NIR prediction models are developed for the determination of relevant parameters to evaluate olive oil quality such as acidity (FFA), peroxide value (PV), UV-absorption at 232 and at 268/270 nm, p-anisidine value (AnV), isomeric diacylglycerols (DG), and pyropheophytin A (PPP). In addition a new NIR method to estimate the age of olive oil is presented. The relevant wavenumbers are given for the calculation of the parameters and the precision data are presented in comparison to the chemical reference methods. The calibration and validation of the methods are executed with independent data sets (test in test instead of cross-validation) to cover the wide range of variability of the analytical parameters including the corresponding accurate analytical results from the reference chemical methods. The correctness and accuracy of the developed NIR methods are verified by analyzing certified materials. Finally, a simple statistical approach has been developed to describe the quality of olive oils using the parameters FFA, PV, K-232 and K-270, DG and PPP. The probability of presence of a sensory defect (100% estimates 1; 0% estimates 0) is calculated using the following equation: Pred (BIN) = 1/(1 + exp(-(-9 + 37 x FFA-0.9 x PV-2.9 x K232 + 14 x K270 + 3.7 x PPP-0.17 x DG))). Practical Applications: The use of NIR allows the analysis of different parameters relevant for the quality of olive oil in one run in comparison to the individual chemical reference methods. That allows saving time and money, and a skilled operator is not necessary, making the method interesting for routine analysis. The use of a simple equation developed from the logistic regression using FFA, PV, K-values, DG, and PPP measured by NIR as variables enables for the first time to describe the sensory quality of olive oils without making sensory testing. This criterion without setting limits for the individual parameters estimates the probability of the presence of sensory defects on basis of chemical parameters. This tool can easily be used to differentiate the two categories extra virgin and virgin olive oils both using the traditional laboratory methods and the corresponding NIR-methods. In the present study, NIR prediction models are developed for the determination of free fatty acids, peroxide value, UV-absorption at 232 and at 268/270 nm, p-anisidine value, isomeric di-acyl-glycerols, and pyropheophytin A to evaluate olive oil quality. On basis of NIR, a new method is developed to estimate the age of olive oil. The parameters FFA, PV, K232 and K270, DG and PPP have been used to describe the quality of olive oils by a simple statistical approach.
