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基于近红外光谱法的米糠层中纤维素快速准确的定量分析

A Rapid and Accurate Quantitative Analysis of Cellulose in the Rice Bran Layer Based on Near-Infrared Spectroscopy.

作者信息

Fan Shuang, Qin Chaoqi, Xu Zhuopin, Wang Qi, Yang Yang, Ni Xiaoyu, Cheng Weimin, Zhang Pengfei, Zhan Yue, Tao Liangzhi, Wu Yuejin

机构信息

Anhui Key Laboratory of Environmental Toxicology and Pollution Control Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China.

Science Island Branch, Graduate School of USTC, Hefei 230026, China.

出版信息

Foods. 2023 Aug 9;12(16):2997. doi: 10.3390/foods12162997.

Abstract

Cultivating rice varieties with lower cellulose content in the bran layer has the potential to enhance both the nutritional value and texture of brown rice. This study aims to establish a rapid and accurate method to quantify cellulose content in the bran layer utilizing near-infrared spectroscopy (NIRS), thereby providing a technical foundation for the selection, screening, and breeding of rice germplasm cultivars characterized by a low cellulose content in the bran layer. To ensure the accuracy of the NIR spectroscopic analysis, the potassium dichromate oxidation (PDO) method was improved and then used as a reference method. Using 141 samples of rice bran layer (rice bran without germ), near-infrared diffuse reflectance (NIRdr) spectra, near-infrared diffuse transmittance (NIRdt) spectra, and fusion spectra of NIRdr and NIRdt were used to establish cellulose quantitative analysis models, followed by a comparative evaluation of these models' predictive performance. Results indicate that the optimized PDO method demonstrates superior precision compared to the original PDO method. Upon examining the established models, their predictive capabilities were ranked in the following order: the fusion model outperforms the NIRdt model, which in turn surpasses the NIRdr model. Of all the fusion models developed, the model exhibiting the highest predictive accuracy utilized fusion spectra (NIRdr-NIRdt (1st der)) derived from preprocessed (first derivative) diffuse reflectance and transmittance spectra. This model achieved an external predictive R of 0.903 and an RMSEP of 0.213%. Using this specific model, the rice mutant O2 was successfully identified, which displayed a cellulose content in the bran layer of 3.28%, representing a 0.86% decrease compared to the wild type (W7). The utilization of NIRS enables quantitative analysis of the cellulose content within the rice bran layer, thereby providing essential technical support for the selection of rice varieties characterized by lower cellulose content in the bran layer.

摘要

培育麸皮层纤维素含量较低的水稻品种,有可能提高糙米的营养价值和口感。本研究旨在建立一种利用近红外光谱(NIRS)快速准确地定量分析麸皮层纤维素含量的方法,从而为筛选、选育麸皮层纤维素含量低的水稻种质品种提供技术依据。为确保近红外光谱分析的准确性,对重铬酸钾氧化(PDO)法进行了改进,并将其用作参照方法。以141份水稻麸皮层(无胚米糠)样品为材料,利用近红外漫反射(NIRdr)光谱、近红外漫透射(NIRdt)光谱以及NIRdr与NIRdt的融合光谱建立纤维素定量分析模型,并对这些模型的预测性能进行比较评价。结果表明,优化后的PDO法比原始PDO法具有更高的精度。在考察所建立的模型时,其预测能力排序如下:融合模型优于NIRdt模型,NIRdt模型又优于NIRdr模型。在所有建立的融合模型中,预测准确性最高的模型采用了由预处理(一阶导数)漫反射和透射光谱得到的融合光谱(NIRdr-NIRdt(一阶导数))。该模型的外部预测相关系数R为0.903,预测均方根误差RMSEP为0.213%。利用该特定模型成功鉴定出水稻突变体O2,其麸皮层纤维素含量为3.28%,与野生型(W7)相比降低了0.86%。近红外光谱技术的应用能够对水稻麸皮层中的纤维素含量进行定量分析,从而为筛选麸皮层纤维素含量较低的水稻品种提供重要的技术支持。

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