Faced with the continuous improvement of counterfeiting methods,cheap olive pomace oil will probably become a potential raw material for adulterating extra virgin olive oil.Therefore,this study focuses on the deep learning algorithm-assisted non-contact,non- destructive spectral detection technology to quantify the adulteration behavior of extra virgin olive oil.Mix expired olive pomace oil and extra virgin olive oil in different volume propor- tions to prepare different adulterated,mixed oil concentrations.The 785 nm portable Raman spectrometer was used to collect the Raman spectra of these mixed oils,and the quantitative analysis model of adulteration was established by combining the one-dimensional convolu- tional neural network algorithm.The density functional theory B3LYP/6-31+G(d,p) basis set  was  used  to  calculate  the  Raman  spectrum  of  linoleic  acid  molecules  to  further  analyze  the Raman   spectrum   of   extra   virgin   olive   oil.The   experimental   results   show   that   the   technical solution  based  on  combining  deep  structured  feedforward  neural  networks  and  785  nm  porta- ble  Raman  spectroscopy  technology  is   a  powerful  tool   for  quantitative   analysis  of  plant  oil   a- dulteration.The  decision  coefficients  of  4000   spectral  data  quantitative  models  from  80  mixed oil  products   are   all   better   than   0.97,and   the   decision   coefficient   of  quantitative   analysis   in the evaluation model test set reaches 0.9704,with a root mean square error less than

0.0499.This   technology   has   great   application   potential   in   quickly   evaluating   the   adulteration of  extra  virgin  olive  oil,providing  a  beneficial  reference   scheme   for  regulating  the  domestic olive   oil   market    and   safeguarding    consumers'legitimate   rights    and   interests.