The soluble solid content (SSC) in grapes significantly influences their flavour and plays an integral role in evaluation of the quality and consumer acceptance. This study employed visible near-infrared (Vis-NIR) spectroscopy to rapidly quantify SSC in table grapes during storage. A predictive model was developed to construct a correlation between the spectral data and the measured SSC, while a comparative analysis was undertaken to assess the effects of various spectral preprocessing techniques. Successive projection algorithms (SPA), uninformative variable elimination (UVE), and the competitive adaptive reweighting algorithm (CARS) were adopted to eliminate redundant variables from both the original and preprocessed spectral data. The partial least squares regression (PLSR), and support vector regression (SVR) algorithms were adopted to establish a predictive model. Comparing the modelling results derived from whole-band spectral data with those obtained from selected spectral variables, the optimal spectral prediction model was formulated utilizing PLSR. The model, which incorporated filtered characteristic wavelength spectral data obtained through CARS following standard normal variate (SNV) preprocessing yielded optimum results with the correlation coefficients of the calibration set (RC), and the prediction set (RP) were 0.956 and 0.940, respectively. The root mean square errors of the calibration set (RMSEC), and prediction set (RMSEP) were 0.683 and 0.769, respectively, while the ratio of prediction to deviation (RPD) was 2.899. These results suggest that the application of Vis-NIR spectroscopy technology could effectively detect the SSC in grapes during storage, and it can provide a valuable reference for the rapid assessment of the table grape quality.

The potential of employing hyperspectral imaging (HSI) in the near-infrared (NIR) range (386.82−1,004.50 nm) for predicting the firmness of 'Fuji' apples cultivated in Aksu has been evaluated. The performance of seven preprocessing algorithms and two feature selection algorithms was evaluated. The coefficient of determination (R2) and root mean square error (RMSE) of Partial Least Squares (PLS) models are contrasted using various inputs. These results confirm that the Multiplicative Scatter Correction (MSC) preprocessing algorithm was the optimal choice (\begin{document}$ {R}_{p}^{2} $\end{document} = 0.7925, RMSEP = 0.6537), and the Competitive Adaptive Reweighted Sampling (CARS) feature selection algorithm demonstrated superior performance (\begin{document}$ {R}_{p}^{2} $\end{document} = 0.8325, RMSEP = 0.6257). Based on the aforementioned findings, PLS, Multiple Linear Regression (MLR), Heterogeneous Transfer Learning (HTL), and Back Propagation Neural Network (BPNN) models were constructed for cross-validation purposes. The experimental results indicate that the CARS-BPNN model exhibits the optimal prediction performance, with an \begin{document}$ {R}_{p}^{2} $\end{document} value of 0.9350 and an RMSEP value of 0.4654. The results of the research indicated that a deep learning method combined with hyperspectral imaging technology could be utilized to non-destructively detect the firmness of 'Fuji' apples, which will be beneficial and potentially applicable for post-harvest fruit firmness monitoring. This research provides a reference point for the non-destructive detection of apple in the selection of preprocessing, feature selection algorithms, and predicting firmness model.

MicroRNAs (miRNAs) play important roles in various physiological activities in plants. However, their role in protecting grapes against gray mold (Botrytis cinerea) invasion remains largely unexplored. This study focuses on the phenotypic and physiological responses of 'Shine Muscat' (Vitis vinifera × V. labrusca) to gray mold infestation. High-throughput sequencing implicates several miRNAs, including miR398 and miR319, involved in the plant's defense mechanisms. Notably, miR395 emerges as a key player, positively influencing grape disease resistance. Specifically, miR395 downregulated the expression of its target gene APS1, which encodes ATP sulfurylase, a crucial enzyme in the plant's sulfur metabolic pathway. Concurrently, ATP sulfurylase downregulation increased the content of sulfate ions and glutathione (GSH). These findings were corroborated by our study of APS1. Collectively, these results suggest that miR395-APS1 modulates sulfur metabolism in grapes, thereby enhancing resistance to B. cinerea. The observed miRNA-mediated interactions between grapes and B. cinerea elucidate the role of miR395 in grape resistance to gray mold and offer new insights into the molecular mechanisms of grape disease resistance.

