A chemometric evaluation of the underlying physical and chemical patterns that support near infrared spectroscopy of barley seeds as a tool for explorative classification of endosperm genes and gene combinations

Identification of biomarkers in hydrosoluble extracts from spelt and wheat cultivated in different production systems

BACKGROUND

In the present paper, a method for differentiation between common and spelt wheat grown in different farming systems (biodynamic, ecological, integrated, conventional), based on biomarkers identified from aqueous flour extracts (nitrogen and 14 soluble carbohydrates) was employed.

RESULTS

The identification and determination of soluble carbohydrate content were carried out using gas chromatography-mass spectrometry, with the UV spectrum generated by mass spectrometry for comparison with the WILEY database. Soluble carbohydrates were determined in the peak area between 21.92 and 43.63 min^-1 retention time. The obtained data set was analyzed by multivariate statistical techniques. It was revealed that common wheat exerted a much more pronounced tendency than spelt wheat to be influenced by the farming system.

CONCLUSION

This differentiation was particularly well visualized after subjecting the data set to principal component analysis (PCA) and cluster analysis (CA). In the PCA graph, all spelt samples were positioned closer to the corresponding control sample, in contrast to the wheat samples, which were distributed over a huge area in the factor space. CA showed that the spelt samples grown under different farming systems were highly similar and grouped into one cluster. Common wheat samples cultivated under organic, biodynamic and integrated system were similar and represented the second cluster, whereas that cultivated under the conventional system was clearly separated from other samples. © 2020 Society of Chemical Industry.

Physiological Genetics Reformed: Bridging the Genome-to-Phenome Gap by Coherent Chemical Fingerprints - the Global Coordinator

Forward-focused molecular genetics is successfully framing DNA diversity and mapping primary gene functions. However, abandoning the classic Linnaean fingerprint link between the phenome and genome by suppressing gene interaction (pleiotropy), has resulted in a genome-to-phenome gap and poor utilization of molecular data. We demonstrate how to bridge this gap by using an example of a barley mutant seed model, where pleiotropy is observed as covarying global molecular patterns that define each endosperm. Global coherence was discovered as a covariate coordinator within and between local genotype specific fingerprints. This implies that any of these fingerprints can select its recombinant global phenotype variant, including composition. Introducing the law of coherence, and the movement of gene complexes by chemical fingerprint traits as selectors, introduces a revolution in understanding physiological molecular genetics and plant-breeding.

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.

Identification of Soybean Varieties Using Hyperspectral Imaging Coupled with Convolutional Neural Network

Soybean variety is connected to stress resistance ability, as well as nutritional and commercial value. Near-infrared hyperspectral imaging was applied to classify three varieties of soybeans (Zhonghuang37, Zhonghuang41, and Zhonghuang55). Pixel-wise spectra were extracted and preprocessed, and average spectra were also obtained. Convolutional neural networks (CNN) using the average spectra and pixel-wise spectra of different numbers of soybeans were built. Pixel-wise CNN models obtained good performance predicting pixel-wise spectra and average spectra. With the increase of soybean numbers, performances were improved, with the classification accuracy of each variety over 90%. Traditionally, the number of samples used for modeling is large. It is time-consuming and requires labor to obtain hyperspectral data from large batches of samples. To explore the possibility of achieving decent identification results with few samples, a majority vote was also applied to the pixel-wise CNN models to identify a single soybean variety. Prediction maps were obtained to present the classification results intuitively. Models using pixel-wise spectra of 60 soybeans showed equivalent performance to those using the average spectra of 810 soybeans, illustrating the possibility of discriminating soybean varieties using few samples by acquiring pixel-wise spectra.

Long wavelength near-infrared transmission spectroscopy of barley seeds using a supercontinuum laser: Prediction of mixed-linkage beta-glucan content

A supercontinuum laser was used to perform the first transmission measurements on intact seeds with long wavelength near-infrared spectroscopy. A total of 105 barley seeds from five different barley genotypes (Bomi, lys5.f, lys5.g, lys16 and lys95) were measured from 2275 to 2375 nm. The mixed-linkage (1→3,1→4)-β-D-glucan (BG) and protein content was measured with wet chemical analysis for each single seed. A partial least squares model correlated the BG % (w/w) with the spectral measurements with a R²_CV and R²_PRED of 0.83 and 0.90, respectively. The predictive model for BG could be improved by averaging spectra from the same seed and by replacing the individual seed BG content with the average BG of each barley genotype.

