Deep learning in food quality and safety management: A review of architectures, applications, and future directions

Abstract

As a cornerstone of the modern food industry, process control in food processing operations undergirds food quality assurance, safety compliance and economic performance of an enterprise. With artificial intelligence technology further penetrating into the food sector, deep learning has evolved from the early basic models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Artificial Neural Network (ANN) to a diversified repertoire of advanced architectures like YOLO, U-Net, Bidirectional Long Short-Term Memory neural network (BiLSTM) and Vision Transformer (ViT). These advanced architectures have been systematically investigated and deployed across multiple facets of process control in food processing stages. This review summarizes the multi-domain applications of deep learning in food process control, encompassing raw material inspection, dynamic monitoring, safety traceability, and quality evaluation. It further discusses diverse architectural strategies, including single-model and multi-model fusion approaches, and explores their integration with other intelligent analytical technologies to enhance processing efficiency and product consistency. Finally, current limitations regarding model interpretability and generalization are identified, and future research directions are proposed, focusing on the development of rapid, adaptive, and explainable deep learning systems to advance intelligent transformation across the entire food industry chain.

Graphical abstract

Highlights

• •Deep learning enhances food quality and safety by intelligent monitoring.
• •Deep learning enables efficient real-time optimization in food processing control.
• •Deep learning boosts food inspection reliability by non-destructive analysis.
• •Dynamic process control and intelligent data fusion as key future challenges.

1. Introduction

Food processing operations involve stages such as raw produce screening, processing (including procedures such as cleaning, heating, seasoning, grinding, mixing, etc.), packaging, storage and transportation, and a quality inspection and monitoring system running through the whole process. To ensure the quality and safety of final products, it is necessary to implement intelligent real-time detection and monitoring in core links such as raw produce selection, processing environmental hygiene, control of key process parameters, and product quality inspection. However, the conventional food processing relies heavily on manual work and basic mechanical automation. Under large-scale production conditions, this conventional approach appears to be too slow, inaccurate and weak in data handling to meet today's demands for speed and precision, thereby capping production efficiency. Due to lack of advanced detection tools and data collection and analysis approaches, quality decisions often rest on subjective experience and personal judgment, further increasing the complexity and uncertainty of product quality control.

Therefore, it is necessary to integrate advanced information technology into the food production and processing (Fig. 1). It is conducive to improving the precision and efficiency of food processing, achieving high-quality and low-cost output, meeting the needs for development of modern food industry, enhancing food safety and competitiveness of enterprises, and realizing the transformation from traditional experience-driven manual work to information-based data-driven automation.

Fig. 1.

Application of deep learning in process control of food processing.

As a subfield of machine learning, deep learning uses multi-level deep neural networks to process and analyze raw data from low to high levels with its powerful feature learning ability. In fields such as image processing (Jiao and Zhao, 2019), natural language processing (Otter et al., 2021), and time series analysis (J. Wang et al., 2022), it utilizes multiple ways to automatically learn features of image, text data and time series data, extracts useful information, and achieves a highly flexible and adaptive effect. Deep learning thrives on the diversity and complexity of food matrices, turning vast, tangled data into clear answers (Class et al., 2021). It is gradually proving its worth for raw produce detection, quality inspection, intelligent process monitoring and sensory characteristic prediction (Zhou et al., 2019).

A comprehensive literature search was conducted across multiple authoritative electronic databases, including Web of Science, Science Direct, and PubMed, to identify relevant studies on the application of deep learning in food quality and safety management. The search was performed using structured keyword combinations with Boolean operators: (“deep learning” OR “convolutional neural network” OR “CNN” OR “recurrent neural network” OR “RNN” OR “LSTM” OR “YOLO” OR “U-Net” OR “Transformer” OR “Vision Transformer” OR “BiLSTM”) AND (“food processing” OR “food quality control” OR “food safety” OR “food defect detection” OR “maturity detection” OR “freshness evaluation” OR “adulteration detection” OR “process monitoring” OR “hyperspectral imaging” OR “Raman spectroscopy”). The retrieval time span was set from January 2015 to April 2026, with particular emphasis on publications from the last five years (2021–2026) to ensure the inclusion of cutting-edge advances. Inclusion criteria were established as follows: (1) peer-reviewed original research articles or high-quality review papers published in English; (2) studies explicitly employing deep learning architectures (e.g., CNN, RNN, LSTM, YOLO, U-Net, Transformer) for food process control applications; (3) research covering at least one of the four core domains: raw material inspection (defect, maturity, freshness, and adulteration detection), in-process monitoring and optimization (drying, fermentation, crystallization, and real-time quality prediction), packaging defect inspection, and final product quality assessment (sensory attributes, nutritional composition, and shelf-life prediction); and (4) studies providing quantitative performance metrics (e.g., accuracy, precision, recall, R², RMSE) to enable comparative analysis.

Against this background, this review aims to sort through how deep learning, from workhorse models like CNN and RNN to newer architectures such as YOLO, U-Net, BiLSTM, and ViT, is being applied in food quality and safety management. Unlike earlier reviews that focus on isolated applications, we emphasize the integration of the entire supply chain for food quality and safety management. covering raw material inspection, process monitoring, safety traceability, and final quality evaluation. To make this concrete, the review is organized around four key control points: raw produce detection, including defects, maturity, freshness, and adulteration; processing operation monitoring and optimization covering real-time quality prediction, in-line grading, and packaging through storage; safety detection and traceability addressing harmful substances and pathogenic bacteria; and final product quality assessment focusing on sensory attributes, nutritional composition, and shelf life. These four areas span the entire production line from incoming materials to finished products and represent where deep learning has shown the most practical impact. We also examine architectural strategies from single-model to multi-model fusion, along with the integration of complementary tools like hyperspectral imaging, Raman spectroscopy, and electronic sensing (Fig. 2). Finally, we discuss current limitations in interpretability, generalization, and deployability, and outline future directions toward rapid, adaptive, and explainable systems that can truly drive intelligent transformation across the food industry.

Fig. 2.

Data flow logic diagram of process control in food processing.

2. Applications of deep learning in the inspection of food ingredients

Deep learning has been widely applied in the detection of food raw produce (Fig. 1). Food raw produce can be compromised by environmental factors during harvest and transportation, resulting in defects such as bruises and rotting. If defects, ripeness, and freshness of food ingredients can be inspected and graded prior to processing, it not only preserves the nutrients and guarantees food safety, but also avoids problems such as downstream stoppages or rework. By learning from food images and processing time-series data, it can automatically identify and classify food. Combined with sensor technology, it has constructed an intelligent technical support system for dynamic control of food processing to deliver both quality and safety, its core data flow logic diagram is shown in Fig. 2.

2.1. Defect detection in food ingredients

Quality inspection of food raw produce is the first gatekeeper in process control for food processing. At present, deep learning technologies have been applied to rapid and non-destructive detection and sorting of food raw produce. Among them, the CNN model in deep learning has been widely seen for its outstanding performance in analyzing the color, texture, and characteristic spectra of foods. CNNs have demonstrated superior performance over traditional machine learning algorithms—such as support vector machines (SVM), k-nearest neighbors, decision trees, and random forests—in food process control applications, primarily due to their distinct architectural advantages (Ding et al., 2025). Unlike conventional methods that depend on manually crafted features like color histograms or texture descriptors, which may inadequately capture subtle defect patterns, CNNs enable automatic hierarchical feature extraction directly from raw data, learning low-, mid-, and high-level representations that enhance model robustness. Furthermore, the convolutional kernel structure equips CNNs with spatial invariance and local receptive fields, allowing them to effectively capture spatial correlations and local patterns in images, which is critical for identifying surface defects, bruises, and contamination. Through mechanisms such as weight sharing and pooling operations, CNNs substantially reduce the number of model parameters compared to fully connected networks, thereby mitigating overfitting and improving generalization. In addition, their architecture is inherently compatible with multimodal inputs, including 1D spectral signals, 2D images, and 3D hyperspectral cubes, offering considerable adaptability across diverse food processing scenarios (J. Zhao et al., 2025). Consequently, CNN-based models consistently outperform conventional machine learning approaches in tasks involving high-dimensional image and spectral data.

Combined with other deep learning techniques and non-destructive testing methods, CNNs offer a new approach to defect detection in food raw materials. However, as the number of layers in a CNN increases, training accuracy tends to decline. To address the issue of performance degradation in deep networks, residual connections are introduced into CNNs, enabling such deep networks to be trained effectively even with hundreds or thousands of layers. Da Costa et al. (2020) proposed a deep residual neural network (Residual Network, ResNet) classifier. The ResNet50 model was trained to learn the typical characteristics of healthy tomatoes. The stems and calyces, as seen in normal tomato appearances, could be accurately recognized without confusing with surface defects of tomatoes. An average precision of 94.6% was achieved. In particular, the optimal classifier delivered a recall rate of 86.6% and a precision rate of 91.7%, greatly reducing the rate of defective fruits in tomato raw produce. Since the deep learning model can automatically learn complex features related to surface defects from the original image data of color, texture and shape, etc. of food without help of manual feature specification, the model can also be generalized for detection of outer defects of other foods. Jahanbakhshi et al. (2020) proposed a data augmentation and random pooling mechanism to improve the results of CNN for distinguishing defective and healthy lemons. Compared with conventional algorithms of manual feature extraction and machine learning classification, it pushed the classification accuracy to 100%, remarkably improving the efficiency for detection of defective lemons. In multi-level classification, Erbaş et al. (2022) used the deep learning technology based on the architecture of Alex Network to classify hazelnuts into five categories, namely cracks, holes, marks, cuts and soundness. It delivered an accuracy of 99%, a precision of 98.7%, and a recall rate of 99.6%, achieving high-precision identification of defects in the hazelnut industry.

