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Using a custom-built 785 nm Raman instrument, a recent study identified 14 pesticides and employed multivariate and machine learning techniques—particularly Random Forests (RF)—to automate classification. Readers will learn practical steps in spectral acquisition, spectral comparison across wavelengths, data preprocessing, and implementing machine learning models for real-world chemical monitoring (1).
Introduction and Relevance
This tutorial explores the integration of Raman spectroscopy and machine learning for pesticide detection. Pesticide contamination in food and the environment poses serious health and regulatory challenges globally. Detecting these compounds quickly, reliably, and non-destructively is crucial for food safety and environmental monitoring. Raman spectroscopy offers a label-free, fast, and molecularly specific method for identifying chemical substances, including pesticides. However, complex spectra and spectral overlap often hinder interpretation, especially when handled by non-experts. This tutorial showcases a practical approach using a 785 nm Raman instrument, combined with multivariate analysis and machine learning, to simplify and improve pesticide identification (1). Drawing on recent work by Yüce and colleagues, we demonstrate how Raman fingerprinting and Random Forest classification can provide a robust, automated workflow for identifying multiple analytes with high accuracy, setting the stage for broader applications in agriculture, diagnostics, and industrial quality control (1–9).
Mini-Tutorial: Raman fingerprinting and machine learning classification of pesticides © marritch -chronicles-stock.adobe.com
Core Tutorial Content
1. Principles and Definitions: Raman spectroscopy relies on inelastic scattering of monochromatic light (typically from a laser) by molecules, providing vibrational information unique to their chemical structure. The spectral range typically used spans from 400 to 1700 cm−1, covering key vibrational modes for functional group identification. Two common excitation wavelengths are 532 nm and 785 nm. While 532 nm can offer resonance enhancement (higher sensitivity for chromophores), it often suffers from fluorescence interference. In contrast, 785 nm offers clearer spectra with reduced fluorescence, making it ideal for complex samples like pesticides (1,3,4,7–9).
Machine learning (ML), particularly Random Forests, allows for classification based on subtle spectral features that may not be easily discernible to the human eye. Random Forest is an ensemble learning technique that constructs multiple decision trees and outputs the most common prediction among them, making it robust to overfitting and noise (5,6).
2. How It Works in Practice
Instrument Setup and Measurement: Yüce and colleagues designed a custom 785 nm Raman system optimized for a 400–1700 cm−1 window (1). They used this instrument to acquire spectra from 14 pesticides, collecting more than 20 technical replicates per compound to ensure robustness.
Each Raman spectrum underwent baseline correction and normalization before analysis. For comparison, identical pesticide samples were measured using a commercial 532 nm Raman system. This dual approach highlighted the impact of excitation wavelength on spectral clarity and feature intensity. The recommended measurement parameters are as follows (1).
3. Application Examples
This combined Raman + ML approach is applicable to:
4. Tips and Common Pitfalls
5. Steps for Data Evaluation and Analysis
When analyzing pesticide samples using Raman spectroscopy, start by collecting high-quality spectra over the 400–1700 cm⁻¹ range. Normalize and baseline-correct the spectra to reduce variability and reveal meaningful features. Look for shared vibrational peaks—such as aromatic ring modes and C–H bending bands—as well as unique peaks in regions like 1200–1400 cm⁻¹ that can help differentiate structurally similar compounds. These distinctive vibrational signatures serve as the foundation for compound identification and classification. Creating a well-annotated spectral library from reference samples is essential for comparative analysis and future model training (1,3,4,7–9).
Principal Component Analysis (PCA) is a powerful tool to visualize the variance in Raman spectra and detect underlying patterns. After preprocessing your spectral data, apply PCA to reduce dimensionality and plot the first two principal components. You’ll often find that replicates of the same compound cluster tightly, while different compounds separate along PC1 or PC2. This clustering helps validate the spectral uniqueness of each analyte and is an effective way to check for outliers or measurement inconsistencies. PCA is also a useful first step before supervised machine learning, offering visual insights into how distinguishable your classes may be (1).
