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A global predictive model was developed for protein, moisture, and grain type, using near infrared (NIR) spectra. The model is a deep convolutional neural network, trained on NIR spectral data captured from wheat, barley, field pea, and lentil whole grains. The deep learning model performs multi-task learning to simultaneously predict grain protein, moisture, and type, with a significant reduction in prediction errors compared to linear approaches (e.g., partial least squares regression). Moreover, it is shown that the convolutional network architecture learns much more efficiently than simple feedforward neural network architectures of the same size. Thus, in addition to improved accuracy, the presented deep network is very efficient to implement, both in terms of model development time, and the required computational resources.

Increased levels of atmospheric CO2 levels (eCO2) have been shown to decrease Grain Protein Concentration (GPC) and alter wheat composition (both protein and carbohydrate components). Lower GPC and altered composition under eCO2 conditions have deleterious impacts on end-product processing, dough rheology and bread loaf volume. To determine if the decline in GPC in wheat under eCO2 could be circumvented, traits hypothesised to improve grain protein were assessed in the Australian Grains Free Air CO2 Enrichment (AGFACE) facility. Wheat genotypes were selected with enhanced nitrogen use efficiency, water use efficiency, root vigour and grain quality, and were exposed to eCO2 (550 μmol mol−1) to determine if grain quality could be retained. Results showed that these trait strategies were not able to prevent the significant decline in GPC or grain quality (P ≤ 0.001). The decline in loaf volume (P ≤ 0.001) was greater under eCO2 across the selected cultivars and environments is likely associated with reduced GPC, changes in gluten protein composition, and significant increases in arabinoxylan content. Larger quantities (increased yield, P > 0.05) of lower quality grain will be produced in the future climates (eCO2), unless crop breeding efforts focus on grain-nitrogen translocation traits so that wheat can retain high quality grain under eCO2.

Maintaining grain quality of wheat under climate change is critical for human nutrition, end-use functional properties, as well as commodity value. This paper reviews the current knowledge of high temperature and elevated atmospheric CO2 on whole-grain and functional properties of wheat. It also considers the utility of contemporary crop models for investigating the impacts of climate change on wheat quality; and discusses opportunities for advancing model capability. Under elevated CO2 wheat yield can increase by up to 36%, but universally grain protein concentration decreases and a shift in composition translates to reduced functional properties. High temperature during the post-anthesis period of crops can cause a step change reduction in grain-set, grain size and milling yield. Numerous crop models including APSIM-Nwheat, CropSyst, Sirius, GLAM-HTS account for high CO2 effects through modification of RUE, TE or critical leaf-N concentration and high temperature by accelerated leaf senescence, grain number, potential grain weight or HI modifications. For grain quality, however, crop models are typically restricted to predicting average grain size and grain-N content (concentration), although the SiriusQuality model accounts for the major storage proteins, gliadin and glutenin. For protein composition, high temperature stress reduces the glutenin/gliadin ratio and limits the synthesis of the larger SDS-insoluble glutenin polymers which causes wheat dough to have weaker viscoelasticity properties. This link provides an opportunity to model high temperature effects on grain functional properties. Further development and testing, utilizing grain quality data from global FACE programmes will be particularly valuable for validating and enhancing the performance of such models. For whole-grain characteristics, a single-spike model approach, which accounts for intra-spike variation in assimilate deposition may provide an opportunity to predict grain size distribution and associated screenings percentage and milling yield. Taken together expanding the predictive capability of our crop models to grain quality is an important step in providing a powerful tool for developing adaptation strategies for combating the impacts of climate change to global crop production and grain quality.

Grain texture defines the structural component of barley and can be quantified by measuring endosperm hardness and grain density. Both components impact on important grain quality traits, in particular water-uptake efficiency, steeping, malt-extract potential, pearling efficiency, milling efficiency, digestibility, and grain appearance. Grain composition, grain size, grain morphology are traits found to impact the expression of endosperm hardness and grain density. However, little is known about the genomic regions that are directly associated with the expression of grain texture and how these regions may be employed to improve grain quality. A combination of small-scale phenotyping tools, consensus maps, proteomics, and genomics will aid in understanding how grain texture is associated with barley processing and malt-quality related traits. This review highlights the interactions between endosperm hardness, grain density, grain size traits, and malt-quality. For grain texture it is clear that even when all the allelic variations at the Hardness locus have been fully described, there will remain a need to understand the pleiotropic effects on grain texture of a range of other gene loci that are currently considered to be important in physical grain size or phenology.

