{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:26:08Z","timestamp":1792790768896,"version":"4.5.0"},"reference-count":96,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T00:00:00Z","timestamp":1770508800000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Food Chemistry: X"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1016\/j.fochx.2026.103654","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:52:25Z","timestamp":1770742345000},"page":"103654","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":5,"special_numbering":"C","title":["Artificial intelligence in bread making: Applications in quality control, formulation and sensory prediction"],"prefix":"10.1016","volume":"34","author":[{"family":"Marimuthu Murugesan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Prakash Pandurangan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Anitha Murugesan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Halan Senthilkumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Krishnamurthy K. Hegde","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Sheela Thangaraj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"family":"Meivelu Moovendhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.fochx.2026.103654_bb0005","doi-asserted-by":"crossref","DOI":"10.3389\/fsufs.2025.1538375","article-title":"Digital twin applications in the food industry: A review","volume":"9","author":"Abdurrahman","year":"2025","journal-title":"Frontiers in Sustainable Food Systems"},{"issue":"2","key":"10.1016\/j.fochx.2026.103654_bb0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.afres.2022.100126","article-title":"Recent advances and applications of artificial intelligence and related technologies in the food industry","volume":"2","author":"Addanki","year":"2022","journal-title":"Applied Food Research"},{"key":"10.1016\/j.fochx.2026.103654_bb0015","doi-asserted-by":"crossref","DOI":"10.3389\/fnut.2025.1553942","article-title":"AI-driven transformation in food manufacturing: A pathway to sustainable efficiency and quality assurance","volume":"12","author":"Agrawal","year":"2025","journal-title":"Frontiers in Nutrition"},{"key":"10.1016\/j.fochx.2026.103654_bb0020","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1038\/srep00196","article-title":"Flavor network and the principles of food pairing","volume":"1","author":"Ahn","year":"2011","journal-title":"Scientific Reports"},{"issue":"1","key":"10.1016\/j.fochx.2026.103654_bb0025","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.jfoodeng.2012.01.017","article-title":"Rheological parameters determination using near-infrared technology in whole wheat grain","volume":"111","author":"Arazuri","year":"2012","journal-title":"Journal of Food Engineering"},{"issue":"1","key":"10.1016\/j.fochx.2026.103654_bb0030","first-page":"142","article-title":"Applications of machine learning models in near-infrared spectroscopy for small-grain quality control","volume":"44","author":"Ardiana","year":"2025","journal-title":"Asian J. Dairy Food Res."},{"key":"10.1016\/j.fochx.2026.103654_bb0035","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI","volume":"58","author":"Arrieta","year":"2020","journal-title":"Info. Fusion"},{"key":"10.1016\/j.fochx.2026.103654_bb0040","series-title":"Explainable artificial intelligence techniques for interpretation of food datasets: a review","author":"Arrighi","year":"2025"},{"key":"10.1016\/j.fochx.2026.103654_bb0045","doi-asserted-by":"crossref","DOI":"10.3389\/fclim.2025.1585331","article-title":"Enhancing system resilience to climate change through artificial intelligence: A systematic literature review","volume":"7","author":"Ayadi","year":"2025","journal-title":"Frontiers in Climate"},{"issue":"7","key":"10.1016\/j.fochx.2026.103654_bb0050","doi-asserted-by":"crossref","first-page":"908","DOI":"10.36347\/sajb.2025.v13i07.005","article-title":"Revolutionizing food product development: Use of AI in product formulation, sensory prediction, and sustainable scaling","volume":"13","author":"Azeem","year":"2025","journal-title":"Scholars Acad. J. Biosci."