From metabolome to phenotype: GC-MS metabolomics of developing mutant barley seeds reveals effects of growth, temperature and genotype

The development of crop varieties tolerant to growth temperature fluctuations and improved nutritional value is crucial due to climate change and global population growth. This study investigated the metabolite patterns of developing barley seed as a function of genotype and growth temperature for ideal vegetable protein production and for augmented β-glucan production. Seeds from three barley lines (Bomi, lys3.a and lys5.f) were sampled eight times during grain filling and analysed for metabolites using gas chromatography-mass spectrometry (GC-MS). The lys3.a mutation disrupts a regulator gene, causing an increase in proteins rich in the essential amino acid lysine, while lys5.f carries a mutation in an ADP-glucose transporter gene leading to a significant increase in production of mixed-linkage β-glucan at the expense of α-glucan. Unique metabolic patterns associated with the tricarboxylic acid cycle, shikimate-phenylpropanoid pathway, mevalonate, lipid and carbohydrate metabolism were observed for the barley mutants, whereas growth temperature primarily affected shikimate-phenylpropanoid and lipid metabolism. The study applied recently developed GC-MS metabolomics methods and demonstrated their successful application to link genetic and environmental factors with the seed phenotype of unique and agro-economically important barley models for optimal vegetable protein and dietary fibre production.

Near-Infrared Spectroscopy Using a Supercontinuum Laser: Application to Long Wavelength Transmission Spectra of Barley Endosperm and Oil

The supercontinuum laser is a new type of light source, which combines the collimation and intensity of a laser with the broad spectral region of a lamp. Using such a source therefore makes it possible to focus the light onto small sample areas without losing intensity and thus facilitate either rapid or high-intensity measurements. Single seed transmission analysis in the long wavelength (LW) near-infrared (NIR) region is one area that might benefit from a brighter light source such as the supercontinuum laser. This study is aimed at building an experimental spectrometer consisting of a supercontinuum laser source and a dispersive monochromator in order to investigate its capability to measure the barley endosperm using transmission experiments in the LW NIR region. So far, barley and wheat seeds have only been studied using NIR transmission in the short wavelength region up to 1100 nm. However, the region in the range of 2260-2380 nm has previously shown to be particularly useful in differentiating barley phenotypes using NIR spectroscopy in reflectance mode. In the present study, 350 seeds (consisting of 70 seeds from each of five barley genotypes) in 1 mm slices were measured by NIR transmission in the range of 2235-2381 nm and oils from the same five barley genotypes were measured in a cuvette with a 1 mm path length in the range of 2003-2497 nm. The spectra of the barley seeds could be classified according to genotypes by principal component analysis; and spectral covariances with reference analysis of moisture, β-glucan, starch, protein and lipid were established. The spectral variations of the barley oils were compared to the fatty acid compositions as measured using gas chromotography-mass spectrometry (GC-MS).

The change of fatty acids composition of Polish biscuits during storage

Commercially available Polish biscuits were stored for 10months under different storage conditions i.e. in temperatures of 5°C and 20°C. The chemical quality alteration caused by chemical reactions occurring within biscuits were studied in terms of change of composition of fat extracted from studied samples in one-month intervals. Correlation of data from standard methods e.g. gas chromatography or classic titration with FT-IR spectroscopy, was followed by calculation of four statistical models that accurately predicted peroxide value, oxidative stability, polar fraction content and unsaturated trans fatty acid content in any samples. On the basis of data obtained, scheme of chemical reactions involved in oxidation process was suggested. A critical time of storage was proposed as an indicator of the period of the highest rate of chemical changes. Among factors considered to influence oxidative stability, the following had the greatest impact: initial water content, initial fat content, and time of storage.

Characterisation of grain quality in diverse sorghum germplasm using a Rapid Visco-Analyzer and near infrared reflectance spectroscopy

BACKGROUND

Twenty-two diverse sorghum landraces, classified as normal and opaque types obtained from Ethiopia, were characterised for grain quality parameters using near infra-red spectroscopy (NIRS), chemical and Rapid Visco-Analyzer (RVA) characteristics.

RESULTS

Protein content ranged from 77 to 182 g kg(-1), and starch content from 514 to 745 g kg(-1). The NIRS analysis indicated the pig faecal digestible energy range from 14.6 to 15.7 MJ kg(-1) as fed, and the ileal digestible energy range from 11.3 to 13.9 MJ kg(-1) as fed. The normal sorghums had higher digestible energy than the opaque sorghums, which exhibited lower RVA viscosities, and higher pasting temperatures and setback ratios. The RVA parameters were positively correlated with the starch content and negatively correlated with the protein content. The normal and opaque types formed two distinct groups based on principal component and cluster analyses.

