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 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.

Bisphenol A widely remains in food and environmental systems. A small amount of bisphenol A can directly affect human health. However, the recent colorimetric detection methods of bisphenol A still meet the challenges such as complex operations and the influence of high-salt solutions, resulting in inaccurate detection results. Herein, Ag3PO4 nanoparticles are prepared through a facile coprecipitation method and have excellent laccase-mimicking catalytic activity. Under the catalytic action of Ag3PO4 nanoparticles, bisphenol A loses electrons and further reacts with 4-amino-antipyrine to form a red substance. Thus, a novel rapid colorimetric method for bisphenol A detection is first established based on the laccase-mimicking activity of Ag3PO4 nanoparticles. The limit of detection of the colorimetric method is determined as low as 0.222 mg·L−1, which is lower than the National Health and Family Planning Commission of China and the United States Food and Drug Administration. Moreover, the colorimetric method displays excellent selectivity against other competitive targets. Further research has confirmed the accuracy, reliability, and rapidity of the colorimetric method for detecting bisphenol A in actual food and water samples, which indicates that such a colorimetric method will play a potentially vital role in practical applications.

Bisphenol A widely remains in food and environmental systems. A small amount of bisphenol A can directly affect human health. However, the recent colorimetric detection methods of bisphenol A still meet the challenges such as complex operations and the influence of high-salt solutions, resulting in inaccurate detection results. Herein, Ag3PO4 nanoparticles are prepared through a facile coprecipitation method and have excellent laccase-mimicking catalytic activity. Under the catalytic action of Ag3PO4 nanoparticles, bisphenol A loses electrons and further reacts with 4-amino-antipyrine to form a red substance. Thus, a novel rapid colorimetric method for bisphenol A detection is first established based on the laccase-mimicking activity of Ag3PO4 nanoparticles. The limit of detection of the colorimetric method is determined as low as 0.222 mg·L−1, which is lower than the National Health and Family Planning Commission of China and the United States Food and Drug Administration. Moreover, the colorimetric method displays excellent selectivity against other competitive targets. Further research has confirmed the accuracy, reliability, and rapidity of the colorimetric method for detecting bisphenol A in actual food and water samples, which indicates that such a colorimetric method will play a potentially vital role in practical applications.

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.

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 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.

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.