Research On Data Processing And Qualitative Analysis Of Raman Spectrum

Raman spectrum analysis technique is gaining a wider use in many fields suchas food, materials and environmental monitoring due to its advantages ofnon-destructive, rich information, no sample preparation etc.. Handheld Ramanspectrometer is widely used in the material identification of industrial productionbecause of its advantages of easy to operate, compact construction, lightweight etc.Currently, major foreign spectrometer manufacturers have already launched variousmodels of handheld Raman spectrometer. The domestic market has beenmonopolized by these products. Therefore the need of development of our ownhandheld Raman spectrometer with independent intellectual property rights isbecoming extremely urgent.In this paper, related research on data processing and qualitative analysis ofhandheld Raman spectrometer is carried out. Because handheld Raman spectrometeris mainly used in qualitative discrimination issues in industrial production, andproduction line operators often do not have the professional knowledge of chemicalanalysis, reducing the need of manual intervention and achievement of automation ofdata processing and spectrum analysis process become a critical point.This paper systematically studied the algorithm of data processing andqualitative analysis of handheld Raman spectroscopy. The major research work is as follows:(1) Studied the quality evaluation methods of Raman spectrum. Noise standarddeviation methods such as small scale wavelet transform method, spatial correlationwavelet transform method, Donoho robust estimation method and improvedsecond-order difference method were realized. Estimation accuracy of noise standarddeviation of these methods were compared, results showed that improvedsecond-order difference method is the most accurate estimation method. Proposed anew method for the calculation of SNR, the new SNR can better characterize thequality of Raman spectrum in comparison with the conventional SNR.(2) Studied the data processing methods of Raman spectrum. The commonspectrum preprocessing methods were realized. The research were focused on thedespeike, debaseline and denoise methods of Raman spectrum. Proposed a novelmethod for spectrum despike, improved iteratively stripping method, which is atotally automated despike method without any parameter need to be specified.Realized an automated noise reduction method based on three points zero-orderSavitzky-Golay filter. It’s denoise performance was compared with the traditionaldenoise methods such as sliding window average, sliding window median method,Savitzky-Golay filter and wavelet threshold filter. Numerical experiments showedthat the method had the best denoising performance. Meanwhile the degradation ofpeak caused by denoising was minimal. It could retain most of the informationcontained in Raman peaks. Proposed a novel baseline estimation method, improvedsmall window moving average method, which is a totally automatic debaselinemethod without any parameters to be set. The novel method’s baseline estimationaccuracy is pretty good.(3) Studied the peak recognition methods of Raman spectrum. Realized the peakrecognition method based on continuous wavelet transform. Proposed two novel peakrecognition methods: bi-scale correlation method and multi-scale local SNR method.Their peak recognition performance of these methods was compared. Simulationresults showed that the multi-scale local SNR method outperformed the continuous wavelet transform method and bi-scale correlation method. The simulationexperiment shows that: for singular peak on the detection limit the recognitionaccuracy of multi-scale local SNR method is95.1%, when the peak SNR is greaterthan or equal to6, the recognition accuracy of proposed algorithm is up to100%, forcongested peak, when the peak SNR is greater than or equal to7, the recognitionaccuracy is up to100%, multi-scale local SNR method had the highest peak positionestimation accuracy.(4) Studied the discriminant analysis methods of Raman spectrum. Directcomparison method and soft independent modeling of class analogy method wererealized. Their performance was compared. The disadvantage of direct comparisonmethod is that when the reference spectrum of unkown sample wasn’t contained inthe reference library, the direct comparison method would still give one best matchresult. Soft independent modeling of class analogy method outperformed the directcomparison method. The soft independent modeling of class analogy method hadmore superior identification performance. When the reference spectrum of unkownsample wasn’t contained in the reference library the soft independent modeling ofclass analogy method could tell us that the unkown sample belonged to a new class.Through soft independent modeling of class analogy method we could get otherinformation such as the similarity between the two classes, the relevance of avariable and the similarity between one sample and certain class.