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<p>In this experimental study, the quality and shelf-life of sea bream were evaluated under different processing conditions (uncleaned, cleaned + unwashed, and cleaned + washed), storage temperatures (4&#xa0;°C and 10&#xa0;°C), and storage days (T<sub>0</sub>, 1st, 3rd, 5th, 7th and 9th days) based on mesophilic and psychrophilic bacterial counts. The aim of the study is to determine the most influential processing and storage variables using Response Surface Methodology (RSM) and to incorporate these significant factors as inputs into a Fuzzy Inference System (FIS) designed to estimate bacterial growth. The mesophilic and psychrophilic bacterial count estimates generated independently by both RSM and FIS provide a consistent basis for evaluating and comparing the predictive performance of the two approaches. By integrating RSM-based factor selection with an FIS predictive model, the study presents a novel decision-support framework for microbial quality evaluation and freshness assessment in seafood. Although the cleaned + washed group generally showed lower mesophilic bacterial loads compared to the other processing types, this difference was not statistically significant (<i>p</i> &gt; 0.05). Day and temperature significantly influenced bacterial growth (<i>p</i> &lt; 0.01), and the adjusted R<sup>2</sup> values of the predictive models were 0.8826 for mesophilic bacteria and 0.8930 for psychrophilic bacteria. The optimum conditions for both bacteria were determined as day 1, 4&#xa0;°C, and cleaned and washed processing type. The expected bacterial counts at these factor levels were 2.3807 log CFU/g for mesophilic bacteria and 1.5672 log CFU/g for psychrophilic bacteria. The findings demonstrated that predictive bacterial load values were obtained using both RSM and FIS models, and the performance metrics (MSE, RMSE, MAE) showed that the FIS model produced lower error levels. This outcome is likely due to the suitability of the membership functions and parameter settings defined for the FIS model in this study.</p>

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Evaluation of the effects of different processing methods on the microbiological quality of sea bream (Sparus aurata) during cold storage using fuzzy inference and response surface methodology

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月31日

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  • Sevcan Demir Atalay,
  • Berna Kılınç,
  • Gözde Kuş,
  • Baran Mis

摘要

In this experimental study, the quality and shelf-life of sea bream were evaluated under different processing conditions (uncleaned, cleaned + unwashed, and cleaned + washed), storage temperatures (4 °C and 10 °C), and storage days (T0, 1st, 3rd, 5th, 7th and 9th days) based on mesophilic and psychrophilic bacterial counts. The aim of the study is to determine the most influential processing and storage variables using Response Surface Methodology (RSM) and to incorporate these significant factors as inputs into a Fuzzy Inference System (FIS) designed to estimate bacterial growth. The mesophilic and psychrophilic bacterial count estimates generated independently by both RSM and FIS provide a consistent basis for evaluating and comparing the predictive performance of the two approaches. By integrating RSM-based factor selection with an FIS predictive model, the study presents a novel decision-support framework for microbial quality evaluation and freshness assessment in seafood. Although the cleaned + washed group generally showed lower mesophilic bacterial loads compared to the other processing types, this difference was not statistically significant (p > 0.05). Day and temperature significantly influenced bacterial growth (p < 0.01), and the adjusted R2 values of the predictive models were 0.8826 for mesophilic bacteria and 0.8930 for psychrophilic bacteria. The optimum conditions for both bacteria were determined as day 1, 4 °C, and cleaned and washed processing type. The expected bacterial counts at these factor levels were 2.3807 log CFU/g for mesophilic bacteria and 1.5672 log CFU/g for psychrophilic bacteria. The findings demonstrated that predictive bacterial load values were obtained using both RSM and FIS models, and the performance metrics (MSE, RMSE, MAE) showed that the FIS model produced lower error levels. This outcome is likely due to the suitability of the membership functions and parameter settings defined for the FIS model in this study.