Food ChemistryPub Date : 2026-10-01Epub Date: 2026-07-31DOI: 10.1016/j.foodchem.2026.150642
Ishita Ghosh, Federico Harte, Yi Zhang
{"title":"A plant protein-based fat texturizer: Enzymatic bioproduction and application in non-fat yogurt.","authors":"Ishita Ghosh, Federico Harte, Yi Zhang","doi":"10.1016/j.foodchem.2026.150642","DOIUrl":"10.1016/j.foodchem.2026.150642","url":null,"abstract":"<p><p>The growing demand for low-fat or non-fat food products highlights the need for fat texturizers that replicate the mouthfeel and texture of fat. This study investigates the enzymatic modification of lentil protein isolates (LPI) to generate amphiphilic polypeptides for fat replacement in non-fat foods, exemplified by yogurt. LPI was hydrolyzed using Alcalase at varying enzyme-to-substrate ratios (1:100-1:2000) and hydrolysis times (2-240 min). Enzymatic hydrolysis significantly modified LPI, with partially hydrolyzed proteins (LPP5-10, <50 kDa) exhibiting balanced functional properties, including surface hydrophobicity (207 A.U.) comparable to lecithin (199 A.U.), high solubility across pH 2-12, and substantially higher oil-holding capacity (752%) than native LPI (174%). Adding 0.05% LPP5-10 to non-fat milk enhanced yogurt gelation, storage modulus, and yield stress, achieving textures comparable to whole-milk yogurt. These findings demonstrate that enzymatically produced lentil polypeptides are effective plant-protein fat texturizers for restoring the texture and structure of low-fat food products.</p>","PeriodicalId":318,"journal":{"name":"Food Chemistry","volume":"525 Pt 3","pages":"150642"},"PeriodicalIF":10.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148667848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Food ChemistryPub Date : 2026-09-28DOI: 10.1016/j.foodchem.2026.151294
Yong Ding, Xiangzhan Zhang, Yanan Wang, Suke Wang, Yanli Su, Long Wang, Huabai Xue
{"title":"Exogenous 3-Hydroxy-4-methoxycinnamic acid inhibits browning in fresh-cut pear flesh","authors":"Yong Ding, Xiangzhan Zhang, Yanan Wang, Suke Wang, Yanli Su, Long Wang, Huabai Xue","doi":"10.1016/j.foodchem.2026.151294","DOIUrl":"https://doi.org/10.1016/j.foodchem.2026.151294","url":null,"abstract":"Fresh-cut pears are highly susceptible to oxidative browning during postharvest storage, which seriously deteriorates fruit quality and nutritional value. Although 3-Hydroxy-4-methoxycinnamic acid (HMCA) is a phenolic metabolite with established antioxidant properties, its inhibitory effects on enzymatic browning remains unexplored compared to conventional antioxidants. In this study, fresh-cut pear slices were treated with exogenous HMCA, and its effects on quality deterioration and browning progression were investigated. Results indicated that HMCA effectively maintained fruit quality, suppressed the increase in browning index, reduced the accumulation of reactive oxygen species (ROS) and malondialdehyde (MDA), and retarded the degradation of ascorbic acid (AsA). Meanwhile, HMCA enhanced the activities of superoxide dismutase (SOD) and catalase (CAT), inhibited the activities of polyphenol oxidase (PPO) and peroxidase (POD), and modulated the expression of genes involved in phenolic metabolism and antioxidant defense. Furthermore, overexpression of <ce:italic>PbrC4H</ce:italic> delayed browning, whereas silencing of this gene accelerated browning. Collectively, these findings demonstrate that HMCA mitigates browning in fresh-cut pears via regulating the phenylpropanoid pathway, thereby providing a theoretical basis for the development of novel natural preservatives for fresh-cut fruits.","PeriodicalId":318,"journal":{"name":"Food Chemistry","volume":"4 1","pages":""},"PeriodicalIF":8.8,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149085820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Effect