本文選題：激光探針　切入點(diǎn)：光譜數(shù)據(jù)處理　出處：《華中科技大學(xué)》2015年碩士論文

【摘要】：自激光探針技術(shù)誕生以來,研究者主要集中于激光誘導(dǎo)等離子體的物理特性、實(shí)驗(yàn)樣品的物理化學(xué)性質(zhì)、實(shí)驗(yàn)參數(shù)優(yōu)化和儀器設(shè)備性能等方面的研究,光譜數(shù)據(jù)處理方法的研究沒有得到足夠重視。然而作為一種有力的軟優(yōu)化方法,數(shù)據(jù)處理方法有著諸多方面的優(yōu)勢。一方面,數(shù)據(jù)處理可以代替某些高精度硬件設(shè)備實(shí)現(xiàn)技術(shù)指標(biāo),降低硬件成本;同時(shí)解決硬件優(yōu)化無法克服的技術(shù)難題,提升光譜分析的質(zhì)量;另一方面,數(shù)據(jù)處理通過提取光譜的有用信息并對數(shù)據(jù)進(jìn)行再加工,可以顯著提高光譜分析的精度。本文通過對光譜數(shù)據(jù)預(yù)處理方法和定量分析方法的研究,有效提高了定性和定量分析的準(zhǔn)確度。首先,本文研究了基于連續(xù)小波變換的激光探針譜峰識別算法,提出了一種自動(dòng)計(jì)算噪聲的新方法,采用連續(xù)小波變換結(jié)合信噪比閾值法實(shí)現(xiàn)譜峰的自動(dòng)識別。將該方法應(yīng)用于土壤樣品的光譜中,結(jié)果表明,該方法能夠有效排除尖峰噪聲的干擾,識別強(qiáng)峰,并且具有較強(qiáng)的重疊峰分辨能力和良好的定性分析能力,為后續(xù)的定量分析奠定了基礎(chǔ)。其次,本文提出了基于離散小波變換背景扣除的改良算法,對傳統(tǒng)的背景擬合算法進(jìn)行修正。通過對微合金鋼樣品的激光探針光譜進(jìn)行背景校正,并對Cr、V、Cu和Mn元素進(jìn)行定量分析。結(jié)果表明,該方法能夠使譜線的背景明顯降低,并能有效避免出現(xiàn)背景的高估現(xiàn)象,與未進(jìn)行背景校正、多項(xiàng)式擬合背景扣除方法和常規(guī)的小波變換方法相比,這種方法能夠改善光譜質(zhì)量,提高回歸模型的準(zhǔn)確性。然后,本文研究了基于遺傳算法和偏最小二乘法相結(jié)合的定量分析模型。通過對11種土壤組成成分Mn、Cr、Cu、Pb、Ba、Al2O3、CaO、Fe2O3、MgO、Na2O和K2O的含量分別進(jìn)行預(yù)測,證明遺傳算法作為譜線選擇的一種預(yù)處理方法,能夠有效去除光譜中重復(fù)、多余或不相關(guān)的變量,減少用于偏最小二乘法建模的光譜譜線數(shù)目,從而減少建模時(shí)間,最終簡化模型。對于大部分土壤組成成分,該模型都能夠顯著改善定量分析的準(zhǔn)確度。最后,本文研究了基于偏最小二乘法和人工神經(jīng)網(wǎng)絡(luò)相結(jié)合的定量分析模型。將該模型應(yīng)用于激光探針土壤定量分析中,對11種土壤組成成分的含量進(jìn)行預(yù)測。結(jié)果表明,該模型能夠?qū)⑵钚《朔ń档妥宰兞慷嘀毓簿�性和人工神經(jīng)網(wǎng)絡(luò)具有非線性處理能力的優(yōu)勢結(jié)合起來,顯著改善了激光探針定量分析的準(zhǔn)確度。
[Abstract]:Since the birth of laser probe technology, researchers have focused on the physical properties of laser-induced plasma, the physical and chemical properties of experimental samples, the optimization of experimental parameters and the performance of instruments and equipment.The research of spectral data processing method has not been paid enough attention to.However, as a powerful soft optimization method, data processing method has many advantages.On the one hand, data processing can replace some high-precision hardware devices to achieve technical targets and reduce the cost of hardware; at the same time, it can solve the technical problems that can not be overcome by hardware optimization, and improve the quality of spectral analysis; on the other hand,The accuracy of spectral analysis can be significantly improved by data processing by extracting useful spectral information and reprocessing the data.In this paper, the preprocessing method and quantitative analysis method of spectral data are studied to improve the accuracy of qualitative and quantitative analysis.Firstly, this paper studies the laser probe spectral peak recognition algorithm based on continuous wavelet transform, and proposes a new method to automatically calculate the noise. The continuous wavelet transform combined with the SNR threshold method is used to realize the automatic recognition of the spectral peak.The method is applied to the spectrum of soil samples. The results show that this method can effectively eliminate the interference of peak noise, identify strong peaks, and have strong resolution ability of overlapping peaks and good qualitative analysis ability.It lays a foundation for further quantitative analysis.Secondly, an improved background deduction algorithm based on discrete wavelet transform is proposed to modify the traditional background fitting algorithm.The laser probe spectra of microalloyed steel samples were calibrated and the elements of Cr (V) Cu and mn were quantitatively analyzed.The results show that the proposed method can reduce the background of spectral lines obviously, and can effectively avoid the phenomenon of background overestimation. Compared with the methods of background correction, polynomial fitting background subtraction and conventional wavelet transform, the proposed method can effectively avoid background overestimation.This method can improve the spectral quality and improve the accuracy of the regression model.Then, the quantitative analysis model based on genetic algorithm and partial least square method is studied.The number of spectral lines used for partial least square modeling is reduced, thus the modeling time is reduced and the model is simplified.For most soil components, the model can significantly improve the accuracy of quantitative analysis.Finally, the quantitative analysis model based on partial least square method and artificial neural network is studied.The model was applied to the quantitative analysis of soil with laser probe, and the contents of 11 soil components were predicted.The results show that the model can combine the advantages of the partial least square method to reduce the independent variable multiple collinearity and the artificial neural network has the ability to deal with nonlinear, and improve the accuracy of laser probe quantitative analysis.
【學(xué)位授予單位】：華中科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP274.2;TN249

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