## 4 Conclusions  
The proposed reflective spectroscopy method, which combines machine learning and spectral analysis, is fast, nondestructive, and simple to use for the identification of ginseng growth years. The random forest method, which has the advantage of assessing the importance of variables, is easy to implement for nonprofessional technical personnel and has a high precision classifier. Using 35 samples from 7 years, through the identification and spectral analysis of the random forest method, the following conclusions can be obtained. Figure 6: The importance of spectral band under food or medicine classification scale provided by random forest. The colorful curves have ten colors in total from experiments repeated 10 times. Figure 7: The importance of spectral bands under younger than 5 years or not provided by random forest. (1) The accuracy of food and medicine identification was 92.9% when the food and medicine identification was distinguished by less than 6 years or not. Incorrect classification only occurred in the fifth and the sixth years and not in other years. Accuracy can reach 100% by using two types recognition less than 5 years or not. These two kinds of identification can be used at the same time to provide more identification information. The age identification accuracies are comparable to 94.8% of ginseng leaves by using Fourier transform infrared spectroscopy [14]. (2) For food and medicine differentiation and identification bounded by five years, only 400-1000 nm (VNIR) information is necessary, and 1000-2500 nm can be discarded. In VNIR, 400-650 nm is particularly important. The reason why VNIR is important is that the year's information is not reflected in the shape of the spectral reflectance curve, but data processing in this study has completed relative and absolute radiometric correction, which is comparable in high and low values of reflectance. Therefore, when designing special instruments, it is not necessary to have very high spectral resolution in this band range, but there is a need to control the absolute precision of radiation measurements. (3) The uncertainty of 5-year ginseng samples is high, indicating that the fifth year is a year with vigorous growth processes and the largest changes in ginseng are observed. Although, from the perspective of the spectrum, it is easier to identify using 5 years as the boundary, however, in traditional Chinese medicine, ginseng for no more than 5 years is regarded as food, and ginseng for 6 years or more is regarded as medicinal material, which fully take into account the instability of 5-year-old ginseng and ensures the accumulation of effective components. The traditional classification method of food and medicine by less 6 years or not is supported by the results of the hyperspectral study provided in this paper. Although spectral information and random forest methods have been used, either individually or in combination, to investigate synthetic pharmaceutical drug molecules, their combined use has not been performed in the area of traditional Chinese medicine, particularly for ginseng quality surveillance. We used the method proposed to achieve the identification of ginseng as food and medicine. The age identification accuracy of the proposed method in this paper is equal to that of other methods using spectral information. The proposed method is mainly based on spectral data, so there is no damage induced toward ginseng samples. In addition, the implementation of this method does not require professional technicians or professional laboratories, which provides great convenience and expands the application's scope. At the moment, only spectral information was used for ginseng growth year identification. However, hyperspectral data also include spatial information in two-dimensional images. The spectrum and image could be jointly used to obtain better accuracies in identification. More samples of different ages could be helpful to improve identification accuracy. Conceptualization, X.C. and S.L. ; methodology, X.C. and L.Z.