Predicting Steam Processing Degree of Prepared Radix Rehmanniae (Shudihuang) Using Machine Learning

Abstract

The classic way for the prepared Radix Rehmanniae (PRR) preparation, “steaming and drying (SD) for several cycles (generally nine times, SD₉),” is the golden standard method from the traditional processing theory. However, the controversy of “optimal SD cycle” still exists, and there has not been an efficient way to identify the processing degree of PRRs. The study aims to determine the optimal processing conditions to make PRR approaching the SD₉ quality and establish validated models to identify the processing degree of PRR unknowns. PPR-SD_1–9 samples were prepared under 1 to 9 SD cycles. A spectrophotometer and a camera were used for color and gloss assessment. The chemical changes during processing were detected by high-performance liquid chromatography and liquid chromatography coupled with mass spectroscopic (LC-MS) technique. Statistical analysis of the LC-MS^E data using principal component analysis showed separation of PRRs with different processing degrees, which led to the use of random forest (RF) for model training. The changes in both appearance and chemicals were obvious before, but slight after 3–5 SD cycles. Two predictors based on the RF classifier were proven to be valid for the identification of steaming time in a wide range (0–78 hours) and with an error rate of 0 in blind verification. Ten commercial samples were then identified as 1–2 SD cycle-processed PRRs. Our results, of which processing 3 to 5 times can get PRR-SD₉, are aligned with the past documented knowledge in QianJinYiFang. Our models can be good tools for quality control in PRR manufacture and supervision.

Keywords

prepared Radix Rehmanniae - steaming and drying for 9 cycles - machine learning - random forest

