首页> 外文期刊>Journal of the Science of Food and Agriculture >Fast and nondestructive determination of protein content in rapeseeds (Brassica napus L.) using Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS)
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Fast and nondestructive determination of protein content in rapeseeds (Brassica napus L.) using Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS)

机译:使用傅里叶变换红外光声光谱法(FTIR-PAS)快速无损测定油菜籽(Brassica napus L.)中的蛋白质含量

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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
机译:背景:快速而无损地确定油菜籽蛋白含量对油菜籽生产具有重要意义。这项研究提出了使用傅里叶变换中红外光声光谱法(FTIR-PAS)量化油菜籽蛋白质含量的首次尝试。首先使用偏最小二乘(PLS)构建全光谱模型。然后采用包括区间偏最小二乘(iPLS),协同间隔偏最小二乘(siPLS),反向消除间隔偏最小二乘(biPLS)和动态反向消除间隔偏最小二乘(dyn-biPLS)的间隔选择方法来选择相关的结果:全谱PLS模型的预测偏差比(RPD)为2.047。相比之下,所有间隔选择方法都比全谱建模产生更好的结果。当将频谱分为25个间隔并选择两个间隔(1198-1335和1614-1753 cm(-1))时,siPLS的RPD为3.215,实现了最佳预测精度。 iPLS优于biPLS和dyn-biPLS,并且dyn-biPLS的性能略优于biPLS。结论:FTIR-PAS被证明是定量油菜籽蛋白质含量的有前途的分析工具。间隔选择可以提取与感兴趣的样本成分相关的相关单个波段或协同波段,然后提高全光谱模型的预测准确性

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