Maca (Lepidium meyenii Walp.), an annual or biennial herb of Brassicaceae family, grows at high altitudes and contains rich nutritional value and bio-health benefits. After being listed as a new resource food in 2011, Maca is gradually becoming familiar to the public, the Maca industry has developed rapidly and price has risen steadily. Due to the fact that the shape of turnip (Brassica rapa L.) is very similar to that of Maca, driven by economic interests, illegal businessmen often pass turnip off as Maca to make Maca powder, slices and drinks in order to make exorbitant profits, which has brought serious negative impact on the orderly development of Maca healthy industry. Therefore, the authenticity identification of Maca is very necessary, but most of methods for the authenticity identification of Maca are traditional, and there are few rapid detection methods. In this study, a new method for rapid and non-destructive identification of Maca and turnip was established by using multispectral imaging technology. The experiment mainly focuses on the authenticity identification of Maca slices and Maca powder. One is to identify the authenticity of Maca slices. A total of 240 Maca and turnip slices (120 Maca slices and 120 turnip slices, respectively) were selected to collect data by the Videometer Lab equipment, which acquired the multispectral images at 19 different wavelengths from the visual region to the lower wavelengths of the NIR region and the detailed information of the measured wavelength were 405, 435, 450, 470, 505, 525, 570, 590, 630, 645, 660, 700, 780, 850, 870, 890, 910, 940 and 970 nm. In order to identify Maca and turnip effectively, the principal component analysis (PCA) was first performed. Then the qualitative analysis model was generated using support vector machine (SVM), genetic algorithm optimization support vector machine (GA-SVM) and back propagation neural network (BPNN) algorithm, and the ratio of the the calibration set to the prediction set is 3∶1. The results demonstrated that clear differences between Maca and turnip could be easily visualized by PCA. The predictive accuracies by SVM model for Maca and turnip slices were 98.33% and 100%, respectively, and the predictive accuracies by GA-SVM and BPNN model could be as high as 100%. The other is the identification of Maca powder. 120 samples of Maca powder were selected and 20%, 40%, 60%, 80%, 4 different adulterated levels (W/W) of turnip powder were mixed for multispectral data acquisition, Combining partial least squares (PLS) and least squares support vector machine (LS-SVM), the adulteration ratio of turnip was quantitatively predicted. The study found that the prediction coefficient (R2P) of PLS and LS-SVM models were 0.992 and 0.994, the predicted root mean square error (RMSEP) were 2.718% and 2.675% and the relative prediction error (RPD) were 12.782 and 12.987, respectively. In comparison, the LS-SVM model had higher R2P, RPD, lower RMSEP, so it was considered to have better predictive performance for the proportion of turnip powder adulterated to Maca powder. In conclusion, the research results provide a method for the rapid and non-destructive identification of Maca authenticity.