Exploring the broad spectrum of machine learning technologies in the food sector: A comprehensive review of techniques and practical applications.

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Learn more: PMC Disclaimer | PMC Copyright Notice Curr Res Food Sci . 2026 Apr 8;12:101406. doi: 10.1016/j.crfs.2026.101406 Search in PMC Search in PubMed View in NLM Catalog Add to search Exploring the broad spectrum of machine learning technologies in the food sector: A comprehensive review of techniques and practical applications Junli Feng Junli Feng a Zhejiang Key Laboratory of Food Microbiology and Nutritional Health, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, 310018, China Find articles by Junli Feng a , Fuguang Zheng Fuguang Zheng a Zhejiang Key Laboratory of Food Microbiology and Nutritional Health, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, 310018, China Find articles by Fuguang Zheng a , Yu Zhang Yu Zhang a Zhejiang Key Laboratory of Food Microbiology and Nutritional Health, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, 310018, China b School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310006, China Find articles by Yu Zhang a, b , Xixi Zeng Xixi Zeng c Panvascular Diseases Research Center, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, 324000, China d Laboratory of Food Nutrition and Clinical Research, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, 310012, China Find articles by Xixi Zeng c, d , Zhijian Wang Zhijian Wang f Zhejiang Provincial Institute for Food and Drug Control, Hangzhou, 310052, China Find articles by Zhijian Wang f , Jingjing Liang Jingjing Liang f Zhejiang Provincial Institute for Food and Drug Control, Hangzhou, 310052, China Find articles by Jingjing Liang f , Zejun Wang Zejun Wang e Laboratory of Medicine-Food Homology Innovation and Achievement Transformation, Linping Hospital of Integrated Traditional Chinese and Western Medicine, Hangzhou, Zhejiang, 311110, China Find articles by Zejun Wang e, ⁎ , Liting Ji Liting Ji b School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310006, China Find articles by Liting Ji b, ⁎⁎ , Qing Shen Qing Shen c Panvascular Diseases Research Center, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, 324000, China d Laboratory of Food Nutrition and Clinical Research, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, 310012, China Find articles by Qing Shen c, d, ⁎⁎⁎ Author information Article notes Copyright and License information a Zhejiang Key Laboratory of Food Microbiology and Nutritional Health, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, 310018, China b School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310006, China c Panvascular Diseases Research Center, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, 324000, China d Laboratory of Food Nutrition and Clinical Research, Institute of Seafood, Zhejiang Gongshang University, Hangzhou, 310012, China e Laboratory of Medicine-Food Homology Innovation and Achievement Transformation, Linping Hospital of Integrated Traditional Chinese and Western Medicine, Hangzhou, Zhejiang, 311110, China f Zhejiang Provincial Institute for Food and Drug Control, Hangzhou, 310052, China ⁎ Corresponding author. [email protected] ⁎⁎ Corresponding author. [email protected] ⁎⁎⁎ Corresponding author. Panvascular Diseases Research Center, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, 324000, China. [email protected] Received 2025 Nov 5; Revised 2026 Apr 1; Accepted 2026 Apr 8; Collection date 2026. © 2026 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). PMC Copyright notice PMCID: PMC13091932  PMID: 42011234 Abstract In the contemporary era, the digital revolution is fundamentally transforming our modes of living, working and thinking by optimizing processes, enabling deeper insights discovery and enhancing decision-making. The realization of this immense potential lies in the ability to extract valuable information from large datasets through machine learning (ML), thereby generating data-driven insights, informed decisions and accurate predictions. By leveraging the powerful modeling capabilities of ML, particularly in handling complex high-dimensional data, the food industry can more accurately predict or identify potential quality issues, safety risks and shifts in consumer trends. In this review, the basic principles of ML in data processing, model training and performance evaluation were introduced, followed by a comprehensive overview of ML applications across various food industry scenarios, including production optimization, origin traceability, adulteration detection, quality control, pathogen or foreign objects identification, preservation techniques, supply chain management, foods innovation and consumption trends. The types of data processing, feature extraction and model algorithms employed in these retrieved studies are systematically categorized and discussed, to assist readers in selecting appropriate algorithms for solving practical problems that may be encountered in food industry. Despite substantial progress in both theoretical foundation and practical applications of ML technique, there are still challenges in terms of data accessibility, model robustness and results interpretability. Addressing these issues is essential for fully realizing the potential benefits that ML offers to food industry. It is expected that the insights presented will contribute to the advancement of ML-based artificial intelligence technologies for smart food industry applications. Keywords: Food industry, Machine learning, Algorithms, Food quality, Intelligent technology Graphical abstract Open in a new tab Highlights • An overview of ML principles and their applications in food industry are presented. • Types of data processing and ML algorithms in food studies are categorized. • The presented insights advance AI technologies for smart food industry applications. 1. Introduction Nowadays, global food industries face mounting challenges in ensuring food security, safety and sustainability, and these issues are exacerbated by the rising consumer's demands and environmental changes ( Kakani et al., 2020 ). For instance, foodstuff shortage impacted over 281 million people across 59 countries in 2023, with more than 705,000 individuals experiencing catastrophic conditions ( Food Security Information Network, 2024 ). Meanwhile, recent events such as the COVID-19 pandemic and the Russia-Ukraine war have exacerbated global food security risks, and the Food Price Index has reached 124.4 points in September 2024. In terms of food safety, a major E. coli outbreak (14 states across the United States) linked to McDonald's Quarter Pounders was reported in 2024 ( https://www.cdc.gov/ecoli/outbreaks/e-coli-O157.html ), which exposed a potentially significant vulnerability in the fast food supply chain. Additionally, consumers who ingested the red yeast rice dietary supplements manufactured by Japan's Kobayashi Pharmaceutical Co. Ltd experienced severe kidney dysfunction ( Guo et al., 2025 ). The red yeast incident not only revealed the company's deficiencies in production management, but also reflected the need for further refinement of Japan's current food safety regulatory framework. To address these issues, digital and intelligence technologies, such as artificial intelligence (AI), big data analytics, block chain and internet of things (IoT), are recognized as effective tools for facilitating the transformation of the food industry towards sustainable development, innovative green strategies and smart farming. As a crucial branch of artificial intelligence (AI), machine learning (ML) is a computer-based system capable of extracting complex patterns from extensive datasets and predicting outcomes to enhance recognition accuracy, while simplifying model prediction and minimizing subjectivity in data analysis ( Ji et al., 2023 ). The application of ML-based intelligent technologies has been employed in the agro-food industry. For example, numerous studies have suggested that ML may exhibit superior performance in identifying key metabolites associated with flavor ( Cai et al., 2024 ; Cui et al., 2025 ; Ji et al., 2023 ; Li et al., 2025 ). The ML model based on computer vision allows the categorization of food products, and this automated sorting process can effectively enhance the uniformity of final products and improve consumer satisfaction ( Guo et al., 2024 ; Karadağ and Kılıç, 2023 ; Sim et al., 2024 ; Yang et al., 2025 ). Additionally, ML algorithms can proactively predict potential contaminant by analyzing historical data, thereby more effectively protecting public health ( Zhu et al., 2024 ). A review of related research over the past five years reveals that most studies have applied ML-powered intelligent technologies to improve modeling accuracy and prediction robustness in food processing optimization, food components analysis, nutritional assessment, quality grading and food safety risk identification. These methodological advances have enabled large-scale deployment of automated control modules, intelligent quality inspection systems, and full-chain digital traceability platforms across mainstream of food field-such as dairy, meat, beverages, and ready-to-eat meals-thereby boosting enterprises' technological innovation capacity and market responsiveness. However, systematic reviews remain scarce in several areas: (1) precise consumer demand identification and dynamic trend forecasting; (2) scientific marketing decision support systems; and (3) targeted functional food design and personalized nutrition interventions for specific populations (e.g., older adults, individuals with metabolic syndrome, athletes). These gaps not only limit enterprises' ability to accurately forecast consumption trends, but also impede the efficient translation of functional foods from lab to clinical testing, industrialization, and precision supply. They further delay the deeper integration of food science and the health industry, particularly with respect to data-driven practice, evidence-based decision-making, and cross-disciplinary innovation. To address the aforementioned research gaps, this review systematically searched the relevant literature published since 2020 across PubMed, ScienceDirect, Web of Science and Google Scholar databases. While not fully adhering to the PRISMA guidelines, key screening steps were rigorously implemented, including predefined inclusion/exclusion criteria, structured keyword searches, and rapid relevance screening of initial results to ensure high topical relevance and academic representativeness. In the search process, keywords “machine learning”, “algorithms”, “intelligent technology”, “food production”, “food quality”, “food safety”, “food nutrition analysis”, “food storage and supply chain”, “food consumption”, and “food innovation”, along with their reasonable variations, were used for combined search, balancing the breadth of term coverage and search accuracy. The search covered diverse food categories (e.g., dairy, meat, fruits, vegetables and grains) and application scenarios (production, processing, distribution and consumption), but only peer-reviewed English-language journal articles were included. Subsequently, the literature screening was conducted with an initial evaluation of article titles and abstracts to exclude those that were clearly irrelevant to the topic of “machine learning-driven development and innovation in the food industry”. Second, full texts of retained studies were thoroughly reviewed and evaluated. For each final included study, the information was extracted on food type, feature extraction methods, ML algorithms, model performance metrics, and efficiency-related parameters. On this basis, the high-quality studies that are highly pertinent to our topic were then selected to provide robust and reliable supports for this review. 