Location: Food and Feed Safety ResearchTitle: Use of visible-near-infrared (Vis/NIR) spectroscopy to detect aflatoxin B1 on peanut kernels
|TAO, FEIFEI - Mississippi State University|
|YAO, HAIBO - Mississippi State University|
|HRUSKA, ZUZANA - Mississippi State University|
|Rajasekaran, Kanniah - Rajah|
Submitted to: Applied Spectroscopy
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/29/2018
Publication Date: 2/20/2019
Citation: Tao, F., Yao, H., Hruska, Z., Liu, Y., Rajasekaran, K., Bhatnagar, D. 2019. Use of visible-near-infrared (Vis/NIR) spectroscopy to detect aflatoxin B1 on peanut kernels. Applied Spectroscopy. 73(4):415-423. https://doi.org/10.1177/0003702819829725.
Interpretive Summary: Aflatoxins are a group of highly toxic secondary metabolites produced predominantly by Aspergillus fungi. Aflatoxin contamination can occur in a wide variety of agricultural products including the most popular edible peanuts during both pre- and post-harvest conditions, posing potential severe hazards to human health. However, current methods for detection of aflatoxin contamination and fungal infection are mainly based on wet chemical analysis and classical microbiological techniques, which are generally expensive, time-consuming, destructive to the test samples and require skilled personnel to perform, making them impossible for large-scale non-destructive screening, detection, or integration in an on-line sorting and production system. In this context, the great necessity of developing rapid and non-destructive techniques for aflatoxin contamination and fungal infection in foods has been highlighted. In this study, we utilized the visible/near-infrared (Vis/NIR) spectroscopy over the spectral range of 400-2500 nm to detect contamination of commercial, shelled peanut kernels (runner type) with the predominant aflatoxin B1. Contaminated peanuts with 20 and 100 ppb aflatoxin levels were accurately detected with 84.6% and 95% success. Our study demonstrated that Vis/NIR spectroscopic technique is useful in identifying the aflatoxin-contaminated peanut kernels thus enabling improved food safety.
Technical Abstract: Current methods for detecting aflatoxin contamination of agricultural and food commodities are generally based on wet chemical analyses, which are time-consuming, destructive to test samples and require skilled personnel to perform, making them impossible for large-scale non-destructive screening and on-site detection. In this study, we utilized the visible/near-infrared (Vis/NIR) spectroscopy over the spectral range of 400-2500 nm to detect contamination of commercial, shelled peanut kernels (runner type) with the predominant aflatoxin B1 (AFB1). The artificially contaminated samples were prepared by dropping different amounts of aflatoxin standard dissolved in methanol, onto peanut kernel surface (with skin) to achieve 10, 20, 50, 100, 500 and 1000 µg/kg (ppb) contamination levels, with each group consisting of 30 peanut kernels. Both contaminated and control samples were scanned using Vis/NIR spectroscopy in absorbance mode. The partial least squares discriminant analysis (PLS-DA) models established using the full spectra over different ranges achieved good prediction results. The best average accuracies of 87.92 and 94% were obtained when taking 20 and 100 µg/kg, respectively, as the classification threshold. Both the variable selection methods of random frog (RF) and Monte Carlo uninformative variable elimination (MC-UVE) were used to find the optimal characteristic wavelengths for identifying the surface AFB1-contamination of peanut kernels, and results of the simplified RF-PLS-DA and MC-UVE-PLS-DA models indicated that RF was superior to MC-UVE in this aspect. Moreover, the further simplified RF-LDA models, based on the first 10 wavelengths selected, were also established, achieving average accuracies of 84.59 and 95%, with the classification threshold of 20 and 100 ppb, respectively. The present study demonstrated that the Vis/NIR spectroscopic technique combined with appropriate chemometric methods could be useful in identifying AFB1 contamination of peanut kernels.