The objectives of this study was to develop partial least square (PLS) models using NIR spectroscopy for the determination of SSC and firmness in intact low chilling 'Aurora-1' peach fruit, and verify the influence of maturity stage and harvest season on the models to be developed (robustness). FT-NIR spectra were obtained as log 1/R with fruit harvested in 2013 at 3 maturity stages and in 2014. The spectra were collected on the background and blush colour skin areas of the each fruit. Model performance was evaluated based on the values of root mean square error for prediction (RMSE P) and coefficient of determination (R P 2) obtained from validation fruit set (Kennard-Stone), and prediction fruit set (2014). PCA could not group the fruit based on blush and background skin colour, maturity stages, and harvest season. The model constructed using the external validation method obtained a RMSE VE of 1.08 % with 11 latent variables (LV S) and a R VE 2 of 0.59. The prediction set, independent data, resulting in a less accurate model (RMSE P 1.04 %, R p 2 0.45 and 11 LV S). The same trend happened for determining firmness with the external validation resulting in better model with RMSE VE 9.51 N and R VE 2 of 0.40 and the prediction set with RMSE P of 13.2 N, R P 2 0.40 with 7 LV S. The NIR spectroscopy showed to be a potential analytical method to determine SSC and firmness of intact low chilling 'Aurora 1' cultivar. However, it is necessary to optimize the models in other to reduce the prediction errors. 2015 Elsevier B.V. All rights reserved.

Lipases are well‐known biocatalysts used in several industrial processes/applications. Thus, as with other enzymes, changes in their surrounding environment and/or their thermodynamic parameters can induce structural changes that can increase, decrease, or even inhibit their catalytic activity. The use of ionic compounds as solvents or additives is a common approach for adjusting reaction conditions and, consequently, for controlling the biocatalytic activity of enzymes. Herein, to elucidate the effects of ionic compounds on the structure of lipase, the stability and enzymatic activity of lipase from Aspergillus niger in aqueous solutions (at 0.05, 0.10, 0.50, and 1.00 M) of six cholinium‐based ionic liquids (cholinium chloride [Ch]Cl; cholinium acetate ([Ch][Ac]); cholinium propanoate ([Ch][Prop]); cholinium butanoate ([Ch][But]); cholinium pentanoate ([Ch][Pent]); and cholinium hexanoate ([Ch][Hex])) were evaluated over 24 hr. The enzymatic activity of lipase was maintained or enhanced in the lower concentrations of all the [Ch]+‐ILs (below 0.1 M). [Ch][Ac] maintained the biocatalytic behavior of lipase, independent of the IL concentration and incubation time. However, above 0.1 M, [Ch][Pent] and [Ch][Hex] caused complete inhibition of the catalytic activity of the enzyme, demonstrating that the increase in the anionic alkyl chain length strongly affected the conformation of the lipase. The hydrophobicity and concentration of the [Ch]+‐ILs play an important role in the enzyme activity, and these parameters can be controlled by adjusting the anionic alkyl chain length. The inhibitory effects of [Ch][Pent] and [Ch][Hex] may be of great interest to the pharmaceutical industry to induce pharmacological inhibition of gastric and pancreatic lipases.

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Background:Ganoderma lucidum (Leyss. Ex. Fr) Karst is a basidiomycete mushroom that has been used for many years as a food supplement and medicine. In Brazil, National Health Surveillance Agency (ANVISA) classified Ganoderma lucidum as a nutraceutical product. The objective of the present work was to observe the effects of an extract from Ganoderma lucidum in rats treated with streptozotocin, and an agent that induces diabetes. Method: Male Wistar rats were obtained from the animal lodging facilities of both University Nove de Julho (UNINOVE) and Lusiada Universitary Center (UNILUS) with approval from the Ethics Committee for Animal Research. Animals were separated into groups: (1) C: Normoglycemic control water; (2) CE: Normoglycemic control group that received hydroethanolic extract (GWA); (3) DM1 + GWA: Diabetic group that received extract GWA; and (4) DM1: Diabetic group that received water. The treatment was evaluated over a 30-day period. Food and water were weighted, and blood plasma biochemical analysis performed. Results: G. lucidum extract contained beta-glucan, proteins and phenols. Biochemical analysis indicated a decrease of plasma glycemic and lipid levels in DM rats induced with streptozotocin and treated with GWA extract. Histopathological analysis from pancreas of GWA-treated DM animals showed preservation of up to 50% of pancreatic islet total area when compared to the DM control group. In plasma, Kyn was present in diabetic rats, while in treated diabetic rats more Trp was detected. Conclusion: Evaluation from G. lucidum extract in STZ-hyperglycemic rats indicated that the extract possesses hypoglycemic and hypolipidemic activities. Support: Proj. CNPq 474681/201.

