Fish, being a good source of nutrients, is often cooked by different methods before consumption, which affect the beneficial quality detrimentally. In this study, Catla catla, and mustard oil are selected as representative of fish and cooking oil for frying, respectively, because of their agricultural importance and worldwide demand. Extensive experiments are performed varying the effective processing variables of conventional frying viz., temperature (140 °C‐240 °C), time (5 min–20 min) and oil amount (25 ml/kg of fish‐100 ml/kg of fish) to correlate the drastic reduction of the nutritional quality indices, that is, ω‐3/ω‐6 and cis/trans‐fatty acids (FAs) profiles of fish after frying. To establish a nonlinear correlation between these inputs and outputs, an exhaustive search of all available artificial neural network (ANN) algorithms and activation functions is executed for the development of a model. The hybrid robust process approach integrating ANN with differential evolution (DE) and simulated annealing (SA) are employed to optimize the cooking parameters for regaining nutritional impact. After frying ω‐3/ω‐6 and cis/trans‐FAs ratio deteriorated by 76.65% and 92.68%, respectively, than the fresh samples. The ANN‐DE and ANN‐SA formalism efficiently enhanced these nutritional parameters up to 33.18% and 79%, respectively.

中文翻译：

---

通过鲁棒的混合人工智能方法对烹饪过程参数进行建模和优化以改善油炸鱼的营养状况

鱼是营养的良好来源，在食用前通常会通过不同的方法烹制，这会不利地影响其有益品质。在这项研究中，卡特拉·卡特拉由于其农业重要性和全球需求，分别选择芥末油和芥末油作为油炸用鱼油和食用油的代表。进行了广泛的实验，改变了常规油炸的有效加工变量，即温度（140°C–240°C），时间（5分钟至20分钟）和油量（25 ml / kg鱼-100 ml / kg鱼）鱼）与油炸后鱼的营养品质指数（即ω-3/ω-6和顺式/反式脂肪酸（FAs））的急剧下降相关。为了在这些输入和输出之间建立非线性关联，将对所有可用的人工神经网络（ANN）算法和激活函数进行详尽搜索，以开发模型。混合鲁棒工艺方法将ANN与差分进化（DE）和模拟退火（SA）相结合，用于优化烹饪参数以恢复营养影响。油炸后，ω-3/ω-6和顺式/反式FAs的比分别比新鲜样品下降了76.65％和92.68％。ANN-DE和ANN-SA形式主义有效地将这些营养参数分别提高了33.18％和79％。