|Maghirang, Elizabeth - KANSAS ST UNIV, MANHATTAN|
|Xie, Feng - KANSAS ST UNIV, MANHATTAN|
Submitted to: Transactions of the ASAE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: April 1, 2001
Publication Date: November 1, 2001
Citation: Pearson, T.C., Wicklow, D.T., Maghirang, E.B., Xie, F., Dowell, F.E. Detecting Aflatoxin in Single Corn Kernels by Using Transmittance and Reflectance Spectroscopy. Transactions of the ASAE. 44(5):1247-1254. Interpretive Summary: A rapid, non-destructive, method for detecting whole corn kernels with aflatoxin was developed. Samples of corn are usually inspected using fluorescence as an indication of aflatoxin content. However, this method requires that the kernels be ground up. Also, screening for aflatoxin contamination by fluorescence can cause many corn samples that have no aflatoxin to be chemically analyzed by expensive and time-consuming methods. This can cause a large delay in the corn material flow. The new method developed for detecting corn with aflatoxin uses near infrared light. This method requires no sample preparation and can provide instant results. In addition, the method can be easily automated so large sample sizes can be used. This method should be able to reduce the number of corn samples with no aflatoxin that need to be chemically analyzed for aflatoxin due to inaccuracies of the fluorescence method. Also, this technology might be implemented into sorting machines so that kernels with high aflatoxin can be removed from process streams.
Technical Abstract: Transmittance spectra (500 to 900 nm) and reflectance spectra (550-1700 nm) were analyzed to determine if they could be used to distinguish aflatoxin contamination in single whole corn kernels. Spectra were obtained on whole corn kernels exhibiting various levels of bright greenish-yellow fluorescence. Afterwards, each kernel was analyzed for aflatoxin following the USDA-FGIS Aflatest affinity chromatography procedures. Spectra were analyzed using discriminate analysis and partial least squares regression. More than 95% of kernels were correctly classified as containing either high (>100 ppb) or low (<10 ppb) levels of aflatoxin. Results were similar when using either transmittance or reflectance, and when using either discriminate analysis or partial least squares regression. The two-feature discriminate analysis of transmittance data gave the best results. However, for automated high-speed detection and sorting, instrumentation that utilizes single-feature reflectance spectra may be more practically implemented. This technology should provide the corn industry with a valuable tool for rapidly detecting aflatoxin in corn.