This study shows the use of time-domain (TD)-NMR transverse relaxation (T2) data and chemometrics in the nondestructive determination of fat content for powdered food samples such as commercial dried milk products. Most proposed NMR spectroscopy methods for measuring fat content correlate free induction decay or echo intensities with the sample's mass. The need for the sample's mass limits the analytical frequency of NMR determination, because weighing the samples is an additional step in this procedure. Therefore, the method proposed here is based on a multivariate model of T2 decay, measured with Carr-Purcell-Meiboom-Gill pulse sequence and reference values of fat content. The TD-NMR spectroscopy method shows high correlation (r = 0.95) with the lipid content, determined by the standard extraction method of Bligh and Dyer. For comparison, fat content determination was also performed using a multivariate model with near-IR (NIR) spectroscopy, which is also a nondestructive method. The advantages of the proposed TD-NMR method are that it (1) minimizes toxic residue generation, (2) performs measurements with high analytical frequency (a few seconds per analysis), and (3) does not require sample preparation (such as pelleting, needed for NIR spectroscopy analyses) or weighing the samples.

18Aguardente is a typical Brazilian spirit produced by the distillation of sugarcane most. The 19 valorisation of this spirit can be attributed to its notoriety, related to the production origin which can 20 influence its quality. Therefore, the objective of this study was to use NIR spectroscopy coupled with 21 discriminant analysis as a non-destructive method to attribute authenticity of aguardentes produced 22 in two geographic regions and to predict the ethanol content. Some chemometric methods were used 23 to discriminate sugarcane aguardente, namely partial least squares-linear discriminant analysis 24 (PLS-LDA), principal component analysis-linear discriminant analysis (PCA−LDA), and variable 2 selection techniques such as successive projection algorithm (SPA−LDA) and genetic algorithm 26 (GA−LDA). NIR spectra were collected using a FT−NIR spectrometer (4,000-10,000 cm -1 ) at a 27 spectral resolution of 16 cm -1 , 8 cm -1 interval, and 64 scans. PCA results were not effective to classify 28 the aguardente samples, but with PLS−DA, PCA−LDA, SPA−LDA, GA−LDA and LDA it was 29 possible to get 87.2% prediction accuracy. Better results were obtained using PLS−DA on raw 30 spectra and GA-LDA using only six wavelengths (namely, 1,025 nm, 1,181 nm, 1,596 nm, 1,610 nm, 31 1,653 nm, 2,125 nm) which gave relatively good accuracy rate (up to 87.2 %). NIR spectroscopy and 32 chemometrics can be used as a non-destructive method to attribute authenticity, and PLSR combined 33 with NIR was a good non-destructive method to predict ethanol content in sugarcane aguardente. 34 35 while the Minas Gerais State is the main alembic producer, both representing 25% of the Brazilian 46 market 3 . In the alembic production of sugarcane spirit the product is divided into three fractions: 47 head, heart, and tail, while in column distillation there is no such separation into fractions and that 48 explains to a range extent the chemical differences produced by the two processes 4 . 49

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A compilation of papers published between 2014 and 2018 was evaluated. Many papers related to multivariate calibration and classification have been reported, as well as, design of experiments applications and artificial intelligence methods. Some applications were highlighted, as medical and pharmaceutical, food analysis, fuels, biological and forensic for the chemometric techniques on this review. Most studies are related to developing methods for practical solutions in industry or routine analysis. A promising scenario is shown considering the number of published papers: a total of 832 for this period using the keywords, multivariate classification, multivariate calibration, analysis, chemometrics, prediction, analytical chemistry, artificial neural networks (ANN), design of experiments (DoE) and factorial design. An useful overview for Analytical Chemistry researchers´ combined with Chemometrics is presented in this review.