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This research examines the impact of different solvents on the chemical composition of oil extracted from Triadica sebifera (a.k.a. Sapium sebiferum) seed, a plant common in China and America. Eight solvents were used, including n-hexane, petroleum ether, diethyl ether, ethyl acetate, ethanol, "Folch" solution (chloroform/methanol, v: v = 2: 1), isopropanol, and acetone. Petroleum ether provided the highest oil yield at 34.3%, while n-hexane extracted oil with the highest unsaturated fatty acids at 89.47%. Ethanol-extracted oil contained the most bioactive substances, including tocopherols (642.7 mg/kg), sterols (1043.7 mg/kg), and polyphenols (55.4 mg/kg), also exhibiting strong antioxidant capacity. Regression analysis showed positive correlations between antioxidant capacity and bioactive compounds like polyphenols and certain sterols, while saturated fatty acids correlated negatively. Principal component analysis highlighted ethanol as a promising solvent. The findings suggest that Triadica sebifera seed oil, particularly when extracted using ethanol, offers valuable applications for integrating natural ingredients with health benefits into various products. • Petroleum ether yielded 34.3% oil, the highest among solvents. • N-hexane oil had 89.47% unsaturated fatty acids, the highest. • Ethanol-extracted oil had the most tocopherols (642.7 mg/kg). • Ethanol oil showed high levels of sterols and polyphenols. • Antioxidant capacity linked to polyphenols and sterols. [ABSTRACT FROM AUTHOR]

Soybean oil (SO), recognized for its nutrients and affordability, often oxidizes, leading to nutrient degradation. This study presents a methodology for tailor oil blends to meet specific requirements, using SO blended with corn oil (CO), sunflower oil (SFO), and flaxseed oil (FSO). We analyzed the physicochemical properties, nutrient composition, and antioxidant capacity of various blends. Pearson's correlation analysis was utilized to examine the interrelation between nutrient composition and antioxidant capacity. The principal component analysis identified suitable blend ratios based on the desired nutrient composition and antioxidant capacity. Blend F3 (60% SO, 15% CO, 16% SFO, 9% FSO) demonstrated superior qualities, including a higher α-tocopherol content (138.8 mg/kg), and enhanced antioxidant capacities (DPPH polar: 12 μmol TE/100g, ABTS: 67.3 μmol TE/100g, FRAP: 112 μmol TE/100g), alongside a lower acid value (0.15 mg/g) compared to other blends. We highlight the roles of squalene, γ-tocopherol, δ-tocopherol, and fatty acid composition in enhancing the antioxidant capacity of oil blends. Specifically, the blend labeled F3 emerged as a nutritionally superior and highly antioxidative option among soybean oil blends. This study provides a framework for tailoring oil blends to meet specific nutritional and antioxidative requirements, providing a more flexible and purpose-driven approach to oil blending. • Innovative oil blending boosts nutritional quality. • Enhanced antioxidant capacity through natural methods. • The synergy of α-tocopherol, squalene, and fatty acids in blends boosts health benefits and stability. [ABSTRACT FROM AUTHOR]

In this study, the effects of different pretreatment (microwave or roasting) and processing (cold pressing, hexane extraction, or aqueous enzymatic extraction) methods on the physicochemical properties, minor components, and antioxidant capacity of Camellia oleifera Abel. seed oil (COSO) were investigated. The results revealed that the fatty acid profiles of COSO were not affected by the different processing methods, while hexane extraction resulted in the highest lipid yield (41.30–44.42 g/100g), saponification value (195.23–197.33 mg/g), and polyphenol content (7.25–8.23 mg/kg). Hierarchical cluster analysis proved that the processing method had a more significant effect than the pretreatment method on the chemical composition of COSO. Principal component analysis of the antioxidant capacity revealed that the microwave–hexane extraction method resulted in the highest content of α-tocopherol (144.7 mg/kg) and polyphenols (8.17 mg/kg) in COSO, indicating a high antioxidant activity (OSI: 6.23 h; DPPH-oil: 237, DPPH-nonpolar: 150, DPPH-polar: 670, FRAP: 77.4, ABTS: 45.9 μmol TE/kg). Consequently, hexane extraction is a promising method for future COSO processing. • Effects of different processing methods on Camellia oleifera seed oil quality. • Microwave–hexane extraction method produced oils with high antioxidant activity. • Pretreatment method had a significant impact on oil nutritional quality. • Extraction method had substantial effects on oil yield and properties. [ABSTRACT FROM AUTHOR]

