反射光谱结合光谱基二维卷积回归网络快速检测食用油中饱和脂肪酸

人们日常膳食中常见的食用油含有丰富的饱和脂肪酸,饱和脂肪酸能为人体提供能量和必须营养物质,但过量摄入会导致多种心血管疾病.结合反射率光谱和深度学习方法发展一种食用油中饱和脂肪酸含量的分析方法.首先,测量了菜籽油、大豆油、葵花籽油、玉米油、橄榄油、芝麻油及花生油等7种食用植物油350～2500 nm范围的反射光谱,并通过...

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 (R2_P) of 0.987 9 and the root mean square error of prediction set (RMSE_P) of 0.510 0. Based on important wavelengths selected by combination RFrog and SFS, the S2DCRN exhibited excellent performance with R2_P=0.967 9 and RMSE_P=0.462 7. Although the results based on important wavelengths obtained is slightly worse, the number of wavelengths is less than 1% of the full spectra. It is convenient for the measurement of spectra and remarkably reduces the complexity of the model, which is helpful for the further development of portable and simplified detection devices. In addition, to further explore the generalization and applicability of S2DCRN, S2DCRN was used to analyze the content of arachidonic acid and behenic acid and gain a prediction result for arachidonic acid R2_P=0.950 1, RMSE_P=0.152 9. Therefore, the proposed method accurately and rapidly analysed various fatty acids in edible vegetable oils by reflectance spectroscopy.