Influence of roasting pretreatment on high‐oleic rapeseed oil quality evaluated by analytical and sensory approaches

Qualitative and quantitative effects on the contents of minor components of cold‐pressed high‐oleic rapeseed oil (HORO) were evaluated in a function of different roasting temperatures (80, 100, 120 and 140 °C). Along with roasting temperature elevation, a significant increase in the content of total tocopherols up to 32% (mainly γ‐T homologue) and a slight increase of total sterols concentration (up to 5%) were observed, whereas no significant changes in the fatty acid composition occurred during seeds thermal pretreatment. Additionally, an increased degree of hydrolysis and lipid oxidation was reported; however, obtained results were within codex limits. The peroxide value of the oil ranged from 1.30 to 2.34 mEq O₂ kg^?1, while the acid value did not exceed 0.46 mg/KOH g. Principal component analysis was capable of differentiating between rapeseed oils acquired from seeds pretreated with different roasting temperatures.     

Automatic monitoring system for individual dairy cows based on a deep learning framework that provides identification via body parts and estimation of body condition score

Body condition score (BCS) is a common tool for indirectly estimating the mobilization of energy reserves in the fat and muscle of cattle that meets the requirements of animal welfare and precision livestock farming for the effective monitoring of individual animals. However, previous studies on automatic BCS systems have used manual scoring for data collection, and traditional image extraction methods have limited model performance accuracy. In addition, the radio frequency identification device system commonly used in ranching has the disadvantages of misreadings and damage to bovine bodies. Therefore, the aim of this research was to develop and validate an automatic system for identifying individuals and assessing BCS using a deep learning framework. This work developed a linear regression model of BCS using ultrasound backfat thickness to determine BCS for training sets and tested a system based on convolutional neural networks with 3 channels, including depth, gray, and phase congruency, to analyze the back images of 686 cows. After we performed an analysis of image model performance, online verification was used to evaluate the accuracy and precision of the system. The results showed that the selected linear regression model had a high coefficient of determination value (0.976), and the correlation coefficient between manual BCS and ultrasonic BCS was 0.94. Although the overall accuracy of the BCS estimations was high (0.45, 0.77, and 0.98 within 0, 0.25, and 0.5 unit, respectively), the validation for actual BCS ranging from 3.25 to 3.5 was weak (the F1 scores were only 0.6 and 0.57, respectively, within the 0.25-unit range). Overall, individual identification and BCS assessment performed well in the online measurement, with accuracies of 0.937 and 0.409, respectively. A system for individual identification and BCS assessment was developed, and a convolutional neural network using depth, gray, and phase congruency channels to interpret image features exhibited advantages for monitoring thin cows.     

Physicochemical properties of steamed bread fortified with ground linseed (Linum usitatissimum)

Chinese steamed bread (CSB) is a staple food among Asian populations. Linseed (Linum usitatissimum) is an emerging ‘healthy’ food ingredient rich in a range of bioactive components. Ground linseeds were incorporated in northern style CSB formulation up to 20%. Various physico-chemical, nutritional and sensory properties of the resulting CSB were studied. Linseed addition decreased the specific volume and spread ratio of CSB, while increasing the hardness and chewiness in a dose-dependent manner. Linseed addition up to 15% little affected the overall sensory acceptability of CSB. Nutritionally, linseed addition increased the in vitro antioxidant activities of CSB, while decreasing in vitro starch digestibility and expected glycaemic index. It may be concluded that linseed has the potential to enhance the nutritional quality of CSB without negatively affecting the eating quality.