This study aimed to develop a reliable method for quantifying fat crystal size in low-solid shortenings using laser diffraction, with a focus on preserving crystal integrity during sample preparation. A systematic approach was taken to evaluate solvent solubility, dispersion protocols, and measurement conditions. Solubility tests showed that ethanol and isopropanol were the most suitable solvents, as they dissolved less than 0.5% of fat, minimizing the risk of altering crystal structure. Dispersion protocols were optimized to ensure effective separation of crystal clusters without damaging their structure, using moderate shear and cooled conditions to prevent dissolution during analysis. Three crystallization conditions (A, B, and C) were applied to generate distinct crystal networks, revealing differences in structural density and shear history significantly influence dispersion behavior and particle size results. Laser diffraction data were validated by microscopy, confirming the reliability of the measurements. The developed method offers a robust, reproducible approach for characterizing fat crystal networks and detecting structural differences induced by processing. Its validation through both particle size analysis and microscopy provides a solid foundation for application in fat-based food systems, including product development, process optimization, and quality control in industrial settings.
This study aimed to develop a reliable method for quantifying fat crystal size in low-solid shortenings using laser diffraction, with a focus on preserving crystal integrity during sample preparation. A systematic approach was taken to evaluate solvent solubility, dispersion protocols, and measurement conditions. Solubility tests showed that ethanol and isopropanol were the most suitable solvents, as they dissolved less than 0.5% of fat, minimizing the risk of altering crystal structure. Dispersion protocols were optimized to ensure effective separation of crystal clusters without damaging their structure, using moderate shear and cooled conditions to prevent dissolution during analysis. Three crystallization conditions (A, B, and C) were applied to generate distinct crystal networks, revealing differences in structural density and shear history significantly influence dispersion behavior and particle size results. Laser diffraction data were validated by microscopy, confirming the reliability of the measurements. The developed method offers a robust, reproducible approach for characterizing fat crystal networks and detecting structural differences induced by processing. Its validation through both particle size analysis and microscopy provides a solid foundation for application in fat-based food systems, including product development, process optimization, and quality control in industrial settings.
Development of a Method for Fat Crystal Size Determination of Low Solid Shortenings
FARAMARZIPALANGAR, ZEINAB
2025/2026
Abstract
This study aimed to develop a reliable method for quantifying fat crystal size in low-solid shortenings using laser diffraction, with a focus on preserving crystal integrity during sample preparation. A systematic approach was taken to evaluate solvent solubility, dispersion protocols, and measurement conditions. Solubility tests showed that ethanol and isopropanol were the most suitable solvents, as they dissolved less than 0.5% of fat, minimizing the risk of altering crystal structure. Dispersion protocols were optimized to ensure effective separation of crystal clusters without damaging their structure, using moderate shear and cooled conditions to prevent dissolution during analysis. Three crystallization conditions (A, B, and C) were applied to generate distinct crystal networks, revealing differences in structural density and shear history significantly influence dispersion behavior and particle size results. Laser diffraction data were validated by microscopy, confirming the reliability of the measurements. The developed method offers a robust, reproducible approach for characterizing fat crystal networks and detecting structural differences induced by processing. Its validation through both particle size analysis and microscopy provides a solid foundation for application in fat-based food systems, including product development, process optimization, and quality control in industrial settings.| File | Dimensione | Formato | |
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Masters Thesis. Zeinab Faramarzi.pdf
embargo fino al 19/03/2029
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https://hdl.handle.net/20.500.12608/105291