Publication History

Received: 17 April 2025

Accepted: 19 September 2025

Article published online:
24 October 2025

• References

• 1 Li M, Jiang H, Hao Y. et al. A systematic review on botany, processing, application, phytochemistry and pharmacological action of Radix Rehmnniae. J Ethnopharmacol 2022; 285: 114820
• 2 National Pharmacopoeia Commission. Pharmacopoeia of the People's Republic of China. Beijing: China Medical Science Press; 2020: 129-130
• 3 Henan Provincial Drug Administration. Henan province specifications for processing of TCM (2022 Edition). Zhengzhou: Henan Science and Technology Press; 2022: 138-139
• 4 Xie Y, Zhong LY, Wang Z. et al. Historical evolution and modern research progress of Rehmanniae Radix . Zhongguo Shiyan Fangjixue Zazhi 2022; 24: 273-282
• 5 Li SL, Song JZ, Qiao CF. et al. A novel strategy to rapidly explore potential chemical markers for the discrimination between raw and processed Radix Rehmanniae by UHPLC-TOFMS with multivariate statistical analysis. J Pharm Biomed Anal 2010; 51 (04) 812-823
• 6 Boccard J, Kalousis A, Hilario M. et al. Standard machine learning algorithms applied to UPLC-TOF/MS metabolic fingerprinting for the discovery of wound biomarkers in Arabidopsis thaliana . Chemom Intell Lab Syst 2010; 104: 20-27
• 7 Li Y, Fan J, Cheng X. et al. New Revolution for quality control of TCM in Industry 4.0: focus on artificial intelligence and bioinformatics. TrAC Trends Anal Chem 2024; 181: 118023
• 8 Li H, Zhang S, Zhao Y, He J, Chen X. Identification of raffinose family oligosaccharides in processed Rehmannia glutinosa Libosch using matrix-assisted laser desorption/ionization mass spectrometry image combined with machine learning. Rapid Commun Mass Spectrom 2023; 37 (22) e9635
• 9 Zhou L, Xu JD, Zhou SS. et al. Integrating targeted glycomics and untargeted metabolomics to investigate the processing chemistry of herbal medicines, a case study on Rehmanniae Radix. J Chromatogr A 2016; 1472: 74-87
• 10 Zhou L. Holistic evaluation on quality and efficacy of Rehmanniae Radix Praeparata in “nine cycles of steaming and drying” processing [in Chinese]. [Master's thesis]. Nanjing: Nanjing University of Chinese Medicine; 2017
• 11 Li Y. Processing technology and mechanism of “repeated steaming and air-exposing” of Rehmanniae Radix Praeparata [in Chinese]. [Master's thesis]. Jinan: Shandong University; 2023
• 12 Su C, Chen D. A chromometer that can inspect and measure aperture sizes. CN Patent 222069915U. November, 2024
• 13 Li C, Guo Y, Dong S, Hu Y, Zhang F. Dynamic range adjustment method of the aerospace camera based on histogram distribution. Spacecr Recover Remote Sens 2017; 38: 36-43
• 14 Gao Q, Jiang H, Tang F. et al. Evaluation of the bitter components of bamboo shoots using a metabolomics approach. Food Funct 2019; 10 (01) 90-98
• 15 Morales EF, Escalante HJ. Chapter 6 - A brief introduction to supervised, unsupervised, and reinforcement learning. In: Torres-García AA, Reyes-García CA, Villaseñor-Pineda L, Mendoza-Montoya O. eds. Biosignal Processing and Classification Using Computational Learning and Intelligence. Academic Press; 2022: 111-129
• 16 Dalal N, Sáiz MJ, Caporale AG, Baldini F, Babayan SA, Adamo P. Fishy forensics: FT-NIR and machine learning based authentication of Mediterranean anchovies (Engraulis encrasicolus). J Food Compos Anal 2024; 136: 106847
• 17 Saeed MH, Hama JI. Cardiac disease prediction using AI algorithms with SelectKBest. Med Biol Eng Comput 2023; 61 (12) 3397-3408
• 18 Brownlee J. Data preparation for machine learning: data cleaning, feature selection, and data transforms in Python. Machine Learning Mastery; 2020: 158-163
• 19 Sha Y, Jiang M, Luo G. et al. HerbMet: enhancing metabolomics data analysis for accurate identification of Chinese herbal medicines using deep learning. Phytochem Anal 2025; 36 (01) 261-272
• 20 Shafizadeh F. Acidic hydrolysis of glycosidic bonds. Tappi 1963; 46: 381-383
• 21 Xue S, Fu Y, Sun X, Chen S. Changes in the chemical components of processed rehmanniae radix distillate during different steaming times. Evid Based Complement Alternat Med 2022; 2022: 3382333
• 22 Yang J, Zhang L, Zhang M. et al. Exploration of the dynamic variations of the characteristic constituents and the degradation products of catalpol during the process of Radix Rehmanniae . Molecules 2024; 29 (03) 705
• 23 Liao J, Zhang Y, Zhang W. et al. Different software processing affects the peak picking and metabolic pathway recognition of metabolomics data. J Chromatogr A 2023; 1687: 463700
• 24 Ringnér M. What is principal component analysis?. Nat Biotechnol 2008; 26 (03) 303-304
• 25 Lin H, Gui SH, Yu BB, Que XH, Zhu JQ. Analysis of polysaccharide monosaccharides of Radix Rehmanniae by different processing processes and their effects on ovarian granulosa cells. Zhongchengyao 2019; 12: 2958-2963
• 26 Jia H, Zhang WF, Lei JW, Li YY, Yang CJ, Fan KF. UV combined with MIR spectroscopy to discuss the dynamic changes of sugar during the processing of Rehmannia glutinosa . Lishizhen Med Materia Medica Res 2023; 2023: 96-99
• 27 Wang XC, Ma XL, Liu JN. et al. A comparison of feature extraction capabilities of advanced UHPLC-HRMS data analysis tools in plant metabolomics. Anal Chim Acta 2023; 1254: 341127
• 28 Tamrakar S, Huerta B, Chung-Davidson YW, Li W. Plasma metabolomic profiles reveal sex- and maturation-dependent metabolic strategies in sea lamprey (Petromyzon marinus). Metabolomics 2022; 18 (11) 90
• 29 Ponce de Leon-Sanchez ER, Dominguez-Ramirez OA, Herrera-Navarro AM, Rodriguez-Resendiz J, Paredes-Orta C, Mendiola-Santibañez JD. A deep learning approach for predicting multiple sclerosis. Micromachines (Basel) 2023; 14 (04) 749
• 30 Benes E, Bajusz D, Gere A, Fodor M, Rácz A. Comprehensive chemometric classification of snack products based on their near infrared spectra. Lebensm Wiss Technol 2020; 133: 110130
• 31 Wang Q, Zhao YX, Gu J. et al. Establishment of traditional Chinese medicine standards reflecting the quality characteristics of Chinese herbal pieces based on processing. Chung Kuo Yao Hsueh Tsa Chih 2025; 40: 114-120
• 32 Xue R, Zhang Q, Mei X. et al. Research on quality marker based on the processing from Aconiti lateralis radix praeparata to Heishunpian. Phytochem Anal 2024; 35 (06) 1443-1456