Rancidity and moisture estimation in shelled almond kernels using NIR hyperspectral imaging and chemometric analysis

The current work focused on rapid and non-destructive determination of moisture content (MC), free fatty acids (FFA) and peroxide value (PV) in shelled almonds, using reflectance NIR-hyperspectral imaging (HSI). Sample set of 354 and 235 almond kernels were treated to vary the MC, rancidity (FFA and PV), respectively and used for model development. The sample set for each response was divided into calibration and testing set in the ratio of 70:30. Reference MC, FFA and PV were measured using wet analysis methods and their association with the spectral data were modelled using partial least squares regression. Superior models were obtained from the full-spectrum (900-1700 nm) data with R-p(2) values of 0.957, 0.970, 0.955 and RMSEP values of 0.41%, 0.108%, 0.453 mEq for MC, FFA and PV, respectively. Competitive adaptive reweighted sampling method was used to select the feature wavelengths for rapid quantification. Multiple linear regression models were developed using the feature wavelengths having good predictability (R-p(2) values: 0.941, 0.903, 0.886 and RMSEP values: 0.494%, 0.162%, 0.658 mEq for MC, FFA and PV, respectively). The current findings demonstrated great feasibility in industrial deployment of HSI technique for non-destructive estimation of moisture and rancidity indices in almonds and other nuts.