Oil addition increases the heat resistance of Clostridium sporogenes spores in braised sauce beef: Perspectives from spore surface characteristics and microstructure.

During thermal processing of braised sauce beef, the lipid content of circularly used sauce increased accordingly because of lipid migration from beef to sauce, which may impact the bacterial heat resistance in the products. This study aims to characterize the heat resistance of Clostridium sporogenes spores in braised sauce beef, and investigate the effects of oil on the spore surface characteristics and microstructure. The results indicated that the heat resistance of C. sporogenes spores in beef was significantly higher than that in sauce. Oil addition remarkably enhanced the spore heat resistance in sauce, with D95°C value three times more than that without oil added, and even higher than that in beef. The results of spore surface characteristics indicated that oil addition led to an increase of hydrophobicity and a decrease of zeta potential, which ultimately increased spore heat resistance. Microstructure analysis indicated that exosporium maintenance and cortex expansion induced by oil addition might contribute to the increase of spore heat resistance. This study has sufficiently verified the importance of oil content on the heat resistance of C. sporogenes spores, which should be taken into consideration when developing thermal processes for controlling the spores in food matrices.

Stilbenes-enriched peanut sprouts alleviated physical fatigue via regulating interactions of nutrients-microbiota-metabolites revealed by multi-omics analysis.

In this study, the antifatigue effect and mechanism of peanut sprouts were explored. BALB/c mice divided into three groups (control, dark and UV-C) were respectively supplemented with a normal diet, peanut sprouts (dark germination) added diet and stilbenes-enriched peanut sprouts (UV-C radiated germination) added diet. Results showed that swimming time and levels of blood glucose and antioxidant enzymes significantly increased, while contents of triglyceride and malondialdehyde notably decreased by peanut sprout supplementation. Besides, combined analysis of gut microbiota gene sequencing and targeted metabolomics of fecal metabolites revealed that peanut sprout supplementation up-regulated abundances and metabolic transformations of Catenibacillus, Odoribacter, Prevotellaceae-UCG-001 and Butyricicoccus while it down-regulated the abundance of Parabacteroides. Consequently, contents of sebacic acid, azelaic acid, suberic acid, heptanoic acid, pimelic acid, aminoadipic acid and mono-phenolics notably increased, which were markedly correlated with the antifatigue effect. Compared with the dark group, the swimming time, glutathione peroxidase activity, methylmalonylcarnitine content and abundances of Butyricicoccus, Catenibacillus and Lachnospiraceae NK4A136 were higher in the UV-C group, while opposite results were obtained for the levels of triglyceride, malondialdehyde, alpha-linolenic acid, gamma-linolenic acid, 10Z-heptadecenoic acid and palmitelaidic acid. Overall, peanut sprout supplementation could alleviate fatigue by modulating gut microbiota composition to promote fatty acid oxidation and lysine and stilbene catabolism to increase energy supply and regulate redox balance. UV-C-radiated peanut sprout supplementation could alleviate fatigue more effectively by up-regulating abundances of Butyricicoccus, Catenibacillus and Lachnospiraceae NK4A136 to promote long-chain fatty acid oxidation and catabolism of flavonoids and stilbenes efficiently.

A Novel Variable Selection Method Based on Ordered Predictors Selection and Successive Projections Algorithm for Predicting Gastrodin Content in Fresh Gastrodia elata Using Fourier Transform Near-Infrared Spectroscopy and Chemometrics.

Gastrodin is one of the most important biologically active components of Gastrodia elata, which has many health benefits as a dietary and health food supplement. However, gastrodin measurement traditionally relies on laboratory and sophisticated instruments. This research was aimed at developing a rapid and non-destructive method based on Fourier transform near infrared (FT-NIR) to predict gastrodin content in fresh Gastrodia elata. Auto-ordered predictors selection (autoOPS) and successive projections algorithm (SPA) were applied to select the most informative variables related to gastrodin content. Based on that, partial least squares regression (PLSR) and multiple linear regression (MLR) models were compared. The autoOPS-SPA-MLR model showed the best prediction performances, with the determination coefficient of prediction (Rp2), ratio performance deviation (RPD) and range error ratio (RER) values of 0.9712, 5.83 and 27.65, respectively. Consequently, these results indicated that FT-NIRS technique combined with chemometrics could be an efficient tool to rapidly quantify gastrodin in Gastrodia elata and thus facilitate quality control of Gastrodia elata.