of the ripening stage on the hot-air drying behavior of tomato (Solanum lycopersicum L.): Cell wall polysaccharides, ultrastructure, and mechanisms of bioactive and flavor evolution","authors":"Xiao-Xiao Niu, Huan Li, Yu-Ting Ding, Wei-Peng Zhang, Ming-Qiang Xu, Feng-Lun Zhang, Cheng Zhang, Jun Zhang, Jing-Shou Zhang, Hong-Wei Xiao","doi":"10.1016/j.foodchem.2026.151277","DOIUrl":"https://doi.org/10.1016/j.foodchem.2026.151277","url":null,"abstract":"Industrial tomato drying is strongly influenced by ripening stage. This study evaluated mature green, breaker, and red tomatoes for physicochemical traits, cell-wall polysaccharides, microstructure, drying behavior, bioactives, and volatiles. With ripening, polygalacturonase and pectin methylesterase activities increased, water- and chelator-soluble pectins accumulated, Na₂CO₃-soluble pectin and hemicellulose declined, and SEM/TEM observations showed apparent parenchyma loosening and middle-lamella blurring. Red ripe tomatoes had higher effective moisture diffusivity than mature green fruit (1.62 × 10<ce:sup loc=\"post\">−9</ce:sup> vs 1.30 × 10<ce:sup loc=\"post\">−9</ce:sup> m<ce:sup loc=\"post\">2</ce:sup>/s) and 24.44% shorter drying time. Lycopene increased after ripening and drying, total phenolic equivalents rose with maturity on a dry-matter basis, and ascorbic acid declined after drying. Electronic-nose and HS-SPME-GC–MS analyses indicated maturity-dependent volatile fingerprints, with breaker tomatoes enriched in tentatively annotated aroma-related aldehydes and esters. These findings underscore maturity-based grading as a vital strategy to balance drying efficiency, nutritional quality, and flavor development.","PeriodicalId":318,"journal":{"name":"Food Chemistry","volume":"4 1","pages":"151277"},"PeriodicalIF":8.8,"publicationDate":"2026-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149085848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Food ChemistryPub Date : 2026-09-25DOI: 10.1016/j.foodchem.2026.151321
Ming Cheng, Yuhan Zhao, Xiujie Yu, Lulu Zhu, Tingting Yang, Yan Fan, Hu Hou
{"title":"Co-production of gelatin, hydroxyapatite and calcium-binding peptides from Tilapia scales: Structural characteristics and physicochemical properties","authors":"Ming Cheng, Yuhan Zhao, Xiujie Yu, Lulu Zhu, Tingting Yang, Yan Fan, Hu Hou","doi":"10.1016/j.foodchem.2026.151321","DOIUrl":"https://doi.org/10.1016/j.foodchem.2026.151321","url":null,"abstract":"A novel co-production strategy was developed to valorize tilapia scale waste into gelatin (TSG), hydroxyapatite (TSHA), and calcium-binding peptides. TSG was obtained by hot-water extraction, and the residues were calcined at 700 °C for 3 h to produce TSHA, which served as a self-prepared affinity medium. Alkaline protease hydrolysis yielded the highest calcium-binding capacity (CBC, 4.86 μg/mg). After TSHA-based affinity purification, the recovered fraction (THAC) showed improved CBC (5.71 μg/mg) and comprised predominantly <500 Da peptides enriched in aspartic acid (Asp, 14.10%) and glutamic acid (Glu, 10.00%). Isothermal titration calorimetry suggested apparent spontaneous and exothermic calcium-binding (K<ce:inf loc=\"post\">a</ce:inf> = 2.4 × 10<ce:sup loc=\"post\">4</ce:sup> M<ce:sup loc=\"post\">−1</ce:sup>; <ce:italic>n</ce:italic> = 2.26). Spectroscopic analyses demonstrated that Ca<ce:sup loc=\"post\">2+</ce:sup> coordination through carboxyl and amino groups induced a random-coil-to-β-sheet transition. Peptidomics identified acidic-motif-rich sequences (GDDGE, DVSGGYDE, GEPGDDGAK, and GDRGDEGPK), and molecular docking indicated bidentate/multidentate coordination. This integrated strategy provides a feasible route for converting aquaculture by-products into value-added calcium-binding peptide ingredients.","PeriodicalId":318,"journal":{"name":"Food Chemistry","volume":"1 1","pages":"151321"},"PeriodicalIF":8.8,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149085863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Food ChemistryPub Date : 2026-09-16DOI: 10.1016/j.foodchem.2026.151199
