title: [Research on detection method of adulterated olive oil by Raman spectroscopy and least squares support vector machine].
abstract: For the purpose of the authentication of sorts as well as the prediction of contents of the oils which were adulterated into olive oil, 117 olive oil samples adulterated with sunflower seed oil, soybean oil and corn oil were detected by Raman spectroscopy, and least squares support vector machine (LS-SVM) based on multiple iterative optimization was used to identify the type of the adulterant oil, and the composite recognition rate was 97%. In addition, methods such as LS-SVM, ANNs and PLSR were used to build the Raman spectra calibration model of the adulterant oil (sunflower seed oil, soybean oil and corn oil) contents respectively, the results indicated that LS-SVM had the best predictive performance, and the root mean square error of prediction (RMSEP) ranged from 0.007 4 to 0.014 2. Research results showed the method based on Raman spectroscopy and LS-SVM was accurate, fast, simple and non-destructive for adulterated olive oil detection.
authors.affiliation: Department of Optical Engineering State Key Laboratory for Modern Optical Instrumentation, Zhejiang University, Hangzhou 310027, China. whqs007@zju.edu.cn
authors.forename: Ying-Qiang
authors.initials: YQ
authors.lastname: Zhang
authors.forename: Wei
authors.initials: W
authors.lastname: Dong
authors.forename: Bing
authors.initials: B
authors.lastname: Zhang
authors.forename: Xiao-Ping
authors.initials: XP
authors.lastname: Wang
mesh_terms: D003314:Corn Oil; D005506:Food Contamination / Q000032:analysis*; D016018:Least-Squares Analysis; D000069463:Olive Oil; D010938:Plant Oils / Q000032:analysis*; D013024:Soybean Oil; D013059:Spectrum Analysis, Raman; D000074242:Sunflower Oil; D060388:Support Vector Machine
chemical_list: D000069463:Olive Oil; D010938:Plant Oils; D000074242:Sunflower Oil; D013024:Soybean Oil; D003314:Corn Oil