},{"key":"10.1016\/j.fochx.2026.103654_bb0055","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.spc.2025.10.007","article-title":"Advancing life cycle assessment through data science: A critical review of algorithms, tools, and data challenges","volume":"61","author":"Bahmutsky","year":"2025","journal-title":"Sustainable Production and Consumption"},{"key":"10.1016\/j.fochx.2026.103654_bb0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.tifs.2025.105153","article-title":"Artificial intelligence for food safety: From predictive models to real-world safeguards","volume":"163","author":"Balakrishnan","year":"2025","journal-title":"Trends in Food Science and Technology"},{"key":"10.1016\/j.fochx.2026.103654_bb0065","series-title":"R&D report: Pilot-scale implementation of LSTM-based fermentation monitoring and dough proofing control. Barilla Group Reports","author":"Barilla Group","year":"2023"},{"key":"10.1016\/j.fochx.2026.103654_bb0070","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.jmsy.2020.06.004","article-title":"Towards IoT-enabled dynamic service optimal selection in multiple manufacturing clouds","volume":"56","author":"Chen","year":"2020","journal-title":"Journal of Manufacturing Systems"},{"key":"10.1016\/j.fochx.2026.103654_bb0075","doi-asserted-by":"crossref","first-page":"5040","DOI":"10.1039\/D5RA07499A","article-title":"Rheological analysis in food processing: Factors, applications, and future outlooks with machine learning integration","volume":"16","author":"Chen","year":"2026","journal-title":"RSC Advances"},{"issue":"8","key":"10.1016\/j.fochx.2026.103654_bb0080","doi-asserted-by":"crossref","first-page":"4538","DOI":"10.3390\/app15084538","article-title":"Innovative approaches in sensory food science: From digital tools to virtual reality","volume":"15","author":"Cosme","year":"2025","journal-title":"Applied Sciences"},{"key":"10.1016\/j.fochx.2026.103654_bb0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.foodchem.2024.139230","article-title":"Development of machine learning-based shelf-life prediction models for multiple marine fish species and construction of a real-time prediction platform","volume":"450","author":"Cui","year":"2024","journal-title":"Food Chemistry"},{"issue":"11","key":"10.1016\/j.fochx.2026.103654_bb0090","doi-asserted-by":"crossref","first-page":"2227","DOI":"10.3390\/foods12112227","article-title":"IoT-enabled electronic nose system for beef quality monitoring and spoilage detection","volume":"12","author":"Damdam","year":"2023","journal-title":"Foods"},{"key":"10.1016\/j.fochx.2026.103654_bb0095","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.nutos.2023.07.001","article-title":"Artificial intelligence, nutrition, and ethical issues: A mini review","volume":"50","author":"Detopoulou","year":"2023","journal-title":"Clinical Nutrition Open Science"},{"key":"10.1016\/j.fochx.2026.103654_bb0100","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.jfoodeng.2017.04.024","article-title":"Fusion of electronic nose, electronic tongue and computer vision for animal source food authentication and quality assessment: A review","volume":"210","author":"Di Rosa","year":"2017","journal-title":"Journal of Food Engineering"},{"key":"10.1016\/j.fochx.2026.103654_bb0105","series-title":"Artificial intelligence in food systems","author":"Food and Agriculture Organization of the United Nations","year":"2023"},{"key":"10.1016\/j.fochx.2026.103654_bb0110","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.ijpe.2019.01.004","article-title":"Industry 4.0 technologies: Implementation patterns in manufacturing companies","volume":"210","author":"Frank","year":"2019","journal-title":"International Journal of Production