Potential of hyperspectral imaging for nondestructive determination of chlorogenic acid content in Flos Lonicerae
| العنوان: | Potential of hyperspectral imaging for nondestructive determination of chlorogenic acid content in Flos Lonicerae |
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| المؤلفون: | Qingqing Wang, Yunhong Liu, Xiuwei Gao, Anguo Xie, Huichun Yu |
| المصدر: | Journal of Food Measurement and Characterization. 13:2603-2612 |
| بيانات النشر: | Springer Science and Business Media LLC, 2019. |
| سنة النشر: | 2019 |
| مصطلحات موضوعية: | biology, Calibration (statistics), General Chemical Engineering, 010401 analytical chemistry, Hyperspectral imaging, Flos, Feature selection, 04 agricultural and veterinary sciences, biology.organism_classification, 040401 food science, 01 natural sciences, Industrial and Manufacturing Engineering, 0104 chemical sciences, 0404 agricultural biotechnology, Standard normal deviate, Least squares support vector machine, Partial least squares regression, Variable elimination, Safety, Risk, Reliability and Quality, Biological system, Food Science, Mathematics |
| الوصف: | Chlorogenic acid (CGA), as a major active component, is an important index for evaluating the quality of FlosLonicerae. Hyperspectral imaging (HSI) technology was applied for nondestructive estimating CGA content in FlosLonicerae. In order to obtain the best performance of calibration models, nine different pretreatment methods were investigated and compared based on partial least squares regression (PLSR) models. The optimal method was determined as a standard normal variable (SNV) method with RP2 of 0.9766 and RMSEP of 2.711 for further analysis. To simplify calibration models, different variables selection methods, including the uninformative variable elimination (UVE), successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), UVE–CARS, UVE–SPA, CARS–SPA, and UVE–CARS–SPA, were used to extracted characteristic wavelengths from the full spectrum. And then PLSR and least squares support vector machine (LS-SVM) were established based on full spectrum and the selected characteristic wavelengths, respectively. The results showed that the nonlinear UVE–CARS–LS–SVM model (RP2 = 0.9785 and RMSEP = 2.496) was the optimal model for predicting CGA content in FlosLonicerae. Therefore, this study revealed that the combination of HSI with SNV preprocessing method, UVE–CARS variable selection method and LS-SVM modeling had great potential to nondestructively and rapidly determine CGA content in Flos Lonicerae during storage. |
| تدمد: | 2193-4134 2193-4126 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::f4a2cb73c53f4c3e49a08e0c09bfa21c https://doi.org/10.1007/s11694-019-00180-x |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........f4a2cb73c53f4c3e49a08e0c09bfa21c |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 21934134 21934126 |
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