Food Chemistry最新文献

Retraction notice to "Photochromic biodegradable film based on polyvinyl alcohol modified with silver chloride nanoparticles and spirulina; investigation of physicochemical, antimicrobial and optical properties" [Food Chem. 411 (2023) 135459]. 《氯化银纳米颗粒和螺旋藻改性聚乙烯醇光致变色生物降解薄膜》撤回通知；理化、抗菌和光学性能的研究”[食品化学，411(2023)135459]。

IF 10.4 1区农林科学

Food Chemistry Pub Date : 2026-10-01 Epub Date: 2026-08-21 DOI: 10.1016/j.foodchem.2026.150737

Aref Erfani, Mir Khalil Pirouzifard, Sajad Pirsa

引用次数: 0

Retraction notice to "Investigating the causes of yolk mudding during storage of salted eggs: Water migration accelerates protein and lipid oxidation" [Food Chem. 481 (2025) 144112]. 《咸蛋贮藏过程中蛋黄混浊的原因研究：水分迁移加速蛋白质和脂质氧化》[食品化学，481(2025)144112]。

IF 10.4 1区农林科学

Food Chemistry Pub Date : 2026-10-01 Epub Date: 2026-08-21 DOI: 10.1016/j.foodchem.2026.150738

Jiyu Zhang, Songyi Lin, Sichen Lu, Xunze Yuan, Yue Tang, Zhijie Bao

引用次数: 0

Ultrasound/electrostatic field combined with ε-polylysine for improving the quality of ready-to-eat pork jelly and freshness classification based on convolutional neural network. 超声/静电场结合ε-聚赖氨酸提高即食猪肉果冻质量及基于卷积神经网络的新鲜度分类

IF 10.4 1区农林科学

Food Chemistry Pub Date : 2026-10-01 Epub Date: 2026-07-31 DOI: 10.1016/j.foodchem.2026.150630

Qing Guo, Min Zhang, Bhesh Bhandari, Qi Yu, Luming Rui

{"title":"Ultrasound/electrostatic field combined with ε-polylysine for improving the quality of ready-to-eat pork jelly and freshness classification based on convolutional neural network.","authors":"Qing Guo, Min Zhang, Bhesh Bhandari, Qi Yu, Luming Rui","doi":"10.1016/j.foodchem.2026.150630","DOIUrl":"10.1016/j.foodchem.2026.150630","url":null,"abstract":"This study investigated the effects of ultrasound (US, 300 W, 20 kHz, 20 min) and electrostatic field (EF, 3.5 kV/cm, 60 min) combined with ε-polylysine (PL, 0.5%, w/w) on the quality of ready-to-eat pork jelly (Shuijingyaorou, SJYR) during 40-day storage at 4 °C. Physicochemical properties such as lipid oxidation, total plate count, texture, flavor, and moisture distribution were evaluated. The results showed that the combined treatment (US + EF + ε-polylysine) exhibited the strongest preservation effect for SJYR, significantly inhibiting lipid oxidation, protein degradation, pH increase, and microbial growth, meanwhile maintaining texture and flavor of SJYR, resulting in the shelf-life extended approximately 10 days compared with the blank control group. Furthermore, four convolutional neural network (CNN) models including Xception, ResNet-50, MobileNet, and GhostNet were developed for SJYR freshness classification. Xception model achieved the highest test accuracy (83.05% mean precision), while GhostNet model showed promising accuracy (77.48%) with lower computational cost, making it suitable for mobile applications. This study provides a feasible strategy for improving SJYR shelf-life and rapid freshness identification.","PeriodicalId":318,"journal":{"name":"Food Chemistry","volume":"525 Pt 3","pages":"150630"},"PeriodicalIF":10.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148700339","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}

引用次数: 0

Corrigendum to "Metabolomic profile of edible Amazonian Arecaceae fruits by FT-ICR-MS: Insights into chemical, nutritional and antioxidant profiles" [Food Chem. 525 (2026) 150319]. “利用FT-ICR-MS分析亚马逊槟子科可食用水果的代谢组学特征：化学、营养和抗氧化特征的见解”[食品化学，525(2026)150319]。

IF 10.4 1区农林科学

Food Chemistry Pub Date : 2026-10-01 Epub Date: 2026-08-31 DOI: 10.1016/j.foodchem.2026.150767

Gesiane da S Lima, Lanaia I L Maciel, Tamara Lenz, Hugo G Machado, Nerilson M Lima, Jorge L S Simão, Vanessa G P Severino, Michael Rychlik, Boniek Gontijo, Stefan A Pieczonka, Philippe Schmitt-Kopplin

引用次数: 0

A plant protein-based fat texturizer: Enzymatic bioproduction and application in non-fat yogurt. 植物蛋白脂肪增构剂：酶法生物合成及其在脱脂酸奶中的应用。

IF 10.4 1区农林科学

Food Chemistry Pub Date : 2026-10-01 Epub Date: 2026-07-31 DOI: 10.1016/j.foodchem.2026.150642

