AIMC Topic: Rhizome

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Characterization of Gastrodiae Rhizoma from different geographical origins by HS-GC-IMS and authenticity identification combined with deep learning.

Journal of chromatography. A
Given the growing demand for Gastrodiae Rhizoma (GR), it is important to establish a strategy for origin authentication and adulteration screening to ensure the high-quality products in the market. Currently, efficient and dependable methods for iden...

Species discrimination and VIP-stacking quantitative models for Curcumae Rhizoma utilizing multi-modal spectra combined with machine learning algorithm.

Journal of pharmaceutical and biomedical analysis
Curcumae Rhizoma (Ezhu) is a multi-species herbal medicine with excellent medicinal value and development potential. However, challenges such as the difficulty in differentiating its varieties and the limitations of current methods for determining mi...

From image to insight deep learning solutions for accurate identification and object detection of Acorus species slices.

Scientific reports
Given the morphological similarity and medicinal efficacy differences between Acorus tatarinowii Rhizoma and Acorus calamus Rhizoma, both belonging to the Acorus rhizome slices, as well as the phenomenon of their mixed use in the market, this study a...

Intelligent identification method of origin for Alismatis Rhizoma based on image and machine learning.

Scientific reports
Alismatis Rhizoma (AR) is widely utilized as a natural medicine across many Asian countries. However, in China, due to its complex origins, AR quality varies, which can affect clinical efficacy. Therefore, there is a need for a method that is both fa...

High-precision identification of highly similar Pinelliae Rhizoma and adulterated Rhizoma pinelliae pedatisectae through deep neural networks based on vision transformers.

Journal of food science
Pinelliae Rhizoma is a key ingredient in botanical supplements and is often adulterated by Rhizoma Pinelliae Pedatisectae, which is similar in appearance but less expensive. Accurate identification of these materials is crucial for both scientific an...

Polygonati Rhizoma varieties and origins traceability based on multivariate data fusion combined with an artificial intelligence classification algorithm.

Food chemistry
This study collected multidimensional feature data such as spectra, texture, and component contents of Polygonati Rhizoma from different origins and varieties (Polygonatum kingianum Coll. et Hemsl from Yunnan and Guizhou; Polygonatum cyrtonema Hua fr...

Study on the identification and evaluation of growth years for Paris polyphylla var. yunnanensis using deep learning combined with 2DCOS.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Paris polyphylla var. yunnanensis, as perennial plants, its quality is closely related to growth period. Different harvest years determine the dry matter accumulation of its medicinal parts and the dynamic accumulation of active ingredients, as well ...

Identification of the lipid-lowering component of triterpenes from Alismatis rhizoma based on the MRM-based characteristic chemical profiles and support vector machine model.

Analytical and bioanalytical chemistry
It has been demonstrated that triterpenes in Alismatis rhizoma (Zexie in Chinese, ZX) contributed to the lipid-lowering effect on high-fat diet-induced hyperlipidemia. Alisol B 23-acetate, one of the abundant triterpenes in ZX, was used as the marker...

Machine learning and chemometric methods for high-throughput authentication of 53 Root and Rhizome Chinese Herbal using ATR-FTIR fingerprints.

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences
To address the identification challenges caused by morphological similarities in Root and Rhizome Chinese Herbal (RRCH), this study developed a discrimination system integrating Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (AT...