Comprehensive Raman Fingerprinting and Machine Learning-Based Classification of 14 Pesticides Using a 785 nm Custom Raman Instrument.
Journal:
Biosensors
PMID:
40136965
Abstract
Raman spectroscopy enables fast, label-free, qualitative, and quantitative observation of the physical and chemical properties of various substances. Here, we present a 785 nm custom-built Raman spectroscopy instrument designed for sensing applications in the 400-1700 cm spectral range. We demonstrate the performance of the instrument by fingerprinting 14 pesticide reference samples with over twenty technical repeats per sample. We present molecular Raman fingerprints of the pesticides comprehensively and distinguish similarities and differences among them using multivariate analysis and machine learning techniques. The same pesticides were additionally investigated using a commercial 532 nm Raman instrument to see the potential variations in peak shifts and intensities. We developed a unique Raman fingerprint library for 14 reference pesticides, which is comprehensively documented in this study for the first time. The comparison shows the importance of selecting an appropriate excitation wavelength based on the target analyte. While 532 nm may be advantageous for certain compounds due to resonance enhancement, 785 nm is generally more effective for reducing fluorescence and achieving clearer Raman spectra. By employing machine learning techniques like the Random Forest Classifier, the study automates the classification of 14 different pesticides, streamlining data interpretation for non-experts. Applying such combined techniques to a wider range of agricultural chemicals, clinical biomarkers, or pollutants could provide an impetus to develop monitoring technologies in food safety, diagnostics, and cross-industry quality control applications.