Prediction of 3D printability and rheological properties of different pineapple gel formulations based on LF-NMR

• Published in: Food Chemistry: X Vol. 24; p. 101906
• Main Authors: Bao, Yunfei, Li, Linlin, Chen, Junliang, Cao, Weiwei, Liu, Wenchao, Ren, Guangyue, Luo, Zhenjiang, Pan, Lifeng, Duan, Xu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 30-12-2024; Elsevier
• Subjects: 3D printability; BP-ANN; Low-field nuclear magnetic resonance; Partial least squares; Pineapple gels; Pineapple gels; Low-field nuclear magnetic resonance; 3D printability; Partial least squares; BP-ANN
• ISSN: 2590-1575; 2590-1575
• DOI: 10.1016/j.fochx.2024.101906

In this study, a pineapple-starch-xanthan gum system was prepared using fresh pineapple juice, maize starch, and xanthan gum (XG). The feasibility of using low-field nuclear magnetic resonance (LF-NMR) to predict pineapple gels' rheological properties and printability was evaluated. Results indicated that as maize starch and XG increased, the gel transformed from unable to support printed models to a stable shape, eventually becoming too viscous for printing. Principal component analysis and Fisher discriminant analysis classified the gels into four categories based on their rheological properties, aligning with the actual printing results. Pearson correlation analysis showed a strong correlation between the LF-NMR parameters and the rheological properties of gels. The partial least squares (PLS) and back-propagation artificial neural network (BP-ANN) models constructed using the LF-NMR parameters can effectively predict the rheological properties of pineapple gels. Therefore, LF-NMR is a valuable, non-destructive method for quickly assessing pineapple gels' 3D printing suitability.
•Formulation printability was affected by the addition of maize starch and xanthan gum.•Classification results of PCA and FDA analysis agreed with actual printing results.•Low-field nuclear magnetic parameters had correlations with rheological parameters.•Two models were developed to predict the rheological properties of the formulations.•Back-propagation artificial neural network model has better prediction performance.