Random Forest is a robust supervised learning method ideal for classifying Raman spectra of multiple analytes. Begin by splitting your spectral dataset into training and test sets. After training the model, evaluate performance using a confusion matrix, which compares predicted vs. actual labels. A well-performing model will have high accuracy with most samples falling along the matrix diagonal, indicating correct classification. Precision and recall scores offer further insight into which compounds are most reliably identified. Use cross-validation and hyperparameter tuning to optimize performance, and monitor for overfitting, especially when dealing with structurally similar compounds (1,5,6).
The choice of laser excitation wavelength has a critical impact on Raman spectral quality. While 532 nm excitation may provide stronger signals for certain chromophores through resonance enhancement, it often introduces high fluorescence background—particularly problematic for complex organic samples like pesticides. Using a 785 nm laser generally reduces fluorescence and yields cleaner, more interpretable spectra. To determine the optimal setup, measure the same sample with both wavelengths and compare spectral clarity, baseline stability, and peak visibility. For routine analysis and library building, 785 nm is typically more suitable for organic compounds due to its superior signal-to-noise characteristics (1,3,4,9).
6. Conclusion and Practical Takeaways
This study demonstrates the power of integrating Raman spectroscopy with ML for pesticide detection. By using a custom 785 nm Raman system and Random Forest classification, it is possible to automate and simplify the identification of chemically similar compounds. This workflow is highly relevant to labs focused on food safety, agricultural monitoring, and environmental testing. Key takeaways include the importance of excitation wavelength selection, spectral preprocessing, and rigorous model validation. As Raman instrumentation becomes more accessible and machine learning tools easier to implement, such integrated techniques will play a growing role in analytical science (1–9).
References
(1) Yüce, M.; Öncer, N.; Çınar, C. D.; Günaydın, B. N.; Akçora, Z. İ.; Kurt, H. Comprehensive Raman Fingerprinting and Machine Learning-Based Classification of 14 Pesticides Using a 785 nm Custom Raman Instrument. Biosensors 2025, 15 (3), 168. DOI 10.3390/bios15030168
(2) Workman, J., Jr.; Weyer, L. Practical Guide and Spectral Atlas for Interpretive Near-Infrared Spectroscopy; CRC Press: Boca Raton, FL, 2012.
(3) Smith, E.; Dent, G. Modern Raman Spectroscopy: A Practical Approach, 2nd ed.; John Wiley & Sons: Chichester, U.K., 2019.
(4) Lewis, I. R.; Edwards, H. G. M., Eds. Handbook of Raman Spectroscopy: From the Research Laboratory to the Process Line; CRC Press: Boca Raton, FL, 2001.
(5) Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning, 2nd ed.; Springer: New York, 2009. DOI: 10.1007/978-0-387-84858-7
(6) Breiman, L. Random Forests. Mach. Learn. 2001, 45, 5–32. DOI: 10.1023/A:1010933404324
(7) Zavaleta, C. L.; Garai, E.; Liu, J. T.; et al. A Raman-Based Endoscopic Strategy for Multiplexed Molecular Imaging. Proc. Natl. Acad. Sci. U.S.A. 2013, 110 (25), E2288–E2297. DOI: 10.1073/pnas.1211309110
(8) Xu, M. L.; Gao, Y.; Han, X. X.; Zhao, B. Detection of Pesticide Residues in Food Using Surface-Enhanced Raman Spectroscopy: A Review. J. Agric. Food Chem. 2017, 65 (32), 6719–6726. DOI: 10.1021/acs.jafc.7b02504
(9) Lieber, C. A.; Mahadevan-Jansen, A. Automated Method for Subtraction of Fluorescence from Biological Raman Spectra. Appl. Spectrosc. 2003, 57 (11), 1363–1367. DOI: 10.1366/000370203322554518
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