Optical diagnosis of hepatitis B virus infection in blood plasma using Raman spectroscopy and chemometric techniques,Journal of Raman Spectroscopy

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Optical diagnosis of hepatitis B virus infection in blood plasma using Raman spectroscopy and chemometric techniques
Journal of Raman Spectroscopy ( IF 2.4 ) Pub Date : 2020-05-04 , DOI: 10.1002/jrs.5896
M. Saleem ₁ , Safdar Ali ₂ , M. Bilal Khan ₃ , Ayyaz Amin ₁ , M. Bilal ₄ , Haq Nawaz ₅ , Mehdi Hassan ₆

Affiliation

The potential of Raman spectroscopy has been utilized for the diagnosis of hepatitis B virus (HBV) infection in blood plasma. Raman spectra of 24 diseased and 10 healthy samples were used to develop distinct types of support vector machine (SVM) models, including linear, quadratic, and radial basis function (RBF) using multivariate method of principal component analysis (PCA) to reduce the dimensions of the obtained datasets. To assess the diagnostic power of these algorithms, developed models were tested on independent dataset. RBF‐based PCA‐SVM model achieved the best performance and yielded accuracy of 98.82%, sensitivity of 98.89%, and specificity of 98.80%. The performance of the SVM models was compared with rerated chemometric method of partial least square regression (PLSR), which has been developed by using the same dataset. The PLSR model attained the diagnostic accuracy of 88%, sensitivity of 93%, and specificity of 78% for same dataset. Our developed model has established promising results compared with state‐of‐the‐art approaches. The results reveal the improved performance of the developed chemometric techniques and clinical prediction potential of HBV by PCA‐SVMs in conjunction with Raman spectroscopy.

中文翻译：

使用拉曼光谱和化学计量学技术对血浆中乙型肝炎病毒感染的光学诊断

拉曼光谱法的潜力已被用于诊断血浆中的乙型肝炎病毒（HBV）感染。使用24个患病样本和10个健康样本的拉曼光谱，使用多元主成分分析（PCA）方法来开发不同类型的支持向量机（SVM）模型，包括线性，二次和径向基函数（RBF），以减小尺寸获得的数据集。为了评估这些算法的诊断能力，在独立的数据集上测试了开发的模型。基于RBF的PCA-SVM模型获得了最佳性能，产生的准确度为98.82％，灵敏度为98.89％，特异性为98.80％。将SVM模型的性能与偏最小二乘回归（PLSR）的重估化学计量方法进行了比较，后者是使用相同的数据集开发的。对于同一数据集，PLSR模型的诊断准确性达到88％，灵敏度为93％，特异性为78％。与最先进的方法相比，我们开发的模型已经建立了令人鼓舞的结果。结果揭示了PCA-SVM结合拉曼光谱技术对已开发的化学计量学技术的性能提高和HBV的临床预测潜力。

更新日期：2020-05-04

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