ArticleFoods (Basel, Switzerland)2023
Rapid Test for Adulteration of Fritillaria Thunbergii in Fritillaria Cirrhosa by Laser-Induced Breakdown Spectroscopy.
Article in Foods (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 14 citations in OpenAlex.
- Non-Targeted Hyperspectral Imaging Screening of Adulterants and Congeneric Species inFoods (Basel, Switzerland) · 2026Article
- Cross-Instrument Data Utilization Based on Laser-Induced Breakdown Spectroscopy (LIBS) for the Identification ofBioengineering (Basel, Switzerland) · 2025Article
- A Dual-Technology Approach: Handheld NIR Spectrometer and CNN forFoods (Basel, Switzerland) · 2025Article
- Molecular quantification of fritillariae cirrhosae bulbus and its adulterants.Chinese medicine · 2024Article
- Botanical characterization, phytochemistry, biosynthesis, pharmacology clinical application, and breeding techniques of the Chinese herbal medicineFrontiers in pharmacology · 2024Review
Corrections and comments
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Authors and funding
10 authors at 1 institution in 2 countries.
Funding
Abstract
Fritillaria has a long history in China, and it can be consumed as medicine and food. Owing to the high cost of Fritillaria cirrhosa, traders sometimes mix it with the cheaper Fritillaria thunbergii powder to make profit. Herein, we proposed a laser-induced breakdown spectroscopy (LIBS) technique to test the adulteration present in the sample of Fritillaria cirrhosa powder. Experimental samples with different adulteration levels were prepared, and their LIBS spectra were obtained. Partial least squares regression (PLSR) was adopted as the quantitative analysis model to compare the effects of four data standardization methods, namely, mean centring, normalization by total area, standard normal variable, and normalization by the maximum, on the performance of the PLSR model. Principal component analysis and least absolute shrinkage and selection operator (LASSO) were utilized for feature extraction and feature selection, and the performance of the PLSR model was determined based on its quantitative analysis. Subsequently, the optimal number of features was determined. The residuals were corrected using support vector regression (SVR). The mean absolute error and root mean square error of prediction obtained from the quantitative analysis results of the combined LASSO-PLSR-SVR model for the test set data were 5.0396% and 7.2491%, respectively, and the coefficient of determination R
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