Quantitative characterizations of visual fibrousness in meat analogues using automated image analysis.

A desirable quality of plant-based meat analogues is to resemble the fibrous structure of cooked muscle meat. While texture analysis can characterize fibrous structures mechanically, assessment of visual fibrous structures remains subjective. Quantitative assessment of visual fibrous structures of meat analogues relies on expert knowledge, is resource-intensive, and time-consuming. In this study, a novel image-based method (Fiberlyzer) is developed to provide automated, quantitative, and standardized assessment of visual fibrousness of meat analogues. The Fiberlyzer method segments fibrous regions from 2D images and extracts fiber shape features to characterize the fibrous structure of meat analogues made from mung bean, soy, and pea protein. The computed fiber scores (the ratio between fiber length and width) demonstrate a strong correlation with expert panel evaluations, particularly on a per-formulation basis (r2 = 0.93). Additionally, the Fiberlyzer method generates fiber shape features including fiber score, fiber area, and the number of fiber branches, facilitating comparisons of structural similarity between meat analogue samples and cooked chicken meat as a benchmark. With a simple measurement setup and user-friendly interface, the Fiberlyzer method can become a standard tool integrated into formulation development, quality control, and production routines of plant-based meat analogue. This method offers rapid, cheap, and standardized quantification of visual fibrousness, minimizing the need for expert knowledge in the process of quality control.

Quantitative analysis of 3D food printing layer extrusion accuracy: Contextualizing automated image analysis with human evaluations: Quantifying 3D food printing accuracy.

3D food printing can customize food appearance, textures, and flavors to tailor to specific consumer needs. Current 3D food printing depends on trial-and-error optimization and experienced printer operators, which limits the adoption of the technology by general consumers. Digital image analysis can be applied to monitor the 3D printing process, quantify printing errors, and guide optimization of the printing process. We here propose an automated printing accuracy assessment tool based on layer-wise image analysis. Printing inaccuracies are quantified based on over- and under-extrusion with reference to the digital design. The measured defects are compared to human evaluations via an online survey to contextualize the errors and identify the most useful measurements to improve printing efficiency. The survey participants marked oozing and over-extrusion as inaccurate printing which matched the results obtained from automated image analysis. Although under-extrusion was also quantified by the more sensitive digital tool, the survey participants did not perceive consistent under-extrusion as inaccurate printing. The contextualized digital assessment tool provides useful estimations of printing accuracy and corrective actions to avoid printing defects. The digital monitoring approach may accelerate the consumer adoption of 3D food printing by improving the perceived accuracy and efficiency of customized food printing.