Identification of the botanic source of honey by 1 H nuclear magnetic resonance and support vector machine

Objective To establish a method for identification of the botanic source of honey by 1H nuclear magnetic resonance (1H NMR) spectroscopy and support vector machine. Methods Spectral information of 122 samples were collected including 4 kinds of honey such as vitex honey samples, rape honey samples, acacia honey samples and sunflower honey samples. Classification models were established based on 4 different integration intervals including full spectrum (δ 0.10‒δ 9.50), fat zone (δ 0.10‒δ 3.00), carbohydrate zone(δ 3.00‒δ 6.00) and aromatic zone (δ 6.00‒δ 9.50), and the discriminant model was further optimized by screening feature variables with principal component weight coefficient. Results Based on the principal component weight coefficient, 267 integral variables were screened in the range of variables δ 3.40‒δ 3.90 and δ 4.60‒δ 4.70. The classification model of support vector machine was established with the regional integral variable as input variable, the discriminant accuracy of the training set was 97.53%, and the discriminant accuracy of the test set was 100%. Conclusion The weight coefficients of principal components can effectively pick the characteristic variables, reduce the input variables and 第 1 期 周 密, 等: 氢核磁共振结合支持向量机鉴别蜂蜜植物源 17 improve the robustness and accuracy of the model. The classification model based on 1H NMR combines with support vector machine can identify honey from different plants effectively.