Machine learning methods to predict the cultivation age of Panacis Quinquefolii Radix

Xiaowen Hu; Hua Yan; Xiaodong Wang; Zonghu Wang; Yuanpeng Li; Lianjun Zheng; Jianbo Yang; Wenguang Jing; Xianlong Cheng; Feng Wei; Shuangcheng Ma

doi:10.1186/s13020-021-00511-5

Machine learning methods to predict the cultivation age of Panacis Quinquefolii Radix

Chin Med. 2021 Oct 9;16(1):100. doi: 10.1186/s13020-021-00511-5.

Authors

Affiliations

¹ National Institutes for Food and Drug Control, Institute for Control of Chinese Traditional Medicine and Ethnic Medicine, Beijing, 100050, China.
² XtalPi-AI Research Center (XARC), Tower A, Dongsheng Building, No. 8, Zhongguancun East Road, Haidian District, Beijing, 100083, China.
³ National Institutes for Food and Drug Control, Institute for Control of Chinese Traditional Medicine and Ethnic Medicine, Beijing, 100050, China. weifeng@nifdc.org.cn.
⁴ National Institutes for Food and Drug Control, Institute for Control of Chinese Traditional Medicine and Ethnic Medicine, Beijing, 100050, China. masc@nifdc.org.cn.

^# Contributed equally.

Abstract

Background: American ginseng (AG) is a valuable medicine widely consumed as a herbal remedy throughout the world. Huge price difference among AG with different growth years leads to intentional adulteration for higher profits. Thus, developing reliable approaches to authenticate the cultivation ages of AG products is of great use in preventing age falsification.

Methods: A total of 106 batches of AG samples along with their 9 physicochemical features were collected and measured from experiments, which was then split into a training set and two test sets (test set 1 and 2) according to the cultivation regions. Principle component analysis (PCA) was carried out to examine the distribution of the three data sets. Four machine learning (ML) algorithms, namely elastic net, k-nearest neighbors, support vector machine and multi-layer perception (MLP) were employed to construct predictive models using the features as inputs and their growth years as outputs. In addition, a similarity-based applicability domain (AD) was defined for these models to ensure the reliability of the predictive results for AG samples produced in different regions.

Results: A positive correlation was observed between the several features and the growth years. PCA revealed diverse distributions among different cultivation regions. The most accurate model derived from MLP shows good prediction power for the fivefold cross validation and the test set 1 with mean square error (MSE) of 0.017 and 0.016 respectively, but a higher MSE value of 1.260 for the test set 2. After applying the AD, all models showed much lower prediction errors for the test samples within AD (IDs) than those outside the AD (ODs). MLP remains the best predictive model with an MSE value of 0.030 for the IDs.

Conclusion: Cultivation years have a close relationship with bioactive components of AG. The constructed models and AD are also able to predict the cultivation years and discriminate samples that have inaccurate prediction results. The AD-equipped models used in this study provide useful tools for determining the age of AG in the market and are freely available at https://github.com/dreadlesss/Panax_age_predictor .

Keywords: Applicability domain; Cultivation age; Machine learning; Panax quinquefolium L..

Abstract

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