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The authentication of Slovak wines in comparison to other similar wines from various geographical regions, namely Hungary, France, Austria, and Ukraine, was conducted using the OC-PLS, DD-SIMCA, and PLS-DM models, all of them operating in rigorous way. The study involved 63 samples, of which 41 originated from Slovakia, covering diverse wine types such as varietal wines, cuvée selections (different "putnový"), and essence. To capture digital images under controlled conditions, a custom-made cardboard box with white inner surfaces was devised and equipped with a smartphone. During the training phase, sensitivities of 96%, 100%, and 96% were attained for OC-PLS, DD-SIMCA, and PLS-DM, respectively. In the subsequent stages of validation and testing for DD-SIMCA and PLS-DM, the proposed methods displayed optimal efficiency, achieving both sensitivity and specificity rates of 100%. However, such results were not achieved in the case of OC-PLS, which exhibited efficiency levels of 90% in validation and 80% in testing.

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Banisteriopsis (Malpighiaceae) is an important genus of neotropical savannas with related biological and medicinal activities but under-explored metabolomic profiles. We present a chemometric analysis for discriminating secondary metabolites of three species of Banisteriopsis (B. laevifolia, B. malifolia, and B. stellaris) leaves. Initially, each species was separately extracted with ethanol:water (4 : 1, v/v) and analysed by Ultra Performance Liquid Chromatography coupled with Mass Spectrometry (UPLC-MS/MS). The chromatographic profiles were subjected to Global Natural Product Social (GNPS) and Partial Least Squares Discriminant Analysis (PLS-DA). Eighty-nine compounds (cosine≥0.90) were annotated, including flavonoids, phenolics, and acids. The chemometric analysis (VIP Score) showed each species' relative concentration of the more relevant compounds. In addition, four compounds that discriminate the metabolomic profiles of B. laevifolia, B. malifolia, and B. stellaris were identified by PLS-DA.

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Maize (Zea mays L.) is of socioeconomic importance as an essential food for human and animal nutrition. However, cereals are susceptible to attack by mycotoxin-producing fungi, which can damage health. The methods most commonly used to detect and quantify mycotoxins are expensive and time-consuming. Therefore, alternative non-destructive methods are required urgently. The present study aimed to use near-infrared spectroscopy with hyperspectral imaging (NIR-HSI) and multivariate image analysis to develop a rapid and accurate method for quantifying fumonisins in whole grains of six naturally contaminated maize cultivars. Fifty-eight samples, each containing 40 grains, were subjected to NIR-HSI. These were subsequently divided into calibration (38 samples) and prediction sets (20 samples) based on the multispectral data obtained. The averaged spectra were subjected to various pre-processing techniques (standard normal variate (SNV), first derivative, or second derivative). The most effective pre-treatment performed on the spectra was SNV. Partial least squares (PLS) models were developed to quantify the fumonisin content. The final model presented a correlation coefficient (R2) of 0.98 and root mean square error of calibration (RMSEC) of 508 µg.kg-1 for the calibration set, an R2 of 0.95 and root mean square error of prediction (RMSEP) of 508 µg.kg-1 for the test validation set and a ratio of performance to deviation of 4.7. It was concluded that NIR-HSI with partial least square regression is a rapid, effective, and non-destructive method to determine the fumonisin content in whole maize grains.

