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What is Starch Granule?
Starch molecules generally is deposited as discrete semi-crystalline aggregates known as starch granule in the plant. Starch granules are the most vital energy reserve in plants. They are mainly composed of amylopectin with a minor fraction of amylose. The structure of starch Granule is proposed to consist of concentric shells, semi-crystalline, and soft amorphous layers. The shape, volume and structure of starch granule are the most important factors contributing to starch quality. The starch granule number, volume and starch composition are distinct in different starch species.
The granule size distribution of starch is a very important characteristic that strongly affects its physicochemical properties and chemical composition which in turn may affect the functionality. It directly affects the ratio of amylopectin to amylose, the gelatinization properties, and the properties of the starch. Starch granule size distribution is an important factor that influences the quality of many processed products.
Each plant species has its own unique starch granule size. It is generally accepted that wheat starch granules can be divided into large A and small B types according to their size and shape. The large A type starch granules are discoid or lenticular in shape with an average diameter that ranges from 10 to 35 µm, and it accounts for 3% of the total number of wheat starch granules. The small B-type starch granules are usually spherical or polygonal in shape with diameter less than 10 µm, and it contributes to more than 90% of the total number of starch granules. Wheat starch granules in some literatures also can be categorized into three types with different size ranges: A type granules with diameter larger than 15 μm, B type granules with diameter ranges from 5 to 15 µm, and C type granules diameter smaller than 5 μm. The granule size of maize starch usually ranges from 1 to 7 μm for small granules and 15 to 20 μm for large granules. Rice starch granules generally have diameters that ranges from 3 to 5 μm in size. The large A type and the small B type starch granules have different physicochemical and functional properties.
Creative Proteomics has established itself as a leader in the field of biochemical analysis, providing a wide array of glycomics service tailored to meet the specific needs of our clients. Our starch granule size distribution analysis services are designed to help researchers and industry professionals understand the physical properties of starch granules, which can significantly impact the functionality and performance of starch in various applications.
Starch Granule Size Distribution Analysis at Creative Proteomics
We offer an extensive range of starch granule size distribution analysis services, each tailored to the unique requirements of different industries and research fields. Our services include:
Particle Size Distribution Analysis:
Utilizing state-of-the-art laser diffraction technology, we accurately determine the size distribution of starch granules. This analysis is crucial for applications where the granule size affects the texture, stability, or processing characteristics of the final product.
Microscopic Imaging and Analysis:
By employing advanced microscopy techniques, we provide detailed imaging of starch granules. This service allows for the visualization of granule morphology, providing insights into the structural characteristics that influence functional properties.
Dynamic Light Scattering (DLS) Analysis:
DLS is used to measure the size distribution of smaller starch granules with high precision. This technique is particularly useful in studying nano-sized starch particles, which are increasingly being used in innovative applications such as drug delivery systems.
Scanning Electron Microscopy (SEM):
SEM offers high-resolution imaging that reveals the surface topography of starch granules. This service is essential for understanding the granular structure at a micro-scale, which can affect the behavior of starch in different environments.
Customized Analytical Solutions:
Understanding that each project is unique, Creative Proteomics offers customized analytical services. We work closely with our clients to develop tailored protocols that meet their specific research or industrial needs.
Analytical Techniques for Granule Size Distribution Analysis
Laser Diffraction: Precision in Particle Sizing
Laser diffraction is one of the most widely used techniques for starch granule size distribution analysis. It works by measuring the angle and intensity of light scattered by the granules as they pass through a laser beam. The resulting data is then analyzed to determine the size distribution of the granules.
- High Throughput: Capable of analyzing large sample volumes quickly.
- Wide Size Range: Effective for analyzing particles ranging from sub-micron to millimeter scales.
- Non-Destructive: The technique does not alter the sample, allowing for further analysis.
Microscopy Techniques: Detailed Granular Imaging
Microscopy techniques, such as light microscopy and electron microscopy, are integral to starch granule analysis. These methods allow for direct observation of the granule shape, size, and surface characteristics.
Light Microscopy:
- Useful for observing and measuring larger starch granules.
- Provides a quick assessment of granule morphology.
Electron Microscopy:
- Provides high-resolution images that reveal detailed surface features and internal structures of starch granules.
- Particularly valuable for analyzing the microstructure of starch in different environments.
Dynamic Light Scattering (DLS): Analyzing Nano-sized Granules
DLS is a powerful technique for measuring the size distribution of smaller, often nano-sized starch granules. This method analyzes the fluctuations in light scattering caused by the Brownian motion of particles in suspension.
- Ideal for applications requiring precise measurement of small particle sizes, such as in the development of starch-based nanomaterials.
- Provides rapid and accurate size distribution data for particles in the nanometer range.
Scanning Electron Microscopy (SEM): Surface Topography and Morphology
SEM is an advanced imaging technique that provides detailed information on the surface structure of starch granules. By scanning the surface with a focused electron beam, SEM generates high-resolution images that reveal the granular morphology in great detail.
