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The development of techniques and methods for rapidly and reliably detecting and analysing food quality and safety products is of significance for the food industry. Traditional machine learning algorithms based on handcrafted features normally have poor performance due to their limited representation capacity for complex food characteristics. Recently, the convolutional neural network (CNN) emerges as an effective and potential tool for feature extraction, which is considered the most popular architecture of deep learning and has been increasingly applied for the detection and analysis of complex food matrices. In the current review, the structure of CNN, the method of feature extraction based on 1-D, 2-D and 3-D CNN models, and multi-feature aggregation methods are introduced. Applications of CNN as a depth feature extractor for detecting and analyzing complex food matrices are discussed, including meat and aquatic products, cereals and cereal products, fruits and vegetables, and others. In addition, data sources, model architecture and overall performance of CNN with other existing methods are compared, and trends of future studies on applying CNN for food detection and analysis are also highlighted. CNN combined with nondestructive detection techniques and computer vision system show great potential for effectively and efficiently detecting and analysing complex food matrices, and the features based on CNN show better performance and outperform the features handcrafted or those extracted by machine learning algorithms. Although there still remains some challenges in using CNN, it is expected that CNN models will be deployed on mobile devices for real-time detection and analysis of food matrices in future. • The principle and architectures of convolutional neural network are presented. • Feature extraction methods based on convolutional neural network are investigated. • Recent applications of convolutional neural network in food are summarized. • Challenges and future work of convolutional neural network are discussed. [ABSTRACT FROM AUTHOR]

Objective: To develop an artificial intelligence (AI)-based algorithm which can automatically detect food items from images acquired by an egocentric wearable camera for dietary assessment.Design: To study human diet and lifestyle, large sets of egocentric images were acquired using a wearable device, called eButton, from free-living individuals. Three thousand nine hundred images containing real-world activities, which formed eButton data set 1, were manually selected from thirty subjects. eButton data set 2 contained 29 515 images acquired from a research participant in a week-long unrestricted recording. They included both food- and non-food-related real-life activities, such as dining at both home and restaurants, cooking, shopping, gardening, housekeeping chores, taking classes, gym exercise, etc. All images in these data sets were classified as food/non-food images based on their tags generated by a convolutional neural network.Results: A cross data-set test was conducted on eButton data set 1. The overall accuracy of food detection was 91·5 and 86·4 %, respectively, when one-half of data set 1 was used for training and the other half for testing. For eButton data set 2, 74·0 % sensitivity and 87·0 % specificity were obtained if both 'food' and 'drink' were considered as food images. Alternatively, if only 'food' items were considered, the sensitivity and specificity reached 85·0 and 85·8 %, respectively.Conclusions: The AI technology can automatically detect foods from low-quality, wearable camera-acquired real-world egocentric images with reasonable accuracy, reducing both the burden of data processing and privacy concerns. [ABSTRACT FROM AUTHOR]