Rapid and accurate screening of cystic echinococcosis in sheep based on serum Fourier‐transform infrared spectroscopy combined with machine learning algorithms

Abstract

AbstractCystic echinococcosis (CE) in sheep is a serious zoonotic parasitic disease caused by Echinococcus granulosus sensu stricto (s.s.). Presently, the screening technology for CE in sheep is time‐consuming and inaccurate, and novel screening technology is urgently needed. In this work, we combined machine‐learning algorithms with Fourier transform infrared (FT‐IR) spectroscopy of serum to establish a quick and accurate screening approach for CE in sheep. Serum samples from 77 E. granulosus s.s.‐infected sheep to 121 healthy control sheep were measured by FT‐IR spectrometer. To optimize the classification accuracy of the serum FI‐TR method for the E. granulosus s.s.‐infected sheep and healthy control sheep, principal component analysis (PCA), linear discriminant analysis, and support vector machine (SVM) algorithms were used to analyze the data. Among all the bands, 1500–1700 cm−1 band has the best classification effect; its diagnostic sensitivity, specificity, and accuracy of PCA‐SVM were 100%, 95.74%, and 96.66%, respectively. The study showed that serum FT‐IR spectroscopy combined with machine learning algorithms has great potential for rapid and accurate screening methods for the CE in sheep.