Dongni Tong1, Cheng Chen2, JingJing Zhang3, GuoDong Lv4, Xiangxiang Zheng2, Zhaoxia Zhang5, Xiaoyi Lv6. 1. Department of Laboratory Medicine, The First Affiliated Hospital of Xinjiang Medical University, Urumuqi 83001, China. 2. College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China. 3. Imaging Center, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, China. 4. State Key Laboratory of Pathogenesis, Prevention, and Treatment of Central Asian High Incidence Diseases, The First Affiliated Hospital of Xinjiang Medical University, Urumqi 830054, Xinjiang, China. 5. Department of Laboratory Medicine, The First Affiliated Hospital of Xinjiang Medical University, Urumuqi 83001, China. Electronic address: 285715300@qq.com. 6. College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China. Electronic address: xiaoz813@163.com.
Abstract
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.
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-HBVpersons. 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-HBVhuman 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.
Authors: Alexandre Girard; Anneli Cooper; Samuel Mabbott; Barbara Bradley; Steven Asiala; Lauren Jamieson; Caroline Clucas; Paul Capewell; Francesco Marchesi; Matthew P Gibbins; Franziska Hentzschel; Matthias Marti; Juan F Quintana; Paul Garside; Karen Faulds; Annette MacLeod; Duncan Graham Journal: PLoS Pathog Date: 2021-11-15 Impact factor: 6.823