本文選題：近紅外光譜法 + 洗凈絨　； 參考：《北京化工大學(xué)》2016年碩士論文

【摘要】：天然纖維是紡織工業(yè)的重要材料來源。近年來,面對(duì)相關(guān)產(chǎn)業(yè)結(jié)構(gòu)的調(diào)整優(yōu)化,以及企業(yè)降低成本和保證產(chǎn)品質(zhì)量的巨大需求,亟待解決纖維的現(xiàn)場(chǎng)或在線快速檢測(cè)技術(shù)。但在應(yīng)用近紅外技術(shù)進(jìn)行現(xiàn)場(chǎng)和在線分析時(shí),光散射以及濕度引起的樣品水分變化等因素會(huì)影響模預(yù)測(cè)性能,從而影響現(xiàn)場(chǎng)和在線測(cè)量準(zhǔn)確度和精度。此外,市場(chǎng)上還缺乏某些纖維的現(xiàn)場(chǎng)和在線快速分析系統(tǒng)。因此,本論文研究了水和光散射對(duì)山羊絨光譜的影響以及光譜處理方法,并建立了山羊絨凈絨率快速分析方法；研制了一套天然纖維素纖維漿粕性質(zhì)在線分析系統(tǒng),并建立了在線分析方法。(1)本文研究了水分對(duì)山羊絨洗凈絨纖維漫反射近紅外光譜的影響。發(fā)現(xiàn)光譜受到水分的嚴(yán)重影響。提出了一種算法—扣水算法來減小水分的影響。該算法基于向量-子空間夾角判據(jù)算法,通過從樣品光譜中扣除水的光譜信息來消除水的影響。分別利用未扣水的光譜和利用該方法預(yù)處理后的光譜與凈絨率相結(jié)合,利用偏最小二乘法建立洗凈絨的定量模型。未扣水模型的預(yù)測(cè)決定系數(shù)為0.87,預(yù)測(cè)均方根誤差(SEP)為8.53%,而扣水模型的預(yù)測(cè)決定系數(shù)為0.94, SEP為5.28%。結(jié)果表明,扣水算法可明顯提高模型的預(yù)測(cè)性能。(2)本文研究了洗凈絨中主要組成羊絨和粗毛的漫反射近紅外光譜特征。研究發(fā)現(xiàn)由化學(xué)組成等化學(xué)信息引起的羊絨和粗毛的光譜差異極小,而差異主要來源于物理性質(zhì)例如直徑不同帶來的光散射效應(yīng)。研究了多元散射校正(MSC)對(duì)消除散射效應(yīng)的校正效果,并對(duì)MSC處理后的光譜殘差進(jìn)行了主成分分析,研究發(fā)現(xiàn)在MSC光譜殘差中包含對(duì)洗凈絨定量校正有用的物理信息。為充分利用樣品的化學(xué)和物理信息,本文提出光譜重建方法。該算法利用主成分分析分解殘差光譜,然后根據(jù)主成分光譜與凈絨率的相關(guān)系數(shù)曲線,來選擇與凈絨率相關(guān)關(guān)系高的主成分光譜,并將其添加到MSC校正后的光譜,構(gòu)建新的用來建模的樣品光譜。利用該方法建立的PLS定量模型,預(yù)測(cè)決定系數(shù)為0.92,SEP為6.10。(3)本文研究了扣水算法和光譜重構(gòu)方法聯(lián)合使用對(duì)光譜與凈絨率相關(guān)關(guān)系以及模型性能的影響。研究發(fā)現(xiàn),兩種方法聯(lián)用可有效提高光譜與凈絨率的相關(guān)系數(shù),并獲得最佳的模型,模型預(yù)測(cè)決定系數(shù)為0.95,SEP為5.18%,可滿足凈絨率的檢測(cè)要求,從而實(shí)現(xiàn)洗凈絨凈絨率的快速分析。(4)本文研制了一種快速在線分析漿粕性質(zhì)的近紅外光譜儀。利用該在線分析儀動(dòng)態(tài)采集86個(gè)漿粕的漫反射光譜圖,利用PLS方法結(jié)合S-G導(dǎo)數(shù)、均值中心化和MSC預(yù)處理方法,分別建立α-纖維素含量和聚合度的在線分析模型。其模型的決定系數(shù)分別0.89和0.98,SEP為0.94和25.1,且具有良好的預(yù)測(cè)重復(fù)性。研究結(jié)果可實(shí)現(xiàn)漿粕連續(xù)生產(chǎn)過程的在線監(jiān)控。
[Abstract]:Natural fiber is an important material source of textile industry. In recent years, in the face of the adjustment and optimization of related industrial structure, as well as the huge demand of enterprises to reduce the cost and ensure the quality of products, the field or on-line rapid detection technology of fiber urgently needs to be solved. However, in the field and on-line analysis using near-infrared technique, light scattering and moisture change of sample caused by humidity will affect the prediction performance of the model, thus affecting the accuracy and accuracy of field and on-line measurement. In addition, the market also lacks some fiber field and on-line rapid analysis system. Therefore, the effect of water and light scattering on cashmere spectrum and the method of spectrum processing were studied in this paper, and a rapid analysis method of cashmere net velvet was established, and a set of on-line analysis system for the properties of natural cellulose fiber pulp was developed. The effect of water content on the diffuse reflectance near infrared spectroscopy of cashmere washed-down fiber was studied. It was found that the spectrum was seriously affected by moisture. In this paper, an algorithm, the water impingement algorithm, is proposed to reduce the influence of water content. Based on the vector-subspace angle criterion algorithm, the influence of water is eliminated by deducting the spectral information of