This study aims to detect peanut flour adulteration in chocolate powder using near-infrared (NIR) hyperspectral imaging. Fifteen samples were prepared by mixing both food products in different proportions (0%, 0.1%, 1%, 10% and 100% of peanut) and measured using the hyperspectral camera. A preliminary Principal Component Analysis (PCA) was performed to investigate the structure of the data. Next, the Multivariate Curve Resolution - Alternating Least Squares (MCR-ALS) chemometric method combined with a selectivity constraint in the concentration matrix was applied to untangle the spectral data into a set of components representative of the main constituents found in the samples. Moreover, a detection algorithm based on the calculation of the Mahalanobis distance for every pixel to the model distribution of chocolate powder was implemented. This analysis revealed the complexity of the unmixing problem, allegedly due to the spectral signature overlap in the pixel field of view and because the pure products presented similar spectral signatures. MCR-ALS results were improved after the application of a selectivity constraint, which resulted in a higher performance of the detection algorithm. MCR-ALS detected from 0% to 2.2% of adulterated pixels in mixed samples. On the other hand, the selectivity-constrained MCR-ALS method provided detections from 0.03% to 17.0% in those samples. This pipeline showed that peanut adulteration can be detected even for the lowest concentration level tested (0.1% of peanut). This work highlights the potential of NIR hyperspectral imaging combined with chemometrics for detection purposes.