2. Basic principles of ML ML represents a remarkable advancement in computer technology, differing significantly from traditional programming approaches. By providing extensive input data and the requirements of corresponding outputs, ML empowers computers to autonomously extract patterns and rules from the data, thus completing the learning process. This procedure involves a thoroughly exploration of the underlying relationships between the dataset and its outputs, and then generating algorithms accordingly. The resulting algorithms are not only applicable for processing current data but also have robust generalization capabilities, allowing them to be applied for predicting future unknown data. Therefore, these models offer substantial supports for decision-making across various domains ( Yang et al., 2025 ). ML can be broadly classified into several primary paradigms based on its learning approaches, the most representative of which are supervised learning, unsupervised learning, and semi-supervised learning-a hybrid approach situated between the former two ( Tseng et al., 2023 ). Supervised learning uses datasets with known answers to develop predictive models for classifying or predicting new data. Unsupervised learning allows computers to autonomously explore datasets without corresponding answers, discern the underlying connections between features and subsequently cluster the data based on these findings. Bridging these two paradigms, semi-supervised learning trains models using a small amount of labeled data alongside a large pool of unlabeled data. This hybrid approach overcomes the limitations of purely supervised methods (which require extensive labeling) and purely unsupervised methods (which may lack guidance), thereby enhancing model performance in real-world scenarios where labeled data are scarce. When categorized by learning task, ML can be divided into three principal types: classification, regression and clustering ( Palei et al., 2025 ). The classification model generates outputs based on input features expressed as discrete values, while the regression model learns mapping relationships from training data to predict continuous values for specific attributes. Both classification and regression belong to the category of supervised learning. In contrast, cluster analysis is an unsupervised learning method that groups samples into clusters based on similarity or distance measures. Thus, samples within the same cluster exhibit a greater similarity while those in different clusters show more pronounced differences. A variety of ML algorithms have been developed to address problems of varying complexity. As shown in Fig. 1 , common algorithms of ML include the decision trees (DT), random forest (RF), support vector machines (SVM), Naive Bayes (NB) and artificial neural networks (ANNs) ( Tseng et al., 2023 ; Zhang et al., 2021 ). Among them, DT, RF, SVM as well as NB are classic algorithms predominantly used for supervised learning tasks, such as classification and regression. The ANNs, on the other hand, are noteworthy for their flexibility-they can be adapted for both supervised and unsupervised learning method, depending on the training data and learning objectives. It is worth noting that the boundary between learning paradigms is not rigid. The variations of DT, for instance, can also be employed for certain unsupervised tasks such as clustering, highlighting the importance of understanding algorithm capabilities beyond their conventional applications. Each algorithm possesses its distinct characteristics, operating principles and mechanisms, and thus exhibits different advantages and disadvantages when dealing with complex datasets. The DT algorithm consists of a root node representing all input data, internal nodes (also called child nodes) that split the data based on certain features, and leaf nodes indicating final classification or regression results after several splits. RF algorithm can be viewed as a “forest” made up of multiple DTs, and the result of a RF classifier is determined by the majority vote of these trees. Therefore, RF is more effective in reducing the risk of over-fitting, although DT offers an advantage in training time ( Cai et al., 2024 ; Wang et al., 2024 ). SVM algorithm, which uses a linear separating hyperplane to create a classifier with a maximal margin, is well-suited for handling both linear and nonlinear data. For datasets characterized by mutually independent features, the NB algorithm, which is relied on conditional probability as described by Bayes' theorem, represents a suitable choice ( Wickramasinghe and Kalutarage, 2021 ). However, features often interact complexly in food industry and thus leading to suboptimal performance of NB algorithm. ANNs are ML algorithms designed to mimic the function of human nervous system, aiming to replicate analogous behaviors in an artificial environment ( Shi et al., 2023 ). The simplest ANN is a multi-layer perceptron, which includes the input layer, hidden layer, and output layer. In addition to the algorithms mentioned above, deep learning (DL) represents another significant branch of ML that can extract features from raw data more effectively for tasks such as classification, regression and detection. DL utilizes deep ANNs consisting of multiple neuron layers to learn complex patterns. With its powerful feature learning capability, DL has demonstrated significant advantages in handling numerous complex tasks like image classification and target detection, thus is widely employed in food nutrition analysis, safety and authenticity detection ( Kaushal et al., 2024 ; Wang et al., 2024 ). Fig. 1. Open in a new tab A concise overview of supervised learning (classification and regression). 3. Applications of ML in food field In food industry, the integration of advanced technologies, especially data science methodologies, has emerged as a crucial strategy for enterprises to sustain their competitive advantages. These technologies can not only monitor and analyze the food production process in real-time but also improve the accurate detection and identification of food components, nutrition and quality, so as to effectively address food safety issues. Due to their robust capabilities in handling large datasets, ML algorithms can significantly enhance big data-driven decision-making process, which facilitates the identification of consumer trends, enables more effective responses in supply chain management, and ultimately enhances customer experiences. In this section, the specific applications of ML in these aspects will be elaborated. To systematically present the diverse application scenarios covered in this section, this study summarizes the representative research works in Table 1 . The table structurally categorizes the existing literature by application sub-scenarios, data sources/technology, and core algorithms employed-with the aim of enabling readers to efficiently grasp both mainstream methodologies and emerging trends across subfields of the food industry. Following this overview, Sections 3.1 to 3 .7 will delve into each application domain in detail, discussing specific case studies and their implications. Table 1. Summary of machine learning applications in the food industry. Application scenario Data source/technology Main algorithms Reference Food Production Precipitation prediction Satellite images + radar data RF, ANN, SVM, NB, k-NN, Kmeans++ (multi-classifier fusion) Lazri et al. (2020) Crop yield prediction Historical weather + crop yield data Not specified (SMART-CYPS system) Kuradusenge et al. (2024) Milk production prediction Body weight + age at first calving ANN (Bayesian regularization) Bhardwaj et al. (2023) Milk yield prediction Behavior + health + productivity data Automated ML framework Ji et al. (2022) Pig body weight prediction 2D digital images Multiple linear regression Cunha et al. (2024) Cattle body weight prediction Imaging data DL models Afridi et al. (2024) Automated fish processing Image data CNN Mainali and Li (2025) Gluten-free biscuit optimization Process parameters Response surface methodology + ANN Olawoye et al. (2020) Baking moisture prediction Process parameters ANN, adaptive neuro-fuzzy inference system Emerald et al. (2020) Frozen food design Mechanistic modeling + Bayesian networks BN Yang et al. (2021) Food Quality Egg freshness estimation Portable NIR spectrometer PLS, SVM Cruz-Tirado et al. (2021) Tea impurity removal Hyperspectral imaging SVM Guo et al. (2024) Pistachio sorting Dual-camera images DL object detection (YOLO-based) Karadağ and Kılıç (2023) Flavor prediction (review) Flavoromics data k-NN, SVM, DT, DL, PCA, PLS, RF Cai et al. (2024) Flavor prediction (review) Molecular structure + physicochemical properties + E-nose/tongue SVM, DT, RF, k-NN, DL, ANN Ji et al. (2023) Sensor fusion E-nose + E-tongue + sensor fusion ML techniques Mahanti et al. (2024) Black tea quality grading NIR hyperspectral imaging DT (fine tree, medium tree) Ren et al. (2020) Coffee origin classification HSI-NIR SVM Sim et al. (2024) Apple defect detection Image/video data CNN (YOLOAPPLE) Karthikeyan et al. (2024) Potato dry rot detection Thermal imaging ANN, SVM Farokhzad et al. (2024) Blueberry freshness prediction Wireless sensor network (T, RH, CO 2 , O 2 , C 2 H 4 ) SVM, BP-NN, RBF-NN, extreme ML Huang et al. (2023) Honey adulteration Raman spectroscopy CNN, SVM, probabilistic NN Hu et al. (2022) Honey origin tracing Metagenomic data ML models Sabater et al. (2024) Milk adulteration Lactoscan parameters + HSI LR, DT, SVM, LDA Aqeel et al. (2025) Food Safety Pesticide residues Terahertz imaging CNN Nie et al. (2021) Pesticide residues Hyperspectral imaging Logistic regression, SVM, RF, CNN, ResNet Ye et al. (2022) Pesticide detection SERS spectra SERSFormer (CNN + Transformer) Hajikhani et al. (2024) Heavy metal contamination NIR reflectance spectroscopy Extreme learning machine Liu et al., 2022 Moldy chestnut detection Hyperspectral imaging PLS-DA, SVM, RF, CNN, LSTM Zhong et al. (2023) Foodborne pathogen detection Microscopic images CNN Chen et al. (2024) Malaria parasite detection Cell images Stacked CNN Umer et al. (2020) Malaria parasite detection Smartphone images Faster R-CNN Davidson et al. (2021) Parasite detection (review) Parasite images Deep CNN Kumar et al. (2023) Mycotoxin detection E-nose ANN, Logistic regression Leggieri et al. (2021) Patulin prediction Lesion diameter, depth, weight loss, respiration rate BPNN, RF, XGBoost, GBRT Cheng et al. (2024) Mycotoxin screening Molecular/physicochemical descriptors RF, NN Cova et al. (2025) Mycotoxin detection (review) Multiple Multiple (mainly NN) Inglis et al. (2024) Olive foreign object detection Image/sensor data NN Pascual et al. (2020) Chili foreign object detection Hyperspectral imaging R-CNN, Fast R-CNN, YoloV5 Shu et al. (2023) Food Nutrition Analysis Allergen detection Images (Allergen30 dataset) YOLOv5, YOLOR Mishra et al. (2022) Cellulose/sugar prediction Food label nutrition data k-NN Davies et al. (2022) Micronutrient prediction Food label nutrition data k-NN, LR, NN, RF, SVM Razavi and Xue (2023) Fish PUFA detection Skin hyperspectral imaging RBF, other ML methods Cao et al. (2024) AI in nutrition (review) Multiple ML/DL techniques Theodore Armand et al. (2024) Food Storage and Supply Chain Microbial spoilage assessment Visible spectroscopy, FTIR, multispectral imaging SVR, PLS regression Manthou et al. (2022) Fish TVB-N prediction Hyperspectral imaging Linear DNN, PLS regression, LS-SVM Moosavi-Nasab et al. (2021) Lamb TVB-N prediction E-nose + HSI CNN Liu et al. (2022) Meat freshness monitoring Colorimetric barcodes + E-nose scent fingerprints DCNN Guo et al. (2020) Supply chain traceability Blockchain + DL DL model Sheriff and Aravindhar (2024) Sustainable supply chain Mathematical optimization + NN NN + mathematical optimization Mamoudan et al. (2023) Cold chain energy optimization Agricultural cold chain data Algorithm-based AI Zhang et al. (2022) Food Consumption Consumer willingness to pay Survey data SVM, RF, ANN, Logistic regression Shen et al. (2021) Online purchase intention E-commerce platform data Stacking classifier Trivedi et al. (2022) Restaurant demand forecasting Restaurant historical data ML algorithms + DL architectures Chae et al. (2024) Personalized meal recommendation User data Ensemble ML algorithms Yaiprasert and Hidayanto (2024) Consumer insight extraction User-generated content (comments, reviews) NLP + ML Mustak et al. (2024) Organic food consumer behavior Consumer survey DT Boccia and Tohidi (2024) Food Innovation 3D food printing Algae-based biopolymers ML Bin Abu Sofian et al. (2024) Rice quality improvement Temperature, moisture profiling ANN, adaptive neuro-fuzzy inference system, CFD Chakraborty et al. (2025) Dried carrot classification Image texture 8 ML algorithms Günaydın et al. (2024) Multi-taste prediction Chemical/sensory data Multi-objective ML Androutsos et al. (2024) AI + probiotics (review) Probiotic-related data AI/ML Han et al. (2025) Functional food screening (review) Compound structural information ML models Zhang et al. (2024) Functional food candidate screening Compound structural information RF, k-NN, DNN, SVM (ensemble) Lee et al. (2023) Open in a new tab 3.1. Food production With the growing global population, frequent climate changes and the shrinkage of arable land, meeting the current population's food needs has become a formidable challenge ( Khan et al., 2023 ). Precision agriculture, also called smart farming, is a revolutionary approach to address contemporary challenges in sustainable food production. And the driving force behind this advanced technology is the various algorithms of ML, which is expected to be widely applied in the agriculture and food industries to enhance productivity, reduce resource consumption and improve decision-making. 