International audience | An innovative chemometric method was developed to exploit visible and near-infrared (Vis-NIR) spectroscopy to guide food formulation to reach the anticipated and constant quality of final products. First, a total of 671 spectral variables related to the puree quality characteristics were identified by spectral variable selection methods. Second, the concentration profiles from multivariate curve resolution-alternative least squares (MCR-ALS) made it possible to reconstruct the identified spectral variables of formulated purees. Partial least square based on the reconstructed Vis-NIR spectral variables was evidenced to predict the final puree quality, such as a* values (RPD = 3.30), total sugars (RPD = 2.64), titratable acidity (RPD = 2.55) and malic acid (RPD = 2.67), based only on the spectral data of composed puree cultivars. These results open the possibility of controlling puree formulation: a multiparameter optimization of the color and taste of final puree products can be obtained using only the VisNIR spectral data of single-cultivar purees.

An innovative chemometric method was developed to exploit visible and near-infrared (Vis-NIR) spectroscopy to guide food formulation to reach the anticipated and constant quality of final products. First, a total of 671 spectral variables related to the puree quality characteristics were identified by spectral variable selection methods. Second, the concentration profiles from multivariate curve resolution-alternative least squares (MCR-ALS) made it possible to reconstruct the identified spectral variables of formulated purees. Partial least square based on the reconstructed Vis-NIR spectral variables was evidenced to predict the final puree quality, such as a* values (RPD = 3.30), total sugars (RPD = 2.64), titratable acidity (RPD = 2.55) and malic acid (RPD = 2.67), based only on the spectral data of composed puree cultivars. These results open the possibility of controlling puree formulation: a multiparameter optimization of the color and taste of final puree products can be obtained using only the Vis-NIR spectral data of single-cultivar purees.

An innovative chemometric method was developed to exploit visible and near-infrared (Vis-NIR) spectroscopy to guide food formulation to reach the anticipated and constant quality of final products. First, a total of 671 spectral variables related to the puree quality characteristics were identified by spectral variable selection methods. Second, the concentration profiles from multivariate curve resolution-alternative least squares (MCR-ALS) made it possible to reconstruct the identified spectral variables of formulated purees. Partial least square based on the reconstructed Vis-NIR spectral variables was evidenced to predict the final puree quality, such as a* values (RPD = 3.30), total sugars (RPD = 2.64), titratable acidity (RPD = 2.55) and malic acid (RPD = 2.67), based only on the spectral data of composed puree cultivars. These results open the possibility of controlling puree formulation: a multiparameter optimization of the color and taste of final puree products can be obtained using only the Vis-NIR spectral data of single-cultivar purees.

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.

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.

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.

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.

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.

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.

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.

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.

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.