Hyperspectral imaging as an effective tool for prediction the moisture content and textural characteristics of roasted pistachio kernels

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. The objective of this study was to develop calibration models for prediction of moisture content and textural characteristics (fracture force, hardness, apparent modulus of elasticity and compressive energy) of pistachio kernels roasted in different conditions (temperatures 90, 120 and 150 °C; times 20, 35 and 50 min and air velocities 0.5, 1.5 and 2.5 m/s) using Vis/NIR hyperspectral imaging and multivariate analysis. The effects of different pre-processing methods and spectral treatments such as normalization [multiplicative scatter correction (MSC), standard normal variate transformation (SNV)], smoothing (median filter, Savitzky–Golay and Wavelet) and differentiation (first derivative, D1 and second derivative, D2) on the obtained data were investigated. The prediction models were developed by partial least square regression (PLSR) and artificial neural network (ANN). The results indicated that ANN models have higher potential to predict moisture content and textural characteristics of roasted pistachio kernels comparing to PLSR models. High correlation was observed between reflectance data and fracture force (R2 = 0.957 and RMSEP = 3.386) using MSC, Savitzky–Golay and D1, compressive energy (R2 = 0.907 and RMSEP = 15.757) using the combination of MSC, Wavelet and D1, moisture content (R2 = 0.907 and RMSEP = 0.179) and apparent modulus of elasticity (R2 = 0.921 and RMSEP = 2.366) employing combination of SNV, Wavelet and D1, respectively. Moreover, Vis–NIR data correlated well with hardness (R2 = 0.876 and RMSEP = 5.216) using SNV, Wavelet and D2. These results showed the capability of Vis/NIR hyperspectral imaging and the central role of multivariate analysis in developing accurate models for prediction of moisture content and textural properties of roasted pistachio kernels