近红外光谱鉴别鲜枣品种及裂果研究

我国的鲜食枣品种多样, 不同的品种其肉质有差异, 其食用品质也有差别。此外, 鲜枣收获时有裂果，裂果导致其果肉外露，腐烂变酸，失去食用价值, 并且腐烂变质的水果，很容易传染给完整果，因此有必要将裂果和完整果进行分开贮藏，以减少损失。本研究采用近红外光谱快速鉴别鲜枣品种及裂果。以室温(20 ℃)贮藏的鲜枣为研究对象，应用近红外光谱，对2个不同品种的鲜枣进行光谱分析, 采用平滑法、多元散射校正、一阶微分和二阶微分方法对光谱进行预处理, 再用回归关系法和主成分分析法进行波数选择，建立3层人工神经网络鉴别模型, 结果表明, 鲜枣品种和裂果鉴别的正确率均可达100%, 可鉴别鲜枣品种和裂果, 以减少鲜枣贮藏的损失。说明近红外光谱可以实现鉴别冬枣和梨枣品种, 以及正确的识别完整果和裂果，为鲜枣的分类贮藏和加工提供理论依据。

Discrimination of the Fresh Jujube Varieties and Dehiscent Fruit by NIR Spectroscopy

There are many fresh jujube varieties. The different variety has different quality. In addition, dehiscent fruit easily rot and the rotten fruit contaminated the full fruit very rapidly. It is necessary to discriminate the jujube varieties and dehiscent fruit to reduce the storage loss. The objective is to discriminate varieties and dehiscent Fruit of fresh jujube using near infrared (NIR) spectroscopy. Two jujube varieties were investigated. Smoothing, multiplicative scatter correction, the first derivative and second derivative methods were adopted to pretreat the spectra. The regression coefficient and principal component analysis were used to select wavenumber. Multilayer perceptron artificial neural network was used to build varieties and dehiscent fruit qualitative discrimination model. The results showed that the varieties and dehiscent fruit could be correctly discriminated and both the discrimination accuracy rates were 100%. Hence, near infrared spectroscopy could achieve to identify the variety of Dongzao and Lizao, and dehiscent fruit and intact fruit.