Abstract: Abstract: To establish a method for rapid detection of honey adulteration，in this paper, Acacia honey, Jing nectar, Date honey of market liquidity greatly and rape honey, rice syrup of typical adulterated substances were studied, the electronic nose (e-nose) information of natural honey and adulterated honey that was made by incorporation 10%, 20%, 30%, 40%, 50%, 60%, 70% rape honey and rice syrup were collected by FOX4000 e-nose. The data processing method of principal component analysis (PCA) was employed to study information variation characteristics of the electronic nose of natural honey and adulterated honey, and the minimum amount added that adulterated substances cause honey aroma system changes. On the basis of information variation characteristics of the electronic nose was as the guidance, the pattern recognition algorithm of Linear Discriminant Analysis (LDA) was employed to study the ability of qualitative recognition of electronic nose for adulterated honey. The results showed that there was a linear relationship between e-nose signals and the level of adulteration. Adulterated honey samples array from right to left with the increase in the content of adulteration in the direction of the principal component 1, adulterated samples of incorporation rape honey and rice syrup both located on the left side of pure acacia honey and arrayed from right to left in the direction of the principal component 1. The linear relationship of adulterated information was not affected by nectar, and the adulterated samples of date honey, Jing nectar, acacia honey had the same regular pattern, and the linear relationship of adulterated information had universality in honey. The minimum amount to be added of rape honey and rice syrup as adulterated substances that can cause honey aroma system changes were 2% and 1%, respectively. Honey aroma system can easily be changed by added substances. This showed that the electronic nose had a strong discriminable ability for honey adulteration. The linear discriminant analysis linear that was pattern recognition algorithm was employed to distinguish honey adulteration. The results showed that pure honey and adulterated honey can be distinguished by LDA pattern recognition algorithms, and the accuracy of discriminant model was 94.7%, and this achieved the rapid identification for honey adulteration. The pure honey and adulterated honey can be well distinguished, and was far away each other, this show the difference between pure honey and adulterated honey can be discovered by Linear Discriminant Analysis (LDA). The method of electronic nose combined with LDA pattern recognition algorithm provided a reference for the identification of fast and accurate of honey adulteration. The study may provide an identification method of effective and rapid for honey adulteration, and had great significance to ensure the healthy development of honey industry.