首页|Research from Zhejiang A&F University Provides New Study Findings o n Food Research (Identification of Dendrobium Using Laser- Induced Breakdown Spec troscopy in Combination with a Multivariate Algorithm Model)
Research from Zhejiang A&F University Provides New Study Findings o n Food Research (Identification of Dendrobium Using Laser- Induced Breakdown Spec troscopy in Combination with a Multivariate Algorithm Model)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – New study results on food research have been publ ished. According to news reporting out of Hangzhou, People’s Republic of China, by NewsRx editors, research stated, “Dendrobium, a highly effective traditional Chinese medicinal herb, exhibits significant variations in efficacy and price am ong different varieties. Therefore, achieving an efficient classification of Den drobium is crucial.” Funders for this research include Scientific Research Foundation of Zhejiang A A nd F University. Our news reporters obtained a quote from the research from Zhejiang A& F University: “However, most of the existing identification methods for Dendrobi um make it difficult to simultaneously achieve both non-destructiveness and high efficiency, making it challenging to truly meet the needs of industrial product ion. In this study, we combined Laser-Induced Breakdown Spectroscopy (LIBS) with multivariate models to classify 10 varieties of Dendrobium. LIBS spectral data for each Dendrobium variety were collected from three circular medicinal blocks. During the data analysis phase, multivariate models to classify different Dendr obium varieties first preprocess the LIBS spectral data using Gaussian filtering and stacked correlation coefficient feature selection. Subsequently, the constr ucted fusion model is utilized for classification. The results demonstrate that the classification accuracy of 10 Dendrobium varieties reached 100% . Compared to Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Ne ighbors (KNN), our method improved classification accuracy by 14%, 20%, and 20%, respectively. Additionally, it outperfor ms three models (SVM, RF, and KNN) with added Principal Component Analysis (PCA) by 10 %, 10%, and 17%.”
Zhejiang A&F UniversityHa ngzhouPeople’s Republic of ChinaAsiaAlgorithmsFood Research
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