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.

Storage Properties and Shelf-Life Prediction of Fresh-Cut Radishes Treated by Photodynamic Technology.

Fresh-cut radishes are susceptible to quality loss and microbial contamination during storage, resulting in a short shelf life. This study investigated the effects of photodynamic technology (PDT) on fresh-cut radishes stored at 4 °C for 10 d and developed appropriate models to predict the shelf life. Results showed that curcumin-mediated PDT maintained sensory acceptability, color, and firmness, decreased weight loss, and increased ascorbic acid and total phenolics of samples by inactivating polyphenol oxidase and peroxidase, resulting in improved antioxidant capacity and quality. The total bacteria count in samples was significantly (p < 0.05) reduced by 2.01 log CFU g-1 after PDT and their shelf life was extended by 6 d compared to the control. To accurately predict the shelf life, the kinetic models based on microbial growth were established, while weight loss, b* value, firmness, and ascorbic acid were selected as representative attributes for developing quality-based prediction models through correlation analysis. Modeling results showed prediction models based on ascorbic acid best fitted PDT-treated samples, while the modified Gompertz model based on bacteria growth was the best for control and samples treated by sodium hypochlorite. This study suggests that PDT is promising in extending the shelf life of fresh-cut radishes, and using critical indexes to establish the prediction model can provide a more reliable shelf-life estimation.

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.

New insights into the relationship between optical response and physicochemical properties in apple flesh: Hyperspectral microscope imaging technology.

Hyperspectral microscope imaging (HMI) technique was employed to assess the changes in physicochemical parameters and microstructure of 'Golden Delicious' apples flesh during storage. Four regions of interest (ROIs), including whole-cell ROI, intercellular space ROI, cytoplasm ROI, and cell wall ROI were investigated to assess their relationships with physicochemical parameters. Different ROIs presented similar vibrational profiles, but with slight differences in spectral intensity, especially in the range of 800-1000 nm. Spectral angle mapper (SAM) was applied to the HMI of apple tissues at different storage stages to clearly show the structural changes of parenchyma cells, while principal component analysis (PCA) could highlight the distribution of sugars, water and pigments in apple flesh at the cellular scale. Simultaneously with the degradation of acid-soluble pectin (ASP), middle lamella dissolution and increased intercellular space were observed using SEM and TEM. Single feature variables were used to construct linear models based on pearson correlation analysis, with R2 of 0.96 for moisture at 982 nm, 0.85 for water-soluble pectin (WSP) at 420 nm, 0.82 for L* at 946 nm, 0.77 for soluble solids content (SSC) at 484 nm, and 0.66 for firmness at 490 nm. This work demonstrated the great potential of HMI technology as a fast, accurate and efficient solution for assessing the quality of 'Golden Delicious' apples.

Assessment of optical properties and Monte-Carlo based simulation of light propagation in citrus infected by Penicillium italicum.

This original work investigated the optical properties and Monte-Carlo (MC) based simulation of light propagation in the flavedo of Nanfeng tangerine (NF) and Gannan navel orange (GN) infected by Penicillium italicum. The increase of absorption coefficient (µa) at around 482 nm and the decrease at around 675 nm were both observed in infected NF and GN during storage, indicating the accumulation of carotenoids and loss of chlorophyll. Particularly, the µa in NF varied more intensively than GN, but the limited differences of reduced scattering coefficient (µs') were detected while postharvest infection. Besides, MC simulation of light propagation indicated that the photon packets weight and penetration depth at 482 nm in NF were reduced more than in GN flavedo, while there were almost no changes at the relatively low absorption wavelength of 926 nm. The simulated absorption energy at 482 nm in NF and GN presented more changes than those at 675 nm during infection, thus could provide better detection of citrus diseases. Furthermore, PLS-DA models can discriminate healthy and infected citrus, with the accuracy of 95.24 % for NF and 98.67 % for GN, respectively. Consequently, these results can provide theoretical fundamentals to improve modelling prediction robustness and accuracy.

Comparing visible and near infrared 'point' spectroscopy and hyperspectral imaging techniques to visualize the variability of apple firmness.

In this work, visible and near-infrared 'point' (Vis-NIR) spectroscopy and hyperspectral imaging (Vis-NIR-HSI) techniques were applied on three different apple cultivars to compare their firmness prediction performances based on a large intra-variability of individual fruit, and develop rapid and simple models to visualize the variability of apple firmness on three apple cultivars. Apples with high degree of intra-variability can strongly affect the prediction model performances. The apple firmness prediction accuracy can be improved based on the large intra-variability samples with the coefficient variation (CV) values over 10%. The least squares-support vector machine (LS-SVM) models based on Vis-NIR-HSI spectra had better performances for firmness prediction than that of Vis-NIR spectroscopy, with the with the Rc2 over 0.84. Finally, The Vis-NIR-HSI technique combined with least squares-support vector machine (LS-SVM) models were successfully applied to visualize the spatial the variability of apple firmness.

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.