Quality Evaluation of Shelled and Unshelled Macadamia Nuts by Means of Near‐Infrared Spectroscopy (NIR)

Abstract

The quality of shelled and unshelled macadamia nuts was assessed by means of Fourier transformed near-infrared (FT-NIR) spectroscopy. Shelled macadamia nuts were sorted as sound nuts; nuts infected by Ecdytolopha aurantiana and Leucopteara coffeella; and cracked nuts caused by germination. Unshelled nuts were sorted as intact nuts (<10% half nuts, 2014); half nuts (March, 2013; November, 2013); and crushed nuts (2014). Peroxide value (PV) and acidity index (AI) were determined according to AOAC. PCA-LDA shelled macadamia nuts classification resulted in 93.2% accurate classification. PLS PV prediction model resulted in a square error of prediction (SEP) of 3.45 meq/kg, and a prediction coefficient determination value (Rp (2) ) of 0.72. The AI PLS prediction model was better (SEP = 0.14%, Rp (2) = 0.80). Although adequate classification was possible (93.2%), shelled nuts must not contain live insects, therefore the classification accuracy was not satisfactory. FT-NIR spectroscopy can be successfully used to predict PV and AI in unshelled macadamia nuts, though.

An automated non-destructive prediction of peroxide value and free fatty acid level in mixed nut samples

“…This is the first study that used a VNIR (400-1000 nm) HSI system for classification of pooled nuts spectra and automatically predicted PV and FFA in the nut samples. In other studies, the concentrations of PV and FFA in corn kernels, sunflower seeds and macadamia single nuts have been predicted using HSI/VNIR spectroscopy operated in various spectral regions from 950 nm to 2500 nm (Canneddu et al, 2016;Cantarelli et al, 2009;Weinstock et al, 2006). In our study, however, HSI (400-1000 nm) was the system used to predict PV and FFA in canarium and macadamia samples.…”

“…Other studies have also used data transformation techniques to distinguish the nuts having almost similar spectra. For example, a combination of SNV and 2dv has been used prior to separating the marketable shelled macadamia nuts from those defected with citrus fruit borer (Ecdytolopha aurantiana) using NIR spectroscopy (Canneddu et al, 2016). The 1dv and 2dv have also been used to distinguish insect-damaged and fungal-infected chestnuts and almonds using NIR spectroscopy (Liang et al, 2015;Moscetti et al, 2014).…”

Using Intact Nuts and Near Infrared Spectroscopy to Classify Macadamia Cultivars

“…Overall, there was no difference of NIR spectra according to macadamia cultivar and the observed peaks were related to the presence of the main components of macadamia nut shell, which are lignin (47.6%), cellulose (25.8%), and hemicellulose (11.7%) (Toles et al 1998). For cellulose and hemicellulose, the most important spectral variables were observed at a broadband between 6800 and 6400 cm −1 , related to the first OH stretching overtone and the peak between 5000 and 4500 cm −1 corresponds to the first CH, CH 2 overtone region, and CH, CH 2 , CH 3 combination band region (Guimarães et al 2014;Canneddu et al 2016). For lignin, the peaks are at about 5230 cm −1 , first O-H stretching overtone of aromatics, 4415 cm −1 , O-H combination bands, and C-O stretches (Guimarães et al 2014).…”

“…The NIRS has been successfully applied to develop models for macadamia nut classification. Canneddu et al (2016) classified macadamia nuts in marketable and non-marketable models constructed using partial least squares-discriminant analysis (PLS-DA) reporting 93.2% correctly classified macadamia nuts. Guthrie et al (2004) also discriminated macadamia nuts according to their defects, such as rancidity, brown centers, discoloration, mold growth, germination, and decomposition, using principal components analysis (PCA).…”

Near-Infrared Spectroscopy Coupled Chemometric Algorithms for Rapid Origin Identification and Lipid Content Detection of Pinus Koraiensis Seeds

“…NIRS is applied to many characteristics of agricultural products, such as the quantification of protein content in sweet potato [ 14 ] and the prediction of the level of astringency in persimmon [ 15 ]. When it comes to the use of NIR in nuts, in addition to conventional properties (such as moisture content), NIR has also been used to discern textural characteristics of roasted pistachio kernels [ 16 ] and aspects of quality evaluation in Macadamia nuts [ 17 ]. NIR detection technology has been used for more complex work in nuts, including the characterization of the chemical morphology of areca nuts [ 18 ], the detection of mold-damaged chestnuts [ 19 , 20 ], and the discrimination of peanuts in bulk cereals [ 21 ].…”