- High Resolution: Capable of imaging at magnifications up to 500,000x.
- Surface Detail: Reveals surface features that are not visible with other techniques.
- Versatility: Applicable to a wide range of starch types and conditions.
Sample Requirements for Granule Size Distribution Analysis
| Parameter | Requirement |
|---|---|
| Sample Type | Starch granules (native, modified, or processed) |
| Sample Quantity | 1-5 grams (depending on the method used) |
| Sample Purity | ≥95% purity recommended to avoid interference during analysis |
| Sample Preparation | Dried and ground into a fine powder; free from contaminants (e.g., fibers, proteins) |
| Particle Size Range | 0.1 µm to 1 mm (varies with the method) |
| Moisture Content | Preferably <10% to ensure consistency in measurements |
| Storage Conditions | Store in a dry, cool environment; avoid exposure to humidity and light |
| Packaging | Sealed, airtight container (e.g., glass vial or plastic container) |
| Additional Information | Include details on the origin, type, and any modifications of the starch sample |
| Lead Time | Typically 1-2 weeks, depending on the analysis method and sample condition |
Effect of climatic conditions on wheat starch granule size distribution, gelatinization and flour pasting properties.
Journal: Agronomy
Published: 2023
Background
Bread wheat (Triticum aestivum L.) is a crucial global cereal crop, extensively cultivated across diverse environments that significantly influence its productivity and quality. The demand for wheat is projected to rise by 50% by 2050, necessitating the development of wheat varieties with high yields and consistent technological traits. Variations in wheat quality are attributed to genotype, environmental factors, and their interactions. Environmental conditions, such as temperature and precipitation, notably impact starch accumulation in wheat grains, affecting their quality. High temperatures during grain filling can alter starch structure, granule size distribution, and its gelatinization properties, which are essential for determining the quality of wheat flour and its final products. To improve wheat breeding and adaptation strategies, understanding how genotype and environmental conditions influence starch properties is crucial.
Materials & Methods
Materials
Wheat Samples: Ninety-two wheat grain samples (Triticum aestivum) from eight different varieties, grown at seven locations in Vojvodina, Serbia. The samples were collected from three different years (Year 1, Year 2, and Year 3) with varying climatic conditions:
- Year 1: Average temperatures and precipitation were close to long-term averages with a moderate number of days exceeding 30°C.
- Year 2: Characterized by high temperatures and low precipitation, indicative of pronounced heat stress.
- Year 3: Featured cooler temperatures and higher precipitation, close to long-term averages.
Equipment:
- Laboratory mill (MLU 202, Bühler)
- Laser light scattering particle size analyzer (Mastersizer 2000, Malvern Instruments)
- Differential scanning calorimeter (DSC) (204 F1 Phoenix, Netzsch)
- Glutomatic (PerkinElmer)
- Amylograph (Brabender Co.)
Methods
1. Wheat Sample Preparation:
Wheat grains were harvested, cleaned, and milled to obtain wheat flour using a laboratory mill (MLU 202, Bühler).
2. Alpha-Amylase Activity:
Measured using the Megazyme Ceralpha assay kit. Results were expressed in Ceralpha Units (CU) on a dry basis.
3. Amylograph Peak Viscosity:
Determined using an Amylograph (Brabender Co.) according to ICC Method 126/1.
4. Falling Number:
Determined using ICC Methods 107/1.
5. Amylose Content:
Assessed using the Megazyme amylose/amylopectin assay kit.
6. Starch Isolation:
Starch was isolated from wheat flour using a Glutomatic apparatus (PerkinElmer) and purified through multiple centrifugation cycles.
7. Granule Size Analysis:
The size distribution of starch granules was measured using laser light scattering (Mastersizer 2000, Malvern Instruments) in wet cell mode. Granule volumes were calculated assuming spherical shape.
8. Gelatinization Properties:
Gelatinization characteristics were analyzed using a differential scanning calorimeter (DSC) (204 F1 Phoenix, Netzsch). Parameters including onset temperature (To), peak temperature (Tp), end temperature (Te), and enthalpy of gelatinization (∆H) were determined.
9. Statistical Analysis:
Data were analyzed using Principal Component Analysis (PCA) and ANOVA with Statistica software, version 12. Models were validated using coefficients of determination (r²), reduced chi-square (χ²), mean bias error (MBE), root mean square error (RMSE), mean percentage error (MPE), and Akaike's Information Criterion (AIC).
Results
1. Correlation Analysis
Alpha-Amylase Activity (AA): Showed a negative correlation with Maximum Viscosity (MV) (r = -0.587) and Falling Number (FN) (r = -0.620).
MV and FN: Positively correlated with each other (r = 0.650).
Amylose Content (AM): Inversely correlated with MV (r = -0.484).
