Elizabeth B. Maghirang

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 nthey could be used to distinguish aflatoxin contamination in single whole corn kernels. Spectra were obtained on whole corn nkernels exhibiting various levels of bright greenish–yellow fluorescence. Afterwards, each kernel was analyzed for aflatoxin nfollowing the USDA–FGIS Aflatest affinity chromatography procedures. Spectra were analyzed using discriminant analysis nand partial least squares regression. More than 95% of the kernels were correctly classified as containing either high n(>100 ppb) or low (<10 ppb) levels of aflatoxin. Classification accuracy for kernels between 10 and 100 ppb was only about n25%, but these kernels do not usually affect total sample concentrations and are not as important. Results were similar when nusing either transmittance or reflectance, and when using either discriminant analysis or partial least squares regression. The ntwo–feature discriminant analysis of transmittance data gave the best results. However, for automated high–speed detection nand sorting, instrumentation that uses single–feature reflectance spectra may be more practically implemented. This ntechnology 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...

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.

AUTOMATED DETECTION OF SINGLE WHEAT KERNELS CONTAINING LIVE OR DEAD INSECTS USING NEAR--INFRARED REFLECTANCE SPECTROSCOPY

An automated near–infrared (NIR) reflectance system was used over a two–month storage period to detect single nwheat kernels that contained live or dead internal rice weevils at various stages of growth. Correct classification of sound nkernels plus kernels containing live pupae, large larvae, medium–sized larvae, and small larvae averaged 94%, 93%, 84%, nand 63%, respectively. Pupae + large larvae calibrations were developed for live (day 1) and dead (days 7, 14, 28, 42, and n56) internal insects. Validation results showed that the live pupae +live large larvae calibration correctly classified 86% to n96% of dead pupae + dead large larvae validation samples. The dead pupae + dead large larvae calibration correctly detected nthe presence of live pupae + live large larvae with an accuracy of 92% to 93%. Thus, wheat kernels containing either live nor dead insects can be used to develop calibrations for detecting both live and dead insects in wheat. These findings will nimpact how calibration sample sets can be handled. Results indicated that immediate sample processing for creating ncalibrations may no longer be necessary; internal insects can be killed and calibrations created at a later time without nsacrificing accuracy. Additionally, laboratories can share these same calibration samples to save time and resources.

Predicting Wheat Quality Characteristics and Functionality Using Near-Infrared Spectroscopy

Cereal Chem. 83(5):529–536 The accuracy of using near-infrared spectroscopy (NIRS) for predicting 186 grain, milling, flour, dough, and breadmaking quality parameters of 100 hard red winter (HRW) and 98 hard red spring (HRS) wheat and flour samples was evaluated. NIRS shows the potential for predicting protein content, moisture content, and flour color b* values with accuracies suitable for process control (R 2 > 0.97). Many other parameters were predicted with accuracies suitable for rough screening including test weight, average single kernel diameter and moisture content, SDS sedimentation volume, color a* values, total gluten content, mixograph, farinograph, and alveograph parameters, loaf volume, specific loaf volume, baking water absorption and mix time, gliadin and glutenin content, flour particle size, and the percentage of dark hard and vitreous kernels. Similar results were seen when analyzing data from either HRW or HRS wheat, and when predicting quality using spectra from either grain or flour. However, many attributes were correlated to protein content and this relationship influenced classification accuracies. When the influence of protein content was removed from the analyses, the only factors that could be predicted by NIRS with R 2 > 0.70 were moisture content, test weight, flour color, free lipids, flour particle size, and the percentage of dark hard and vitreous kernels. Thus, NIRS can be used to predict many grain quality and functionality traits, but mainly because of the high correlations of these traits to protein content. Quality characteristics of wheat (Triticum aestivum L.) whole grain, flour, dough, and bread can be measured by various qualitative and quantitative tests. These measurements are typically used to determine value or used to predict functionality and end use quality. There are standard or recommended measurement methods for many of these quality parameters such as those found in the Approved Methods of AACC International (2000) and the United States Department of Agriculture (USDA) Grain Inspection Handbook (USDA 2004). These methods are generally difficult and time-consuming, and most cannot be used to rapidly measure quality characteristics and functionality.

An Automated Near-Infrared System for Selecting Individual Kernels Based on Specific Quality Characteristics

ABSTRACT An automated sorting system was developed that nondestructively measured quality characteristics of individual kernels using near-infrared (NIR) spectra. This single-kernel NIR system was applied to sorting wheat (Triticum aestivum L.) kernels by protein content and hardness, and proso millet (Panicum miliaceum L.) into amylose-bearing and amylose-free fractions. Single wheat kernels with high protein content could be sorted from pure lines so that the high-protein content portion was 3.1 percentage points higher than the portion with the low-protein kernels. Likewise, single wheat kernels with specific hardness indices could be removed from pure lines such that the hardness index in the sorted samples was 29.4 hardness units higher than the soft kernels. The system was able to increase the waxy, or amylose-free, millet kernels in segregating samples from 94% in the unsorted samples to 98% in the sorted samples. The portion of waxy millet kernels in segregating samples was increased from 32% in t...

