Surface-Enhanced Raman Spectroscopy (SERS) for the rapid detection of mycotoxins in agricultural products

Mycotoxin contamination in food and feed crops remains one of the greatest global health concerns. Therefore, there is a need for rapid, low-cost, and sensitive techniques to detect mycotoxins in agricultural products. Surface-Enhanced Raman Spectroscopy (SERS) is a vibrational surface-sensitive technique which enhances conventional Raman scattering through the adsorption of molecules close to the surface of roughened noble metals, commonly made of gold (Au) or silver (Ag). Its unique fingerprinting ability allows for individual analytes to be detected and identified at low levels. In this study, four nanosubstrates were fabricated and applied to determine the limit of detection (LOD) for deoxynivalenol (DON). The SERS enhancement of fabricated Au nanoparticles (AuNPs), Ag nanoparticles (AgNPs), Au nanostars (AuNSs) and Au@Ag core-shell nanoparticles (CSNPs) was determined in the presence of a fluorescent dye; Rhodamine 6G (R6G). The particles were applied to detect the fingerprint spectra of DON standards either by mixing in solution or by assembling the mixture onto a solid substrate (filter paper or aluminum tin foil). The LOD for DON was determined as 3 µg/kg (ppb), which is much lower than the European recommended limits for DON in cereals intended for human consumption (750 µg/kg). Wheat samples naturally contaminated with DON were extracted and analysed using the same approach. It was found that matrix interferences from the wheat sample reduced the SERS intensity and therefore this is an area that requires future improvements. To observe whether chemometric modelling could improve matrix effects and multiplexing capabilities two interfering mycotoxins; aflatoxin B1 (AFB1) and ochratoxin A (OTA) were also analysed. By exploiting supervised Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA), the model could successfully discriminate between three mycotoxins: DON, OTA and AFB1. The internal validation of the OPLS-DA model produced a R2 and Q2 value of 0.974 and 0.979. Overall, the results from this preliminary study reveal that there is substantial merit in developing SERS-based applications combined with statistical modelling to help quantify levels of mycotoxins within agricultural products.