基于近紅外光譜的發(fā)酵過程濕度軟測量技術(shù)研究

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摘要 固態(tài)發(fā)酵是一個(gè)多相多變量、強(qiáng)耦合的非線性系統(tǒng)。在實(shí)際的固態(tài)發(fā)酵生產(chǎn)過程中，由于硬件檢測設(shè)備缺乏和價(jià)格過高的原因，一些關(guān)鍵變量的信息只能通過離線檢測獲得，往往造成信息滯后，這嚴(yán)重制約了固態(tài)發(fā)酵系統(tǒng)控制性能的提高。為有效提高固態(tài)發(fā)酵過程檢測與控制的效率，本文以蛋白飼料固態(tài)發(fā)酵為研究對象，開展了基于近紅外光譜技術(shù)的固態(tài)發(fā)酵過程檢測研究，著重探討了基于近紅外光譜技術(shù)的固態(tài)發(fā)酵過程濕度軟測量方法。
首先對獲取的固態(tài)發(fā)酵物樣本的近紅外光譜，采用離散小波變換（DWT）結(jié)合主成分分析（PCA）對其進(jìn)行濾噪和特征提取；然后利用提取的特征變量建立基于支持向量機(jī)（SVM）的固態(tài)發(fā)酵過程濕度軟測量模型。研究結(jié)果表明，利用近紅外光譜技術(shù)來實(shí)現(xiàn)固態(tài)發(fā)酵過程濕度軟測量是可行的。
本研究為固態(tài)發(fā)酵過程檢測與控制帶來新思路，旨在提高固體發(fā)酵過程參數(shù)檢測和過程狀態(tài)監(jiān)測的準(zhǔn)確度和時(shí)效性，研究成果可為固態(tài)發(fā)酵過程監(jiān)控儀器裝備的研發(fā)提供研究基礎(chǔ)
關(guān)鍵詞：固態(tài)發(fā)酵 濕度 近紅外光譜 支持向量機(jī) 軟測量 過程檢測

Soft sensor of humidity based on near infrared spectroscopy for fermentation process

Abstract Solid-state fermentation is a multi-phase and multi-variable nonlinear system with high coupling. Due to the lack of hardware detection equipment and the over-high price in the actual production process of solid-state fermentation , some key variables can only be obtained through off-line testing , resulting in information lag. This has seriously hampered the control performance of solid-state fermentation system from being improved. In order to improve the efficiency of process detection and control of solid-state fermentation (SSF), this work attempted to the feasibility and method of the measurement of process parameters of SSF of protein feed by use of near-infrared spectroscopy (NIRS) techniques. In addition, soft sensor of humidity in solid-state fermentation was also focused on by use of NIRS in this work.
Firstly, the raw spectra of all fermented samples obtained were preprocessed by use of the discrete wavelet transform (DWT), and the feature vectors were extracted by use of principal component analysis (PCA) from the spectral data preprocessed. Then, the identified model was developed by use of support vector machine(SVM). The overall results demonstrate that SVM is a prominent approach, and NIRS technique combined with SVM has high potential to monitor the process state of SSF in a no-invasion way.
This study provides a new idea for the process detection and control of SSF. The main aim of improving the accuracy and timeliness for the measurement of process parameters and the monitoring of process state of SSF has been achieved. The results in this work can provide research foundation for developing instruments and equipment for the monitoring of SSF process.
Keywords solid-state fermentation; humidity; near-infrared spectroscopy; support vector machine; soft sensor; process detection

目 錄
第一章 緒 論 1
1.1 本文研究背景及對象 1
1.2 近紅外光譜技術(shù)研究 2
1.2.1 近紅外光譜技術(shù)概述 2
1.2.2 近紅外光譜技術(shù)發(fā)展歷程 2
1.2.3 近紅外光譜技術(shù)特點(diǎn) 5
1.2.4 基于近紅外光譜技術(shù)的發(fā)酵過程研究現(xiàn)狀 7
1.3 基于支持向量機(jī)的軟測量方法 8
1.4 本文主要研究內(nèi)容 8
第二章 固態(tài)發(fā)酵過程試驗(yàn)及數(shù)據(jù)采集 12
2.1 引言 12
2.2 樣品制備 12
2.2.1 實(shí)驗(yàn)材料 12
2.2.2 發(fā)酵實(shí)驗(yàn) 12
2.3 光譜采集 13
2.4 濕度測定 13
2.5 本章小結(jié) 14
第三章 基于近紅外光譜技術(shù)的固態(tài)發(fā)酵軟測量建模方法研究 15
3.1 樣本劃分 15
3.2 近紅外光譜預(yù)處理 16
3.2.1 平滑處理 16
3.2.2 導(dǎo)數(shù)計(jì)算 17
3.3 近紅外光譜濾噪及特征提取 18
3.3.1 離散小波變換 18
3.3.2 特征提取 20
3.4 近紅外光譜分析技術(shù)步驟 22
3.5 支持向量回歸建模 24
3.5.1 模型評價(jià)標(biāo)準(zhǔn) 24
3.5.2 基于支持向量回歸（SVR）的模型建立及預(yù)測 24
3.6 本章小結(jié) 28
第四章 總結(jié)與展望 29
4.1 工作總結(jié) 29
4.2 研究展望 29
參考文獻(xiàn) 31
致 謝 34