A computer implemented method of determining a property of an unknown tobacco sample (26) comprising using
hyperspectral imaging in combination with a
machine-learning-based prediction framework. The method comprises: creating a first, reference spectral
fingerprint dataset by scanning a plurality of known tobacco samples (26) using
hyperspectral imaging techniques, generating hyperspectral images of each of said known tobacco samples and forming a reference spectral
fingerprint, and storing each of said reference spectral fingerprints into said first, reference spectral
fingerprint dataset. The method also comprises providing a second, known property, dataset, comprising known properties / compositions of said known tobacco sample, and inputting said first, reference spectral fingerprint dataset and said second, known property, dataset into said
machine learning-based prediction framework. The method further comprises via said
machine learning- based prediction framework, correlating said first, spectral fingerprint dataset with said known property or properties of said second, known property, dataset; said machine-
learning based prediction framework generating a prediction model that is configured to determine one or more mathematical functions based on said first reference spectral fingerprint dataset, and said second, known property dataset. The method further comprises generating (100) hyperspectral images of said unknown tobacco sample (26), and based on said hyperspectral images of said unknown tobacco sample, generating a spectral fingerprint of said unknown tobacco sample. The method also comprises inputting said spectral fingerprint of said unknown tobacco sample into said
machine learning-based prediction framework, and said machine-learning-based prediction framework applying said one or more mathematical functions to obtain and output said determined property of said unknown tobacco sample (26).