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The Coronavirus Disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has required the search for sensitive, rapid, specific, and lower-cost diagnostic methods to meet the high demand. The gold standard method of laboratory diagnosis is real-time reverse transcription polymerase chain reaction (RT-PCR). However, this method is costly and results can take time. In the literature, several studies have already described the potential of Fourier transform infrared spectroscopy (FTIR) as a tool in the biomedical field, including the diagnosis of viral infections, while being fast and inexpensive. In view of this, the objective of this study was to develop an FTIR model for the diagnosis of COVID-19. For this analysis, all private clients who had performed a face-to-face collection at the Univates Clinical Analysis Laboratory (LAC Univates) within a period of six months were invited to participate. Data from clients who agreed to participate in the study were collected, as well as nasopharyngeal secretions and a saliva sample. For the development of models, the RT-PCR result of nasopharyngeal secretions was used as a reference method. Absorptions with high discrimination (p < 0.001) between GI (28 patients, RT-PCR test positive to SARS-CoV-2 virus) and GII (173 patients who did not have the virus detected in the test) were most relevant at 3512 cm-1, 3385 cm-1 and 1321 cm-1 after 2nd derivative data transformation. To carry out the diagnostic modeling, chemometrics via FTIR and Discriminant Analysis of Orthogonal Partial Least Squares (OPLS-DA) by salivary transflectance mode with one latent variable and one orthogonal signal correction component were used. The model generated predictions with 100 % sensitivity, specificity and accuracy. With the proposed model, in a single application of an individual's saliva in the FTIR equipment, results related to the detection of SARS-CoV-2 can be obtained in a few minutes of spectral evaluation.

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The search for knowledge related to the Pitaya (Hylocereus polyrhizus [F.A.C. Weber] Britton & Rose, family Cactaceae) is commonly due to its beneficial health properties e aesthetic values. But process to obtain pitaya pulp is a first and important step in providing information for the subsequent use of this fruit as colorant, for example. Therefore, the effects of the pulping process on the metabolomic and chemometric profile of non-volatile compounds of pitaya were assessed for the first time. The differences in metabolic fingerprints using UPLC-QTOF-MSE and multivariate modeling (PCA and OPLS-DA) was performed in the following treatments: treatment A, which consists of pelled pitaya and no ascorbic acid addition during pulping; treatment B, use of unpelled pitaya added of ascorbic acid during pulping; and control, unpelled pitaya and no ascorbic acid addition during pulping. For the metabolomic analysis, UPLC-QTOF-MSE shows an efficient method for the simultaneous determination of 35 non-volatile pitaya metabolites, including isorhamnetin glucosyl rhamnosyl isomers, phyllocactin isomers, 2'-O-apiosyl-phylocactin and 4'-O-malonyl-betanin. In addition, the chemometric analysis efficiently distinguished the metabolic compounds of each treatment applied and shows that the use of unpelled pitaya added of ascorbic acid during pulping has an interesting chemical profile due to the preservation or formation of compounds, such as those derived from betalain, and higher yields, which is desirable for the food industry.

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Fungal melanin contributes to the survival and virulence of pathogenic fungi, such as Fonsecaea pedrosoi, which is responsible for causing chromoblastomycosis. The objective of this study was to employ Fourier transform infrared spectroscopy (FTIR) to predict the melanin content of F. pedrosoi. The melanin content, in percentage, was previously determined using gravimetry for twenty-six clinical isolates. Quintuplicate spectra of each isolate were obtained using attenuated total reflection (ATR) within the range of 4000 to 650 cm-1. To predict the melanin content, modeling was performed using partial least squares regression (PLS) in the region 1800 - 750 cm-1. Two models were tested: PLS and successive projections algorithms for interval selection in partial least squares (iSPA-PLS). The best modeling results were achieved using iSPA-PLS with one factor. The calibration set exhibited a determination coefficient (R2) of 0.9745 and a root mean square error of cross-validation (RMSECV) of 0.0977. In the prediction set, the R2 value was 0.9711, and the root mean square error of prediction (RMSEP) was 0.0999. Modeling with FTIR and multivariate calibration provides a valuable means of predicting fungal melanin content, which is simpler and more robust, thereby contributing to the advancement of this field of study.

