Study Of Methodology For Identification Of Blood Products Based On Raman/SERS Spectroscopy

Raman spectrum technology was of many advantages in biochemical analysis,because of its features,such as the ability to study the structure of biomolecules at the molecular level,strong penetration force,without sample pretreatment and small water interference and so on.However,when Raman spectrum technique was used in the anialysis and identification of blood products,it was prone to produce some problems such as complex sample composition,poor Raman signal of sample,strong signals of background fluorescence,large interference of instrument signals,serious drift of spectral baselines and small difference of spectral.In this study,to improve the detection sensitivity by using the surface enhanced Raman spectroscopy（SERS）technology and to improve the validity and reliability of Raman identification and analysis in combination with statistical methods were proposed.Species discriminant model for human blood,canine blood and rabbit blood was established based on the optimization of the Raman test conditions and methods,combined with mathematical statistics methods,which was successfully applied to the portable blood discriminator in Raman soectrum technology developed by the laboratory itself.The preparation methods of integrated nano-SERS substrate and the serum testing method on SERS chips were studied.Furthermore,the serum diseases were successfully tested efficiently and identified rapidly by combining multivariate stastisical analysis methods,which provide a new way and methold for the analysis of complex biological samples.The related technologies and methods have imporment research value and application prospects in the fields of disease diagnosis,species identification,and criminal investigation and detection.The main research works and the results are as follows:1.Study on identification of human blood and animal blood species based on Raman spectral analysisThe non-destructive testing method based on the non-contact Raman spectroscopy was adopted for the efficient identification and the testing requirements of human blood and animal blood.Human blood,canine blood and rabbit blood were successfully tested efficiently and identified rapidly by species discriminant model,which was established based on the optimization of the Raman test conditions and methods,combined with mathematical statistics methods in commercially-manufactured Raman spectrometer and laboratory-developed portable Raman blood analyzer.the optimal conditions of non-destructive testing human and animal blood was blood anticoagulant tube made of borosilicate glass,the objective lens distance of 1 mm from the outer wall of the anticoagulant tube as the focus position with a 5 s integration time with 2 accumulating times with 633 nm excitation.Laser power was selected approximately 8 mW.The pretreatment method of wavelet denoising and piecewise polynomial baseline correction for the series of Raman spectra was adopted.The model of partial least squares discriminant analysis（PLS-DA）was established based on 102 spectra from human blood,80 spectra from dog blood and 65 spectra from rabbit blood.The classification accuracy of proposed model for the calibration set and the blind test were 100%（202/202）and 93.33%（42/45）.The portable Raman blood analyzer which was loaded a PLS-DA model based on 115 spectra from human blood,119 spectra from dog blood and 92 spectra from rabbit blood was used to identify species of blood in some companies.The accuracy rate was above 95.00%.The established method based on Raman spectroscopy for efficiently and quickly defiling the blood of other people and animals has important research significance and practical prospects in the fields of export inspection and quarantine,criminal investigation and detection.2.Testing and analysis of liver cancer patients based on microfluidic SERS chipA high-throughput microfluidic SERS chip integrated Ag/Au nanocomposites was designed and applied to test the serum SERS for normal people and liver cancer patients,the new approach for rapidly and efficiently identifying the alpha fetoprotein（AFP）which was the characteristic indicators of liver cancer and acetyl coenzyme a carboxylase was established based on microfluidic SERS chip and principal component analysis with linear discriminant analysis（PCA-LDA）.The detection limit for rhodamine 6G（R6G）on microfluidic SERS chip integrated nano-Ag@Au was1.00×10^-1010 M,the enhancement factor was 3.10×10⁷,and the relative standard deviation（RSD）between the different channels of the same chip was less than 3.95%.The results showed that the microfluidic SERS chip had good detection sensitivity and test repeatability.AFP,the serum of 60 normal individuals and the serum of 60 patients with liver cancer were tested by microfluidic SERS chip.According to the characteristic peaks of AFP,the classification model was established by analysising the characteristic peaks of AFP on serum SERS spectra of normal people and liver cancer patient with PCA-LDA.The accuracy rate was above 90.00%.A new diagnosis model for liver cancer was established by combining serum SERS analysis based on microfluidic SERS chip and PCA-LDA statistical with the range of 400-1700 cm^-1 wave of serum SERS spectra to synthesize the change of AFP and other substances in serum.The classification accuracy of proposed model for the calibration set and the blind test were95.00%（76/80）and 92.56%（37/40）.Through the results of PCA-LDA could find that the characteristic peaks of 634cm^-1 and 725 cm^-1 were the biggest difference peaks.634cm^-1 was the C-S stretching vibration of tyrosine,725 cm^-1 is the C-C stretching vibration of coenzyme A and acetyl-CoA,and the tyrosine is increased.Coenzyme A and acetyl-CoA were decreased,which is consistent with the increase of AFP content in serum of patients with liver cancer in the literature,increase of acetyl-CoA carboxylase,and reduction of coenzyme A and acetyl-CoA.The new method was established for serum SERS analysis effectively combines microfluidic technology,SERS analysis techniques,and multivariate statistical methods,and has a good application prospect in disease diagnosis and disease screening.