多波长激光诱导荧光麦卢卡蜂蜜掺杂分类识别

Manuka honey is produced in New Zealand and has strong antibacterial and antioxidant effects. The price is relatively high, and adulteration incidents have occurred frequently in recent years. This paper uses laser-induced fluorescence (LIF) technology to classify and identify Manuka honey adulterated with syrup. Four commonly used lasers of 266, 355, 405 and 450 nm are selected as excitation sources, and three brands of New Zealand Manuka honey (No. A, B and C) adulterated with baking syrup are used as experimental samples. The adulteration ratio ranged from 0% to 90%, with an interval of 10%. Each sample solution has been tested 60 times under different excitation wavelengths, with a total of 7 200 sets of data. For the spectral data, firstly, pretreat with fluorescence band interception, smoothing and normalization; Then, randomly select 80% of the data as training and 20% as test sets. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) is used to reduce the dimension of the training set data; Finally, K-nearest neighbor (KNN) and support vector machine (SVM) classification models are established for the dimensionality reduction data respectively, and the test set data are classified and identified by the models. After 50 times of random grouping and classification calculation, the recognition rate's average value and standard deviation are obtained. The experimental results show that the excitation wavelength greatly influences the final recognition results. The recognition rate of 266 nm excitation is the highest. The recognition rates of the three Manuka adulterated solutions are more than 98. 5%, and the highest can reach 100% ; 355 and 405 nm excitation are the second, and the recognition rates of all samples are greater than 92%; The classification effect of 450 nm excitation is the worst, with the recognition rates less than 66%. Therefore, the comparison of classification algorithms only uses the spectral data excited by 266, 355 and 405 nm. The analysis results show that the classification effect of the KNN algorithm is better than the SVM algorithm. For the three honey adulterated solutions excited by 266 nm, the recognition rates of the KNN algorithm are more than 1% higher than that of the SVM algorithm. According to the experimental results, using LIF to classify and identify adulterated Manuka honey is feasible. For Manuka honey adulterated with syrup, among all combinations used in this paper, 266nm excitation combined with PCA-LDA and KNN algorithms has the highest recognition rate and the best classification effect, which provides an effective method for rapid and accurate identification of adulterated Manuka honey.