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Coffee is one of the most consumed beverages worldwide. Espírito Santo is the largest Brazilian producer of conilon coffee, and invested in the creation of new cultivars, such as "Conquista ES8152", launched in 2019. Therefore, the present study aimed to evaluate the effects of maturation and roasting on the chemical and sensorial composition of the new conilon coffee cultivar "Conquista ES8152". The coffee was harvested containing 3 different percentages of ripe fruits: 60%, 80%, and 100%, and roasted at 3 different degrees of roasting: light, medium, and dark, to evaluate the moisture and ash content, yield of soluble extract, volatile compound profile, chlorogenic acid and caffeine content, and sensory profile. "Conquista ES8152" coffee has a moisture content between 1.38 and 2.62%; ash between 4.34 and 4.72%; and yield between 30.7 and 35.8%. Sensory scores ranged between 75 and 80 and the majority of volatile compounds belong to the pyrazine, phenol, furan, and pyrrole groups. The content of total chlorogenic acids was drastically reduced by roasting, with values between 2.40 and 9.33%, with 3-caffeoylquinic acid being the majority. Caffeine was not influenced by either maturation or roasting, with values between 2.16 and 2.41%. The volatile compounds furfural, 5-methylfurfural, and 2-ethyl-5-methylpyrazine were positively correlated with the evaluated sensory attributes and 5-methylfurfural was the only one significantly correlated with all attributes. Ethylpyrazine, furfuryl acetate, 1-furfurylpyrrole, 4-ethyl-2-methoxyphenol, and difurfuryl ether were negatively correlated. The stripping did not affect the quality and composition of this new cultivar, however, the roasting caused changes in both the chemical and sensorial profiles, appropriately indicated by the principal component analysis.

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In this study, twenty free amino acids (FAA) were investigated in samples of bracatinga (Mimosa scabrella) honeydew honey (BHH) from Santa Catarina (n = 15) and Paraná (n = 13) states (Brazil), followed by chemometric analysis for geographic discrimination. The FAA determination was performed by gas chromatography-mass spectrometry (GC-MS) after using a commercial EZ:faast™ kits for GC. Eight FAA were determined, being proline, asparagine, aspartic and glutamic acids found in all BHH, with significant differences (p < 0.05). In addition, with the exception of proline, the others FAA (asparagine, aspartic and glutamic) normally showed higher concentrations in samples from Santa Catarina state, being that in these samples it was also observed higher FAA sums (963.41 to 2034.73 mg kg-1) when compared to samples from Paraná state. The variability in the results did not show a clear profile of similarity when the heatmap and hierarchical grouping were correlated with the geographic origin and the concentration of eight determined FAA. However, principal component analysis (PCA) demonstrated that serine, asparagine, glutamic acid, and tryptophan were responsible for the geographic discrimination among samples from Santa Catarina and Paraná states, since they were the dominant variables (r > 0.72) in the PCA. Therefore, these results could be useful for the characterization and authentication of BHH based on their FAA composition and geographic origin.

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Brazilian Cerrado is recognised as a biodiversity hotspot due to the presence of endemic species with great biological potential. Particularly, Lomatozona artemisiifolia, is a rare species found in the Cerrado region in midwestern Brazil. Efforts have been made for its conservation in the Cerrado, such as the use of in vitro micropropagation, demanding a comparative analysis between grown plants and those collected from nature. For this purpose, we performed the chemical study of L. artemisiifolia by LC-ESI-MS/MS and molecular networking analysis in the Global Natural Products Social Molecular Networking (GNPS) with in silico annotation using Network Annotation Propagation (NAP), which led to the observation of labdane diterpenes and flavonoid subclasses as the most representative specialised metabolites of this plant. In addition, molecular networking and chemometric analysis were correlated, allowing the metabolite profile emerging from field growth and micropropagation conditions to be observed.

