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Rapid variety identification of pure rough rice by Fourier-transform near-infrared spectroscopy.

机译:通过傅里叶变换近红外光谱法快速鉴定纯糙米。

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Rice variety is considered as an important factor influencing cooking and processing quality because of variations in size, shape and constitution. Difficulty in the management of rough rice with lower varietal purity becomes a significant problem in rice production and can result in the reduction of rice quality. Fourier-transform near-infrared (FT-NIR) spectroscopy was used to identify the variety of rough rice through wholegrain techniques. Moist rough rice samples (n = 259) comprising 5 varieties (Khao Dawk Mali 105 (KDML105), Pathum Thani 1, Suphan Buri 60, Chainat 1 and Pitsanulok 2) were gathered from different locations around Thailand and scanned in the NIR region of 9088-4000 cm--1 in reflectance mode. Soft independent modelling of class analogies (SIMCA) and partial least squares discriminant analysis (PLSDA) methods were used for identification by utilizing preprocessed spectra. The highest identification accuracy achieved was 74.42% by the SIMCA model and 99.22% by the PLSDA model. The best PLSDA model demonstrated approx. 97% correct identification for KDML105 samples and 100% for the others. It is suggested that this study may raise the possibility of applying FT-NIR spectroscopy as a nondestructive technique for rapidly identifying moist rough rice varieties in routine quality assurance testing.
机译:由于大小,形状和组成的差异,水稻品种被认为是影响烹饪和加工质量的重要因素。具有较低品种纯度的糙米的管理困难成为稻米生产中的重要问题,并可能导致稻米质量下降。傅立叶变换近红外(FT-NIR)光谱用于通过全麦技术鉴定糙米的品种。从泰国各地收集了湿糙米样品( n = 259),其中包括5个品种(Khao Dawk Mali 105(KDML105),Pathum Thani 1,Suphan Buri 60,Chainat 1和Pitsanulok 2)。在反射模式下在9088-4000 cm -1 的NIR区域中扫描。利用类比的软独立建模(SIMCA)和偏最小二乘判别分析(PLSDA)方法通过预处理光谱进行识别。 SIMCA模型达到的最高识别准确度为74.42%,PLSDA模型达到的99.22%。最好的PLSDA模型展示了约。 KDML105样品的正确识别率达97%,其他样品的正确率达100%。建议这项研究可能会增加使用FT-NIR光谱技术作为无损检测技术,以在常规质量保证测试中快速鉴定潮湿的糙米品种的可能性。

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