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This research aims to enhance the determination of carbamate pesticides using a machine learning-assisted PARAFAC model.

June 19, 2026Microchemical JournalOpen Access

Machine learning-assisted spectral optimization for improved PARAFAC modeling in fluorescence-based determination of carbamate pesticides

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This research aims to enhance the determination of carbamate pesticides using a machine learning-assisted PARAFAC model.
Integrates PARAFAC with a machine learning workflow for spectral variable selection.
Applies the method to excitation-emission matrices (EEMs) of carbamate pesticides.
Tested in complex matrices like tap water and orange juice to assess performance.
Achieved coefficients of determination (R²) above 0.99 for tap water and 0.98 for orange juice.
Limits of detection of 7 nM for carbaryl and 72 nM for aminocarb were established.
Demonstrated superior sustainability with an AGREE score of 0.77 compared to 0.64 for conventional methods.

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Randomized trial demonstrates enhanced detection of carbamate pesticides in complex matrices, indicating improved analytical methods.

Zarta et al. (2026) studied this question.

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