3.1.1. Crop yield and livestock production prediction Crop yield might be influenced by various parameters, such as the soil fertility, weather conditions, sunlight intensity and harvesting schedules. The systematic application of ML models is anticipated to predict events that may influence crop yield, thus early corrective strategies can be implemented in time. Based on the atmospheric synoptic patterns, Lazri et al. (2020) have built a multi-classifier model by combination the RF, ANN, SVM, NB, k-nearest neighbours (k-NN) and the Kmeans++ algorithms, and the training and validation of this model were conducted using the satellite images and radar data. Compared with classifiers developed using separate ML algorithm, the proposed multi-classifier model showed a clear improvement in predicting precipitation intensities. Kuradusenge et al. (2024) reported the design and development of a SMART-CYPS system, which integrated ML and IoT technologies, to predict seasonal yields of potato and maize in Musanze District of Rwanda. By utilizing historical weather data and crop yield information across various agricultural seasons, the developed SMART-CYPS system demonstrated favorable predictive accuracy, with mean absolute percentage errors (MAPE) of 0.339 and 0.309 for Irish potatoes across two seasons, and 0.177 for maize over one season. For livestock production prediction, Bhardwaj et al. (2023) reported the utilization of three distinct ANN algorithms to train ML models, to predict milk production and investment based on body weight and age at first calving as input variables. The findings showed that ML model developed by Bayesian regularization algorithm had superior prediction ability, achieving an R 2 of 0.999 and a mean squared error below 10 −6 . Besides, it is more possible to achieve higher milk production with lower investment when the first calving age is 768 days and the body weight is approximately 281 kg. Ji et al. (2022) reported the development of a ML framework to automatically train models for predicting daily milk yield and quantity in the subsequent 28 days, based on the behaviour, heath and productivity data from 80 cows in a robotic dairy farm. The developed models achieved an R 2 greater than 0.9 and an overall prediction accuracy exceeding 80%. Cunha et al. (2024) developed a multiple linear regression-based mathematical model for body weight prediction in crossbred pigs, with the aid of two-dimensional digital images. Support vector regression (SVR) outperformed multiple linear regression, achieving an R 2 of 0.91 for real morphometric measurements and 0.88 for digital image-derived measurements. Afridi et al. (2024) also reported the development of DL models to estimate cattle body weight from imaging data. The best-performing model achieved a MEA of 15.06 kg, the RMSE of 20.01 kg, the MAPE of 3.57%, and an R 2 of 0.97. All these efforts collectively enhance informed decision-making in precision livestock management systems. 3.1.2. Food processing optimization The application of ML models in food processing is an effective approach for optimizing management processes and process parameters, thereby reducing energy consumption and processing time while ensuring product quality and maximizing profit. The integration of ML with advanced techniques, such as various sensors, inspection methods or big data analytics, not only enables automated and intelligent production but also transforms processing management. For example, Ropelewska et al. (2023) reported the development of ML models using RF, multi-class classifiers, logistic and SMO algorithms, to evaluate texture changes in freeze-dried carrot slices based on imaging analysis. All four algorithms achieved perfect classification accuracy (100%), demonstrating the feasibility of non-destructively estimating chemical property changes in processed carrot samples over time. The findings had practical significance for estimating changes in the chemical properties of processed carrot samples over time by non-destructive procedures. Cruz-Tirado et al. (2021) trained various ML models using portable near infrared spectrometer data, to estimate egg freshness. The results indicated that the PLS model had superior performance (classification accuracy of 87.0%) compared to SVM in classifying fresh and stale eggs. Guo et al. (2024) reported the development of a SVM model utilizing both full-spectrum data and characteristic wavelength data for pixel-level classification of hyperspectral images. This approach successfully achieved the removal of impurities in Puer tea, with an accuracy rate as high as 97.8%. Mainali and Li (2025) reported the design of a comprehensive automatic fish processing line utilizing ML models. The convolution neural network (CNN) algorithm was employed to set criteria for actions across several critical steps, including fish type identification, fish orientation determination and chopping point identification. The CNN model achieved 100% accuracy in fish type classification, while a neural network model for fish orientation determination reached 87.5% accuracy. The implementation of ML in this study not only realized the automation of fish processing, but also minimized fish meat waste during cutting process, therefore achieving the maximization of economic benefits. On the other hand, food processing is a complex procedure, in which changes of process conditions and parameters can significantly affect the nutritional value, safety, and flavor characteristics of food. Olawoye et al. (2020) employed response surface methodology and ANN algorithm based ML models to optimize the processing parameters for gluten-free biscuit production. The ANN model demonstrated excellent predictive performance on test data, with an R 2 of 0.9904 and RMSE of 0.337. The optimal conditions for production of biscuits with high resistant starch, low glycemic index and low glycemic load were identified as the baking temperature of 158 °C and baking duration of 20 min. Emerald et al. (2020) developed the ANN and adaptive neuro-fuzzy inference system models using four different algorithms. The multi-layer feed-forward ANN model outperformed others in predicting moisture ratio, achieving an R 2 of 0.9946 and RMSE of 0.0188. The authors also suggested the proposed model represented a simple and effective approach for predicting the complex water transfer phenomena in baking, and might be helpful for industry to optimize baking conditions. Yang et al. (2021) reported the development of an integrated mechanistic-modeling and Bayesian networks (BN) based ML approach for optimizing food product design (the thickness of frozen microwaveable foods), to achieve improved heating uniformity and quality. Karadağ and Kılıç (2023) proposed a method to distinguish open-shelled from closed-shelled pistachios using a deep learning object detection algorithm with a dual-camera setup. The detection accuracy reached 98% for open-shelled pistachios and 85% for closed-shelled pistachios. Furthermore, to facilitate the physical sorting of pistachios, they developed a Cartesian manipulator equipped with a gripper and conveyor system. Additionally, the integration of ML with various food detection technologies such as electronic nose (E-nose), electronic tongue (E-tongue) and headspace-gas chromatography-ion mobility spectrometry, as well as omics technologies, can significantly enhance food quality and flavor. For example, Cai et al. (2024) provided a comprehensive summary of the latest advances in the integrated application of food flavoromics analysis and ML algorithms. They noted that models such as k-NN, SVM, DT and DL are capable of handling complex and large-scale datasets but prone to over-fitting, whereas models such as PCA, PLS, and RF are simpler in structure yet may exhibit underfitting when applied to highly complex data. Ji et al. (2023) reviewed common ML methods like SVM, DT, RF, k-NNs, DL and ANN, and their prospects in predicting food flavors based on molecular structure, physical or chemical properties, and data obtained from E-nose or tongue. They concluded that combining of multiple ML methods yields better flavor prediction performance than using a single model. Mahanti et al. (2024) have reported that the individual use of E-nose and E-tongue systems faced limitations such as sensor drift, lack of standardization and data interpretation complexities, whereas these issues can be addressed through a fusion approach that combined various sensors with ML techniques. This performance enhancement can be attributed to the complementary nature of multi-modal data. While individual sensors capture specific chemical or sensory dimensions (e.g., volatile compounds or taste substances), their fusion provides a holistic fingerprint of the food matrix. By integrating this data, ML models can leverage both the correlations and unique variances across modalities, leading to more robust feature representation and reduced sensitivity to single-sensor drift or noise. In such multi-sensor integration, the manner in which data from different sources are combined, referred to as fusion strategy, critically influences model performance. Two common approaches are typically employed: (1) feature-level fusion, where features extracted from each sensor are concatenated into a combined feature vector before being fed into the machine learning model; and (2) decision-level fusion, where separate models are trained on each sensor's data and their outputs are subsequently integrated via voting, averaging or a meta-learner. Feature-level fusion is advantageous for capturing cross-sensor correlations, whereas decision-level fusion offers greater flexibility when sensors produce heterogeneous data types or when model interpretability for each modality is desired. Furthermore, for flavor improvement, researchers can explore and develop new flavor compounds in a digital framework with the aid of AI algorithms. This not only saves time and resources, but also facilitates the rapid optimization of product formulas and the enhancement of flavors to meet consumers' diverse preferences, thus driving food industry towards a smarter and sustainable future ( Cui et al., 2025 ). Across the food production studies reviewed in Sections 3.1.1 , 3.1.2 , several common patterns emerge. First, predictive tasks in agriculture and livestock farming predominantly rely on integrating multi-source data-combining environmental parameters (weather, soil), biological indicators (body weight, age) and imaging data-to capture the complex factors influencing yield and quality. Second, while traditional algorithms like ANN, SVM, and RF remain widely used for their robustness and interpretability, there is a growing trend toward hybrid or ensemble approaches that combine multiple models to improve prediction accuracy. Third, in food processing optimization, ML is increasingly employed not merely for endpoint quality prediction, but for real-time process control and parameter optimization, enabled by non-destructive sensors (e.g., hyperspectral imaging (HSI), near-infrared (NIR) spectroscopy) that provide continuous data streams. A notable gap, however, is the limited translation of these optimized parameters into industrial-scale applications, as most studies remaining at the laboratory or pilot scale. Future efforts should focus on developing models that maintain their predictive performance under the variability and noise inherent in real production environments. 