This work (purchase required, USD$35.95) presents an algorithm to transform the LC-MS spectra of one instrument, to make it appear as though it came from another instrument. This allows a machine learning model trained on one set of samples using one LC-MS platform to classify new honey samples on a different LC-MS platform. The authors used a non-targeted honey botanical origin database as a proof of concept. They trainsferred a dataset of 262 monofloral honey samples across 4 LC-MS platforms using a logistic regression classifier model that predicts whether a sample is blueberry, buckwheat, clover, or “other” monofloral honey.
They report that same-instrument classification was comparable to cross-instrument classification. In 75% of cases, there was no significant difference (p>0.05) between same-instrument and cross-instrument performance.
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