While using deep learning model alone for defect detection only thrives on outer surface spectral detection tackles inner defects using spatial and chemical spectral information. The combined use of deep learning technology and spectral information technology speed up detection of food defects. Pourdarbani et al. (2023) used hyperspectral imaging (HSI) technology coupled with two-dimensional (2D) and three-dimensional (3D) CNN models to detect bruises incurred at different growth stages of oranges. The classification accuracy of the 2D model appeared to be lower than 83%, while that of the 3D model exceeded 90%. It indicated that the 3D-CNN model can rapidly and accurately screen out the bruised oranges. Zhang et al. (2025) combined HSI technology with a CNN model based on competitive adaptive re-weighted sampling for feature wavelength selection, achieving a high accuracy of 97.27% in identification of outer defects of Nanfeng mandarin. It provided technical support for evaluating the inner and outer quality of other fruits.

While deep learning CNN and its variants ResNet, AlexNet and 3D-CNN achieve high accuracy in food defect detection, some other CNN-based detection models, as shown in Table 1, provide new technical options for food defect grading. Deep learning models automatically learn the defect-related features, effectively avoiding the limitations caused by manual design and demonstrating strong generalization. In complex situations with multiple types of food defects, CNN and its variant models offer much higher classification accuracy than the traditional machine learning. At the same time, their combination with spectral data enables rapid and non-destructive evaluation and classification of the inner and outer quality of food, minimizing the cost and error rate of manual sorting to the greatest extent.

Table 1.

The application of deep learning in food defect detection.

Category	Object	Analysis technology	Model	Results	References
Defect	Tomato	CV	ResNet50	The average precision of the test set is 94.6%, the recall rate is 86.6%, and the accuracy is 91.7%.	Da Costa et al. (2020)
	Carrot	CV	ShuffleNe	The accuracy rate of binary classification reaches 99.82%, and the accuracy rate in multi-class classification reaches 93.01%.	Deng et al. (2021)
	Potato	Visible-Near IR	YOLOv5s, CNN	The average precision of external defect detection by YOLOv5s is 0.892, and the mean average precision is also 0.892. The accuracy of internal defect detection by CNN is 98.2%.	Wei et al. (2023)
	Coffee beans	-	ResNet-18	The accuracy rate is 93%.	P. Wang et al. (2021)
	Hazelnut	-	AlexNet	The accuracy rate reaches 99%, the precision is 98.7%, and the recall rate is 99.6%.	Erbaş et al. (2022)
	Pine nuts	HSI	1D CNN, 3D CNN	The accuracy of the 1D CNN model on the training set is 90.23% and 81.32% on the validation set. The 3D CNN model performs well on the training set but shows overfitting on the validation set.	Peng et al. (2024)
	Walnut	-	U-Net	The mean IoU is 96.71% and the mean pixel accuracy is 98.52%.	J. Wang et al. (2024)
	Almonds	HSI	VGG16, MobileNet	The highest accuracy of the MobileNet model is 96.60%.	S.-Y. Chen et al. (2024)
	Orange	HSI	2D-CNN, 3D-CNN	The accuracy rate of 2D-CNN is 83%, and the accuracy rate of 3D-CNN reaches 94%.	Pourdarbani et al. (2023)
		Structured-illumination reflectance imaging	Unet	An overall accuracy of 99.4% and an IoU of 0.903.	H. Zhang et al. (2024)
	Apple	NIR, RGB	AlexNet, Inception-V3, VGG16	The accuracy rates of the AlexNet, Inception-V3, and VGG16 models reach 99.33%, 100%, and 100% respectively.	Ünal et al. (2024)
		HSI	YOLOv5	The recognition rate is 95%.	C. Zhang et al. (2024)
	Akidzuki pears	CV	YOLOv5	The average precision is 0.939, and the recall rate (R) is 0.921.	(B. Wang et al., 2024)
Defect	Citrus	CV	SPMUNet	The mean pixel accuracy is 89.1%, the mean IoU is 83.8%, and the Dice coefficient is 88.6%.	X. Xu et al. (2024)
	Nanfeng mandarin	HSI	CNN	The accuracy rate is 97.27%.	J. Zhang et al. (2025)

2.2. Fruit maturity detection

Maturity affects the taste and flavor of fruits, and fruits at different maturity stages can be made into different types of products. Therefore, accurately detecting the maturity of raw produce is the key to improving food quality. Classifying fruits by maturity is a complex process (Mahmood et al., 2022). As conventional practices of destructive detection are proven less satisfactory in speed and efficiency, efforts are now directed towards using non-destructive detection based on deep learning to meet the industry needs.

While the CNN model in deep learning has demonstrated excellent performance in image data processing and object detection, it judges the maturity of fruits mainly by colors. To improve sorting of hawthorns, Azadnia et al. (2023) managed to enhance the captured image dataset of hawthorns with data augmentation from 600 to 3000. Based on the Inception V3 of the CNN model, hawthorn fruits were classified as immature, mature and overripe with an accuracy of 100%. It helped effectively reduce waste of hawthorns and labor costs, realizing automatic classification of hawthorn fruits by maturity and enhancing quality of the fruits. Similarly, Arunima et al. (2024) trained a 9-layer CNN model to recognize four ripening stages of bananas (immature, medium-ripe, ripe, and overripe), with an accuracy rate of 95%, outperforming the conventional classification models such as Visual Geometry Group (VGG) 16, VGG19, InceptionV3, ResNet50 and Efficient Network B0. More recently, some researchers detected the maturity of mangoes by color. They used CNN and VGG16 to extract the image features and train the dataset. The results showed that the CNN model delivered an accuracy rate up to 96.28%, far higher than that of the VGG16 model (Sikder et al., 2025).

However, for fruits such as kiwifruit, it is very difficult to judge their maturity solely by outer features like colors. An integrated approach combining deep learning with spectroscopy and acoustic vibration technology offers a viable solution for maturity detection of kiwifruit. Tian et al. (2023) proposed a method for detecting the soluble solids content and hardness of ‘Hongyang’ kiwifruit. Using the starch-staining index as reference maturity scale, it combined VIS-NIR and acoustic vibration technologies with 1D-CNN model in deep learning to detect the maturity of kiwifruit. Following an analysis of the spectral and acoustic vibration data with the 1D-CNN model, the soluble solids content and hardness of kiwifruit were estimated with accuracies of 93.08% and 92.31% respectively, substantially improving the efficiency for detection of kiwifruit maturity.

Deep learning CNN models and their extended architectures have shown significant advantages in detection of food maturity, enabling non-destructive, high-precision, and real-time sorting operations. By fusing multi-dimensional data, such as images, spectra and acoustic vibration information, these models overcome the constraints of traditional visual inspection and effectively suppress overfitting, further enhancing generalization with the help of data augmentation strategies. The deep learning-based intelligent classification model not only significantly reduces loss of raw produce, but also achieves differential classification of products according to varied grading criteria, which is of great significance in improving the flavor quality and commercial value of food. Other applications of deep learning in detection of fruit maturity are shown in Table 2.

Table 2.

The application of deep learning in fruit maturity detection.

Category	Object	Analysis technology	Model	Results	References
Fruit maturity	Strawberry	HIS	1D ResNet, 3D ResNet	All achieved a classification accuracy of over 84%.	Su et al. (2021)
		-	GoogleNet, ResNet18, ShuffleNe	The accuracies of GoogleNet, ResNet18, and ShuffleNet are 98.85%, 96.88%, and 95.63% respectively.	Azizi et al. (2024)
	Jujube fruits	-	AlexNet, VGG16	The accuracy rate of AlexNet is 98.26%, and the accuracy rate of VGG16 is 99.17%.	Mahmood et al. (2022)
	Banana	-	CNN	The accuracy rate is 91.25%.	Shuprajhaa et al. (2023)
		CV	CNN	The accuracy rate is 95%.	Arunima et al. (2024)
	Orange	-	cResNet18, ResNet50, ShuffleNet, MobileNetv2, Dense Convolutional Networks (DenseNet)201, GoogleNet	The accuracy rate of ResNet50 is 99.5%, which is superior to other models.	Momeny et al. (2022)
	Winter Jujube	CV	iResNet-50	The accuracy rate is 98.35%.	Ban et al. (2023)
	Kiwifruit	VIS-NIR and acoustic vibration technology	1D-CNN	The accuracy rate is 99.4%.	Tian et al. (2023)
	Kiwifruit Hawthorn fruit	Fluorescence hyperspectral imaging	CNN-MLP	The CNN-MLP with the improved whale optimization algorithm achieves an accuracy of 91.6% on the test set.	(Zou et al., 2024a)
	Kiwifruit Hawthorn fruit Apple	Machine vision	Inception-V3, ResNet-50, CNN	The accuracies of Inception-V3, ResNet-50, and CNN are 100%, 99.63%, and 99.63% respectively.	Azadnia et al. (2023)
		-	YOLOv5, Transformer	The accuracy of different weight versions of YOLOv5 is above 90%. When the two are combined, the accuracy is increased to 99.63%.	B. Xiao et al. (2024)
Fruit maturity	Apple Mango	VIS-NIR	1D-CNN	The classification accuracy rates increased by 4%, 18% and 15% respectively in the new season.	Pan et al. (2024)
	Apple Mango Tomato	-	CNN, VGG16	The accuracy rate of CNN is 96.28%. Overall, it outperforms VGG16.	Sikder et al. (2025)
		NIR-HSI	RNN	The accuracy rate is 96.6%.	M. Zhao et al. (2023)
	Rice	Terahertz imaging	VGG	The accuracy rate is 99.8%, the precision is 99.8%, and the recall rate is 99.8%.	Q. Wang et al. (2023)

2.3. Freshness detection of raw materials

During logistics and storage process, environmental factors can easily induce spoilage, accelerate proliferation of microorganisms and lead to the deterioration of food, which significantly increases the risk of foodborne diseases and poses a serious threat to public health (J. Liu et al., 2024). To tackle the problem, a combination of deep learning and food freshness detection can help evaluate food freshness rapidly, accurately and efficiently. Deep learning architectures, ranging from CNNs such as Squeeze Network (SqueezeNet), ResNet50, and GoogLeNet to RNN variants including BiLSTM, as well as hybrid models, have been integrated with multi-modal data (spectroscopic, imaging, and sensor data) to achieve high-precision freshness detection of agricultural products. Specific applications are detailed in Table 3.

