Serum Raman spectroscopy combined with multiple algorithms for diagnosing thyroid dysfunction and chronic renal failure.

In this study, 60 samples taken from patients with thyroid dysfunction, 40 samples taken from patients with chronic renal failure (CRF) and 60 samples taken from healthy people were classified. We used partial least squares (PLS) to extract features to reduce the dimension of the spectral data to discriminate among the different samples. The Decision Trees (DT), Extreme Learning Machine (ELM), Probabilistic Neural Network (PNN), Back Propagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) algorithms were used to build classification models and compare the results. The PLS-PNN algorithm distinguished between patients with thyroid dysfunction and patients with chronic renal failure with up to a 96.67 % accuracy rate, the PLS-BP algorithm distinguished between patients with chronic renal failure and healthy people with up to a 98.33 % accuracy rate, and the PLS-PNN algorithm and the PLS-DT algorithm distinguished between healthy people and patients with chronic renal failure with up to a 100 % accuracy rate. The results showed that serum Raman spectroscopy can be used in conjunction with classification algorithms to rapidly and accurately diagnose and distinguish between thyroid dysfunction and chronic renal failure.

Exploration research on the fusion of multimodal spectrum technology to improve performance of rapid diagnosis scheme for thyroid dysfunction.

The spectral fusion by Raman spectroscopy and Fourier infrared spectroscopy combined with pattern recognition algorithms is utilized to diagnose thyroid dysfunction serum, and finds the spectral segment with the highest sensitivity to further advance diagnosis speed. Compared with the single infrared spectroscopy or Raman spectroscopy, the proposal can improve the detection accuracy, and can obtain more spectral features, indicating greater differences between thyroid dysfunction and normal serum samples. For discriminating different samples, principal component analysis (PCA) was first used for feature extraction to reduce the dimension of high-dimension spectral data and spectral fusion. Then, support vector machine (SVM), back propagation neural network, extreme learning machine and learning vector quantization algorithms were employed to establish the discriminant diagnostic models. The accuracy of spectral fusion of the best analytical model PCA-SVM, single Raman spectral accuracy and single infrared spectral accuracy is 83.48%, 78.26% and 80%, respectively. The accuracy of spectral fusion is higher than the accuracy of single spectrum in five classifiers. And the diagnostic accuracy of spectral fusion in the range of 2000 to 2500 cm-1 is 81.74%, which greatly improves the sample measure speed and data analysis speed than analysis of full spectra. The results from our study demonstrate that the serum spectral fusion technique combined with multivariate statistical methods have great potential for the screening of thyroid dysfunction.

Application of Raman spectroscopy in the detection of hepatitis B virus infection.

OBJECTIVE: Detection of hepatitis B virus (HBV) using Raman spectroscopy. METHODS: Raman spectroscopy was used to examine the serum samples of 500 patients with HBV and 500 non-HBV persons. First, the adaptive iterative weighted penalty least squares method (airPLS) was used to deduct the fluorescence background in Raman spectra. Then, a principal component analysis (PCA) was used to extract the processed Raman spectra, and a support vector machine (SVM) was used for modeling and prediction. The particle swarm optimization (PSO) algorithm was selected to optimize the parameters of the SVM instead of a traditional grid search. Finally, 600 serum samples were detected by Raman spectroscopy, and the results wereverified using a double-blind method. RESULTS: In the Raman spectra, the non-HBV human Raman peaks at 509, 957, 1002, 1153, 1260, 1512, 1648 and 2305 cm-1 were different from those of patients with HBV. The reported accuracy, sensitivity and specificity of the HBV serum model established using airPLS-PCA-PSO-SVM was 93.1%, 100% and 88%, respectively. The two groups were verified by a double-blind method. In the first group sensitivity was 87%, specificity was 92%, and the KAPPA value was 0.79; in the second group sensitivity was 80%, specificity was 79%, and the KAPPA value was 0.59. CONCLUSION: This preliminary study shows that serum Raman spectroscopy combined with the airPLS-PCA-PSO-SVM model can be used for hepatitis B virus detection.