Research on Rapid and Low-Cost Spectral Device for the Estimation of the Quality Attributes of Tea Tree Leaves.

Tea polyphenols, amino acids, soluble sugars, and other ingredients in fresh tea leaves are the key parameters of tea quality. In this research, a tea leaf ingredient estimation sensor was developed based on a multi-channel spectral sensor. The experiment showed that the device could effectively acquire 700-1000 nm spectral data of tea tree leaves and could display the ingredients of leaf samples in real time through the visual interactive interface. The spectral data of Fuding white tea tree leaves acquired by the detection device were used to build an ingredient content prediction model based on the ridge regression model and random forest algorithm. As a result, the prediction model based on the random forest algorithm with better prediction performance was loaded into the ingredient detection device. Verification experiment showed that the root mean square error (RMSE) and determination coefficient (R2) in the prediction were, respectively, as follows: moisture content (1.61 and 0.35), free amino acid content (0.16 and 0.79), tea polyphenol content (1.35 and 0.28), sugar content (0.14 and 0.33), nitrogen content (1.15 and 0.91), and chlorophyll content (0.02 and 0.97). As a result, the device can predict some parameters with high accuracy (nitrogen, chlorophyll, free amino acid) but some of them with lower accuracy (moisture, polyphenol, sugar) based on the R2 values. The tea leaf ingredient estimation sensor could realize rapid non-destructive detection of key ingredients affecting tea quality, which is conducive to real-time monitoring of the current quality of tea leaves, evaluating the status during tea tree growth, and improving the quality of tea production. The application of this research will be helpful for the automatic management of tea plantations.

FumDSB Can Reduce the Toxic Effects of Fumonisin B1 by Regulating Several Brain-Gut Peptides in Both the Hypothalamus and Jejunum of Growing Pigs.

Fumonisin B1 (FB1) is the most common food-borne mycotoxin produced by the Fusarium species, posing a potential threat to human and animal health. Pigs are more sensitive to FB1 ingested from feed compared to other farmed livestock. Enzymatic degradation is an ideal detoxification method that has attracted much attention. This study aimed to explore the functional characteristics of the carboxylesterase FumDSB in growing pigs from the perspective of brain-gut regulation. A total of 24 growing pigs were divided into three groups. The control group was fed a basal diet, the FB1 group was supplemented with FB1 at 5 mg/kg feed, and the FumDSB group received added FumDSB based on the diet of the FB1 group. After 35 days of animal trials, samples from the hypothalamus and jejunum were analyzed through HE staining, qRT-PCR and immunohistochemistry. The results demonstrated that the ingestion of FB1 can reduce the feed intake and weight gain of growing pigs, indicating that several appetite-related brain-gut peptides (including NPY, PYY, ghrelin and obestatin, etc.) play important roles in the anorexia response induced by FB1. After adding FumDSB as detoxifying enzymes, however, the anorexia effects of FB1 were alleviated, and the expression and distribution of the corresponding brain-gut peptides exhibited a certain degree of regulation. In conclusion, the addition of FumDSB can reduce the anorexia effects of FB1 by regulating several brain-gut peptides in both the hypothalamus and the jejunum of growing pigs.