Table 3.

The application of deep learning in food freshness detection.

Category	Object	Analysis technology	Model	Results	References
Food freshness	Egg	NIR	CNN	The accuracy rate is 90.7%.	Yao et al. (2024)
	Milk	-	3D-CNN	The accuracy rate is 93.25%, the precision is 3.42%, the recall rate is 92.90%, and the F1 score is 93.07%.	J. Zhang et al. (2024)
	Fish	Colorimetric paper sensing	ResNet-50, InceptionV4, VGG16, WISeR-50	The highest accuracy rate of WISeR-50 is 99.2%.	Ma et al. (2023)
	Fish Beef	FHSI	LSTM + NGO	The R2 values of protein and TVB-N are 0.910 and 0.987 respectively.	(Zou et al., 2024b)
	Fish Beef Beef Chicken	Raman Spectroscopy	CNN	The accuracy rate is 90.6%, and the testing time is shortened to 0.44 s, which is superior to traditional methods.	K. Wang et al. (2023)
		-	SqueezeNet, VGG19	The highest accuracy of VGG19 combined with the ANN is 77.3%.	Yildiz et al. (2024)
		Color sensing array	ResNet-50	The accuracy rate is 99.27%.	(Jia et al., 2023a)
		Colorimetric sensor array	ResNet34, VGG16, GoogleNet	The highest accuracy of ResNet34 is 99.46%.	(Lin et al., 2024a)
	Beef Chicken Chicken Pork	Dual hyperspectral imaging	Combine CNN, AM and pyramid structure	The Coefficient of Determination (R2) values of TVB-N and TVC are 0.920 and 0.961 respectively, which are superior to traditional chemometric methods.	Cai et al. (2024)
		-	InceptionV3	The accuracy rate is 96.3%.	Hasan et al. (2024)
	Chicken Pork	Two-Dimensional Correlation Spectroscopy, VIS-NIR	CNN	The R2 for the TVB-N content is 0.9579.	Sun et al. (2024)
Food freshness	Pork Shrimp	VIS-NIR	CNN-PLSR, CNN-SVR	CNN-SVR has the highest prediction accuracy for pH value. The correlation coefficient of the prediction set (Predictionset Correlation Coefficients, Rp) is 0.9625. CNN-SVR also has the highest prediction accuracy for TVB-N content, and the Rp is 0.9697.	X. Zhao et al. (2025)
		-	YOLO-Shrimp	The accuracy is 95.23%, the recall rate is 89.05%, the F1-score is 92.04%, the mAP is 5092.32%, and the mAP50:95 is 70.2%.	Hou et al. (2025)
	Shrimp Beef, chicken and pork	NIR	1D-CNN	For TVB-N, the coefficient of determination in prediction ($R_P^2$) is 0.9397, and the RPD is 2.8279. For TVC, the $R_P^2$ is 0.9318 and the RPD is 2.7560.	Q. Han et al. (2025)
		Fluorescent sensing array	SqueezeNet	The accuracy rate is 98.17%, and the recognition can be completed within 5–7 s.	(Lin et al., 2024b)
	Shrimps and fish	Chemical reaction-based dye odor sensor array	ResNet-18	The overall accuracy rate reaches 99.25%.	Jiang et al. (2024)
	Chicken, beef	Fluorescent sensor array	DCNN	The accuracy rate is 97.1%.	M. Li et al. (2024)
	Fruits and vegetables	-	CNN + BiLSTM	The accuracy rate has reached 97.76%.	Yuan et al. (2024)
	Fruits and vegetables	-	GoogLeNet + DenseNet-201+ResNeXt-101	The highest accuracy rate reaches 96.98%.	Yuan and Chen (2024)
	Beef	Low-cost color imaging system	Deep Neural Network	The accuracy rate is 99.33%, the precision rate is 98.68%, and the recall rate is 98.67%.	Setiadi et al. (2022)

2.3.1. Freshness detection of meat products

In meat inspection, deep learning is typically used in conjunction with various sensor technologies and spectroscopic analysis techniques. Deep learning models, such as CNNs, excel at processing image data and demonstrate exceptional stability in assessing meat freshness by analyzing visual and spectral information (Y. Han et al., 2026). Lin et al., 2024b developed a non-destructive method for detecting meat freshness based on a fluorescence sensor array combined with the deep learning model SqueezeNet. It delivered an accuracy rate of freshness detection as high as 98.17%, enabling real-time and dynamic detection and early warning of meat freshness. Cai et al. (2024) used VIS-NIR and short-wave near-infrared spectroscopy (IR), combined with a deep learning Pyramid Attention Features Fusion Model, CNN, attention mechanism (AM), and pyramid structure. By extracting the characteristic information of total volatile basic nitrogen (TVB-N) and total viable count (TVC) in chicken, they found that the accuracy of the model in predicting the content of TVB-N and TVC reached 92% and 96%, respectively. RNNs are neural networks capable of processing sequential data with internal memory mechanisms; they can handle various input-output mappings, including one-to-one, many-to-one, and many-to-many relationships (Jia et al., 2023b). Variants such as LSTM and BiLSTM, when integrated with multimodal sensory data, can accurately predict food freshness. Z. Liu et al. (2023) obtained deeply penetrating Raman images of shrimp using spatially offset Raman spectroscopy. An attention-based LSTM network was employed to automatically learn the relative importance of spectral bands and spatial locations, enabling end-to-end freshness prediction without manual intervention. The model achieved an R² of 0.93, an RMSE of 0.48, and an RPD of 4.06, significantly outperforming traditional machine learning methods. He et al. (2025) developed a wearable detection system that integrates flexible impedance and spectral sensors. By combining this system with a 1D-CNN-BiLSTM-ATT deep learning model, they achieved non-destructive, real-time grading of lamb freshness with 94.57% accuracy. The method combining spectral technology with a deep learning model demonstrated excellent stability and generalization, providing a reliable technical solution for high-precision detection and comprehensive analysis of meat freshness.

2.3.2. Freshness detection of fruits and vegetables

For detection of fruit and vegetable freshness, Yuan et al. (2024) deployed a deep learning model that combines a CNN and a BiLSTM, achieving accuracy rates of 98.25% and 98.46%, respectively, for detecting the freshness of bananas and sweet peppers. D. Wang et al. (2024) used a fluorescence sensor array prepared from three natural plant dyes, curcumin, puerarin and myricetin, to monitor the acid-base changes of volatile organic compounds in vegetables in real time. Coupled with a ResNet50-driven deep CNN, the model obtained accuracy rates of 98.58%, 97.15% and 92.89%, respectively, for the freshness of kidney beans, spinach and sweet corn. Yuan et al. (2024) proposed a fruit-and-vegetable freshness detector that fuses pre-trained deep learning models of GoogLeNet, DenseNet201 and ResNet101 with principal-component analysis. Following extracting and dimension-reducing the image features of five vegetables (tomato, cucumber, carrot, potato and sweet pepper) and five fruits (banana, orange, mango, apple and strawberry), the model achieved an accuracy of 96.98%. These efficient detection strategies provide technical guarantees for the freshness of fruits. Although CNNs have shown capability in correlating environmental factors with biochemical reactions, the efficacy of such deep learning systems remains critically dependent on data quality and sensor sensitivity (Jia et al., 2026). Future efforts should therefore transition to a co-design paradigm that unifies sensor hardware, signal processing algorithms, and deep learning models into an end-to-end optimization framework, thereby improving the accuracy of fruit and vegetable freshness assessment.

2.3.3. Continuous monitoring of freshness during storage and transportation

Managing cold-chain transport with big data is inherently complicated as shipments consist of many small lots, each requiring continuous tracking of moisture, temperature and humidity. Deep learning excels at handling such complex data. Combined with sensor technology, it ensures consistent quality of products in transport and is now widely applied in monitoring freshness of eggs and temperature deviations in packaged foods. Kim et al. (2022) introduced a model that combined CNN and LSTM to predict the freshness of eggs under cold-chain storage conditions. Compared with traditional random-forest and ANN models, it reduced RMSE from 6.62 to 2.02 and MAE from 3.16 to 1.39, demonstrating high accuracy and reliability in real-time cold chain environments. Zou et al. (2023) incorporated multiple data streams, such as cargo-air temperature, initial product temperature, carriage door status, and loading and unloading duration, into an ANN to predict food temperature dynamically. Compared with the traditional model that only uses in-cabin air temperature, the RMSE fell by 77% and 79% in chilled zone and frozen zone, respectively. The model could track food temperature in real time under changing ambient temperatures, offering an effective monitoring and early-warning tool for cold-chain logistics.

CNN, CNN-LSTM, AM, ANN and ViT models can automatically verify package integrity, locate internal foreign objects and read shelf-life markings, through image classification and segmentation, object detection and temporal data processing. By closely tracking temperature, humidity and quality drifts, they enable real-time surveillance and risk alerts, offering a digital solution that slows spoilage and cuts losses across storage and transportation.

3. Deep learning-based monitoring and optimization for food processing control

Automation in food processing operations coupled with intelligent quality monitoring is crucial to guarantee product quality consistency and safety. CNN-based models and their derivative architectures can learn visual features such as surface edges, textures and structures from images, enabling real-time identification of quality and precise and consistent decisions. On the other hand, RNN-based models and their variants can adapt to temporal dynamics of process streams and effectively process sensor-generated time-series signals such as temperature and gas composition, thereby enabling real-time detection and prediction of quality indicators in food processing (Balakrishnan et al., 2025; Ghazi et al., 2024). This provides a safe, intelligent technical means for real-time quality prediction and grading during processing, as well as for tracking of dynamic changes in quality throughout packaging, storage and transportation. This chapter focuses specifically on how deep learning enables real-time monitoring and optimization of processing operations, with an emphasis on improving production efficiency, product consistency, and intelligent decision-making during processing, packaging, and storage.

