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Food-grade filler particles as an alternative method to modify the texture and stability of myofibrillar gels.

ABSTRACT: A series of food grade particles were characterized for their potential as fillers in myofibrillar gels. The fillers were separated into (i) hydrophilic, insoluble, crystalline particles and (ii) starch granules. The particles used were microcrystalline cellulose, oat fiber and walnut shell flour, as well as potato and tapioca starches. Crystalline particles increased hardness and decreased recovery properties. Although all of these fillers decreased the T₂ relaxation time of water, this was dependent on particle type and size. An increase in gel strength was observed with increasing filler content, which was attributed to particle crowding. Native potato starch was the most efficient at increasing liquid retention, while native tapioca was the least effective. Gel strength increased significantly only for the native potato and modified tapioca starches, but no effect on recovery attributes were observed for any of the starch varieties. The potato starches became swollen and hydrated to a similar extent during the protein gelation process, while the native tapioca starch gelatinized at higher temperatures, and the modified tapioca showed little evidence of swelling. T₂ relaxometry supported this finding, as the meat batters containing native potato starch displayed two water populations, while the remaining starches displayed only a single population.

SUBMITTER: Gravelle AJ

PROVIDER: S-EPMC5599672 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Food-grade filler particles as an alternative method to modify the texture and stability of myofibrillar gels.

Gravelle Andrew J AJ Barbut Shai S Marangoni Alejandro G AG

Scientific reports 20170914 1

A series of food grade particles were characterized for their potential as fillers in myofibrillar gels. The fillers were separated into (i) hydrophilic, insoluble, crystalline particles and (ii) starch granules. The particles used were microcrystalline cellulose, oat fiber and walnut shell flour, as well as potato and tapioca starches. Crystalline particles increased hardness and decreased recovery properties. Although all of these fillers decreased the T<sub>2</sub> relaxation time of water, t ...[more]

PMID: 28912434

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Project description:BackgroundCT texture analysis (CTTA) has been successfully used to assess tissue heterogeneity in multiple diseases. The purpose of this work is to demonstrate the value of three-dimensional CTTA in the evaluation of diffuse liver disease. We aimed to develop CTTA based prediction models, which can be used for staging of fibrosis in different anatomic liver segments irrespective of variations in scanning parameters.MethodsWe retrospectively collected CT scans of thirty-two chronic hepatitis patients with liver fibrosis. The CT examinations were performed on either a 16- or a 64-slice scanner. Altogether 354 anatomic liver segments were manually highlighted on portal venous phase images, and 1117 three-dimensional texture parameters were calculated from each segment. The segments were divided between groups of low-grade and high-grade fibrosis using shear-wave elastography. The highly-correlated features (Pearson r > 0.95) were filtered out, and the remaining 453 features were normalized and used in a classification with k-means and hierarchical cluster analysis. The segments were split between the train and test sets in equal proportion (analysis I) or based on the scanner type (analysis II) into 64-slice train 16-slice validation cohorts for machine learning classification, and a subset of highly prognostic features was selected with recursive feature elimination.ResultsA classification with k-means and hierarchical cluster analysis divided segments into four main clusters. The average CT density was significantly higher in cluster-4 (110 HU ± SD = 10.1HU) compared to the other clusters (c1: 96.1 HU ± SD = 11.3HU; p < 0.0001; c2: 90.8 HU ± SD = 16.8HU; p < 0.0001; c3: 93.1 HU ± SD = 17.5HU; p < 0.0001); but there was no difference in liver stiffness or scanner type among the clusters. The optimized random forest classifier was able to distinguish between low-grade and high-grade fibrosis with excellent cross-validated accuracy in both the first and second analysis (AUC = 0.90, CI = 0.85-0.95 vs. AUC = 0.88, CI = 0.84-0.91). The final support vector machine model achieved an excellent prediction rate in the second analysis (AUC = 0.91, CI = 0.88-0.94) and an acceptable prediction rate in the first analysis (AUC = 0.76, CI = 0.67-0.84).ConclusionsIn conclusion, CTTA-based models can be successfully applied to differentiate high-grade from low-grade fibrosis irrespective of the imaging platform. Thus, CTTA may be useful in the non-invasive prognostication of patients with chronic liver disease.

Dataset Information

Food-grade filler particles as an alternative method to modify the texture and stability of myofibrillar gels.

Publications

Food-grade filler particles as an alternative method to modify the texture and stability of myofibrillar gels.

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