Application of ATR-FTIR Spectrum Combined With Ensemble Learning and Deep Learning for Identification of Amomum tsao-ko at Different Drying Temperatures
文献类型： 外文期刊
作者： He, Gang 1 ; Yang, Shao-bing 1 ; Wang, Yuan-zhong 1 ;
作者机构： 1.Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming, Peoples R China
2.Jiangnan Univ, Sch Biotechnol, Wuxi, Jiangsu, Peoples R China
关键词： Amomum tsao-ko; deep learning; drying temperatures; ensemble learning; machine learning
期刊名称：JOURNAL OF CHEMOMETRICS （ 影响因子：2.1； 五年影响因子：2.3 ）
ISSN： 0886-9383
年卷期： 2025 年 39 卷 3 期
页码：
收录情况： SCI
摘要： Amomum tsao-ko Crevost et Lemaire (A. tsao-ko) is an important medicinal plant and flavoring spice. A. tsao-ko dried at different drying temperatures has different nutritional and medicinal values, leading to the phenomenon of substandard products in the market from time to time. In this study, attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) data were pre-processed with SD, normalization, EWMA, SNV to compare their effects on the recognition ability of SVM, RF, XGBoost, and CatBoost models. Meanwhile, full-band and local-band 2DCOS profiles were obtained to characterize the differences in chemical features of A. tsao-ko dried by different drying temperatures and classified in conjunction with the ResNet model. The results show that although traditional machine learning can obtain better classification results, the classification efficiency is very unsatisfactory, and the correct classification rate is improved to 97% after derivative (SD) preprocessing. The 2DCOS atlas is able to visualize the feature information in the samples, which is further combined with the ResNet model to obtain 100% classification correctness with excellent generalization ability and convergence effect. The above study was able to provide new ideas for quality evaluation of A. tsao-ko.
- 相关文献
作者其他论文 更多>>
-
FT-NIR Spectra of Different Dimensions Combined with Machine Learning and Image Recognition for Origin Identification: An Example of Panax notoginseng
作者：Zuo, Zhi-Tian;Yao, Zeng-Yu;Zuo, Zhi-Tian;Wang, Yuan-Zhong
关键词：
-
Based on metabolomics and fourier transforms near infrared spectroscopy characterization of Lanxangia tsaoko chemical profile differences among fruit types and development of rapid identification and nutrient prediction models
作者：Fu, Deng-Ke;Yang, Wei-Ze;Yang, Mei-Quan;Yang, Tian-Mei;Wang, Yuan-Zhong;Zhang, Jin-Yu;Fu, Deng-Ke
关键词：Lanxangia tsao-ko; Phenotype; Metabolic differences; 2DCOS; ResNet; PLSR
-
Spatial and temporal distribution characteristics of Paris polyphylla var. yunnanensis and the prediction of steroidal saponins content
作者：Zhong, Chen;Li, Li;Zhong, Chen;Wang, Yuan-Zhong
关键词：Paris polyphylla var. yunnanensis; Habitat suitability; FT-IR spectroscopy; Chemometrics; Steroidal saponins
-
A rapid method for identification of Lanxangia tsaoko origin and fruit shape: FT-NIR combined with chemometrics and image recognition
作者：He, Gang;Yang, Shao-bing;Wang, Yuan-zhong;He, Gang;Yang, Shao-bing;Wang, Yuan-zhong
关键词：chemometrics; classification; Fourier transform-near infrared spectroscopy; image recognition; Lanxangia tsaoko
-
Analysis of Chemical Changes during Maturation of Amomum tsao-ko Based on GC-MS, FT-NIR, and FT-MIR
作者：He, Gang;Yang, Shao-bing;Wang, Yuan-zhong;He, Gang
关键词：
-
FT-IR spectroscopy coupled with HPLC for qualitative and quantitative analysis of different parts of Gentiana rigescens Franch
作者：He, Gang;Zhu, Xin-yan;Wang, Yuan-zhong;He, Gang;Shen, Tao
关键词：Gentiana rigescens; Total secoiridoids; FT-IR; HPLC; Content prediction
-
Suitable habitat prediction and identification of origin of Lanxangia tsao-ko
作者：He, Gang;Yang, Shao-bing;Wang, Yuan-zhong;He, Gang
关键词：Medicinal plant; FT-NIR spectroscopy; Machine learning; Suitable habitats; Origin identification
