A Rapid, Univariate FT-NIR Procedure to Determine Moisture Concentration in Olive Oil

Nondestructive Detection of Moisture Content in Palm Oil by Using Portable Vibrational Spectroscopy and Optimal Prediction Algorithms

Rapid and nondestructive measurement of moisture content in crude palm oil is essential for promoting the shelf-stability and quality. In this research, micro NIR spectrometer coupled with a multivariate calibration model was used to collect and analyse fingerprinted information from palm oil samples at different moisture contents. Several preprocessing methods such as standard normal variant (SNV), multiplicative scatter correction (MSC), Savitzky-Golay first derivative (SGD1), Savitzky-Golay second derivative (SGD2) together with partial least square (PLS) regression techniques, full PLS, interval PLS (iPLS), synergy interval PLS (SiPLS), genetic algorithm PLS (GAPLS), and successive projection algorithm PLS (SPA-PLS) were comparatively employed to construct an optimum quantitative prediction model for moisture content in crude palm oil. The models were evaluated according to the coefficient of determination and root mean square error in calibration (Rc and RMSEC) and prediction (Rp and RMSEC) set, respectively. The model SGD1 + SiPLS was the optimal novel algorithm obtained among the others with the performance of Rc = 0.968 and RMSEC = 0.468 in the calibration set and Rp = 0.956 and RMSEP = 0.361 in the prediction set. The results showed that rapid and nondestructive determination of moisture content in palm oil is feasible and this would go a long way to facilitating quality control of crude palm oil.

Determination of Moisture in Olive Oil: Rapid FT-NIR Spectroscopic Procedure Based on the Karl-Fischer Reference Method

According to CODEX, moisture and volatile matter are olive oil quality parameters and the development of a rapid screening method for the determination of moisture is of interest. We recently demonstrated for the first time that the weak near-infrared (NIR) band near 5260 cm^-1 is primarily attributed to a water O-H combination band. To determine the intensity of this band, we measured the peak-to-peak (p-p) height of its first derivative and generated exponential calibration curves for p-p height versus gravimetrically determined concentrations of spiked water in olive oils that had been purged of their initial moisture contents. To further optimize this univariate calibration method, calibration curves were generated in the present study based on plotting the moisture band first derivative p-p heights for neat olive oils (that were neither purged nor spiked) versus the moisture concentrations obtained by the Karl-Fischer (KF) primary reference method. To enhance the speed of FT-NIR data collection, measurements were carried in the transmission mode using disposable glass tubes. We also developed and compared a multivariate partial least squares approach to the univariate one. All the spectra were collected in two separate laboratories using two FT-NIR spectrometers of the same brand and model and no significant difference (p > 0.05) was found between the two laboratory determinations and the KF reference values at a 95% confidence interval. High accuracies were found with the two FT-NIR instruments used, as indicated by the low root mean squared error (RMSE, %) for predicted values obtained with the univariate procedure (RMSE = 0.008% and 0.010%) and the multivariate one, which yielded an even lower value (RMSE= 0.007% for both instruments). These results suggest that, once validated, the FT-NIR approach could potentially be a rapid substitute for the KF method.