Detection of aflatoxins in ground maize using a compact and automated Raman spectroscopy with machine learning

Authors: KIM, YONG-KYOUNG - National Agricultural Products Quality Management Service; Qin, Jianwei - Tony Qin; Baek, Insuck; LEE, KYUNG-MIN - National Agricultural Products Quality Management Service; KIM, SUNG-YOUN - National Agricultural Products Quality Management Service; KIM, SEYEON - National Agricultural Products Quality Management Service; Chan, Diane; HERRMAN, TIMOTHY - Texas A&M Agrilife; KIM, NARMKUK - National Agricultural Products Quality Management Service; Kim, Moon

Submitted to: Current Research in Food Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/16/2023
Publication Date: 11/21/2023
Citation: Kim, Y., Qin, J., Baek, I., Lee, K., Kim, S., Kim, S., Chan, D.E., Herrman, T.J., Kim, N., Kim, M.S. 2023. Detection of aflatoxins in ground maize using a compact and automated Raman spectroscopy with machine learning. Current Research in Food Science. 7: Article e100647. https://doi.org/10.1016/j.crfs.2023.100647.
DOI: https://doi.org/10.1016/j.crfs.2023.100647

Interpretive Summary: Aflatoxin contamination of maize is becoming an important issue in human food and animal feed supply. There is a need for an effective and efficient detection method that can be used for rapid onsite inspection of aflatoxin contamination. This study developed an aflatoxin detection method based on a custom developed compact and automated laser Raman spectroscopy system. Using a customized sample holder, Raman spectral data were automatically collected from ground maize samples naturally contaminated with aflatoxin. The data were analyzed using a machine learning method. A classification accuracy was achieved at 95.7% using a machine learning model based on linear discriminant analysis to differentiate aflatoxin levels in ground maize samples. The system and the detection method have the potential to be used at onsite processing locations to rapidly screen food and feed for aflatoxin and other hazardous substances affecting human and animal health. The technique would benefit the food industry and the regulatory agencies (e.g., FDA and USDA FSIS) in enforcing standards of the safety and quality of the maize-related food products.

Technical Abstract: Aflatoxin contamination of maize is emerging as a serious problem in food and feed supply. Therefore, it is necessary to develop a rapid detection method for on-site field use. An aflatoxin detection method was developed using a compact Raman device that can be used in the field. Data were obtained using maize samples naturally contaminated with aflatoxin, and the data were analyzed using a machine learning method. Of the multiple classification models and spectral preprocessing methods evaluated, the best classification accuracy was achieved at 95.7% using linear discriminant analysis (LDA) in combination with Savitzky-Golay 2nd derivative (SG2) preprocessing. Two PLSR models performed similarly, using standard normal variate (SNV) and multiplicative scatter correction (MSC) preprocessing methods, with determination of coefficient values of R2C and R2V of 0.9998 and 0.8322 respectively for SNV, and 0.9916 and 0.8387 respectively for MSC. Based on the results, the new compact automated system may be considered as an equally effective and efficient detection method for analyzing aflatoxins in ground maize compared to conventional analytical methods. This system and method can be used as a tool at on-site field processing locations to rapidly screen food and feed for hazardous substances affecting human and animal health.