This study was to evaluate the phenolic content and composition of Carthamus tinctorius L. seed extract (CSE) and to further assess its antioxidant and anti-adipogenic activities using various radical scavenging systems and 3T3-L1 cells. Our results show that the total phenolic and flavonoid contents of CSE were 126.0 ± 2.4 mg GAE/g and 62.2 ± 1.9 mg QE/g, respectively. The major phenolic compounds in CSE was (−)-epigallocatechin (109.62 mg/g), with a 4-hydroxy benzhydrazide derivative and gallocatechin present at 18.28 mg/g and 17.02 mg/g, respectively. CSE exhibited remarkable radical scavenging activities, FRAP (ferric reducing antioxidant power) and reducing power in a dose-dependent manner. Moreover, the oxygen radical absorbance capacity (ORAC) value of CSE (0.1 mg/mL) was 62.9 ± 4.7 μM TE (trolox equivalent)/g. During adipogenesis, CSE significantly inhibited fat accumulation in 3T3-L1 cells compared with control cells. Overall, these results indicate that CSE might be a valuable source of bioactive compounds that impart functional food and natural antioxidant properties.

Advances in machine learning and artificial intelligence have led to many promising solutions for challenging issues in agriculture. One of the remaining challenges is to develop practical applications, such as an automatic sorting system for after-ripening crops such as tomatoes, according to ripeness stages in the post-harvesting process. This paper proposes a novel method for detecting tomato ripeness by utilizing multiple streams of convolutional neural network (ConvNet) and their stochastic decision fusion (SDF) methodology. We have named the overall pipeline as SDF-ConvNets. The SDF-ConvNets can correctly detect the tomato ripeness by following consecutive phases: (1) an initial tomato ripeness detection for multi-view images based on the deep learning model, and (2) stochastic decision fusion of those initial results to obtain the final classification result. To train and validate the proposed method, we built a large-scale image dataset collected from a total of 2712 tomato samples according to five continuous ripeness stages. Five-fold cross-validation was used for a reliable evaluation of the performance of the proposed method. The experimental results indicate that the average accuracy for detecting the five ripeness stages of tomato samples reached 96%. In addition, we found that the proposed decision fusion phase contributed to the improvement of the accuracy of the tomato ripeness detection.