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Maize (Zea mays L.) is of socioeconomic importance as an essential food for human and animal nutrition. However, cereals are susceptible to attack by mycotoxin-producing fungi, which can damage health. The methods most commonly used to detect and quantify mycotoxins are expensive and time-consuming. Therefore, alternative non-destructive methods are required urgently. The present study aimed to use near-infrared spectroscopy with hyperspectral imaging (NIR-HSI) and multivariate image analysis to develop a rapid and accurate method for quantifying fumonisins in whole grains of six naturally contaminated maize cultivars. Fifty-eight samples, each containing 40 grains, were subjected to NIR-HSI. These were subsequently divided into calibration (38 samples) and prediction sets (20 samples) based on the multispectral data obtained. The averaged spectra were subjected to various pre-processing techniques (standard normal variate (SNV), first derivative, or second derivative). The most effective pre-treatment performed on the spectra was SNV. Partial least squares (PLS) models were developed to quantify the fumonisin content. The final model presented a correlation coefficient (R2) of 0.98 and root mean square error of calibration (RMSEC) of 508 µg.kg-1 for the calibration set, an R2 of 0.95 and root mean square error of prediction (RMSEP) of 508 µg.kg-1 for the test validation set and a ratio of performance to deviation of 4.7. It was concluded that NIR-HSI with partial least square regression is a rapid, effective, and non-destructive method to determine the fumonisin content in whole maize grains.

O milho (Zea mays L.) possui importância socioeconômica por constituir um dos alimentos básicos na nutrição humana e animal. Porém, o cereal é suscetível ao ataque de fungos produtores de micotoxinas que podem causar danos à saúde. Os métodos mais utilizados para detectar e quantificar micotoxinas são caros e demorados e métodos alternativos para a detecção das micotoxinas são uma necessidade. O presente trabalho tem como objetivo utilizar espectroscopia no infravermelho próximo com imagem hiperespectral (NIR-HSI) e análise multivariada de imagens para desenvolver um método rápido e preciso para quantificação de fumonisinas em grãos inteiros de seis cultivares de milho naturalmente contaminadas. Cinquenta e oito amostras, cada uma contendo 40 grãos, foram submetidas ao NIR-HSI e posteriormente divididas em um conjunto de calibração (38 amostras) e um conjunto de predição (20 amostras) com base nos dados multiespectrais obtidos. Os espectros médios foram submetidos a diversas técnicas de pré-processamento (variação normal padrão - SNV, primeira derivada ou segunda derivada). O melhor pré-processamento dos espectros foi SNV e um modelo de mínimos quadrados parciais (PLS) foi desenvolvido para quantificar o teor de fumonisinas. O modelo final apresentou coeficiente de correlação (R2) de 0,98 e raiz quadrada do erro médio quadrático de calibração (RMSEC) de 508 µg.kg-1 para o conjunto de calibração, R2 de 0,95 e raiz quadrada do erro médio quadrático de predição (RMSEP) de 508 µg.kg-1 para o conjunto de validação do teste e relação desempenho/desvio de 4,7. Conclui-se que o NIR-HSI com regressão parcial de mínimos quadrados pode ser um método rápido, eficaz e não destrutivo para determinar o teor de fumonisinas em grãos integrais de milho.

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INTRODUCTION: In general, two characteristics are ever present in NMR-based metabolomics studies: (1) they are assays aiming to classify the samples in different groups, and (2) the number of samples is smaller than the feature (chemical shift) number. It is also common to observe imbalanced datasets due to the sampling method and/or inclusion criteria. These situations can cause overfitting. However, appropriate feature selection and classification methods can be useful to solve this issue. OBJECTIVES: Investigate the performance of metabolomics models built from the association between feature selectors, the absence of feature selection, and classification algorithms, as well as use the best performance model as an NMR-based metabolomic method for prostate cancer diagnosis. METHODS: We evaluated the performance of NMR-based metabolomics models for prostate cancer diagnosis using seven feature selectors and five classification formalisms. We also obtained metabolomics models without feature selection. In this study, thirty-eight volunteers with a positive diagnosis of prostate cancer and twenty-three healthy volunteers were enrolled. RESULTS: Thirty-eight models obtained were evaluated using AUROC, accuracy, sensitivity, specificity, and kappa's index values. The best result was obtained when Genetic Algorithm was used with Linear Discriminant Analysis with 0.92 sensitivity, 0.83 specificity, and 0.88 accuracy. CONCLUSION: The results show that the pick of a proper feature selection method and classification model, and a resampling method can avoid overfitting in a small metabolomic dataset. Furthermore, this approach would decrease the number of biopsies and optimize patient follow-up. 1H NMR-based metabolomics promises to be a non-invasive tool in prostate cancer diagnosis.

