Systems and methods for material classification

A method for classifying material samples, comprising the steps of:

• receiving a set of spectrum values extracted from a spectrum of a sample;
• reducing, in a feature extraction block comprising at least one convolutional layer and at least one pooling layer, the set of spectrum values to a set of derived values each indicative of a spectral feature of the spectrum of the sample;
• and
• classifying, in a classification block comprising at least one dense layer and an output layer, the set of derived values as indicative of one or more materials in the sample.

A non-transitory computer readable storage medium comprising instructions executable by a processor for:

• converting spectrum signal data related to a spectrum of a sample into a set of spectrum values;
• reducing, in a feature extraction block comprising at least one convolutional layer and at least one pooling layer, the set of spectrum values to a set of derived values each indicative of a spectral feature of the spectrum of the sample;
• and
• classifying, in a classification block comprising at least one dense layer and an output layer, the set of derived values as indicative of one or more materials in the sample.