Near-infrared (NIR) spectroscopy has emerged as a popular technique for assessing food quality due to its advantages over complex chemical analysis methods. However, the application of NIR spectroscopy for evaluating fish quality based on histamine content has not been extensively explored. This study investigates the use of NIR spectroscopy in combination with machine learning (ML) techniques to classify fish samples into two safety classes, Safe and Unsafe, based on their histamine content. A comprehensive NIR dataset comprising 11,360 spectra collected at eight distinct positions within the fish body was obtained from 284 fish samples of mackerel, tuna, and pompano species. ML experiments were conducted to classify fish samples based on whether their histamine content exceeded the permissible limit of 100 ppm. To address class imbalance and optimize ML models, various data pre-processing and feature extraction techniques as well as ML algorithms were explored. The results demonstrated that utilizing NIR data specifically obtained from the tail’s flesh, a specific location within the fish, yielded superior models for fish safety classification. A feature extraction method employing pre-processed NIR spectra and their second derivatives, combined with an optimized convolutional neural network architecture, outperformed traditional ML classifiers with an accuracy of approximately 93%.

Spices are an important component of both commercial and homemade foods. Many spices have been shown to possess an antioxidant effect. In our study, we tested the activity of different allspice extracts (ethanol, chloroform, diethylether, benzene and hexane) on the stability of rapeseed oil. The order of antioxidant effectiveness was as follows: ethanol extract > chloroform extract> diethylether extract > benzene extract > hexane extract. The ethanol extract exhibited a remarkable antioxidant effect.

Near-infrared (NIR) spectroscopy has become a popular technique for assessing food quality due to its advantages over complex chemical analysis methods. However, the application of NIR spectroscopy for evaluating fish quality based on urea content has not been extensively explored. This study investigates the use of NIR spectroscopy in combination with machine learning (ML) techniques to classify fish samples into two safety classes—Safe and Unsafe—based on their urea content. A comprehensive NIR dataset comprising 11,960 spectra collected from eight distinct positions within the fish body was obtained from 299 fish samples of mackerel, tuna, and pompano species. ML experiments were conducted to classify fish samples based on whether their urea content exceeded the permissible limit of 1000 ppm. To address class imbalance and optimize ML models, various data pre-processing and feature extraction techniques, as well as ML algorithms, were explored. The results demonstrated that utilizing NIR data specifically obtained from the outer skin of the stomach yielded superior models for fish safety classification. A feature extraction method employing pre-processed NIR spectra and their first derivatives, combined with an optimized convolutional neural network architecture, outperformed traditional ML classifiers, achieving an accuracy of up to 83.9%.

This investigation involved the addition of Trametes versicolor mycelium to Pleurotus ostreatus extract, which was subsequently fermented using Lactobacillus plantarum to improve the quality of the fermented beverage. The concentration of total dissolved solids (^oBrix) generally diminishes with prolonged fermentation time, whereas it increases with the addition of T. versicolor mycelium. The pH values markedly decreased after 12 h, thereafter stabilizing after 24 and 48 h of fermentation, oscillating between 3.04 and 3.32. The total acid content (TAC) escalated in direct correlation with the mycelium ratio and fermentation length, with TAC values spanning from 6.617 to 10.678 g/L at a 1.0% mycelium addition. The ABTS+ antioxidant capacity ascended in direct correlation with the quantity of T. versicolor mycelium incorporated, attaining values of 58.13%, 49.63%, and 48.44% after 12, 24, and 48 h in CT6, respectively. Nonetheless, this ability exhibited a progressive decline over the fermenting period. The addition of T. versicolor mycelium effectively stimulated the proliferation of L. plantarum WCFS1, achieving a high cell density in the treatment supplemented 0.6% T. versicolor mycelium (CT4) after 12, 24, and 48 h of fermentation. The capacity of inhibiting harmful bacteria was maximized with the addition of 1.0% mycelium, resulting in an inhibition zone width of 13.42 mm for Escherichia coli ATCC 85922 after 48 h and 16.65 mm for Staphylococcus aureus ATCC 25023 after 24 h of fermentation. The fermented beverage had the greatest sensory score with the addition of 0.6% mycelium after 24 h of fermentation. The findings revealed significant potential for the manufacture of natural-based functional beverage industry.

Most of industrial lipases are derived from microbial sources, following by a wide variety of plants. Among plant lipases, lipase from Carica papaya latex has been the focus of intense and growing research due to low cost, easy acceptance by consumers and its unique characteristics. This enzyme has been successfully applied for lipid modification and synthesis of some organic compounds. However, research for its molecular structure has been limited due to the difficulty to isolate the enzyme from the latex matrix. In this study, we suggested a modified approach using sodium lauroyl sarcosinate to solubilize the latex, then the protein was precipitated by ammonium sulphate. We also carried out the characterization of the lipase obtained from Carica papaya latex. The results showed that freeze-drying the fresh latex could improve significantly lipase activity of latex powder in comparison with sun-drying or oven-drying. The zwitterion sodium lauroyl sarcosinate could solubilize nearly 50% of the latex and the achieved supernatant exhibited great lipase activity. There was no need to use an organic solvent to delipidate the latex prior to solubilization with sodium lauroyl sarcosinate due to possible denaturation of enzymes. The proteins which were fractionally precipitated with 50 - 60%, 60 - 70% and 70 - 80% ammonium sulphate saturation showed lipolytic activity. The fraction from 50 - 60% saturation with the greatest mass was subjected to ion exchange chromatography, SDS electrophoresis and kinetic parameter determination. The results showed the presence of two proteins with molecular mass ranging from 35 kDa to 55 kDa and both presented lipase activity. The Km and Vmax of the lipase fraction from 50 - 60% saturation was 1.12 mM and 1.2 x 10-6 mM.min-1.mL-1 respectively. So, the freeze-drying of papaya latex could help to preserve its lipase activity and the usage of sodium lauroyl sarcosinate could improve the isolation of the lipase from the papaya latex and pave the way for research on the molecular structure of Carica papaya latex lipases.