Scholarlink

<p>The growing demand for plant-based cheese analogues (PBCA) reflects increasing consumer interest in dairy-free products with desirable texture and sensory attributes. This study applied a hybrid optimization framework combining fractional factorial design (FFD) and artificial neural network (ANN) modeling to identify key formulation factors influencing chickpea-based (<i>Cicer arietinum</i>) cheese analogues. Initially, several plant-based milks (mung bean, whole mung bean, black-eyed pea, vigna mungo, chickpea, and soybean) were evaluated with different coagulants to determine milk-clotting efficiency. Chickpea milk exhibited superior coagulation with papain, and was subsequently selected for formulation optimization. The effects of water, vegetable oil, starch, papain, and carrageenan on textural properties were analyzed. The FFD results showed that starch significantly increased hardness, gumminess, and chewiness, while carrageenan enhanced cohesiveness and structural stability (<i>p</i> &lt; 0.05). The ANN model demonstrated high predictive accuracy (R² = 0.999 for hardness, 0.913 for chewiness, 1.000 for cohesiveness), capturing nonlinear interactions beyond the quadratic limitations of FFD. The integration of FFD and ANN effectively minimized experimental variability and revealed starch and carrageenan as critical determinants of plant-based cheese analogue (PBCA) texture and mouthfeel. The integrated modeling strategy reduces experimental trials and material use, supporting resource-efficient formulation development. This keeps sustainability without invoking policy goals.</p> Graphical Abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Fractional factorial design and machine learning reveal key formulation factors in chickpea-based (Cicer arietinum) cheese analogues

  • 期刊论文

  • 发表日期：

    2026年5月2日

查看全文
  • Anis Asyila Marzlan,
  • Aliah Zannierah Mohsin,
  • Fatema Hossain Brishti,
  • Sylvie Marchesseau,
  • Charles Cunault,
  • Anis Shobirin Meor Hussin

摘要

The growing demand for plant-based cheese analogues (PBCA) reflects increasing consumer interest in dairy-free products with desirable texture and sensory attributes. This study applied a hybrid optimization framework combining fractional factorial design (FFD) and artificial neural network (ANN) modeling to identify key formulation factors influencing chickpea-based (Cicer arietinum) cheese analogues. Initially, several plant-based milks (mung bean, whole mung bean, black-eyed pea, vigna mungo, chickpea, and soybean) were evaluated with different coagulants to determine milk-clotting efficiency. Chickpea milk exhibited superior coagulation with papain, and was subsequently selected for formulation optimization. The effects of water, vegetable oil, starch, papain, and carrageenan on textural properties were analyzed. The FFD results showed that starch significantly increased hardness, gumminess, and chewiness, while carrageenan enhanced cohesiveness and structural stability (p < 0.05). The ANN model demonstrated high predictive accuracy (R² = 0.999 for hardness, 0.913 for chewiness, 1.000 for cohesiveness), capturing nonlinear interactions beyond the quadratic limitations of FFD. The integration of FFD and ANN effectively minimized experimental variability and revealed starch and carrageenan as critical determinants of plant-based cheese analogue (PBCA) texture and mouthfeel. The integrated modeling strategy reduces experimental trials and material use, supporting resource-efficient formulation development. This keeps sustainability without invoking policy goals.

Graphical Abstract