Using FT-NIR spectroscopy technique to determine arginine content in fermented Cordyceps sinensis mycelium.
Using FT-NIR spectroscopy technique to determine arginine content in fermented Cordyceps sinensis mycelium.
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DOI：
10.1016/j.saa.2015.05.028
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发表时间：
2015-10
期刊：
影响因子：
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通讯作者：
Chuanqi Xie;N. Xu;Y. Shao;Yong He
中科院分区：
文献类型：
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作者：
Chuanqi Xie;N. Xu;Y. Shao;Yong He
本研究探讨了利用傅里叶变换近红外（FT-NIR）光谱技术测定发酵冬虫夏草（C. sinensis）菌丝体中精氨酸含量的可行性。采用三种不同的模型来预测精氨酸含量。使用竞争性自适应重加权采样（CARS）和连续投影算法（SPA）等波数选择方法来识别最重要的波数并降低原始光谱数据的高维性。 CARS 和 CARS-SPA 分别仅选择了少数波数作为最佳波数。在预测模型中，CARS-最小二乘支持向量机（CARS-LS-SVM）模型表现最好，其预测确定系数最高（R p 2= 0.8370）和残差预测偏差（RPD= 2.4741），预测均方根误差最低（RMSEP= 0.0841）。而且，输入变量的数量为45个，仅占全波数的2.04%。结果表明，FT-NIR光谱技术有潜力成为一种客观、无损检测发酵冬虫夏草菌丝体中精氨酸含量的方法。
This research investigated the feasibility of using Fourier transform near-infrared (FT-NIR) spectral technique for determining arginine content in fermented Cordyceps sinensis (C. sinensis) mycelium. Three different models were carried out to predict the arginine content. Wavenumber selection methods such as competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA) were used to identify the most important wavenumbers and reduce the high dimensionality of the raw spectral data. Only a few wavenumbers were selected by CARS and CARS–SPA as the optimal wavenumbers, respectively. Among the prediction models, CARS–least squares-support vector machine (CARS–LS-SVM) model performed best with the highest values of the coefficient of determination of prediction (R p 2= 0.8370) and residual predictive deviation (RPD= 2.4741), the lowest value of root mean square error of prediction (RMSEP= 0.0841). Moreover, the number of the input variables was forty-five, which only accounts for 2.04% of that of the full wavenumbers. The results showed that FT-NIR spectral technique has the potential to be an objective and non-destructive method to detect arginine content in fermented C. sinensis mycelium.