Floyd E. Dowell

Predicting Scab, Vomitoxin, and Ergosterol in Single Wheat Kernels Using Near-Infrared Spectroscopy

ABSTRACT Near-infrared spectroscopy (NIRS) was used to detect scab damage and estimate deoxynivalenol (DON) and ergosterol levels in single wheat kernels. Results showed that all scab-damaged kernels identified by official inspectors were correctly identified by NIRS. In addition, this system identified more kernels with DON than did a visual inspection. DON and ergosterol were predicted with standard errors of ≈40 and 100 ppm, respectively. All samples with visible scab had single kernels with DON levels >120 ppm, and some kernels contained >700 ppm of DON. This technology may provide a means of rapidly screening samples for potential food safety and quality problems related to scab damage.

Transgenic avidin maize is resistant to storage insect pests

Avidin is a glycoprotein found in chicken egg white, that sequesters the vitamin biotin. Here we show that when present in maize at levels of ≥100 p.p.m., avidin is toxic to and prevents development of insects that damage grains during storage. Insect toxicity is caused by a biotin deficiency, as shown by prevention of toxicity with biotin supplementation. The avidin maize is not, however, toxic to mice when administered as the sole component of their diet for 21 days. These dates suggest that avidin expression in food or feed grain crops can be used as a biopesticide against a spectrum of stored-produce insect pests.*

DETECTING AFLATOXIN IN SINGLE CORN KERNELS BY TRANSMITTANCE AND REFLECTANCE SPECTROSCOPY

Transmittance spectra (500 to 950 nm) and reflectance spectra (550 to 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 discriminant analysis and partial least squares regression. More than 95% of the kernels were correctly classified as containing either high (>100 ppb) or low (<10 ppb) levels of aflatoxin. Classification accuracy for kernels between 10 and 100 ppb was only about 25%, but these kernels do not usually affect total sample concentrations and are not as important. Results were similar when using either transmittance or reflectance, and when using either discriminant analysis or partial least squares regression. The two–feature discriminant analysis of transmittance data gave the best results. However, for automated high–speed detection and sorting, instrumentation that uses 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.

Reflectance and Transmittance Spectroscopy Applied to Detecting Fumonisin in Single Corn Kernels Infected with Fusarium verticillioides

ABSTRACT Reflectance and transmittance visible and near-infrared spectroscopy were used to detect fumonisin in single corn kernels infected with Fusarium verticillioides. Kernels with >100 ppm and <10 ppm could be classed accurately as fumonisin positive or negative, respectively. Classification results were generally better for oriented kernels than for kernels that were randomly placed in the spectrometer viewing area. Generally, models based on reflectance spectra have higher correct classification than models based on transmittance spectra. Statistical analyses indicated that including near-infrared wavelengths in calibrations improved classifications, and some calibrations were improved by including visible wavelengths. Thus, the color and chemical constituents of the infected kernel contribute to classification models. These results show that this technology can be used to rapidly and nondestructively screen single corn kernels for the presence of fumonisin, and may be adaptable to on-line detection...

Automated Color Classification of Single Wheat Kernels Using Visible and Near-Infrared Reflectance

ABSTRACT Modification of an existing single kernel wheat characterization system allowed collection of visible and near-infrared (NIR) reflectance spectra (450–1,688 nm) at a rate of 1 kernel/4 sec. The spectral information was used to classify red and white wheats in an attempt to remove subjectivity from class determinations. Calibration, validation, and prediction results showed that calibrations using partial least squares regression and derived from the full wavelength profile correctly classed more kernels than either the visible region (450–700 nm) or the NIR region (700–1,688 nm). Most results showed >99% correct classification for single kernels when using the visible and NIR regions. Averaging of single kernel classifications resulted in 100% correct classification of bulk samples.

Effects of Integrating Cultivar Resistance and Fungicide Application on Fusarium Head Blight and Deoxynivalenol in Winter Wheat

Fusarium head blight (FHB) or scab, incited by Fusarium graminearum, can cause significant economic losses in small grain production. Five field experiments were conducted from 2007 to 2009 to determine the effects on FHB and the associated mycotoxin deoxynivalenol (DON) of integrating winter wheat cultivar resistance and fungicide application. Other variables measured were yield and the percentage of Fusarium-damaged kernels (FDK). The fungicides prothioconazole + tebuconazole (formulated as Prosaro 421 SC) were applied at the rate of 0.475 liters/ha, or not applied, to three cultivars (experiments 1 to 3) or six cultivars (experiments 4 and 5) differing in their levels of resistance to FHB and DON accumulation. The effect of cultivar on FHB index was highly significant (P < 0.0001) in all five experiments. Under the highest FHB intensity and no fungicide application, the moderately resistant cultivars Harry, Heyne, Roane, and Truman had less severe FHB than the susceptible cultivars 2137, Jagalene, Overley, and Tomahawk (indices of 30 to 46% and 78 to 99%, respectively). Percent fungicide efficacy in reducing index and DON was greater in moderately resistant than in susceptible cultivars. Yield was negatively correlated with index, with FDK, and with DON, whereas index was positively correlated with FDK and with DON, and FDK and DON were positively correlated. Correlation between index and DON, index and FDK, and FDK and DON was stronger in susceptible than in moderately resistant cultivars, whereas the negative correlation between yield and FDK and yield and DON was stronger in moderately resistant than in susceptible cultivars. Overall, the strongest correlation was between index and DON (0.74 ≤ R ≤ 0.88, P ≤ 0.05). The results from this study indicate that fungicide efficacy in reducing FHB and DON was greater in moderately resistant cultivars than in susceptible ones. This shows that integrating cultivar resistance with fungicide application can be an effective strategy for management of FHB and DON in winter wheat.