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Liver enzymes alterations (activity or quantity increase) have been recognized as biomarkers of obesity-related abnormal liver function. The intake of healthy foods can improve the activity of enzymes like aspartate and alanine aminotransferases (AST, ALT), γ-glutaminyl transferase (GGT), and alkaline phosphatase (ALP). Beans have a high concentration of several phytochemicals; however, Restriction Irrigation (RI) during plant development amends their synthesis. Using chemometric tools, we evaluated the capacity of RI-induced phytochemicals to ameliorate the high activity of liver enzymes in obese rats. The rats were induced with a high-fat diet for 4 months, subsequently fed with 20% cooked beans from well-watered plants (100/100), or from plants subjected to RI at the vegetative or reproduction stage (50/100, 100/50), or during the whole cycle (50/50) for 3 months. A partial least square discriminant analysis indicated that mostly flavonols have a significant association with serum AST and ALT activity, while isoflavones lowered GGT and ALP. For AST and ALT activity in the liver, saponins remained significant for hepatocellular protection and flavonoids remained significant as hepatobiliary protectants by lowering GGT and ALP. A principal component analysis demonstrated that several flavonoids differentiated 100/50 treatment from the rest, while some saponins were correlated to 50/100 and 50/50 treatments. The intake of beans cultivated under RI improves obesity-impaired liver alterations.

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Green coffee is the hulled coffee bean, rich in chemical compounds indicative of quality before roasting, making the classification special or traditional. This work aimed to determine compounds in green coffee beans and find the differentiation of green coffee beans into special or traditional ones through chemometrics. For that, the levels of phenolic compounds, reducing, nonreducing, and total sugars were quantified by spectrophotometry: caffeine, trigonelline, 5-hydroxymethylfurfural (5-HMF), 3-hydroxybenzoic, 4-hydroxybenzoic, chlorogenic, caffeic, and nicotinic acids (NAs) by high-performance liquid chromatography-UV-Vis; acetaldehyde, acetone, methanol, ethanol, and isoamyl by HS-GC-FID. Principal component analysis (PCA) was used to differentiate green coffee beans through the levels obtained in spectrophotometric and chromatographic analyses. Statistically, the contents of total phenolic compounds, caffeine, nonreducing sugars, total sugars, NA, 5-HMF, acetaldehyde, ethanol, and ethanol/methanol showed significant differences. The PCA made it possible to classify green coffee beans into special and traditional, in addition to understanding the attributes that influenced the differentiation between coffees. In addition, it was possible to classify green coffee beans into special and traditional, either using all parameters evaluated or only using spectrophotometric analyses. In this way, some advantages allow classification without using a trained and experienced evaluator as their previous experience can influence the results due to their expertise in a certain type of coffee, in addition to being faster and cheaper, especially regarding spectrophotometric analyses.

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Brazil nut oil is highly valued in the food, cosmetic, chemical, and pharmaceutical industries, as well as other sectors of the economy. This work aims to use the Fourier transform infrared (FTIR) technique associated with partial least squares regression (PLSR) and principal component analysis (PCA) to demonstrate that these methods can be used in a prior and rapid analysis in quality control. Natural oils were extracted and stored for chemical analysis. PCA presented two groups regarding the state of degradation, subdivided into super-degraded and partially degraded groups in 99.88% of the explained variance. The applied PLS reported an acidity index (AI) prediction model with root mean square error of calibration (RMSEC) = 1.8564, root mean square error of cross-validation (REMSECV) = 4.2641, root mean square error of prediction (RMSEP) = 2.1491, R2cal (calibration correlation coefficient) equal to 0.9679, R2val (validation correlation coefficient) equal to 0.8474, and R2pred (prediction correlation coefficient) equal to 0, 8468. The peroxide index (PI) prediction model showed RMSEC = 0.0005, REMSECV = 0.0016, RMSEP = 0.00079, calibration R2 equal to 0.9670, cross-validation R2 equal to 0.7149, and R2 of prediction equal to 0.9099. The physical-chemical analyses identified that five samples fit in the food sector and the others fit in other sectors of the economy. In this way, the preliminary monitoring of the state of degradation was reported, and the prediction models of the peroxide and acidity indexes in Brazil nut oil for quality control were determined.

