Patrícia Valderrama

Thermal edible oil evaluation by UV-Vis spectroscopy and chemometrics.

Edible oils such as colza, corn, sunflower, soybean and olive were analysed by UV-Vis spectroscopy and Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS). When vegetable oils were heated at high temperatures (frying), oxidation products were formed which were harmful to human health in addition to degrading the antioxidants present, and this study aimed to evaluate tocopherol (one antioxidant present in oils) and the behaviour of oxidation products in edible oils. The MCR-ALS results showed that the degradation started at 110°C and 85°C, respectively, for sunflower and colza oils, while tocopherol concentration decreased and oxidation products increased starting at 70°C in olive oil. In soybean and corn oils, tocopherol concentration started to decrease and oxidation products increased at 50°C. The results suggested that sunflower, colza and olive oils offered more resistance to increasing temperatures, while soybean and corn oils were less resistant.

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.

Simultaneous determination of aflatoxins B2 and G2 in peanuts using spectrofluorescence coupled with parallel factor analysis

In the present study a method for the simultaneous determination of aflatoxins B2 and G2 in peanuts has been developed. The method uses second order standard addition method and excitation-emission fluorescence data together with parallel factor analysis (PARAFAC). The aflatoxin analysis was based on extraction with methanol-water and carried out using immunoaffinity clean-up. The results of PARAFAC on a set of spiked and naturally contaminated peanuts indicated that the two aflatoxins could be successfully determined. The method was validated and analytical figures of merit were obtained for both analytes. The limits of detection (LOD) were 0.05 and 0.04 μg kg(-1) for aflatoxins B2 and G2, respectively. The limits of quantification (LOQ) were 0.16 and 0.12 μg kg(-1) for aflatoxins B2 and G2, respectively. Coupling of spectrofluorimetry with PARAFAC can be considered as an alternative method for quantification of aflatoxins in the presence of unknown interferences obtained through analysis of highly complex matrix of peanuts samples at a reduced cost per analysis.

Analysis of volatile compounds in Capsicum spp. by headspace solid-phase microextraction and GC × GC-TOFMS

A suitable method based on headspace solid-phase microextraction (HS-SPME), comprehensive two-dimensional gas chromatography, with a time-of-flight mass spectrometry detector (GC × GC-TOFMS) and a chemometric approach was used aimed at the investigation of the volatile fraction of Brazilian Capsicum peppers: malagueta (C. frutescens), dedo-de-moca (C. baccatum) and murupi (C. chinense). A total of 184 volatiles compounds were identified in the three pepper samples and 123 of these compounds were described in Brazilian peppers for the first time. In addition, during Brazilian chili pepper maturation, as the maturation time increases, it was noted that the majority of volatile compounds responsible for green odor notes disappeared being replaced by others with fruity notes. Chemometric analysis (PCA) was able to separate samples according to their constituents, where malagueta was characterized by branched esters, murupi by terpenes and dedo-de-moca by the presence of aldehydes and terpenes.

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.

Validation of the near infrared spectroscopy method for determining soil organic carbon by employing a proficiency assay for fertility laboratories

Current research indicates that near infrared (NIR) spectroscopy is the most promising alternative technique for the determination of soil organic carbon (SOC) in laboratories worldwide and that it may serve as a total or partial replacement for the traditional wet chemistry methods. In this research, NIR spectroscopy was developed and validated as a method for the determination of SOC aiming to promote the use of NIR technology in the soil laboratories of Brazil as a routine analysis method. To this end, multivariate calibration models were constructed from a large number of soil samples (1490 samples) that encompassed the variability of Brazilian soils. These models were validated by submitting the concentrations of soil organic matter (SOM) as determined by NIR spectroscopy to the proficiency assay for fertility laboratories, which is coordinated by the Brazilian Agricultural Research Corporation (Embrapa Soils). The proposed methodology using NIR spectroscopy received the excellence index of quality, which gives a certificate seal issued by the interlaboratory programme. Therefore, the robustness of the NIR method was proved in a conclusive way by a proficiency test programme dedicated to evaluating the reference method for SOM determination employed by over a hundred soil laboratories in Brazil.

Resolução multivariada de curvas com mínimos quadrados alternantes: descrição, funcionamento e aplicações

Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS) is a resolution method that has been efficiently applied in many different fields, such as process analysis, environmental data and, more recently, hyperspectral image analysis. When applied to second order data (or to three-way data) arrays, recovery of the underlying basis vectors in both measurement orders (i.e. signal and concentration orders) from the data matrix can be achieved without ambiguities if the trilinear model constraint is considered during the ALS optimization. This work summarizes different protocols of MCR-ALS application, presenting a case study: near-infrared image spectroscopy.

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.

Vitamin C Determination by Ultraviolet Spectroscopy and Multiproduct Calibration

In this work the vitamin C was determined in industrialized nectar juices through ultraviolet (UV) spectroscopy and multiproduct multivariate calibration, based on partial least squares (PLS) regression. Since samples with different flavors, sugar content (light or not) were together in the model construction, it can be considered as a multiproduct and, due to the heterogeneity of the samples, it was necessary to optimize the calibration and validation sets by outliers elimination. The model was developed and validated by the evaluation of the figures of merit such as: accuracy, sensitivity, analytical sensitivity, adjust, linearity, relative prediction deviation, limits of detection and quantification, indicating that the multiproduct model developed from UV spectroscopy and PLS regression can be used in the industrial routine analysis as an alternative to titration or other time and reagent consuming methods. Here, it was evidenced that the UV-PLS multiproduct model provides advantages as being free of sample preparation steps, is suitable to be updated in order to measure other parameters, does not generates residues and is feasible to be implemented for on-line monitoring. Furthermore, the application of multivariate calibration in multiproduct models is extremely attractive from the industrial point of view.

Degradação térmica de tocoferol e produtos de oxidação em diferentes classes de azeite de oliva utilizando espectroscopia UV-Vis e MCR-ALS

Extra virgin olive oil, virgin olive oil and mixed oil were analyzed by UV-Vis spectroscopy and Multivariate Curve Resolution with Alternating Least Squares (MCR-ALS). When the oils were heated at high temperatures (frying), oxidation products were formed which were harmful to human health and antioxidants present were degraded. The aim of this study was to evaluate tocopherol (an antioxidant present in oils) and the behaviour of oxidation products in different olive oil classes. MCR-ALS results showed that degradation started at 70 oC for extra virgin olive oil, while tocopherol concentration decreased and oxidation products increased starting at 40 oC in virgin olive oil. In mixed oil, commercialized as a mixture of 85% soybean oil and 15% olive oil, tocopherol concentration started to decrease and oxidation products increase at 30 oC. The results suggested that extra virgin olive oil offered more resistance to increasing temperatures, while virgin olive oil and mixed oil were less resistant.