Ishita Ghosh, Federico Harte, Yi Zhang

引用次数: 0

Corrigendum to "Oxidation-driven oil release in liquid salted egg yolks: mechanistic roles of NaCl and free radicals" [Food Chem. 522 (2026) 150106]. “液体咸蛋黄中氧化驱动的油脂释放：NaCl和自由基的机制作用”[食品化学，522(2026)150106]的勘误表。

IF 10.4 1区农林科学

Food Chemistry Pub Date : 2026-09-30 Epub Date: 2026-07-01 DOI: 10.1016/j.foodchem.2026.150180

Jing Guo, Ke Tian, Jiaxuan Ma, Yongguo Jin

引用次数: 0

Corrigendum to "High-performance of starch-protein edible films with interpenetrating networks via reactive extrusion for food milk powder preservation" [Food Chem. 509 (2026) 148621]. “通过反应挤压制备具有互穿网络的淀粉-蛋白质可食用薄膜，用于食品奶粉的保存”[食品化学，509(2026)148621]的更正。

IF 10.4 1区农林科学

Food Chemistry Pub Date : 2026-09-15 Epub Date: 2026-07-14 DOI: 10.1016/j.foodchem.2026.150344

Xuliang Wang, Yanxi Wang, Wenya Ma, Kunpeng Qian, Jiahao Zhang, Qingfeng Dong, Min Fan, Li Li

引用次数: 0

Corrigendum to "Oriented structure design of pectin/Ag nanosheets film with improved barrier and long-term antimicrobial properties for edible fungi preservation" [Food Chem. 484 (2025) 144451]. “果胶/银纳米片薄膜的定向结构设计，具有改善的屏障和长期抗菌性能，用于食用菌保存”[食品化学]. 484(2025)144451。

IF 10.4 1区农林科学

Food Chemistry Pub Date : 2026-09-15 Epub Date: 2026-07-02 DOI: 10.1016/j.foodchem.2026.150184

Ying Zhou, Weina Wu, Kseniya Hileuskaya, Ping Shao

引用次数: 0

Non-destructive ripeness classification of apricot (Prunus armeniaca L.) using a physically informed deep learning approach. 使用物理信息深度学习方法对杏（Prunus armeniaca L.）进行无损成熟度分类。

IF 10.4 1区农林科学

Food Chemistry Pub Date : 2026-09-15 Epub Date: 2026-06-27 DOI: 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":"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 (R2 = 0.442, p < 0.001), demonstrating that learned visual features capture biologically meaningful ripening signals and enabling more reliable AI-assisted fruit grading.","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}

引用次数: 0

MMSE-GC-MS volatile organic compound profiling of twelve defect categories in Robusta coffee beans: chemical markers and multivariate discrimination of quality defects. 罗布斯塔咖啡豆12种缺陷类型的MMSE-GC-MS挥发性有机物分析：品质缺陷的化学标记和多变量判别

IF 10.4 1区农林科学

Food Chemistry Pub Date : 2026-09-15 Epub Date: 2026-06-27 DOI: 10.1016/j.foodchem.2026.150212

Februadi Bastian, Trivena Patricia Bongga Pasilong, Andi Dirpan, Olly Sanny Hutabarat, Irham Rasyid, Justasya Nanda Putri Buntu Payung

{"title":"MMSE-GC-MS volatile organic compound profiling of twelve defect categories in Robusta coffee beans: chemical markers and multivariate discrimination of quality defects.","authors":"Februadi Bastian, Trivena Patricia Bongga Pasilong, Andi Dirpan, Olly Sanny Hutabarat, Irham Rasyid, Justasya Nanda Putri Buntu Payung","doi":"10.1016/j.foodchem.2026.150212","DOIUrl":"10.1016/j.foodchem.2026.150212","url":null,"abstract":"Defective coffee beans significantly compromise the flavor quality of Robusta coffee (Coffea canephora L.). This study presents a comprehensive volatile organic compound (VOC) profiling of twelve defect categories in Robusta green coffee beans from Bulukumba, Indonesia, using Monolithic Material Sorptive Extraction coupled with gas chromatography-mass spectrometry (MMSE-GC-MS). Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) in independent triplicates were applied to discriminate volatile patterns across defect types, with validation on commercial-grade unsorted Robusta samples from three origins. A total of 42 volatile compounds were tentatively identified, spanning pyrazines, furans, aldehydes, alcohols, phenols, esters, acids, terpenes, and sulfur compounds. PCA explained 57.8% of total variance, with bean versus non-bean distinction as the primary axis of differentiation, HCA resolved three major clusters. Selected markers remained detectable in commercial-grade samples across origins. MMSE-GC-MS combined with multivariate analysis offers a sensitive, chemistry-informed framework for objective Robusta defect discrimination, complementing conventional visual inspection.","PeriodicalId":318,"journal":{"name":"Food Chemistry","volume":"523 ","pages":"150212"},"PeriodicalIF":10.4,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148343707","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}

引用次数: 0