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A Near Infrared Spectroscopic Discrimination of Noodle Flours Using a Principal-Component Analysis Coupled with Chemical Information

机译:基于主成分分析和化学信息的面条粉近红外光谱鉴别

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Using a portable near infrared (NIR) spectrometer, we discriminated flours for making Japanese noodles (Soba), not only relying on a statistical and mathematical approach, but also on a chemical interpretation of the NIR spectra. In original NIR spectra, the particle-size difference, which results in an undesired systematic variation, was extracted and interpreted as the first-principal component factor by a principal-component analysis. The discrimination of flour materials cannot be satisfied by this factor. However, after a standardized treatment for the original spectra, the particle-size effects were eliminated; alternatively, differences in the chemical contents were extracted as principal-component factors. Using these factors, flour material discrimination was achieved much better. This study suggests a novel idea of utilizing the wavelength contribution ratio spectra for interpreting the factors extracted from the principal-component analysis for the NIR spectra. This report also describes the relationship between the NIR spectra and the chemical-analysis data.
机译:我们使用便携式近红外(NIR)光谱仪,不仅依靠统计和数学方法,还依靠对NIR光谱的化学解释,来区分用于制造日本面条(Soba)的面粉。在原始的近红外光谱中,导致主观分析的粒径差异被提取出来,并通过主成分分析解释为第一主要成分因子。面粉材料的区分不能通过该因素来满足。但是,在对原始光谱进行标准化处理之后,消除了粒径影响;或者,提取化学成分的差异作为主成分因子。利用这些因素,可以更好地实现面粉材料的区分。这项研究提出了一种利用波长贡献率光谱来解释从近红外光谱主成分分析中提取的因素的新颖思路。该报告还描述了近红外光谱和化学分析数据之间的关系。

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