Quantification of adulteration in extra virgin olive oil based on Raman spectroscopy and CNN algorithm
To provide a reference for the rapid quantitative analysis of adulteration in extra virgin olive oil(EVOO),taking EVOO adulterated with rapeseed oil as an example,the Raman spectral data of the oil samples were obtained using a laser Raman spectroscopy experimental system.The convolutional neural network(CNN)algorithm based on the Inception V2 structure was used to extract Raman spectral features and complete the nonlinear relationship mapping between spectral features and adulteration amount.The results showed that there was a significant difference between the Raman spectra of extra virgin olive oil and rapeseed oil,and the Raman characteristic peaks generated by carotenoids,C=C,methyl,and methylene groups were the main factors causing the differences.The established CNN model performed well,with determination coefficients greater than 0.99 for the training,validation,test sets and root mean square errors less than 0.026.In low-dose adulteration,the model's predictive performance still had a specific reference value.In summary,the model established by combining Raman spectroscopy with CNN algorithm based on the Inception V2 structure can meet the rapid detection of adulteration amount in extra virgin olive oil.
extra virgin olive oilRaman spectroscopyquantification of adulterationInception V2 structureconvolutional neural network
NETL
NSTL
万方数据
维普
