Self-supervised learning for inter-laboratory variation minimization in surface-enhanced Raman scattering spectroscopy
Seongyong Park, Abdul Wahab, Minseok Kim, Shujaat Khan
Abstract
). The leave-one-lab-out cross-validation (LOLABO-CV) results indicate that the MVNet also minimizes the variance of completely unseen laboratory datasets while improving the reproducibility and linear fit of the regression model. The Python implementation of MVNet and the code for the analysis can be found on the GitHub page https://github.com/psychemistz/MVNet.
Topics & Concepts
Python (programming language)Computer scienceLinear regressionRegressionMean squared errorVariance (accounting)MinificationArtificial intelligenceCross-validationAnalyteRegression analysisLinear modelMachine learningPattern recognition (psychology)Data miningStatisticsMathematicsChemistryAccountingOperating systemBusinessPhysical chemistryProgramming languageSpectroscopy and Chemometric AnalysesSpectroscopy Techniques in Biomedical and Chemical ResearchWater Quality Monitoring and Analysis