Spectroscopy and Spectral Analysis, Volume. 42, Issue 5, 1490(2022)
Reflectance Spectroscopy for Accurate and Fast Analysis of Saturated Fatty Acid of Edible Oil Using Spectroscopy-Based 2D Convolution Regression Network
The edible oil in the humandaily diet is rich in saturated fatty acids, which can provide energy and other healthy nutrients for the human body, but excessive intake of saturated fatty acids can lead to a variety of cardiovascular diseases. In this study, a method for analyzing the content of saturated fatty acidsin edible oils was developed by combining reflectance spectroscopy and machine learning. Firstly, the reflectance spectra of 7 edible vegetable oils, such as rapeseed oil, soybean oil, sunflower seed oil, corn oil, olive oil, sesame oil and peanut oil, were measured in the range of 350~2 500 nm, as well as the contents of palmitic acid, arachidonic acid and behenic acid were obtained by GC-MS. Spectral preprocessing algorithms were employed to eliminate the noise in spectra, including centralization, multiple scattering correction, standard normal variable transformation and standardization. Then, a novel two-dimensional spectral convolution regression network (S2DCRN) was constructed for fatty acids analysis, and a full convolutional network (FCN), partial least squares regression (PLSR), support vector regression (SVR) and random forest (RF) were compared with S2DCRN. Finally, sequential forward selection (SFS), random frog (RFrog) and genetic algorithm were used to select important wavelength spectra to re-build more simple and robust analysis models. The results showed that the S2DCRN obtained optimal performance after pretreatment of edible oil spectra with the determination coefficient of prediction set (
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Shi-zhuang WENG, Zhao-jie CHU, Man-qin WANG, Nian WANG. Reflectance Spectroscopy for Accurate and Fast Analysis of Saturated Fatty Acid of Edible Oil Using Spectroscopy-Based 2D Convolution Regression Network[J]. Spectroscopy and Spectral Analysis, 2022, 42(5): 1490
Category: Research Articles
Received: Jan. 6, 2021
Accepted: --
Published Online: Nov. 10, 2022
The Author Email: WENG Shi-zhuang (weng_1989@126.com)