This study aimed to use Raman spectroscopy to identify the producing areas of peanut oil and build a robust discriminant model to further screen out the characteristic spectra closely related to the origin. Raman spectra of 159 peanut oil samples from different provinces and different cities of the same province were collected. The obtained data were analyzed by stepwise linear discriminant analysis (SLDA), k-nearest neighbor analysis (k-NN), support vector machine (SVM) and multi-way analysis of variance. The results showed that the overall recognition rate of samples based on full spectra was higher than 90%. The producing origin, variety and their interaction influenced Raman spectra of peanut oil significantly, and 1 400–1 500 cm–1 and 1 600–1 700 cm–1 were selected as the characteristic spectra of origin and less affected by variety. The best classification model established by SLDA combined with characteristic spectra could rapidly and accurately identify peanut oil’s origin.
High-moisture extrusion technology should be considered one of the best choices for producing plant-based meat substitutes with the rich fibrous structure offered by real animal meat products. Unfortunately, the extrusion process has been seen as a “black box” with limited information about what occurs inside, causing serious obstacles in developing meat substitutes. This study designed a high-moisture extrusion process and developed 10 new plant-based meat substitutes comparable to the fibrous structure of real animal meat. The study used the Feature-Augmented Principal Component Analysis (FA-PCA) method to visualize and understand the whole extrusion process in three ways systematically and accurately. It established six sets of mathematical models of the high-moisture extrusion process based on 8 000 pieces of data, including five types of parameters. The FA-PCA method improved the R2 values significantly compared with the PCA method. The Way 3 was the best to predict product quality (Z), demonstrating that the gradually molecular conformational changes (Yn´) were critical in controlling the final quality of the plant-based meat substitutes. Moreover, the first visualization platform software for the high-moisture extrusion process has been established to clearly show the “black box” by combining the virtual simulation technology. Through the software, some practice work such as equipment installation, parameter adjustment, equipment disassembly, and data prediction can be easily achieved.
This study investigated the effects of grape seed extract (GSE) on fresh and cooked meat color and premature browning (PMB) in ground meat patties (85% beef and 15% pork back fat) packaged under high-oxygen modified atmospheres (HiOx-MAP). The GSE was added to patties at concentrations of 0, 0.10, 0.25, 0.50 and 0.75 g kg–1. This study evaluated the surface color, pH, lipid oxidation, and total viable counts (TVC) of raw patties, and the internal color and pH of patties cooked to a temperature of 66 or 71°C over 10-day storage at 4°C. Compared with the control (0 g kg–1 GSE), GSE improved the color stability (P<0.05) and significantly inhibited the lipid and myoglobin oxidation of raw patties from day 5 to 10, but GSE had no effect (P>0.05) on TVC. Patties containing 0.50 and 0.75 g kg–1 GSE cooked to 66°C exhibited greater (P<0.05) interior redness than the control and reduced the PMB of cooked patties in the late storage stage. These results suggested that 0.50 and 0.75 g kg–1 GSE can improve fresh meat color and minimize PMB of HiOx-MAP patties.
The metabolomics variations among rice, brown rice, wet germinated brown rice, and processed wet germinated brown rice
Germination and processing are always accompanied by significant changes in the metabolic compositions of rice. In this study, polished rice (rice), brown rice, wet germinated brown rice (WGBR), high temperature and pressure-treated WGBR (WGBR-HTP), and low temperature-treated WGBR (WGBR-T18) were enrolled. An untargeted metabolomics assay isolated 6 122 positive ions and 4 224 negative ions (multiple difference ≥1.2 or ≤0.8333, P<0.05, and VIP≥1) by liquid chromatography-mass spectrum. These identified ions were mainly classified into three categories, including the compounds with biological roles, lipids, and phytochemical compounds. In addition to WGBR-T18 vs. WGBR, massive differential positive and negative ions were revealed between rice of different forms. Flavonoids, fatty acids, carboxylic acids, and organoxygen compounds were the dominant differential metabolites. Based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, there 7 metabolic pathways (phenylalanine/tyrosine/tryptophan biosynthesis, histidine metabolism, betalain biosynthesis, C5-branched dibasic acid metabolism, purine metabolism, zeatin biosynthesis, and carbon metabolism) were determined between brown rice and rice. Germination changed the metabolic pathways of porphyrin and chlorophyll, pyrimidine, and purine metabolisms in brown rice. In addition, phosphonate and phosphinate metabolism, and arachidonic acid metabolism were differential metabolic pathways between WGBR-HTP and WGBR-T18. To sum up, there were obvious variations in metabolic compositions of rice, brown rice, WGBR, and WGBR-HTP. The changes of specific metabolites, such as flavonoids contributed to the anti-oxidant, anti-inflammatory, anti-cancer, and immunomodulatory effects of GBR. HTP may further improve the nutrition and storage of GBR through influencing specific metabolites, such as flavonoids and fatty acids.
Copyright © Journal of Integrative Agriculture
Sponsored by Chinese Academy of Agricultural Sciences (CAAS)
Co-sponsored by China Association of Agricultural Science Societies (CAASS)
Publishing Service by Elsevier B.V.
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