Improving the accuracy of spectroscopic identification of geographical origins of agricultural samples through cooperative combination of near-infrared and laser-induced breakdown spectroscopy

Highlights: • NIRS and LIBS were cooperatively employed to improve the accuracy of discriminant analysis. • The strategy was evaluated for discrimination of the geographical origins of milk vetch root samples. • Initially, whole NIR feature were converted to a single coefficient by SVR. • The SVR coefficient was then merged with discrete LIBS intensities for discriminant analysis. • Use of both data improved the accuracy of discrimination compared to each method separately. As a versatile strategy to improve accuracy for identification of the geographical origin of agricultural samples, milk vetch root samples in this study, both near-infrared spectroscopy (NIRS) and laser-induced breakdown spectroscopy (LIBS) have been cooperatively combined. The motivation was based on the potential of accuracy improvement by utilization of these two methods providing complementary spectral information, compositions of organic compounds and elements. For initial evaluation, NIRS and LIBS were separately employed to discriminate domestic milk vetch root samples from imported ones using support vector machine (SVM). The near-infrared (NIR) information in a full spectral range was used for the analysis, while in LIBS spectra, the intensities of 35 selected discrete element peaks were used. The use of NIR information providing organic compositions of the samples resulted in a discrimination accuracy of 91.5%, better than that of using LIBS elemental peak intensities (73.1%). Next, to utilize both sets of spectral data for discrimination, support vector regression (SVR) was used to represent NIR spectral feature of a sample as a SVR coefficient, and then it was merged with the existing discrete LIBS intensity data; accuracy was improved to 95.8%. The cooperative combination of information on organic and elemental composition of the samples was the root of improvement.