CONCLUSION

The landraces were different for the various grain quality parameters with some landraces displaying unique RVA and NIRS profiles. This study will guide utilisation of the sorghum landraces in plant improvement programs, and provides a basis for further studies into how starch and other constituents behave in and affect the properties of these landraces.

Use of near infrared reflectance and transmittance coupled to robust calibration for the evaluation of nutritional value in naked oats

A rapid accurate and precise method for simultaneous determination of β-glucan and protein content in naked oat samples, based on the coupling of near-infrared spectroscopy and chemometrics, is presented. In particular, three different spectroscopic approaches [near infrared reflectance (NIR) and transmittance (NIT) on flour and NIT on whole grains] and various spectral pretreatments were considered. To account for the possibility of outlying samples, a robust version of the PLS algorithm (namely partial robust M-regression) was used. All the models resulted as accurate as the reference methods, reflectance spectroscopy being the technique providing the best outcomes. Variable reduction by inclusion of the most relevant predictors only (as evaluated by VIP scores) resulted in simpler and, in one case, more parsimonious models, without loss in accuracy.

Near-infrared reflectance models for the rapid prediction of quality of brewing raw materials

Calibration models for quickly and reliably predicting moisture content and total nitrogen, both "as is" and "dry matter" on malt, as well as moisture content and total lipids, both "as is" and "dry matter", on maize by means of near-infrared (NIR) spectroscopy were developed. The FT-NIR spectra recorded on the finely ground cereals were correlated to the analytical data by means of the multivariate PLS algorithm. In particular, these models were developed on the raw materials, which are used by the main Italian brewing industries. Validation was carried out both by means of cross-validation and test set validation. Regression coefficients (R(2)) were higher than 97 for both malt and maize moisture content and higher than 85 and 88 for malt total nitrogen and maize total lipids, respectively. The RMSE values (both RMSECV and RMSEP) were lower than 0.1% m/m for both malt and maize moisture contents, whereas they ranged from 0.024 to 0.042% m/m for malt total nitrogen and from 0.042 to 0.055% m/m for maize total lipids. Repeatability was tested by taking into account more than one sample for each calibration and compared, when possible, to those of the standard methods. Repeatability (r(95)) ranged from 0.060 to 0.158% m/m and from 0.020 to 0.055% m/m for malt moisture and total nitrogen contents, respectively, and from 0.094 to 0.160% m/m and from 0.076 to 0.208% m/m for maize moisture and total lipids contents, respectively.

Innovative uses of near-infrared spectroscopy in food processing

Near-infrared spectroscopy (NIRS) has experienced widespread use as an analytical tool in the last 3 decades. Researchers today are exploring ways of applying NIRS that expand beyond compositional analyses into process control. Processes such as meat tenderness evaluation, curd cutting, and dough mixing have traditionally been controlled by highly skilled master craftsmen; new NIRS research applications are demonstrating that these complex processes can be monitored and controlled in situ to produce consistent, high quality end products with online NIRS technology. Additionally, researchers also now have the potential ability to develop new nondestructive spectroscopic techniques to probe the underlying molecular evolution of these products during processing.

Near-infrared analysis of whole kernel barley: comparison of three spectrometers

This study was conducted to develop calibration models for determining quality parameters of whole kernel barley using a rapid and nondestructive near-infrared (NIR) spectroscopic method. Two hundred and five samples of whole barley grains of three winter-habit types (hulled, malt, and hull-less) produced over three growing seasons and from various locations in the United States were used in this study. Among these samples, 137 were used for calibration and 68 for validation. Three NIR instruments with different resolutions, one Fourier transform instrument (4 cm(-1) resolution), and two dispersive instruments (8 nm and 10 nm bandpass) were utilized to develop calibration models for six components (moisture, starch, beta-glucan, protein, oil, and ash) and the results were compared. Partial least squares regression was used to build models, and various methods for preprocessing of spectral data were used to find the best model. Our results reveal that the coefficient of determination for calibration models (NIR predicted versus reference values) ranged from 0.96 for moisture to 0.79 for beta-glucan. The level of precision of the model developed for each component was sufficient for screening or classification of whole kernel barley, except for beta-glucan. The higher resolution Fourier transform instrument gave better results than the lower resolution instrument for starch and beta-glucan analysis. The starch model was most improved by the increased resolution. There was no advantage of using a higher resolution instrument over a lower resolution instrument for other components. Most of the components were best predicted using first-derivative processing, except for beta-glucan, where second-derivative processing was more informative and precise.

