Globally, the proportion of the consumption of fruits in the human diet shows an increasing trend. However, fruits may incur significant losses during the post-harvest storage and transportation process due to metabolic activities and mechanical damage. Post-harvest coating technology has been proven to be an effective means of reducing quality loss, and it offers the advantages of being environmentally friendly, energy-efficient, and free of chemical residues. This article begins with an introduction to the three main mechanisms of coating preservation, including physical barrier effects, physiological metabolism regulation, and antibacterial and antioxidant effects. Secondly, this paper comprehensively reviews the latest progress of coating application technology in the field of fruit preservation, and summarizes the development of coating application technology in recent years, which is divided into two categories: traditional technology and fiber coating formation technology. Among these, the spraying method in traditional technology and microfluidic spinning technology in fiber coating formation technology are emphasized. This information will help to further develop coating application techniques to improve post-harvest fruit preservation.

This paper presents a comprehensive review on near-infrared (NIR) spectroscopy applied in food analysis, systematically elaborating its core principles, widespread industrial applications, advanced chemometric strategies, and cutting-edge technological progress. NIR spectroscopy (760–2500 nm), characterized by rapid, non-destructive detection and minimal sample preparation, has been widely implemented in quality evaluation and safety monitoring of grains, meat, fruits and vegetables, dairy, fermented products, tea, coffee, and other processed foods, realizing quantitative analysis of nutrients, freshness assessment, texture prediction, adulteration identification, origin tracing, and rapid preliminary screening of toxin/pesticide residues. A series of chemometric methods, including spectral preprocessing (SNV, MSC, S-G smoothing), feature extraction, and variable selection (CARS, PSO-CMW, ICPA), as well as linear/nonlinear modeling algorithms (PLS, SVM, BP-ANN, fuzzy clustering) significantly boost the accuracy and robustness of spectral analysis. Meanwhile, portable NIR devices and online monitoring systems promote on-site and real-time detection in food supply chains. Despite existing challenges such as calibration transfer, matrix interference, and model generalization, innovations like multimodal data fusion, deep learning integration, and intelligent algorithm optimization offer effective solutions. This review not only summarizes the latest research advances of NIR technology in the food field but also emphasizes its significant advantages as a rapid, non-destructive complementary tool to traditional destructive detection methods, providing theoretical support and technical reference for accelerating the industrial translation and standardized application of NIR spectroscopy, and ultimately safeguarding global food quality and safety.