2018 ASHS Annual Conference

Brazil is the seventh largest macadamia producer in the world with a cultivated area of 6,500 hectares. The main existing macadamia cultivars were introduced from Hawaii Agricultural Experiment Station (HAES) and some were selected by Instituto Agronômico de Campinas (IAC) based on Hawaiian cultivars. Although growers can recognize macadamia cultivars based on nut appearance and morphology, some cultivars produce nuts quite similar to each other, which makes difficult to classify the fruit. As near infrared spectroscopy (NIRS) has been used to classify various food produces, the objective of this study was to evaluate the feasibility of near infrared spectroscopy (NIRS) to classify intact shelled macadamia nuts. A total of 255 shelled macadamia nuts were collected from the following cultivars: IAC 4-20 ‘Keaumi’ (n=80), IAC 5-10 ‘Kakeré’ (n=60), IAC 8-17 ‘Waiaré’ (n=80), IAC 2-23 ‘Keaudo’ (n=40), HAES 246 ‘Keauhou’ (n=80). Two spectra were acquired per nut using a NIR spectrometer (Tango, Bruker, Ettlingen, Germany) in the reflectance mode over the wavelength range of 11,544 a 3,952 cm^-1, resolution of 16 cm^-1, and 64 scans. Different pre-processings were applied to spectra, namely standard normal variate (SNV), De-trend, multiplicative scatter correction (MSC), and first derivative of Savitzky-Golay. Spectra were subjected to principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) and models were developed for each cultivar. The best results were obtained for the cultivars IAC4-20 ‘Keaumi’ (SEP = 0.24%, R_p² = 0.68, with 9 LVs) and IAC 2-23 ‘Keaudo’ (SEP = 0.19%, R_p² = 0.68, with 11 LVs). The other cultivars could not be separated from each other due to genetic similarities (progeny). These results indicate that NIR spectroscopy can be useful for the classification of macadamia cultivars based on their nuts and could be a valid and simple tool to reduce the quality control costs of monitoring macadamia nuts’ quality. However, is recommended more experimentation to include more data variability in order to increase the classification accuracy to 100%.