Development of a Single Kernel NIR Barley Protein Calibration and Assessment of Variation in Protein on Grain Quality

Multi-Locus GWAS of Quality Traits in Bread Wheat: Mining More Candidate Genes and Possible Regulatory Network

In wheat breeding, improved quality traits, including grain quality and dough rheological properties, have long been a critical goal. To understand the genetic basis of key quality traits of wheat, two single-locus and five multi-locus GWAS models were performed for six grain quality traits and three dough rheological properties based on 19, 254 SNPs in 267 bread wheat accessions. As a result, 299 quantitative trait nucleotides (QTNs) within 105 regions were identified to be associated with these quality traits in four environments. Of which, 40 core QTN regions were stably detected in at least three environments, 19 of which were novel. Compared with the previous studies, these novel QTN regions explained smaller phenotypic variation, which verified the advantages of the multi-locus GWAS models in detecting important small effect QTNs associated with complex traits. After characterization of the function and expression in-depth, 67 core candidate genes involved in protein/sugar synthesis, histone modification and the regulation of transcription factor were observed to be associated with the formation of grain quality, which showed that multi-level regulations influenced wheat grain quality. Finally, a preliminary network of gene regulation that may affect wheat quality formation was inferred. This study verified the power and reliability of multi-locus GWAS methods in wheat quality trait research, and increased the understanding of wheat quality formation mechanisms. The detected QTN regions and candidate genes in this study could be further used for gene cloning and marker-assisted selection in high-quality breeding of bread wheat.

The Brewing Industry and the Opportunities for Real-Time Quality Analysis Using Infrared Spectroscopy

Brewing is an ancient process which started in the middle east over 10,000 years ago. The style of beer varies across the globe but modern brewing is very much the same regardless of the style. While there are thousands of compounds in beer, current methods of analysis rely mostly on the content of only several important processing parameters such as gravity, bitterness, or alcohol. Near infrared and mid infrared spectroscopy offer opportunities to predict dozens to hundreds of compounds simultaneously at different stages of the brewing process. Importantly, this is an opportunity to move deeper into quality through measuring wort and beer composition, rather than just content. This includes measuring individual sugars and amino acids prior to fermentation, rather than total °Plato or free amino acids content. Portable devices and in-line probes, coupled with more complex algorithms can provide real time measurements, allowing brewers more control of the process, resulting in more consistent quality, reduced production costs and greater confidence for the future.

Protein content prediction in single wheat kernels using hyperspectral imaging

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HSI was applied for non-destructive prediction of total protein content in wheat kernels.

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Above 2100 wheat kernels were taken from ~200 batches and individually analysed.

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PLS regression models had R² = 0.82 and prediction error lower than 0.93%.

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Protein distribution had wide range (6–20%) and was visualised by applying the calibration.

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The performance of HgGcTe was superior to the one built by simulating InGaAs sensors.

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 increased R² value (R_c² = 0.82 vs 0.73).

Applications of single kernel conventional and hyperspectral imaging near infrared spectroscopy in cereals

Single kernel (SK) near infrared (NIR) reflectance and transmittance technologies have been developed during the last two decades for a range of cereal grain physical quality and chemical traits as well as detecting and predicting levels of toxins produced by fungi. Challenges during the development of single kernel near infrared (SK-NIR) spectroscopy applications are modifications of existing NIR technology to present single kernels for scanning as well as modifying reference methods for the trait of interest. Numerous applications have been developed, and cover almost all cereals although most have been for key traits including moisture, protein, starch and oil in the globally important food grains, i.e. maize, wheat, rice and barley. An additional benefit in developing SK-NIR applications has been to demonstrate the value in sorting grain infected with a fungus or mycotoxins such as deoxynivalenol, fumonisins and aflatoxins. However, there is still a need to develop cost-effective technologies for high-speed sorting which can be used for small grain samples such as those from breeding programmes or commercial sorting; capable of sorting tonnes per hour. Development of SK-NIR technologies also includes standardisation of SK reference methods to analyse single kernels. For protein content, the use of the Dumas method would require minimal standardisation; for starch or oil content, considerable development would be required. SK-NIR, including the use of hyperspectral imaging, will improve our understanding of grain quality and the inherent variation in the range of a trait. In the area of food safety, this technology will benefit farmers, industry and consumers if it enables contaminated grain to be removed from the human food chain.

Limitations and current applications of Near Infrared Spectroscopy for single seed analysis

Near Infrared Spectroscopy (NIRS) analysis at the single seed level is a useful tool for breeders, farmers, feeding facilities, and food companies according to current researches. As a non-destructive technique, NIRS allows for the selection and classification of seeds according to specific traits and attributes without alteration of their properties. Critical aspects in using NIRS for single seed analysis such as reference method, sample morphology, and spectrometer suitability are discussed in this review. A summary of current applications of NIRS technologies at single seed level is also presented.

Variation in caffeine concentration in single coffee beans

Twenty-eight coffee samples from around the world were tested for caffeine levels to develop near-infrared reflectance spectroscopy (NIRS) calibrations for whole and ground coffee. Twenty-five individual beans from five of those coffees were used to develop a NIRS calibration for caffeine concentration in single beans. An international standard high-performance liquid chromatography method was used to analyze for caffeine content. Coffee is a legal stimulant and possesses a number of heath properties. However, there is variation in the level of caffeine in brewed coffee and other caffeinated beverages. Being able to sort beans on the basis of caffeine concentration will improve quality control in the level of caffeine in those beverages. The range in caffeine concentration was from 0.01 mg/g (decaffeinated coffee) to 19.9 mg/g (Italian coffee). The majority of coffees were around 10.0-12.0 mg/g. The NIRS results showed r(2) values for bulk unground and ground coffees were >0.90 with standard errors <2 mg/g. For the single-bean calibration the r(2) values were between 0.85 and 0.93 with standard errors of cross validation of 0.8-1.6 mg/g depending upon calibration. The results showed it was possible to develop NIRS calibrations to estimate the caffeine concentration of individual coffee beans. One application of this calibration could be sorting beans on caffeine concentration to provide greater quality control for high-end markets. Furthermore, bean sorting may open new markets for novel coffee products.