3.1. Real-time quality prediction for food processing

The intrinsic properties of food can change significantly during processing as a result of variables such as processing method, temperature and pressure. To improve production efficiency and guarantee product quality, it is necessary to monitor and predict in real time the key quality indicators such as appearance, size and color of food during processing. Deep learning technology offers an innovative solution for real-time quality prediction and intelligent regulation of food processing operations, allowing processing parameters such as time, temperature and pressure to be adjusted automatically on the fly.

In food drying process, three models, Unet-Xception, 1D-CNN and CNN-BiLSTM-SE have been deployed to monitor and predict the changes in parameters such as color, moisture content, soluble solids, acidity and hardness. Iheonye et al. (2025) constructed a deep learning system of Unet-Xception to monitor the appearance attributes of green peas during the drying process on fluidized bed. It achieved semantic segmentation of pre-processed pea images and predicted color, texture and size with a mean intersection over union (IoU) of 0.9464, far exceeding traditional computer vision (CV) techniques. You et al. (2024) used CV technology to extract the spatial images of dried Tencha samples. By combining with a 1D-CNN deep learning model, they predicted the dynamics of moisture content during the drying process. The R_p value was 0.9548, indicating that the image information alone could yield moisture-related patterns that were highly correlated with reference values and confirming the architecture's suitability for industrial, intelligent Tencha production. With continuous exploration of drying technologies, a high prediction rate in complex processes has been achieved recently, by integrating spectral data with hybrid deep learning models. Some used HSI imaging technology combined with a hybrid model of CNN, BiLSTM and the Squeeze-and-Excitation Network (CNN-BiLSTM-SE) to monitor and predict, in real time, the quality parameters of red dates during the hot-air drying process, such as soluble solids, acidity, moisture and hardness. The $R_P^2$ valued 0.955, 0.919, 0.975 and 0.940, respectively. Compared with the conventional machine learning approaches of partial least squares regression and the SVM model, the CNN-BiLSTM-SE achieved higher accuracy, better demonstrating the effectiveness of visualizing quality parameters in different drying stages of red dates. It also offers a novel tool for monitoring and quality control of the drying process (Q. Liu et al., 2025).

In fermentation processes, LSTM, CNN and 2D-CNN models provide real-time, rapid and non-destructive solutions for fermentation of beer, Oolong Tea and kombucha beverages. Bowler et al. (2021) adopted an ultrasonic probe and a temperature sensor to collect signal data during the beer fermentation process and feed it to an LSTM model to predict alcohol content. The optimal prediction rate R² of the 1st and 2nd reflection features and temperature readings turned out to be 0.952, indicating the great potential of the model for alcohol-content prediction in beer. Zheng et al. (2024) discriminated under-fermented, moderately fermented and over-fermented Oolong Tea online by combining VIS-NIR and RGB images with CNN. The system achieved an accuracy of 97.14%, offering a technical solution for real-time and precise quality prediction during the Oolong Tea fermentation process. S. Zhao et al. (2025) introduced a VIS-NIR calibration strategy assisted by 2D-CNN and feature reconstruction to monitor the residual sugar and bacterial concentration in real time during kombucha fermentation process. The resulting predicted degrees of RPD were 4.49 and 6.88, respectively for sugar and bacterial concentration, which outperformed the traditional partial least squares approach and cut prediction errors by 72.3% and 33.8% respectively, meeting the demands of real-time quality control in industrial scenarios. In addition, in crystallization process of cane sugar, Zhang et al. (2020) deployed a hybrid InceptionResNetV2 model to classify five crystallization types, attaining an overall accuracy of 90.1%. The category-1 and category-3 crystals were recognized as high as 99% and 97% respectively, and a single-image inference latency of around 0.5s enabled real-time quality monitoring of the crystallization process.

Models such as Unet-Xception, 1D-CNN and CNN-BiLSTM-SE fuse hyperspectral and image data, and LSTM combines with ultrasonic signals, CNN and 2D-CNN, InceptionResNetV2 and MLP. By integrating multimodal data and extracting complex temporal features, they enable real-time characterization of non-linear dynamic processes, and drive the digital upgrade of real-time prediction of quality indicators across food processing.

3.2. Quality inspection in food manufacturing

In food processing operations, manual or off-line inspection is error-prone and directly compromises product consistency and market value. Real-time monitoring, by contrast, identifies non-conforming items the moment they deviate from the specifications, so they can be removed or diverted immediately to secondary-product lines. Deep learning technology automatically learns multi-dimensional surface features and tolerates visual clutters in complex processing environments, enabling fully automated pipelines run from image acquisition to grading without hand-crafted features. It provides the key technical foundation for constructing a quality control system that delivers both high accuracy and high efficiency.

In fermentation process, LSTM networks deliver an efficient technical solution for grading Kimchi and black tea, while Yolov model classifies stages of dough fermentation. Kim et al. (2024) coupled wireless temperature and humidity sensors with an LSTM model to predict the acidity of Kimchi throughout 60 days of low-temperature storage and to assign one of five maturity levels. The validation set yielded a Mean Absolute Error (MAE) of 0.1% acidity and 96.7% grading accuracy. In a word, it achieved on-line monitoring and grading of Kimchi in real time. Huang et al. (2024) used a CNN-LSTM deep learning hybrid model PSO-CNN-LSTM that combines HSI with the Particle Swarm Optimization (PSO) algorithm to classify fermenting black-tea leaves at different stages. The system delivered a classification accuracy of 96.78% with an inference latency below 1 s, offering non-contact and high-precision sorting of black-tea leaves for the production line in real time. Gilbert Murengami et al. (2025) employed YOLOv8s to extract the contours and bounding box coordinates from the RGB images of fermented dough, which were then used for subsequent dough feature extraction and classification. The model achieved 99% precision and recall and a mAP of 98%. Moreover, the rapid decline and stabilization of loss value further confirmed that YOLOv8s could efficiently extract features. The verification-related loss declined steadily, but remained lower than the training-related loss, suggesting robust generalization without overfitting. The pipeline could process large image volumes in seconds, ensuring accurate quality grading during food processing.

In addition, CNN-based MobileNetV2, YOLO and DNN have been applied to assess coffee-bean roast degree, detect candy defects and predict beverage formulation and processing parameters. Hakim et al. (2020) employed the MobileNetV2 model to determine the roast degree of coffee beans by color during the roasting process, achieving an accuracy rate of 97.75%, a recall rate of 96.44% and a precision rate of 96.33%. It realized an accurate prediction of roast degree in real time. Li et al. (2024) collected inline candy images and used StyleGAN2 to generate defective samples to address data imbalance. An improved YOLOv7 was deployed to detect candies in normal, bulging, sunken and fragmented states in real time, delivering an accuracy of 98.1% and a recall rate of 96.2% on the test set. The approach supports rapid and accurate defect detection and online grading on the candy production line. Ren et al. (2023) trained a DNN model to learn the complex non-linear relationships between the electronic-sensory data of taste, odor, color and texture of fruit and vegetable beverages and the formula and process parameters. The model achieved R² = 0.88 on the test set and R² = 0.83 in external validation, demonstrating good generalization and allowing rapid prediction of formula and process parameters. It provides strong support for instant quality evaluation in the beverage industry.

In real-time grading of food processing, deep learning forms a multi-model cluster, achieving automated grading through multi-modal data fusion and temporal-spatial feature extraction. The YOLO series is adapted to high-speed lines with its high precision, strong resistance to overfitting and millisecond-level inference. The hybrid model PSO-CNN-LSTM shows potential in specific tasks. However, stand-alone LSTM, MobileNetV2 and DNN yield slightly lower accuracy and are less robust when generalized across varying scenes. Consequently, selection of technologies has to be aligned with specific throughput demands and on-site resource constraints if deep learning is to deliver its full value of real-time grading in food processing.

3.3. Monitoring packaging defects in food production

High-quality packaging is a proven means of retarding spoilage and deterioration of food during transportation and storage. At present, for detection of seal defects in food packaging, deep learning research has converged on CNN-derived architectures such as Faster R-CNN, ResNet18, DenseNet161 and U-Net, as well as the ViT model based on the Transformer architecture. These models automatically identify faulty seals faster than conventional manual inspection and prevents spoilage and deterioration typically following seal defects. The deep learning system of Faster R-CNN, first proposed by (Adem and Közkurt, 2019), was applied to quality control of aseptic packaging. It delivered an accuracy rate of 99.25% for defect detection, outperforming the conventional image processing and SVM models. Moreover, it could detect various seal defects under both high and low temperatures and pressure. Banus et al. (2021) used ResNet18 for 100% on-line pizza-seal inspection, achieving an accuracy rate up to 99.87% and doubling inspection speed relative to manual inspection. In response to the high false-negative rate inherent in manual inspection of thermoformed packaging, Banús et al. (2021) proposed to monitor seal defects in thermoformed packaging based on the pre-trained DenseNet161 model of CNN. It delivered an accuracy rate of 99.93%, a false-positive rate below 0.30% and a false-negative rate below 0.07%, allowing direct installation on production lines to improve food safety. Yang et al. (2025) introduced a semantic-adaptive, multi-task vacuum packaging defect detector. A U-Net segmentation network was used to divide the image into food and seal regions, eliminating semantic interference, followed by defect classification that attained an AUC of 99.47% and an accuracy of 97.82% on highly imbalanced distribution of packaging defects. Guo et al. (2025) identified leaks in gas-flushed pillow packs using semantic contour extraction combined with a multi-dimensional fusion ViT. The model delivered a classification accuracy of 97.5%, 19.17% higher than that of a stand-alone ViT.