Economics"},{"issue":"8","key":"10.1016\/j.fochx.2026.103654_bb0115","doi-asserted-by":"crossref","first-page":"3421","DOI":"10.3390\/app14083421","article-title":"Enhancing food integrity through artificial intelligence and machine learning: A comprehensive review","volume":"14","author":"Gbashi","year":"2024","journal-title":"Applied Sciences"},{"key":"10.1016\/j.fochx.2026.103654_bb0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.jare.2025.10.018","article-title":"Machine learning for food flavor prediction and regulation: Models, data integration, and future perspectives","author":"Ge","year":"2025","journal-title":"Journal of Advanced Research"},{"issue":"1","key":"10.1016\/j.fochx.2026.103654_bb0125","doi-asserted-by":"crossref","first-page":"48","DOI":"10.4018\/IJAEIS.2020010103","article-title":"Big data challenges and opportunities in agriculture","volume":"11","author":"Gopal","year":"2020","journal-title":"Int. J. Agric. Environ. Inf. Syst."},{"issue":"13","key":"10.1016\/j.fochx.2026.103654_bb0130","doi-asserted-by":"crossref","first-page":"4063","DOI":"10.3390\/s25134063","article-title":"Machine learning-based shelf life estimator for dates using a multichannel gas sensor: Enhancing food security","volume":"25","author":"Haque","year":"2025","journal-title":"Sensors"},{"key":"10.1016\/j.fochx.2026.103654_bb0135","series-title":"Artificial intelligence in food science","first-page":"263","article-title":"Flavor profiling and sensory analysis using artificial intelligence and machine learning","author":"Hiregoudar","year":"2026"},{"issue":"9","key":"10.1016\/j.fochx.2026.103654_bb0140","first-page":"3321","article-title":"Automatic modulation recognition based on the optimized linear combination of higher-order cumulants","volume":"22","author":"Hussain","year":"2022","journal-title":"Sensors"},{"issue":"11","key":"10.1016\/j.fochx.2026.103654_bb0145","first-page":"233","article-title":"Adaptive IoT and AI-enhanced quality assurance system for sustainable bakery production","volume":"9","author":"Hussain","year":"2024","journal-title":"International Journal of Innovative Science Research & Technology"},{"issue":"1","key":"10.1016\/j.fochx.2026.103654_bb0150","doi-asserted-by":"crossref","DOI":"10.3390\/foods15010090","article-title":"Machine learning in transforming the food industry","volume":"15","author":"Hussain","year":"2026","journal-title":"Foods"},{"issue":"3","key":"10.1016\/j.fochx.2026.103654_bb0155","doi-asserted-by":"crossref","first-page":"88","DOI":"10.3390\/technologies13030088","article-title":"Comprehensive analysis of random forest and XGBoost performance with SMOTE, ADASYN, and GNUS under varying imbalance levels","volume":"13","author":"Imani","year":"2025","journal-title":"Technologies"},{"key":"10.1016\/j.fochx.2026.103654_bb0160","article-title":"How AI is reshaping product development: Accelerating product formulation and ingredient optimization","author":"Institute of Food Technologists","year":"2025","journal-title":"Food Technology Magazine"},{"key":"10.1016\/j.fochx.2026.103654_bb0165","doi-asserted-by":"crossref","first-page":"2045","DOI":"10.1007\/s00231-020-02837-6","article-title":"Prediction of baking quality using machine learning-based intelligent models","volume":"56","author":"Isleroglu","year":"2020","journal-title":"Heat and Mass Transfer"},{"issue":"3","key":"10.1016\/j.fochx.2026.103654_bb0170","doi-asserted-by":"crossref","first-page":"278","DOI":"10.3390\/biomimetics8030278","article-title":"A comprehensive review of bio-inspired optimization algorithms including applications in microelectronics and nanophotonics","volume":"8","author":"Jak\u0161i\u0107","year":"2023","journal-title":"Biomimetics"},{"issue":"13","key":"10.1016\/j.fochx.2026.103654_bb0175","doi-asserted-by":"crossref","DOI":"10.3390\/ani13132096","article-title":"Precision livestock farming research: A global scientometric