Comparison of hyperspectral imaging and spectrometers for prediction of cheeses composition.

Artisanal cheeses are part of the heritage and identity of different countries or regions. In this work, we investigated the spectral variability of a wide range of traditional Brazilian cheeses and compared the performance of different spectrometers to discriminate cheese types and predict compositional parameters. Spectra in the visible (vis) and near infrared (NIR) region were collected, using imaging (vis/NIR-HSI and NIR-HSI) and conventional (NIRS) spectrometers, and it was determined the chemical composition of seven types of cheeses produced in Brazil. Principal component analysis (PCA) showed that spectral variability in the vis/NIR spectrum is related to differences in color (yellowness index) and fat content, while in NIR there is a greater influence of productive steps and fat content. Partial least squares discriminant analysis (PLSDA) models based on spectral information showed greater accuracy than the model based on chemical composition to discriminate types of traditional Brazilian cheeses. Partial least squares (PLS) regression models based on vis/NIR-HSI, NIRS, NIR-HSI data and HSI spectroscopic data fusion (vis/NIR + NIR) demonstrated excellent performance to predict moisture content (RPD > 2.5), good ability to predict fat content (2.0 < RPD < 2.5) and can be used to discriminate between high and low protein values (∼1.5 < RPD < 2.0). The results obtained for imaging and conventional equipment are comparable and sufficiently accurate, so that both can be adapted to predict the chemical composition of the Brazilian traditional cheeses used in this study according to the needs of the industry.

Thiamine, cysteine and xylose added to the Maillard reaction of goat protein hydrolysate potentiates the formation of meat flavoring compounds.

A protein hydrolysate of goat viscera added with xylose, cysteine, and thiamine under different pH was used to prepare a meat flavoring. Goat viscera hydrolysate and flavoring were subjected to analysis of physicochemical characteristics, amino acid profile, sugars, fatty acids, and volatile profile. Meat aroma characteristics were initiated in the hydrolysate, in which Strecker's pyrazines and aldehydes were identified, which also had fatty acids and amino acids available for the formation of 96 volatile compounds in the flavorings via lipid manipulation, Maillard occurrence, Strecker manipulation and interactions among these means. Maillard reaction products with intense meat aroma, such as 2-methyl-3-furanthiol, 2-furfurylthiol and, bis(2-methyl-3-furyl) disulfide were isolated only in the flavoring at pH 4. In contrast, the flavoring at pH 6 showed a higher concentration than all the other compounds, providing a lower meat characteristic, but an intense sweet, fatty and goat aroma.

Juá (Ziziphus joazeiro Mart.) mucilage and juá by-product phenolic extract improve quality parameters and retain bioactive compounds in fresh-cut pineapple during storage.

This study evaluated the effects of coatings with juá mucilage (JM), juá mucilage incorporated with phenolic extract from juá (JMPE), and juá mucilage with gum arabic (JM-GA) on quality parameters, bioactive compounds and antioxidant activity of fresh-cut pineapple during 9 days at 5 ± 1 °C. JM and JMPE coatings were effective in reducing enzymatic activity, consequently reducing changes in fruit color compared to uncoated fresh-cut pineapple (C). JM coating was more efficient in reducing metabolic activity with the lowest conversion of sugars into glucose, fructose and organic acids. In addition, JMPE coating showed the highest retention of phenolics. On the other hand, JM had a minor reduction in antioxidant activity in FRAP (39.55 %) and ORAC (33.46 %) assay compared to other coatings (p ≤ 0.05). Findings indicate that JM and JMPE are promising for application to preserve the overall quality and to extend the shelf life of fresh-cut pineapple.