In addition, foreign-object detection inside food packs has been addressed with YOLOv4 model by Cut-Paste data augmentation, Patch training, and post-processing algorithms. Applied to images of pasta, snacks, pistachios and red beans, the pipeline delivered an accuracy over 94% while keeping the false alarm rate below 5% (K. Kim et al., 2021). A clear, correctly printed expiration date on food package is also one of the key elements to monitor on production lines. De Sousa Ribeiro et al. (2018) constructed a dual deep-neural-network scheme to flag packs with blurred or missing date codes. A Fully Convolutional Networks first segmented the image to locate the expiration date region. The subsequent classifier then achieved accuracy ranges of 90%-97% and 97%-98% for blurred and missing dates, respectively. The scheme could handle the complexity of various texts and images in food packaging design.

4. Deep learning-based detection and traceability for food safety

During the production, processing, storage, and transportation of food, food safety issues such as contamination, spoilage, and adulteration may arise. Detection of physical, chemical, and microbial contamination has become a critical measure for ensuring food safety, hygiene, and harmlessness. Conventional chemical assays and microbiological tests, though precise and reliable, are cumbersome, time-consuming and destructive. Deep learning technology overcomes these drawbacks with efficient recognition algorithms that automatically extract microscopic features from large data sets and fuse them with multi-source imagery, providing a powerful tool for early warning against hazardous substances and foodborne pathogens (Fig. 3). In contrast to the process optimization focus of the previous chapter, this chapter concentrates on deep learning applications for food safety assurance, particularly the early detection of chemical contaminants and microbial pathogens that pose direct risks to consumer health.

Fig. 3.

Application of deep learning in harmful substances and pathogens in food.

4.1. Detection of hazardous substances in food

Harmful food contaminants are present at every stage of the supply chain. Pesticide residues originate from the cultivation process in the field, while carcinogens may form during high-temperature processing. Improper storage can lead to the production of mycotoxins. Prolonged contact between packaging and food can also cause toxic compounds to leach into the food. These pollutants pose significant health risks. Traditional testing methods are unable to provide real-time detection of such substances and are relatively costly. Therefore, there is an urgent need for novel detection approaches. With the advancement of deep learning, it has become possible to detect them effectively in food.

4.1.1. Detection of pesticide residues in raw materials

In agricultural production, non-water-soluble highly toxic pesticides are often applied close to harvest to protect fruits and vegetables from pests and diseases. These pesticides, insoluble, remain on the surface of fruits and vegetables, leaving residues that endanger consumer health. He et al. (2021) processed the leek leaves treated with a pesticide mixture through short-wave infrared hyperspectral images using a mean-filtering algorithm. A 1D-CNN was adopted to identify pesticide residues, achieving recognition accuracies of 98.5%, 98.0% and 97.9% on the training, development and test sets, respectively. It suggested that the model could detect and identify pesticide residues rapidly, accurately and non-destructively, providing an effective means for food safety detection. Y. Zhao et al. (2025) proposed a deep learning model integrating CNN, BiGRU, and self-attention, which combines GAN spectral reconstruction with RFF reverse feature fusion for key wavelength selection, enabling high-precision, non-destructive detection of four types of pesticide residues on apple surfaces. In the detection of pesticide residues on apples, this model achieved an F1 score of 0.9630, a precision of 0.9632, a recall of 96.31%, and an accuracy of 96.31%, significantly outperforming traditional methods such as SVM.

4.1.2. Detection of hazardous substances in the manufacturing process

A carcinogenic substance called acrylamide is produced when a certain kind of food is deep-fried at high temperatures. It is of great significance for food-safety assurance if acrylamide could be identified and detected after processing. Arora et al. (2021) trained a deep learning-based deep CNN model to identify acrylamide in potato chips. The pre-trained MobileNetv2 completed the task in 3.33 s with an accuracy of 99.12%, achieving real-time detection of acrylamide. Similarly, Garg & Singla. (2025) developed a new Deep-CNN architecture by training features extracted from models such as VGG19, DenseNet121, ResNet50 and MobileNetV2 using the XGBoost algorithm of a machine learning classifier. The accurate detection rate of acrylamide in potato chips reached 95.83% while using markedly fewer training parameters. Moreover, it effectively reduced the computational complexity, allowing producers to adjust cooking conditions in real time to optimize product quality and ensure food safety.

4.1.3. Detection of hazardous substances in storage

Aflatoxins are highly toxic secondary metabolites produced by the fungi Aspergillus flavus and Aspergillus parasiticus following host infection. Among them, aflatoxin B1 is the most toxic and a Class I carcinogen (Z. Guo et al., 2025). Its molecular structure is highly stable, exhibiting strong resistance to heat, pH and degradation, making it difficult to destroy at normal cooking and processing temperatures. Aflatoxin only begins to decompose at temperatures above 280 °C. It is affected by storage temperature, humidity, water activity, pH and moisturecontent (Ouma et al., 2024). At present, liquid chromatography-tandem mass spectrometry and high-performance liquid chromatography with fluorescence detection, which are analytical techniques with high sensitivity, are often used for aflatoxin detection. However, they are expensive, time-consuming and destructive, preventing real-time and non-destructive detection. To address that, Zhu et al. (2022) developed a sub-pixel model combined with an improved ResNet18 transformation regression prediction model based on deep learning. The model achieved an R² of 0.8898 when detecting aflatoxin B1 in peanuts, enabling rapid, real-time online monitoring of aflatoxin level, and providing theoretical support for food enterprises to develop efficient sorting systems. Z. Guo et al. (2024) integrated spatial and spectral features using 2D-CNN and 3D-CNN architectures and developed a novel Dual-aspect Attention Spatial-spectral Transformer (DAASST) model. It achieved accuracy rates of 99.40% in detecting Aspergillus flavus contamination in peanuts and 100% in identifying contamination time, indicating its high efficiency in the field of food safety detection.

In grain storage process, in order to prevent mildew, it is necessary to monitor temperature and humidity in real time, issue early warnings, and predict quality drifts. The combination of the AM of deep learning technology, CNN, RNN and sensing technology provide an effective solution. Tang & Wang. (2024) combined multi-branch AM with an electronic nose to classify volatiles emitted by rice under varying temperatures and humidity conditions. The system delivered an accuracy rate well above 96%, enabling rapid, non-destructive, on-line surveillance that is readily generalizable to other agricultural products. In addition, in order to assess the quality inside a grain pile fast in real time, and to monitor the internal and external temperature, Li et al., 2025, Li et al., 2025 used 3D-DenseNet to extract thermal signatures and discriminate six storage states, namely normal, empty bin, ventilation, fresh-grain intake, condensation and mildew. The 3D-CNN-LSTM was fed with historical grain temperature and absolute moisture records to predict the dynamic temperature evolution in the granary. A classification accuracy of 97.38% was achieved, with a temperature MAE of 0.24 °C and an RMSE of 0.28 °C, providing solid support for real-time monitoring and management of grain storage.

Deep learning models such as 1D-CNN, MobileNetV2, Deep-CNN, ResNet18 and DAASST achieve deep fusion and intelligent analysis of multi-source heterogeneous data through efficient computing algorithms. With their lightweight design, the models reduce the computational complexity significantly. They can automatically capture the characteristic expressions of harmful substances and the spectral signals of toxins, thus eliminating the need for hand-crafted features and removing detection lags inherent in conventional assays. The framework provides a breakthrough technical path for detection of harmful substances during food processing and storage. It facilitates the transformation of food safety supervision from passive spot-checks to full-process intelligent early warning, delivering immediate practical value and forward-looking risk prevention capabilities.

4.2. Detection of foodborne pathogens

The accurate detection of foodborne pathogens is a cornerstone of food safety monitoring system. The deep learning-based CNN facilitates automatic extraction of key features of pathogens, such as their morphology and size, effectively identifies signs of contamination in food samples, and uncovers subtle microscopic features otherwise invisible to manual microscopy, enabling high-precision identification and classification of different types of pathogens.

Qiu et al. (2023) detected Staphylococcus aureus in chicken breast with a deep learning-based CNN paired with visible-near infrared hyperspectral imaging. By effectively extracting spectral features, the model raised overall accuracy from 83.88% of traditional classifiers to 91.38%, further promoting application of hyperspectral imaging technology in detection of foodborne pathogens in chicken products. In addition, Chen et al. (2024) constructed a data set of foodborne pathogens. By using a CNN to identify six common foodborne pathogens, namely Escherichia coli, Vibrio parahaemolyticus, Staphylococcus aureus, Bacillus cereus, Salmonella typhi and Streptococcus hemolyticus, they achieved an accuracy rate of 90%-100%, reducing the error rate of manual microscopy and providing rapid, accurate and intuitive discrimination results. In pursuit of low-cost, rapid and efficient detection of foodborne pathogens, Kang et al. (2024) used 3D nanostructured swabs to capture foodborne bacteria. A portable Raman instrument combined with a 1D-CNN model was employed to collect the Raman spectral signals of bacteria and classify them. Classification accuracy rates of 100% and close to 100% were achieved respectively for single species and mixed cultures, offering a practical route for real-time pathogen monitoring in catering operations.

In foodborne pathogen detection, CNN has become one of the most commonly used options. It automatically captures the microscopic morphological features of pathogens that escape manual microscopy, significantly reducing errors from subjective judgment. When integrated with spectral technologies such as HSI and RS, it enables efficient dimensionality reduction and feature distillation from high-dimensional, complex spectral data. However, it remains dependent significantly on large-scale, high-quality datasets. At the same time, when facing unknown strains and diverse food matrices, it requires further improvement of generalization.

4.3. Detection of food adulteration

Authenticating raw produce is pivotal for food safety, consumer protection and a robust quality-control system. Conventional adulteration tests, such as sensory evaluation, chemical assays and microbiological analyses, achieve high accuracy only after labor-intensive steps of sample preparation, extraction and purification. These procedures not only take a long time but also demand well trained personnel (Goyal et al., 2024). Deep learning features strong analytical power to handle high-throughput samples. When paired with rapid, low-cost spectroscopic techniques, it can significantly improve the accuracy and efficiency of adulteration screening.

