Soybean (Glycine max) oil bodies and their associated phytochemicals

Abstract:  Soybean oil bodies were isolated from 3 cultivars (Ustie, K98, and Elena) and the occurrence of 2 classes of phytochemicals (tocopherol isoforms and isoflavones) and strength of their association with isolated oil bodies was evaluated. Tocopherol is shown to be closely associated with soybean oil bodies; δ-tocopherol demonstrated a significantly greater association with oil bodies over other tocopherol isoforms. Isoflavones do not show a significant physical association with oil bodies, although there is some indication of a passive association of the more hydrophobic aglycones during oil body isolation. Practical Application:  Oil bodies are small droplets of oil that are stored as energy reserves in the seeds of oil seeds, and have the potential to be used as future food ingredients. If oil body suspensions are commercialized on a large scale, knowledge of the association of phytochemicals with oil bodies will be valuable in deciding species of preference and predicting shelf life and nutritional value

Soybean (Glycine max) oil bodies and their associated phytochemicals

Abstract:  Soybean oil bodies were isolated from 3 cultivars (Ustie, K98, and Elena) and the occurrence of 2 classes of phytochemicals (tocopherol isoforms and isoflavones) and strength of their association with isolated oil bodies was evaluated. Tocopherol is shown to be closely associated with soybean oil bodies; δ-tocopherol demonstrated a significantly greater association with oil bodies over other tocopherol isoforms. Isoflavones do not show a significant physical association with oil bodies, although there is some indication of a passive association of the more hydrophobic aglycones during oil body isolation. Practical Application:  Oil bodies are small droplets of oil that are stored as energy reserves in the seeds of oil seeds, and have the potential to be used as future food ingredients. If oil body suspensions are commercialized on a large scale, knowledge of the association of phytochemicals with oil bodies will be valuable in deciding species of preference and predicting shelf life and nutritional value

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

Non-destructive characterisation of mesenchymal stem cell differentiation using LC-MS-based metabolite footprinting

Bone regeneration is a complex biological process where major cellular changes take place to support the osteogenic differentiation of mesenchymal bone progenitors. To characterise these biological changes and better understand the pathways regulating the formation of mature bone cells, the metabolic profile of mesenchymal stem cell (MSC) differentiation in vitro has been assessed non-invasively during osteogenic (OS) treatment using a footprinting technique. Liquid chromatography (LC)-mass spectrometry (MS)-based metabolite profiling of the culture medium was carried out in parallel to mineral deposition and alkaline phosphatase activity which are two hallmarks of osteogenesis in vitro. Metabolic profiles of spent culture media with a combination of univariate and multivariate analyses investigated concentration changes of extracellular metabolites and nutrients linked to the presence of MSCs in culture media. This non-invasive LC-MS-based analytical approach revealed significant metabolic changes between the media from control and OS-treated cells showing distinct effects of MSC differentiation on the environmental footprint of the cells in different conditions (control vs. OS treatment). A subset of compounds was directly linked to the osteogenic time-course of differentiation, and represent interesting metabolite candidates as non-invasive biomarkers for characterising the differentiation of MSCs in a culture medium

Impact of salt crystal size on in-mouth delivery of sodium and saltiness perception from snack foods

Fried, sliced potato crisps were flavored with sodium chloride of varying size fractions to investigate the impact of salt crystal size on the delivery rate of sodium to the tongue and resultant saltiness, measured over 65 s with a defined chew protocol (three chews, then holding the bolus in the mouth without swallowing). Salt crystal size impacted upon the delivery rate and perceived saltiness. The smallest crystal size fraction dissolved and diffused throughout the mouth to the tongue saliva faster than the medium and the largest ones; the smallest crystal size fraction also had the highest maximum concentration and greatest total sodium. These results correlated well with the sensory perceived saltiness, where the smallest crystal size fraction resulted in the fastest Tmax, highest maximum saltiness intensity and maximum total saltiness. The different delivery rates can be explained by differential dissolution kinetics and enhanced mass transfer of sodium across the saliva

