Monitoring of solid-state fermentation of wheat straw in a pilot scale using FT-NIR spectroscopy and support vector data description
Monitoring of solid-state fermentation of wheat straw in a pilot scale using FT-NIR spectroscopy and support vector data description
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DOI：
10.1016/j.microc.2011.12.003
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发表时间：
2012-05-01
影响因子：
4.8
通讯作者：
Yu, Shuang
中科院分区：
文献类型：
--
作者：
Jiang, Hui;Liu, Guohai;Yu, Shuang
傅里叶变换近红外光谱(FT-NIR)结合支持向量数据描述(SVDD)是一种快速、准确地监测秸秆固态发酵过程中物理化学变化的理想工具，而不需要进行化学分析。在10,000-4000 cm(-1)的波长范围内获得了发酵样品的原始光谱。采用支持向量机算法建立一类分类模型，并在模型标定中通过交叉验证优化了支持向量机算法的部分参数。同时，对四种传统的两类分类方法(线性判别分析，LDA；K近邻，KNN；反向传播神经网络)进行了研究。支持向量机。支持向量机)用于监测SSF期间发生的与时间相关的变化。与四种模型相比，支持向量机模型在处理训练集不均衡问题上显示出了无可比拟的优势。当固定阶段样本与其他阶段样本之比为1：8时，SVDD模型在验证集中的识别率为90%。本研究表明，傅立叶变换近红外光谱结合支持向量机是建立SSF快速监测的一类分类模型的有效方法。(C)爱思唯尔2011年。版权所有。
Fourier transform near-infrared (FT-NIR) spectroscopy coupled with support vector data description (SVDD) as an ideal tool was attempted to rapidly and accurately monitor physical and chemical changes in solid-state fermentation (SSF) of crop straws without the need for chemical analysis. Raw spectra of fermented samples were acquired with wavelength range of 10,000-4000 cm(-1). SVDD algorithm was employed to build a one-class classification model, and some parameters of SVDD algorithm were optimized by cross-validation in calibrating model. Simultaneously, four traditional two-class classification approaches (i.e., linear discriminant analysis, LDA; K-nearest neighbor, KNN; back propagation neural networks. BPNN; support vector machine. SVM) were comparatively utilized for monitoring time-related changes that occur during SSF. Compared to the four models, SVDD model revealed its incomparable superiority in handling the problem of imbalance training sets. The discrimination rate of SVDD model was 90% in the validation set when the ratio of samples from stationary stage to those from other stages was one to eight. This study demonstrates that FT-NIR spectroscopy combined with SVDD is an efficient method to develop one-class classification model for the rapid monitoring of SSF. (C) 2011 Elsevier ay. All rights reserved.