Identification and quantification of counterfeit sesame oil by 3D fluorescence spectroscopy and convolutional neural network

Food Chemistry
Xijun WuHailong Liu

Abstract

The method of 3D fluorescence spectroscopy combined with convolutional neural network (CNN) was developed to identify the counterfeit sesame oil. AlexNet, a pre-trained CNN architecture, was transferred to extract spectral characteristics. Then these features extracted by AlexNet were used as the input of the support vector machine (SVM) to determine whether the sample was counterfeit and its ingredients simultaneously, and both the accuracy were 100%. According to different counterfeit ingredients, these features extracted by AlexNet were used as the input of partial least squares (PLS) to predict the volume percentage concentration of sesame oil essence. There was a good linear relationship between the predicted and actual values of the three sets of counterfeit samples (R2 > 0.99), and the root mean square error of prediction (RMSEP) values were 0.99%, 2.20% and 1.64%, respectively. The results confirmed the validity of this novel method in sesame oil identification.

Related Concepts

Vegetable Oils
Sesame Oil
Fluorescence Spectroscopy
Rietveld Refinement
Neural Network Simulation
Support Vector Machines
Genetic Vectors
Oils
Fluorescence Spectroscopy
Plant Roots

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