3.2. Food quality Changes in food quality significantly affect sensory characteristics like appearance, flavor and texture, which might influence consumer's purchasing preferences. However, the detection of food quality is often subjective, laborious and destructive. The integration of ML with non-destructive imaging technologies such as HSI, Raman imaging, fluorescence imaging, soft X-ray imaging, magnetic resonance imaging, laser light scattering and infrared spectroscopy will become the mainstream tools for food quality inspection in the future. 3.2.1. Quality defects The majority of the retrieved studies focused on food quality analysis were conducted by ML models developed based on algorithms such as ANN, CNN, RF, SVM or DT, primarily using features extracted from hyperspectral images, transmittance, spectral information or sensor data. For example, Ren et al. (2020) analyzed near-infrared hyperspectral images of black tea samples across seven quality levels and extracted the spectral features. Subsequently, they employed three DT algorithms for modeling the hyperspectral data. The fine tree algorithm achieved the highest correct classification rate of 93.13% in predicting black tea quality, followed by the medium tree algorithm. Sim et al. (2024) reported the application of HSI-NIR and different ML models for rapid and non-destructive origin classification of coffee. All SVM models achieved classification accuracies exceeding 93%, demonstrating near-perfect performance for this task. Karthikeyan et al. (2024) introduced YOLOAPPLE, a multi-class detection and recognition system based on CNN features of DL, to identify the normal, damaged and red delicious apples using image or video data. YOLOAPPLE can achieve a mean precision of 99.13%. Farokhzad et al. (2024) proposed a reliable thermography-based method for detecting healthy potato tubers from those with dry rot disease. The ANN and SVM models achieved sensitivities of 96% and 100%, respectively, in diagnosing healthy tubers. For detecting the severity of tissue damage, the overall accuracies were 93% and 97%, respectively. In addition, Huang et al. (2023) constructed ML models based on various algorithms (including SVM, BP neural network, radial basis neural network, and extreme ML), to predict freshness of blueberry using critical gas information such as temperature, relative humidity, CO 2 , O 2 , C 2 H 4 and other environmental parameter data. All ML models achieved prediction accuracies exceeding 90%, notably higher than traditional freshness prediction models based on the Arrhenius equation ( Fig. 2 A). Fig. 2. Open in a new tab Numerous applications of ML models in food industry. (A) Freshness prediction of blueberries during cold chain transportation utilizing ML model and multi-source sensing techniques ( Huang et al., 2023 ). (B) The work flow for detecting multiple benzimidazole pesticide residues in leaves of Toona sinensis using terahertz (THz) imaging and deep learning ( Nie et al., 2021 ). https://www.mdpi.com/1422-0067/22/7/3425 (Open access). (C) Brief overview of discrimination the moldy chestnuts from normal ones with spectroscopy and ML. The spectral imaging data were analyzed using five algorithms to reduce the dimensionality, and then feature-selected data were employed for training the random forest and convolutional neural network models. The image was drawn based on research methodology reported by Zhong et al. (2023) https://www.mdpi.com/2304-8158/12/10/2089 (Open access). (D) Integration of colorimetric barcodes with deep convolutional neural networks presented as a simple and portable platform for accurate real-time monitoring of meat freshness and shelf-life ( Guo et al., 2020 ). Copyright 2020, Wiley-VCH. (E) The role of ML models AI in the supply chain of fresh agricultural products ( Zhang et al., 2022 , Free Access). 3.2.2. Food adulteration Producers of food products may mix inferior or non-food ingredients to enhance economic returns or to meet certain standards ( Haji et al., 2023 ). Since adulterated food samples often closely resemble authentic ones in appearance, distinguishing between them is challenging. However, ML has shown significant advantages in this area. Through analyzing extensive datasets derived from various detection methods, ML models can extract key features based on subtle variations. Moreover, ML models have the capacity to continuously improve their accuracy and robustness through training processes, thereby increasing their effectiveness in identifying adulterated samples. Honey adulteration usually occurs by adding cheaper sweeteners like corn syrup and glucose syrup to honey, or by feeding these sweeteners to bees. The adulterated honey is hard to be recognized by taste. To address this problem, Hu et al. (2022) proposed an approach by combining Raman spectroscopy with ML models to effectively identify the adulterated honey. In their study, the partial least squares method was used to preprocess Raman spectral data and extracted the key features, which were subsequently input into CNN, SVM, and probabilistic neural network (PNN) ML models. Experimental validation showed all three ML models achieved extremely high recognition accuracy in detecting honey adulteration. The PNN and SVM models both achieved 100% recognition accuracy, while the CNN model achieved 99.75% accuracy. Sabater et al. (2024) reported tracing the origin of Spanish PDO honey using metagenome-based ML models, achieving classification accuracies exceeding 90% for discriminating both geographical origin and honey variety. Milk is more common in daily life compared to honey, thus detecting milk adulteration is crucial for consumer health and the advancement of dairy industry. Aqeel et al. (2025) reported the establishment of an efficient multi-class model for detecting milk adulteration through both destructive and non-destructive approaches. The destructive Lactoscan systems quantified essential milk parameters, including density, fat content, lactose concentration, conductivity, solids content, protein level and pH value, to provide foundational data. Non-destructive detection employed high-resolution HSI data in conjunction with ML algorithms such as LR, DT, SVM and linear discriminant analysis (LDA), to identify milk adulteration. The results indicate that one-to-one LDA verification achieved an accuracy of 100% in assessing milk quality. The application examples of ML in food quality assessment, as discussed in Sections 3.2.1 , 3.2.2 , reveal a strong reliance on non-destructive spectroscopic and imaging techniques. The HSI and NIR dominate the field due to their ability to capture both physical and chemical attributes of food products without sample destruction. From an algorithmic perspective, a clear dichotomy emerges: for classification tasks such as origin tracing or defect detection, SVM and RF are frequently preferred for their balance of accuracy and interpretability; for more complex pattern recognition, such as identifying subtle adulteration signals, deep learning models (particularly CNN) demonstrate superior feature extraction capabilities. However, a critical observation across these studies is the lack of standardized datasets and benchmarking protocols. Each study typically constructs its own dataset under specific conditions, making cross-study comparisons of algorithm performance difficult. The field would benefit from community-wide efforts to establish reference datasets and standardized evaluation metrics, enabling more systematic progress in ML-based food quality assessment. 3.3. Food safety Each year, millions of individuals suffer from various diseases and tens of thousands lose their lives due to consumption of unsafe foods. Therefore, ensuring safety is undeniably a critical research issue for food industry. Foods may be contaminated by chemical, biological or physical hazards throughout production, processing, preservation or supply chains ( Wang et al., 2022 ). This section will discuss the application of ML models in detection of these hazards. 3.3.1. Chemical hazards The potential chemical hazards in foods include process contaminants, environmental pollution, pesticide residues, veterinary drug and disinfectant residues. For detection these chemical contaminants, spectroscopy-based imaging or computer vision are predominantly employed to generate raw data, while NN and SVM algorithms are the most commonly used for training the ML models. For instance, Nie et al. (2021) used the high-throughput terahertz technology imaging and CNN model for detecting benzimidazole pesticide residues, such as benzoyl, carbendazim, thiabendazole and their mixtures. All algorithms achieved classification accuracies above 94% for different pesticide residue scenarios ( Fig. 2 B). Ye et al. (2022) reported the detection of pesticide residues on grape surfaces using the HSI and the ML models trained by logistics regression, SVM, RF, CNN, and residual neural networks algorithms. The results indicated that almost all models performed well and achieved over 80% classification accuracy for different grape types and pesticide residues. However, the performance of RF model was slightly inferior due to over-fitting. Hajikhani et al. (2024) introduced an innovative strategy for the rapid and accurate identification of five pesticides in spinach, by combining surface-enhanced Raman spectroscopy (SERS) with a transformer model called SERSFormer. The SERSFormer employed advanced data processing techniques and CNN model, supported by an architecture featuring weight-shared multi-head self-attention transformer encoder layers, and it achieved the qualitative analysis accuracy of 98.4%. Compared to pesticide residues, the presence of heavy metals in foods resulting from environmental pollution has received relatively less attention. Liu, Xu et al. (2022) employed near-infrared reflectance spectroscopy in conjunction with an extreme learning machine to detect heavy metal contamination in mussels. The developed model achieved classification accuracies of 97.53% for zinc, 95.67% for lead, 99.00% for cadmium, and 98.80% for copper contamination. 3.3.2. Biological hazards Food-borne illnesses caused by biological hazard are a serious food safety issue. Crops like wheat, maize and rice may harbor fungi that pose serious risks to human health. In addition to causing infections or allergies, the mycotoxins released by fungi may also cause acute poisoning, liver and gastrointestinal disorders or even cancer ( Morya et al., 2020 ). In the retrieved studies, spectroscopy-based methods are mainly applied to obtain data for biological hazards analyzing, whereas NN, SVM and RF are the most commonly utilized algorithms for training ML models. For example, Zhong et al. (2023) combined algorithms of PLS-DA, SVM, RF, CNN, and long short-term memory with HSI techniques, to discriminate moldy chestnuts from normal ones. In their study, spectral images were preprocessed using various methods, and a feature selection algorithm was applied to reduce the dimensionality of spectral data. The integration of these raw data processing, feature selection procedures, and various ML algorithms yielded diverse outcomes. Notably, the long short-term memory model trained using the first-order derivative method achieved the highest accuracy of 99.72% in detecting chestnuts quality. Furthermore, CNN showed excellent performance by combining the first-order derivative processing with uninformative variable elimination for dimensionality reduction, resulting in the highest accuracy of 97.33% ( Fig. 2 C). Chen et al. (2024) reported the construction of a high-quality and large-scale microscopic dataset of food-borne pathogenic bacteria, which allowed the identification of each bacterium using CNN model designed to extract input features. This approach could assist or replace the manual microscopic inspection, with accuracy rates of 90% or higher for six common food-borne pathogens. Parasites are another kind of food contamination that is hard to detect with the naked eyes. Umer et al. (2020) proposed a stacked CNN architecture for the automatic detection of malaria parasites using 27,558 cell images. After five-fold cross-validation, this approach achieved an accuracy of 99.96% and a precision of 100%. Davidson et al. (2021) employed a pre-trained Faster Region-based CNN model to detect malaria infection and stages of malaria parasites from smartphone images. The model achieved an accuracy of 99.8% and an AUC of 0.979, and was made available as an online web tool (PlasmoCount, available at