Browse
Submit Data
Databases
API
Help

Dataset Information

14 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A Rapid Detection of Whole Wheat Gluten Quality by a Novel Chemometric Technique-GlutoPeak.

ABSTRACT: The study aims to accurately detect gluten quality of whole wheat flour without a refining process by measuring gluten aggregation properties with a novel and non-destructive chemometric technique called GlutoPeak, coupled with principal component analyses (PCA) and hierarchical cluster analyses (HCA). For this purpose, whole wheat flour samples from 125 common bread wheat cultivars were analyzed for protein content (PC), wet gluten content (WGC), and Zeleny sedimentation value (SV). The correlations of GlutoPeak indices (peak maximum time, PMT; maximum torque, MT; torque 15 s before MT, AM; torque 15 s after MT) with other conventional wheat quality parameters were evaluated. Results indicated that MT had high correlations with WGC (r = 0.627, p < 0.05) and PC (r = 0.589, p < 0.05) while PC (r = 0.511, p < 0.05) and WGC (r = 0.566, p < 0.05) values had moderate correlations with the GlutoPeak PM index. Considering the effect of regions, the MT and PM GlutoPeak indices are powerful parameters to discriminate whole wheat flour samples by their gluten strengths. In conclusion, the GlutoPeak test can be a powerful and reliable tool for prediction of refined and unrefined wheat quality without being time-consuming.

SUBMITTER: Turksoy S

PROVIDER: S-EPMC9265637 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Predicting vital wheat gluten quality using the gluten aggregation test and the microscale extension test.

Project description:Vital gluten is a by-product of wheat starch production and commonly used in bread making, but its quality is difficult to predict. The most accurate method to determine vital gluten quality is the baking experiment, but this approach is time- and labor-intensive. Therefore, the aim was to identify faster and easier ways to predict vital gluten quality. Three different approaches, the gliadin/glutenin ratio, the gluten aggregation test and the microscale extension test, were assessed for their predictive value regarding the baking performance of 46 vital gluten samples using two recipes. Hierarchical clustering classified the vital gluten samples into 23 samples with good, 15 with medium and eight with poor quality. Protein-related parameters, such as the gliadin/glutenin ratio, were not reliable to predict gluten quality, because the correlations to the bread volumes were weak. The gluten aggregation test and the microscale extension test were reliable methods to predict vital gluten quality for use in baking based on a scoring system. Both methods need less material, time and labor compared to baking experiments. Especially, maximum torque, peak maximum time, the ratio between peak30 and peak180 as well as the corresponding distance at maximum resistance to extension seem to be suitable alternatives to predict vital gluten quality.

| S-EPMC7749392 | biostudies-literature

Rapid detection of Staphylococcus aureus using a novel multienzyme isothermal rapid amplification technique

Project description:Staphylococcus aureus is a common pathogen that causes various infections. Therefore, it is crucial to develop a fast and easy detection method for diagnosing and preventing S. aureus infections. In this study, MIRA assay was developed and validated (specificity; 100%) for the detection of S. aureus with nuc as the target gene. The reaction temperature and reaction time were then optimized, and the best reaction was at 40°C, 20 min. The assay could detect S. aureus in only 25 min. Additionally, the limit of detection of MIRA was 5 × 102 CFU/ml, 10-fold lower than that of the traditional PCR. Furthermore, this assay efficiently detected 219 S. aureus of 335 strains obtained from different bacterial samples (detection accuracy; 99.40%). In conclusion, this study provides a rapid and easy-to-operate method for the detection of S. aureus, and thus can be used for the timely diagnosis and prevention of S. aureus infection.

| S-EPMC9606696 | biostudies-literature

Preparation and quality evaluation of potato steamed bread with wheat gluten.

