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Rapid identification of fermentation stages of bioethanol SSF using FT-NIR spectroscopy: Comparisons of linear and non-linear algorithms for multiple classification issues

Abstract

Solid state fermentation (SSF) is a critical step in achieving bioethanol product, and an effective monitoring of SSF process is on the urgent need due to the rapid changes in the SSF industry, which demands fast tools providing real time information to ensure the quality of the final product. The aim of this study is to investigate FT-NIR spectroscopy technique associated with supervised pattern recognition methods, to monitor time-related molecular changes that occur during SSF of bioethanol. Principal component analysis as an exploratory tool was employed to uncover molecular modification of the spectral data during the SSF process. Furthermore, identification models were constructed using partial least squares discriminant analysis (PLS-DA), back propagation neural network (BPNN), support vector machine (SVM) and extreme learning machine (ELM) algorithms, respectively. The parameters of the four models were optimized by leave-one-out cross-validation (LOOCV) in identification model calibration. Experimental results showed that the non-linear identification models showed strong classification performance to identify fermentation stages in SSF of bioethanol. Meanwhile, compared with BPNN and SVM models, the ELM model obtained slightly better generalization performance with the identification rate of 92.60% in the validation process. The overall results show that the ELM-FT-NIR methodology is efficient in accurately identifying the fermentation stages during the SSF of bioethanol, demonstrating potential for apply in in-situ monitoring and control of large-scale industrial processes.

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Publication details

The article was received on 31 Jul 2017, accepted on 14 Sep 2017 and first published on 14 Sep 2017


Article type: Paper
DOI: 10.1039/C7AY01861D
Citation: Anal. Methods, 2017, Accepted Manuscript
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    Rapid identification of fermentation stages of bioethanol SSF using FT-NIR spectroscopy: Comparisons of linear and non-linear algorithms for multiple classification issues

    H. Jiang, C. Mei and Q. Chen, Anal. Methods, 2017, Accepted Manuscript , DOI: 10.1039/C7AY01861D

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