Shuyue Fan, Keren A. Bindon, David W. Jeffery
{"title":"Fluorescence fingerprinting to predict multiple chromatic and phenolic parameters for a diversified set of red wines","authors":"Shuyue Fan, Keren A. Bindon, David W. Jeffery","doi":"10.1016/j.foodchem.2026.151199","DOIUrl":"10.1016/j.foodchem.2026.151199","url":null,"abstract":"<div><div>Absorbance-transmittance and fluorescence excitation-emission matrix (A-TEEM) spectroscopy was utilised to study red wines (<em>n</em> = 113) undergoing maturation or bottle ageing. Chromatic and phenolic parameters including wine colour density, hue, monomeric pigments, various forms of polymeric pigments, tannin concentration using precipitation assays, SO<sub>2</sub> resistant pigments, total anthocyanins, and total phenolics were modelled. Machine learning models using extreme gradient boosting (XGB) regression were developed for EEM data and fused A-TEEM data based on reference chemistry, achieving high R<sup>2</sup> and low RMSE values of Train and cross-validation (CV) models across all parameters. XGBR Test models based on A-TEEM showed enhanced prediction accuracy for certain parameters than using EEM alone. Overall, this study provided insights into effectively determining colour and phenolic profile of wines with a rapid fluorescence spectroscopy approach, offering a valuable tool that could be used to facilitate research, wine quality control, and decision-making in wineries.</div></div>","PeriodicalId":318,"journal":{"name":"Food Chemistry","volume":"532 ","pages":"Article 151199"},"PeriodicalIF":10.4,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148980425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Food ChemistryPub Date : 2026-09-15Epub Date: 2026-06-27DOI: 10.1016/j.foodchem.2026.150226
Batuhan Inanlar, Filiz Altay
{"title":"Non-destructive ripeness classification of apricot (Prunus armeniaca L.) using a physically informed deep learning approach.","authors":"Batuhan Inanlar, Filiz Altay","doi":"10.1016/j.foodchem.2026.150226","DOIUrl":"10.1016/j.foodchem.2026.150226","url":null,"abstract":"<p><p>Accurate ripeness assessment is essential for optimizing harvest timing, storage management and commercial grading in climacteric fruit supply chains. However, conventional methods rely on destructive physicochemical measurements that limit scalability and real-time application. Here we develop a physically informed deep learning framework for non-destructive apricot (Prunus armeniaca L.) ripeness classification by integrating physicochemical clustering with image-based analysis. Ripeness labels were defined using combined hardness, °Brix and color parameters, with an independent firmness-based subset used for external validation. A ResNet18 convolutional neural network trained on segmented and augmented images achieved 88.6% accuracy in cross-validation (Macro F1 = 0.888). Performance declined to 63.08% accuracy (Macro F1 = 0.459) on the independent subset, reflecting the continuous nature of ripening transitions. CNN prediction scores were significantly associated with physicochemical ripeness indicators (R<sup>2</sup> = 0.442, p < 0.001), demonstrating that learned visual features capture biologically meaningful ripening signals and enabling more reliable AI-assisted fruit grading.</p>","PeriodicalId":318,"journal":{"name":"Food Chemistry","volume":"523 ","pages":"150226"},"PeriodicalIF":10.4,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148343652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}