N-Way NIR Data Treatment through PARAFAC in the Evaluation of Protective Effect of Antioxidants in Soybean Oil.

The use of chemometric tools is progressing to scientific areas where analytical chemistry is present, such as food science. In analytical food evaluation, oils represent an important field, allowing the exploration of the antioxidant effects of herbs and seeds. However, traditional methodologies have some drawbacks which must be overcome, such as being time-consuming, requiring sample preparation, the use of solvents/reagents, and the generation of toxic waste. The objective of this study is to evaluate the protective effect provided by plant-based substances (directly, or as extracts), including pumpkin seeds, poppy seeds, dehydrated goji berry, and Provençal herbs, against the oxidation of antioxidant-free soybean oil. Synthetic antioxidants tert-butylhydroquinone and butylated hydroxytoluene were also considered. The evaluation was made through thermal degradation of soybean oil at different temperatures, and near-infrared spectroscopy was employed in an n-way mode, coupled with Parallel Factor Analysis (PARAFAC) to extract nontrivial information. The results for PARAFAC indicated that factor 1 shows oxidation product information, while factor 2 presents results regarding the antioxidant effect. The plant-based extract was more effective in improving the frying stability of soybean oil. It was also possible to observe that while the oxidation product concentration increased, the antioxidant concentration decreased as the temperature increased. The proposed method is shown to be a simple and fast way to obtain information on the protective effects of antioxidant additives in edible oils, and has an encouraging potential for use in other applications.

Metabolomic kinetics investigation of Camellia sinensis kombucha using mass spectrometry and bioinformatics approaches.

Kombucha is created through the fermentation of Camellia sinensis tea leaves, along with sucrose, utilizing a symbiotic consortium of bacteria and yeast cultures. Nonetheless, there exists a dearth of comprehensive information regarding the spectrum of metabolites that constitute this beverage. To explore this intricate system, metabolomics was used to investigate fermentation kinetics of Kombucha. For that, an experimental framework was devised to assess the impact of varying sucrose concentrations and fermentation temperatures over a ten-day period of kombucha fermentation. Following fermentation, samples were analyzed using an LC-QTOF-MS system and a distinctive metabolomic profile was observed. Principal component analysis was used to discriminate between metabolite profiles. Moreover, the identified compounds were subjected to classification using the GNPS platform. The findings underscore notable differences in compound class concentrations attributable to distinct fermentation conditions. Furthermore, distinct metabolic pathways were identified, specially some related to the biotransformation of flavonoids. This comprehensive investigation offers valuable insights into the pivotal role of SCOBY in driving metabolite production and underscores the potential bioactivity harbored within Kombucha.

Proof-of-concept on the effect of human milk storage time: Lipid degradation and spectroscopic characterization using portable near-infrared spectrometer and chemometrics.

Human milk (HM) modifications over time represent an important issue. This work proposed to evaluate the changes in HM during one-year storage through total lipids (TL) degradation and portable near-infrared (NIR) spectrometer combined with chemometrics. Colostrum, transition, and mature stages were obtained from donors and considered in the raw and pasteurized forms. Principal component analysis in TL content showed changes in the mature stages for both forms after 75 days. Multivariate curve resolution with alternating least squares in NIR spectral data reveals a decrease in protein and triacylglycerol contents while an increase in free fatty acids (palmitic acid) contents were observed through the storage after around 5-6 months. Therefore, more than 5-6 months of storage suggest possible biochemical changes in the HM nutritional composition. Moreover, the chemometrics investigation was crucial in extracting information, bringing coherent results, and helping to understand the chemical changes in human milk during storage.

Multivariate optimization approach applied to natural polymers from Ceratonia siliqua L. and Moringa oleifera Lam as coagulating/flocculating agents.

In this study, a multivariate 23 experimental design was applied to optimize the operational conditions (seed mass, salt concentration, and pH) to employ Ceratonia siliqua L. (carob) and Moringa oleifera Lam (moringa) as coagulating/flocculating agents for water treatment. Currently, the coagulation stage in water treatment uses aluminium compounds, due to the characteristic reaction to natural alkalinity in raw water, and for its low market value. Considering that aluminium effects on human health are not sufficiently studied to acknowledge its toxicity, and its significant environmental impacts, it is suitable for the studies to search for alternatives to be employed in the water treatment that will be distributed to human consumption. This study was carried out with raw water of high turbidity level, 83.7 NTU. The raw water collected was also characterized according to pH, colour, Total Organic Carbon (TOC), Dissolved Organic Carbon (DOC), and Dissolved Organic Matter (DOM), with values of 6.7, 178 NTU, 6.80, 2.45 and 138.58 mg/L, respectively. The optimized results showed that with 2 g of seed, 0.5 mol L-1 of NaCl, and pH 11.0 In these conditions, moringa coagulant reached 90%, 86%, 6%, 67%, and 81% for turbidity, colour, DOC, TOC, and DOM removal, respectively, whereas the carob coagulant achieved 85%, 76%, 5%, 55.6%, 66.7%, respectively for the same parameters' removal. Both coagulants presented lower sludge formation, 1.1 mL L-1 for moringa coagulant, and 1.1 mL L-1 for carob coagulant. The results could be considered promises, and natural polymers carob and moringa can be suggested as alternatives agents in coagulation/flocculation stages for water treatment.

