The potential use of ultrasound-assisted bleaching in removing heavy metals and pigments from soybean oil using kinetic, thermodynamic and equilibrium modeling

Abedi, Elahe; Amiri, Mohammad Javad; Sayadi, Mehran

doi:10.1007/s11356-021-14180-2

Cited by 16 publications

(1 citation statement)

References 54 publications

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“…The magnitude of reduction of both pigment contents was comparable to results reported by Aachary et al (2016). In hempseed oil especially, the chlorophyll removal efficiency of ultrasound was clearly improved by earths presence that may be effective, after thermal degradation, in pigment absorption and removal, as already reported by several authors (Abedi et al, 2021;Su et al, 2013). The level of tocopherols was just slightly negatively affected by ultrasound treatments in both oils (Figure 4).…”

Section: Methodssupporting

confidence: 87%

Bleaching techniques impact on some quality parameters in two different cold‐pressed oils obtained at farm scale

Ferfuia

Zuliani

Piani

et al. 2023

J Food Process Engineering

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The consumption of cold-pressed oil, such as linseed (Linum usitatissimum L.) and hemp (Cannabis sativa L.) oil has increased recently. In these oils obtained on the farm, a large number of components able to affect oil quality (mainly chlorophylls) are present. Given this, an experiment was conducted with the following aims: (1) to compare different bleaching methods by applying ultrasound waves and earths in cold-pressed hempseed and linseed oils and (2) to evaluate the effect of different types (acid-activated and natural) and amount (2% and 4%) of earths with different processing temperatures (60 and 80 C) in hempseed oil, utilizing a bleaching method with earths alone. Regarding the

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Section: Methodssupporting

confidence: 87%

Bleaching techniques impact on some quality parameters in two different cold‐pressed oils obtained at farm scale

Ferfuia

Zuliani

Piani

et al. 2023

J Food Process Engineering

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A comparative study on bath and horn ultrasound‐assisted modification of bentonite and their effects on the bleaching efficiency of soybean and sunflower oil: Machine learning as a new approach for mathematical modeling

Abedi,

Sayadi,

Mousavifard

et al. 2024

Food Science & Nutrition

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In this study, the effect of high‐power bath and horn ultrasound at different powers on specific surface area (SBET), total pore volume (Vtotal), and average pore volume (Dave) of bleaching clay was examined. After subjecting the bleaching clay to ultrasonication treatment, the SBET values demonstrated an escalation from 31.4 ± 2.7 m2 g−1 to 59.8 ± 3.1 m2 g−1 for HU200BC, 143.8 ± 3.9 m2 g−1 for HU400BC, 54.4 ± 3.6 m2 g−1 for BU400BC, and 137.5 ± 2.8 m2 g−1 for BU800BC. The mean pore diameter (Dave) declined from 29.7 ± 0.14 nm in bleaching clay to 11.3 ± 0.13 nm in HU200BC, 8.3 ± 0.12 nm in HU400BC, 16.7 ± 0.14 nm in BU400BC, and 9.6 ± 0.12 nm in BU800BC. Therefore, horn ultrasound‐treated bleaching clay significantly increased SBET and Vtotal, indicating improved adsorption capacity. Moreover, to establish the relationship between bleaching parameters, seven multi‐output ML regression models of Feedforward Neural Network (FNN), Random Forest (RF), Support Vector Regression (SVR), Multi‐Task Lasso, Ridge regression, Extreme Gradient Boosting (XGBoost), and Gradient Boosting are used, and compared with response surface methodology (RSM). ML has revolutionized the understanding of complex relationships between ultrasonic parameters, oil color, and pigment degradation, providing insights into how various factors such as temperature, ultrasonic power, and time can influence the bleaching process, ultimately enhancing the efficiency and precision of the treatment. The XGBoost model showed outstanding performance in predicting the target variables with a high R2‐train up to 1, R2‐test up to .983, and a minimum mean absolute error (MAE) of 0.498. The lower error between the predicted and experimental values implies the superiority of the XGBoost model to predict outcomes rather than RSM. It represents the suitability of bath ultrasound as a mild condition for low‐pigmented oil bleaching. Finally, the Bayesian optimization method in conjunction with XGBoost was used to optimize the amount of bleaching clay and energy consumption, and its performance was compared with RSM. It was observed that the consumption of bleaching clay was reduced by approximately 60% for sunflower oil and 30%–35% for soybean oil.

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Comment on “The potential use of ultrasound-assisted bleaching in removing heavy metals and pigments from soybean oil using kinetic, thermodynamic and equilibrium modeling”

Balderas

Taylor

Kang

2022

Environ Sci Pollut Res

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The potential use of ultrasound-assisted bleaching in removing heavy metals and pigments from soybean oil using kinetic, thermodynamic and equilibrium modeling

Cited by 16 publications

References 54 publications

Bleaching techniques impact on some quality parameters in two different cold‐pressed oils obtained at farm scale

Bleaching techniques impact on some quality parameters in two different cold‐pressed oils obtained at farm scale

A comparative study on bath and horn ultrasound‐assisted modification of bentonite and their effects on the bleaching efficiency of soybean and sunflower oil: Machine learning as a new approach for mathematical modeling

Comment on “The potential use of ultrasound-assisted bleaching in removing heavy metals and pigments from soybean oil using kinetic, thermodynamic and equilibrium modeling”

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