Development of Texture Evaluation Model Based on Viscoelastic Testing Methods for Cheddar Cheese

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  • MORITA Airi
    Department of Global Agricultural Sciences, Graduate School of Agricultural and Life Sciences, The University of Tokyo
  • ARAKI Tetsuya
    Department of Global Agricultural Sciences, Graduate School of Agricultural and Life Sciences, The University of Tokyo
  • IKEGAMI Shoma
    Food Science and Technology Institute, Morinaga Milk Industry, Co., Ltd.
  • OKAUE Misako
    Product Evaluation Center, Morinaga Milk Industry, Co., Ltd
  • SUMI Masahiro
    Product Evaluation Center, Morinaga Milk Industry, Co., Ltd
  • UEDA Reiko
    Food Kansei Communications, Corp.
  • SAGARA Yasuyuki
    Food Kansei Communications, Corp.

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  • チェダーチーズの粘弾性試験法に基づいた食感評価モデルの開発
  • チェダーチーズ ノ ネンダンセイ シケンホウ ニ モトズイタ ショッカン ヒョウカ モデル ノ カイハツ

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Abstract

Novel methodologies have been developed to predict the values of sensory evaluation score by selecting the combination of measurement methods for grouping viscoelastic parameters, and coupled PLS and ANN modeling have been presented as a quality evaluation model based on the relationship analysis between viscoelastic parameters and sensory scores for Cheddar cheese. Ten Cheddar cheeses were selected as test samples and viscoelastic characteristics of the samples were obtained by three measurement methods of fracture, creep and texture tests. Thirty-eight sensory terms including 10 terms of texture attribute were selected and then sensory evaluation using the terms as well as consumer palatability test were conducted for collecting both sensory and palatability scores, respectively. The relationships between viscoelastic parameters and texture changes in human mouth were explainable by adapting the relationships between the viscoelastic characteristics and texture in the mastication process of the force vs. time curve obtained from the two bites process of the sample, and thus selection manner for the combinations of viscoelastic measurement methods corresponding to sensory texture scores were demonstrated by comparing the accuracy of PLS regression equations. Furthermore, ANN models to predict sensory texture and palatability scores were successfully presented by applying each viscoelastic parameter obtained by the selected combinations of the measurement methods. By the modeling process proposed in this study, ANN models were operational to utilize the optimal viscoelastic measurement methods contributing to the sensory and palatability scores.

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