چکیده مقاله
Food spoilage remains one of the leading causes of economic loss and public health concerns worldwide Conventional detection methods, including microbial assays and chromatographic analyses, are often time consuming, destructive, and unsuitable for real time monitoring in cold chain systems This systematic review investigates recent advancements in integrating artificial intelligence AI models with biosensor technologies to predict food spoilage more accurately and efficiently Comprehensive searches were performed across international databases following PRISMA 2020 guidelines Eligible studies implemented AI algorithms such as convolutional neural networks CNN , support vector machines SVM , random forests RF , and long short term memory LSTM networks to analyze biosensor data detecting key spoilage indicators, including total volatile basic nitrogen TVB N , volatile organic compounds VOCs , and pH Deep learning models demonstrated outstanding predictive performance, frequently achieving accuracies above 90%, surpassing traditional machine learning methods Electrochemical, optical, and electronic nose sensors integrated with AI showed enhanced stability and rapid detection performance across meat, dairy, seafood, and produce categories The findings emphasize that AI assisted biosensors can enable early, non destructive detection of spoilage and contribute to smart monitoring within Food Safety 4 0 frameworks However, challenges, including sensor calibration drift, heterogeneous data, and limited real world standardization, hinder large scale implementation Future research should prioritize dataset expansion, harmonized validation protocols, and integration with Internet of Things IoT and edge computing systems for practical, real time deployment Overall, the synergy between AI and biosensing offers a transformative path toward predictive, sustainable, and highly reliable food safety management
کلیدواژهها
نویسندگان
Shima Omidi
Department of Food Science and Technology, Shah C , Islamic Azad University, Shahreza, Iran
Somayeh Sanjari
Department of Food Science and Technology, NT C , Islamic Azad University, Tehran, Iran
Sarina Khajeh-Heidari
Department of Experimental Sciences, Farzanegan School, Sirjan, Iran
Sahar Sanjari
Department of Medicine, Zah C , Islamic Azad University, Zahedan, Iran
شیوه ارجاع
Omidi, Shima and Sanjari, Somayeh and Khajeh-Heidari, Sarina and Sanjari, Sahar,1404,Artificial Intelligence Applications for Predicting Food Spoilage Using Biosensor Data in the Cold Chain: A Systematic Review,The first national conference environment, smart agriculture, food security,Varamin
ارائهشده در
مجموعه مقالات اولین همایش ملی محیط زیست، کشاورزی هوشمند، امنیت غذایی28 آبان 1404 · ورامین