Multivariate classification for the direct determination of cup profile in coffee blends via handheld near-infrared spectroscopy.

Professional cupping is a reliable methodology for the coffee industry and its professionals. However, it faces barriers for its implementation on an industrial scale. To date, no study has determined a coffee cup profile using a handheld near-infrared (NIR) spectrometer. Therefore, the aim of this study was to evaluate directly cup profiles in roasted and ground coffee blends via handheld NIR spectroscopy combined with partial least squares with discriminant analysis (PLS-DA), in an industrial case study. The sensitivity and specificity of the model obtained ranged from 91 to 100%, 84-100%, and 73-95% in the training, prediction, and internal cross-validation sets, respectively. These results are therefore promising for the industrial reality and the methodology could assist coffee professionals in their decisions during cup evaluation in further tests. The proposed method is viable for the direct determination of cup profile at industrial scale since it is portable, fast, simple, robust, and less expensive compared to the benchtops equipment.

Attenuated total reflectance Fourier transform (ATR-FTIR) spectroscopy and chemometrics for organic cinnamon evaluation.

Organic food consumption has increased significantly over time. This contributes to the increased demand and price of this kind of food. Among the organic products, cinnamon stands out for its characteristic flavor and bioactive compounds. Thus, the work aimed to verify the potentials of attenuated total reflectance Fourier transform mid-infrared spectroscopy (ATR-FT-MIR) coupled with Parallel Factor Analysis (PARAFAC) for evaluation of cinnamon organic samples. As result, the proposal is feasible in the differentiation of organic cinnamon powder, in which ATR-FT-MIR coupled with PARAFAC showed the differentiation of organic from non-organic ones on the scores mode, the precision at repeatability level on one loading mode, and the spectral region, on the other loading mode, above 2600 cm-1 was related to the differentiation of the organic and non-organic samples.

Thermal rice oil degradation evaluated by UV-Vis-NIR and PARAFAC.

Thermal rice oil degradation was evaluated by UV-Vis and NIR in three-way arrays assembled with samples, different temperatures, and the absorbance at different wavelengths by applying PARAFAC chemometric method. The loadings matrix of the mode corresponding to the samples (scores) contains the information related to the samples. The loadings on the temperature mode resemble kinetics profiles. These profiles change with the nature of the component responsible for the factor and evolve with the heating temperature. The loadings on the spectral mode reveal the antioxidants γ-oryzanol and tocopherol, and oxidation products are the components responsible for the two factors. The results achieved showed that the antioxidants concentration decreases starting at 70 °C while oxidation products start to increase more pronounced after 90 °C. The proposed method is shown to be a simple and fast way to obtain information about the oxidative stability of rice oils.

Rapid discrimination of fungal strains isolated from human skin based on microbial volatile organic profiles.

Studies of the microbiota of human skin have gained attention mainly because of its high complexity. Volatile metabolites that emerge from the microbiota play a significant role in fungus metabolism, acting on fungal development, defense, and protection against stress, communication, and pathogenicity. The present study evaluated volatile organic profiles, based on headspace-solid-phase microextraction-gas chromatography-mass spectrometry. We sought to define the optimal experimental conditions for such identification. Chromatograms from 15 fungi were evaluated and discriminated by principal component analysis. The volatile metabolite profiles that were putatively identified in the present study (e.g 2­isopropyl­5­methyl­cyclohex­3­en­1­one, 3/2­methyl­1­butanol, isopentyl ethanoate, phenyl ethanol) allowed the discrimination of different microorganisms from human skin. The present methodology may be a more rapid way of identifying microorganisms compared with conventional methods of microbiological identification.

Quantitative assessment of specific defects in roasted ground coffee via infrared-photoacoustic spectroscopy.

Chemical analyses and sensory evaluation are the most applied methods for quality control of roasted and ground coffee (RG). However, faster alternatives would be highly valuable. Here, we applied infrared-photoacoustic spectroscopy (FTIR-PAS) on RG powder. Mixtures of specific defective beans were blended with healthy (defect-free) Coffea arabica and Coffea canephora bases in specific ratios, forming different classes of blends. Principal Component Analysis allowed predicting the amount/fraction and nature of the defects in blends while partial Least Squares Discriminant Analysis revealed similarities between blends (=samples). A successful predictive model was obtained using six classes of blends. The model could classify 100% of the samples into four classes. The specificities were higher than 0.9. Application of FTIR-PAS on RG coffee to characterize and classify blends has shown to be an accurate, easy, quick and "green" alternative to current methods.

Data on roasted coffee with specific defects analyzed by infrared-photoacoustic spectroscopy and chemometrics.

