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Abstract. Macadamia nut is rich in a variety of nutrients and active ingredients, with good economic and medicinal value. The shell of this nut is hard and thick; internal defects assessment is a challenging task. Traditionally, the quality macadamia nut is manually inspected by skilled workers who classify the nut by their color and the incidence of visible deformations. The disadvantage of this method is its inaccuracy, slow, and requires an expert. Currently, the emerging spectral analysis technology is also used to assess the quality of nut, but most of the studies based on the shelled macadamia nut. Thus, there is a necessity to develop a method that can be effectively used to precisely evaluate the internal quality of intact macadamia nut. Therefore, the objectives of this study to assess the internal defects of intact macadamia kernel by near-infrared (NIR) spectroscopy combined with chemometrics tools. In this study, a total of 160 macadamia nuts was used to acquire the transmission spectral data in the wavelength range of 980-1680 nm, where the light source mounted at 180° concerning the sample and integrating sphere. The internal defects of macadamia nut were identified visually based on the kernel's quality defects and classified as good, marketable kernels without defects; bad, non-marketable kernel with various defects after unshelled each nut. Partial least square-discriminant analysis (PLS-DA), and support vector machine classification (SVM-C) techniques were used to develop the classification models with different spectral preprocessing techniques; the PLS-DA resulted in an accuracy of 88.2% using multiple scattering correction (MSC) preprocessed spectra, and SVM-C resulted in an accuracy of 93% using Savitzky-Golay first derivatives preprocessed spectra. The results of this study indicated that NIR spectroscopy is useful for the rapid and non-destructive evaluation of the internal defects for intact macadamia nut.