Color influences sensory perception and liking of orange juice

Background: This study assesses the effect of slight hue variations in orange juice (reddish to greenish) on perceived flavour intensity, sweetness, and sourness, and on expected and actual liking. A commercial orange juice (COJ) was selected as a control, and colour-modified orange juices were prepared by adding red or green food dyes (ROJ and GOJ) that did not alter the flavour of the juice. A series of paired comparison tests were performed by 30 naive panellists to determine the influence of orange juice colour on flavour intensity, sweetness, and sourness. Then, 100 orange juice consumers were asked to rate expected liking of orange juice samples initially by visual evaluation and subsequently for actual liking upon consumption, using a labelled affective magnitude scale. Results: Results of pair comparison tests indicated that colour changes did not affect flavour intensity and sweetness, but the greenish hue (GOJ) significantly increased the perceived sourness. Results of the consumers’ study indicated significant differences in expected liking between the orange juice samples, with ROJ having the highest expected liking. However, scores of actual liking after consumption were not significantly different. COJ and GOJ showed a significant increase in actual liking compared to expected liking. Conclusions: This study shed light on how slight variations in orange juice hue (reddish to greenish hues) affect the perceived flavour intensity, sweetness, and sourness, and the expected and actual liking of orange juice

Resonance Broadening and Heating of Charged Particles in Magnetohydrodynamic Turbulence

The heating, acceleration, and pitch-angle scattering of charged particles by MHD turbulence are important in a wide range of astrophysical environments, including the solar wind, accreting black holes, and galaxy clusters. We simulate the interaction of high-gyrofrequency test particles with fully dynamical simulations of subsonic MHD turbulence, focusing on the parameter regime with beta ~ 1, where beta is the ratio of gas to magnetic pressure. We use the simulation results to calibrate analytical expressions for test particle velocity-space diffusion coefficients and provide simple fits that can be used in other work. The test particle velocity diffusion in our simulations is due to a combination of two processes: interactions between particles and magnetic compressions in the turbulence (as in linear transit-time damping; TTD) and what we refer to as Fermi Type-B (FTB) interactions, in which charged particles moving on field lines may be thought of as beads spiralling around moving wires. We show that test particle heating rates are consistent with a TTD resonance which is broadened according to a decorrelation prescription that is Gaussian in time. TTD dominates the heating for v_s >> v_A (e.g. electrons), where v_s is the thermal speed of species s and v_A is the Alfven speed, while FTB dominates for v_s << v_A (e.g. minor ions). Proton heating rates for beta ~ 1 are comparable to the turbulent cascade rate. Finally, we show that velocity diffusion of collisionless, large gyrofrequency particles due to large-scale MHD turbulence does not produce a power-law distribution function.Comment: 20 pages, 15 figures; accepted by The Astrophysical Journal; added clarifying appendices, but no major changes to result

Comparison of ambient solvent extraction methods for the analysis of fatty acids in non-starch lipids of flour and starch

BACKGROUND: Lipids are minor components of flours, but are major determinants of baking properties and end-product quality. To the best of our knowledge, there is no single solvent system currently known that efficiently extracts all non-starch lipids from all flours without the risk of chemical, mechanical or thermal damage. This paper compares nine ambient solvent systems (monophasic and biphasic) with varying polarities: Bligh and Dyer (BD); modified Bligh and Dyer using HCl (BDHCL); modified BD using NaCl (BDNaCl); methanol–chloroform–hexane (3:2:1, v/v); Hara and Radin (hexane–isopropanol, 3:2, v/v); water-saturated n-butanol; chloroform; methanol and hexane for their ability to extract total non-starch lipids (separated by lipid classes) from wheat flour (Triticum aestivum L.). Seven ambient extraction protocols were further compared for their ability to extract total non-starch lipids from three alternative samples: barley flour (Hordeum vulgare L.), maize starch (Zea mays L.) and tapioca starch (Manihot esculenta Crantz). RESULTS: For wheat flour the original BD method and those containing HCl or NaCl tended to extract the maximum lipid and a significant correlation between lipid extraction yield (especially the glycolipids and phospholipids) and the polarity of the solvent was observed. For the wider range of samples BD and BD HCl repeatedly offered the maximum extraction yield and using pooled standardized (by sample) data from all flours, total non-starch lipid extraction yield was positively correlated with solvent polarity (r=0.5682,P<0.05) and water ratio in the solvent mixture (r=0.5299,P<0.05). CONCLUSION: In general, BD-based methods showed better extraction yields compared to methods without the addition of water and, most interestingly, there was much greater method dependence of lipid yields in the starches when compared to the flour samples, which is due to the differences in lipid profiles between the two sample types (flours and starches)

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)