Machine learning, in combination with optical sensing, extracts key features from high-dimensional data for non-destructive food quality assessment. This approach overcomes the limitations of traditional destructive and labor-intensive methods, facilitating real-time decision-making for food quality profiling and robotic handling. This mini-review highlights various optical techniques integrated with machine learning for assessing food quality, including chemical profiling methods such as near-infrared, Raman, and hyperspectral imaging spectroscopy, as well as visual analysis such as RGB imaging. In addition, the review presents the application of robotics and computer vision techniques to assess food quality and then drives the automation of food harvesting, grading, and processing. Lastly, the review discusses current challenges and opportunities for future research.

Protected cultivation is an effective measure for high-end grape production. Nevertheless, the long-time application of plastic film negatively influences the light environment, and results in a certain decrease in berry quality. In this study, six different light treatments, including white (W), red (R), blue (B), and three different combinations with different ratios of red and blue light (1:1, 4:1, 1:4, respectively), were applied to monitor the quality and sensory properties of 'Queen Nina' grapes. Compared to the control group (without supplemental light), all light treatments significantly increased the size and weight of berries, as well as improved their sugar, anthocyanins, flavonoids, and volatile organic compounds (VOCs) content, whereas all light treatments decreased the levels of chlorophylls and organic acids. Furthermore, the R1B4 treatment improved the content of cyanidin-3-O-glucoside (Cy) and peonidin-3-O-glucoside (Pn), which are the dominant anthocyanin compounds in red grape berry. Additionally, esters, accounting for more than 42% of the VOCs, are the main volatile compounds in 'Queen Nina' grape, and R1B4 treatment was the most favorable treatment for VOCs accumulation. The combination of red and blue light at the 1:4 ratio (R1B4) obtained the highest composite and sensory scores and had the most positive impact on berry coloration, sugars, anthocyanins, flavonoids, and VOCs accumulation, followed by the blue light treatment. In summary, the present results highlight the effective strategy of R1B4 light treatment to increase the berry quality of 'Queen Nina' grape berries.

The potential of employing hyperspectral imaging (HSI) in the near-infrared (NIR) range (386.82−1,004.50 nm) for predicting the firmness of 'Fuji' apples cultivated in Aksu has been evaluated. The performance of seven preprocessing algorithms and two feature selection algorithms was evaluated. The coefficient of determination (R2) and root mean square error (RMSE) of Partial Least Squares (PLS) models are contrasted using various inputs. These results confirm that the Multiplicative Scatter Correction (MSC) preprocessing algorithm was the optimal choice (\begin{document}$ {R}_{p}^{2} $\end{document} = 0.7925, RMSEP = 0.6537), and the Competitive Adaptive Reweighted Sampling (CARS) feature selection algorithm demonstrated superior performance (\begin{document}$ {R}_{p}^{2} $\end{document} = 0.8325, RMSEP = 0.6257). Based on the aforementioned findings, PLS, Multiple Linear Regression (MLR), Heterogeneous Transfer Learning (HTL), and Back Propagation Neural Network (BPNN) models were constructed for cross-validation purposes. The experimental results indicate that the CARS-BPNN model exhibits the optimal prediction performance, with an \begin{document}$ {R}_{p}^{2} $\end{document} value of 0.9350 and an RMSEP value of 0.4654. The results of the research indicated that a deep learning method combined with hyperspectral imaging technology could be utilized to non-destructively detect the firmness of 'Fuji' apples, which will be beneficial and potentially applicable for post-harvest fruit firmness monitoring. This research provides a reference point for the non-destructive detection of apple in the selection of preprocessing, feature selection algorithms, and predicting firmness model.

