Methods and systems for polymeric fingerprint analysis and identification

A system comprising:

• a memory;
• a display;
• and
• at least one processor coupled to the memory, the at least one processor configured to:
• store, on the memory, a trained machine learning model, wherein:
• the trained machine learning model was trained with training data comprising:
• a plurality of pairs of datasets, wherein each of the pairs of datasets comprises a reference dataset and a sample dataset, and wherein:
• the reference dataset is indicative of first results of a first plastic sample analysis, and
• the sample dataset is indicative of second results of a second plastic sample analysis;
• and
• an indication, for each of the pairs of datasets, that features of the sample dataset and the reference dataset are a match;
• receive a new sample dataset;
• determine, based on the trained machine learning model, that the new sample dataset matches at least one of one or more new reference datasets, the reference dataset of one of the plurality of pairs of datasets in the training data, or the sample dataset of one of the plurality of pairs of datasets in the training data;
• and
• send to the display data indicative of whether a match for the new sample dataset was identified, wherein the data sent to the display further comprises curve data configured to cause the display to show a representation of:
• a first curve representative of the new sample dataset, and
• a second curve representative of a dataset determined to match the new sample dataset.

A non-transitory computer readable medium having instructions stored thereon that, upon execution by a computing device, cause the computing device to perform operations comprising:

• receiving a dataset, wherein the dataset is a first portion of a total available dataset and wherein the dataset comprises:
• a plurality of data pairs, wherein each of the data pairs comprises reference data and sample data, and wherein:
• the reference data is indicative of first results of a first plastic sample analysis, and
• the sample data is indicative of second results of a second plastic sample analysis;
• and
• an indication, for each of the data pairs, that features of the sample data and the reference data are a match;
• training a machine learning model using the dataset;
• receiving new sample data;
• and
• determining, using the trained machine learning model, that the new sample data matches at least one of new reference data, the reference data of one of the plurality of data pairs in the dataset, or the sample data of one of the plurality of data pairs in the dataset;
• receiving a second portion of the total available dataset;
• and
• determining, using the trained machine learning model, whether sample data in the second portion of the total available dataset match any of reference data in the second portion of the total available dataset, the reference data of one of the plurality of data pairs in the dataset, or the sample data of one of the plurality of data pairs in the dataset.