Assessment of metabolic, mineral, and cytotoxic profile in pineapple leaves of different commercial varieties: A new eco-friendly and inexpensive source of bioactive compounds.

Pineapple is among the most produced and consumed fruits worldwide, and consequently, its agroindustrial production/processing generates high amounts of agricultural waste, which are routinely discarded. Thus, it is crucial to seek alternatives to reuse this agricultural waste that are in high availability. Therefore, this work aims to evaluate the chemical composition of a specific residue (leaves) of seven commercial varieties of pineapples, to attribute high added value uses, and to evaluate its potential as a source of secondary metabolites and minerals. Thereby, twenty-eight metabolites were annotated by UPLC-QTOF-MSE, including amino acids, organic acids, and phenolic compounds. The following minerals were quantitatively assessed by ICP-OES: Zn (5.30-19.77 mg kg-1), Cr, Cd, Mn (50.80-113.98 mg kg-1), Cu (1.05-4.01 mg kg-1), P (1030.77-6163.63 mg kg-1) and Fe (9.06-70.17 mg kg-1). In addition, Cr and Cd (toxic materials) present concentration levels below the limit of quantification of the analytical method (LOQCr and LOQCd = 0.02 mg kg-1) for all samples. The multivariate analysis was conceived from the chemical profile, through the tools of PCA (principal component analysis) and HCA (hierarchical cluster analysis). The results show that pineapple leaves have similarities and differences concerning their chemical composition. In addition, the cytotoxicity assays of the extracts against tumor and non-tumor strains shows that the extracts were non-toxic. This fact can corroborate and enhance the prospection of new uses and applications of agroindustrial co-products from pineapple, enabling the evaluation and use in different types of industries, such as pharmacological, cosmetic, and food, in addition to the possibility of being a potential source of bioactive compounds.

One-class classification of special agroforestry Brazilian coffee using NIR spectrometry and chemometric tools.

The near-infrared spectrometry combined with the one-class classification method was applied as quality control of the agroforestry-grown specialty coffee. A total of 34 samples were analyzed in this study. Spectral data were obtained using a NIR portable and different pre-treatment strategies for baseline correction were evaluated. Unsupervised pattern recognition (PCA and HCA) techniques were performed. The construction of the classification model was carried out using the dd-SIMCA algorithm with 19 samples acquired directly from producers that are recognized for the best quality control of the specialty type coffee. In order to test the model, 15 samples of non-specialty type, obtained in local markets, were evaluated. The classification model with the highest correct classification rate (CCR) scored 100% and 87% in the validation and test groups, respectively. The results demonstrated that the application of this strategy was successful in verifying the authenticity of specialty type agroforestry-grown coffee samples.

Detection of adulteration in hydrated ethyl alcohol fuel using infrared spectroscopy and supervised pattern recognition methods.

This paper proposes an analytical method to detect adulteration of hydrated ethyl alcohol fuel based on near infrared (NIR) and middle infrared (MIR) spectroscopies associated with supervised pattern recognition methods. For this purpose, linear discriminant analysis (LDA) was employed to build a classification model on the basis of a reduced subset of wavenumbers. For variable selection, three techniques are considered, namely the successive projection algorithm (SPA), the genetic algorithm (GA) and a stepwise formulation (SW). For comparison, models based on partial least squares discriminant analysis (PLS-DA) were also employed using full-spectrum. The method was validated in a case study involving the classification of 181 hydrated ethyl alcohol fuel samples, which were divided into three different classes: (1) authentic samples; (2) samples adulterated with water and (3) samples contaminated with methanol. LDA/GA and PLS-DA models were found to be the best methods for classifying the spectral data obtained in NIR region, which achieved a correct prediction rate of 100% in the test set, while the LDA/SPA and LDA/SW were correctly classified at 84.4% and 97.8%, respectively. For MIR data, all models (PLS-DA and LDA coupled with the SW, SPA and GA) employed in this study correctly classified all samples in the test set.

Classification of biodiesel using NIR spectrometry and multivariate techniques.

This article describes the classification of biodiesel samples using NIR spectroscopy and chemometric techniques. A total of 108 spectra of biodiesel samples were taken (being three samples each of four types of oil, cottonseed, sunflower, soybean and canola), from nine manufacturers. The measurements for each of the three samples were in the spectral region between 12,500 and 4000 cm(-1). The data were preprocessed by selecting a spectral range of 5000-4500 cm(-1), and then a Savitzky-Golay second-order polynomial was used with 21 data points to obtain second derivative spectra. Characterization of the biodiesel was done using chemometric models based on hierarchical cluster analysis (HCA), principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA) elaborated for each group of biodiesel samples (cotton, sunflower, soybean and canola). For the HCA and PCA, the formation of clusters for each group of biodiesel was observed, and SIMCA models were built using 18 spectral measurements for each type of biodiesel (training set), and nine spectral measurements to construct a classification set (except for the canola oil which used eight spectra). The SIMCA classifications obtained 100% accurate identifications. Using this strategy, it was feasible to classify biodiesel quickly and nondestructively without the need for various analytical determinations.