The Quality Assessment And Rapid Analytical Method Development Of Edible Oil

Edible oil is one of the six nutrients for human survival and health.At present,during the production process or selling of high-quality edible oil,illegal peddlers impersonate products provenances or adulterate the expensive products by cheaper oils to chase the exorbitant profits,which has become a serious issue and attracted more attention to the quality of edible oil.Therefore,it is important to develop new detection methodologies of adulterated oil for the identification of the quality of edible oil.As the first part of this thesis,LF-NMR technology combined with chemometric method was employed to distinguish the walnut samples from three different provenances(Xinjiang,Yunnan and Liaoning),and to determine the moisture content and oil content in these walnut samples.The Carr–Purcell–Meiboom–Gill(CPMG)data were analyzed and processed by principle component analysis(PCA)method,and the obtained PCA score plot enables the discrimination of specific walnut sample from aforementioned provenance.Moreover,a rapid prediction model on the determination of oil and moisture content of walnut was established by the combination of LF-NMR technology and partial least squares regression(PLSR)analysis.The correlation coefficients of the predictive values and actual values(determined by direct drying method and Soxhlet extraction method)in the established PLSR model are 0.973 for oil and 0.975 for moisture,respectively.The oil and moisture contents of the walnut samples from these three different provenances were predicted by the established PLSR model.Compared to the experimental values,the relative errors ranges of the predicted ones are 3.12%-3.59% for oil and 0.330%-0.380% for moisture,respectively,indicating a reliability of the established model.Furthermore,the LF-NMR technology was combined with chemometrics to determine the adulteration level of cheap edible oil(soybean oil,corn oil and sunflower oil)in walnut oil.The obtained PCA score plot also enables the discrimination of the pure walnut oil sample and the adulterated ones.Moreover,quantitative models on the adulteration ratio were established by the combination of LF-NMR and PLSR analysis.The correlation coefficients of the predicted values and actual values in PLSR models for the walnut oil adulteration of soybean oil,corn oil and sunflower oil are 0.993,0.992 and 0.993,respectively.The root mean square errors of prediction(RMSEP)of the corresponding value are 2.06%,1.72% and 2.06%,respectively.Therefore,LF-NMR coupled with PLSR method can achieve the quantitative determination of adulteration ratio of walnut oil with a relatively high accuracy.Finally,LF-NMR technology combined with PCA,PLSR and principal component regression(PCR)analysis is applied to detect the adulteration of soybean oil in sesame oil.Analogously,the PCA score plot enables the discrimination of adulterated sesame oil samples and the pure one.The prediction models,established by PLSR and PCR method,were used for the quantitative determination of the adulteration ratios.The correlation coefficients between experimental and predicted values in PLSR and PCR model were 0.970 and 0.975,respectively;and RMSEP were 0.736% and 0.0710%,respectively.Therefore,the combination of LF-NMR and above chemometrics provides a rapid and non-destructive methodology to identify the quality of sesame oil sample.