generating, by infrared spectroscopy, spectra data identifying quantities and associated wavelengths of radiation absorption for each of a plurality of wine samples as determined by the infrared spectroscopy;

converting the spectra data for each wine sample to a set of discretized data;

transforming the discretized data into a visual image representation of each respective wine sample, the visual image representation of each wine being an optically recognizable representation of the corresponding converted set of discretized data;

storing a record including the visual image representation of each wine sample in a memory;

building a trained computer vision classification system, the trained computer vision classification system being built based on transfer learning applied to a pretrained computer vision model trained using training data including a subset of the visual image representations of the plurality of wine samples, each visual image representation in the training data associated with at least one identified classification;

receiving an indication of a selection of one or more of the visual image representations of the plurality of wine samples other than the subset of the visual image representations in the training data; and

executing the trained computer vision classification system using the selected one or more visual image representations of the plurality of wine samples to generate an output including an indication of whether the selected one or more visual image representations of the plurality of wine samples corresponds to the at least one identified classification.