Project description:The present study aimed to study the preparation and quality evaluation of potato steamed bread by using potato flour, wheat flour, and gluten at the presence of yeast and inorganic additives. As the rheological properties of the potato-wheat formulated flour negatively related to the potato flour, the potato-wheat formulated flour with 35% potato flour was set as the basic flour (100%). The effects of wheat gluten on the rheological properties of the dough were also evaluated, and gluten addition amount was set at 6.5%. The effects of yeast, sodium bicarbonate, citric acid, and monocalcium phosphate addition on steamed bread properties have been studied and optimized by orthogonal test. The obtained potato steamed bread formula was 100% basic flour (potato/wheat mass ratio of 35:65), 6.5% wheat gluten, 1.1% yeast, 1.4% NaHCO3, 0.75% citric acid, and 0.50% Ca(H2PO4)2. The prepared potato steamed bread has good sensory and texture properties, with natural potato flavor.

| S-EPMC7455941 | biostudies-literature

Gluten quality of bread wheat is associated with activity of RabD GTPases.

Project description:In the developing endosperm of bread wheat (Triticum aestivum), seed storage proteins are produced on the rough endoplasmic reticulum (ER) and transported to protein bodies, specialized vacuoles for the storage of protein. The functionally important gluten proteins of wheat are transported by two distinct routes to the protein bodies where they are stored: vesicles that bud directly off the ER and transport through the Golgi. However, little is known about the processing of glutenin and gliadin proteins during these steps or the possible impact on their properties. In plants, the RabD GTPases mediate ER-to-Golgi vesicle transport. Available sequence information for Rab GTPases in Arabidopsis, rice, Brachypodium and bread wheat was compiled and compared to identify wheat RabD orthologs. Partial genetic sequences were assembled using the first draft of the Chinese Spring wheat genome. A suitable candidate gene from the RabD clade (TaRabD2a) was chosen for down-regulation by RNA interference (RNAi), and an RNAi construct was used to transform wheat plants. All four available RabD genes were shown by qRT-PCR to be down-regulated in the transgenic developing endosperm. The transgenic grain was found to produce flour with significantly altered processing properties when measured by farinograph and extensograph. SE-HPLC found that a smaller proportion of HMW-GS and large proportion of LMW-GS are incorporated into the glutenin macropolymer in the transgenic dough. Lower protein content but a similar protein profile on SDS-PAGE was seen in the transgenic grain.

| S-EPMC4345403 | biostudies-literature

Validation and optimization of the loop-mediated isothermal amplification (LAMP) technique for rapid detection of wheat stripe mosaic virus, a wheat-infecting pathogen

Project description: Not available

| S-EPMC10997836 | biostudies-literature

Drought stress detection technique for wheat crop using machine learning.

Project description:The workflow of this research is based on numerous hypotheses involving the usage of pre-processing methods, wheat canopy segmentation methods, and whether the existing models from the past research can be adapted to classify wheat crop water stress. Hence, to construct an automation model for water stress detection, it was found that pre-processing operations known as total variation with L1 data fidelity term (TV-L1) denoising with a Primal-Dual algorithm and min-max contrast stretching are most useful. For wheat canopy segmentation curve fit based K-means algorithm (Cfit-kmeans) was also validated for the most accurate segmentation using intersection over union metric. For automated water stress detection, rapid prototyping of machine learning models revealed that there is a need only to explore nine models. After extensive grid search-based hyper-parameter tuning of machine learning algorithms and 10 K fold cross validation it was found that out of nine different machine algorithms tested, the random forest algorithm has the highest global diagnostic accuracy of 91.164% and is the most suitable for constructing water stress detection models.

| S-EPMC10280683 | biostudies-literature

Identification of novel antibody-reactive detection sites for comprehensive gluten monitoring.

Project description:Certain cereals like wheat, rye or barley contain gluten, a protein mixture that can trigger celiac disease (CD). To make gluten-free diets available for affected individuals the gluten content of foodstuff must be monitored. For this purpose, antibody-based assays exist which rely on the recognition of certain linear gluten sequence motifs. Yet, not all CD-active gluten constituents and fragments formed during food processing/fermentation may be covered by those tests. In this study, we therefore assayed the coverage of reportedly CD-active gluten components by currently available detection antibodies and determined the antibody-inducing capacity of wheat gluten constituents in order to provide novel diagnostic targets for comprehensive gluten quantitation. Immunizations of outbred mice with purified gliadins and glutenins were conducted and the linear target recognition profile of the sera was recorded using synthetic peptide arrays that covered the sequence space of gluten constituents present in those preparations. The resulting murine immunorecognition profile of gluten demonstrated that further linear binding sites beyond those recognized by the monoclonal antibodies α20, R5 and G12 exist and may be exploitable as diagnostic targets. We conclude that the safety of foodstuffs for CD patients can be further improved by complementing current tests with antibodies directed against additional CD-active gluten components. Currently unrepresented linear gluten detection sites in glutenins and α-gliadins suggest sequences QQQYPS, PQQSFP, QPGQGQQG and QQPPFS as novel targets for antibody generation.

