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Post-fermented Pu-erh tea (PFPT) is a microbial fermented tea characterized by unique sensory attributes and multiple health benefits. Aspergillus niger is the dominant fungus involved in the fermentation process and plays a significant role in imparting the distinct characteristics of PFPT. To investigate the role of Aspergillus niger in the fermentation of Pu-erh tea, this study inoculated unsterilized sun-dried green tea with Aspergillus niger isolated from Pu-erh tea to enhance the fermentation process. Metabolites and microbial communities in sun-dried green tea (CK), fortified fermented tea (TF), and naturally fermented tea (NF) were analyzed using non-targeted metabolomics, 16S rDNA, and internal transcribed spacer sequencing. Non-targeted metabolomics revealed that Aspergillus niger significantly altered the metabolite profile of the tea samples, identifying a total of 200 different metabolites, with 95 showing significant increases and 105 significant decreases, predominantly enriched in metabolic pathways associated with amino acid biosynthesis and degradation. High-throughput sequencing revealed that although the relative abundance of the fungal community remained largely unchanged, the inoculation of Aspergillus niger significantly increased the abundance of Bacillales and Pseudomonas within the bacterial community, thereby influencing the dynamic balance of the microbial ecosystem. Collectively, the inoculation of Aspergillus niger altered the composition of the microbial community and metabolic activities, resulting in changes to the content of amino acid-dominated metabolites, thereby enhancing the flavor profile and overall quality of Pu-erh tea. These findings provide important insights for optimizing the production processes of Pu-erh tea and the application of microorganisms in other fermented foods.

Understanding the correlation between soil chemical properties and tea quality is essential for the comprehensive management of ancient tea gardens. However, the specific links between these factors in ancient tea gardens remain underexplored. This study analyzes the soil chemical properties of four distinct research regions in Nanhua County to explore their effects on key chemical components in ancient tea garden teas, providing a scientific basis for improving the quality of ancient tea garden teas through soil management. Employing high performance liquid chromatography (HPLC) and inductively coupled plasma mass spectrometry (ICP-MS), the chemical components of tea and the chemical properties of the soil were meticulously quantified. Following these measurements, the integrated fertility index (IFI) and the potential ecological risk index (PERI) were evaluated and correlation analysis was conducted. The results revealed that ancient tea garden tea quality is closely linked to soil chemical properties. Soil’s total nitrogen (TN), total sulfur (TS), and available potassium (AK) negatively correlate with tea’s catechin gallate (CG) component and AK also with polyphenols. Most other soil properties show positive correlations with tea components. The research also evaluated soil heavy metals’ IFI and PERI. IFI varied significantly among regions. Hg’s high pollution index indicates ecological risks; Cd in Xiaochun (XC) region poses a moderate risk. PERI suggests moderate risk for XC and Banpo (BP), with other areas classified as low risk. Implementing reasonable fertilization and soil amelioration measures to enhance soil fertility and ensure adequate supply of key nutrients will improve the quality of ancient tea gardens. At the same time, soil management measures should effectively control heavy metal pollution to ensure the quality and safety of tea products. Insights from this study are crucial for optimizing soil management in ancient tea gardens, potentially improving tea quality and sustainability.

China’s Yunnan Province, known for its tea plantations, faces significant challenges in smart pest management due to its ecologically intricate environment. To enable the intelligent monitoring of pests within tea plantations, this study introduces a novel image recognition algorithm, designated as YOLOv8n-WSE-pest. Taking into account the pest image data collected from organic tea gardens in Yunnan, this study utilizes the YOLOv8n network as a foundation and optimizes the original loss function using WIoU-v3 to achieve dynamic gradient allocation and improve the prediction accuracy. The addition of the Spatial and Channel Reconstruction Convolution structure in the Backbone layer reduces redundant spatial and channel features, thereby reducing the model’s complexity. The integration of the Efficient Multi-Scale Attention Module with Cross-Spatial Learning enables the model to have more flexible global attention. The research results demonstrate that compared to the original YOLOv8n model, the improved YOLOv8n-WSE-pest model shows increases in the precision, recall, mAP50, and F1 score by 3.12%, 5.65%, 2.18%, and 4.43%, respectively. In external validation, the mAP of the model outperforms other deep learning networks such as Faster-RCNN, SSD, and the original YOLOv8n, with improvements of 14.34%, 8.85%, and 2.18%, respectively. In summary, the intelligent tea garden pest identification model proposed in this study excels at precise the detection of key pests in tea plantations, enhancing the efficiency and accuracy of pest management through the application of advanced techniques in applied science.

