Equilibrium Moisture Content of Kabuli, Chickpea, Black Sesame, and White Sesame Seeds

Sesame and chickpea are important crops in Ethiopia because both are major export crops that generate much revenue for both small farmers and the country as a whole. However, there is a lack of information about the fundamental equilibrium moisture content (EMC) relationships among these crops, which would help facilitate better monitoring and storage. Therefore, EMC adsorption and desorption prediction models based on temperature (T) and relative humidity (RH) were developed for the modified Chung-Pfost and modified Henderson models for Kabuli chickpea (KC), black sesame (BS), and white sesame (WS) seeds. The samples for conducting the adsorption and desorption tests were conditioned to various moisture content (MC) levels for the EMC test models. The samples (~500 g) were placed in multiple sealed enclosures equipped with T and RH sensors, which were placed in an environmental chamber where they were exposed to three temperatures (15°C, 25°C, and 35°C). The MCdb ranges used for model development for adsorption and desorption were, respectively, 11.6% to 19.5% and 8.9% to 16.9% for KC samples, 5.0% to 8.7% and 4.3% to 6.9% for BS, and 4.2% to 8.7% and 3.5% to 7.6% for WS. Nonlinear regression was used to determine the model coefficients for the modified Henderson and modified Chung-Pfost equations. The prediction statistics for the adsorption and desorption models yielded an SEE of, respectively, 0.53% and 0.68% MCdb for KC, 0.23% and 0.13% for BS, and 0.28% and 0.25% for WS. The model coefficients obtained in this study will be used in a moisture meter based on EMC measurement, which is currently being used as part of a USAID postharvest project in various African and Asian countries. These EMC models may also be important for other grain operations, which include harvesting, drying, storage, conditioning, and processing

Insect Fragments in Flour: Relationship to Lesser Grain Borer (Coleoptera: Bostrichidae) Infestation Level in Wheat and Rapid Detection Using Near-Infrared Spectroscopy

We determined that the number of insect fragments, quantified using the standard flotation method, in flour milled from wheat infested with larvae, pupae, or preemergent adults of the lesser grain borer, Rhyzopertha dominica (F.), was proportional to infestation level. Wheat infested with a single preemergent adult contributed 28 and 10X as many fragments as wheat infested with a single larva or pupa, respectively. Using regression models that were developed from these data, we predicted that the maximum infestation level that would result in flour with fragment counts below the Food and Drug Administration defect action level (75 fragments/50 g of flour) was 0.95 and 1.5% (380-640 infested kernels/kg of wheat) for pupae and larvae, but it decreased to 0.05% (20 infested kernels/kg) when the grain was infested with preemergent adults. We also reexamined the accuracy and sensitivity of near-infrared spectroscopy (NIRS) for detecting insect fragments in flour by testing three different NIR spectrometers. NIRS-predicted numbers of insect fragments were correlated with the actual number of fragments. NIRS is less precise than the standard flotation method, but it is rapid, nondestructive, does not require extensive sample preparation, and could easily be automated for a more sophisticated sampling protocol for flour based on prescreening samples with NIRS followed up by use of the standard flotation method when necessary

Near-Infrared Spectroscopy Used to Predict Soybean Seed Germination and Vigour

Rapid, non-destructive methods for measuring seed germination and vigour are valuable. Standard germination and seed vigour were determined using 81 soybean seed lots. From these data, seed lots were separated into high and low germinating seed lots as well as high, medium and low vigour seed lots. Near-infrared spectra (950–1650 nm) were collected for training and validation samples for each seed category and used to create partial least squares (PLS) prediction models. For both germination and vigour, qualitative models provided better discrimination of high and low performing seed lots compared with quantitative models. The qualitative germination prediction models correctly identified low and high germination seed lots with an accuracy between 85.7 and 89.7%. For seed vigour, qualitative predictions for the 3-category (low, medium and high vigour) models could not adequately separate high and medium vigour seeds. However, the 2-category (low, medium plus high vigour) prediction models could correctly identify low vigour seed lots between 80 and 100% and the medium plus high vigour seed lots between 96.3 and 96.6%. To our knowledge, the current study is the first to provide near-infrared spectroscopy (NIRS)-based predictive models using agronomically meaningful cut-offs for standard germination and vigour on a commercial scale using over 80 seed lots

Identification of Termite Species and Subspecies of the Genus Zootermopsis Using Near-Infrared Reflectance Spectroscopy

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

Insect Fragments in Flour: Relationship to Lesser Grain Borer (Coleoptera: Bostrichidae) Infestation Level in Wheat and Rapid Detection Using Near-Infrared Spectroscopy

We determined that the number of insect fragments, quantified using the standard flotation method, in flour milled from wheat infested with larvae, pupae, or preemergent adults of the lesser grain borer, Rhyzopertha dominica (F.), was proportional to infestation level. Wheat infested with a single preemergent adult contributed 28 and 10X as many fragments as wheat infested with a single larva or pupa, respectively. Using regression models that were developed from these data, we predicted that the maximum infestation level that would result in flour with fragment counts below the Food and Drug Administration defect action level (75 fragments/50 g of flour) was 0.95 and 1.5% (380-640 infested kernels/kg of wheat) for pupae and larvae, but it decreased to 0.05% (20 infested kernels/kg) when the grain was infested with preemergent adults. We also reexamined the accuracy and sensitivity of near-infrared spectroscopy (NIRS) for detecting insect fragments in flour by testing three different NIR spectrometers. NIRS-predicted numbers of insect fragments were correlated with the actual number of fragments. NIRS is less precise than the standard flotation method, but it is rapid, nondestructive, does not require extensive sample preparation, and could easily be automated for a more sophisticated sampling protocol for flour based on prescreening samples with NIRS followed up by use of the standard flotation method when necessary

Visible and near-infrared spectroscopy detects queen honey bee insemination

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