摘要
Raohe honey (Honey in Raohe) is the only product which has obtained China’s national geographical mark for honey;however, it is always counterfeited by some producers due to its excellent quality. In this research, Raohe honey was identified by geographical sourcing, where the detection on 166 Raohe honey samples and 31 non-Raohe honey samples was conducted with Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Additionally, the method of Primary Composite Analysis accomplished dimensionality reduction by transforming the abundance ratios variables of 13 isotopes to 4 primary composites, and could explain 91.17% of the total variables. There were five models: Decision Tree, Naive Bayes, Neural Network, Partial Least Square Discriminate and Support Vector Machine, built on the four new variables of primary composites with the Agilent MPP Software. The validation of the models was performed with 11 Raohe honey samples and 5 non-Raohe honey samples randomly selected. The accuracies of the Decision Tree and Support Vector Machine models were both 93.97%, and those of the Naive Bayes and Neural Network models were both 87.5%, while the contribution rate of the Partial Least Square Discriminate model was only 75%. It was concluded that the Decision Tree and Support Vector Machine models could be used for indentifying Raohe honey, and the Naive Bayes and Neural Network models could work as references, while the Partial Least Square Discriminate model was not suitable for identifying Raohe honey.
Raohe honey (Honey in Raohe) is the only product which has obtained China’s national geographical mark for honey;however, it is always counterfeited by some producers due to its excellent quality. In this research, Raohe honey was identified by geographical sourcing, where the detection on 166 Raohe honey samples and 31 non-Raohe honey samples was conducted with Inductively Coupled Plasma Mass Spectrometry (ICP-MS). Additionally, the method of Primary Composite Analysis accomplished dimensionality reduction by transforming the abundance ratios variables of 13 isotopes to 4 primary composites, and could explain 91.17% of the total variables. There were five models: Decision Tree, Naive Bayes, Neural Network, Partial Least Square Discriminate and Support Vector Machine, built on the four new variables of primary composites with the Agilent MPP Software. The validation of the models was performed with 11 Raohe honey samples and 5 non-Raohe honey samples randomly selected. The accuracies of the Decision Tree and Support Vector Machine models were both 93.97%, and those of the Naive Bayes and Neural Network models were both 87.5%, while the contribution rate of the Partial Least Square Discriminate model was only 75%. It was concluded that the Decision Tree and Support Vector Machine models could be used for indentifying Raohe honey, and the Naive Bayes and Neural Network models could work as references, while the Partial Least Square Discriminate model was not suitable for identifying Raohe honey.
