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利用傅里叶变换红外光声光谱法(FTIR-PAS)快速无损测定油菜籽(甘蓝型油菜)中的蛋白质含量。

Fast and nondestructive determination of protein content in rapeseeds (Brassica napus L.) using Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS).

作者信息

Lu Yuzhen, Du Changwen, Yu Changbing, Zhou Jianmin

机构信息

Institute of Soil Science, National Key Laboratory of Soil and Sustainable Agriculture Chinese Academy of Sciences, Nanjing, 21008, China.

出版信息

J Sci Food Agric. 2014 Aug;94(11):2239-45. doi: 10.1002/jsfa.6548. Epub 2014 Jan 24.

Abstract

BACKGROUND

Fast and non-destructive determination of rapeseed protein content carries significant implications in rapeseed production. This study presented the first attempt of using Fourier transform mid-infrared photoacoustic spectroscopy (FTIR-PAS) to quantify protein content of rapeseed. The full-spectrum model was first built using partial least squares (PLS). Interval selection methods including interval partial least squares (iPLS), synergy interval partial least squares (siPLS), backward elimination interval partial least squares (biPLS) and dynamic backward elimination interval partial least squares (dyn-biPLS) were then employed to select the relevant band or band combination for PLS modeling.

RESULTS

The full-spectrum PLS model achieved an ratio of prediction to deviation (RPD) of 2.047. In comparison, all interval selection methods produced better results than full-spectrum modeling. siPLS achieved the best predictive accuracy with an RPD of 3.215 when the spectrum was sectioned into 25 intervals, and two intervals (1198-1335 and 1614-1753 cm(-1) ) were selected. iPLS excelled biPLS and dyn-biPLS, and dyn-biPLS performed slightly better than biPLS.

CONCLUSION

FTIR-PAS was verified as a promising analytical tool to quantify rapeseed protein content. Interval selection could extract the relevant individual band or synergy band associated with the sample constituent of interest, and then improve the prediction accuracy of the full-spectrum model.

摘要

背景

快速无损测定油菜籽蛋白质含量对油菜籽生产具有重要意义。本研究首次尝试使用傅里叶变换中红外光声光谱法(FTIR-PAS)对油菜籽蛋白质含量进行定量分析。首先使用偏最小二乘法(PLS)建立全光谱模型。然后采用区间选择方法,包括区间偏最小二乘法(iPLS)、协同区间偏最小二乘法(siPLS)、反向消除区间偏最小二乘法(biPLS)和动态反向消除区间偏最小二乘法(dyn-biPLS),为PLS建模选择相关波段或波段组合。

结果

全光谱PLS模型的预测偏差比(RPD)为2.047。相比之下,所有区间选择方法的结果均优于全光谱建模。当光谱被划分为25个区间并选择两个区间(1198 - 1335和1614 - 1753 cm(-1))时,siPLS获得了最佳预测精度,RPD为3.215。iPLS优于biPLS和dyn-biPLS,dyn-biPLS的表现略优于biPLS。

结论

FTIR-PAS被证实是一种用于定量油菜籽蛋白质含量的有前景的分析工具。区间选择可以提取与目标样品成分相关的单个波段或协同波段,进而提高全光谱模型的预测精度。

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