Sub-Sample Analysis:
The correlation patterns in a reduced dataset, focusing on three wheat varieties (Apač, Pobeda, Zvezdana), three locations (Bačka Topola, Sremska Mitrovica, Sombor), and three years, confirmed the general trends observed in the full dataset.
2. Principal Component Analysis (PCA)
PCA Analysis: Identified patterns and groupings among wheat samples based on variety, location, and year.
Principal Components:
- First Component: Positive contributions from MV (10.2%), A-granule (13.4%), and gelatinization temperatures (To, Tp, Te), and negative contributions from B-granule (11.0%) and C-granule (10.0%).
- Second Component: Negative influence from AM (24.2%) and A-granule (12.4%), and positive influence from B-granule (11.2%), C-granule (12.5%), and MV (13.8%).
PCA Plot:
The PCA biplot effectively discriminates between different wheat varieties, growing locations, and harvest years.
Figure 1. Biplot graphic of three wheat varieties, grown at three different locations in three years.
3. Second-Order Polynomial (SOP) Model
- MV: Influenced by quadratic terms of variety (Var) and year (Y), with the highest values in Apač and Year 2.
- FN: Affected by quadratic terms of year and location × year interactions, with the highest values in Year 2.
- AA: Influenced by quadratic term of year and linear terms of variety and year, with the lowest values in Year 2.
- AM: Affected by quadratic terms of variety, with the highest content in Pobeda and Year 2.
- Granule Sizes: A-granule had the highest proportion in Year 2, B- and C-granules in Year 3.
- Gelatinization Temperatures (To, Tp, Te): Highest in Year 2, lowest in Year 3, with Apač having the highest temperatures.
Model Fit:
The SOP model showed a good fit with high r2r^2r2 values and low error metrics, indicating effective prediction of wheat characteristics.
Reference
- Rakita, Slađana, et al. "Effect of climatic conditions on wheat starch granule size distribution, gelatinization and flour pasting properties." Agronomy 13.6 (2023): 1551.
How should a sample be prepared for accurate Starch Granule Size Distribution Analysis?
Sample preparation is critical for obtaining accurate results:
- Drying: Ensure that the starch sample is completely dry. Moisture can cause granules to swell or clump together, leading to inaccurate size measurements. Drying can be achieved through air drying, oven drying, or using a desiccator.
- Grinding: If the starch sample is in bulk or in larger granules, grinding it into a fine powder may be necessary to ensure a uniform size distribution and avoid clumping.
- Dispersion: For methods like laser diffraction or DLS, dispersing the starch in an appropriate medium (e.g., water or a suitable solvent) is crucial to prevent agglomeration. Proper dispersion ensures that the granules are individually suspended and can be measured accurately.
- Filtration: Filtering the sample through a sieve or filter can help remove any larger particles or impurities that might interfere with the analysis.
What challenges are associated with Starch Granule Size Distribution Analysis, and how can they be overcome?
Agglomeration: Starch granules often tend to clump together, which can skew results. To mitigate this, use dispersants to prevent clumping, and ensure thorough mixing and dispersion of the sample.
Moisture Content: Variability in moisture can affect granule size and distribution. Accurate drying and sample preparation techniques are essential to minimize moisture-related issues.
Instrument Calibration: Regular calibration of analytical instruments is necessary to ensure accuracy. Follow the manufacturer's guidelines and perform routine maintenance and calibration checks.
Sample Preparation Consistency: Inconsistent sample preparation can lead to variability in results. Adhere to standardized procedures for sample handling and preparation to ensure reliability.
Can Starch Granule Size Distribution Analysis be used to compare different starch sources, and if so, how?
Yes, Starch Granule Size Distribution Analysis is a valuable tool for comparing different starch sources. By analyzing the size distribution of granules from various starch types, you can:
Identify Differences: Determine variations in granule size and distribution between starch sources, which may affect their functionality and suitability for specific applications.
Optimize Formulations: Select the most appropriate starch based on its granule size characteristics for desired properties such as viscosity, texture, and gelatinization behavior.
Quality Control: Assess the consistency and quality of starch from different suppliers or batches to ensure that the starch meets the required specifications for your application.
Alternative glycosylation controls endoplasmic reticulum dynamics and tubular extension in mammalian cells.
Kerselidou, Despoina, Bushra Saeed Dohai, David R. Nelson, Sarah Daakour, Nicolas De Cock, Zahra Al Oula Hassoun, Dae-Kyum Kim et al.
Journal: Science advances
Year: 2021
https://doi.org/10.1126/sciadv.abe8349
Identification of the O-Glycan Epitope Targeted by the Anti-Human Carcinoma Monoclonal Antibody (mAb) NEO-201
Tsang, K. Y., Fantini, M., Zaki, A., Mavroukakis, S. A., Morelli, M. P., Annunziata, C. M., & Arlen, P. M.
Journal: Cancers
Year: 2022
https://doi.org/10.3390/cancers14204999
