A rapid analysis method of safflower (Carthamus tinctorius L.) using combination of computer vision and near-infrared
| Autor: | Manfei Xu, Yanjiang Qiao, Ling Lin, Fang-Yu Zhang, Jing-Qi Zeng, Lijuan Ma, Zhisheng Wu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
China
Carthamus tinctorius 02 engineering and technology 010402 general chemistry 01 natural sciences Analytical Chemistry Chalcone Partial least squares regression Food Quality Image Processing Computer-Assisted Computer vision Chinese pharmacopoeia Least-Squares Analysis Instrumentation Spectroscopy Analysis method Spectroscopy Near-Infrared biology Plant Extracts business.industry Chemistry Carthamus Nondestructive analysis Near-infrared spectroscopy Quinones 021001 nanoscience & nanotechnology biology.organism_classification Standard normal variate Atomic and Molecular Physics and Optics 0104 chemical sciences Artificial intelligence Safflower yellow 0210 nano-technology business Food Analysis |
| Zdroj: | Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy. 236:118360 |
| ISSN: | 1386-1425 |
| DOI: | 10.1016/j.saa.2020.118360 |
| Popis: | The quality of safflower (Carthamus tinctorius L.) in the market is uneven due to the problems of dyeing and adulteration of safflower, and there is no perfect standard for the classification of quality grade of safflower at present. In this study, computer vision (CV) and near-infrared (NIR) were combined to realize the rapid and nondestructive analysis of safflower. First, the partial least squares discrimination analysis (PLS-DA) model was used to qualitatively identify the dyed safflower from 150 samples. Then the partial least squares (PLS) model was used for quantitative analysis of the hydroxy safflower yellow pigment A (HSYA) and water extract of undyed safflower. Herein, the discrimination rate of PLS-DA model reached 100%, and the residual predictive deviation (RPD) values of the PLS models for HSYA and water extract were 2.5046 and 5.6195, respectively. It indicated that the rapid analysis method combining CV and NIR was reliable, and its results can provide important reference for the formulation of safflower quality classification standards in the market. |
| Databáze: | OpenAIRE |
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