Volatile Fingerprinting and Interpretable Machine Learning for Quality Differentiation of Astragali Radix from Different Cultivation Patterns
Shulin Yu, Ziyue Song, Yunqi Sun, Wanying Li, Jiayi Dong, Huiqin Zou, Yonghong Yan · 2026
Read Volatile Fingerprinting and Interpretable Machine Learning for Quality Differentiation of Astragali Radix from Different Cultivation Patterns by Shulin Yu, Ziyue Song, Yunqi Sun, Wanying Li, Jiayi Dong, Huiqin Zou, Yonghong Yan (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/PMC13465424.txt.
Description
Shulin Yu and six co-authors fingerprint Astragali Radix, a prized food-medicine root, by its scent chemistry to tell how it was grown. Wild, near-wild, and farmed roots fetch different prices, yet looks alone cannot sort them. One hundred seventeen roots from the three patterns faced two complementary scent-reading machines.
The two machines caught overlapping but distinct scent sets, with farmed roots showing the richest list on one platform. Pattern models separated the three growth styles, more cleanly on the second platform. Explanation tools nominated ten candidate marker compounds across both machines, from gamma-hexalactone to 1-octen-3-one. The markers trace back to fat, scent, and terpene pathways in the plant, which gives graders a chemical footing beyond appearance.
Catalog record
| CREATOR | Shulin Yu, Ziyue Song, Yunqi Sun, Wanying Li, Jiayi Dong, Huiqin Zou, Yonghong Yan |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 38 KB |
| LICENSE | https://creativecommons.org/licenses/by/4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |