Comparative analysis between PCA model and logistic regression model for qualitative identification of flavor oil-tea camellia seed oil adulteration with leaching oil-tea camellia seed oil based on volatile components
  
DOI:
KeyWord:flavor oil-tea camellia seed oil  leaching oil-tea camellia seed oil  volatile component  principal component analysis model  logistic regression model  qualitative identification
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Author NameAffiliation
SUN Tingting1,2, CHEN Zhiqing1,2, LIU Jianbo3, REN Jiali1,2,ZHONG Haiyan1,2, ZHOU Bo1,2 1.Hunan Key Laboratory of Forestry Edible Sources Safety and Processing, Changsha 410004China
2.School of Food Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, China
3.Food and Drug Inspection Institute of Yueyang City Inspection and Testing Center, Yueyang 414000, Hunan, China 
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Abstract:
      Based on volatile components, principal component analysis (PCA) and logistic regression (LR)models were constructed and compared to solve the problem of qualitative identification of flavor (original/roasted) oil-tea camellia seed oil adulterated with leaching oil-tea camellia seed oil under high and low adulteration gradients.The results showed that LR model had a good ability to qualitatively identify the flavor oil-tea camellia seed oil adulterated with leaching oil-tea camellia seed oil, which was better than that of PCA model. Under the high adulteration gradient, the detection limit of the PCA model for original/roasted oil-tea camellia seed oil adulterated with leaching oil-tea camellia seed oil was 20%/60%, and the detection limit of the LS model was 10%/10%, respectively. Under the low adulteration gradient, the detection limit of LR model for original/roasted oil-tea camellia seed oil adulterated with leaching oil-tea camellia seed oil was 4%/4%, but the discriminative ability of the PCA model was not accurate.The LR model can qualitatively identify flavor oil-tea camellia seed oil adulterated with leaching oil-tea camellia seed oil.
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