Rheological determination of molecular weight of honey adulterated with high fructose corn syrup

Anidiobu, Vincent Okechukwu

doi:10.1080/00218839.2021.1874708

Cited by 2 publications

(1 citation statement)

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“…A few examples include the routine rheology assessment of honey, being a known driving factor of organoleptic perception, which allows predictions on the honey quality to be made [10]. Furthermore, rheology measurements are a reliable tool to uncover adulteration of honey blended with other amorphous sugars, because rheology is such an inherent fluid property and adulteration leads to shifts of the expectable rheological profiles compared to pure, unadulterated honey [11]. In the beverage industries, rheology is measured for the assessment of emulsion stability and cloudiness of juice-type products (oil-in-water emulsions, where the oil is a combination of essential and vegetable oils [12]), in addition to the assessment of the foam stabil-ity of light and dark beers by off-line large-amplitude oscillatory shear measurements [13].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Rheological Changes Using Acoustic Emissions for Complex Formulated Fluids Manufacturing

Hefft,

Blake,

Farhoud

et al. 2023

Chem Eng & Technol

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The measurement capabilities of a newly developed in‐situ rheometric device based on a single passive acoustic emission sensor and machine learning algorithms were investigated. Two surfactant structured fluids demonstrating complex non‐Newtonian rheology (Power‐law and Herschel‐Bulkley models) were examined. Furthermore, a static evaluation on the laboratory scale in comparison to dynamic processing on the pilot scale was conducted. The results indicate that the machine learning algorithms of this technology can identify, in > 90 % of scenarios, the correct type of rheology or the manufacturing process step across both scales. This identification is based on solving a classification problem using quadratic support vector machine learning algorithms, which have proven to deliver the most robust predictions across a choice of 24 different algorithms tested. Additionally, a new format of in situ rheology display was introduced, referred to as RRF™ factor.

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