Indirect prediction of 3D printability of mashed potatoes based on LF-NMR measurements. (December 2020) - Liu, Zhenbin; Zhang, Min; Ye, Yufen
Abstract: It is time consuming to assess 3D printability of food material by evaluating printing results. The aim of this study was to establish one effective method to quickly predict 3D printability of mashed potatoes (MP) without conducting printing experiments. According to 3D printing performance or the principal components analysis (PCA) based on rheological properties, MP could be categorized into three groups: self-supportable but not extrudable, self-supportable and extrudable, extrudable but not self-supportable. Fisher discriminant analysis indicated that it is reliable to predict MP's 3D printing behavior based on rheological properties. Principal Component Regression (PCR) and Partial Least Squares (PLS) were proven to be effective to predict MP's rheology based on LF-NMR parameters, thus indirectly to quickly predict 3D printability without conducting time consuming 3D printing tests. This will facilitate the rate of evaluation 3D printing behavior of specific food material. Highlights: Different formulation of mashed potato was classified by 3D printing. Principal Component Analysis based on rheology can predict 3D printability correctly. Principal Component Regression using NMR parameters can predict rheology correctly. NMR measurement was correct and efficient to indirectly predict 3D printability.