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The objective of this work was to evaluate elemental changes in pepper exposed to Cd stress through different chemometric tools. For this purpose, pepper plants were grown under five different treatments with different Cd concentrations in the nutrient solution. Considering the hypothesis that pepper plants exposed to Cd stress during growth undergo changes in the macro- and microelemental distribution in leaves, stems, and roots, principal component analysis (PCA) and parallel factor (PARAFAC) analysis were applied to compare bidirectional and multivariate chemometric strategies to assess elemental changes in pepper plants. Since the number of variables and the data generated were large and complex, the application of chemometric tools was justified to facilitate the visualization and interpretation of results. The mineral composition, namely the Ca, Cd, Cu, Fe, K, Mg, Mn, N, and P contents, was assessed in 180 samples of leaves, stems, and roots of the cultivated peppers. Then, PCA and PARAFAC analysis were applied to compare bidirectional and multivariate chemometric strategies to assess elemental changes throughout pepper plants. The visualization of the trend on each sample and their intrinsic relationship with the variables were possible with the application of PCA. The use of PARAFAC analysis permitted the simultaneous study of all samples in a straightforward representation of the information that facilitated a quick and comprehensive understanding of the spatial distribution of elements in plants. Thus, macroelements (Ca, K, Mg, N, and P) that were found in higher concentrations in leaves did not present significant differences in the distribution along the plants under different treatment conditions. In contrast, a significant impact on the microelement (Cu, Fe, and Mn) distribution was produced between uncontaminated and contaminated samples. This analysis revealed a significant accumulation of Cd in roots and adverse effects on normal plant growth, demonstrating their level of phytotoxicity to pepper.

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Wine is a temperature, light, and oxygen-sensitive product, so its physicochemical characteristics can be modified by variations in temperature and time when samples are either sampled, transported, and/or analyzed. These changes can alter its metabolomic fingerprinting, impacting further classification tasks and quality/quantitative analyses. For these reasons, the aim of this work is to compare and analyze the information obtained by different chemometric methods used in a complementary form (PCA, ASCA, and PARAFAC) to study 1H-NMR spectra variations of four red wine samples kept at different temperatures and time lapses. In conjunction, distinctive changes in the spectra are satisfactorily tracked with each chemometric method. The chemometric analyses reveal variations related to the wine sample, temperature, and time, as well as the interactions among these factors. Moreover, the magnitude and statistical significance of the effects are satisfactorily accounted for by ASCA, while the time-related effects variations are encountered by PARAFAC modeling. Acetaldehyde, formic acid, polyphenols, carbohydrates, lactic acid, ethyl lactate, methanol, choline, succinic acid, proline, acetoin, acetic acid, 1,3-propanediol, isopentanol, and some amino acids are identified as some of the metabolites which present the most important variations.

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Yerba mate, a popular plant consumed mainly as an infusion, possesses nutritional and medicinal properties attributed to its secondary metabolites. This study aimed to develop strategies to elucidate the phenolic composition of yerba mate samples from Brazil, Argentina, Uruguay, and Paraguay. Optimization of ultrasonic-assisted extraction (UAE) was performed, and the extracted compounds were characterized using ultra-high-pressure liquid chromatography coupled with quadrupole/time-of-flight mass spectrometry (UHPLC-QTOF-MS), molecular fluorescence and high-pressure liquid chromatography with diode-array detection (HPLC-DAD). Chemometric analysis, including parallel factor analysis (PARAFAC) and principal component analysis (PCA) explored metabolite profiles and identify patterns. PARAFAC modelling of the molecular fluorescence results revealed higher pigment content in Brazilian samples, while other countries' samples exhibited higher phenolic content. PCA modeling of HPLC-DAD results indicated that cultivated yerba mate contained higher chlorogenic acids levels, and samples from Argentina, Paraguay, and Uruguay exhibited higher concentrations of chlorogenic acids and flavonoids.

