News
Article
Author(s):
A team of scientists from institutions in Brazil have demonstrated the effectiveness of handheld near-infrared (NIR) spectroscopy in rapidly detecting pesticide residues in intact fruits.
Portable Tech Aims to Clean Up the Fruit Aisle
With growing concern over pesticide residues in fruits, researchers from Brazil have developed a novel approach using portable near-infrared (NIR) spectroscopy to screen produce for chemical contamination without the need for lab-based tests (1). Published in the Journal of Food Composition and Analysis, the study, titled “Monitoring pesticides with portable NIR spectroscopy in different intact fruits,” offers a significant advance in rapid, non-invasive food safety screening (1).
Led by I.J.S. Ferreira, the study was conducted by a multidisciplinary team including D. dos Santos Costa, L.A. Rolim, S.T. de Freitas, N.A.C. de Souza, and B. Teruel. All authors are affiliated with Brazilian research institutions, notably the University of São Paulo (USP) and the Federal University of São Carlos (UFSCar) (1).
Portable NIR tech detects pesticides in fresh produce © exclusive-design -chronicles-stock.adobe.com
How the Technology Works
The team employed a handheld NIR spectrometer to measure pesticide presence in three commonly consumed fruits: apples, bananas, and oranges. Unlike traditional laboratory methods that require destructive sampling and chemical reagents, this portable device collects reflectance spectra directly from the fruit surface in a matter of seconds (1). Vibrational spectroscopic methods have been demonstrated to reveal pesticide contamination in food products (1,2)
NIR spectroscopy works by measuring how light in the near-infrared region (typically 780–2500 nm) interacts with molecular bonds in a sample. Chemical differences—such as the presence or absence of pesticides—alter the way light is absorbed and reflected, creating a spectral fingerprint that can be analyzed with multivariate statistical tools.
Chemometric Models Reveal Pesticide Residues
To interpret the complex spectral data, the researchers applied advanced chemometric techniques. These included data preprocessing steps—such as baseline correction and normalization—as well as modeling approaches like principal component analysis (PCA) and partial least squares discriminant Analysis (PLS-DA) (1).
The results showed a clear ability to distinguish between untreated fruits and those contaminated with pesticides. The spectral differences were especially pronounced in regions associated with C–H, N–H, and O–H bond vibrations, which are sensitive to both the fruit matrix and the chemical nature of the pesticide residues (1,2).
Notably, each fruit type had its own spectral behavior and required individual calibration models to ensure accurate classification. Nonetheless, the portable NIR method proved capable of providing a rapid and non-invasive solution for on-site pesticide screening (1).
A Step Forward for Food Safety and Sustainability
This study presents an important breakthrough in the field of food composition analysis, offering a sustainable and efficient tool for quality control in the supply chain. As noted by Ferreira and colleagues, the method supports broader goals of reducing chemical exposure and promoting consumer safety while minimizing waste and testing costs (1).
Furthermore, the researchers emphasize that the ability to apply this technique in-field—without the need for sample destruction—makes it well-suited for use by agricultural inspectors, food vendors, and even consumers in the near future (1).
The findings pave the way for the broader application of NIR spectroscopy in food safety monitoring, especially as portable devices become more accessible and machine learning algorithms improve classification accuracy.
References
(1) Ferreira, I. J. S.; dos Santos Costa, D.; Rolim, L. A.; de Freitas, S. T.; de Souza, N. A. C.; Teruel, B. Monitoring Pesticides with Portable NIR Spectroscopy in Different Intact Fruits. J. Food Compos. Anal. 2025, 124, 108024. DOI: 10.1016/j.jfca.2025.108024
(2) 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
Get essential updates on the latest spectroscopy technologies, regulatory standards, and best practices—subscribe today to Spectroscopy.
