Simultaneous Determination of Four Catechins in Black Tea via NIR Spectroscopy and Feature Wavelength Selection: A Novel Approach.

As a non-destructive, fast, and cost-effective technique, near-infrared (NIR) spectroscopy has been widely used to determine the content of bioactive components in tea. However, due to the similar chemical structures of various catechins in black tea, the NIR spectra of black tea severely overlap in certain bands, causing nonlinear relationships and reducing analytical accuracy. In addition, the number of NIR spectral wavelengths is much larger than that of the modeled samples, and the small-sample learning problem is rather typical. These issues make the use of NIRS to simultaneously determine black tea catechins challenging. To address the above problems, this study innovatively proposed a wavelength selection algorithm based on feature interval combination sensitivity segmentation (FIC-SS). This algorithm extracts wavelengths at both coarse-grained and fine-grained levels, achieving higher accuracy and stability in feature wavelength extraction. On this basis, the study built four simultaneous prediction models for catechins based on extreme learning machines (ELMs), utilizing their powerful nonlinear learning ability and simple model structure to achieve simultaneous and accurate prediction of catechins. The experimental results showed that for the full spectrum, the ELM model has better prediction performance than the partial least squares model for epicatechin (EC), epicatechin gallate (ECG), epigallocatechin (EGC), and epigallocatechin gallate (EGCG). For the feature wavelengths, our proposed FIC-SS-ELM model enjoys higher prediction performance than ELM models based on other wavelength selection algorithms; it can simultaneously and accurately predict the content of EC (Rp2 = 0.91, RMSEP = 0.019), ECG (Rp2 = 0.96, RMSEP = 0.11), EGC (Rp2 = 0.97, RMSEP = 0.15), and EGCG (Rp2 = 0.97, RMSEP = 0.35) in black tea. The results of this study provide a new method for the quantitative determination of the bioactive components of black tea.

Free school meals and cognitive ability: Evidence from China's student nutrition improvement plan.

The China Student Nutrition Improvement Plan (SNIP) covers 40.6 million students in the compulsory education stage, accounting for 42% of all students enrolled in rural compulsory education in 2021. This paper utilizes the county-by-county rollout of the SNIP and estimates the effect of this nutritional intervention on students' cognitive outcomes. We find that SNIP increases math test scores but has a statistically insignificant effect on verbal achievement. The effect is greater for middle school students and children from disadvantaged families. The SNIP affects the cognitive performance of students by improving their health status, increasing school attendance, fostering good study habits, raising educational expectations, and improving the human capital of peers.

Individual and combined effects of arbuscular mycorrhizal fungi and phytohormones on the growth and physiobiochemical characteristics of tea cutting seedlings.

Both arbuscular mycorrhizal fungi (AMF) and phytohormones collectively regulate plant growth and root development, but their individual and combined effects on tea [Camellia sinensis (L.) O. Kuntze] cutting seedings remain unclear. This study examined the individual and combined effects of two species of AMF (Rhizophagus intraradices, RI and Funneliformis mosseae, FM) and two types of palnt hormones (strigolactones, SLs; polyamines, PAs) on tea cutting seedings, by evaluating the growth and physiobiochemical characteristics of plants treated with the AMFs and/or hormones. The results showed that inoculation with either AMF individually or hormones treatment alone could significantly enhanced mycorrhizal colonization, growth target and physiobiochemical characteristics of tea cutting seedlings. Interestingly, the addition of a combination of AMFs and hormones showed superior effects, while SL and RI exhibited the most improvements to the colonization rate, plant growth, root-morphological traits, root DHA activity, photosynthesis, chlorophyll content, soluble sugar content in leaves, and the activities of antioxidant enzymes (SOD, POD, and CAT), compared to other treatment combinations (SL + FM, PA + RI, and PA + FM). Correlation analyses revealed a significantly (p < 0.05) positive correlation of root AMF colonization with root-related traits (e.g., DHA, root total length, surface area, and volume) and leaf-related traits (e.g., leaf area, shoot biomass, total chlorophyll, and antioxidant enzyme activities). This study demonstrated that while the apllication of individual AMF or plant hormones had a certain good effects on most growth and physiobiochemical characteristics parameters of tea cutting seedings, the additive effect was from specific combined of AMF and plant hormones. These results highlight the possibility for combined of AMF and plant hormones to improve the asexual reproduction of tea plants via cuttings.