review","volume":"13","author":"Jiang","year":"2023","journal-title":"Animals"},{"key":"10.1016\/j.fochx.2026.103654_bb0180","doi-asserted-by":"crossref","first-page":"4257","DOI":"10.1007\/s11540-025-09928-z","article-title":"Leveraging machine learning to predict potato shelf life: A comprehensive analysis in an evaporative cooling structure","volume":"68","author":"Jubayer","year":"2025","journal-title":"Potato Research"},{"issue":"1\u20132","key":"10.1016\/j.fochx.2026.103654_bb0185","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2200000083","article-title":"Advances and open problems in federated learning","volume":"14","author":"Kairouz","year":"2021","journal-title":"Foundations and Trends in Machine Learning"},{"key":"10.1016\/j.fochx.2026.103654_bb0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.jfoodeng.2025.112918","article-title":"Digital twins for autonomous thermal food processing: A model predictive control study with reduced-order models of augmented neural ordinary differential equation type","volume":"410","author":"Kannapinn","year":"2026","journal-title":"Journal of Food Engineering"},{"issue":"3","key":"10.1016\/j.fochx.2026.103654_bb0195","first-page":"2419","article-title":"Machine learning-based modeling in food processing applications: State of the art","volume":"21","author":"Khan","year":"2022","journal-title":"Comprehensive Reviews in Food Science and Food Safety"},{"issue":"2","key":"10.1016\/j.fochx.2026.103654_bb0200","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.foodcont.2012.01.011","article-title":"A new method for dynamic modelling of bread dough kneading based on artificial neural network","volume":"26","author":"Lamrini","year":"2012","journal-title":"Food Control"},{"issue":"17","key":"10.1016\/j.fochx.2026.103654_bb0205","doi-asserted-by":"crossref","first-page":"5251","DOI":"10.3390\/s25175251","article-title":"Pressure-guided LSTM modelling for fermentation quantification prediction","volume":"17","author":"Lee","year":"2025","journal-title":"Sensors"},{"issue":"22","key":"10.1016\/j.fochx.2026.103654_bb0210","doi-asserted-by":"crossref","first-page":"4160","DOI":"10.3390\/foods12224160","article-title":"The study of an adaptive bread maker using machine learning","volume":"12","author":"Lee","year":"2023","journal-title":"Foods"},{"issue":"19","key":"10.1016\/j.fochx.2026.103654_bb0215","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.3390\/foods13193025","article-title":"Research progress of machine learning in extending and regulating the shelf life of fruits and vegetables","volume":"13","author":"Li","year":"2024","journal-title":"Foods"},{"key":"10.1016\/j.fochx.2026.103654_bb0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.108412","article-title":"Label-efficient learning in agriculture: A comprehensive review","author":"Li","year":"2023","journal-title":"Computers and Electronics in Agriculture"},{"issue":"19","key":"10.1016\/j.fochx.2026.103654_bb0225","doi-asserted-by":"crossref","first-page":"3424","DOI":"10.3390\/foods14193424","article-title":"Machine learning for quality control in the food industry: A review","volume":"14","author":"Liakos","year":"2025","journal-title":"Foods"},{"issue":"5","key":"10.1016\/j.fochx.2026.103654_bb0230","doi-asserted-by":"crossref","first-page":"2884","DOI":"10.3390\/app13052884","article-title":"Digital twin model quality optimization and control methods based on workflow management","volume":"13","author":"Luo","year":"2023","journal-title":"Applied Sciences"},{"issue":"8","key":"10.1016\/j.fochx.2026.103654_bb0235","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.3390\/foods14081295","article-title":"Prediction of extensibility and toughness of wheat-flour dough using bubble inflation\u2013structured light scanning 3D imaging technology and an enhanced 3D VGG11 model","volume":"14","author":"Luo","year":"2025","journal-title":"Foods"},{"key":"10.1016\/j.fochx.2026.103654_bb0240","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.tifs.2022.04.025","article-title":"Artificial