Determining mosquito age is important to evaluate vector control programs because the ability to transmit diseases is age dependent. Current age-grading techniques require dissection or RNA extraction. Near infrared spectroscopy has been used to rapidly and nondestructively determine the age of fresh mosquitoes and specimens stored in RNAlater, but other preservation techniques have not been examined. Thus, in this study, we investigate whether age can be predicted from insects preserved by various common methods. Results from this study show that age can be predicted from mosquitoes preserved with desiccants, ethanol, Carnoy, RNAlater, or refrigeration with confidence intervals < 1.4 days. The best results were generally obtained from mosquitoes stored using desiccants, RNAlater, or refrigeration.

Early stages of lineage divergence in insect herbivores are often related to shifts in host plant use and divergence in mating capabilities, which may lead to sexual isolation of populations of herbivorous insects. We examined host preferences, degree of differentiation in mate choice, and divergence in cuticular morphology using near-infrared spectroscopy in the grasshopper Hesperotettix viridis aiming to understand lineage divergence. In Kansas (USA), H. viridis is an oligophagous species feeding on Gutierrezia and Solidago host species. To identify incipient mechanisms of lineage divergence and isolation, we compared host choice, mate choice, and phenotypic divergence among natural grasshopper populations in zones of contact with populations encountering only one of the host species. A significant host-based preference from the two host groups was detected in host-paired feeding preference studies. No-choice mate selection experiments revealed a preference for individuals collected from the same host species independent of geographic location, and little mating was observed between individuals collected from different host species. Female mate choice tests between males from the two host species resulted in 100% fidelity with respect to host use. Significant differentiation in colour and cuticular composition of individuals from different host plants was observed, which correlated positively with host choice and mate choice. No evidence for reinforcement in the zone of contact was detected, suggesting that divergent selection for host plant use promotes sexual isolation in this species.

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.

The abdomens of honey bee queens and semen from drone bees were analyzed by visible and near-infrared spectroscopy. Mated honey bee queens could be distinguished from virgin queens by their absorption spectra with 100% accuracy. Spectra of semen showed that classifications of queens were likely influenced by the presence or absence of semen in the queen spermathecae. However, physiological or morphological changes that occur in the queens after mating probably influenced the classifications also.

ABSTRACTThis 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, the loaf volume and bake mix time models still looked promising for screening samples. When including only variables that could be rapidly measured (protein content, test weight, single kernel moisture content, single kernel diameter, single kernel hardness, bulk moisture content, and dark hard and vitreous kernels), only loaf volume could be predicted with accuracies adequate for screening samples.

We developed automated visible and near-infrared (NIR) spectroscopy procedures and instrumentation to select kernels with specific hardness, amylose content, protein content, and color traits to enhance the development of wheat breeding lines. The system also shows potential to sort for other characteristics such as scab damage, vomitoxin levels, ergosterol levels, vitreousness, sprout damage, moisture content, selenium content, and Karnal bunt-infected kernels. Our single kernel near-infrared system can sort single kernels based on specified properties at a rate of about one kernel/2s (500-1000g/day). We also have high-speed sorting technology that can sort visible defects at rates as high as 80,000 kernels/s (300 bu/hr). While most of our work has been with wheat, we have also shown applications for proso millet, barley, rice, and sorghum.

Dampwood termites of the genus Zootermopsis (Isoptera: Termopsidae) are an abundant group of basal termites found in temperate forests of western North America. Three species are currently recognized in the genus and one of these species is subdivided into two subspecies. Although morphological and genetic characters are useful in differentiating among the three species and the two subspecies, respectively, only hydrocarbon analysis can enable differentiation both among the three species and the two subspecies. Due to the limitations of hydrocarbon analysis, such as the need for fresh specimens, alternative methods that could rapidly and accurately identify Zootermopsis would be useful. Using a partial least squares analysis of near-infrared spectra, each of the Zootermopsis species and subspecies were identified with greater than 95% and 80% accuracy, respectively. Neural network analysis of the near-infrared spectra successfully enabled the identification of the species and subspecies with greater than 99% accuracy. The inexpensive, reproducible, and rapid nature of near-infrared spectroscopy makes it a viable alternative to morphological, hydrocarbon, or genetic analysis for identifying Zootermopsis.

ABSTRACTAn 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 the unsorted samples to 55% after sorting. Thus, this technology can be used to enrich the desirable class within segregating populations in breeding programs, to increase the purity of heterogeneous advanced or released lines, or to measure the distribution of quality within samples during the marketing process.

ABSTRACTVarious 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 differences still existed. Values that were higher for HRW wheat flour were color b*, free polar lipids content, falling number, and farinograph tolerance. Values that were higher for HRS wheat flour were geometric mean diameter, quantity of insoluble polymeric proteins and gliadins, mixograph mix time, alveograph configuration ratio, dough weight, crumb grain score, and SDS sedimentation volume. This research showed that the grain and flour quality of HRS wheat generally exceeds that of HRW wheat whether or not samples are grouped to include a similar protein content range.

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.