Almonds are nutrient-rich nuts, their health benefits are potentially linked to the high consumption worldwide. Due to relatively higher price, the producers are targeting it as an illegal practice for earning more profit. The most common adulterants are based on superficially matching, which the apricot nut as an adulterant comparatively are inexpensive, almost identical in color, texture, odor, and other physicochemical characteristics with almonds. In addition, apricots nuts contain amygdalin component which is converted to hazardous toxic cyanide in the digestive system. In the past decades, hyperspectral imaging (HSI) has attracted good attention as a rapid, real-time and non-destructive measurement method for food quality and safety analysis. In this study, near-infrared hyperspectral imaging (NIR-HSI system) with the wavelength of 900-1700 nm synchronized to a conveyor belt was used for online detection of added apricot nuts in almond. A total of 448 samples from different varieties of almond and apricot nuts (112x4) were scanned while the samples are moving on the conveyor belt. The spectral data were extracted from each imaged nuts and used for developing a PLS-DA model coupled with different preprocessing techniques. The PLS-DA model showed over 95% accuracy for the validation set. Additionally, the obtained beta coefficient from the developed model was used for pixel-based classification. An image processing algorithm was developed for chemical visualization mapping of almond and apricot nuts. The online classification system feedback with an overall accuracy of 87% for the classification of nuts. The developed online prototype (NIR-HIS) system combined with multivariate analysis exhibit strong potential for classification of adulterated almond, and the result indicates the system can be used effectively for high-throughput industrial classification of adulterated almond nuts in an industrial environment.