Department of Agricultural Engineering, National Institute of Agricultural Sciences, Rural Development Administration, 310 Nongsaengmyeng-ro, Wansan-gu, Jeonju 54875, Korea.
Microbial Safety Team, National Institute of Agricultural Sciences, Rural Development Administration, 166 Nongsaengmyeong-ro, Iseo-myeon, Wanju-gun 55365, Korea.
Sensors (Basel). 2017 Sep 30;17(10):2258. doi: 10.3390/s17102258.
The purpose of this study is to use near-infrared reflectance (NIR) spectroscopy equipment to nondestructively and rapidly discriminate -infected hulled barley. Both normal hulled barley and -infected hulled barley were scanned by using a NIR spectrometer with a wavelength range of 1175 to 2170 nm. Multiple mathematical pretreatments were applied to the reflectance spectra obtained for discrimination and the multivariate analysis method of partial least squares discriminant analysis (PLS-DA) was used for discriminant prediction. The PLS-DA prediction model developed by applying the second-order derivative pretreatment to the reflectance spectra obtained from the side of hulled barley without crease achieved 100% accuracy in discriminating the normal hulled barley and the -infected hulled barley. These results demonstrated the feasibility of rapid discrimination of the -infected hulled barley by combining multivariate analysis with the NIR spectroscopic technique, which is utilized as a nondestructive detection method.
本研究旨在利用近红外反射光谱（NIR）设备，对受感染的带壳大麦进行非破坏性、快速鉴别。使用波长范围为 1175 至 2170nm 的 NIR 光谱仪对正常带壳大麦和受感染带壳大麦进行扫描。对获得的反射光谱进行了多种数学预处理，用于鉴别，并采用偏最小二乘判别分析（PLS-DA）的多元分析方法进行判别预测。对未褶皱带壳大麦侧面获得的反射光谱应用二阶导数预处理，建立的 PLS-DA 预测模型对正常带壳大麦和受感染带壳大麦的判别准确率达到 100%。这些结果表明，通过将多元分析与 NIR 光谱技术相结合，可以作为一种无损检测方法，快速鉴别受感染的带壳大麦。