| S-EPMC5536345 | biostudies-literature

Rapid Detection of Carbendazim Residue in Apple Using Surface-Enhanced Raman Scattering and Coupled Chemometric Algorithm.

Project description:In order to achieve rapid and precise quantification detection of carbendazim residues, surface-enhanced Raman spectroscopy (SERS) combined with variable selected regression methods were developed. A higher sensitivity and greater density of "hot spots" in three-dimensional (3D) SERS substrates based on silver nanoparticles compound polyacrylonitrile (Ag-NPs @PAN) nanohump arrays were fabricated to capture and amplify the SERS signal of carbendazim. Four Raman spectral variable selection regression models were established and comparatively assessed. The results showed that the bootstrapping soft shrinkage-partial least squares (BOSS-PLS) method achieved the best predictive capacity after variable selection, and the final BOSS-PLS model has the correlation coefficient (RP) of 0.992. Then, this method used to detect the carbendazim residue in apple samples; the recoveries were 86~116%, and relative standard deviation (RSD) is less than 10%. The 3D SERS substrates combined with the BOSS-PLS algorithm can deliver a simple and accurate method for trace detection of carbendazim residues in apples.

| S-EPMC9103909 | biostudies-literature

The Landscapes of Gluten Regulatory Network in Elite Wheat Cultivars Contrasting in Gluten Strength.

Project description:Yangmai-13 (YM13) is a wheat cultivar with weak gluten fractions. In contrast, Zhenmai-168 (ZM168) is an elite wheat cultivar known for its strong gluten fractions and has been widely used in a number of breeding programs. However, the genetic mechanisms underlying the gluten signatures of ZM168 remain largely unclear. To address this, we combined RNA-seq and PacBio full-length sequencing technology to unveil the potential mechanisms of ZM168 grain quality. A total of 44,709 transcripts were identified in Y13N (YM13 treated with nitrogen) and 51,942 transcripts in Z168N (ZM168 treated with nitrogen), including 28,016 and 28,626 novel isoforms in Y13N and Z168N, respectively. Five hundred and eighty-four differential alternative splicing (AS) events and 491 long noncoding RNAs (lncRNAs) were discovered. Incorporating the sodium-dodecyl-sulfate (SDS) sedimentation volume (SSV) trait, both weighted gene coexpression network analysis (WGCNA) and multiscale embedded gene coexpression network analysis (MEGENA) were employed for network construction and prediction of key drivers. Fifteen new candidates have emerged in association with SSV, including 4 transcription factors (TFs) and 11 transcripts that partake in the post-translational modification pathway. The transcriptome atlas provides new perspectives on wheat grain quality and would be beneficial for developing promising strategies for breeding programs.

| S-EPMC10253585 | biostudies-literature

Low-gluten, nontransgenic wheat engineered with CRISPR/Cas9.

Project description:Coeliac disease is an autoimmune disorder triggered in genetically predisposed individuals by the ingestion of gluten proteins from wheat, barley and rye. The ?-gliadin gene family of wheat contains four highly stimulatory peptides, of which the 33-mer is the main immunodominant peptide in patients with coeliac. We designed two sgRNAs to target a conserved region adjacent to the coding sequence for the 33-mer in the ?-gliadin genes. Twenty-one mutant lines were generated, all showing strong reduction in ?-gliadins. Up to 35 different genes were mutated in one of the lines of the 45 different genes identified in the wild type, while immunoreactivity was reduced by 85%. Transgene-free lines were identified, and no off-target mutations have been detected in any of the potential targets. The low-gluten, transgene-free wheat lines described here could be used to produce low-gluten foodstuff and serve as source material to introgress this trait into elite wheat varieties.

| S-EPMC5867031 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data