Near-infrared spectroscopy (NIRS) combined with pattern recognition technique has become an important type of non-destructive discriminant method. This review first introduces the basic structure of the qualitative analysis process based on near-infrared spectroscopy. Then, the main pretreatment methods of NIRS data processing are investigated. Principles and recent developments of traditional pattern recognition methods based on NIRS are introduced, including some shallow learning machines and clustering analysis methods. Moreover, the newly developed deep learning methods and their applications of food quality analysis are surveyed, including convolutional neural network (CNN), one-dimensional CNN, and two-dimensional CNN. Finally, several applications of these pattern recognition techniques based on NIRS are compared. The deficiencies of the existing pattern recognition methods and future research directions are also reviewed.

Food packaging plays a vital role in the food supply chain by acting as an additional layer to protect against food contamination, but the main function of traditional conventional packaging is only to isolate food from the outside environment, and cannot provide related information about food spoilage. Intelligent packaging can feel, inspect, and record external or internal changes in food products to provide further information about food quality. Importantly, intelligent packaging indicators will account for a significant proportion of the food industry’s production, with promising application potential. In this review, we mainly summarize and review the upcoming progress in the classification, preparation, and application of food packaging indicators. Equally, the feasibility of 3D printing in the preparation of intelligent food packaging indicators is also discussed in detail, as well as the limitations and future directions of smart food packaging. Taken together, the information supported in this paper provides new insights into monitoring food spoilage and food quality.