Wu et al. (2023) combined 2D-COS with the RS and the Deep Residual Shrinkage Network (DRSN) to address the challenging quantitative detection of adulteration in Manuka and locust honeys. The strategy delivered high-precision concentration predictions, yielding relatively low Root Mean Square Errors of Prediction (RMSEP) of 3.1166 and 2.3188 respectively, and demonstrating strong potential of quantitative analysis of complex food matrices. J. Wang et al. (2025) achieved high-precision detection of adulteration of multi-species vegetable-oil in camellia oil by integrating RS with deep learning. In adulteration classification tasks, the Convolutional Long Short-Term Memory Network (ConvLSTM) and Transformer model performed significantly better than traditional machine learning methods, attaining a classification accuracy of 100%. In terms of quantitative prediction, the ConvLSTM model demonstrated excellent performance, with an $R_P^2$ of 0.999, an RMSEP of 1.2% and an RPD of 25.2. The comprehensive indicators above confirm that the ConvLSTM model possesses superior predictive ability and robustness compared with the LSTM and Transformer models.

Deep learning models are capable of both qualitative and quantitative analysis of adulterants in tea-seed oil, providing a new technical means for authenticity verification of edible oils. In addition, Kuang et al. (2025) developed a rapid screening protocol for horse-fat adulteration based on IR combined with deep learning. The standard normal variate transformation and detrending algorithm was used to pre-process the IR data of horse fat adulterated with butter, sheep fat and lard at varying proportions. The fine-tuned ResNet model delivered an accuracy of 100% in identifying the adulterated samples, outperforming the comparative models as CNN, RNN and Transformer.

Derived from CNN and RNN architectures, models such as DRSN, ConvLSTM, Transformer and fine-tuned ResNet, improve remarkably the efficiency of identifying agricultural product adulteration and provide intelligent tools for quality control in the agricultural product processing industry, when paired with spectroscopy and mass spectrometry technologies. These deep learning models, through automated feature extraction and multi-task learning, outperform conventional chemometric approaches and can be widely applied for qualitative and quantitative detection of adulterants in high-nutritional-value foods such as honey, vegetable oils and animal fats. They provide a new technical route toward multi-dimensional, high-precision quality and safety assessment. This not only helps to reinforce the quality and safety control system of agricultural products but also drives the analytical workflow toward greater intelligence and systematization. Some specific applications of deep learning in detection of raw produce adulteration are shown in Table 4. Since food process control involves heterogeneous datasets (images, spectra, and time-series signals) and varying operational constraints, no single algorithm is universally optimal. Table 5 provides a practical mapping between common algorithms and the datasets/tasks they best fit, together with the associated assumptions and limitations.

Table 4.

The application of deep learning in food adulteration detection.

Category	Object	Analysis technology	Model	Results	References
Adulteration	Turmeric	Multi-spectral images	CNN, DenseNet201, MobileNet, VGG16	The CNN model is superior, with a training accuracy of 100% and a validation accuracy of 94.35%.	Lanjewar et al. (2022)
	Honey	RS combined with 2D-COS technology	DRSN, 2D-CNN、 Inception V3, MTx-Net.	The RMSEPs of the adulteration concentrations of Luca honey and locust honey are 3.1166 and 2.3188 respectively.	X. Wu et al. (2023)
	Pistachio	-	ResNet-50, VGGNet-19, DenseNet201	The accuracy of VGGNet-19 reaches 100% in the LAB color space, and the accuracy of ResNet-50 reaches 98.8% in the YUV color space.	Çinarer et al. (2023)
	Panax notoginseng powder	NIR, VIS	ANN, LSTM	It achieved a 100% classification accuracy rate on the test set.	Guan et al. (2024)
	Lycium barbarum	HSI	CNN	The accuracies of the training set and the prediction set are 99.4% and 95.4% respectively.	Y. Zhang et al. (2024)
	Polygonati Rhizoma	Laser-Induced Breakdown Spectroscopy, VIS-NIR	CNN + Bi-LSTM	The accuracy rate is 98.75%.	F. Chen et al. (2024)
	Coconut Milk	Fourier transform near-IR and micro near-infrared spectroscopy	CNN, S-AlexNET, ResNET, GoogleNet	All models performed excellently when predicting the adulteration degree of coconut milk. The coefficient of determination (R2) ranged from 0.886 to 0.999, and the RMSE ranged from 0.370 to 6.108%.	Sitorus and Lapcharoensuk (2024)
	Horse oil	IR	ResNet	The indicators of Area Under the Curve, ACC, Pre, and F1-Score all reach 100%.	Kuang et al. (2025)
	Camellia oil	RS	LSTM, ConvLSTM, Transformer	For the optimal ConvLSTM model, the $R_P^2$ is 0.999, the RMSEP is 0.9%, and the RPD is 31.5.	J. Wang et al. (2025)

Table 5.

Algorithm suitability analysis for food process control: data type, assumptions, strengths and limitations.

Algorithm	Suitable data type/task	Assumptions	Strengths	Limitations	References
CNN(2D-CNN))	RGB/multispectral/HSI spatial maps; surface defect detection, sorting, quality grading	Local spatial correlation; enough representative images	Hierarchical feature learning; strong for visual defects & spatial patterns	Labeling cost; domain shift across batches/factories	(Y. Xiao et al., 2025; J. Zhao et al., 2025)
1D-CNN	NIR spectra, single point spectral signals; rapid nondestructive regression/classification	Local spectral pattern stationarity; adequate calibration transfer	Efficient feature extraction for spectra; often beats PLSR/SVR with proper preprocessing	Sensitive to instrument shift; still needs enough labeled samples	Paiva-Peredo et al. (2025)
LSTM	Multivariate sensor time series; fermentation/drying dynamics prediction; soft sensors	Temporal dependency; historical states informative	Captures nonlinear dynamics & lag effects; good for fermentation kinetics	Training cost; drift/domain shift; interpretability limited	(Rivera et al., 2026; Saetae, 2025)
Transformer	Long-range dependencies in spectra/time-series; HSI token/patch modeling; multi-task prediction	Global context matters; sufficient data or strong regularization	Captures long range correlations; flexible multi task learning	Requires large training data	(X. Li et al., 2025; Singh et al., 2024)
ANN (BP)	Low-dimensional numeric data	Nonlinear mapping	Simple structure	Poor scalability
YOLO	Real-time object/defect detection on production lines; counting/localization	Object presence assumption	Fast inference; suitable for edge/line deployment	Small object sensitivity; dataset bias impacts robustness	Badgujar et al. (2024)

5. Deep learning for food quality prediction

Conventional quality prediction models suffer from low efficiency, limited accuracy, and offline operation. They struggle to meet the urgent needs of the modern food industry for rapid, highly accurate and non-destructive detection technologies. On the other hand, deep learning, with its powerful non-linear modeling capabilities, automatically reveals hidden patterns and high-level features within complex data sets, enabling precise predictions of multiple key indicators of food products, such as sensory attributes, nutritional composition and shelf life (Fig. 4). Not only does this contribute to consistence of product quality, but it also significantly enhances consumer acceptance and satisfaction with the final products.

Fig. 4.

Application of deep learning in product sensory attributes, nutritional composition, and shelf life.

5.1. Prediction of food sensory properties

The sensory attributes of food, such as shape, color, hardness, chewiness and cohesiveness, govern consumer acceptance. Traditionally, food sensory quality is assessed by specially trained panelists or determined by instrumental analysis. However, these practices are subjective, time-consuming and often destructive (Gul et al., 2024). A rapid and non-destructive intelligent system that evaluates food sensory characteristics is therefore essential for improving consumer satisfaction. In recent years, the combination of oral processing simulation, non-invasive brain-computer interfaces, and deep learning has provided a new perspective for objectively decoding sensory evaluation data.

5.1.1. Prediction of multisensory attributes

Deep learning-based CNN, RNN and the continuously evolving ResNet and LSTM models now offer effective technical means for predicting food sensory attributes (Ma et al., 2024). The consistency of taste, color and flavor determines how consumers accept the fruits. Siricharoen et al. (2023) used Mask R-CNN model with a ResNet-101 backbone to segment, crop and adjust the outer images of pineapples, and predict their taste. The system delivered an F1 score of 0.9025 and mAP of 0.8351, well matching panel-assessed flavor. Xu et al. (2024) verified the relationship between moisture content and color changes during drying process by combining RGB images of sea buckthorn color with LSTM. They predicted the appearance and color of sea buckthorn fruits and compared them with actually captured images. The R² valued 0.86, indicating the accuracy of the model's prediction.

In production and processing of meat products, gel strength affects the sensory attributes, texture and taste of the products, and it is an important indicator for evaluating product quality. H. Li et al. (2024) combined a hybrid CNN-LSTM model with the RS method to predict the gel strength and whiteness of pork batter. The system outperformed CNN and LSTM models used alone. The R_p values of the gel strength and whiteness were close to 1, indicating high prediction accuracy. Nunekpeku et al. (2025) used ultrasonic treatment and fused NIR spectra with RS data to establish CNN and LSTM models for prediction of gel strength of chicken meat paste. The LSTM model yielded a R² of 0.9882 and a RPD of 9.2091, presenting better results than CNN model. The strategy provides a rapid, non-destructive means for predicting gel strength of chicken meat paste, and highlights that spectral technology can reveal food structure and chemical characteristics without any sample pre-treatment.

Deep learning-based architectures, such as Mask R-CNN, LSTM and CNN-LSTM, by fusing multi-modal data, have achieved high-precision prediction of sensory attributes such as fruit flavor and meat texture. They not only eliminate the subjectivity of manual sensory evaluation and the destructiveness of conventional instrumentation but also reveal the formation mechanism of sensory attributes. Furthermore, they establish quantitative maps between outer appearance and inner quality, providing a science-based estimate of consumer acceptance.