water from the spectrum of the sample. The quantitative model of washed velvet was established by partial least square method by combining the spectrum of uncapped water and the spectrum after pretreatment with the net velvet ratio. The predictive decision coefficient of unincorporated water model is 0.87, the root mean square error (SEP) of prediction is 8.53, while the predictive decision coefficient of water model is 0.94 and SEP is 5.28. The results show that the water buckling algorithm can improve the predictive performance of the model. (2) the diffuse reflectance near infrared spectra of cashmere and coarse wool are studied in this paper. It is found that the spectral difference between cashmere and coarse wool caused by chemical information such as chemical composition is very small and the difference is mainly due to the light scattering effect caused by physical properties such as different diameters. The correction effect of multivariate scattering correction (MSCI) on the elimination of scattering effect was studied. The spectral residuals after MSC treatment were analyzed by principal component analysis. It was found that the spectral residuals of MSC contained physical information useful for the quantitative correction of washed velvet. In order to make full use of the chemical and physical information of the sample, a spectral reconstruction method is proposed in this paper. In this algorithm, principal component analysis (PCA) is used to decompose the residual spectrum, and then according to the correlation coefficient curve between principal component spectrum and net velvet ratio, the principal component spectrum with high correlation with net velvet ratio is selected and added to the corrected spectrum of MSC. A new sample spectrum for modeling was constructed. Using the PLS quantitative model established by this method, the predictive decision coefficient is 0.92g SEP is 6.10.3.In this paper, the influence of the combination of water blocking algorithm and spectral reconstruction method on the correlation between the spectrum and the net velvet ratio and the performance of the model is studied. It is found that the two methods can effectively improve the correlation coefficient between the spectrum and the net velvet rate, and obtain the best model. The prediction decision coefficient of the model is 0.95g SEP 5.18, which can meet the requirements of the net velvet ratio detection. In this paper, a near infrared spectrometer for fast on-line analysis of pulp properties has been developed. The diffuse reflectance spectra of 86 pulps were dynamically collected by the on-line analyzer. The on-line analytical models of 偽 -cellulose content and degree of polymerization were established by PLS method combined with S-G derivative, mean centralization and MSC pretreatment. The determination coefficients of the model are 0.89 and 0.98SEP 0.94 and 25.1respectively, and the model has good prediction repeatability. The results can realize the on-line monitoring of continuous pulp production process.
【學(xué)位授予單位】：北京化工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TS102;O657.33

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