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Hypericum perforatum L. (St. John's wort) is one of the world's most consumed medicinal plants for treating depression and psychiatric disorders. Counterfeiting can occur in the medicinal plant trade, either due to the lack of active ingredients or the addition of substances not mentioned on the labels, often without therapeutic value or even harmful to health. Hence, 43 samples of St. John's wort commercially acquired in different Brazilian regions and other countries were analyzed by paper spray ionization mass spectrometry (PS-MS) and modeled by principal component analysis. Hence, samples (plants, capsules, and tablets) were extracted with ethanol in a solid-liquid extraction. For the first time, PS-MS analysis allowed the detection of counterfeit H. perforatum samples containing active principles typical of other plants, such as Ageratum conyzoides and Senna spectabilis. About 52.3% of the samples were considered adulterated for having at least one of these two species in their composition. Furthermore, out of 35 samples produced in Brazil, only 13 were deemed authentic, having only H. perforatum. Therefore, there is a clear need to improve these drugs' quality control in Brazil.

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Among several complications related to physiotherapy, osteosarcopenia is one of the most frequent in elderly patients. This condition is limiting and quite harmful to the patient's health by disabling several basic musculoskeletal activities. Currently, the test to identify this health condition is complex. In this study, we use mid-infrared spectroscopy combined with chemometric techniques to identify osteosarcopenia based on blood serum samples. The purpose of this study was to evaluate the mid-infrared spectroscopy power to detect osteosarcopenia in community-dwelling older women (n = 62, 30 from patients with osteosarcopenia and 32 healthy controls). Feature reduction and selection techniques were employed in conjunction with discriminant analysis, where a principal component analysis with support vector machines (PCA-SVM) model achieved 89% accuracy to distinguish the samples from patients with osteosarcopenia. This study shows the potential of using infrared spectroscopy of blood samples to identify osteosarcopenia in a simple, fast and objective way.

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Cachaça is a Brazilian beverage obtained from the fermentation of sugarcane juice (sugarcane spirit) and is considered one of the most consumed alcoholic beverages in the world with a strong economic impact on the northeastern Brazil, more specifically in the Brejo. This microregion produces sugarcane spirits with high quality associated to edaphoclimatic conditions. In this sense, analysis for sample authentication and quality control that uses solvent-free, environmentally friendly, rapid and non-destructive methods is advantageous for cachaça producers and production chain. Thus, in this work commercial cachaça samples using near-infrared spectroscopy (NIRS) were classified based on geographical origin using one-class classification Data-Driven in Soft Independent Modelling of Class Analogy (DD-SIMCA) and One-Class Partial Least Squares (OCPLS) and predicted quality parameters of alcohol content and density based on different chemometric algorithms. A total of 150 sugarcane spirits samples were purchased from the Brazilian retail market being 100 from Brejo and 50 from other regions of Brazil. The one-class chemometric classification model was obtained with DD-SIMCA using the Savitzky-Golay derivative with first derivative, 9-point window and 1st degree polynomial as preprocessing algorithm and sensibility was 96.70 % and specificity 100 % in the spectral range 7,290-11,726 cm-1. Satisfactory results were obtained in the model constructs for density and the chemometric model, iSPA-PLS algorithm with baseline offset as preprocessing, obtained root mean square errors of prediction (RMSEP) of 0.0011 mg/L and Relative Error of Prediction (REP) of 0.12 %. The chemometric model for alcohol content prediction used the iSPA-PLS algorithm with Savitzky-Golay derivative with first derivative, 9-point window and 1st degree polynomial as algorithm as preprocessing obtaining RMSEP and REP of 0.69 and 1.81 % (v/v), respectively. Both models used the spectral range from 7,290-11,726 cm-1. The results reflected the potential of vibrational spectroscopy coupled with chemometrics to build reliable models for identifying the geographical origin of cachaça samples for predicting quality parameters in cachaça samples.