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 the trained computer vision classification system based on transfer learning applied to a pretrained computer vision model trained using training data including a subset of a plurality of visual image representations of the plurality of wine samples;

acquiring an image including an indication of at least one wine;

identifying, by optical character recognition, the at least one wine in the acquired image;

correlating each of the identified at least one wines to a visual image representation of each wine; and

executing the trained computer vision classification system using (1) the visual image representation of each of the identified wines and (2) labeled visual image representations of at least one wine associated with a user flavor profile including at least one classification as inputs to generate an output including, for each of the identified wines, an indication of whether the identified wine corresponds to the at least one classification associated with the user flavor profile.