Near infrared spectroscopy combined with chemometrics for quantitative analysis of corn oil in edible blend oil
作者：Huan Zhang, Xiaoyun Hu, Limei Liu, Junfu Wei, Xihui Bian*
關(guān)鍵字：Edible blend oil, Quality control, Chemometrics, Near infrared spectroscopy, Multivariate calibration
論文來(lái)源：期刊
具體來(lái)源：Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2022, 270, 120841
發(fā)表時(shí)間：2022年
In this study, near infrared (NIR) spectroscopy combined with chemometrics was used for the quantitative analysis of corn oil in binary to hexanary edible blend oil. Sesame oil, soybean oil, rice oil, sunflower oil and peanut oil were mixed with corn oil subsequently to form binary, ternary, quaternary, quinary and hexanary blend oil datasets. NIR spectra for the five order blend oil datasets were measured in a transmittance mode in the range of 12000-4000 cm-1. Partial least square (PLS) was used to build models for the five datasets. Six spectral preprocessing methods and their combinations were investigated to improve the prediction performance. Furthermore, the optimal preprocessing-PLS models were further optimized by uninformative variable elimination (UVE), Monte Carlo uninformative variable elimination (MCUVE), and randomization test (RT) variable selection methods. The optimal models acquire root mean square error of prediction (RMSEP) of 1.1758, 1.8321, 2.0294, 2.3532 and 2.5577 for binary, ternary, quaternary, quinary and hexanary blend oil datasets, respectively. The determination coefficients of prediction set (R2P) and residual predictive deviations (RPDs) for the five datasets are all above 0.93 and 3. Results show that the prediction accuracy is gradually decreased with the increasing of mixture order of blend oil. However, with proper spectral preprocessing and variable selection, the optimal models present good prediction accuracy even for the higher order blend oil. It demonstrates that NIR technology is feasible for determination pure oil contents for binary to hexanary blend oil.