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This proof-of-concept study explored the use of an RGB colour sensor to identify different blends of vegetable oils in avocado oil. The main aim of this work was to distinguish avocado oil from its blends with canola, sunflower, corn, olive, and soybean oils. The study involved RGB measurements conducted using two different light sources: UV (395 nm) and white light. Classification methods, such as Linear Discriminant Analysis (LDA) and Least Squares Support Vector Machine (LS-SVM), were employed for detecting the blends. The LS-SVM model exhibited superior classification performance under white light, with an accuracy exceeding 90%, thus demonstrating a robust prediction capability without evidence of random adjustments. A quantitative approach was followed as well, employing Multiple Linear Regression (MLR) and LS-SVM, for the quantification of each vegetable oil in the blends. The LS-SVM model consistently achieved good performance (R2 > 0.9) in all examined cases, both for internal and external validation. Additionally, under white light, LS-SVM models yielded root mean square errors (RMSE) between 1.17-3.07%, indicating a high accuracy in blend prediction. The method proved to be rapid and cost-effective, without the necessity of any sample pretreatment. These findings highlight the feasibility of a cost-effective colour sensor in identifying avocado oil blended with other oils, such as canola, sunflower, corn, olive, and soybean oils, suggesting its potential as a low-cost and efficient alternative for on-site oil analysis.

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Garlic and salt spice is widely used in Brazilian cookery, but it has a high sodium content; as high sodium intake has been strongly correlated to the incidence of chronic diseases. This study aimed to develop a garlic and salt spice with reduced sodium intake. Sensory evaluation was conducted by applying the spices to cooked rice. First, the optimal concentration of spice added during rice preparation was determined. Subsequently, seasonings (3:1) were prepared containing 0%, 50% and 25% less NaCl using a mixture of salts consisting of KCl and monosodium glutamate; a seasoning with a 0% NaCl reduction was established as a control. Three formulations of rice with different spices were assessed according to sensory testing acceptance, time-intensity and temporal domain of sensations. The proportions of salts used in the garlic and salt spice did not generate a strange or bad taste in the products; instead, the mixtures were less salty. However, the seasonings with lower sodium levels (F2 and F3) were better accepted in comparison to the traditional seasoning (F1). Therefore, a mixture of NaCl, KCl and monosodium glutamate is a viable alternative to develop a garlic and salt spice with reduced sodium intake.

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The use of univariate, bivariate, and multivariate statistical techniques, such as analysis of variance, multiple comparisons of means, and linear correlations, has spread widely in the area of Food Science and Technology. However, the use of supervised and unsupervised statistical techniques (chemometrics) in order to analyze and model experimental data from physicochemical, sensory, metabolomics, quality control, nutritional, microbiological, and chemical assays in food research has gained more space. Therefore, we present here a manuscript with theoretical details, a critical analysis of published work, and a guideline for the reader to check and propose mathematical models of experimental results using the most promising supervised and unsupervised multivariate statistical techniques, namely: principal component analysis, hierarchical cluster analysis, linear discriminant analysis, partial least square regression, k-nearest neighbors, and soft independent modeling of class analogy. In addition, the overall features, advantages, and limitations of such statistical methods are presented and discussed. Published examples are focused on sensory, chemical, and antioxidant activity of a wide range of fruit juices consumed worldwide.

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The antibacterial activity against Bacillus subtilis, Staphylococcus aureus and Escherichia coli, as well as the antifungal activity against Aspergillus niger of a series of anilide derivatives have been modeled using augmented multivariate image analysis applied to quantitative structure-activity relationship (aug-MIA-QSAR). This QSAR approach is based on 2D molecular shape, as well as atomic sizes and colors to encode chemical, physical and biological properties. Predictive models with r(2) from 0.65 to 0.83 were used to estimate the antimicrobial activities of novel anilide analogs, which were built from the combination of substructures of the most active antimicrobial compounds along the series. Given the synergistic effect of different substituents to provide new molecules, promising compounds were proposed, highlighting a considerable multi-antimicrobial activity.

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Multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR) is a very simple correlative method that uses pixels (binaries) of chemical structures built from 2D viewer programs as descriptors; structural changes correspond to different pixel coordinates, which explain the variance in the bioactivities block. The MIA-QSAR method has shown to be predictive and capable of encoding some chemical information, but introduction of more descriptive information, such as atom size and colors to differentiate atom types, would improve predictability and interpretability. The bioactivities of a series of chemokine receptor (CCR2) inhibitors have been modeled using both conventional and atom color/size-dependent MIA-QSAR (namely aug-MIA-QSAR); the latter showed to be better. Moreover, the results were comparable to those obtained by 3D methodologies, indicating that 2D shape and substituent size are more significant descriptors than the conformational profiles required by field fit techniques.

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BACKGROUND: A screening of the chemical composition of eight commercial classes of raw Brazilian green propolis throughout the seasons was carried out. A multivariate exploratory analysis of chemical composition obtained by gas chromatography associated with mass spectrometry with headspace extraction (HS-GC/MS), and by mass spectrometry with electrospray ionization (ESI-MS) was carried out using principal component analysis (PCA). RESULTS: Differences in the volatile and polyphenolic profiles of propolis samples were verified during the seasons. Within each season, the high quality commercial classes of propolis presented similar characteristics, while the low quality classes presented distinct compositions. In spring and summer, propolis of the trimming class, commonly considered of low quality by beekeepers, presented a composition similar to the superior quality propolis. CONCLUSION: Seasonality influences the chemical composition of the commercial classes of raw Brazilian green propolis. Headspace-GC/MS and ESI-MS assisted by PCA are effective to characterize volatile and non-volatile compounds of the propolis samples, and to correlate it to the seasons.

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A goethite-based catalyst was evaluated using in-situ X-ray absorption near-edge structure (XANES) spectroscopy during methane oxidation under increasing reaction temperature. Determination of rank by median absolute deviation (DRMAD), evolving factor analysis (EFA), and multivariate curve resolution (MCR) were used to detect the species present in the catalyst during the reaction and determine their concentration profiles and their pure spectra. The reactants and reaction products were monitored on-line by mass spectrometer. The goethite-based catalyst was active for methane oxidation, with CO(2) and H(2)O as the main products. DRMAD and EFA were useful to determine the number of chemical species present in the catalyst structure during reactions. The catalyst presented phase transition during the reaction from goethite to maghemite according to XANES spectra determined by MCR. On the other hand, it was verified that the catalyst presented phase transition from goethite to wüstite in the process in the absence of the oxidant (O(2)).

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The syringyl/guaiacyl ratio was determined for six different Eucalyptus spp. wood clones cultivated in four regions in Brazil. The determinants were made by pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS) and the results were compared with those obtained by alkaline nitrobenzene oxidation method. The S/G ratios were obtained considering all the identified lignin derivatives in the pyrograms and also using two groups of markers. The first group of markers consisted of guaiacol, 4-methylguaiacol, 4-vinylguaiacol, trans-isoeugenol, syringol, 4-methylsyringol, 4-vinylsyringol and trans-4-propenylsyringol compounds as markers. The second group included guaiacol, 4-methylguaiacol, 4-vinylguaiacol, vanillin, 4-ethylsyringol, 4-vinylsyringol, syringaldehyde, syringylacetone and trans-4-propenylsyringol. It was observed from the statistical analysis that the values of S/G obtained by Py-GC-MS using the two groups of markers did not differ significantly from those obtained by nitrobenzene oxidation method.

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