Short wave infrared (SWIR) hyperspectral imaging technique for examination of aflatoxin B1 (AFB1) on corn kernels

Abstract Aflatoxins are toxic metabolites produced by the fungi Aspergillus flavus and Aspergillus parasiticus. They can contaminate a wide range of crops before harvest and during storage. Contaminated grains are associated with economic losses for cultivators as well as potential health hazards to both humans and animals. In this study, a short-wave infrared (SWIR) hyperspectral imaging technique was utilized to detect aflatoxin contamination on corn kernels. Corn samples were inoculated with four different aflatoxin B1 (AFB1) concentrations (10, 100, 500 and 1000 μg/kg) while control samples were surface-disinfected with a PBS solution. Both infected and control samples were scanned with an SWIR hyperspectral system over the spectral range of 1100–1700 nm. A partial least squares discriminant analysis (PLS-DA) model was developed to categorize control and infected kernels and the highest overall classification accuracy yielded from the developed model was 96.9%. Spectral deviation was observed between the control and inoculated samples as the AFB1 concentrations increased. In addition, the contamination map generated with the PLS-DA model provided the visual appearance of infected samples. Our results suggest that SWIR hyperspectral imaging is a rapid, accurate, and non-destructive technique for the detection of toxic metabolites in grains and could be an alternative to manual techniques.

[1]  Y. R. Chen,et al.  HYPERSPECTRAL REFLECTANCE AND FLUORESCENCE IMAGING SYSTEM FOR FOOD QUALITY AND SAFETY , 2001 .

[2]  B. A. Weinstock,et al.  Prediction of Oil and Oleic Acid Concentrations in Individual Corn (Zea Mays L.) Kernels Using Near-Infrared Reflectance Hyperspectral Imaging and Multivariate Analysis , 2006, Applied spectroscopy.

[3]  M. Shahin,et al.  Original paper: Detection of Fusarium damaged kernels in Canada Western Red Spring wheat using visible/near-infrared hyperspectral imaging and principal component analysis , 2011 .

[4]  R. Barnes,et al.  Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra , 1989 .

[5]  A. Marocco,et al.  Rapid detection of kernel rots and mycotoxins in maize by near-infrared reflectance spectroscopy. , 2005, Journal of agricultural and food chemistry.

[6]  Xingyi Huang,et al.  Review on the Potential Use of Near Infrared Spectroscopy (NIRS) for the Measurement of Chemical Residues in Food , 2013 .

[7]  Tom Fearn,et al.  Practical Nir Spectroscopy With Applications in Food and Beverage Analysis , 1993 .

[8]  Noel D.G. White,et al.  Detection of insect-damaged wheat kernels using near-infrared hyperspectral imaging , 2009 .

[9]  Charles R. Hurburgh,et al.  Feasibility of near infrared spectroscopy for analyzing corn kernel damage and viability of soybean and corn kernels , 2012 .

[10]  Y. R. Chen,et al.  Multispectral detection of fecal contamination on apples based on hyperspectral imagery: Part II. Application of hyperspectral fluorescence imaging , 2002 .

[11]  Fei Liu,et al.  Ripeness Classification of Astringent Persimmon Using Hyperspectral Imaging Technique , 2014, Food and Bioprocess Technology.

[12]  D. Massart,et al.  The influence of data pre-processing in the pattern recognition of excipients near-infrared spectra. , 1999, Journal of Pharmaceutical and Biomedical Analysis.

[13]  Gerald W. Heitschmidt,et al.  Identification of aflatoxin B1 on maize kernel surfaces using hyperspectral imaging , 2014 .

[14]  Moon S. Kim,et al.  Detection of cuticle defects on cherry tomatoes using hyperspectral fluorescence imagery , 2013 .

[15]  Junhong Liu,et al.  Single-Kernel Maize Analysis by Near-Infrared Hyperspectral Imaging , 2004 .

[16]  M. Klich.  Aspergillus flavus: the major producer of aflatoxin. , 2007, Molecular plant pathology.

[17]  Panmanas Sirisomboon,et al.  Application of near infrared spectroscopy to detect aflatoxigenic fungal contamination in rice , 2013 .

[18]  R. Casagrande,et al.  An outbreak of aflatoxin poisoning in dogs associated with aflatoxin B1–contaminated maize products , 2013, Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc.

[19]  Stephen J. Symons,et al.  Quantification of Mildew Damage in Soft Red Winter Wheat Based on Spectral Characteristics of Bulk Samples: A Comparison of Visible-Near-Infrared Imaging and Near-Infrared Spectroscopy , 2013, Food and Bioprocess Technology.

[20]  A. Soldado,et al.  Application of near infrared spectroscopy for rapid detection of aflatoxin B1 in maize and barley as analytical quality assessment , 2009 .

[21]  Paul J. Williams,et al.  Investigation of fungal development in maize kernels using NIR hyperspectral imaging and multivariate data analysis , 2012 .

[22]  Deepak Bhatnagar,et al.  Detecting maize inoculated with toxigenic and atoxigenic fungal strains with fluorescence hyperspectral imagery , 2013 .

[23]  Huazhou Chen,et al.  The Combined Optimization of Savitzky-Golay Smoothing and Multiplicative Scatter Correction for FT-NIR PLS Models , 2013 .

[24]  D. Bhatnagar,et al.  Correlation and classification of single kernel fluorescence hyperspectral data with aflatoxin concentration in corn kernels inoculated with Aspergillus flavus spores , 2010, Food additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment.

[25]  Woo-Il Kim,et al.  The study on characteristics of heat treatment of the direct squeeze cast 7075 wrought Al alloy , 2001 .

[26]  J. Do,et al.  Aflatoxins: Detection, toxicity, and biosynthesis , 2007 .