intelligence and ethics within the food sector: Developing a common language for technology adoption across the supply chain","volume":"125","author":"Manning","year":"2022","journal-title":"Trends in Food Science and Technology"},{"issue":"24","key":"10.1016\/j.fochx.2026.103654_bb0245","doi-asserted-by":"crossref","first-page":"4236","DOI":"10.3390\/foods14244236","article-title":"A machine learning model to reframe the concept of shelf-life in bakery products: PDO sourdough as a technological preservation model","volume":"14","author":"Marianelli","year":"2025","journal-title":"Foods"},{"issue":"2","key":"10.1016\/j.fochx.2026.103654_bb0250","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.foodcont.2012.06.001","article-title":"Predictive microbiology theory and application: Is it all about rates?","volume":"29","author":"McMeekin","year":"2013","journal-title":"Food Control"},{"key":"10.1016\/j.fochx.2026.103654_bb0255","article-title":"Machine vision based intelligent oven for baking inspection of cupcake: Design and implementation","volume":"82","author":"Mehdizadeh","year":"2022","journal-title":"Mechatronics"},{"issue":"14","key":"10.1016\/j.fochx.2026.103654_bb0260","doi-asserted-by":"crossref","first-page":"3660","DOI":"10.3390\/en18143660","article-title":"Digital twin for energy intelligent bakery operations: Concepts and applications","volume":"18","author":"Melesse","year":"2025","journal-title":"Energies"},{"issue":"239","key":"10.1016\/j.fochx.2026.103654_bb0265","first-page":"2","article-title":"Docker: Lightweight Linux containers for consistent development and deployment","volume":"2014","author":"Merkel","year":"2014","journal-title":"Linux J."},{"issue":"3","key":"10.1016\/j.fochx.2026.103654_bb0270","doi-asserted-by":"crossref","first-page":"41","DOI":"10.3390\/jcp5030041","article-title":"A systematic review on hybrid AI models integrating machine learning and federated learning","volume":"5","author":"Moussaoui","year":"2025","journal-title":"J. Cybersecurity Priv."},{"key":"10.1016\/j.fochx.2026.103654_bb0275","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1016\/j.jmsy.2022.01.010","article-title":"Towards edge computing in intelligent manufacturing: Past, present and future","volume":"62","author":"Nain","year":"2022","journal-title":"Journal of Manufacturing Systems"},{"key":"10.1016\/j.fochx.2026.103654_bb0280","doi-asserted-by":"crossref","first-page":"3650","DOI":"10.3390\/s24113650","article-title":"Challenges and opportunities in calibrating low-cost environmental sensors","volume":"24","author":"Nalakurthi","year":"2024","journal-title":"Sensors"},{"key":"10.1016\/j.fochx.2026.103654_bb0285","doi-asserted-by":"crossref","DOI":"10.1016\/j.foodcont.2025.111292","article-title":"The role of artificial intelligence in advancing food safety: A strategic path to zero contamination","volume":"175","author":"Naseem","year":"2025","journal-title":"Food Control"},{"key":"10.1016\/j.fochx.2026.103654_bb0290","article-title":"Vibrational spectroscopy and machine learning in bakery processes","author":"Niu","year":"2025","journal-title":"Journal of Spectroscopy, Machine Learning and Food Systems"},{"key":"10.1016\/j.fochx.2026.103654_bb0295","article-title":"Artificial intelligence in sensory and consumer studies of food products: Advantages, limitations, and prospects","volume":"51","author":"Nunes","year":"2023","journal-title":"Current Opinion in Food Science"},{"key":"10.1016\/j.fochx.2026.103654_bb0300","doi-asserted-by":"crossref","DOI":"10.1111\/1541-4337.13385","article-title":"Innovations in nondestructive assessment of baked products: Current trends and future prospects","volume":"23","author":"Olakanmi","year":"2024","journal-title":"Comprehensive Reviews in Food Science and Food Safety"},{"issue":"22","key":"10.1016\/j.fochx.2026.103654_bb0305","doi-asserted-by":"crossref","first-page":"3919","DOI":"10.3390\/foods14223919","article-title":"Artificial