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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Development of Electronic Nose and Near Infrared Spectroscopy Analysis Techniques to Monitor the Critical Time in SSF Process of Feed Protein
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Development of Electronic Nose and Near Infrared Spectroscopy Analysis Techniques to Monitor the Critical Time in SSF Process of Feed Protein

机译：电子鼻和近红外光谱分析技术的发展以监测饲料蛋白SSF过程中的关键时间

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摘要

In order to assure the consistency of the final product quality, a fast and effective process monitoring is a growing need in solid state fermentation (SSF) industry. This work investigated the potential of non-invasive techniques combined with the chemometrics method, to monitor time-related changes that occur during SSF process of feed protein. Four fermentation trials conducted were monitored by an electronic nose device and a near infrared spectroscopy (NIRS) spectrometer. Firstly, principal component analysis (PCA) and independent component analysis (ICA) were respectively applied to the feature extraction and information fusion. Then, the BP_AdaBoost algorithm was used to develop the fused model for monitoring of the critical time in SSF process of feed protein. Experimental results showed that the identified results of the fusion model are much better than those of the single technique model both in the training and validation sets, and the complexity of the fusion model was also less than that of the single technique model. The overall results demonstrate that it has a high potential in online monitoring of the critical moment in SSF process by use of integrating electronic nose and NIRS techniques, and data fusion from multi-technique could significantly improve the monitoring performance of SSF process.
机译：为了确保最终产品质量的一致性，固态发酵（SSF）工业中对快速有效的过程监控的需求日益增长。这项工作研究了非侵入性技术与化学计量学方法相结合的潜力，以监测饲料蛋白SSF过程中与时间相关的变化。通过电子鼻装置和近红外光谱仪（NIRS）监测四项发酵试验。首先，将主成分分析（PCA）和独立成分分析（ICA）分别应用于特征提取和信息融合。然后，使用BP_AdaBoost算法建立了融合模型，用于监测饲料蛋白SSF过程中的关键时间。实验结果表明，在训练集和验证集上，融合模型的识别结果均比单一技术模型的识别结果好得多，并且融合模型的复杂度也小于单一技术模型。总体结果表明，通过集成电子鼻和NIRS技术，它具有在线监测SSF过程关键时刻的潜力，而来自多种技术的数据融合可以显着提高SSF过程的监测性能。

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