Camel milk powder possesses high nutritional and economic value. The adulteration of camel milk powder with other varieties seriously compromises consumer rights. In the practical application of hyperspectral analysis for camel milk powder detection, the sample categories used for testing often differ from those used to construct the model. As a learning approach adept at domain-shifted and few shot scenarios, meta-learning is employed to tackle this issue. In this study, we used camel milk powder adulterated with cow milk powder as training samples and adulterated with goat milk powder as test samples. In the detection of eleven adulteration levels, the detection accuracy for pure camel milk powder reached 98.92%. Notably, the detection accuracy for the less conspicuous 70% adulteration level achieved 77.69%. The comprehensive detection accuracy of meta-learning reached 84.4%, showcasing notable improvements compared to SVM, BP, and CNN, which saw increases of 24.67%, 28.16%, and 18.4%, respectively. The detailed analysis of feature vectors and contributions substantiates the reliability and stability of the meta-learning-based qualitative analysis. The introduction of meta-learning methods is poised to make significant contributions to rapid detection by relevant testing agencies and the protection of consumer rights. • A domain-shifted hyperspectral dataset was created for adulterated camel milk powder. • Under domain-shift, meta-learning achieved 84.4% detection performance. • The influence of different spectral bands on the model was elucidated. [ABSTRACT FROM AUTHOR]

This study investigated the effects of different reducing sugars (glucose, fructose, and maltose) on quality and flavor during the enzymatic preparation of fragrant rapeseed oils (FROs) through Maillard reaction. The FROs was determined using sensory and physiochemical analyses. The fructose group had a reasonable fatty acid composition, lower acid value (1.28 mg/g), peroxide value (0.12 g/100g), moisture (0.08%), and benzo(a)pyrene content (1.43 μg/kg); higher total tocopherol content (597.78 mg/kg), sterol content (790.00 mg/kg), and sensory evaluation score (3.81 score); a sweeter nutty/toasted flavor and a weaker spicy flavor. The flavor was attributed to the presence of furfural, a sweet-tasting compound, and the nitrile content, which was responsible for the spicy flavor, was lower than that of traditional FROs, resulting in weaker spiciness. Overall, the results suggest that fructose is the best reducing sugar for enzymatic preparation of FROs, providing key directives for the industrial production of FROs. • Fragrant rapeseed oils were prepared by enzymatic methods to produce flavor substances. • The substance that produces the sweet flavor was hypothesized and the possible pathways for its production were proposed. • The correlation between flavor and quality indicators was analyzed. [ABSTRACT FROM AUTHOR]

Processing tomato (Lycopersicon esculentum Mill.) is rich in vitamins and lycopene, which is favored by consumers. In this study, near-infrared hyperspectral imaging (HSI) technology (980–1660 nm) was used to detect the firmness, soluble solids, lycopene, and titratable acid content of processing tomatoes and to classify fruits at three maturity stages. Savitzky-Golay (SG) smoothing was used to reduce the noise of hyperspectral images. The average spectrum of the tomato fruit was extracted for model development. Random forest (RF), partial least squares (PLS), and recurrent neural network (RNN) were used to develop models for predicting the four quality attributes and identifying the maturity level. Results showed that the RNN model had a classification accuracy of 40% higher than RF and 17% higher than PLS. In the prediction of quality parameters, RNN models had the highest R2 value (>0.87), followed by PLS and RF models. Important wavelengths were identified by calculating its contribution values and were used to interpret the model. The results illustrated that near-infrared hyperspectral imaging technology combined with deep learning could effectively predict the quality and maturity of processing tomatoes. The work can provide a perspective on the application of HSI as a nondestructive testing approach for other agricultural products. • Use of hyperspectral imaging to predict multi-quality of processing tomatoes. • Use of hyperspectral imaging to classify maturity of processing tomatoes. • Comparison of recurrent neural networks with traditional machine learning methods. • Identification of important wavelengths in contributing to model performance. [ABSTRACT FROM AUTHOR]