Quantitative analysis of blended corn-olive oil based on Raman spectroscopy and one-dimensional convolutional neural network. (15th August 2022)

• Record Type: Journal Article
• Authors: Wu, Xijun; Gao, Shibo; Niu, Yudong; Zhao, Zhilei; Ma, Renqi; Xu, Baoran; Liu, Hailong; Zhang, Yungang
• Is Part Of: Food chemistry. Volume 385(2022)
• Publication Date: 2022-08-15
• Subjects: Raman spectra -- Quantitative analysis -- 1D CNN -- PLSR -- SVR; Food -- Analysis -- Periodicals; Food -- Composition -- Periodicals; 664
• DOI: 10.1016/j.foodchem.2022.132655
• Languages: English
• ISSNs: 0308-8146

Abstract

Highlights: CNN was used to quantitatively identify blended corn-olive oil. 1D CNN model was established based on 315 Raman spectra with seven ratios. The model achieved comparable prediction results with chemometric methods. Abstract: Blended vegetable oil is a vital product in the vegetable oil market, and quantifying high-value vegetable oil is of great significance to protect the rights and interests of consumers. In this study, we established a one-dimensional convolutional neural network (1D CNN) quantitative identification model based on Raman spectra to identify the amount of olive oil in a corn-olive oil blend. The results show that the 1D CNN model based on 315 extended average Raman spectra can quantitatively identify the content of olive oil, with R 2 p and RMSEP values of 0.9908 and 0.7183 respectively. Compared with partial least squares regression (PLSR) and support vector regression (SVR), although the index is not optimal, it provides a new analytical method for the quantitative identification of vegetable oil.