Background and objectives Lentil (Lens culinaris M.) is a high-value grain used traditionally as a minimally processed product. Lentil crops are well-suited to Mediterranean-type climates with mild winters and hot dry summers which results in the production of high-value grain. However, extreme weather conditions, such as frost, can impact the lentil seed, for example, a darkened seed color and distorted seed shape resulting in a downgrade of its market value. The quality parameters of lentil when milled as wholemeal flour is not reliant on visual or physical seed traits such as seed size and color which impact on the market value in traditional market specifications. Instead, quality parameters such as those applied in the assessment of wheat, these include flour yield and color would be appropriate parameters to determine the value of lentil flour. It is proposed that flour, even from down-graded lentil could be supplemented with wheat flour and used to enhance the quality profile of baked products including breads, pastas, and cookies. Findings This study investigated the use of premium and frost-damaged lentil flour in cookie making. Overall cookies made from wheat–lentil composite blends resulted in flatter and wider cookies that were darker than the cookies made from 100% wheat flour. Cookies made by incorporating lentil and wheat flour resulted in a significant increase in total protein, insoluble fiber, and oligosaccharides (p < .05). In addition, the phenolic acids kaempferol and procyanidin were detected in cookies made using wheat–lentil contributing to an increase in antioxidant activity (p < .05). These phenolic acids were not detected in cookies made from 100% wheat flour. Our results show that using concentrations of up to 25% lentil flour resulted in cookies that had expectable hardness and color characteristics in comparison with a 100% wheat biscuit. Conclusion This research showed that the nutritional value and functional properties of cookies can be significantly enhanced by using either premium or down-graded lentil and the quality of the cookies was not impacted by the market-grade of the raw material. Significance and novelty Investigations into the use of pulse flours such as lentil in novel food products including pasta and snack foods are gaining popularity particularly by the health-conscious consumer. The visual appearance of lentil is not critical when utilized in such products. Lentil that has been visually or physically affected by adverse weather conditions, but its nutritional and functional values have not been compromised may prove a viable inclusion in the creation of highly nutritious food alternatives. The use of down-graded lentil as flour in novel food production could ultimately add value to pulse grain affected by adverse environmental conditions.

The objectives of this study were to investigate the relationship between milling yield and grain hardness. A preliminary study was carried out with 20 samples (both hard and soft wheats) using the Brabender hardness tester (BHT) with two grind settings: one-step grind (0-10) and two-step grind (2-12: coarse; 0-8: fine). The two-step grind was correlated with particle size index, single-kernel characterization system (SKCS) hardness, break yield, and reduction yield (P < 0.05), whereas there was no correlation with the one-step grind method. An additional 64 samples were ground with the two-step grind setting to further validate this method. In terms of the BHT crush profile, no discernible differences were observed between varieties for the coarse grind, whereas for the fine grind, hard wheat gave a higher BHT maximum peak height and shorter grinding time compared with soft wheat. The break and reduction yields were significantly correlated with both BHT and SKCS hardness (P < 0.05). The findings indicat...

Wheat is one of the major crops grown commercially and an important source of nutrition for humans. Changes in grain quality due to increasing levels of atmospheric CO2 could have far-reaching impacts to human diets globally. Future wheat-growing conditions are expected to be impacted by climate change, particularly increases in carbon dioxide. Elevated CO2 (eCO2) has been shown to increase plant growth and yield, yet changes in grain properties also affect product quality.Using Australian Grains Free Air CO2 Enrichment (AGFACE) technology we investigated the impacts of eCO2 on grain quality over three seasons.Elevated CO2 consistently decreased baking quality and grain protein percentage was consistently lower under eCO2, and protein composition changed towards a greater glutenin/gliadin ratio. Starch damage and amylograph peak viscosity were consistently reduced in the eCO2 treatment in all years and for all cultivars. For other traits such as grain weight, milling quality, dough rheology, A-type starch granules and pasting viscosity the same altered effects were not consistent for cultivar or over years. The results suggest that grain and product quality of wheat grown under eCO2 will be compromised in a high CO2 atmosphere, and this may offset some of the potential yield benefits of eCO2.

