Raman Spectroscopy Aflatoxin Detection Enhances Peanut Safety
A research team from Jiangsu University has developed a Raman spectroscopy-based method to detect aflatoxin B1 (AFB1) in peanuts with improved accuracy and efficiency. By employing a two-step hybrid strategy integrating backward interval partial least squares (BiPLS) and variable combination population analysis (VCPA), the new model significantly enhances the precision of AFB1 detection, providing a more reliable approach for food safety monitoring.
Aflatoxin B1 molecule, a carcinogenic toxin produced by fungi Aspergillus © Jotch -chronicles-stock.adobe.com
Aflatoxin B1 (AFB1) is one of the most toxic and carcinogenic contaminants found in peanuts and other food products. Classified as a Group Ⅰ carcinogen by the International Agency for Research on Cancer (IARC) (1–3), AFB1 poses severe health risks when ingested in contaminated food. Conventional detection methods such as thin-layer chromatography (TLC) and high-performance liquid chromatography (HPLC) are widely used but come with limitations, including high costs, complex sample preparation, and time-consuming procedures (1).
In response to these challenges, researchers from the School of Electrical and Information Engineering at Jiangsu University, China, have pioneered a novel approach utilizing Raman spectroscopy for AFB1 detection in infected peanuts. Their study, recently published in Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, presents an advanced detection framework combining feature selection methods to enhance predictive accuracy and model efficiency (1).
Advanced Spectroscopic Method for AFB1 Detection
The study employs a portable Raman spectrometer to analyze peanut samples at varying levels of mold contamination. Raman spectroscopy, an optical vibrational spectroscopy technique, offers a rapid and non-destructive alternative to traditional chemical analysis. Unlike HPLC and TLC, Raman spectroscopy does not require extensive sample preparation, making it a promising candidate for real-time food safety monitoring (1).
To optimize the detection process, the research team introduced a two-step hybrid strategy for feature selection (1). The first step was to apply backward interval partial least squares (BiPLS), which refines the spectral data by selecting optimal feature intervals, reducing redundancy and improving model performance. The second step used is variable combination population analysis (VCPA), which further selects key wavelength variables, ensuring a high level of predictive accuracy (1). This combined strategy is abbreviated as BiPLS-VCPA-PLS.
Key Findings and Performance Metrics
The optimized BiPLS-VCPA-PLS model demonstrated superior predictive capability, achieving a root mean square error of prediction (RMSEP) of 33.3147 μg/kg; a prediction correlation coefficient (RP) of 0.9558, and a relative percent deviation (RPD) of 3.4896%.These results indicate that the two-step feature optimization method effectively identifies the most relevant spectral variables, enhancing both detection efficiency and accuracy (1).
Implications for Food Safety and Industry Applications
The implications of this research extend beyond academic interest. The improved Raman spectroscopy-based method provides a practical, cost-effective solution for monitoring aflatoxin contamination in food production and storage. With the growing demand for rapid and precise food safety diagnostics, the adoption of advanced spectroscopic techniques could assist in contamination detection processes in the agricultural and food industries.
By offering a robust, real-time monitoring system, this new methodology could reduce the risks associated with aflatoxin exposure, and help to safeguard public health and ensure compliance with food safety regulations.
The study marks a serious advance in food safety measurement technology. By leveraging Raman spectroscopy with an innovative two-step hybrid feature selection chemometrics strategy, the team has developed an accurate and efficient method for detecting AFB1 in peanuts. This research paves the way for improved food safety protocols and further demonstrates the use of spectroscopic analysis in agricultural quality control (1).
As researchers continue to refine and expand the applications of this technology, its impact on food safety monitoring is expected to grow, providing a useful tool to monitor food contamination and minimize related health risks.
References
(1) Jiang, H.; Zhao, Y.; Li, J.; Zhao, M.; Deng, J.; Bai, X. Quantitative Detection of Aflatoxin B1 in Peanuts Using Raman Spectra and Multivariate Analysis Methods. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2024, 312, 124322. DOI: 10.1016/j.saa.2024.124322
(2) International Agency for Research on Cancer (IARC). Aflatoxin Page. Available at: https://www.iarc.who.int/news-events/mycotoxin-exposure-and-human-cancer-risk-a-systematic-review-of-epidemiological-studies/ (accessed 2025-03-04).
(3) Claeys, L.; Romano, C.; De Ruyck, K.; Wilson, H.; Fervers, B.; Korenjak, M.; et al. Mycotoxin Exposure and Human Cancer Risk: A Systematic Review of Epidemiological Studies. Compr. Rev. Food Sci. Food Saf. 2020, 19 (3), 1447–1465. DOI: 10.1111/1541-4337.12567
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