Measuring the food microstructure by two-point cluster function. (March 2016)

• Record Type: Journal Article
• Authors: Derossi, A.; Severini, C.; De Pilli, T.
• Is Part Of: Journal of food engineering. Volume 173(2016:Mar.)
• Page Start: 42
• Page End: 48
• Publication Date: 2016-03
• Subjects: Microstructure -- Counterfeit food -- Correlation functions -- Bread; Food industry and trade -- Periodicals; Food -- Analysis -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Analyse -- Périodiques; Aliments -- Recherche -- Périodiques; 664.005
• DOI: 10.1016/j.jfoodeng.2015.10.035
• Languages: English
• ISSNs: 0260-8774

Abstract

Abstract: The clustering of pores of three types of bread was studied by measuring the two point cluster function, C 2 (r) . The ability of this function in describing the connectivity among the structure elements of bread was proved by comparing the C 2 (r) of a "reference" and a "reconstructed" bread image having the same porosity fraction and lineal path distribution function, L(r) . The two point cluster function of the void phase of two commercial, "Pan Brioscè" and "Pancarrè", and a non commercial, "White", bread enabled to highlight significant differences of their topological properties. "White" bread was characterized from more homogeneous pores with a lower degree of connectivity while the "Pancarrè" samples exhibited voids highly connected producing pores bigger in size and with a shape extremely complex. Furthermore, for the first time it was proved as the microstructure information of the two point cluster function allows discriminating the 90% of the total variace of bread samples. Highlights: The clustering of food microstructure was studied by two-point cluster function. Two types of breads were studied: the first manually prepared and the second locally purchased. A specific algorithm based on burning method was implemented. Significant difference in terms of clustering of void phase were highlighted. The 90% of the bread samples were discriminated by using the clustering information.