Postharvest preservation of fruits is one of the key issues in the current agriculture and food processing industry. Surface coating treatment, a promising technology for postharvest fruit preservation, has gathered significant attention due to its ability to reduce water loss, regulate gas exchange, and inhibit respiration, thereby achieving postharvest fruit preservation. Among them, chitosan-based coating has a wide application prospect due to its superior film-forming capability, high biosecurity, wide range of sources, etc. This review summarizes the structural features, physicochemical properties, modification strategies, and preservation mechanisms of chitosan-based coatings, focusing on their applications in postharvest fruit storage. Unlike prior works, it highlights advanced modifications (e.g., nanocomposite, multifunctional grafting) that enhance antimicrobial activity, mechanical strength, and environmental adaptability. Challenges in fruit preservation—such as microbial resistance and stability—are analyzed, with solutions proposed via material innovation. The discussion on industrial scalability emphasizes chitosan’s biodegradability, cost-effectiveness, and alignment with sustainable agriculture, while addressing technical bottlenecks. This work bridges fundamental research and practical use, advancing chitosan-based coatings toward greener, safer, and scalable postharvest solutions.

A VIS/NIR hyperspectral imaging system was used to classify three different degrees of freeze-damage in corn seeds. Using image processing methods, the hyperspectral image of the corn seed embryo was obtained first. To find a relatively better method for later imaging visualization, four different pretreatment methods (no pretreatment, multiplicative scatter correction (MSC), standard normal variation (SNV) and 5 points and 3 times smoothing (5-3 smoothing)), four wavelength selection algorithms (successive projection algorithm (SPA), principal component analysis (PCA), X-loading and full-band method) and three different classification modeling methods (partial least squares-discriminant analysis (PLS-DA), K-nearest neighbor (KNN) and support vector machine (SVM)) were applied to make a comparison. Next, the visualization images according to a mean spectrum to mean spectrum (M2M) and a mean spectrum to pixel spectrum (M2P) were compared in order to better represent the freeze damage to the seed embryos. It was concluded that the 5-3 smoothing method and SPA wavelength selection method applied to the modeling can improve the signal-to-noise ratio, classification accuracy of the model (more than 90%). The final classification results of the method M2P were better than the method M2M, which had fewer numbers of misclassified corn seed samples and the samples could be visualized well.

The quality assurance of corn seeds is of utmost significance in all stages of production, storage, circulation, and breeding. However, the traditional detection method has some disadvantages, such as high labor intensity, strong subjectivity, low efficiency, cumbersome operation, etc. In view of this, it is of great significance to study more advanced detection methods. In this paper, the application of near-infrared spectroscopy and its imaging technology in the quality detection of corn seeds was reviewed. Firstly, the principles of these two technologies were introduced, and their components, data acquisition, and processing methods, as well as portability, were compared and discussed. Then, the application of these methods to the main quality of corn seeds (including variety and purity, vigor, internal components, mycotoxins, and other qualities such as frost damage, hardness, and maturity, etc.) was reviewed. Breakthroughs and innovations have been made in detection methods, spectral preprocessing methods and recognition algorithms. The significance of corn quality characteristics and the function of the applied algorithm were emphasized. Finally, the challenges and future research direction of spectral and its imaging technology was proposed, aiming to further enhance the accuracy, reliability, and practicability of the detection technology. With the rapid development of spectral and its imaging technology, the detection methods of corn quality are also advancing with the times. This is not just for corn, but more and more crops can be accurately detected by these technologies. It will become an important means of agricultural production inspection in the future.