This article contains data related to the research article entitled "Quantitative assessment of specific defects in roasted ground coffee via infrared-photoacoustic spectroscopy" (Dias et al., 2018) [1]. A method potentially able for assessing the quality of roasted ground coffees is described in the origin paper. Infrared spectroscopy and photoacoustic detection (FTIR-PAS) associated with multivariate calibration were used. The samples were obtained blending whole and healthy coffee beans (C. arabica and C. canephora) with specific blends of defects, named selections, which contain broken, sour, and black beans, skin, woods and healthy beans still not collected. In addition to a reduction in commercial value, the presence of defects compromises the sensory attributes of coffee. On the other hand, selections are commonly found in coffee crops and can be added intentionally to the product. Twenty-five selections were used to obtain a panel of 154 blends. The FTIR-PAS spectra of each sample generated the prediction model of Partial Least Squares Regression parameters, which are also presented here.

Multi-block data analysis using ComDim for the evaluation of complex samples: Characterization of edible oils.

The ComDim chemometrics method for multi-block analysis was employed to evaluate thirty-two vegetable oil samples analyzed by near infrared (NIR) and ultraviolet-visible (UV-Vis) spectroscopy, and by Gas Chromatography with flame ionization detection (GC-FID) for their fatty acids composition. This unsupervised pattern recognition method was able to extract information from the tables of results that could be presented in informative graphs showing the relationship between the samples through the scores, the predominance of information in particular tables through the saliences and the contribution of the variables in each table which were responsible for the similarities observed in the samples, through the loadings plots. It was possible to infer similarities and differences among the samples studied according to the specific absorption in the UV-Vis and NIR region, as well as their fatty acids composition. The proposed methodology demonstrates the applicability of ComDim for the characterization of samples when different variables (different techniques) describe the same samples. In this particular study, the ComDim chemometrics method was able to discriminate samples by their characteristics and compositions.

Near infrared spectroscopy and multivariate calibration for simultaneous determination of glucose, triglycerides and high-density lipoprotein in animal plasma.

The quantitative analysis of glucose, triglycerides and high-density lipoprotein (HDL) in rat plasma without sample pre-treatment using direct near-infrared spectroscopy was studied. Comparison was made of several multivariate calibration techniques and algorithms for data pre-processing and variable selection, including partial least squares (PLS), interval partial least squares (iPLS), genetic algorithm (GA) and successive projections algorithm (SPA). Variable selection yielded good results for the correlation coefficient and Root Mean Square Error of Prediction (RMSEP) values for the three parameters, especially triglycerides. The RMSEP values for glucose, triglycerides and HDL produced by the PLS model were 6.08, 16.07 and 2.03 mg dl(-1), respectively. F tests and t-tests were performed to compare the results of the models with each other and with a reference method. These results suggests that the PLS method can be used to simultaneously determine the concentrations of glucose, triglycerides and HDL in complicated biological fluids with NIR spectroscopy, offering an alternative analysis in animals.

Non-destructive detection of adulterated tablets of glibenclamide using NIR and solid-phase fluorescence spectroscopy and chemometric methods.

This study describes a method for non-destructive detection of adulterated glibenclamide tablets. This method uses near infrared spectroscopy (NIRS) and fluorescence spectroscopy along with chemometric tools such as Soft Independent Modeling of Class Analogy (SIMCA), Partial Least Squares-Discriminant Analysis (PLS-DA) and Unfolded Partial Least Squares with Discriminant Analysis (UPLS-DA). Both brand name (Daonil) and generic glibenclamide tablets were used for analysis. The levels of glibenclamide in each type of tablet were evaluated by derivative spectrophotometry in the ultraviolet region. The results obtained from the NIR and fluorescence spectroscopy along with those obtained from multivariate data classification show that this combined technique is an effective way to detect adulteration in drugs for the treatment of diabetes. In the future, this method may be extended to detect different types of counterfeit medications.

Q-mode curve resolution of UV-vis spectra for structural transformation studies of anthocyanins in acidic solutions.

Chemometric analysis of ultraviolet-visible (UV-vis) spectra for pH values 1.0, 3.3, 5.3, and 6.9 was used to investigate the kinetics and the structural transformations of anthocyanins in extracts of calyces of hibiscus flowers of the Hibiscus acetosella Welw. ex Finicius for the first time. Six different species were detected: the quinoidal base (A), the flavylium cation (AH+), the pseudobase or carbinol pseudobase (B), cis-chalcone (C(C)), trans-chalcone (C(t)), and ionized cis-chalcone (C(C)-). Four equilibrium constant values were calculated using relative concentrations, hydration, pK(h) = 2.60 +/- 0.01, tautomeric, K(T) = 0.14 +/- 0.01, acid-base, pK(a) = 4.24 +/- 0.04, and ionization of the cis-chalcone, pK(C(C)) = 8.74 +/- 1.5 x 10(-2). The calculated protonation rate of the tautomers is K(H+) = 0.08 +/- 7.6 x 10(-3). These constants are in excellent agreement with those measured previously in salt form. From a kinetic viewpoint, the situation encountered is interesting since the reported investigation is limited to visible light absorption in acid medium. These models have not been reported in the literature.