近红外光谱法预测毛竹笋中不溶性膳食纤维含量

Xinjie Yu, Yongjian Yu, Shangyong Zhang
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引用次数: 0

摘要

采用近红外光谱法测定了毛竹笋中不溶性膳食纤维(IDF)的含量。将120个样本分为两组,分别作为校准集和预测集。采用AOAC 991.43法测定样品中IDF含量为0.046% ~ 0.209%。利用样品收集了400 ~ 1000 nm的光谱数据。在对光谱数据进行分析之前,采用标准正态变量(SNV)、乘法散射校正(MSC)和Savitzky-Golay一阶导数变换(S. Golay 1st-Der)三种预处理方法。对校准集进行偏最小二乘(PLS)分析,并利用不同的预处理光谱数据建立校准模型。计算模型的均方根校正误差(RMSEC)和相关系数(r),以选择最佳模型。该模型的r值为0.950,RMSEC值为0.008,具有较好的预测效果。因此,选择MSC模型作为预测IDF含量的最佳模型。结果表明,近红外光谱联用PLS可作为快速、准确测定毛竹中IDF含量的替代方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of insoluble dietary fiber content in moso bamboo shoot using near-infrared spectroscopy
Near-Infrared spectroscopy (NIRS) was used to determine the insoluble dietary fiber (IDF) content of moso bamboo shoot. A total of 120 samples were separated into two groups, as calibration set and prediction set. The IDF content of samples ranged from 0.046% to 0.209% were measured by AOAC 991.43 method. Spectral data from 400 nm to 1000 nm were collected using the samples. Three types of preprocessing methods including standard normal variate (SNV), multiplicative scattering correction (MSC), and Savitzky-Golay first derivative transformation (S. Golay 1st-Der) were used before the spectral data were analyzed. Partial least squares (PLS) analysis was performed on the calibration set, and calibration models were developed using the different preprocessed spectral data. The root mean square error of calibration (RMSEC) and correlation coefficient (r) of the models were calculated to select the best model. The MSC-PLS model with r of 0.950 and RMSEC of 0.008 showed an excellent prediction performance. Thus, MSC model was selected as the best model for prediction of IDF content. The over all results show that NIRS technique coupled with PLS could be applied as an alternative fast and accurate method to determine IDF content in moso bamboo shoot.
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