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Meat analogues: The relationship between mechanical anisotropy, macrostructure, and microstructure
Miek Schlangen1,2, Iris van der Doef1, Atze Jan van der Goot1
1Laboratory of Food Process Engineering, Wageningen University, PO Box 17, 6700 AA, Wageningen, the Netherlands.
Abstract:
Texture of meat analogues is crucial for consumer acceptance, yet it remains poorly defined, but it known that it is influenced by mechanical properties and structure at different length scales. This study describes the relationships between macrostructure, microstructure, and mechanical anisotropy in meat analogues. Two distinct meat analogue product sets are produced with shear cell technology varying in formulations and processing conditions to obtain a wide range of product structures: one based on mung bean protein-rich fractions and the other based on combinations of soy protein isolate and pectin. Mechanical properties are assessed using tensile testing, microstructure is studied using X-ray tomography and confocal laser scanning microscopy, and macrostructure is quantified using a computer vision algorithm based on segmentation and shape features. Both correlation analyses on the response parameters and parameter variance are studied to distinguish the product sets. Strong correlations are found between anisotropy-related parameters, such as fibre score in macrostructure, air anisotropy in microstructure, and the toughness anisotropy index from mechanical properties. Some correlations are found to be product-set independent, such as air bubble anisotropy and fibre score, indicating universal relationships within this study, while other correlations are product-set dependent, such as between fibre score and the anisotropy index of the Young's Modulus in the mung bean fine fraction product set. The relationship between microstructural air bubbles and macrostructure and mechanical properties is apparent in all correlation analyses. Last, univariate feature selection provided insight into which parameters are most important for selected target features.
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