Variations in the concentration of glucoraphanin (GRA) and glucoerucin (GER), as well as the corresponding breakdown products, isothiocyanates (ITCs) and nitriles, were investigated during the growth of broccoli sprouts. The concentrations of GRA and GER decreased sharply from 33.66 µmol/g to 11.48 µmol/g and 12.98 µmol/g to 8.23 µmol/g, respectively, after seed germination. From the third to the seventh day, both GRA and GER were maintained as relatively stable. The highest concentrations of sulforaphane (17.16 µmol/g) and erucin (12.26 µmol/g) were observed on the first day. Hereafter, the concentrations of nitrile hydrolyzed from GRA or GER were higher than those of the corresponding ITCs. Moreover, the ratio of sulforaphane to sulforaphane nitrile decreased from 1.35 to 0.164 from 1 d to 5 d, with a similar trend exhibited for erucin/erucin nitrile after 2 d. RNA-seq analysis showed that BolMYB28 and BolCYP83A1, involved in aliphatic glucosinolate (GSL) biosynthesis, remained largely unexpressed until the third day. In contrast, the genes operating within the GSL-myrosinase hydrolysis pathway were highly expressed right from the beginning, with their expression levels increasing significantly after the third day. Additionally, we identified two BolESPs and six BolNSPs that might play important roles in promoting the production of nitriles during the development of broccoli sprouts.

To investigate the variation in flavonoids content in ancient tree sun–dried green tea under abiotic stress environmental conditions, this study determined the flavonoids content in ancient tree sun−dried green tea and analyzed its correlation with corresponding factors such as the age, height, altitude, and soil composition of the tree. This study uses two machine−learning models, Least Absolute Shrinkage and Selection Operator (LASSO) regression and Cox regression, to build a predictive model based on the selection of effective variables. During the process, bootstrap was used to expand the dataset for single−factor and multi−factor comparative analyses, as well as for model validation, and the goodness−of−fit was assessed using the Akaike information criterion (AIC). The results showed that pH, total potassium, nitrate nitrogen, available phosphorus, hydrolytic nitrogen, and ammonium nitrogen have a high accuracy in predicting the flavonoids content of this model and have a synergistic effect on the production of flavonoids in the ancient tree tea. In this prediction model, when the flavonoids content was >6‰, the area under the curve of the training set and validation set were 0.8121 and 0.792 and, when the flavonoids content was >9‰, the area under the curve of the training set and validation set were 0.877 and 0.889, demonstrating good consistency. Compared to modeling with all significantly correlated factors (p < 0.05), the AIC decreased by 32.534%. Simultaneously, a visualization system for predicting flavonoids content in ancient tree sun−dried green tea was developed based on a nomogram model. The model was externally validated using actual measurement data and achieved an accuracy rate of 83.33%. Therefore, this study offers a scientific theoretical foundation for explaining the forecast and interference of the quality of ancient tree sun−dried green tea under abiotic stress.