5.1.2. Dynamic assessment of oral processing simulations

Emerging multi-sensory oral simulation technologies have opened up novel avenues for investigating the dynamic sensory processes of the oral cavity. The in vitro bionic oral mastication simulator developed by Alejandro et al. (2025) enables the partial replication of solid food processing from ingestion to the pre-swallowing stage, providing a controlled platform for investigating how the oral environment, masticatory kinematics, salivary dynamics, and tongue activity collectively shape bolus properties. Building on this platform, Cao et al. (2026) further integrated an environmental temperature control module into the biomimetic mastication simulator, enabling not only precise regulation of the dynamic changes in food texture during the fragmentation process but also allowing the study to account for the effects of temperature on the enzymatic hydrolysis of salivary enzymes. However, the sheer volume of data generated by multi-modal oral simulations pose a central challenge to subsequent mechanistic analysis. Various machine learning models, including CNN, LSTM networks, SVM, extreme gradient boosting frameworks, and multiple linear regression, possess the ability to learn, identify, and process complex nonlinear relationships, enabling high-precision predictions. Among these, LSTM networks excel at processing time-series data and have demonstrated the best performance in predicting chewing behavior (Ma et al., 2023).

Looking ahead, the convergence of wearable sensing technologies (e.g., wireless bite-force arrays, conformable tongue-pressure patches, and intraoral thermal probes) with graph-based or attention-driven deep learning frameworks holds promise for closing the loop between multi-physiological signal acquisition and real-time bolus state inference. Such integration would not only resolve current data fragmentation but also establish patient-specific predictive models adaptable to aging-related masticatory decline and dysphagic pathophysiology.

5.1.3. Spatiotemporal neural decoding for brain-computer interfaces

Flavor perception is fundamentally a multisensory integration process: sensory afferents ascend from peripheral receptors to higher-order cortical areas, while top-down feedback from associative regions concurrently modulates primary sensory cortices, yielding a unified perception. Recent advances in non-invasive neuroimaging, such as electroencephalography (EEG), coupled with multivariate decoding algorithms, have furnished high-temporal-resolution tools for dissecting the spatiotemporal dynamics of cross-modal flavor integration in the human brain.

Deep learning algorithms—particularly those employing CNNs, RNNs, and AMs—can effectively capture temporal dynamics in sensory signals. As an example, a multiscale residual network applied to EEG data decodes signals representing five basic taste sensations via multiscale spatiotemporal convolution and residual learning, striking an optimal balance between parameter efficiency and predictive accuracy (H. Gao et al., 2023). To better capture temporal dynamics, RNNs are often used to complement CNNs (T. Yang et al., 2026). S. Zhang et al. (2024) A multi-layer RNN based on multi-random-segment search has simultaneously improved EEG recognition in terms of accuracy, interpretability, and generalization.

Compared to the two classic deep learning algorithms—CNNs and RNNs—AMs are less susceptible to interference from redundant information during multisensor data fusion. They enable the model to automatically focus on task-relevant key features, ultimately improving the overall performance of classification tasks (Men et al., 2022). In a study on the aroma prediction of grilled lamb skewers, Shen et al. (2026) proposed the EEG-MambaFusionNet model based on the Transformer architecture. This model incorporates a self-AM and, through a dual-path adaptive fusion module, achieves effective integration of three types of heterogeneous data: EEG, time-dominant sensory analysis, and gas chromatography–ion mobility spectrometr. The final model achieved an aroma prediction accuracy of 92.5%. Furthermore, the study validated the central role of the prefrontal and parietal regions in flavor pleasantness assessment and multisensory integration. Similarly, the frequency band attention network integrates multi-frequency EEG information through frequency band feature mining and frequency band feature self-AMs, enabling it to identify the odors of eight products—including orange juice, shrimp paste, and garlic paste—with 98.92% accuracy (Xia et al., 2024).

Deep learning provides powerful capabilities for feature extraction and fusion in EEG data. However, food preferences currently demonstrate significant individual variability and are heavily influenced by cultural factors. Future EEG-based deep learning systems are expected to facilitate the development of personalized food preference prediction models, thereby providing a neurophysiological basis for precision nutrition and personalized dietary design.

5.2. Prediction of nutritional components

In analyzing food nutritional composition, spectral technology stands out for its simple operation, non-destructive detection and environmental friendliness compared with chromatographic and mass spectrometric protocols. Demanding only sample pre-treatment, it has received extensive attention in the field of food quality detection (Shi et al., 2025). However, the spectral data obtained with this technology are typically high-dimensional, strongly non-linear and contaminated by redundant or noisy variables. Therefore, the most urgent challenge is to extract effective and informative features that drive accurate prediction.

In this aspect, deep learning technology demonstrates significant advantages. In particular, the AM model, as a representative, can achieve intelligent selection of effective wavelengths from spectral imaging data. By learning dynamic weightings that highlight key characteristic bands, it improves the prediction accuracy of food nutritional composition and the interpretability of the model. Y. Wang et al. (2023) proposed a hybrid deep learning ACLSTM model that combines HSI with AM, CNN and LSTM to select effective wavelengths based on AM. When predicting oil, protein and starch content of coix seeds, it yielded a RPD over 3.0, achieving rapid, non-destructive and effective quality evaluation. Y. Wu et al. (2023) predicted the soluble solid content, total acidity, acid-to-sugar ratio, hardness and Vitamin C quality parameters of citrus using a novel deep learning network (GCNN-LSTM-AT) that combines VIS-NIR with Graph Convolutional Network, 2D-CNN, BiLSTM and AM. Overall, the system achieved the lowest root mean square error of cross-validation, the largest R² and the smallest MAE, indicating great potential for on-line application on packaging lines. With continuous research on deep learning technology, it has been found that the multi-head self-AM in Transformer model built on and extended from AM can simultaneously focus on multiple parts of spectral input, effectively identifying and weighing relevant features from different spectral regions. Singh et al. (2024) predicted protein content in lentils by combining near-infrared reflectance spectroscopy with a Transformer model. Superior to traditional modified partial least squares and 1D-CNN, the model delivered R² = 0.977 and RPD = 13.92, demonstrating excellent predictive ability. In addition, the Temporal Convolutional Network model, a CNN derivative, effectively captured long-term dependencies and local features in time series, and simultaneously processed various types of measurements and quantifiable parameters. Rashvand et al., 2025a developed a TCNA deep learning model built on AMs. By combining orthogonal signal correction preprocessing and feature selection algorithms with hyperspectral technology, they extracted characteristic data that reflect carbohydrate-content variations in common-bean flour. With quantitative prediction of carbohydrates enabled in common-bean flour, the approach achieved an R² of 0.982, an RMSE of 0.165 and an RPD of 4.905, indicating its capability for precise prediction of internal indicators in common-bean flour.

Within the deep-learning toolbox, models such as AM, Transformer, ACLSTM, GCNN-LSTM-AT and TCNA neutralize spectral redundancy and noise by optimizing spectral features and sequence modeling, enabling the intelligent extraction and optimization of key wavelength features. They then utilize the deep non-linear architectures to auto-map spectral responses to nutrient composition, analyze the implicit non-linear action mechanism, and achieve high-precision synchronous prediction and comprehensive evaluation of multiple nutrient composition indicators with a multi-model fusion strategy. They provides a non-destructive and low-cost alternative to conventional chemical assays as chromatography and mass-spectrometry, etc.

5.3. Prediction of shelf-life

Once the expiration date is passed, food spoils rapidly due to proliferation of microorganisms and changes of chemical composition, which leads to substantial waste of food. Classic microbiological or chemical assays and sensory panels are often time-consuming and difficult to achieve real-time and high-throughput detection. Although enhanced predictive mathematical and kinetic modeling can help to describe the quality changes of food, they still rely heavily on specific product data and struggle to comprehensively reflect the multi-component evolution in complex environments(Rashvand et al., 2025b). Deep learning, with its power to process multi-variable and large-scale data, can achieve more accurate shelf-life prediction and risk assessment.

Currently, when predicting the shelf life of food, the ANN model built on the crucial Backpropagation (BP) algorithm is considered as the most basic option of deep learning architecture. By iteratively learning from input data and dynamically adjusting weights and thresholds, it can automatically adapt to input changes, learn the patterns and rules, and use variable weights to capture complex non-linear relationships. After Antarctic krill sauce temperature and storage duration were fed into a BP neural network and standardized with the mapminmax function, the model predicted peroxide and thiobarbituric acid values with relative errors of 11.76% and 13.39%, respectively, demonstrating high accuracy in predicting the shelf life of Antarctic krill sauce (Chi et al., 2024). W. Li et al. (2025) verified the accuracy of conventional kinetic models in predicting growth of Listeria monocytogenes using a BP ANN. The shelf life of salads was optimized indirectly using a formula. The RMSE for training, validation, test and overall data sets were valued at 0.58694, 0.65719, 0.66347 and 0.58775, respectively, confirming reliable, low-error predictions of the model. Xiao et al. (2025) analyzed transverse relaxation data generated by low-field nuclear magnetic resonance using a BP ANN to determine the total number of colonies in dried tofu and predict its shelf life. The model delivered a relative error below 10% between the predicted and actual shelf life of dried tofu, offering a rapid, non-destructive and environmentally friendly alternative to plate counting.

By synergistically analyzing environmental parameters and chemical composition, the deep learning-based BP-ANN adjusts weights to accurately capture the complex mechanisms of food spoilage, mirroring the results of non-destructive detection such as spectroscopy and nuclear magnetic resonance. Breaking through the destructiveness and lag of conventional microbial assays, it constructs an intelligent quality-control system for the whole process from production to warehousing for perishable and deteriorating foods, significantly reducing waste caused by expiration.

6. Limitations of deep learning in food industry applications

6.1. Bottlenecks in data processing

The diversity of food matrices and the complexity and dynamics of processing environments make it challengeable to acquire data during food processing. When the background is noisy or the lighting conditions are poor, the deep learning algorithm would acquire a large number of irrelevant pixels and differential data. Annotation of these complex data would hamper the consistency and accuracy of data due to subjective human factors. As generalization remains the central technical hurdle in process control for food processing operations, most current studies use developed deep learning models only to conduct quality inspections on a single commodity. When faced with new raw produce or changes in processing conditions and assays, the model cannot accurately predict and control, resulting in a declined performance.

