Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration
文献类型：期刊论文
| 作者 | Su, Ning2,3; Weng, Shizhuang1; Wang, Liusan2,3; Xu, Taosheng2,3 |
| 刊名 | BIOSENSORS-BASEL
 |
| 出版日期 | 2021-12-01 |
| 卷号 | 11 |
| 关键词 | oil adulteration Vis-NIR reflectance spectroscopy multivariate analysis CARS |
| DOI | 10.3390/bios11120492 |
| 通讯作者 | Wang, Liusan(lswang@iim.ac.cn) ; Xu, Taosheng(xtsvm@mail.ustc.edu.cn) |
| 英文摘要 | The visible and near-infrared (Vis-NIR) reflectance spectroscopy was utilized for the rapid and nondestructive discrimination of edible oil adulteration. In total, 110 samples of sesame oil and rapeseed oil adulterated with soybean oil in different levels were produced to obtain the reflectance spectra of 350-2500 nm. A set of multivariant methods was applied to identify adulteration types and adulteration rates. In the qualitative analysis of adulteration type, the support vector machine (SVM) method yielded high overall accuracy with multiple spectra pretreatments. In the quantitative analysis of adulteration rate, the random forest (RF) combined with multivariate scattering correction (MSC) achieved the highest identification accuracy of adulteration rate with the full wavelengths of Vis-NIR spectra. The effective wavelengths of the Vis-NIR spectra were screened to improve the robustness of the multivariant methods. The analysis results suggested that the competitive adaptive reweighted sampling (CARS) was helpful for removing the redundant information from the spectral data and improving the prediction accuracy. The PLSR + MSC + CARS model achieved the best prediction performance in the two adulteration cases of sesame oil and rapeseed oil. The coefficient of determination (R-Pcv(2)) and the root mean square error (RMSEPcv) of the prediction set were 0.99656 and 0.01832 in sesame oil adulterated with soybean oil, and the R-Pcv(2) and RMSEPcv were 0.99675 and 0.01685 in rapeseed oil adulterated with soybean oil, respectively. The Vis-NIR reflectance spectroscopy with the assistance of multivariant analysis can effectively discriminate the different adulteration rates of edible oils. |
| WOS关键词 | OLIVE OIL ; RAMAN-SPECTROSCOPY ; RAPID IDENTIFICATION ; GAS-CHROMATOGRAPHY ; VEGETABLE-OILS ; SPECTROMETRY |
| 资助项目 | Natural Science Foundation of Anhui Province, China[2008085QF292] ; Major scientific and technological innovation project of Shandong Province, China[2019JZZY010730] |
| WOS研究方向 | Chemistry ; Science & Technology - Other Topics ; Instruments & Instrumentation |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:000736330900001 |
| 资助机构 | Natural Science Foundation of Anhui Province, China ; Major scientific and technological innovation project of Shandong Province, China |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/127151]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Wang, Liusan; Xu, Taosheng |
| 作者单位 | 1.Anhui Univ, Natl Engn Res Ctr, Agroecol Big Data Anal & Applicat, Hefei 230601, Peoples R China 2.Intelligent Agr Engn Lab Anhui Prov, Hefei 230031, Peoples R China 3.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Su, Ning,Weng, Shizhuang,Wang, Liusan,et al. Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration[J]. BIOSENSORS-BASEL,2021,11. |
| APA | Su, Ning,Weng, Shizhuang,Wang, Liusan,&Xu, Taosheng.(2021).Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration.BIOSENSORS-BASEL,11. |
| MLA | Su, Ning,et al."Reflectance Spectroscopy with Multivariate Methods for Non-Destructive Discrimination of Edible Oil Adulteration".BIOSENSORS-BASEL 11(2021). |
入库方式： OAI收割
来源：合肥物质科学研究院
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