Non-thermal pasteurization of milk by an innovative energy-saving moderate electrical field equipped with elongated electrodes and process optimization

Alsaedi, Ali Wali M.; Al-Mousawi, Azhar J.; Al-Hilphy, Asaad R.; Gavahian, Mohsen

doi:10.1016/j.ifset.2023.103445

Cited by 7 publications

(1 citation statement)

References 39 publications

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“…Non-thermal food processing techniques have been employed to prevent the adverse effects of heat treatment and could improve food quality, consume less water, reduce carbon emissions, increase energy efficiency, ensure clean labeling, and decrease food waste (Yudhistira et al, 2023). For example, our research team designed a continuous moderate electric field (MEF) system with elongated electrodes for milk sustainable non-thermal pasteurization (NP) (Alsaedi et al, 2023), and optimized to yield milk with desirable chemical and microbiological properties (Alsaedi et al, 2024). This recently emerged MEF pasteurizer could be used as an example of a novel non-thermal process to apply mathematical modeling and ANFIS to elaborate on the effect of such a process on the CIE color components to predict fat and moisture content accordingly.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence‐based modeling of novel non‐thermal milk pasteurization to achieve desirable color and predict quality parameters during storage

Alsaedi,

Al‐Hilphy,

Al‐Mousawi

et al. 2024

J Food Process Engineering

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This study proposed using color components as artificial intelligence (AI) input to predict milk moisture and fat contents. In this sense, an adaptive neuro‐fuzzy inference system (ANFIS) was applied to milk processed by moderate electrical field‐based non‐thermal (NP) and conventional pasteurization (CP). The differences between predicted and experimental data were not significant (p > 0.05) for lightness (L*), redness‐greenness (a*), yellowness‐blueness (b*), total color differences (∆E), hue angle (h), chroma (C), whiteness (WI), yellowness (YI), and browning index (BI). ANFIS well‐predicted milk fat and moisture content using quadratic and two‐factor interaction models with mean errors of .00858–.01260 and correlation coefficient of .8051–.8205. Stability tests showed L* and WI reduced while a*, b*, ΔE, h, C, YI, and BI increased during the storage. NP milk had 77.21% higher half‐life than CP, as predicted by ANFIS modeling. Findings indicated milk quality characteristics could be estimated based on physical parameters (e.g., color components), contributing to sustainable food production.Practical applicationsThe findings offer practical applications of artificial intelligence (AI) as an innovative monitoring and prediction technique to enhance food quality and sustainability. The proposed methodology makes the real‐time prediction of milk quality feasible by leveraging AI and physical parameters. An adaptive neuro‐fuzzy inference system (ANFIS) accurately predicts moisture and fat contents according to color values, facilitating quality assessment. Stability tests during cold storage provide insights into milk quality changes over time, aiding in determining key parameters in predictive modeling. The proposed approach was found to be applicable to both conventional and non‐thermal pasteurized milk. This study also provides a step‐by‐step protocol, facilitating the implementation of emerging technologies in the food industry.

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Section: Introductionmentioning

confidence: 99%