Nutmeg Authentication via Hyperspectral Imaging and AI Models

Kiani, S., van Ruth, S. M., van Raamsdonk, L. W. D., & Minaei, S. (2019). Hyperspectral imaging as a novel system for the authentication of spices: A nutmeg case study. LWT, 104, 61–69. https://doi.org/10.1016/j.lwt.2019.01.045

This study explores a novel method for authenticating nutmeg powder using hyperspectral imaging (HSI) in the 400–1000 nm range. Researchers analyzed 15 authentic samples, 7 adulterants, 31 retail samples, and several artificially adulterated mixtures. A handheld hyperspectral imaging device captured spectral data, which was then processed through advanced mathematical models.

Principal Component Analysis (PCA) enabled clear spatial differentiation between authentic and adulterated materials. To further classify the samples, Partial Least Squares-Discriminant Analysis (PLS-DA) and Artificial Neural Networks (ANN) were applied. The ANN model demonstrated superior accuracy, detecting adulteration levels as low as 5%.

These findings suggest that hyperspectral imaging, paired with machine learning, offers a powerful, non-destructive method for quality control in spice authentication. This approach could be implemented as a visual inspection tool in food safety and fraud prevention.