Identifying camellia oil adulteration with selected vegetable oils by characteristic near-infrared spectral regions

In this paper,a methodology based on characteristic spectral bands of near infrared spectroscopy(1000-2500 nm)and multivariate analysis was proposed to identify camellia oil adulteration withvegetable oils,Sunflower,peanut and corn oils were selected to conduct the test.Pure camlia oiland that adulterated with varying concentrations(1-10%with the gradient of 1%,10-40%withthe gradient of 5%,40-100%with the gradient of 10%)of each type of the three vegetable oilswere prepared,respectively.For each type of adulterated oil,full-spectrum partial least squarespartial least squares(PLS)models and synergy interval partial least squares(SI-PLS)modelswere developed.Parameters of these models were optimized simultaneously by cross-validation,The SI-PLS models were proved to be better than the full-spectrum PLS models.In SI-PLSmodels,the correlation coefficients of predition set(Rp)were 0.9992,0.9998 and 0.9999 foradulteration with sunflower oil,peanut oiloil seperately;the corresponding root meansquare errors of prediction set(RMSEP).66nd 0.37.Furthermore,a new genericPLS model was built based on the chalselected from the intervals of thethree SI-PLS models to identify the oil adulterantsardless of the adultrated oil types.Themodel achieved with Rp=0.9988 and RMSEP==1.52,These results indicated that the charac-teristic near infrared spectral regions could determine the level of adulteration in the camllia oil.

Shortwave infrared hyperspectral imaging for detection of pH value in Fuji apple

pH value is regarded as one of the most important attributes that affect sensory characteristics and edible quality of apple.The objective of the research was to explore the feasibility of applying shortwave infrared hyperspectral imaging system to detect the pH value of apple.A shortwave infrared hyperspectral imaging system was developed over the wavelength region of 1000-2500 nm and used to acquire hyperspectral images of apple samples.After reflectance calibration,mean reflectance spectral was calculated by averaging the intensity of all pixels within the roundness region of interest(ROI).Synergy interval partial least squares(siPLS)algorithms as an effective multivariable method was conducted on the calibration of regression model to estimate the pH value in Fuji apple.The performance of the final model was back-evaluated according to root mean square error of calibration(RMSEC)and correlation coefficient(Rc)in calibration set,and tested in prediction set.The optimal prediction siPLS model was obtained with correlation coefficient(Rp)of 0.8474 and mean square error of prediction(RMSEP)of 0.0398.The results indicated that shortwave infrared hyperspectral imaging combined with siPLS chemometrics could be an accurate and fast method for nondestructive prediction of pH value in Fuji apple.