Ultra-small-angle neutron scattering as a structural fingerprinting tool for commercial cheeses

Ultra-small-angle neutron scattering (USANS) was evaluated as a structural fingerprinting tool for the comparative characterization of commercial cheeses over length scales extending into the micrometer regime. Representative cheeses spanning a broad range of compositions and manufacturing styles exhibited distinct low-q scattering profiles, which were analyzed using simplified diffuse-scattering expressions to extract apparent power-law slopes as shape-based structural descriptors. The apparent low-q power-law slope varied systematically among cheese types, ranging from approximately –1.6 for ricotta to –2.9 for brie, indicating substantial differences in the scale dependence of mesoscale structural heterogeneity. Within the present sample set, fresher and higher-moisture cheeses such as ricotta and mozzarella displayed the shallowest apparent exponents, consistent with more open and coarse heterogeneity over the measured range, whereas ripened and firmer cheeses generally exhibited steeper decays. A cheddar ripening series further revealed that while 12-month and 2-year cheddar were adequately described by single apparent scaling regimes, 6-month cheddar exhibited a crossover between two slopes, suggesting hierarchical mesoscale organization. These results demonstrate that USANS provides a sensitive, non-destructive means of distinguishing cheese classes through compact scattering descriptors derived from the low-q intensity decay. The findings support the use of apparent power-law behavior as a practical comparative fingerprint of cheese mesoscale structure.

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Publication Details

Journal
Food Hydrocolloids
Published
2026-06-17
DOI
https://doi.org/10.1016/j.foodhyd.2026.113010
Primary Topic
Probiotics and Fermented Foods
Type
article
Field-Weighted Citation Impact
0.00

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article

Ultra-small-angle neutron scattering as a structural fingerprinting tool for commercial cheeses

Alejandro G. Marangoni, N. Callaghan-Patrachar, C.P. Adams
Food Hydrocolloids
Probiotics and Fermented Foods
article

Ultra-small-angle neutron scattering as a structural fingerprinting tool for commercial cheeses

Alejandro G. Marangoni, N. Callaghan-Patrachar, C.P. Adams
article en

Abstract

Ultra-small-angle neutron scattering (USANS) was evaluated as a structural fingerprinting tool for the comparative characterization of commercial cheeses over length scales extending into the micrometer regime. Representative cheeses spanning a broad range of compositions and manufacturing styles exhibited distinct low-q scattering profiles, which were analyzed using simplified diffuse-scattering expressions to extract apparent power-law slopes as shape-based structural descriptors. The apparent low-q power-law slope varied systematically among cheese types, ranging from approximately –1.6 for ricotta to –2.9 for brie, indicating substantial differences in the scale dependence of mesoscale structural heterogeneity. Within the present sample set, fresher and higher-moisture cheeses such as ricotta and mozzarella displayed the shallowest apparent exponents, consistent with more open and coarse heterogeneity over the measured range, whereas ripened and firmer cheeses generally exhibited steeper decays. A cheddar ripening series further revealed that while 12-month and 2-year cheddar were adequately described by single apparent scaling regimes, 6-month cheddar exhibited a crossover between two slopes, suggesting hierarchical mesoscale organization. These results demonstrate that USANS provides a sensitive, non-destructive means of distinguishing cheese classes through compact scattering descriptors derived from the low-q intensity decay. The findings support the use of apparent power-law behavior as a practical comparative fingerprint of cheese mesoscale structure.

Food HydrocolloidsVol. 181
St. Francis Xavier University (CA), University of Guelph (CA)
Natural Sciences and Engineering Research Council of Canada
Openalex Percentile: Top 9%
Probiotics and Fermented Foods
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