In this study, gas chromatography (GC) and ultra-high performance liquid chromatography-quadrupole-time-offlight tandem mass spectrometry (UPLC-Q-TOF-MS) were used to explore the effects of feeding Schizochytrium spp. on the fatty acid and lipid composition of goat milk, and the differences in fatty acid and lipid composition between goat milk and commercially available pure bovine milk and docosahexaenoic acid (DHA)-enriched bovine milk were analyzed. The results showed that the lipid content was significantly higher in goat milk than in bovine milk (P < 0.05), the major fatty acids in both goat and bovine milk were myristic acid, palmitic acid and oleic acid. The content of DHA in goat milk was significantly increased by feeding Schizochytrium spp.. Triglycerides were the major lipid components in goat and bovine milk, and the phospholipid content in goat milk was significantly higher than that in bovine milk. DHA was mainly present in an glyceride esterified form in dairy milk lipids, and goat milk contained more phospholipid (PL)-bound DHA than bovine milk. Feeding Schizochytrium spp. at 50 g/day provided the highest DHA enrichment in goat milk. In a nutshell, this study demonstrated that dietary DHA supplementation can improve the nutritional value of goat milk, thereby solving the problem of the single source of dietary DHA.

In this study, the flavor components of double-low fragrant rapeseed oils (cv. “Zhongyouza 19”) from nine different planting areas were qualitatively and quantitatively analyzed by headspace solid phase microextraction (HS-SPME) and gas chromatography-mass spectrometry coupled with olfactometry (GC-MS-O). The results showed that a total of 63 aroma-active compounds were identified using two columns (polar and non-polar) at a total concentration of 222.0 to 468.9 mg/kg. The total concentration of aroma compounds was highest in the Hunan sample and lowest in the Jiangxi sample. Out of the identified flavor substances, 38 had odor activity value (OAV) greater than 10. The results of partial least squares-discriminant analysis (PLS-DA) and sensory evaluation showed that 2-methylpyrazine, 2,3,5-trimethylpyrazine, and 2-methyl-3,5-diethylpyrazine were the main contributors to roasted aroma notes, 3-butenylisothiocyanate provided spicy notes, and 5-hexenenitrile and 4-methylthionitrile led to green notes. Based on variable important in the projection (VIP) values, nine key differential substances were selected, namely, methanthiol, 2-methylpyrazine, 2,3-diethyl-5-methylpyrazine, dimethyl disulfide, 5-methylthiopentonitrile, 5-hexenenitrile, (E,Z)-2,4-pentadienonitrile, hexanal and dimethyl trisulfide. They could be considered as potential flavor markers for the discrimination of double-low fragrant rapeseed oils from different planting areas. This research can provide a theoretical guide for the screening of raw materials for fragrant rapeseed oils.

In recent years, with rising concerns over food quality and safety, computer vision technology has gradually attracted attention and begun to be widely used in the field of food quality evaluation. Machine learning technologies such as artificial neural networks (ANN), convolutional neural networks (CNN), and support vector machines (SVM) allow automatic assessment and monitoring of food quality by training on large amounts of food images and related data. Particularly, with the development of deep learning, the computer is now able to more accurately recognize food features such as appearance, shape, and color, thereby allowing food classification, prediction and quality monitoring. In addition to its conventional application in food quality assessment, learning technologies also find application in more complex tasks such as defect detection, foreign object detection, and freshness assessment. These technologies not only improve the efficiency of food production and processing but also reduce errors caused by human factors, thereby ensuring food quality and safety. However, despite the significant progress in the application of learning technologies in food quality assessment, there are still challenges that need to be overcome. For instance, the high cost of acquiring and annotating food image datasets, as well as insufficient data quality and quantity, may affect the performance and generalization ability of models. Furthermore, the interpretability and transparency of models are important issues, especially when explaining or making decisions on food quality assessment results. Therefore, further research is needed to explore how to improve the quality and scale of datasets, optimize the robustness and interpretability of models, and develop more efficient and sustainable food quality assessment systems.