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Food science continually requires the development of novel analytical methods to prevent fraudulent actions and guarantee food authenticity. Greek table olives, one of the most emblematic and valuable Greek national products, are often subjected to economically motivated fraud. In this work, a novel ultra-high-performance liquid chromatography-quadrupole time of flight tandem mass spectrometry (UHPLC-QTOF-MS) analytical method was developed to detect the mislabeling of Greek PDO Kalamata table olives, and thereby establish their authenticity. A non-targeted screening workflow was applied, coupled to advanced chemometric techniques such as Principal Component Analysis (PCA) and Partial Least Square Discriminant Analysis (PLS-DA) in order to fingerprint and accurately discriminate PDO Greek Kalamata olives from Kalamata (or Kalamon) type olives from Egypt and Chile. The method performance was evaluated using a target set of phenolic compounds and several validation parameters were calculated. Overall, 65 table olive samples from Greece, Egypt, and Chile were analyzed and processed for the model development and its accuracy was validated. The robustness of the chemometric model was tested using 11 Greek Kalamon olive samples that were produced during the following crop year, 2018, and they were successfully classified as Greek Kalamon olives from Kalamata. Twenty-six characteristic authenticity markers were indicated to be responsible for the discrimination of Kalamon olives of different geographical origins.

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The potencial of Coffea arabica leaves as bioindicators of atmospheric carbon dioxide (CO2) was evaluated in a free-air carbon dioxide enrichment (FACE) experiment by using near-infrared reflectance (NIR) spectroscopy for direct analysis and partial least squares discriminant analysis (PLS-DA). A supervised classification model was built and validated from the spectra of coffee leaves grown under elevated and current CO2 levels. PLS-DA allowed correct test set classification of 92% of the elevated-CO2 level leaves and 100% of the current-CO2 level leaves. The spectral bands accounting for the discrimination of the elevated-CO2 leaves were at 1657 and 1698 nm, as indicated by the variable importance in the projection (VIP) score together with the regression coefficients. Seven months after suspension of enriched CO2, returning to current-CO2 levels, new spectral measurements were made and subjected to PLS-DA analysis. The predictive model correctly classified all leaves as grown under current-CO2 levels. The fingerprints suggest that after suspension of elevated-CO2, the spectral changes observed previously disappeared. The recovery could be triggered by two reasons: the relief of the stress stimulus or the perception of a return of favorable conditions. In addition, the results demonstrate that NIR spectroscopy can provide a rapid, nondestructive, and environmentally friendly method for biomonitoring leaves suffering environmental modification. Finally, C. arabica leaves associated with NIR and mathematical models have the potential to become a good biomonitoring system.

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Commercial cigarettes are made from a blend of different tobacco varieties, which in turn are the results of different agronomic practices and post-harvest curing processes. The highly complex mixture of smoke compounds reflects each tobacco variety and the levels of sensory-relevant markers. Therefore, the aim of this work was to identify potential relevant chemosensory markers in the mainstream smoke of four main types of commercial tobaccos and establish any possible relationship between them and the tobacco growing/curing practices. The tobacco samples were segregated into four segments: (1) three curing stages of flue-cured Virginia, (2) three curing stages of air-cured Burley, (3) three geo-regions of sun-cured Oriental and (4) three different process applied to tobacco. One hundred and twenty cigarettes (10 batches per flavour category) were produced and smoked under standard machine-smoking protocols. The mainstream smoke samples collected were extracted and analysed by GC × GC TOFMS. The processed data was analysed by partial least square discriminant analysis (PLS-DA) and the selectivity ratio was used to identify key chemosensory markers responsible for the four segments. All models had sensitivity and specificity equal to unity. Flue-cured Virginia (193 markers) and air-cured Burley (184 markers) showed a similar trend for O-heterocycles markers in the lighter leaf colours and N-heterocycles in the darker leaf colours post-processing, but they had compounds of different flavour descriptions, e. g. sweet and nutty. The three geo-regions of sun-cured Oriental (290 markers) also presented O-heterocycles markers in correlation with leaf sugar contents in addition of sucrose esters markers. The three unusually processed tobacco generated many chemical markers (436 markers), some derived from the so-called Cavendish fermentation process with sweet, spicy and peppery notes, whereas the dark fermented air-cured tobacco presented similar descriptors as air-cured Burley. In addition, some polycyclic aromatic hydrocarbons (PAH) were detected as markers from the fire-curing process. The PLS-DA with selectivity ratio evidenced total of 1098 chemosensory markers in cigarette smoke, in which 173 were tentatively identified.

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The aim of this work was to use spectroscopic methods and partial least squares discriminant analysis (PLS-DA) for the early prediction of genotype resistance or susceptibility to sugarcane borer. The sugarcane leaf +1 was directly analyzed with no sample preparation by ultraviolet-visible-near-infrared (UV-VIS-NIR), middle-infrared (MID), and near-infrared (NIR) spectroscopies. Also, laser-induced breakdown spectroscopy (LIBS) was used to analyze pellets of dried and ground leaves and stalks of sugarcane. Classification models were built using PLS-DA. The models built using UV-VIS-NIR, MID or NIR spectra exhibited ideal sensitivity, specificity, and classification errors, i.e., 1 for both sensitivity and specificity and 0 for classification errors. Regarding the models built using LIBS spectra, those using spectra of pellets made from dried and ground leaves also presented ideal sensitivity, specificity, and classification errors; on the other hand, models built using the spectra of pellets made of dried and ground stalks did not present ideal values for these parameters. Thus, the models built, except for the one using LIBS of pellets made of stalks, showed excellent predictive capacity, making them suitable for predicting the resistance or susceptibility of sugarcane genotypes in the early stages of a plant's life.

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1H NMR spectroscopy combined with chemometrics was employed to discriminate lager beer samples from two different classes, according to their style and information provided on the label. Partial replacement of barley malt by adjuncts is a common practice adopted by large breweries, which can lead to a decrease in diastatic power, requiring the use of exogenous enzymes. For this reason, small variations in the spectral profile can occur in the carbohydrates region. Many studies have focused on differentiating beers according to type and brewing process. However, there have no studies concerning the discrimination of beers of the same type that differ only in style, using 1H NMR spectroscopy. In this study PCA (first three components explained 81.5% of the dataset variability), PLS-DA and SIMCA models proved to be powerful tool with predict power higher than 90% for distinguishing lager beers based on the raw materials employed in the brewing process.

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Several new psychoactive substances (NPS) have reached the illegal drug market in recent years, and ecstasy-like tablets are one of the forms affected by this change. Cathinones and tryptamines have increasingly been found in ecstasy-like seized samples as well as other amphetamine type stimulants. A presumptive method for identifying different drugs in seized ecstasy tablets (n=92) using ATR-FTIR (attenuated total reflectance - Fourier transform infrared spectroscopy) and PLS-DA (partial least squares discriminant analysis) was developed. A hierarchical strategy of sequential modeling was performed with PLS-DA. The main model discriminated four classes: 5-MeO-MIPT, methylenedioxyamphetamines (MDMA and MDA), methamphetamine, and cathinones. Two submodels were built to identify drugs present in MDs and cathinones classes. Models were validated through the estimate of figures of merit. The average reliability rate (RLR) of the main model was 96.8% and accordance (ACC) was 100%. For the submodels, RLR and ACC were 100%. The reliability of the models was corroborated through their spectral interpretation. Thus, spectral assignments were performed by associating informative vectors of each specific modeled class to the respective drugs. The developed method is simple, fast, and can be applied to the forensic laboratory routine, leading to objective results reports useful for forensic scientists and law enforcement.

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