Image analysis offers a rapid and non-destructive method for a wide range of agricultural applications including grading of grains. In this study, a method was developed for seed size grading by analysis of three-dimensional digital image information on single lentil kernels. Predicted length, single-seed mass, bulk-sample mass and Seed Size Index (SSI) were all highly correlated with the physically measured values giving Lin’s concordance test statistics of 0.98, 0.97, >0.99 and >0.99 respectively. Sieves were found to have a 10% misclassification rate and Seed Size Distribution was predicted within error of the sieving method. Results also indicated that image analysis can give much more detailed and precise descriptions of grain size and shape characteristics than can be practically achieved by manual quality assessment

This study applies near infrared (NIR) data for phenotyping genetic populations to determine genomic regions associated with malting quality in barley. To date, most such phenotyping has used reference testing, but this is expensive when a large number of samples must be tested. NIR is a cost-effective tool, but the precision in identifying genomic regions associated with a quality trait is dependent on the accurate collection of data. To ensure that NIR is accurate for use in genomic studies, samples from genetic populations need to be validated independently. Useful interpretive statistics for both calibration and validation data, in combination, include the standard error of prediction ( SEP), the RPD value (the standard deviation of the reference data divided by the SEP) and the coefficient of determination ( R2 and r2). The most useful of these three statistics used in this study were RPD and SEP. We demonstrated that when using NIR-predicted protein and malt extract on wholegrain barley for quantitative trait loci (QTL) analyses, an RPD value greater than 4.00 was required to ensure significant QTL were identified. We have demonstrated that NIR is an appropriate tool to phenotype barley grain for protein content and malt extract in barley mapping studies. However, when the error in the NIR data increased, and RPD values less than 4.00 were observed, which also increased the SEP values, the number of significant QTLs decreased, and the number of spurious QTLs increased.

Using NIR and NMR predictions of quality traits overcomes a major barrier for the application of genomic selection to accelerate improvement in grain end-use quality traits of wheat. Grain end-use quality traits are among the most important in wheat breeding. These traits are difficult to breed for, as their assays require flour quantities only obtainable late in the breeding cycle, and are expensive. These traits are therefore an ideal target for genomic selection. However, large reference populations are required for accurate genomic predictions, which are challenging to assemble for these traits for the same reasons they are challenging to breed for. Here, we use predictions of end-use quality derived from near infrared (NIR) or nuclear magnetic resonance (NMR), that require very small amounts of flour, as well as end-use quality measured by industry standard assay in a subset of accessions, in a multi-trait approach for genomic prediction. The NIR and NMR predictions were derived for 19 end-use quality traits in 398 accessions, and were then assayed in 2420 diverse wheat accessions. The accessions were grown out in multiple locations and multiple years, and were genotyped for 51208 SNP. Incorporating NIR and NMR phenotypes in the multi-trait approach increased the accuracy of genomic prediction for most quality traits. The accuracy ranged from 0 to 0.47 before the addition of the NIR/NMR data, while after these data were added, it ranged from 0 to 0.69. Genomic predictions were reasonably robust across locations and years for most traits. Using NIR and NMR predictions of quality traits overcomes a major barrier for the application of genomic selection for grain end-use quality traits in wheat breeding.

We investigated wheat (Triticum aestivum) grain quality under Free Air CO2 Enrichment (FACE) of 550 ± 10% CO2 μmol mol−1. In each of two full growing seasons (2008 and 2009), two times of sowing were compared, with late sowing designed to mimic high temperature during grain filling. Grain samples were subjected to a range of physical, nutritional and rheological quality assessments. Elevated CO2 increased thousand grain weight (8%) and grain diameter (5%). Flour protein concentration was reduced by 11% at e[CO2], with the highest reduction being observed at the late time of sowing in 2009, (15%). Most of the grain mineral concentrations decreased under e[CO2] - Ca (11%), Mg (7%), P (11%) and S (7%), Fe (10%), Zn (17%), Na (19%), while total uptake of these nutrients per unit ground area increased. Rheological properties of the flour were altered by e[CO2] and bread volume reduced by 7%. Phytate concentration in grains tended to decrease (17%) at e[CO2] while grain fructan concentration remained unchanged. The data suggest that rising atmospheric [CO2] will reduce the nutritional and rheological quality of wheat grain, but at high temperature, e[CO2] effects may be moderated. Reduced phytate concentrations at e[CO2] may improve bioavailability of Fe and Zn in wheat grain.