intelligence-enabled ingredient substitution in food systems: A review and conceptual framework for sensory, functional, nutritional, and cultural optimization","volume":"14","author":"\u00d6z","year":"2025","journal-title":"Foods"},{"key":"10.1016\/j.fochx.2026.103654_bb0310","first-page":"8024","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"24","key":"10.1016\/j.fochx.2026.103654_bb0315","doi-asserted-by":"crossref","DOI":"10.3390\/app152412904","article-title":"Systematic review of reinforcement learning in process industries: A contextual and taxonomic approach","volume":"15","author":"Paz Ramos","year":"2025","journal-title":"Applied Sciences"},{"issue":"2","key":"10.1016\/j.fochx.2026.103654_bb0320","doi-asserted-by":"crossref","DOI":"10.15414\/jmbfs.3294","article-title":"Application of D-optimal mixture design and artificial neural network in optimizing the composition of flours for preparation of gluten-free bread","volume":"11","author":"Pradhan","year":"2021","journal-title":"Journal of Microbiology, Biotechnology and Food Sciences"},{"key":"10.1016\/j.fochx.2026.103654_bb0325","doi-asserted-by":"crossref","DOI":"10.1016\/j.tifs.2025.104989","article-title":"Artificial intelligence for prediction of shelf life of various food products: Recent advances and ongoing challenges","volume":"159","author":"Rashvand","year":"2025","journal-title":"Trends in Food Science and Technology"},{"key":"10.1016\/j.fochx.2026.103654_bb0330","doi-asserted-by":"crossref","DOI":"10.1016\/j.foodcont.2025.111857","article-title":"AI-driven real-time monitoring and predictive control system for yoghurt fermentation","volume":"182","author":"Ray","year":"2026","journal-title":"Food Control"},{"issue":"6","key":"10.1016\/j.fochx.2026.103654_bb0335","doi-asserted-by":"crossref","first-page":"922","DOI":"10.3390\/foods14060922","article-title":"Applications of machine learning in food safety and HACCP monitoring of animal-source foods","volume":"14","author":"Revelou","year":"2025","journal-title":"Foods"},{"key":"10.1016\/j.fochx.2026.103654_bb0340","article-title":"Natural variability of wheat flour composition and its impact on dough rheological properties","volume":"162","author":"Rezette","year":"2025","journal-title":"Food Hydrocolloids"},{"key":"10.1016\/j.fochx.2026.103654_bb0345","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.ijfoodmicro.2014.04.005","article-title":"Use of sourdough fermentation and mixture of wheat, chickpea, lentil and bean flours for enhancing the nutritional, texture and sensory characteristics of white bread","volume":"180","author":"Rizzello","year":"2014","journal-title":"International Journal of Food Microbiology"},{"issue":"12","key":"10.1016\/j.fochx.2026.103654_bb0350","doi-asserted-by":"crossref","first-page":"1410","DOI":"10.3390\/bioengineering10121410","article-title":"A systematic review of machine-learning solutions in anaerobic digestion","volume":"10","author":"Rutland","year":"2023","journal-title":"Bioengineering"},{"key":"10.1016\/j.fochx.2026.103654_bb0355","doi-asserted-by":"crossref","DOI":"10.1016\/j.lwt.2025.118326","article-title":"Machine learning-based prediction of microbial growth and acidification in yogurt fermentation at industrial temperatures","volume":"231","author":"Saetae","year":"2025","journal-title":"LWT"},{"issue":"Part 2","key":"10.1016\/j.fochx.2026.103654_bb0360","article-title":"Low-cost machine learning-integrated optical spectrophotometer for non-destructive color and shelf-life analysis: A study on sliced bread","volume":"492","author":"Sahu","year":"2025","journal-title":"Food Chemistry"},{"key":"10.1016\/j.fochx.2026.103654_bb0365","doi-asserted-by":"crossref","first-page":"176986","DOI":"10.1109\/ACCESS.2020.3026925","article-title":"Prediction of microbial spoilage and shelf life of bakery products through hyperspectral