https://www.baumlab.com/plasmocount ) for malaria research. Kumar et al. (2023) conducted a comprehensive investigation in the field of image detection and classification of parasites using deep CNN model, by reviewing technical publications science 2012. Collectively, these publications have demonstrated encouraging results in the field of parasite detection and classification, which can be attributed to the plasticity of ML architectures and the availability of large sets of parasite images available in public medical libraries. For detection toxins secreted by pathogens, Leggieri et al. (2021) analyzed the fumonisin and aflatoxin B1 contaminations using E-nose, followed by data processing using the ANN and logistic regression algorithms. The models achieved accuracies of 78% for aflatoxin B1 and 77% for fumonisin detection, with ANN demonstrating relatively superior performance. Cheng et al. (2024) reported predicting patulin levels in apples infected with Penicillium expansum using BPNN, RF, XGBoost and GBRT models. For Golden Delicious apples, the best model achieved a RMSE of 0.72, a MAE of 0.217, and the R 2 of 0.982; for Fuji apples, the best model achieved a RMSE of 0.289, a MAE of 0.08, and the R 2 of 0.994. In a recent study, Cova et al. (2025) proposed a rapid non-target screening approach for multiple types of mycotoxins, which integrated RF and NN models alongside molecular or physicochemical descriptors. The results indicate that molecular descriptors—including structural complexity and diversity, chirality and symmetry, connectivity, atomic charge and polarization, as well as those characterizing lipophilicity, absorption, and permeability—are essential for accurate toxicity prediction. Inglis et al. (2024) conducted a systematic review on recent ML applications for the detection/prediction of mycotoxins in various foods. Their findings indicated that many studies lacked detailed reporting on parameters and open-source code, raising concerns about the reproducibility of the ML models employed. Furthermore, they also noted that although most studies primarily used neural networks for mycotoxin detection, there was significant diversity in the types of network architectures employed, with CNN model being the most prevalent. Overall, the application of ML algorithms enables researchers to achieve a deeper understanding of toxin properties and simultaneously improves the efficiency, cost-effectiveness, and scalability of foodborne toxin detection. 3.3.3. Physical hazard Physical hazards in food refer to foreign objects that can threaten consumer's health, such as glass, stones, metals, leaves and small branches ( Onyeaka et al., 2023 ). These objects may originate from the collection, processing and transportation of agriculture products. Therefore, variations in raw materials and processing procedures result in the presence of distinct foreign substances in different foods. Bones and plastics are more commonly found in meat products, followed by insects and metal fragments. To identify these foreign substances effectively, traditional tools like sieves and magnets are used. Nowadays, advanced techniques such as optical and thermal imaging, as well as nuclear magnetic resonance are also employed to address this issue. However, these methods have practical limitations. Pascual et al. (2020) reported the development of an NN algorithm-based ML model to effectively classify four types of olive pocket cases: normal, empty, incorrectly de-stoned olives at any angles, and foreign objects such as leaves, stones and small branches. At high pixel resolutions, the model achieved classification accuracies exceeding 96%. Shu et al. (2023) reported the identification of foreign objects in chilli peppers by analyzing hyperspectral images using Region-CNN (R-CNN), Fast R-CNN and YoloV5 algorithms. The YoloV5-based model outperformed others, achieving detection accuracies exceeding 96% with superior operational speed. Overall, these studies highlight the use of non-destructive imaging for generating raw data (e.g., colors, pixels, shapes) in identifying foreign objects, while the NN was the most commonly used algorithm for training ML models. The food safety studies reviewed in Sections 3.3.1–3.3.3 highlight both the promise and the current limitations of ML in detecting chemical, biological, and physical hazards. Across all three hazard categories, spectroscopy-based methods (Raman, SERS, HSI and terahertz) emerge as the dominant data acquisition technologies, valued for their sensitivity to molecular-level changes induced by contaminants. CNN and its variants are increasingly becoming the preferred algorithms, particularly for image-based detection tasks, while SVM and RF maintain strong relevance for spectral data analysis. Several cross-cutting insights emerge: (1) model performance is highly dependent on data quality and preprocessing, with feature selection and dimensionality reduction proving critical for spectral data; (2) the detection of biological hazards (pathogens, parasites, toxins) presents unique challenges due to the dynamic nature of biological systems and the need for extremely low detection limits; (3) despite high reported accuracies in laboratory settings, few studies have validated their models under real-world conditions-characterized by lower contamination levels and more complex sample matrices. Addressing this laboratory-to-field translation gap, perhaps through transfer learning or domain adaptation techniques, represents a key priority for advancing ML-enabled food safety monitoring. 3.4. Food nutrition analysis Different individuals have diverse nutrition needs. While healthy people can enjoy a wide range of foods and maintain a balanced diet, individuals of obese, diabetic or allergic sufferers may face serious health risks associated with the consumption of inappropriate foods. However, the characteristics of raw materials often undergo significant changes during food processing, which increase the difficulty of food ingredients recognition. In this context, ML models can elucidate the intricate relationships among various nutrients, thus possessing distinct advantages in ingredients prediction. For example, Mishra et al. (2022) reported the development of the Allergen30 dataset, which comprises over 6000 annotated images of 30 food items known to potentially trigger allergic reactions. Using YOLOv5 and YOLOR algorithms for detection, all models achieved mean average precision scores above 0.74. Davies et al. (2022) trained the k-NN-based ML models to predict the contents of cellulose and added sugar in foods and beverages. The models achieved a classification accuracy of 0.89 and an the R 2 of 0.96 for prediction tasks. On the other hand, many individuals suffer from micronutrient deficiencies such as vitamins and minerals, yet food packaging often lacks clear labeling of these micronutrients. Razavi and Xue (2023) employed various ML algorithms, such as k-NN, LR, NN, RF and SVM, to construct models for predicting unspecified nutrient contents based on the label information. For classification into low, medium, and high nutrient levels, the models achieved accuracies ranging from 81% to 94%, depending on the nutrient. For regression tasks predicting continuous nutrient contents, the R 2 values ranged from 0.28 to 0.92. The developed models can accurately predict the contents of most nutrients such as vitamin A, B6 and E, as well as minerals including magnesium and zinc. Furtherly, they categorized these nutrients into low, medium and high levels, which would provide consumers with more clearer nutritional information. The n-3 polyunsaturated fatty acids (PUFAs) such as DHA and EPA are essential nutrients that can improve insulin resistance on a high-fat diet ( Yang et al., 2024 ). Cao et al. (2024) integrated skin HSI with multiple ML methods to evaluate muscular PUFAs contents in carp. Their findings revealed that RBF model achieved an R 2 of 0.9914 for predicting PUFAs levels, outperforming other models, suggesting its potential for the rapid and non-destructive selection of living fish with high n-3 PUFA contents. Theodore Armand et al. (2024) systematically explored the current applications of AI in the field of nutrition, demonstrating the versatility of ML and DL technologies in personalized nutrition, dietary assessment, food recognition and tracking, predictive modeling for disease prevention, as well as disease diagnosis and monitoring. Their study underscored the role of AI in facilitating evidence-based decision-making in nutrition and dietary guidance. In the future, the integration of AI and vision-based technologies into nutritional science is expected to significantly improve individual nutritional status and promote overall human health. ML applications in food nutrition analysis, summarized in Section 3.4 , address a diverse range of tasks from allergen detection to micronutrient prediction and functional compound screening. A common thread across these studies is the creative use of available data sources: food labels, public datasets (e.g., Allergen30), and non-destructive sensors (hyperspectral imaging) are all leveraged to extract nutritional information without traditional wet chemistry. Algorithm selection varies with task complexity, simpler models like k-NN and LR suffice for well-structured tabular data, while deep learning is reserved for image-based tasks. Notably, several studies have demonstrated the feasibility of predicting unlabeled nutritional contents (e.g., micronutrients not declared on packaging) from labeled information, thereby opening new possibilities for consumer empowerment and dietary monitoring. However, a significant limitation is the reliance on Western-centric food databases and labeling conventions; extending these approaches to diverse global food cultures and traditional foods remains an open challenge. Future developments should also explore integration with emerging technologies like smart packaging and IoT-enabled kitchen appliances to enable real-time, personalized nutritional guidance. 3.5. Food storage and supply chain Food products in the storage and supply chain are constantly exposed to external factors like temperature, humidity and microorganisms, leading to quality deterioration. According to previous reports, SVM and PLS regression algorithms are commonly used to predict microbial content in the storage of meat and seafood products ( Kang et al., 2022 ). Manthou et al. (2022) compared the integration of various sensor data and ML models to assess microbial spoilage in ready-to-eat leafy vegetables (baby spinach and rocket). The results showed that microbial spoilage in baby spinach was more effectively assessed using ML models developed from visible spectroscopy data, whereas Fourier-transform infrared spectroscopy and multispectral imaging data were found to be more appropriate for rocket. In terms of algorithms, the SVR algorithm can improve the model performance when applied to Fourier-transform infrared, visible, and near-infrared sensors. Conversely, the partial least squares regression algorithm yielded superior modeling results for multispectral imaging sensors. Across the different sensor-algorithm combinations, the models achieved R 2 values of at least 0.7. Results of their study suggest that no single combination of analytical methods or algorithms can be universally applied across all food types and throughout the entire food supply chain. Total volatile basic nitrogen (TVB-N) is an index of the freshness of animal foods. Common models for predicting TVB-N content and classifying freshness employ DL such as CNN algorithms. For example, Moosavi-Nasab et al. (2021) employed a linear deep neural network algorithm to predict the TVB-N content of rainbow trout fillets. In their study, the established DL model achieved 90% accuracy in classifying fillet freshness, yet its performance in predicting TVB-N content was inferior to PLS regression and LS-SVM models. Liu et al. (2022) combined E-nose and HSI techniques with a CNN-based ML model to predict TVB-N content in lamb. This study employed a feature-level fusion strategy, in which spectral features from HSI and sensor responses from the E-nose were concatenated into a unified input vector for the CNN model. As discussed in Section 3.1.2 , feature-level fusion is particularly advantageous for capturing cross-sensor correlations, allowing the model to leverage complementary information