A digital imaging method was developed to determine the size, weight, volume and density of individual barley grains. Calibrations were developed using morphological measurements based on 2-D digital images, and were validated using a range of bulk barley samples. The validation of the grain diameter (minor) and grain weight algorithms gave high correlation coefficients (r = 0.93 and 0.97, respectively, P Estimated grain volume and density from image data, using three different mathematical approaches were compared: Model 1, a general ellipsoid; Model 2, a combination of two cone frustums with an ellipse top and base; and Model 3, a set of elliptical cylinders representing pixels along the image length. Derived grain volume data using Model 2 when compared to gas displacement volume measurements gave significant correlations (r = 0.97, P

This study reports the measurement of grain density and grain volume of barley grains using a gas displacement technique. The densities of single kernels were determined and were highly correlated with measurements of larger quantities of kernels (10, 25 or 800). Grain weight and volume were significantly correlated when measuring 800 kernels (0.99, P P P P The relationship between grain density and endosperm hardness was significantly correlated (0.57, P P P P

The potential impact of elevated atmospheric carbon dioxide concentration ([CO2]) and future climate predicted for 2050 on wheat marketing grades and grain value was evaluated for Victoria, Australia. This evaluation was based on measured grain yield and quality from the Australian Grains FACE program and commercial grain delivery data from Victoria for five seasons (2009–13). Extrapolation of relationships derived from field experimentation under elevated [CO2] to the Victorian wheat crop indicated that 34% of grain would be downgraded by one marketing grade (range 1–62% depending on season and region) because of reduced protein concentration; and that proportions of high-protein wheat grades would reduce and proportions of lower protein grades would increase, with the largest increase in the Australian Standard White (ASW1) grade. Simulation modelling with predicted 2050 [CO2] and future climate indicated reduced wheat yields compared with 2009–13 but higher and lower grain quality depending on region. The Mallee Region was most negatively affected by climate change, with a predicted 43% yield reduction and 43% of grain downgraded by one marketing grade. Using 2016 prices, the value of Victorian wheat grain was influenced mainly by production in the different scenarios, with quality changes in different scenarios having minimal impact on grain value.

The effect of different nitrogen (N) management strategies (i.e. N rate; 0, 25, 50, 100 kg ha–1, split N application, foliar N application, legume precropping) were assessed for how they may reverse the reduction of grain protein concentration (GPC) under elevated CO2 (eCO2; 550 µmol mol–1) of wheat (cv. Yitpi) using the Australian Grains Free Air CO2 Enrichment facility. GPC did not increase significantly under eCO2 for most of the N management strategies assessed when compared with ambient CO2 (aCO2; 390 µmol mol–1). Grain yield of cv. Yitpi under aCO2 increased by 43% (P

A breeding objective for the malting barley industry is to produce lines with softer, plumper grain containing moderate protein content (9-12%) as they are more likely to imbibe water readily and contain more starch per grain, which in turn produces higher levels of malt extract. In a malting barley mapping population, 'Arapiles' × 'Franklin', the most significant and robust quantitative trait locus (QTL) for endosperm hardness was observed on the short arm of chromosome 1H, across three environments over two growing seasons. This accounted for 22.6% (Horsham 2000), 26.8% (Esperance 2001), and 12.0% (Tarranyurk 2001) of the genetic variance and significantly increased endosperm hardness by 2.06-3.03 SKCS hardness units. Interestingly, Arapiles and Franklin do not vary in Ha locus alleles. Therefore, this region, near the centromere on chromosome 1H, may be of great importance when aiming to manipulate endosperm hardness and malting quality. Interestingly, this region, close to the centromere on chromosome 1H, in our study, aligns with the region of the genome that includes the HvCslF9 and the HvGlb1 genes. Potentially, one or both of these genes could be considered to be candidate genes that influence endosperm hardness in the barley grain. Additional QTLs for endosperm hardness were detected on chromosomes 2H, 3H, 6H and 7H, confirming that the hardness trait in barley is complex and multigenic, similar to many malting quality traits of interest.

A method is presented for the direct determination of uranium in uranium compounds containing up to several percent impurities with a precision of 0.04% at the 95% confidence interval. The method is simple and is a significant improvement over existing methods for analysis of this type of material. A sample containing about 2.5 g of uranium is dissolved in strong phosphoric acid and uranium(VI) is reduced with iron(II). After selective oxidation of the excess of iron(II) with nitrate in presence of molybdate catalyst, a small excess of potassium dichromate is added to oxidize uranium(IV) to uranium(VI). The excess of chromium(VI) is determined colorimetrically with diphenylcarbazide. The method requires some prior knowledge of the uranium content of the sample. It can be applied even in the presence of percent levels of impurities such as iron, nickel, and chromium. Copper, however, at concentrations of more than 1% of the uranium causes a low bias.

Cows fed a milk fat—depressing ration for five to seven weeks before parturition and continued on this feed after calving exhibited a steadily declining milk fat content for the first six to eight weeks of lactation, at which time the fat tests were substantially below controls on conventional rations. These changes appeared to parallel changes in the ratio of acetate to propionate in rumen fluid. The milk fat tests were significantly (P 12 or treated with cobalt. This may have been due to the increased feed consumption observed in this group. The pattern of changes in the fatty acid composition of the milk fat accompanying reduction in milk fat synthesis during early lactation was in marked contrast with that observed in other cows, on the same feed, later in lactation.

