Generalized local adaptive fusion regression process based on physicochemical and physiochemical underlying hidden properties for quantitative analysis of molecular based spectroscopic data

Methodology for searching a large library of spectral measurement (Library X, y) using a generalized local adaptive fusion regression (LAFR) process for quantitative analysis of molecular based spectroscopic data (xnew) from a target sample of analytes, comprising:

• define process parameters and obtain all possible hyperparameter combinations (HPPC’s) thereof;
• for each HPPC:
• reduce Library to N most spectrally similar to the target sample,
• form calibration sets (CalSets) by clustering analyte ranged windows,
• remove all CalSets for which the target is an outlier to produce Approved CalSets,
• use matrix matching to identify a Selected CalSet from the Approved CalSets which best matches to the target sample, and
• store the Selected CalSet for each HPPC;
• use matrix matching to select best N sets from the stored Selected CalSets;
• use matrix matching to select best K samples from the N Selected CalSets;
• form a calibration model with the K samples;
• and
• apply the LAFR process to a new target sample to predict the analysis thereof.

Methodology for predicting the quantitative analysis of a target sample, comprising:

• searching through a library of spectral samples with corresponding analyte values (Library X, y) using a generalized local adaptive fusion regression (LAFR) process for identifying subsets of samples with similar matrix effects;
• forming linear training sets defined by the LAFR process identified subsets of samples;
• forming a final local training set from the linear training sets;
• forming a prediction model with the final local training set;
• and
• using the prediction model to predict the quantitative analysis of a target sample, where the final local training set is composed of samples with the analyte amount highly similar to the unknown analyte amount in the target sample to be predicted.