Food processing operations require the control system to monitor product quality changes in real time and make adjustments and optimizations to process parameters accordingly. However, there is a contradiction between computing power demand of deep learning models and real-time performance. The complexity of the model itself and the huge parameter space used to generate training data can be a source of overfitting (Ghosh and Datta, 2023). Many food processing enterprises can hardly afford the high-performance computing equipment to run complex deep learning models.

Future process-control strategies, therefore, have to prioritize the development of more efficient, rapid and easily deployable deep learning models to meet the actual needs of food processing. The food processing generates various types of data such as images, sounds, physical data and chemical composition analysis. By effectively integrating these multi-source data, comprehensive information can be fed to deep learning models to achieve more accurate full-process detection and prediction. At present, the strategy of combining HSI, RS, VIS, IR, fluorescence spectroscopy detection and deep learning technology has been widely applied in raw produce sorting, process monitoring, quality and safety detection and product quality prediction in the process control of food processing (Yuan et al., 2025). In particular, many spectroscopic techniques have strong qualitative and quantitative potential in identifying and predicting the authenticity of food (Shi et al., 2025). The next wave of research will focus on matching spectroscopic techniques to the specific chemical composition and spectroscopic principles of food and on developing portable spectroscopic instruments that are small, light and factory-floor ready.

6.2. Bottlenecks in food processing control

Although deep learning has demonstrated remarkable advantages in feature extraction and nonlinear modeling, its application in food quality and safety management still faces intrinsic theoretical and practical limitations. First, deep learning models are fundamentally data-driven and rely heavily on large-scale, well-annotated, and representative datasets (Lagemann et al., 2023). In food processing environments, acquiring high-quality labeled data is often costly, time-consuming, and constrained by seasonal variability, product heterogeneity, and equipment differences. Insufficient or biased datasets may lead to poor generalization and unstable performance under changing industrial conditions (J. Gao et al., 2025). Second, most deep learning models function as black-box systems, lacking mechanistic interpretability. In process control scenarios where safety and regulatory compliance are critical, the inability to clearly explain decision pathways limits industrial trust and large-scale deployment (Hassija et al., 2024). Third, deep neural networks generally require high computational power and memory resources (Sze et al., 2017). In real-time industrial environments, particularly in small and medium-sized enterprises, limited hardware capacity may restrict deployment of large-scale models such as Transformers or deep CNN ensembles. Fourth, distribution shift and domain transfer remain unresolved challenges. Variations in raw materials, environmental humidity, temperature, and sensor calibration may significantly degrade model performance. Seasonal variability in raw materials—driven by climatic fluctuations during cultivation—causes significant shifts in physicochemical properties (e.g., moisture, sugar-acid ratio, surface morphology), degrading model accuracy when applied across harvest periods. Raw material heterogeneity, encompassing cultivar differences, growing regions, and management practices, introduces substantial intra-class dispersion that exceeds the generalization capacity of models trained on homogeneous reference datasets. Equipment differences between production lines (camera specifications, illumination configurations, optical setups) and sensor calibration drift over time (temperature fluctuations, dust accumulation, component aging) progressively distort input data distributions. Current models often struggle to adapt across factories, batches, or geographical regions without retraining (Gidado et al., 2026). These factors interact synergistically, creating temporal and spatial domain shifts that current architectures cannot autonomously adapt to. Future research must prioritize the development of adaptive models capable of real-time performance monitoring, automatic drift detection, and incremental domain adaptation to address the limitations present in these specific applications.

Finally, deep learning systems do not inherently encode physicochemical principles governing food processing. Unlike mechanistic models, they cannot guarantee extrapolation outside the training domain (Campo-Manzanares et al., 2026), which limits their reliability for extreme or unseen process conditions. Therefore, future research should focus on lightweight architectures, physics-informed neural networks, domain adaptation strategies, transfer learning frameworks, and explainable AI to overcome these structural constraints.

7. Conclusion and future perspectives

7.1. Limitations of this review

While this review systematically summarizes the applications of deep learning in food quality and safety management, several limitations should be acknowledged. First, the literature search was primarily confined to English-language publications indexed in Web of Science, Science Direct, and PubMed, potentially overlooking relevant studies published in other languages or regional databases. Second, given the rapid evolution of deep learning architectures, some emerging models and very recent advances (particularly those published after April 2026) may not be fully captured. Third, the included studies predominantly focus on laboratory-scale or pilot-scale applications, whereas large-scale industrial deployment cases remain relatively underrepresented, which may limit the practical guidance value for industry practitioners. Fourth, the heterogeneity in experimental designs, evaluation metrics, and dataset characteristics across studies precluded quantitative meta-analysis; thus, the performance comparisons presented herein are largely qualitative. Fifth, this review focuses primarily on technical aspects of deep learning applications, with limited discussion on economic feasibility, regulatory frameworks, and ethical considerations associated with AI implementation in food systems. Future reviews could address these gaps by incorporating multi-language literature sources, conducting quantitative meta-analyses where feasible, and integrating techno-economic and socio-legal perspectives to provide more comprehensive guidance for both researchers and industry stakeholders.

7.2. Conclusion

Deep learning techniques have increasingly supplanted traditional destructive laboratory testing methods in numerous applications, offering non-invasive and efficient alternatives. While preserving the integrity of agricultural products, it enables multi-dimensional synchronous evaluation of proliferation of microorganisms, dynamic changes in chemical composition, and sensory deterioration characteristics. The model optimized with data augmentation shows enhanced adaptability and robustness in industrial scenarios. It establishes a systematic evaluation system for freshness of agricultural products, provides key technical input for smart warehousing and logistics decisions, and helps reduce the risk of foodborne diseases. The evolution of deep learning in food quality and safety management is not merely a technological upgrade but a systemic transformation integrating food science, process engineering, sensor physics, and data intelligence. Over the past decade, deep learning models such as CNN, LSTM, Transformer, and hybrid architectures have progressed from proof-of-concept image classification tools to integrated systems capable of real-time monitoring, predictive regulation, and multi-parameter quality evaluation across the entire food processing chain. However, similar to other emerging intelligent technologies, the next stage of development must move beyond algorithm performance improvement toward mechanism-level integration, industrial scalability, and cross-domain robustness.

7.3. Future perspectives

A fundamental challenge lies in the heterogeneity and dynamic variability of food matrices. Unlike standardized industrial materials, food systems exhibit biological variability influenced by raw material origin, storage condition, seasonal variation, and process parameters. Deep learning models trained under limited distributions may experience performance degradation under domain shift. Therefore, future research should emphasize domain adaptation, transfer learning, and federated learning frameworks that allow cross-factory knowledge sharing while preserving data privacy.

Another critical issue concerns interpretability and regulatory reliability. In food safety supervision, decisions must be explainable and traceable. Black-box models, although highly accurate, limit regulatory acceptance. The integration of explainable AI, physics-informed neural networks, and hybrid mechanistic–data-driven modeling will be essential to bridge empirical prediction and physicochemical causality.

From a system-engineering perspective, the integration of multi-modal sensing will define the next generation of intelligent process control. Future food processing platforms are expected to combine hyperspectral imaging, Raman spectroscopy, electronic noses, temperature–humidity sensors, and machine vision systems into unified architectures. Real-time signal decoupling, edge computing, and lightweight model compression will enable deployment in resource-constrained factory environments.

Deep learning will also transition from passive quality prediction to active process optimization. Reinforcement learning and closed-loop adaptive control systems may allow dynamic adjustment of drying temperature, fermentation duration, or packaging parameters based on predicted quality trajectories, achieving autonomous optimization rather than post-process evaluation.

Sustainability considerations further shape the future direction. Intelligent quality prediction can reduce waste caused by over-processing or premature discard. Accurate shelf-life prediction models may support dynamic expiration labeling and cold-chain optimization, contributing to food security and carbon-emission reduction.

In summary, the future of deep learning in food quality and safety management will be determined not only by improved network architectures but by data governance frameworks, cross-domain adaptability, interpretable modeling, and industrial integration capability. Achieving large-scale transformation from laboratory prototypes to intelligent food manufacturing systems requires interdisciplinary collaboration among food scientists, data engineers, equipment manufacturers, and policymakers. Through this convergence, deep learning is poised to become a cornerstone technology for safe, sustainable, and intelligent food production.

CRediT authorship statement

You Ge: Conceptualization, Methodology, Investigation, Writing - original draft. Huifang Liu: Conceptualization, Methodology, Investigation, Writing - original draft. Qiuye Wang: Investigation, Supervision. Yan Zhang: Investigation. Shui Jiang: Investigation. Xiaohai Ma: Investigation. Junjie Zhang: Investigation. Wenli Ma: Investigation. Shuang Bai: Supervision, Writing - review and editing, Funding acquisition. Yuan Liu: Funding acquisition, Supervision.

Disclosure statement

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.

Data availability

No data was used for the research described in the article.

Declaration of competing interest

No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Glossary

CNN

Convolutional Neural Network

RNN

Recurrent Neural Network

ANN

Artificial Neural Network

BiLSTM

Long Short-Term Memory neural network

ViT

Vision Transformer

SVM

Support vector machines

SqueezeNet

Squeeze Network

TVC

Total viable count

TVB-N

Total volatile basic nitrogen

IoU

Intersection over union

CV

Computer Vision

PSO

Particle Swarm Optimization

DAASST

Dual-aspect Attention Spatial-spectral Transformer

AM

Attention Mechanism

DRSN

Deep Residual Shrinkage Network

RMSEP

Root Mean Square Errors of Prediction

ConvLSTM

Convolutional Long Short-Term Memory Network

IR

Infrared spectroscopy

EEG

Electroencephalography

BP

Backpropagation

MAE

Mean Absolute Error

VGG

Visual Geometry Group

Handling Editor: Professor Georgios Leontidis

Appendix A. Supplementary data

The following is the Supplementary data to this article: