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

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

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

An evaluation of a nurse led unit: an action research study

This study is an exemplar of working in a participatory way with members of the public and health and social care practitioners as co-researchers. A Nurse Consultant Older People working in a nurse-led bed, intermediate care facility in a community hospital acted as joint project lead with an academic researcher. From the outset, members of the public were part of a team of 16 individuals who agreed an evaluation focus and were involved in all stages of the research process from design through to dissemination. An extensive evaluation reflecting all these stakeholders’ preferences was undertaken. Methods included research and audit including: patient and carer satisfaction questionnaire surveys, individual interviews with patients, carers and staff, staff surveys, graffiti board, suggestion box, first impressions questionnaire, patient tracking and a bed census. A key aim of the study has been capacity building of the research team members which has also been evaluated. In terms of impact, the co-researchers have developed research skills and knowledge, grown in confidence, developed in ways that have impacted elsewhere in their lives, developed posters, presented at conferences and gained a better understanding of the NHS. The evaluation itself has provided useful information on the processes and outcomes of intermediate care on the ward which was used to further improve the service

Protein content prediction in single wheat kernels using hyperspectral imaging

Hyperspectral imaging (HSI) combines Near-infrared (NIR) spectroscopy and digital imaging to give information about the chemical properties of objects and their spatial distribution. Protein content is one of the most important quality factors in wheat. It is known to vary widely depending on the cultivar, agronomic and climatic conditions. However, little information is known about single kernel protein variation within batches. The aim of the present work was to measure the distribution of protein content in whole wheat kernels on a single kernel basis, and to apply HSI to predict this distribution. Wheat samples from 2013 and 2014 harvests were sourced from UK millers and wheat breeders, and individual kernels were analysed by HSI and by the Dumas combustion method for total protein content. HSI was applied in the spectral region 980-2500 nm in reflectance mode using the push-broom approach. Single kernel spectra were used to develop partial least squares (PLS) regression models for protein prediction of intact single grains. The protein content ranged from 6.2 to 19.8% (“as-is” basis), with significantly higher values for hard wheats. The performance of the calibration model was evaluated using the coefficient of determination (R2) and the root mean square error (RMSE) from 3250 samples used for calibration and 868 used for external validation. The calibration performance for single kernel protein content was R2 of 0.82 and 0.79, and RMSE of 0.86 and 0.94% for the calibration and validation dataset, enabling quantification of the protein distribution between kernels and even visualisation within the same kernel. The performance of the single kernel measurement was poorer than that typically obtained for bulk samples, but is acceptable for some specific applications. The use of separate calibrations built by separating hard and soft wheat, or on kernels placed on similar orientation did not greatly improve the prediction ability. We simulated the use of the lower cost InGaAs detector (1000-1700 nm), and reported that the use of proposed HgCdTe detectors over a restricted spectral range gave a lower prediction error (RMSEC=0.86% vs 1.06%, for HgCdTe and InGaAs, respectively), and 26 increased R2 value (Rc2=0.82 vs 0.73)

Parades, parties and pests: contradictions of everyday life in peacekeeping economies

Based on research studies conducted in the UN peacekeeping mission in Liberia in 2006, 2012 and 2013, this article argues that peacekeepers’ everyday experiences reflect a series of contradictory identities and performances with regard to nation, work and gender. Peacekeepers straddle paradoxical worlds simultaneously and manage oppositional demands and obligations, although it is often assumed that they inhabit peacekeeping economies in homogenous ways. Importantly, the experiences provide opportunities for peacekeepers to invest in, accumulate and deploy military capital; to consolidate their military identities; and to favourably and tactically position themselves as deserving and useful subjects within the peacekeeping landscape

The HII Region KR 140: Spontaneous Formation of a High Mass Star

We have used a multiwavelength data set from the Canadian Galactic Plane Survey (CGPS) to study the Galactic HII region KR 140, both on the scale of the nebula itself and in the context of the star forming activity in the nearby W3/W4/W5 complex of molecular clouds and HII regions. From both radio and infrared data we have found a covering factor of about 0.5 for KR 140 and we interpret the nebula as a bowl-shaped region viewed close to face on. Extinction measurements place the region on the near side of its parent molecular cloud. The nebula is kept ionized by one O8.5 V(e) star, VES 735, which is less than a few million years old. CO data show that VES 735 has disrupted much of the original molecular cloud for which the estimated mass and density are about 5000 $M_{\odot}$ and 100 cm$^{-3}$, respectively. KR 140 is isolated from the nearest star forming activity, in W3. Our data suggest that KR 140 is an example of spontaneous (i.e., non-triggered) formation of, unusually, a high mass star.Comment: 46 pages; includes 15 figures; accepted by the Ap

Near infrared spectroscopy and hyperspectral imaging for non-destructive quality assessment of cereal grains

Hyperspectral imaging (HSI) combines spectroscopy and imaging, providing information about the chemical properties of a material and their spatial distribution. It represents an advance of traditional Near-Infrared (NIR) spectroscopy. The present work reviews the most recent applications of NIR spectroscopy for cereal grain evaluation, then focused on the use of HSI in this field. The progress of research from ground material to whole grains and single kernels is detailed. The potential of NIR-based methods to predict protein content, sprout damage and α-amylase activity in wheat and barley is shown, in addition to assessment of quality parameters in other cereals such as rice, maize and oats, and the estimation of fungal infection. This analytical technique also offers the possibility to rapidly classify grains based on properties such as variety, geographical origin, kernel hardness, etc. Further applications of HSI are expected in the near future, for its potential for rapid single-kernel analysis