Near-infrared analysis of ground barley for use as a feedstock for fuel ethanol production

The objective of this study was to explore the potential of near-infrared spectroscopy for determining the compositional quality properties of barley as a feedstock for fuel ethanol production and to compare the prediction accuracy between calibration models obtained using a Fourier transform near-infrared system (FT-NIR) and a dispersive near-infrared system. The total sample set contained 206 samples of three types of barley, hull-less, malt, and hulled varieties, which were grown at various locations in the eastern U.S. from 2002 to 2005 years. A new hull-less barley variety, Doyce, which was specially bred for potential use in ethanol production, was included in the sample set. One hundred and thirty-eight barley samples were used for calibration and sixty-eight were used for validation. Ground barley samples were scanned on both a FTNIR spectrometer (10 000 to 4000 cm(-1) at 4 cm(-1) resolution) and a dispersive NIR spectrometer (400 to 2498 nm at 10 nm resolution), respectively. Six grain components, moisture, starch, beta-glucan, protein, oil, and ash content, were analyzed as parameters of barley quality. Principal component analysis showed that barley samples could be classified by their types: hull-less, malt, and hulled. Partial least squares regression indicated that both FT-NIR and dispersive NIR spectroscopy have the potential to determine quality properties of barley with an acceptable accuracy, except for beta-glucan content. There was no predictive advantage in using a high-resolution FT-NIR instrument over a dispersive system for most components of barley.

Edaphic Soil Levels of Mineral Nutrients, pH, Organic Matter, and Cationic Exchange Capacity in the Geocaulosphere Associated with Potato Common Scab

ABSTRACT In order to determine possible relationships between geocaulosphere soil properties and severity of common scab of potato caused by Streptomyces scabies, soils were collected from representative commercial potato fields in Canada: in Simcoe and Dufferin Counties, Ontario and across Prince Edward Island (PEI) in August 2004. Soils immediately adjacent to tubers were sampled and analyzed for select edaphic factors and for pathogen presence using polymerase chain reaction (PCR) tests with primers that amplify a region of the TxtA gene involved in regulating the biosynthesis of the thaxtomin toxin family. Individual tubers were assessed visually for scab severity. The relationships between soil chemical factors and disease severity were investigated for each region to detect the strongest relationships. Principal component analysis revealed a distinctive clustering of samples with respect to disease severity in PEI but not in Ontario soils. Total and percent saturation of K (%K) were the only factors found associated with high disease severity in soils from both provinces. In PEI soils, pH, Mg, Ca, Cu, and %K, %Mg, %Ca, and %Na were associated with high disease severity, whereas cation exchange capacity (CEC) and Al were correlated with low disease severity soils. In Ontario, high Mn content was strongly correlated with low disease severity soils, whereas %K and organic matter content were correlated with disease severity. Partitioning samples into presence or absence of the TxtA PCR product with corresponding high or low severity showed further significant relationships in the data. There was an excellent correlation between Streptomyces spp. presence as detected by PCR and disease severity in PEI soils; however, the relationship was not as clear in Ontario soils, where many PCR-positive soils had low disease incidence. Principal component and partial least square analysis indicated that disease severity was predicted by soil factors such as organic matter, CEC, pH, Al, %Ca, %Mg, and %K for PEI but not for Ontario soils. The data reveal that the relationship between scab severity and soil chemical components is complex and potentially soil specific.

Multivariate analysis of 2-DE protein patterns--practical approaches

Practical approaches to the use of multivariate data analysis of 2-DE protein patterns are demonstrated by three independent strategies for the image analysis and the multivariate analysis on the same set of 2-DE data. Four wheat varieties were selected on the basis of their baking quality. Two of the varieties were of strong baking quality and hard wheat kernel and two were of weak baking quality and soft kernel. Gliadins at different stages of grain development were analyzed by the application of multivariate data analysis on images of 2-DEs. Patterns related to the wheat varieties, harvest times and quality were detected on images of 2-DE protein patterns for all the three strategies. The use of the multivariate methods was evaluated in the alignment and matching procedures of 2-DE gels. All the three strategies were able to discriminate the samples according to quality, harvest time and variety, although different subsets of protein spots were selected. The explorative approach of using multivariate data analysis and variable selection in the analyses of 2-DEs seems to be promising as a fast, reliable and convenient way of screening and transforming many gel images into spot quantities.