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.

Carya cathayensis Sarg. is widely cultivated in China as a specialized nut crop, and its discarded husks (outer pericarp) are rich in triterpenoids with known antibacterial properties. In this study, triterpenoids were extracted from Carya cathayensis Sarg. husks (CCSHs) using a surfactant-mediated, ultrasound-assisted extraction method optimized via response surface methodology, the optimized extraction yield was 33.92 ± 0.52 mg UAE/g DW. Ab-8 macroporous resin was used to purify the triterpenoids from the crude extracts, achieving a 4.3-fold increase in purity. Mesoporous chitosan aerogels were prepared, and their morphology, pore size, and specific surface area were evaluated using microscopic and nitrogen adsorption methods. These aerogels were then used to adsorb the purified triterpenoids from CCSH extracts, enhancing their antibacterial effect. Growth curves of Escherichia coli and Staphylococcus aureus demonstrated that the combination of CCSHs-derived triterpenoids and chitosan aerogel spheres resulted in an enhanced antibacterial effect. This study lays the groundwork for adding value to CCSHs while offering a pathway to develop plant-based antibacterial products.

Carya cathayensis Sarg. is widely cultivated in China as a specialized nut crop, and its discarded husks (outer pericarp) are rich in triterpenoids with known antibacterial properties. In this study, triterpenoids were extracted from Carya cathayensis Sarg. husks (CCSHs) using a surfactant-mediated, ultrasound-assisted extraction method optimized via response surface methodology, the optimized extraction yield was 33.92 ± 0.52 mg UAE/g DW. Ab-8 macroporous resin was used to purify the triterpenoids from the crude extracts, achieving a 4.3-fold increase in purity. Mesoporous chitosan aerogels were prepared, and their morphology, pore size, and specific surface area were evaluated using microscopic and nitrogen adsorption methods. These aerogels were then used to adsorb the purified triterpenoids from CCSH extracts, enhancing their antibacterial effect. Growth curves of Escherichia coli and Staphylococcus aureus demonstrated that the combination of CCSHs-derived triterpenoids and chitosan aerogel spheres resulted in an enhanced antibacterial effect. This study lays the groundwork for adding value to CCSHs while offering a pathway to develop plant-based antibacterial products.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.