Turmeric (Curcuma longa L.) is a perennial tuberous plant from the genus Curcuma (Zingiberaceae) and has been widely used in foods for thousands of years. The present study examined the ethanol extract of turmeric for its chemical composition, antimicrobial activity, and free radical scavenging properties. UHPLC-MS/MS analysis tentatively identified eight compounds in the turmeric extract. Potential antimicrobial effects of 0.1, 1.0, and 10 mg turmeric equivalents (TE)/mL were evaluated in vitro against a variety of Gram-negative bacteria (i.e., Escherichia coli, Klebsiella pneumoniae, and Pseudomonas sp.) and Gram-positive bacteria (i.e., Enterococcus faecalis, Listeria innocua, and Staphylococcus aureus). Concentrations of 0.1 and 1.0 mg TE/mL inhibited the growth of S. aureus and significantly suppressed that of Pseudomonas sp., E. faecalis, and L. innocua. The growth of all strains, including E. coli, was inhibited by 10 mg TE/mL. Moreover, free radical scavenging capacities were determined using HO^●, ABTS^●+, and DPPH^● (HOSC, ABTS, and RDSC, respectively) radicals. The turmeric ethanol extract had a TPC value of 27.12 mg GAE/g, together with HOSC, RDSC, and ABTS values of 1524.59, 56.38, and 1.70 μmol TE/g, respectively. Our results suggest that turmeric extract has potential applications for use in functional foods to reduce microbial burdens and oxidative stress-related health problems.

Seven commercial Chinese chrysanthemum tea products were classified by computer vision combined with machine learning algorithms. Without the need of building any specific hardware, the image acquisition was achieved in two computer vision approaches. In the first approach, a series of multivariate classification models were built after morphological feature extraction of the image. The best prediction accuracies when classifying flowering stages and tea types were respectively 90% and 63%. In comparison, the deep neural network was applied directly on the raw image, yielded 96% and 89% correct identifications when classifying flowering stage and tea type, respectively. The model can be applied for rapid and automatic quality determination of teas and other related foods. The result indicated that computer vision, especially when combined with deep learning or other machine learning techniques can be a convenient and versatile method in the evaluation of food quality.

In this study, a new red sorghum variety (Ji Liang No. 1) was investigated for its triacylglycerol (TAG) and fatty acid profiles, carotenoid and tocopherol compositions, total phenolic, total flavonoid and phenolic acid contents, and antioxidant and anti‐inflammatory properties. A total of 17 TAGs were identified in the red sorghum oil. Linoleic and oleic acids were the primary fatty acids, contributing more than 80% of the total fatty acids. β‐Carotene was the primary carotenoid at a level of 26.14 μg/g. α‐, γ‐, and δ‐tocopherols were at levels of 0.19, 4.08, and 0.10 μg/g, respectively. Moreover, acetone–water (60:40, v/v) extract of the red sorghum exhibited the greatest total phenolic content of 2.77 mg GAE/g and total flavonoid content of 5.44 mg RE/g. The extract had scavenging capacities against DPPH, ABTS⁺, and peroxyl radicals and suppressed LPS stimulated IL‐1β, IL‐6, and COX‐2 mRNA expressions in a dose‐dependent manner. Ferulic, p‐coumaric, isoferulic, and p‐hydroxybenzoic acids were found in the red sorghum, with ferulic acid as the predominant phenolic acid and mostly in an insoluble bound form. These data indicated a potential utilization of the red sorghum in health‐promoting functional food or supplemental products.