In this paper, using a single-step method, resveratrol (RES)-loaded egg white protein (EWP) nanospheric particles were successfully prepared. The micelle behavior, micromorphology, molecular structure changes, and emulsifying properties of the nanoparticle were analyzed, and the molecular interaction between EWP and RES and the environmental response stability of the nanoparticle was characterized. The results show that I373/I385 dropped from 1.1 to about 0.8, indicating that high concentration of ethanol induced EWP to form a more hydrophobic and less polar structure. RES promoted the uniformity of the nanoparticle and formed a tightly-packed spherical three-dimensional structure by characterizing microstructure. Raman and infrared spectroscopy revealed enhanced hydrogen bonding between EWP and RES, increased g-g-g and t-g-t disulfide bonds, and the formation of three-dimensional helical structures due to the opening of flexible structural intervals. Molecular docking analysis identified hydrogen bonds and hydrophobic interactions as the main forces facilitating the binding between RES and EWP. Particle size analysis showed that D3,2 decreased from 30.51 to 17.88 μm, indicating better emulsion stability. The preservation of RES at 0.4 mg/mL was 94.49% in 50 mM NaCl and 83.68% in 500 mM NaCl, with no significant stability change (p > 0.05) over 48 h, revealing a concentration dependence of salt ions and storage stability of RES in the nanoparticle. This study establishes a foundation for exploring the incorporation of high-value hydrophobic compounds into EWP.

MicroRNAs (miRNAs) play important roles in various physiological activities in plants. However, their role in protecting grapes against gray mold (Botrytis cinerea) invasion remains largely unexplored. This study focuses on the phenotypic and physiological responses of 'Shine Muscat' (Vitis vinifera × V. labrusca) to gray mold infestation. High-throughput sequencing implicates several miRNAs, including miR398 and miR319, involved in the plant's defense mechanisms. Notably, miR395 emerges as a key player, positively influencing grape disease resistance. Specifically, miR395 downregulated the expression of its target gene APS1, which encodes ATP sulfurylase, a crucial enzyme in the plant's sulfur metabolic pathway. Concurrently, ATP sulfurylase downregulation increased the content of sulfate ions and glutathione (GSH). These findings were corroborated by our study of APS1. Collectively, these results suggest that miR395-APS1 modulates sulfur metabolism in grapes, thereby enhancing resistance to B. cinerea. The observed miRNA-mediated interactions between grapes and B. cinerea elucidate the role of miR395 in grape resistance to gray mold and offer new insights into the molecular mechanisms of grape disease resistance.

Machine learning, in combination with optical sensing, extracts key features from high-dimensional data for non-destructive food quality assessment. This approach overcomes the limitations of traditional destructive and labor-intensive methods, facilitating real-time decision-making for food quality profiling and robotic handling. This mini-review highlights various optical techniques integrated with machine learning for assessing food quality, including chemical profiling methods such as near-infrared, Raman, and hyperspectral imaging spectroscopy, as well as visual analysis such as RGB imaging. In addition, the review presents the application of robotics and computer vision techniques to assess food quality and then drives the automation of food harvesting, grading, and processing. Lastly, the review discusses current challenges and opportunities for future research.

Skeletal muscle atrophy resulting from glucolipid metabolic disorders poses a serious challenge to human health with the rapidly increasing prevalence of diabetes and obesity. Clinical trials investigating treatment interventions against skeletal muscle atrophy yielded limited success. This article addressed novel phytochemicals, such as polyphenols, flavonoids, terpenoids, alkaloids, and plant extracts, that modulated muscle atrophy and suggested avenues for future treatment. Several studies demonstrated an inverse relationship between dietary phytochemical supplementation and the onset of skeletal muscle atrophy caused by glucolipid metabolic disorders, as evidenced by improved muscle quality and function. Insulin-like growth factor 1/protein kinase B signaling pathway activation, protein ubiquitination inhibition, enhancement of mitochondrial function and inflammatory response, reduction of oxidative stress, and regulation of gut microbiota represent the mechanisms underlying the anti-skeletal muscle atrophy effect of phytochemicals. The manuscript also contains the clinical trials and filed patents regarding the beneficial effects of phytochemicals on skeletal muscle health. This review provided fresh perspectives on potentially effective therapeutic or preventive measures (dietary phytochemical intervention) for clinically managing skeletal muscle atrophy associated with diabetes or obesity.