from both modalities. The integrated model achieved an R 2 of 0.92 for TVB-N prediction, outperforming models using either sensor alone. The findings indicated the integration of E-nose and HSI improved the TVB-N prediction accuracy, and the proposed ML model excelled in feature extraction and modeling of data derived from E-nose sensors. The improved prediction accuracy from such data fusion can be mechanistically explained by the multi-faceted nature of food spoilage. For instance, TVB-N production (a chemical change) is often accompanied by subtle shifts in color and texture (physical changes detectable by HSI) and odor profiles (detectable by E-nose). By fusing these data types, the CNN model is effectively trained on a joint feature space that captures both the chemical byproducts and their physical manifestations, enabling a more holistic and accurate assessment of freshness compared to any single data source alone. Guo et al. (2020) reported the development of a meat freshness monitoring system that integrated cross-reactive colorimetric barcodes with deep convolutional neural networks (DCNNs) model, which was trained using scent fingerprints generated by E-noses. A fully supervised training of DCNN model, employing 3475 labeled barcode images, achieved an overall accuracy of 98.5% in predicting meat freshness. Furthermore, incorporating of DCNN model into a smartphone can form a user-friendly platform for rapid barcode scanning and real-time meat freshness identification ( Fig. 2 D). The management of the food supply chain is complex. In China, conventional food transportation methods would lead to approximately 20% of food waste during transportation, particularly for perishable foods like fresh fruits, vegetables and seafood. Industry 4.0 recommends a shift from traditional manufacturing to automated practices, especially in supply chain management. The decision-making process in food supply chain encompasses numerous intricate challenges and information barriers, where ML offers significant advantages when combined with techniques such as block chain, digital twins and IoT. For example, Sheriff and Aravindhar (2024) introduced an approach that combined block chain technology with DL model, to optimize the traceability of agri-food supply chain, improve the quality and shelf life of perishable food and increase profitability for farmers. Mamoudan et al. (2023) proposed a hybrid ML model that integrated NN algorithm with mathematical optimization method, to develop a sustainable agricultural supply chain. The best-performing model achieved an R 2 of 0.97 and an RMSE of 2.97 in predicting key sustainability indicators. This model was designed to simultaneously optimize supply chain profitability and mitigate environmental pollutants, food waste, and water consumption, while also reducing manufacturing costs and lead time. Zhang et al. (2022) discussed the application of algorithm-based AI in addressing the high energy consumption of agricultural cold chain. They concluded that AI employments provided multi-scale and sustainable strategies to resolve the imbalance of spatial, temporal and supply-demand state of cold chain energy consumption. Taken together, the application of AI not only reduces energy consumption but also facilitates predictive analytics to anticipate future trends and patterns within the cold chain ( Fig. 2 E). The studies in Section 3.5 illustrate the expanding role of ML in monitoring food quality throughout storage and distribution, as well as optimizing complex supply chain operations. In storage quality monitoring, a key finding is the task-specific nature of optimal sensor-algorithm combinations—no single approach universally outperforms others across all food types and spoilage indicators, as demonstrated by Manthou et al. (2022) . This underscores the need for customized solutions tailored to specific products and quality metrics. Data fusion strategies, combining complementary sensors (e.g., E-nose + HSI), consistently outperform single-sensor approaches by capturing the multi-faceted nature of food spoilage (chemical, physical, sensory changes). For supply chain management, the integration of ML with block chain, digital twins, and IoT represents a paradigm shift toward proactive, transparent, and sustainable logistics. Yet, these advanced integration remain largely conceptual or at early implementation stages; widespread adoption faces barriers including interoperability challenges between legacy systems and new technologies, data sharing concerns among supply chain partners, and the need for significant infrastructure investment. Demonstrating a clear return on investment through well-designed pilot implementations is critical to facilitating broader organizational adoption. 3.6. Food consumption Undoubtedly, accurately identifying and analyzing consumer preferences pose significant challenges for managing food production, developing competitive products and maximizing profits. The DL or ML models can be used to predict consumer demand, perceptions and purchasing behaviors in food retailing. For example, Shen et al. (2021) constructed ML models using SVM, RF, ANN and logistic regression algorithms, to train the predicted willingness of custom to buy or pay for labeled foods. The results revealed that the four ML models had comparable performance in terms of prediction accuracy, with all models achieving accuracy significantly higher than random guessing. Notably, the SVM model outperformed others in predicting consumers’ willingness to purchase foods labeled as “raised without antibiotics”. Trivedi et al. (2022) proposed a novel algorithm based on the well-known stacking classifiers, to predict the purchase intentions of users on e-commerce websites. The proposed stacking model, along with other classifiers, achieved prediction accuracies above 0.8 and AUC values above 0.9, demonstrating robust performance. The improved ML model outperformed other classifiers, indicating its potential to assist e-commerce sites in offering predictive and personalized recommendations for their users. Chae et al. (2024) evaluated the effectiveness of ML algorithms and DL architectures in forecasting restaurant demands, and provided valuable insights into effective forecasting methods for restaurant industry across different market periods. Yaiprasert and Hidayanto (2024) proposed a novel approach utilizing ensemble ML algorithms to enhance personalized meal services. This algorithm achieved AUC value greater than 0.9, demonstrating strong predictive performance for food recommendation tasks. Beyond technical success, this study also indicated the potential of AI-powered food recommendations within the food delivery and restaurant industry to improve accessibility for individuals with special dietary needs. Furthermore, it promotes healthier lifestyles by offering nutritious meal suggestions. In terms of marketing strategies, the “customer insights” are essential to understand customers' brand preferences and emotional responses. However, numerous businesses encounter challenges in acquiring such insights due to data or analytical approach limitations. Mustak et al. (2024) reported the utilization of natural language processing and AI-powered ML techniques to extract valuable customer insights from online user-generated contents such as comments, reviews and shares. This study provided guidelines for researchers to acquire customers' insights, thereby facilitating the development of more effective and responsive business strategies. Boccia and Tohidi (2024) reported the utilization of DT-based ML model to analyze consumers' behavior patterns among 400 organic saffron consumers in Iran. The decision tree model achieved high classification accuracy, though the specific metrics were not reported in the study. They found more than fifty percent of these consumers were not willing to engage in word-of mouth advertising for organic saffron, and the primary reason for this reluctance was identified as a lack of awareness about organic saffron's characteristics. Therefore, mass-media advertising could effectively enhance consumers' awareness and influence opinion leaders. In the context of contemporary digital marketing, AI, driven by big data analytics, ML and DL algorithms, plays a vital role in business activities by means such as the improved search tools, better-targeted advertising, continuous training, image and voice recognition, sales forecasting and customer segmentation. In actual business settings, AI can adjust prices and promotions in real time based on customers' demands and behaviors, thereby maximizing revenue and profit margins. Overall, these efforts optimize resource allocation, enhance enterprise profitability, and bolster competitiveness by discerning market trends and consumer preferences. ML applications in food consumption analysis, reviewed in Section 3.6 , cover consumer behavior prediction, demand forecasting, personalized recommendations, and marketing strategy optimization. A distinctive characteristic of this domain is the diversity of data sources—surveys, e-commerce platforms, social media, and restaurant transaction records—each with unique biases and noise patterns that must be carefully handled. Compared to physical and chemical sensing applications discussed in earlier sections, the algorithms employed here rely less on deep learning, while ensemble methods, stacking classifiers, and traditional models like SVM and RF remain highly competitive, suggesting that structured tabular data still predominates over image/text data in this field. A clear deficiency is the limited integration of external contextual factors—seasonality, economic conditions, cultural events—that strongly influence food consumption patterns. Currently, most models are trained solely on historical data, which limits their ability to adapt to sudden shifts (e.g., pandemic-induced behavior changes). Future research should explore dynamic models capable of continuous learning and incorporate multi-source external data to improve robustness and adaptability. Additionally, ethical considerations regarding data privacy and algorithmic fairness have become crucial in this field, as consumption data may reveal sensitive personal information. 3.7. Food innovation The integration and application of big data analytics and AI in food industry have revitalized the sector and ushered in a new era known as “Industry 4.0″ or “Smart Factory” ( Feng et al., 2025 ). For the innovation in manufacturing processes, Bin Abu Sofian et al. (2024) discussed role of ML in 3D food printing by enhancing material selection, predictive modeling and quality control. They also explored the viability of algae-based biopolymers (such as alginate and carrageenan) in 3D printing, and underscored the impact of ML in advanced, eco-friendly food manufacturing techniques. To improve the quality of milled rice, Chakraborty et al. (2025) studied the temperature and moisture profiling for the instant controlled pressure drop treated paddy and its effect on the gelatinization kinetics, using ANN, adaptive neuro-fuzzy interface system and computational fluid dynamics approaches. The adaptive neuro-fuzzy inference system-based models achieved R 2 values exceeding 0.99 and mean squared error values ranging from 0.0034 to 0.0084, demonstrating excellent predictive capability for gelatinization kinetics. Günaydın et al. (2024) reported the application of eight different ML algorithms for distinguishing carrot slices subjected to various drying treatments. In their study, the ML models were built based on image textures analysis, and the overall classification accuracies exceeded 96.33% across all algorithms indicating that the classification were effective and successful. For the innovation of novel products, Androutsos et al. (2024) developed a ML algorithms based multi-class taste predictor, to comprehensively characterize taste perception. On the test set, the model achieved AUC values exceeding 0.86 for multiple taste attributes, indicating reliable predictive performance. This design is expected to introduce innovative methods for the rational design of foods, including pre-determining specific flavors and creating complementary diets to augment pharmacological treatments. Han et al. (2025) reviewed the integration of AI and probiotics in advancing fermentation technology, emphasizing the revolution of AI in strain screening and