Apple (Malus × domestica Borkh.) is a globally significant fruit in terms of both production and consumption. Metabolomics characteristics of 22 apple cultivars collected from five major apple-growing regions in Shanxi Province (China) were investigated by using 1H nuclear magnetic resonance (NMR) metabolomics. The analysis revealed significant variations in metabolite profiles among the cultivars, particularly in sugars (glucose, fructose, sucrose), asparagine, quinic acid, L-rhamnitol, phenylalanine, and condensed polyphenols. Notably, the cultivars 'Xinhongxing' and 'NY543' exhibited high levels of asparagine and quinic acid. 'Xinhongxing' had higher glucose levels but lower sucrose and fructose levels than other cultivars. 'Hongjiangjun' from higher altitudes showed elevated malate levels, indicating that environmental factors significantly influence malate metabolism in apple fruits. The study also revealed correlations between metabolites. For example, the content of condensed polyphenols was positively correlated with the level of asparagine, and that of quinic acid with phenylalanine. The study provides valuable insights on factors influencing apple composition and quality, underlining the importance of both genetic and environmental factors. Future research using transcriptomic and proteomic approaches could reveal the impact of gene-environment interaction on biochemical pathways involved in the primary and secondary metabolism of apples.

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.

Aronia melanocarpa is a fruit rich in antioxidant compounds with notable health benefits; however, its astringency limits its widespread consumption. This study examined the effects of fermentation with Lactiplantibacillus plantarum 1243 and Lacticaseibacillus paracasei 139 on the microbiological dynamics, quality indicators, and flavor profile of Aronia melanocarpa juice. The results showed that, compared to the unfermented juice, the microbial count reached 7.03 lg CFU/mL at 24 h of fermentation, followed by a decline to 3.90 lg CFU/mL at 96 h. Soluble sugars experienced an initial decline, subsequently increased, and then decreased again. Acidity firstly reduced and then increased, while pH increased initially and then decreased. Total phenolic and flavonoid contents remained relatively stable at 24 h before showing a significant reduction. The DPPH radical scavenging activity significantly increased during fermentation, reaching a peak of 71.7% at 48 h. Overall improvement was observed in color and sensory acceptance of the juice. Flavor analysis demonstrated an increase in aromatic organic compounds, aliphatic aromatics, and methyl compounds, contributing to aroma enhancement in Aronia melanocarpa juice. These findings establish a basis for the use of lactic acid bacteria fermentation to enhance the quality, flavor, and functionality of Aronia melanocarpa juice, supporting the development of functional beverages.

The production process of fruit wines is significantly hindered by the color instability of fruit juices, primarily due to their high anthocyanin content. Recent advancements have introduced yeast strains that produce hydroxycinnamic acid decarboxylase (HCDC) into the brewing process, which have demonstrated considerable efficacy in enhancing color stability and mitigating undesirable odors in fruit wines. This review aims to elucidate the mechanism by which HCDC facilitates the synthesis of vinylphenolic pyranoanthocyanins (VPA). Additionally, we will discuss methodologies for assessing the enzyme's activity and compare the enzymatic activities derived from various sources. Furthermore, we will summarize the application of HCDC from yeast during fermentation, to provide a comprehensive scientific foundation, and reference for the utilization of this enzyme in fruit wines and other fermented wines.

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.

In the process of post-harvest storage and transportation, the quality of fresh fruits and vegetables are decreased due to the autogenetic physiological effect and microbial pollution, which causes great losses to the food industry. Food packaging using edible film and coatings is an emerging environmentally friendly method of fruits and vegetable preservation. This review provides an overview of various film fabrication techniques, including solution casting, extrusion, electrospinning, and 3D printing, while examining the advantages and limitations of each method. A detailed analysis is offered on the key performance parameters of these films, such as mechanical strength, water vapor permeability, antioxidant activity, antimicrobial properties, and their effectiveness in preserving fruits and vegetables. Additionally, strategies to enhance the performance of edible films through incorporating nanoparticles, natural additives, and crosslinking methods are explored. The review aims to establish a comprehensive theoretical foundation and offer practical insights to support the further development and application of edible film technology in fruits and vegetables preservation.

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.