Near-Infrared Spectroscopic Method for Identification of Fusarium Head Blight Damage and Prediction of Deoxynivalenol in Single Wheat Kernels

Cereal Chem. 87(6):511–517 Fusarium Head Blight (FHB), or scab, can result in significant crop yield losses and contaminated grain in wheat (Triticum aestivum L.). Growing less susceptible cultivars is one of the most effective methods for managing FHB and for reducing deoxynivalenol (DON) levels in grain, but breeding programs lack a rapid and objective method for identifying the fungi and toxins. It is important to estimate proportions of sound kernels and Fusarium-damaged kernels (FDK) in grain and to estimate DON levels of FDK to objectively assess the resistance of a cultivar. An automated single kernel near-infrared (SKNIR) spectroscopic method for identification of FDK and for estimating DON levels was evaluated. The SKNIR system classified visually sound and FDK with an accuracy of 98.8 and 99.9%, respectively. The sound fraction had no or very little accumulation of DON. The FDK fraction was sorted into fractions with high or low DON content. The kernels identified as FDK by the SKNIR system had better correlation with other FHB assessment indices such as FHB severity, FHB incidence and kernels/g than visual FDK%. This technique can be successfully employed to nondestructively sort kernels with Fusarium damage and to estimate DON levels of those kernels. Single kernels could be predicted as having low ( 60 ppm) DON with ≈96% accuracy. Single kernel DON levels of the high DON kernels could be estimated with R 2 = 0.87 and standard error of prediction (SEP) of 60.8 ppm. Because the method is nondestructive, seeds may be saved for generation advancement. The automated method is rapid (1 kernel/sec) and sorting grains into several fractions depending on DON levels will provide breeders with more information than techniques that deliver average DON levels from bulk seed samples.

Relationship of Bread Quality to Kernel, Flour, and Dough Properties

ABSTRACT This study measured the relationship between bread quality and 49 hard red spring (HRS) or 48 hard red winter (HRW) grain, flour, and dough quality characteristics. The estimated bread quality attributes included loaf volume, bake mix time, bake water absorption, and crumb grain score. The best-fit models for loaf volume, bake mix time, and water absorption had R2 values of 0.78–0.93 with five to eight variables. Crumb grain score was not well estimated, and had R2 values ≈0.60. For loaf volume models, grain or flour protein content was the most important parameter included. Bake water absorption was best estimated when using mixograph water absorption, and flour or grain protein content. Bake water absorption models could generally be improved by including farinograph, mixograph, or alveograph measurements. Bake mix time was estimated best when using mixograph mix time, and models could be improved by including glutenin data. When the data set was divided into calibration and prediction sets, th...

Hardness Measurement of Bulk Wheat by Single-Kernel Visible and Near-Infrared Reflectance Spectroscopy

ABSTRACT Reflectance spectra (400 to 1700 nm) of single wheat kernels collected using the Single Kernel Characterization System (SKCS) 4170 were analyzed for wheat grain hardness using partial least squares (PLS) regression. The wavelengths (650 to 700, 1100, 1200, 1380, 1450, and 1670 nm) that contributed most to the ability of the model to predict hardness were related to protein, starch, and color differences. Slightly better prediction results were observed when the 550–1690 nm region was used compared with 950–1690 nm region across all sample sizes. For the 30-kernel mass-averaged model, the hardness prediction for 550–1690 nm spectra resulted in a coefficient of determination (R2) = 0.91, standard error of cross validation (SECV) = 7.70, and relative predictive determinant (RPD) = 3.3, while the 950–1690 nm had R2 = 0.88, SECV = 8.67, and RPD = 2.9. Average hardness of hard and soft wheat validation samples based on mass-averaged spectra of 30 kernels was predicted and compared with the SKCS 4100 re...

Comparison of Quality Characteristics and Breadmaking Functionality of Hard Red Winter and Hard Red Spring Wheat

ABSTRACT Various whole-kernel, milling, flour, dough, and breadmaking quality parameters were compared between hard red winter (HRW) and hard red spring (HRS) wheat. From the 50 quality parameters evaluated, values of only nine quality characteristics were found to be similar for both classes. These were test weight, grain moisture content, kernel size, polyphenol oxidase content, average gluten index, insoluble polymeric protein (%), free nonpolar lipids, loaf volume potential, and mixograph tolerance. Some of the quality characteristics that had significantly higher levels in HRS than in HRW wheat samples included grain protein content, grain hardness, most milling and flour quality measurements, most dough physicochemical properties, and most baking characteristics. When HRW and HRS wheat samples were grouped to be within the same wheat protein content range (11.4–15.8%), the average value of many grain and breadmaking quality characteristics were similar for both wheat classes but significant differen...