Application of calibrations to hyperspectral images of food grains: example for wheat falling number

The presence of a few kernels with sprouting problems in a batch of wheat can result in enzymatic activity sufficient to compromise flour functionality and bread quality. This is commonly assessed using the Hagberg Falling Number (HFN) method, which is a batch analysis. Hyperspectral imaging (HSI) can provide analysis at the single grain level with potential for improved performance. The present paper deals with the development and application of calibrations obtained using an HSI system working in the near infrared (NIR) region (~900–2500 nm) and reference measurements of HFN. A partial least squares regression calibration has been built using 425 wheat samples with a HFN range of 62–318 s, including field and laboratory pre-germinated samples placed under wet conditions. Two different approaches were tested to apply calibrations: i) application of the calibration to each pixel, followed by calculation of the average of the resulting values for each object (kernel); ii) calculation of the average spectrum for each object, followed by application of the calibration to the mean spectrum. The calibration performance achieved for HFN (R2 = 0.6; RMSEC ~ 50 s; RMSEP ~ 63 s) compares favourably with other studies using NIR spectroscopy. Linear spectral pre-treatments lead to similar results when applying the two methods, while non-linear treatments such as standard normal variant showed obvious differences between these approaches. A classification model based on linear discriminant analysis (LDA) was also applied to segregate wheat kernels into low (250 s) HFN groups. LDA correctly classified 86.4% of the samples, with a classification accuracy of 97.9% when using HFN threshold of 150 s. These results are promising in terms of wheat quality assessment using a rapid and non-destructive technique which is able to analyse wheat properties on a single-kernel basis, and to classify samples as acceptable or unacceptable for flour production

SANDBOX CONTRACTING: AN EVALUATION OF GAMIFIED VS. TRADITIONAL CONTRACTING TRAINING METHODS AT THE USAF ENLISTED CONTRACTING TECHNICAL SCHOOL

This thesis involved an evaluation of gamified versus current (traditional) training methods employed by the instructors and faculty at the Air Force’s 344th Training Squadron (344 TRS) at Lackland Air Force Base, Texas, and by the professors at the Naval Postgraduate School (NPS) in Monterey, California. For our project, we designed and developed a first-person shooter (FPS) video game, titled Sandbox Contracting, that teaches the player basic contracting skills. Over the course of six weeks, we utilized this FPS video game to conduct an experiment in which a control group received the current (traditional) training methods employed by 344 TRS and NPS and a treatment group received the gamified version of the training. We assessed each student’s learning as well as their reaction to the assigned learning modality (traditional versus gaming) using post-training evaluation surveys. Traditional training methods outperformed gamified methods in most cases, but not all. We found that game design and mechanics impacted the student’s reactions and ultimately, the success of using gamified methods for learning. Additionally, the results demonstrated a genuine interest in using games for learning among the Air Force contracting students, given the right game design and mechanics. Lastly, we offer suggestions for areas in which further research should be conducted in the gamified versus traditional training arena.Outstanding ThesisCaptain, United States Air ForceCaptain, United States Air ForceCaptain, United States Air ForceApproved for public release. Distribution is unlimited

Gravitational fragmentation and the formation of brown dwarfs in stellar clusters

We investigate the formation of brown dwarfs and very low-mass stars through the gravitational fragmentation of infalling gas into stellar clusters. The gravitational potential of a forming stellar cluster provides the focus that attracts gas from the surrounding molecular cloud. Structures present in the gas grow, forming filaments flowing into the cluster centre. These filaments attain high gas densities due to the combination of the cluster potential and local self-gravity. The resultant Jeans masses are low, allowing the formation of very low-mass fragments. The tidal shear and high velocity dispersion present in the cluster preclude any subsequent accretion thus resulting in the formation of brown dwarfs or very low-mass stars. Ejections are not required as the brown dwarfs enter the cluster with high relative velocities, suggesting that their disc and binary properties should be similar to that of low-mass stars. This mechanism requires the presence of a strong gravitational potential due to the stellar cluster implying that brown dwarf formation should be more frequent in stellar clusters than in distributed populations of young stars. Brown dwarfs formed in isolation would require another formation mechanism such as due to turbulent fragmentation.Comment: 8 pages, 7 figures. MNRAS, in pres

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Hyperspectral imaging for non-destructive prediction of fermentation index, polyphenol content and antioxidant activity in single cocoa beans

