Intact macadamia nut quality assessment using near-infrared spectroscopy and multivariate analysis
2021
Rahman, Anisur | Wang, Shuai | Yan, Jinshan | Xu, Huirong
As production and demand for high quality Macadamia nuts rise worldwide, rapid and accurate in-situ methods for monitoring internal nut quality will need to be developed. Therefore, purpose of this study was to determine the quality (internal nut defects and oil content) of intact macadamia nuts by combining near-infrared spectroscopy with variable selection algorithms and calibration models. Transmission spectra in the ranges of 1,000–1,650 nm from a total of 345 macadamia nuts were acquired. Partial least square-discriminant analysis (PLS-DA) and partial least square-regression (PLS-R) models with three different spectral preprocessing techniques were then evaluated using the full spectra to classify nut defects and predict oil content. A Savitzky-Golay (SG) first derivatives preprocessing technique was selected as the best one. Then, competitive adaptive reweighted sampling (CARS), random frog (RF), and variable importance projection (VIP) algorithms were used to select effective wavelengths (EWs) to build simpler and more robust models for classification of intact macadamia nut defects and prediction of oil content. The PLS-DA-CARS with SG first derivatives preprocessing based model provided the best nut defect classification with a 91.4 % accuracy, whilst a PLS-R-CARS with SG second derivatives preprocessing model for oil content prediction had a coefficient of determination (R²ₚᵣₑd) of 0.88 and standard error of prediction (SEP) of 1.15 %.
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