Ultrasonic Attenuation Measurements of the Mixing, Agglomeration, and Sedimentation of Sucrose Crystals Suspended in Oil

Yucel, Umut; Coupland, John N.

doi:10.1007/s11746-010-1647-1

“…For example, CB is adjusted by a three-fold addition for every one-fold addition of unwanted moisture to correct its rheology (Beckett, 2000;Aidoo et al, 2012). Approaches such as high intensity conching (Nováková et al, 2002) and ultrasound (Yucel and Coupland, 2011) may also remove these aggregates.…”

Section: Rheologymentioning

confidence: 99%

The effect of sugar on the processing behaviour of palm oil: from confectionery fundamentals to predictive regression models

West¹

2021

Preprint

0

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This dissertation explores the impact of confectioner’s sugar and processing on key physical characteristics of palm oil. Though bulk oils such as palm oil have been well-studied, fat-continuous dispersions that include other ingredients such as sugar, cocoa solids, or milk powder are much more complex due to ingredient interactions and the resulting changes in properties such as fat crystal morphology and crystallization pathway. These implications restrict the ability to extrapolate responses from bulk oils towards multi-ingredient systems. This is particularly important given that palm oil in foods rarely exists in bulk but is usually mixed with multiple ingredients. The effects of processing on both bulk oil and oil-sugar blends were explored over four weeks of storage and clearly demonstrated that the existence of a dispersion resulted in large differences in rheology and texture compared to the bulk. Adding sugar significantly increased storage modulus and firmness of the oils while exhibiting increased sensitivity towards processing conditions. Predictive models were generated through multiple regression analysis that overcame limitations derived from extrapolation and corrected for these behavioural differences using a single binary variable accounting for the presence of confectioner’s sugar. As a result, the contributions of sugar to the rheology and texture of oil-sugar blends were modelled, a first in such systems. The results contained within this dissertation are of great industrial relevance as they: i) limit the dependence on anecdote and empiricism to explain results, ii) generate detailed process maps, iii) identify optimal process values to attain desirable rheological and textural responses, and iv) open the door for further investigation into dispersion effects.

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“…For example, CB is adjusted by a three-fold addition for every one-fold addition of unwanted moisture to correct its rheology (Beckett, 2000;Aidoo et al, 2012). Approaches such as high intensity conching (Nováková et al, 2002) and ultrasound (Yucel and Coupland, 2011) may also remove these aggregates.…”

Section: Rheologymentioning

confidence: 99%

The effect of sugar on the processing behaviour of palm oil: from confectionery fundamentals to predictive regression models

West¹

2021

Preprint

0

View full text Add to dashboard Cite

This dissertation explores the impact of confectioner’s sugar and processing on key physical characteristics of palm oil. Though bulk oils such as palm oil have been well-studied, fat-continuous dispersions that include other ingredients such as sugar, cocoa solids, or milk powder are much more complex due to ingredient interactions and the resulting changes in properties such as fat crystal morphology and crystallization pathway. These implications restrict the ability to extrapolate responses from bulk oils towards multi-ingredient systems. This is particularly important given that palm oil in foods rarely exists in bulk but is usually mixed with multiple ingredients. The effects of processing on both bulk oil and oil-sugar blends were explored over four weeks of storage and clearly demonstrated that the existence of a dispersion resulted in large differences in rheology and texture compared to the bulk. Adding sugar significantly increased storage modulus and firmness of the oils while exhibiting increased sensitivity towards processing conditions. Predictive models were generated through multiple regression analysis that overcame limitations derived from extrapolation and corrected for these behavioural differences using a single binary variable accounting for the presence of confectioner’s sugar. As a result, the contributions of sugar to the rheology and texture of oil-sugar blends were modelled, a first in such systems. The results contained within this dissertation are of great industrial relevance as they: i) limit the dependence on anecdote and empiricism to explain results, ii) generate detailed process maps, iii) identify optimal process values to attain desirable rheological and textural responses, and iv) open the door for further investigation into dispersion effects.

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“…The ultrasonic velocity, attenuation, and peak frequency shift were used to determine particle concentration and size. Both Ribeiro et al [11] and Yucel and Coupland [12] used two non-invasive transducers in the pitch-catch mode to characterise laboratory scale systems. However, transmission-based measurements are unable to be used for most mixing systems at the industrial scale.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning

Bowler

¹

,

Bakalis

²

,

Watson

³

2020

Sensors

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Mixing is one of the most common processes across food, chemical, and pharmaceutical manufacturing. Real-time, in-line sensors are required for monitoring, and subsequently optimising, essential processes such as mixing. Ultrasonic sensors are low-cost, real-time, in-line, and applicable to characterise opaque systems. In this study, a non-invasive, reflection-mode ultrasonic measurement technique was used to monitor two model mixing systems. The two systems studied were honey-water blending and flour-water batter mixing. Classification machine learning models were developed to predict if materials were mixed or not mixed. Regression machine learning models were developed to predict the time remaining until mixing completion. Artificial neural networks, support vector machines, long short-term memory neural networks, and convolutional neural networks were tested, along with different methods for engineering features from ultrasonic waveforms in both the time and frequency domain. Comparisons between using a single sensor and performing multisensor data fusion between two sensors were made. Classification accuracies of up to 96.3% for honey-water blending and 92.5% for flour-water batter mixing were achieved, along with R2 values for the regression models of up to 0.977 for honey-water blending and 0.968 for flour-water batter mixing. Each prediction task produced optimal performance with different algorithms and feature engineering methods, vindicating the extensive comparison between different machine learning approaches.

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Ultrasonic Attenuation Measurements of the Mixing, Agglomeration, and Sedimentation of Sucrose Crystals Suspended in Oil

Cited by 15 publications

References 16 publications

The effect of sugar on the processing behaviour of palm oil: from confectionery fundamentals to predictive regression models

The effect of sugar on the processing behaviour of palm oil: from confectionery fundamentals to predictive regression models

The effect of sugar on the processing behaviour of palm oil: from confectionery fundamentals to predictive regression models

Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning

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