The aim of the current work was to use hyperspectral imaging (HSI) in the spectral range 1000-2500 nm to quantitatively predict fermentation index (FI), total polyphenols (TP) and antioxidant activity (AA) of individual dry fermented cocoa beans scanned on a single seed basis. Seventeen cocoa bean batches were obtained and 10 cocoa beans were used from each batch. PLS regression models were built on 170 samples. The developed HSI predictive models were able to quantify three quality-related parameters with sufficient performance for screening purposes, with external validation R2 of 0.50 (RMSEP=0.27, RPD=1.40), 0.70 (RMSEP=34.1 mg ferulic acid g-1, RPD=1.77) and 0.74 (60.0 mmol Trolog kg-1, RPD=1.91) for FI, TP and AA, respectively. The calibrations were subsequently applied at a single bean and pixel level, so that the distribution was visualised within and between single seeds. HSI is thus suggested as a promising approach to estimate cocoa bean composition rapidly and non-destructively, thus offering a valid tool for food inspection and quality control

Gamification in Defense Acquisition Training and Education

Excerpt from the Proceedings of the Nineteenth Annual Acquisition Research SymposiumLeveraging research conducted as part of an Acquisition Research Program sponsored thesis, this paper expands upon an essay written by our research team (submitted to USNI), in which we argue that gamified learning (building games to promote learning of traditional material) presents a unique opportunity for enhancing education and training within the defense workforce. We provide an in-depth explanation of what gamification is and why it might be particularly useful for enhancing learning in non-traditional defense contexts, using defense acquisition as a test case. We present initial evidence from our empirical research to highlight the opportunities and challenges for advancing military education into the present age through gamified learning methods. Finally, we outline future directions for research in gamification for defense applications, bringing attention to the need for collaboration across the defense-focused entities exploring the potential for gaming in future defense education and training.Approved for public release; distribution is unlimited

Prediction of coffee aroma from single roasted coffee beans by hyperspectral imaging

Coffee aroma is critical for consumer liking and enables price differentiation of coffee. This study applied hyperspectral imaging (1000–2500 nm) to predict volatile compounds in single roasted coffee beans, as measured by Solid Phase Micro Extraction-Gas Chromatography-Mass Spectrometry and Gas Chromatography-Olfactometry. Partial least square (PLS) regression models were built for individual volatile compounds and chemical classes. Selected key aroma compounds were predicted well enough to allow rapid screening (R2 greater than 0.7, Ratio to Performance Deviation (RPD) greater than 1.5), and improved predictions were achieved for classes of compounds - e.g. aldehydes and pyrazines (R2 ∼ 0.8, RPD ∼ 1.9). To demonstrate the approach, beans were successfully segregated by HSI into prototype batches with different levels of pyrazines (smoky) or aldehydes (sweet). This is industrially relevant as it will provide new rapid tools for quality evaluation, opportunities to understand and minimise heterogeneity during production and roasting and ultimately provide the tools to define and achieve new coffee flavour profiles

Total lipid prediction in single intact cocoa beans by hyperspectral chemical imaging

© 2020 This work aimed to explore the possibility of predicting total fat content in whole dried cocoa beans at a single bean level using hyperspectral imaging (HSI). 170 beans randomly selected from 17 batches were individually analysed by HSI and by reference methodology for fat quantification. Both whole (i.e. in-shell) beans and shelled seeds (cotyledons) were analysed. Partial Least Square (PLS) regression models showed good performance for single shelled beans (R2 = 0.84, external prediction error of 2.4%). For both in-shell beans a slightly lower prediction error of 4.0% and R2 = 0.52 was achieved, but fat content estimation is still of interest given its wide range. Beans were manually segregated, demonstrating an increase by up to 6% in the fat content of sub-fractions. HSI was shown to be a valuable technique for rapid, non-contact prediction of fat content in cocoa beans even from scans of unshelled beans, enabling significant practical benefits to the food industry for quality control purposes and for obtaining a more consistent raw material

Variability of single bean coffee volatile compounds of Arabica and robusta roasted coffees analysed by SPME-GC-MS

We report on the analysis of volatile compounds by SPME-GC-MS for individual roasted coffee beans. The aim was to understand the relative abundance and variability of volatile compounds between individual roasted coffee beans at constant roasting conditions. Twenty-five batches of Arabica and robusta species were sampled from 13 countries, and 10 single coffee beans randomly selected from each batch were individually roasted in a fluidised bed roaster at 210 °C for 3 min. High variability (CV = 14.0–53.3%) of 50 volatile compounds in roasted coffee was obtained within batches (10 beans per batch). Phenols and heterocyclic nitrogen compounds generally had higher intra-batch variation, while ketones were the most uniform compounds (CV