A method of authenticating material samples, comprising: measuring, via a measurement component , one or more characteristic features for a plurality of material samples, the plurality of material samples being a batch of material samples (S 1 , S 2 , ..., S N ) that comprise substantially the same composition and are produced by substantially the same process, the one or more characteristic features for the batch of material samples (S 1 , S 2 , ..., S N ) having respective variability; and analyzing, via a computation component , the respective variability to extract one or more statistical parameters for each of the material samples, wherein measuring the one or more characteristic features includes obtaining one or more digital images (32; 41; 42) for each of the batch of material samples (S 1 , S 2 , ..., S N ), and extracting the respective variability from the obtained digital images (32; 41; 42), the extracted variability including an inherent randomness of an optical feature of the obtained digital images for each of the batch of material samples, and the method further comprising: generating, via using a principal component analysis, PCA, a plurality of basis functions based on the extracted variability from the digital images of the batch of material samples (S 1 , S 2 , ..., S N ); obtaining one or more digital images for a test material sample to be authenticated; reconstructing, via the computation component , the digital images of the test material sample using a subset of the plurality of basis functions; and evaluating a reconstruction error for the reconstructing to determine whether the test material sample is authentic. measuring, via a measurement component , one or more characteristic features for a plurality of material samples, the plurality of material samples being a batch of material samples (S 1 , S 2 , ..., S N ) that comprise substantially the same composition and are produced by substantially the same process, the one or more characteristic features for the batch of material samples (S 1 , S 2 , ..., S N ) having respective variability; and analyzing, via a computation component , the respective variability to extract one or more statistical parameters for each of the material samples, wherein measuring the one or more characteristic features includes obtaining one or more digital images (32; 41; 42) for each of the batch of material samples (S 1 , S 2 , ..., S N ), and extracting the respective variability from the obtained digital images (32; 41; 42), the extracted variability including an inherent randomness of an optical feature of the obtained digital images for each of the batch of material samples, and the method further comprising: generating, via using a principal component analysis, PCA, a plurality of basis functions based on the extracted variability from the digital images of the batch of material samples (S 1 , S 2 , ..., S N ); obtaining one or more digital images for a test material sample to be authenticated; reconstructing, via the computation component , the digital images of the test material sample using a subset of the plurality of basis functions; and evaluating a reconstruction error for the reconstructing to determine whether the test material sample is authentic. generating, via using a principal component analysis, PCA, a plurality of basis functions based on the extracted variability from the digital images of the batch of material samples (S 1 , S 2 , ..., S N ); obtaining one or more digital images for a test material sample to be authenticated; reconstructing, via the computation component , the digital images of the test material sample using a subset of the plurality of basis functions; and evaluating a reconstruction error for the reconstructing to determine whether the test material sample is authentic. <EOS>