imaging","volume":"8","author":"Saleem","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.fochx.2026.103654_bb0370","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.crfs.2022.02.006","article-title":"Application of bio-inspired optimization algorithms in food processing","volume":"5","author":"Sarkar","year":"2022","journal-title":"Current Research in Food Science"},{"key":"10.1016\/j.fochx.2026.103654_bb0375","article-title":"Food development through co-creation with AI: Bread with a \u201ctaste of love\u201d (Preprint)","author":"Sera","year":"2024","journal-title":"arXiv"},{"issue":"32","key":"10.1016\/j.fochx.2026.103654_bb0380","doi-asserted-by":"crossref","first-page":"12009","DOI":"10.1080\/10408398.2023.2245899","article-title":"Artificial neural network-based shelf life prediction approach in the food storage process: A review","volume":"64","author":"Shi","year":"2024","journal-title":"Critical Reviews in Food Science and Nutrition"},{"key":"10.1016\/j.fochx.2026.103654_bb0385","doi-asserted-by":"crossref","DOI":"10.3389\/fpubh.2021.816226","article-title":"An artificial intelligence approach toward food spoilage detection and analysis","volume":"9","author":"Sonwani","year":"2022","journal-title":"Frontiers in Public Health"},{"key":"10.1016\/j.fochx.2026.103654_bb0390","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.jmsy.2018.01.006","article-title":"Data-driven smart manufacturing","volume":"48","author":"Tao","year":"2018","journal-title":"Journal of Manufacturing Systems"},{"issue":"24","key":"10.1016\/j.fochx.2026.103654_bb0395","doi-asserted-by":"crossref","first-page":"4461","DOI":"10.3390\/foods12244461","article-title":"The use of predictive microbiology for the prediction of the shelf life of food products","volume":"12","author":"Tarlak","year":"2023","journal-title":"Foods"},{"issue":"6","key":"10.1016\/j.fochx.2026.103654_bb0400","first-page":"90","article-title":"Review of technology infrastructure development within confectionery business environments","volume":"1","author":"Ugwu-Oju","year":"2024","journal-title":"Int. J. Future Eng. Innov."},{"issue":"5","key":"10.1016\/j.fochx.2026.103654_bb0405","doi-asserted-by":"crossref","first-page":"161","DOI":"10.3390\/chemosensors13050161","article-title":"A systematic review of the applications of electronic nose and electronic tongue in food quality assessment and safety","volume":"13","author":"Vanaraj","year":"2025","journal-title":"Chemosensors"},{"key":"10.1016\/j.fochx.2026.103654_bb0410","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2021.109616","article-title":"Reinforcement learning for statistical process control in manufacturing","volume":"182","author":"Viharos","year":"2021","journal-title":"Measurement"},{"issue":"2","key":"10.1016\/j.fochx.2026.103654_bb0415","doi-asserted-by":"crossref","first-page":"68","DOI":"10.62762\/ASFP.2025.552607","article-title":"Application of artificial intelligence in food industry: A review","volume":"2","author":"Wang","year":"2025","journal-title":"Agric. Sci. Food Process."},{"key":"10.1016\/j.fochx.2026.103654_bb0420","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.asoc.2016.04.034","article-title":"A survey on metaheuristics for optimization in food manufacturing industry","volume":"46","author":"Wari","year":"2016","journal-title":"Applied Soft Computing"},{"key":"10.1016\/j.fochx.2026.103654_bb0425","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.agsy.2017.01.023","article-title":"Big data in smart farming: A review","volume":"153","author":"Wolfert","year":"2017","journal-title":"Agricultural Systems"},{"issue":"11","key":"10.1016\/j.fochx.2026.103654_bb0430","doi-asserted-by":"crossref","first-page":"2601","DOI":"10.3390\/microorganisms13112601","article-title":"Predictive fermentation control of Lactiplantibacillus plantarum using deep learning convolutional neural networks","volume":"13","author":"Wu","year":"2025","journal-title":"Microorganisms"},{"key":"10.1016\/j.fochx.2026.103654_bb0435","article-title":"A review of machine learning applications in life cycle assessment studies","volume":"912","author":"Xue","year":"2024","journal-title":"Sci. Total Environ."