Chitosan oligosaccharide (COS), a degradation product of chitosan, is easily accessible, highly bioactive, non-toxic, and well-soluble in water. The effects of COS on the qualitative attributes of tomato fruits were investigated in the current study. COS was administered to tomato plants (Solanum lycopersicum cv. Ruixinghongniu) by foliar spray and root irrigation in alternate cycles at concentrations of 0.5 g·L−1 and 0.16 g·L−1, respectively. The experimental outcomes revealed that COS treatment promoted the coloring and softening of tomato fruits. Lycopene, vitamin C, fructose, and glucose levels increased by 49.0%, 25.4%, 30.2%, and 33.4%, respectively, in COS-treated ripe fruits compared to controls. The volatile metabolome showed that COS application also increased the release of ten volatiles correlated with consumer preference (1-penten-3-one, (E)-2-pentenal, (E)-3-hexen-1-ol, (E)-2-heptenal, 2-isobutylthiazole, phenylacetaldehyde, 2-phenylethanol, 6-methyl-5-hepten-2-one, 6-methyl-5-hepten-2-ol, and β-ionone), contributing to an improved tomato flavor. Moreover, increased transcript levels of genes participating in ethylene biosynthesis, perception, and response along with enhanced ethylene production were observed in COS-treated fruits, suggesting that COS may regulate tomato fruit quality via the ethylene pathway. Taken together, our results indicated that the pre-harvest application of COS could improve tomato fruit quality attributes.

The aroma profile of sun-dried black tea (SBT) was identified by headspace solid–phase microextraction (HS–SPME) coupled with gas chromatography–mass spectrometry (GC–MS) and gas chromatography–olfactometry (GC–O). A total of 37 scents were captured by using the GC–O technique, and 35 scents with odor intensities ranging from 1.09 ± 1.93 to 9.91 ± 0.29 were identified. Twenty-one compounds were further identified as key odor-active compounds with odor activity values (OAVs) greater than or equal to one. These key odor-active compounds were restructured with their detected concentrations, and the aroma profile of the selected SBT sample was successfully imitated to a certain extent. An omission test was performed by designing 25 models and confirmed that (E)-β-damascenone, β-ionone, dihydro-β-ionone, linalool, and geraniol were the key odor-active compounds for the aroma profile of SBT. Meanwhile, phenylethyl alcohol, (E)-2-decenal, hexanal, and methyl salicylate were also important to the aroma profile of SBT. This study can provide theoretical support for the improvement of the aroma quality of sun-dried black tea.

Tomato is susceptible to chilling injury during cold storage. In this study, we found that low temperature promoted the expression of brassinosteroid (BR) biosynthetic genes in tomato fruits. The overexpression of SlCYP90B3 (SlCYP90B3-OE), a key BR biosynthetic gene, alleviated the chilling injury with decreased electrical conductivity and malondialdehyde. In SlCYP90B3-OE tomato fruits, the activities of antioxidant enzymes, including ascorbate peroxidase (APX), catalase (CAT), peroxidase (POD), and superoxide dismutase (SOD), were markedly increased, while the activity of membranous lipolytic enzymes, lipoxygenase (LOX), and phospholipase D (PLD), were significantly decreased when compared with the wild-type in response to cold storage. Furthermore, the expression level of the cold-response-system component, SlCBF1, was higher in SlCYP90B3-OE fruits than in the wild-type fruits. These results indicated that SlCYP90B3 might be involved in the chilling tolerance of tomato fruits during cold storage, possibly by regulating the antioxidant enzyme system and SlCBF1 expression.

The effects of long-term freezer storage and different defrosting methods on the retention of glucosinolates, vitamin C, and total phenols in frozen broccoli florets were investigated in the present study. Frozen broccoli florets were stored in a freezer at −20 °C for 165 days or subjected to defrosting by three different house-hold thawing methods (water, air, and refrigerator defrosting). Results showed that all glucosinolates were well preserved, while vitamin C and total phenols were reduced by less than 12% and 19% of the control, respectively, during the storage. Besides, refrigerator and air defrosting were better than water defrosting in glucosinolates retention, and refrigerator defrosting was the best in vitamin C preservation. No difference was observed in reserving phenolic compounds among the three methods. In conclusion, long-term freezer storage is an excellent way to preserve broccoli florets, and refrigerator defrosting is the best way to maintain the nutritional compounds in frozen broccoli florets.