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The effect of puffing treatment on the volatile components of green tea has been studied. A total of 155 volatile compounds were identified by using HS-SPME and SPE extraction methods, combined with gas chromatography–mass spectrometry (GC–MS). The total concentration of volatile compounds in puffed green tea increased by 2.25 times compared to that in before puffing. 12 key volatile compounds in green tea were identified before and after puffing using a combination of multivariate statistical analysis, GC-O, AEDA dilution analysis, and relative odor activity value (rOAV). The puffing process generates the Maillard reaction, where sugars react with amino acids to produce Maillard reaction products (such as pyrazine, pyrrole, furan, and their derivatives), giving them a unique baking aroma. The proportion of these compounds in the total volatile matter increased. The research results provided guidance and a theoretical basis for improving the aroma processing of green tea.

To elucidate the time-dependent swelling behavior of landfill sand-bentonite mixture liners under the effect of organic pollutants, an no loading swelling ratio test was conducted on mixtures with varying sand and diesel content. The evolution characteristics of the swelling time curve morphology were investigated. The micro-mechanisms underlying the swelling time effect evolution were explored. The results indicated that the sand-diesel interaction significantly altered the swelling time curve morphological characteristics of the mixture. As the sand content increased, the swelling coefficient at the stable state decreased. However, during the rapid swell phase, diesel-contaminated mixed soil does not exhibit the increase-then-decrease pattern in swell coefficient seen in unpolluted mixed soil. The duration of both the slow swelling phase and the time to reach a stable state were longer. Mechanistic analysis revealed that cation exchange capacity is insufficient to effectively analyze the evolution characteristics of the swelling time curve. Instead, the initial swelling potential of bentonite is the true indicator of the mixture’s swelling properties. The particle size distribution influences the changes in the swelling coefficient during the rapid swelling phase, but diesel alters the particle size distribution, mitigating its impact. The oil sealing effect within intra-aggregate and inter-aggregate pores not only weakened the swelling characteristics of the mixture at all stages but also increased the permeability resistance, thereby reducing the duration of the rapid swelling phase and extending the slow swelling and stable swelling phases.

Astringency has an important impact on the taste quality of tea infusion, a process which occurs when polyphenols complex with salivary proteins to form an impermeable membrane. (-)-Epigallocatechin gallate (EGCG) is the main astringent compound found in green tea and mucin is the main protein present in saliva. Determining the turbidity of EGCG–mucin mixtures is an effective method to quantify the astringency intensity of EGCG solutions. In this study, the effects of taste-related, substances present during green tea infusion, on the turbidity of EGCG–mucin mixtures was investigated under the reacting conditions of a pH value of 5.0, at 37 °C, and for 30 min. The results showed that epicatechins, caffeic acid, chlorogenic acid, and gallic acid reduced the turbidity of EGCG–mucin mixtures, while rutin increased turbidity. Metal ions increased the turbidity of EGCG–mucin mixtures. These can be arranged by effectiveness as Al3+ > K+ > Mg2+ > Ca2+. Caffeine, theanine, and sodium glutamate all decreased the turbidity values of EGCG–mucin mixtures, but sucrose had a weak effect. Further experiments confirmed that the turbidity of green tea infusion–mucin mixture indicated the astringent intensity of green tea infusion, and that the turbidity was significantly correlated with the contents of tea polyphenols and EGCG.

Foods containing tea could be widely utilized due to the addition of good tea ingredients, especially large-leaf yellow tea, which is rich with a good flavor. Applying this change to bread containing tea would improve its product quality. In this research, large-leaf yellow tea bread (LYB), possessing a special flavor, was developed using ultrafine large-leaf yellow tea powder and flour as the main raw materials. The amount of ultrafine large-leaf yellow tea powder added to bread was optimized using texture, sensation, and specific volume as comprehensive evaluation indicators. At the optimal dosage, the free amino acids, volatile flavor compounds, antioxidant activity, and in vitro starch digestibility of LYB were measured. Response surface optimization experimental results showed that the comprehensive score of bread was highest when the added amount of ultrafine large-leaf yellow tea powder was 3%. In particular, compared to blank bread (BB), adding ultrafine large-leaf yellow tea powder into bread could effectively increase its amino acid composition, enhance its volatile flavor compounds, improve the antioxidant capacity, and reduce the digestibility of starch.

The precise detection of diseases is crucial for the effective treatment of pear trees and to improve their fruit yield and quality. Currently, recognizing plant diseases in complex backgrounds remains a significant challenge. Therefore, a lightweight CCG-YOLOv5n model was designed to efficiently recognize pear leaf diseases in complex backgrounds. The CCG-YOLOv5n model integrates a CA attention mechanism, CARAFE up-sampling operator, and GSConv into YOLOv5n. It was trained and validated using a self-constructed dataset of pear leaf diseases. The model size and FLOPs are only 3.49 M and 3.8 G, respectively. The mAP@0.5 is 92.4%, and the FPS is up to 129. Compared to other lightweight indicates that the models, the experimental results demonstrate that the CCG-YOLOv5n achieves higher average detection accuracy and faster detection speed with a smaller computation and model size. In addition, the robustness comparison test CCG-YOLOv5n model has strong robustness under various lighting and weather conditions, including frontlight, backlight, sidelight, tree shade, and rain. This study proposed a CCG-YOLOv5n model for accurately detecting pear leaf diseases in complex backgrounds. The model is suitable for use on mobile terminals or devices.

Tea stems are a type of tea by-product, and a considerable amount of them is discarded during picking, with their value often being overlooked. To enhance the utilization of tea stems, we investigated the effects of different expansion temperatures on the non-volatile compounds of tea stems. The results showed that the contents of EC, EGC, EGCG, tea polyphenols, and amino acids all decreased with the expansion temperature, while the contents of GA and C increased. The best effect was observed at 220 °C for 20 s. Additionally, as the temperature increased, the umami and aftertaste of astringency values of tea stems decreased, and the value of bitterness increased. Meanwhile, the value of sweetness decreased first and then increased. EGC was identified as the key differential compound of tea stems at different temperatures. In this investigation, determining the optimum expansion temperature was deemed advantageous for enhancing the flavor quality of tea stems, consequently elevating the utilization efficacy of tea stems and tea by-products.