西甜瓜(西瓜和甜瓜)味甘多汁, 营养丰富, 其内部品质的检测对其生产和流通具有重要意义。 西甜瓜内部品质的传统检测方法检测时间长, 成本高且为有损检测, 不能满足现代生产的需要。 随着光谱分析技术的快速发展, 应用近红外光谱分析和高光谱成像进行西甜瓜内部品质的无损检测已成为研究热点。 为跟踪国内外最新研究进展并分析研究现状, 介绍了近红外光谱分析和高光谱成像的技术特点和系统组成, 归纳了光谱信息预处理、 变量筛选、 模型建立和模型评价等光谱信息解析方法, 综述了近红外光谱分析和高光谱成像在西甜瓜内部品质(可溶性固形物含量、 坚实度、 总酸含量、 成熟度、 水分等)无损检测中的应用, 并从技术难点和实际应用两方面讨论了光谱分析技术在西甜瓜内部品质无损检测中的发展趋势, 指出利用深度学习进行光谱信息解析、 建立多特征信息融合的综合评价模型、 开发基于人工智能与移动终端深度融合的快速无损检测系统等将成为新的研究方向。

Watermelon and muskmelon are sweet, juicy and rich in nutrients.There is great significance in manufacture and circulation for its internal quality detection. The traditional detection methods for internal quality of watermelon and muskmelon are inefficient, long time, high cost and destructive, which can not meet the needs of modern production. With the rapid development of spectral analysis techniques, near-infrared spectroscopy (NIRS) and hyperspectral imaging (HSI) for the internal quality non-destructive detectionof watermelon and muskmelon has become a research hotspot. In order to track national and international progress of research, this paper presents the technical characteristics and system composition of NIRS and HIS. The spectral information analysis methods are concluded, including spectral information preprocessing, variable selection, model establishment and evaluation. Afterwards, the recent progress of NIRS and HSI in the non-destructive detection for the internal quality (soluble solids content, firmness, total acid content, maturity and moisture, etc.) of watermelon and muskmelon is summarized. Finally, the future trends of spectral analysis techniques in the internal qualitynon-destructive detection of watermelon and muskmelon are discussed from the technical difficulties and practical applications.This review indicates thatthe following aspects are identified as the direction of future research, using deep learning methods to analyze spectral information, establishing comprehensive evaluation model of multi-feature information fusion, and developing the rapid non-destructive detection system based on the deep integration of artificial intelligence and mobile terminal.