Non-destructive determination of age and species of anopheles gambiae s.l. using near-infrared spectroscopy

Determining malaria vector species and age is crucial to measure malaria risk. Although different in ecology and susceptibility to control, the African malaria vectors Anopheles gambiae sensu stricto and An. arabiensis are morphologically similar and can be differentiated only by molecular techniques. Furthermore, few reliable methods exist to estimate the age of these vectors, which is a key predictor of malaria transmission intensity. We evaluated the use of near-infrared spectroscopy (NIRS) to determine vector species and age. This non-destructive technique predicted the species of field-collected mosquitoes with approximately 80% accuracy and predicted the species of laboratory-reared insects with almost 100% accuracy. The relative age of young or old females was predicted with approximately 80% accuracy, and young and old insects were predicted with > or = 90% accuracy. For applications where rapid assessment of the age structure and species composition of wild vector populations is needed, NIRS offers a valuable alternative to traditional methods.

Detecting counterfeit antimalarial tablets by near-infrared spectroscopy

Counterfeit antimalarial drugs are found in many developing countries, but it is challenging to differentiate between genuine and fakes due to their increasing sophistication. Near-infrared spectroscopy (NIRS) is a powerful tool in pharmaceutical forensics, and we tested this technique for discriminating between counterfeit and genuine artesunate antimalarial tablets. Using NIRS, we found that artesunate tablets could be identified as genuine or counterfeit with high accuracy. Multivariate classification models indicated that this discriminatory ability was based, at least partly, on the presence or absence of spectral signatures related to artesunate. This technique can be field-portable and requires little training after calibrations are developed, thus showing great promise for rapid and accurate fake detection.

Classification of Fungal-Damaged Soybean Seeds Using Near-Infrared Spectroscopy

Abstract Fungal damage has a devastating impact on soybean quality and end-use. The current visual method for identifying damaged soybean seeds is based on discoloration and is subjective. The objective of this research was to classify healthy and fungal-damaged soybean seeds and discriminate among various types of fungal damage using near-infrared (NIR) spectroscopy. A diode-array NIR spectrometer, which measured reflectance [log(1/R)] from 400 to 1700 nm, was used to obtain spectra from single soybean seeds. Partial least square (PLS) and neural network models were developed to differentiate healthy and fungal-damaged seeds. The highest classification accuracy was more than 99% when the wavelength region of 490–1690 nm was used under a two-class PLS model. Neural network models yielded higher classification accuracy than the PLS models for five-class classification. The average of correct classifications was 93.5% for the calibration sample set and 94.6% for the validation sample set. Classification accuracies of the validation sample set were 100, 99, 84, 94, and 96% corresponding to healthy seeds, Phomopsis, Cercospora kikuchii, soybean mosaic virus (SMV), and downy mildew damaged seeds, respectively. #Contribution No. 03-163-J from the Kansas Agricultural Experiment Station. Mention of a trademark or proprietary product does not constitute a guarantee or warranty of the product by the U.S. Department of Agriculture and does not imply its approval to the exclusion of other products that also may be suitable.

Detection of insect fragments in wheat flour by near-infrared spectroscopy

Insect fragments in commercial wheat flour are a major concern to the milling industry because consumers expect high quality and wholesome products at the retail level. Thus, the US Food and Drug Administration (FDA) has established a defect action level of 75 insect fragments per 50 g of flour. Millers routinely test their wheat flour to comply with this federal requirement and to deliver sound flour to their consumers. The current standard flotation method for detecting fragments in flour is expensive and labor intensive. Therefore, we examined the possible use of a rapid, near-infrared spectroscopy (NIRS) method for detecting insect fragments in wheat flour. We also compared the sensitivity and accuracy of the NIRS method with that of the current standard flotation method. Fragment counts with both techniques were significantly correlated with the actual number of fragments present in flour samples. However, the flotation method was more sensitive than the NIRS method with fragment counts below the FDA defect action level. We were unable to predict whether the number of fragments in a sample exceeded the FDA action level with our NIRS instrumentation. However, we were able to predict accurately whether flour samples contained less than or more than 130 fragments. Although current NIRS instruments are unable to detect insect fragments at the FDA action level, this method should be re-examined in the future because NIRS technology is rapidly improving.