optimization, biomarker prediction, as well as probiotic metabolite analysis. The study also discussed the transformative effects of AI on probiotic efficacy research and fermented product innovation. For the innovation in developing functional foods, Zhang et al. (2024) outlined the general process of constructing ML models, and emphasized the progress of ML in screening food compounds with different bioactivities in recent years. As shown in Fig. 3 , Lee et al. (2023) collected structural information on chemicals that have been proved efficacy in alleviating symptoms such as muscle weakness, and constructed ML models using RF, k-NN, DNN and SVM algorithms. The best-performing model achieved a prediction accuracy of 91.2%. Through analysis conducted by the developed models, citrusinol was identified as a promising candidate for functional foods. Subsequently, validation experiments were conducted to confirm the effectiveness of citrusinol in inhibiting muscle atrophy and enhancing muscle function, by blocking TGF-β, p-SMAD3, MAFbx and downstream regulators in C2C12 cells. This finding not only demonstrated the utility of ML in recognizing chemical substance, but also underscored the potential of ML models in the development of novel functional foods designed for specific populations. Fig. 3. Open in a new tab The application of machine learning models in developing novel functional foods. Train a machine learning model by sequentially combining each feature with various algorithms, and subsequently integrate the best-performing models from each algorithm to construct an ensemble model. Finally, the predictions made by the ensemble model were experimentally validated. Imaging figure adapted from Lee et al. (2023) , which is an open-access article distributed under the Creative Commons Attribution License. https://www.sciencedirect.com/science/article/pii/S1756464623001421 . The emerging applications in food innovation, discussed in Section 3.7 , demonstrate ML's potential to accelerate and transform how new food products, processes, and ingredients are developed. Three interconnected innovation pathways emerge: (1) process innovation, where ML optimizes advanced manufacturing techniques like 3D food printing and novel thermal treatments; (2) product innovation, where ML predicts sensory profiles and guides formulation; and (3) functional innovation, where ML screens compound libraries to identify bioactive candidates with specific health benefits. The study by Lee et al. (2023) exemplifies the power of this approach, using ML to predict and experimentally validate citrusinol as a novel functional ingredient-a paradigm that could dramatically accelerate functional food development. Despite these promising demonstrations, most applications remain proof-of-concept. Translation to commercial products requires addressing challenges in scalability, regulatory approval, and consumer acceptance. Furthermore, the full potential of ML in food innovation will likely be realized through integration with other digital technologies—digital twins for virtual product testing, automated high-throughput screening platforms, and generative models for de novo compound design. As these capabilities mature, ML is poised to shift from a supporting tool to a core driver of innovation in the food industry. 4. Data and basic algorithms of ML models used in food industry Data are fundamental for training ML algorithms models, and the lack of reliable data poses the major challenge for applications of ML for food safety monitoring and quality predicting. Data used in the relevant studies can be categorized into monitoring data, open access data, expert knowledge, and others. Monitoring data are the most commonly data source used in the discussed studies. It is based on results from laboratory tests, or obtained from institutions that provide food inspection services (e.g., supervision and inspection departments, and national food safety monitoring systems). Generally, this kind of data is either not available or available only after obtaining permission. Open access data are generated from historic information, or food safety notifications published by the food safety authority (such as specific food safety hazards, the probability of fraud, and the outbreak of food-borne diseases). This kind of data is often readily accessible and can offer a variety of variables for modeling aimed at decision-making. For instance, Liu et al. (2022) used BN algorithm and open-sourced data (Raw milk price, feed maize/wheat/barley price, and average temperature/precipitation), to develop a KNIME workflow system to monitor the safety of liquid milk in six Europe countries. Thite et al. (2024) introduced an open-access “Sugarcane Leaf Dataset,” which comprised 6748 high-resolution images of healthy sugarcane leaves, along with nine categories of diseased sugarcane. This dataset can serve as a valuable resource for researchers and practitioners in developing various ML algorithms for disease detection, monitoring, and management within sugarcane cultivation. Such efforts are expected to enhance agricultural practices and contribute to increased crop yields. Other data sources related to food quality and safety refer to information generated through the IoT, social media, smartphones, satellite images, etc. (Wang et al., 2021 ). IoT and smartphones can provide data such as humidity, temperature, geographical location and traceability within the food supply chain. Social media platforms such as YouTube, Twitter and Facebook can be used to collect information related to outbreaks of food borne illnesses and discussions surrounding food safety incidents. Satellite imagery serves as a valuable tool for gathering visual data related to agricultural product monitoring, including crop growth and harvest assessments. The emerging AI and big data analytics technologies have brought revolutionary changes to the food industry in terms of supply chain optimization and food safety. ML- or DL-based AI technology often requires large amounts of training data to achieve optimal performance. However, due to the practical challenges associated with data sharing in certain instances, data sharing occasionally encounters obstacles. In the agri-food sectors, the need for data protection is understandable, especially regarding commercially sensitive information or data that may affect reputation. Recent advances in ML technology, particularly federated learning, offer an innovative solution. Federated learning enables the aggregation of diverse datasets from multiple sources while ensuring that each dataset remains securely within its original location. On other words, federated learning is a training algorithm that allows for distributed model training without physically sharing of raw data but exchanging of model information only. As a result, it not only safeguards privacy but also dismantles the barriers imposed by data silos. Li et al. (2024) and Durrant et al. (2022) employed the federated learning and model sharing ML methods for soybean yield forecasting, which demonstrated the applicability of this strategy for training distributed datasets. These studies exemplify how privacy and security concerns prevalent in the agri-food industry can be effectively addressed, they also enhance confidence in data sharing and contribute to building a more sustainable future for food industry. In the development of ML models, selecting an appropriate algorithm is very important ( Mavani et al., 2022 ). The initial step in this selection process involves defining and determining the objectives for employing AI within a given research or implementation context. Subsequently, it is necessary to ascertain whether sensors such as E-tongues, E-noses, computer vision systems or NIS are needed for collecting sampling data required for model training. Following this, users should compare and select suitable algorithms based on their specific research needs. Generally, the NN, SVM, RF and BN are the most frequently employed algorithms for monitoring and predicting food safety. Among them, the BN's structure is more intuitive and capable of integrating diverse data sources such as economic factors, climate change impacts and human behavior. DL such as CNN model excels in analyzing various data formats including images or text files, and customized CNN is particularly prevalent in image processing. It is advisable for users to evaluate the complexity of their research when determining the most appropriate algorithm. Once an algorithm has been selected, the relevant data will be integrated with the chosen AI approach. Algorithmic bias and fairness can be addressed by using diverse datasets. Generalization and over-fitting problems can be solved with strategies such as cross-validation and regularization, while computational efficiency and scalability are addressed through distributing computing and hardware accelerations. Finally, the robustness and reliability of the model are assessed. A robust AI model is successfully established once the validation is accepted, otherwise, users should revert to the previous step and reconsider their choice of algorithm. With the rapidly developing of AI technology, achieving more optimal performance by ML models often relies on sophisticated and integrated learning strategies ( Canatan et al., 2025 ). Recent advancements like transfer learning, federated learning, active learning and continuous learning have significantly enhanced the efficiency and effectiveness of AI models. Transfer learning leverages knowledge acquired from pre-existing model to booster performance of a new task, allowing ML models to be adapted with less data requirements ( Zhou et al., 2025 ). Federated learning facilitates model training on distributed data across multiple devices, safeguarding privacy by avoiding centralized data storage and sharing ( Li et al., 2024 ). Active learning emphasizes the selection of the most informative data points for training and optimizing the models Sui and Ghosh (2024) . Continuous learning empowers AI systems to evolve by continuously integrating new information without forgetting previous knowledge ( Sparrow et al., 2024 ). Additionally, the latest developed AutoML focus on automating ML processes for real-world applications while simplifying the complexities in algorithm selection and tuning ( Singh et al., 2024 ). TinyML has introduced ML capabilities into compact, low-powered devices, thereby broadening the scope and applicability of AI models ( Kulkarni and Bhudhwale, 2024 ). All of these advanced methodologies build upon fundamental principles of AI and offer innovative solutions to intricate challenges. 