This study determined antioxidant activity in terms of the 1,1-Diphenyl-2-picrylhydrazyl (DPPH) scavenging ability and total phenolic content of black tea under different infusion and storage conditions. High performance liquid chromatography analysis identified caffeine, (−)-epigallocatechin, (−)-epicatechin-3-gallate, (−)-epigallocatechin-3-gallate and (−)-gallocatechin-3-gallate in the tea sample. The water–tea leaves weight ratio did not affect the DPPH scavenging ability. However, infusion temperature affected the DPPH scavenging activity and the total phenolic content. In the present study, the 50% inhibitory concentrations (IC₅₀) for DPPH of black tea infused at 60 to 100 °C ranged from 100.0 ± 13.7 to 28.4 ± 4.8 μg/mL. The total phenolic content of black tea steeped at 60 to 100 °C ranged from 50.4 ± 5.2 to 178.6 ± 16.4 mg gallic acid equivalent/g dry leaf. Black tea exhibited increased antioxidant activity when the infusion temperature was increased. Regarding short-term storage, the DPPH scavenging ability and total phenolic content of black tea did not significantly change within 15 days. This result was consistent for storage temperatures of 4, 9, and 25 °C.

This study investigated the polyphenol content, antioxidant activity, and inhibition ability of mushroom tyrosinase and melanogenesis of Dendrobium tosaense (DT) extract. Ground DT was extracted using deionized water (W) or 50% ethanol (50E) at room temperature (RT) or 50 °C (50T) for 20 min. The 50T + 50E extract exhibited the highest total phenol content 47.0 ± 4.0 mg gallic acid equivalent/g DT extract, the highest level of 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid) free-radical scavenging 66.0 ± 3.0 mg Trolox equivalent/g DT extract, and the highest reducing power 12.00 ± 0.50 mg vitamin C equivalent/g DT extract. The RT + W extract had the highest total flavonoid content 110.0 ± 3.0 mg quercetin equivalent/g DT extract. The RT + 50E extract had the lowest half maximal inhibitory concentration 1.30 ± 0.00 mg/mL for 2,2-diphenyl-1-picrylhydrazyl free-radical scavenging, and the lowest half maximal inhibitory concentration 6.40 ± 0.30 mg/mL for mushroom tyrosinase inhibition activity. DT extracts, especially RT + W and 50T + W, exhibited potent inhibitory effects on melanogenesis of B16/F10 cells. These results demonstrated the application potential of DT extract for skincare.

This study aimed to establish a machine learning (ML)-based rice blast predicting model to decrease the appreciable losses based on short-term environment data. The average, highest and lowest air temperature, average relative humidity, soil temperature and solar energy were selected for model development. The developed multilayer perceptron (MLP), support vector machine (SVM), Elman recurrent neural network (Elman RNN) and probabilistic neural network (PNN) were evaluated by F-measures. Finally, a sensitivity analysis (SA) was conducted for the factor importance assessment. The study result shows that the PNN performed best with the F-measure (β = 2) of 96.8%. The SA was conducted in the PNN model resulting in the main effect period is 10 days before the rice blast happened. The key factors found are minimum air temperature, followed by solar energy and equaled sensitivity of average relative humidity, maximum air temperature and soil temperature. The temperature phase lag in air and soil may cause a lower dew point and suitable for rice blast pathogens growth. Through this study’s results, rice blast warnings can be issued 10 days in advance, increasing the response time for farmers preparing related preventive measures, further reducing the losses caused by rice blast.

Sericin, a textile waste, can be used for antioxidant and skin-whitening purposes. The hydrothermal method of extracting sericin is more eco-friendly than are chemical and enzymatic methods. In this study, silk cocoons were cut into pieces and then subjected to hydrothermal extraction at three temperatures (160, 200, and 220 °C) to obtain sericin extracts (Sericin160, Sericin200, and Sericin220, respectively). Antioxidant activity and tyrosinase inhibition were measured to determine the extracts’ effectiveness. Sericin220 was the strongest antioxidant, with total phenol content, total flavonoid content, and ferric reducing power of 62.19 ± 0.04 mg gallic acid equivalents/g dry weight, 0.07 ± 0.01 mg quercetin equivalent/g dry weight, and 181.49 ± 0.024 mg vitamin C equivalent/g dry weight, respectively. The half-maximal inhibitory concentrations for DPPH and ABTS free-radical scavenging ability were 6.41 ± 0.05 and 0.79 ± 0.37 mg/mL, respectively. Sericin220 also exhibited the highest tyrosinase inhibition activity (70.82 ± 4.1 mg vitamin C equivalent/g), indicating its whitening potential.

Rice (Oryza sativa L.) growth prediction is key for precise rice production. However, the traditional linear rice growth forecasting model is ineffective under rapidly changing climate conditions. Here we show that growth rate (Gr) can be well-predicted by artificial intelligence (AI)-based artificial neural networks (ANN) and gene-expression programming (GEP), with accumulated air temperatures based on growth degree day (GDD). In total, 10,246 Gr from 95 cultivations were obtained with three cultivars, TK9, TNG71, and KH147, in Central and Southern Taiwan. The model performance was evaluated by the Pearson correlation coefficient (r), root mean square error (RMSE), and relative RMSE (r-RMSE) in the whole growth period (lifecycle), as well as the average and specific key stages (transplanting, 50% initial tillering, panicle initiation, 50% heading, and physiological maturity). The results in lifecycle Gr modeling showed that ANN and GEP models had comparable r (0.9893), but the GEP model had the lowest RMSE (3.83 days) and r-RMSE (7.24%). In stage average and specific key stages, each model has its own best-fit growth period. Overall, GEP model is recommended for rice growth prediction considering the model performance, applicability, and routine farming work. This study may lead to smart rice production due to the enhanced capacity to predict rice growth in the field.

In this study, the effects of different fruit parts and extraction conditions on the antioxidant properties of jujube (Ziziphus jujuba Mill.) fruit were investigated. Five in vitro antioxidant models and statistical analyses were performed. The results revealed that jujube peel with pulp (peel pulp) exhibited better antioxidant capacity than did seeds. Overall, jujube peel pulp extracted using 50% ethanol at 60 °C exhibited the best antioxidant capacity in terms of 2,2-diphenyl-1-picrylhydrazyl radical scavenging activity (0.3 ± 0 mg/mL), 2,2-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) radical scavenging activity (0.5 ± 0 mg/mL), total phenolic content (38.3 ± 0.4 mg gallic acid equivalent per gram dry weight), total flavonoid content (43.8 ± 0.2 mg quercetin equivalent per gram dry weight), and reducing power (41.9 ± 2.2 mg ascorbic acid equivalent per gram dry weight). The results indicated that jujube peel pulp is a more potential natural antioxidant than seeds.