},{"issue":"Part 2","key":"10.1016\/j.fochx.2026.103654_bb0440","article-title":"Artificial intelligence-driven food quality prediction: Applying machine learning ensemble models for dynamic forecasting of pork pH and meat color changes","volume":"492","author":"Yang","year":"2025","journal-title":"Food Chemistry"},{"key":"10.1016\/j.fochx.2026.103654_bb0445","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1007\/s00217-024-04488-x","article-title":"Analysis of selected deep features with CNN-SVM-based for bread wheat seed classification","volume":"250","author":"Yasar","year":"2024","journal-title":"European Food Research and Technology"},{"issue":"6","key":"10.1016\/j.fochx.2026.103654_bb0450","doi-asserted-by":"crossref","DOI":"10.3390\/fermentation11060323","article-title":"Smart fermentation technologies: Microbial process control in traditional fermented foods","volume":"11","author":"Yee","year":"2025","journal-title":"Fermentation"},{"key":"10.1016\/j.fochx.2026.103654_bb0455","series-title":"Proc. 31st Natl. Conf. & 6th Int. Congr. Iranian food Ind., Sari Agric. Sci. Nat. Res. Univ.","article-title":"Data-driven optimization of bakery formulations for targeted sensory texture using interpretable machine learning","author":"Zamanpour Borojuny","year":"2025"},{"key":"10.1016\/j.fochx.2026.103654_bb0460","doi-asserted-by":"crossref","DOI":"10.1016\/j.tifs.2025.105272","article-title":"Domain knowledge, just evaluation, and robust data standards are required to advance AI in food science","volume":"164","author":"Zhang","year":"2025","journal-title":"Trends in Food Science and Technology"},{"key":"10.1016\/j.fochx.2026.103654_bb0465","article-title":"Application of intelligent taste analysis based on random forest algorithm in food quality inspection","volume":"2022","author":"Zhang","year":"2022","journal-title":"Computational Intelligence and Neuroscience"},{"issue":"1","key":"10.1016\/j.fochx.2026.103654_bb0470","doi-asserted-by":"crossref","first-page":"63","DOI":"10.3390\/machines11010063","article-title":"Semi\u2212supervised hybrid modeling of the yeast fermentation process","volume":"11","author":"Zhao","year":"2023","journal-title":"Machines"},{"key":"10.1016\/j.fochx.2026.103654_bb0475","doi-asserted-by":"crossref","DOI":"10.1016\/j.jfoodeng.2025.112467","article-title":"Digital twin for predicting and controlling food fermentation: A case study of kombucha fermentation","volume":"393","author":"Zhao","year":"2025","journal-title":"Journal of Food Engineering"},{"key":"10.1016\/j.fochx.2026.103654_bb0480","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.crfs.2021.03.009","article-title":"Deep learning and machine vision for food processing: A survey","volume":"4","author":"Zhu","year":"2021","journal-title":"Current Research in Food Science"}],"container-title":["Food Chemistry: X"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2590157526001756?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2590157526001756?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:08:04Z","timestamp":1773360484000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2590157526001756"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":96,"alternative-id":["S2590157526001756"],"URL":"https:\/\/doi.org\/10.1016\/j.fochx.2026.103654","relation":{},"ISSN":["2590-1575"],"issn-type":[{"value":"2590-1575","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence in bread making: Applications in quality control, formulation and sensory prediction","name":"articletitle","label":"Article Title"},{"value":"Food Chemistry: X","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.fochx.2026.103654","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"103654"}}