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Proceedings Paper

Rapid and non-destructive detection of aflatoxin contamination of peanut kernels using visible/near-infrared (Vis/NIR) spectroscopy
Author(s): Feifei Tao; Haibo Yao; Zuzana Hruska; Yongliang Liu; Kanniah Rajasekaran; Deepak Bhatnagar
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Paper Abstract

Aflatoxin contamination can occur in a wide variety of agricultural products pre- and post-harvest, posing potential severe health hazards to human and livestock. However, current methods for detecting aflatoxins 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 shelled commercial peanut kernels with the predominant aflatoxin B1 (AFB1). Our results indicated the usefulness of Vis/NIR spectroscopy combined with the chemometric techniques of partial least squares discriminant analysis (PLS-DA) and least squares support vector machine (LS-SVM) in identifying the AFB1 contamination of peanut kernels. Both PLS-DA and LS-SVM methods provided satisfactory classification results using the full spectral information over the ranges of 410-1070 (I), 1120-2470 nm (II) and I+II. Based on the classification threshold of 20 ppb, the best PLS-DA prediction results using the full spectra yielded the average accuracy of 87.9% and overall accuracy of 88.6%. With 100 ppb as the classification threshold, the best PLS-DA model using the full spectra achieved the average accuracy of 94.0% and overall accuracy of 91.4%. Using the full spectra, the best average accuracies recorded by LS-SVM were 90.9% and 98.0%, with the classification thresholds of 20 and 100 ppb, respectively. Correspondingly, the best overall accuracies by LS-SVM were 90.0% and 97.1%. In addition, the simplified models of CARS-PLS-DA and CARS-LS-SVM also demonstrated good prediction capability in identifying the AFB1 contamination from peanut surface. Based on both classification thresholds of 20 and 100 ppb, the best CARS-PLS-DA and CARS-LS-SVM prediction results were ≥ 90.0% in both average accuracy and overall accuracy. Most importantly, the computation complexity and the employed data dimensionality were significantly reduced by using the simplified models.

Paper Details

Date Published: 15 May 2018
PDF: 11 pages
Proc. SPIE 10665, Sensing for Agriculture and Food Quality and Safety X, 106650K (15 May 2018); doi: 10.1117/12.2304399
Show Author Affiliations
Feifei Tao, Mississippi State Univ. (United States)
Haibo Yao, Mississippi State Univ. (United States)
Zuzana Hruska, Mississippi State Univ. (United States)
Yongliang Liu, Agricultural Research Service (United States)
Kanniah Rajasekaran, Agricultural Research Service (United States)
Deepak Bhatnagar, Agricultural Research Service (United States)

Published in SPIE Proceedings Vol. 10665:
Sensing for Agriculture and Food Quality and Safety X
Moon S. Kim; Kuanglin Chao; Bryan A. Chin; Byoung-Kwan Cho, Editor(s)

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