Serum immunoreactive insulin was measured at intervals during lactation in cows consuming restricted roughage or control rations. At 16 weeks postpartum when milk fat depression was greatest, serum immunoreactive insulin was higher in the restricted roughage group. In general there was a negative relationship between serum immunoreactive insulin and milk fat production and percentage. Within treatment, however, such relationships were consistently negative on restricted roughage ration but either nil or positive on control ration. Rumen acetate:propionate ratios were negatively correlated with serum immunoreactive insulin.

A patient monitoring system is described which has been implemented with interconnected minicomputers organized in a functional two-level hierarchy. Data acquisition, preliminary analysis using algorithmic signal processing techniques, output displays, and user interaction are functions of the minicomputer at the first level of the hierarchy, the patient preprocessor (PP). An important capability of this system is that waveform data is sampled and analyzed continuously. The encoded compressed data output from each patient preprocessor is transmitted to the Inquiry Station (IS), the minicomputer at the next higher level of the hierarchy. It serves as an extension of preprocessorlike functions which can be more efficiently time-shared within one computer. The inquiry station provides secondary mass storage for data in all eight trend displays, additional algorithmic processing for ECG arrhythmias, and the generation and transmission of appropriate physiologic and data-quality alarm messages to personnel at the bedside. Medications, nurses' notes, fluid input-output, and similar data for the patient's chart are entered into the charting system. Display summaries of this data are available for correlation with data collected and stored in the monitoring system.

Potato meal was ensiled with a grass-legume forage at 7.5% of fresh weight and substituted for ground corn in concentrate mixtures at 0, 15, and 30% fresh weight. Production, digestion, and nitrogen balance trials were conducted on 12 lactating Holstein cows fed individually for ad libitum intake. Potato meal was consumed readily in the quantities offered. Mean daily milk production for cows receiving the hay-crop silage ensiled with potato meal was 28.6 compared with 26.3 kg for cows consuming untreated silage. Molar percentages of acetate in rumen were lower and propionate higher for cows receiving hay-crop silage ensiled with potato meal. Digestibility coefficients for dry matter, organic matter, acid detergent fiber, neutral detergent fiber, crude protein, and gross energy all tended to be lower for the silage with added potato meal. Nitrogen balance was higher for cows consuming silage with potato meal. When potato meal was added to concentrate mixes replacing corn at 0, 15, and 30%, milk production was 27.5, 26.8, and 28.0 kg/day. Potato meal can be used advantageously as a silage additive and can be included in concentrate mixtures up to 30% of the fresh weight.

Sixteen Holstein cows were assigned to one of two dietary treatments, control or restricted-roughage, for the first 34 weeks of lactation. Vitamin B 12 in rumen fluid, blood serum, liver, milk and urine was monitored at intervals to determine general trends and to test the hypothesis that roughage restriction would alter the B 12 status of animals on this treatment. The per cent of total vitamin B 12 activity in rumen fluid represented by the vitamin itself was reduced by roughage restriction. This treatment resulted in higher serum, lower liver and milk and higher urine vitamin B 12 activity. Lactational trends are described. Interrelationships among variables suggested the possibility that higher serum B 12 of cows on restricted roughage might be due in part to vitamin B 12 analogues.

Using a barley mapping population, 'Vla- mingh' 9 'Buloke' (V 9 B), whole grain analyses were undertaken for physical seed traits and malting quality. Grain density and size were predicted by digital image analysis (DIA), while malt extract and protein content were predicted using near infrared (NIR) analysis. Validation of DIA and NIR algorithms confirmed that data for QTL analysis was highly correlated (R 2 ( 0.82), with high RPD values (the ratio of the standard error of prediction to the standard deviation, 2.31-9.06). Endosperm hardness was measured on this mapping population using the single kernel characterisation system. Grain density and endo- sperm hardness were significantly inter-correlated in all three environments (r ( 0.22, P \ 0.001); however, other grain components were found to interact with the traits. QTL for these traits were also found on different genomic regions, for example, grain density QTLs were found on chromosomes 2H and 6H, whereas endosperm hardness QTLs were found on 1H, 5H, and 7H. In this study, the majority of the genomic regions associated with grain texture were also coincident with QTLs for grain size, yield, flowering date and/or plant development genes. This study highlights the complexity of genomic regions asso- ciated with the variation of endosperm hardness and grain density, and their relationships with grain size traits, agronomic-related traits, and plant development loci.