本文選題：激光誘導(dǎo)擊穿光譜技術(shù) + 煙草��； 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：近年來(lái),由人為活動(dòng)造成的重金屬污染使植物體內(nèi)積累過(guò)量的重金屬元素,重金屬超標(biāo)的水果、蔬菜、糧食等農(nóng)產(chǎn)品會(huì)對(duì)人體健康造成危害。煙草是我國(guó)重要經(jīng)濟(jì)作物之一,重金屬污染不僅影響香煙品質(zhì),而且對(duì)吸煙者的健康產(chǎn)生嚴(yán)重的危害。因此,煙草中重金屬元素檢測(cè)對(duì)香煙品質(zhì)管理及吸煙者身體健康有著重要意義。本論文以重金屬(銅Cu和鎘Cd)脅迫下的煙草為研究對(duì)象,研究煙草重金屬污染程度判別及重金屬元素定量檢測(cè)的方法和模型,具體研究?jī)?nèi)容和結(jié)果如下:(1)研究了煙草鮮葉Cu污染程度LIBS判別方法。在土培條件下,采摘不同程度Cu污染下的煙草葉片并收集其LIBS光譜信號(hào)。對(duì)全譜LIBS數(shù)據(jù)進(jìn)行主成分分析,基于PC1、PC2、PC3的載荷值選取對(duì)應(yīng)于C、Fe、Mg、Ca、Fe、S、N、O等元素的18條譜線作為特征變量。利用所選特征變量建立線性判別分析(LDA)和支持向量機(jī)(SVM)分類(lèi)模型。在LDA模型中,建模集與預(yù)測(cè)集的識(shí)別正確率分別為85%和72%;在SVM模型中,建模集與預(yù)測(cè)集的分類(lèi)正確率分別為97%和82%。結(jié)果表明,基于LIBS技術(shù)結(jié)合化學(xué)計(jì)量學(xué)方法判別新鮮煙草葉片Cu污染程度是可行的,能獲得滿(mǎn)意的判斷結(jié)果。(2)研究基于響應(yīng)面法的煙草重金屬定量分析LIBS儀器參數(shù)優(yōu)化方法。利用BBD響應(yīng)面法針對(duì)LIBS檢測(cè)煙草重金屬含量的三個(gè)儀器因素:①激光脈沖能量,②探測(cè)器相對(duì)于激光脈沖的延時(shí)時(shí)間,③探測(cè)器門(mén)寬(積分時(shí)間),設(shè)計(jì)了實(shí)驗(yàn)方案,確定了最佳的三因素組合。a.在煙草葉樣本Cu含量檢測(cè)的參數(shù)優(yōu)化實(shí)驗(yàn)中,以Cu I:324.75 nm譜線的信背比(Cu I:324.75 nm譜線強(qiáng)度與325.67 nm～326.73 mn范圍光譜平均值之比)作為目標(biāo)函數(shù),以最大化目標(biāo)函數(shù)為目標(biāo)分析實(shí)驗(yàn)結(jié)果,最終確定基于LIBS技術(shù)的煙草Cu定量分析的參數(shù)優(yōu)化組合為:激光能量為109 mJ,延時(shí)時(shí)間為5.19μs,積分時(shí)間為14.66μs。在此條件下得到的最大信背比為20.8857。b.在煙草根樣本Cd定量分析的參數(shù)優(yōu)化實(shí)驗(yàn)中,以Cd I:361.05 nm譜線的信背比(Cd I:361.05 nm譜線強(qiáng)度與359.15 nm～360.45 nm范圍光譜平均值之比)作為目標(biāo)函數(shù),以使目標(biāo)函數(shù)最大化為目的分析實(shí)驗(yàn)結(jié)果,確定基于LIBS技術(shù)的煙草Cd定量分析的參數(shù)優(yōu)化組合為:激光能量為115.34 mJ,延時(shí)時(shí)間為4.41μs,積分時(shí)間為6.48μs。此參數(shù)組合下最大信背比為11.7614。(3)研究建立了煙草重金屬元素LIBS快速定量檢測(cè)方法和模型。①在基于LIBS煙草葉樣本Cu含量分析中,選擇對(duì)應(yīng)Cu I的324.75 nm和327.4 nm譜線作為關(guān)鍵變量。利用上述關(guān)鍵變量分別建立基于單變量的一元線性回歸模型和基于雙變量的MLR模型。此外,利用320 nm～330 nm范圍LIBS光譜數(shù)據(jù)建立PLS模型�；贑u I 324.75 nm譜線的一元線性回歸模型中,建模集決定系數(shù)R_c~2為0.96,預(yù)測(cè)集決定系數(shù)R_p~2為0.94;基于Cu I 327.4 nm譜線的一元線性回歸模型中,R_c~2為0.96,R_p~2為0.91;基于Cu I的324.75 nm和327.4 nm譜線的MLR模型中,R_c~2為0.96,Rp/為0.92;基于320 nm～330 nm范圍光譜的PLS模型中,R_c~2為0.95,Rp/為0.90。②在基于LIBS煙草根樣本Cd含量分析中,選擇對(duì)應(yīng)CdI的346.62 nm和361.05 nm譜線作為關(guān)鍵變量。利用上述關(guān)鍵變量分別建立基于單變量的一元線性回歸模型和基于雙變量的MLR模型。此外,利用340 nm～365 nm范圍LIBBS光譜數(shù)據(jù)建立PLS模型�；贑d I 346.62 nm譜線的一元線性回歸模型中,R_c~2為0.96,R_p~2為0.90;基于CdI 361.05 nm譜線的一元線性回歸模型中,R_c~2為0.96,R_p~2為0.92;基于CdI 346.62 nm和361.05 nm譜線的MLR模型中,R_c~2為0.97,R_p~2為0.92;基于340 nm～365 nm范圍光譜的PLS模型中,R_c~2為0.98,R_p~2為0.94。結(jié)果表明,基于LIBS技術(shù)的煙草重金屬定量分析可行,基于單變量的建模結(jié)果與基于多變量的建模結(jié)果相近,為后續(xù)便攜式LIBS植物重金屬檢測(cè)儀器的開(kāi)發(fā)提供了理論條件。
[Abstract]:In recent years, heavy metal pollution caused by human activities has accumulated excessive heavy metal elements in plants, and agricultural products, such as fruits, vegetables and grain, will be harmful to human health. Tobacco is one of the most important economic crops in China. Heavy metal pollution not only affects the quality of cigarettes, but also produces serious health for smokers. Therefore, the detection of heavy metal elements in tobacco is of great significance to the quality management of cigarettes and the health of smokers. In this paper, the study of tobacco under the stress of heavy metals (copper Cu and CD Cd) is the research object, and the methods and models of heavy metal pollution degree discrimination and quantitative determination of heavy metals are studied. The specific contents and results are as follows (1) the LIBS discrimination method of Cu pollution degree of fresh leaves of tobacco was studied. Under the condition of soil culture, tobacco leaves under different degree of Cu pollution were picked and their LIBS spectral signals were collected. The total spectral LIBS data were analyzed by principal component analysis. The load values of PC1, PC2, PC3 were selected as the characteristic variables of C, Fe, Mg, Ca, Ca, PC3 and other elements. The classification model of linear discriminant analysis (LDA) and support vector machine (SVM) is established by the selected feature variables. In the LDA model, the recognition accuracy of the modeling set and the prediction set is 85% and 72% respectively. In the SVM model, the classification accuracy of the modeling set and the prediction set is 97% and 82%. respectively, and the new method is based on the combination of LIBS and chemometrics methods. The degree of Cu pollution in fresh tobacco leaves is feasible and can obtain satisfactory results. (2) study on the parameter optimization method of quantitative analysis of heavy metals in tobacco based on response surface method and three instrumental factors for detecting heavy metal content of tobacco using BBD response surface method: (1) excitation pulse energy and second detector relative to laser pulse. The time delay, the width of the detector gate (integral time), the experimental scheme is designed, and the best three factor combination.A. is determined in the parameter optimization experiment of Cu content detection in tobacco leaf sample. The target function is the ratio of the signal back ratio of the Cu I:324.75 nm line (the ratio of the spectral line strength of Cu I:324.75 nm and the spectral average of the 325.67 nm to 326.73 Mn range) as the target function. The maximum target function is the result of the target analysis. Finally, the parameters optimization combination of the quantitative analysis of tobacco Cu based on LIBS technology is as follows: the laser energy is 109 mJ, the time delay time is 5.19 s, the integral time is 14.66 U S., the maximum signal back ratio is the parameter optimization experiment of Cd quantitative analysis of 20.8857.b. in the tobacco root sample. In the case of Cd I:361.05 nm spectrum line signal back ratio (the ratio of Cd I:361.05 nm spectral line strength and 359.15 nm to 360.45 nm range spectral mean) as the target function, the objective function maximization is analyzed, and the optimization combination of quantitative analysis of tobacco Cd based on LIBS technology is determined as: laser energy is 115.34 mJ, time delay time The method and model for rapid quantitative detection of heavy metal elements LIBS in tobacco were established for 4.41 Mu s and 6.48 S. with the maximum signal back ratio of 11.7614. (3). (1) 324.75 nm and 327.4 nm spectrum lines corresponding to Cu I were selected as the key variables in the Cu content analysis of LIBS tobacco leaves. A univariate linear regression model based on a single variable and a bivariate MLR model are established. In addition, the PLS model is established using 320 nm ~ 330 nm range LIBS spectral data. In the linear regression model based on the Cu I 324.75 nm line, the modeling set determination coefficient R_c~2 is 0.96, the prediction set coefficient R_p~2 is 0.94, and the Cu I 327.4 spectral line is based. In a linear regression model, R_c~2 is 0.96 and R_p~2 is 0.91; R_c~2 is 0.96 and Rp/ is 0.92 in the MLR model of 324.75 nm and 327.4 nm lines based on Cu I, and 0.95 in PLS model based on 320 nm ~ 330 nm spectrum. Line is a key variable. Using the above key variables, one variable linear regression model and MLR model based on bivariate are established respectively. In addition, PLS model is established by using 340 nm ~ 365 nm range LIBBS spectral data. In the linear regression model based on Cd I 346.62 nm line, R_c~2 is 0.96, R_p~2 is 0.90; CdI 361.05 n is based. In the linear regression model of M line, R_c~2 is 0.96 and R_p~2 is 0.92; R_c~2 is 0.97, R_p~2 is 0.92 in MLR model based on CdI 346.62 nm and 361.05 nm spectrum line, and 0.98 in PLS model based on 340 nm ~ 365 nm range spectrum. The modeling results are similar to those based on multivariate modeling, which provides a theoretical basis for the subsequent development of portable LIBS plant heavy metal detection instruments.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TS411

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本文編號(hào)：1942315