5. Challenges and future development trends Despite their proposed applications in diverse scenarios in food industry, the reliable implementation of ML models remains in its early stages. As shown in Fig. 4 , future research and development should primarily focus on the following aspects: (1) Challenges originating from big data. The fundamental challenge in ML is to address the question, “How much data is sufficient to train a model to reasonably approximate the unknown underlying mapping function?” A prevalent presumption is that ML algorithms can achieve better approximation and enhanced performance with larger volumes of data. However, in the contemporary big data era, traditional ML algorithms may face challenges like scalability and distributed computing, which impede their capabilities to uncover the hidden information of big data. With the continuously expanding universe of big data, ML algorithms must progress to effectively convert big data into actionable intelligence. Advancements in pre-processing and data enhancement techniques are helpful to address issues related to data variability and quality. Furthermore, developing ML models specifically designed to meet the needs of food industry can yield more accurate and interpretable results. (2) Development of more advanced ML algorithms. Due to the diversity and complexity of food materials, as well as the variability of food processing methods, more advanced and optimized algorithms are needed to detect food information and to extract subtle feature changes. For example, traditional ML approaches are limited to handle instances encountered during the training process. Therefore, these ML models often fail to recognize classes absent from training data, which are referred to as unseen classes. The recently developed algorithms, such as the open-world ML, may offer novel techniques for handling such challenges ( Parmar et al., 2023 ). (3) Development of more efficient ML models for on-site detection. For real-world applications, rapid and real-time analysis is essential in food industry. However, certain ML models have high computational demands that may restrict their deployment in resource-constrained settings. Therefore, it is crucial to develop lightweight and efficient models that are suitable for data scarcity scenarios. A more fundamental challenge lies in data acquisition for on-site deployment. Unlike well-curated laboratory datasets, real-world food processing environments generate highly variable data due to factors such as changing lighting conditions, sensor noise, product heterogeneity, and fluctuating temperatures or humidity. This discrepancy between training data and application data, known as domain shift, often leads to significant performance degradation when models are transferred from lab to factory floor. Furthermore, acquiring sufficient labeled data for every possible scenario is often impractical due to cost, time and the perishable nature of food samples. To address these data-centric challenges, future efforts should focus on developing models that are robust to distribution shifts, such as through domain adaptation techniques that align feature distributions across different conditions. Additionally, leveraging semi-supervised or active learning can maximize model performance with limited labeled data by strategically selecting the most informative samples for annotation. Synthetic data generation, using generative models or digital twins, also offers a promising pathway to augment training datasets with rare but critical scenarios (e.g., contamination events). Besides, multi-model fusion can offer complementary information from different modules, thus enhancing detection accuracy and robustness. Additionally, the integration of ML models with advanced techniques such as IoT devices, high-level imaging methods, AI and edge computing technologies would facilitate efficient on-site detection and enable real-time analysis. (4) Enhancement the interpretability of ML models. Generally, ML algorithms are complex and often challenging to interpret, which complicate the understanding of how specific decisions are made within AI decision-making processes. This complexity consequently raises questions regarding the trustworthiness of ML models. Therefore, enhancing the interpretability of ML models is crucial for improving their transparency, credibility, reliability and potential applications. Various interpretability methods including the local interpretable model diagnostic explanations, shapely additive explanations, model visualization and global interpretive methods (such as feature importance analysis) can elucidate the underlying rationale behind the outputs generated by ML models. However, in practical applications, the importance of ML models' interpretability has not been fully recognized when compared to performance and prediction accuracy. In the future, developing more interpretable and transparent ML models, such as integrating advanced generative pre-trained transformers ChatGPT-4 into human-machine interactions, will facilitate broader applications of ML technique in the food industry. Fig. 4. Open in a new tab The global diagram of machine learning procedures, its challenges and future prospects. It is important to clarify that the proposed integration of large language models (LLMs) such as ChatGPT-4 is not intended for core decision-making in food safety-a role that must remain with domain-validated ML/DL models. Rather, LLMs are envisioned as assistive interfaces for enhancing interpretability: they translate quantitative outputs from specialized models (e.g., feature importance values, prediction probabilities) into natural language explanations accessible to non-expert stakeholders, without altering or influencing the underlying decisions. Within this constrained boundary, several strategies can be employed to ensure explanation accuracy and consistency. First, retrieval-augmented generation coupled with rigid prompt engineering can ground LLM outputs in authoritative sources (including national standards, peer-reviewed literature, and model-derived quantitative results), thereby mitigating hallucinations at the source. Second, a three-way consistency verification mechanism can be established, cross-checking the model's raw output, the LLM-generated explanation, and relevant regulatory standards to automatically filter factually inconsistent content. Third, for high-risk applications, an expert-in-the-loop validation layer can provide final oversight, ensuring that all explanations comply with domain-specific requirements before operational use. These safeguards position LLMs as valuable tools for democratizing access to complex model insights, while maintaining the rigor and reliability demanded by food safety applications. (5) Data security and ethics concerns. Advancements of ML in food industry pose a series of ethical, privacy and even societal challenges. For example, food testing devices may gather massive amounts of personal data from consumers, including purchase histories and dietary preferences, thereby increasing the risks of personal privacy leakage. To avoid abused of such sensitive information, it is imperative for the food industry to adopt strong cybersecurity measures to protect against data breaches and to ensure compliance with privacy regulations. It is also important to guarantee the ethical use of ML models in decision-making procedures, particularly concerning issues such as product recommendations and customized dietary guidance. Moreover, factors such as the education level and economic status significantly affect public's acceptance and understanding of food safety knowledge. When an ML model is trained on unbalanced or biased datasets, it may yield unfair or discriminatory outcomes due to biases in the training data. This can lead to the unequal distribution of food resources and exacerbate existing socioeconomic disparities. (6) Emerging frontiers: from traditional ML to foundation models and multi-modal intelligence. Beyond the traditional ML and DL paradigms discussed above, several cutting-edge advancements are emerging as transformative solutions to the core bottlenecks that have hindered the widespread adoption of intelligent technologies in the food industry. For example, self-supervised learning and domain-specific foundation models address the perennial challenge of labeled data scarcity-a critical barrier in food applications where annotation requires specialized expertise and costly experiments. By pre-training on vast amounts of unlabeled food images, spectra, and sensor data, these models learn generalizable feature representations that require only minimal fine-tuning for downstream tasks such as defect detection, adulteration identification, or traceability analysis. This paradigm shift promises to move the field from the current “one task, one model” fragmentation toward a unified “pre-train once, adapt everywhere” framework, dramatically reducing the reliance on expensive manual annotations. Few-shot learning and domain generalization techniques tackle the performance degradation that occurs when models trained in controlled laboratory settings are deployed in variable real-world environments. Few-shot learning enables rapid adaptation to emerging threats (e.g., novel adulterants or pathogens) with only a handful of labeled samples, while domain generalization methods learn invariant features across different devices, origins, and production conditions-facilitating the translation of laboratory innovations to industrial-scale applications. Transformer architectures, with their self-attention mechanisms and exceptional capacity for modeling long-range dependencies, are demonstrating superior performance in processing spectral, temporal, and multi-modal food data. In hyperspectral analysis, Transformers can capture subtle spectral variations indicative of trace contaminants at the nanoscale. In supply chain forecasting, temporal Transformers integrate diverse features to predict demand fluctuations, spoilage risks, and logistics bottlenecks with unprecedented accuracy. Multi-modal fusion-integrating images, spectra, sensor data, text, and physicochemical measurements-represents an inevitable evolutionary direction for holistic food quality and safety assessment. By combining complementary information, multi-modal models enable comprehensive characterization spanning appearance, internal composition, safety indicators, and traceability, significantly outperforming unimodal approaches in tasks such as origin authentication and quality grading. In supply chain management, fusing IoT sensor streams, logistics trajectories, market sentiment, and safety alerts could enable end-to-end risk traceability and global optimization. However, the industrial deployment of these advanced techniques faces substantial challenges that echo the concerns raised throughout this section: ( 1 ) the lack of standardized, large-scale, high-quality datasets across diverse food matrices hinders the development of robust foundation models; (2) the inherent “black-box” nature of large-parameter models intensifies interpretability concerns, complicating regulatory compliance in high-risk food safety applications; (3) the substantial computational demands of Transformers and multi-modal architectures pose barriers to edge deployment and accessibility for small-to-medium enterprises. Addressing these challenges-through standardized data infrastructures, explainable AI methodologies tailored for complex architectures, and lightweight model design—will be essential for translating these cutting-edge innovations into tangible benefits for the global food industry. 6. Conclusion The rapid advancements in computer science over recent decades have extended to various fields, particularly the food industry. ML algorithms are the foundation of these emerging technologies, contributing to the collection, processing and analysis of data. This review focused on the application of ML in food sectors, emphasizing its pivotal role as a powerful tool for data analysis and modeling across diverse aspects of the food industry. The integration of ML with other advanced techniques such as cloud computing, computer vision and AI has revolutionized the food industry, from raw material processing to personalized nutrition solutions. ML also provides cutting-edge solutions for issues that were previously difficult to solve in food industry, especially those related to big data analysis like food adulteration and traceability. However, despite the transformative potential of ML-based ‘smart’ technologies, their full-scale adoption is hindered by challenges including the complexities of implementation scenarios, lack of transparency and interpretability in decision-making processes, and difficulties to integrate with existing systems. Future developments should prioritize portable solutions while enhancing model design by improving algorithms performance, model interpretability and human-machine interface. Additionally, there is an urgent need to strengthen synergies and integration with other technologies to foster the ‘smart’ food industry while addressing ethical concerns regarding consumer privacy and data security. In conclusion, ML-based intelligent technologies stand at the forefront of revolutionizing the food industry. Through continuous innovation, ethical deployment and strategic integration efforts, these technologies have the potential to significantly improve the efficiency, safety and sustainability of the global food industry, thereby enabling it to meet the growing demands of the future. Ethical approval In this systematic review article, no living organisms were used to collect or obtain experimental data. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This research was supported by National Natural Science Foundation of China (32572686), Zhejiang Provincial Key Research and Development Program (2025C02126), Zhuji City "Open List & Take the Lead" Projects (2025J06),State Administration for Market Regulation Science and Technology Project (2023MK057). Contributor Information Zejun Wang, Email: [email protected]. 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