Abstract：
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.
摘要：
基于植物的肉类似物的理想质量是类似于煮熟的肌肉肉的纤维结构。虽然纹理分析可以机械地表征纤维结构，视觉纤维结构的评估仍然是主观的。肉类类似物的视觉纤维结构的定量评估依赖于专家知识，是资源密集型的，而且耗时。在这项研究中,开发了一种新颖的基于图像的方法(Fiberlyzer)来提供自动化，定量,以及肉类类似物视觉纤维性的标准化评估。Fiberlyzer方法从2D图像中分割纤维区域，并提取纤维形状特征，以表征由绿豆制成的肉类似物的纤维结构，大豆，还有豌豆蛋白.计算的纤维分数（纤维长度和宽度之间的比率）与专家小组评估具有很强的相关性，特别是在每个配方的基础上(r2=0.93)。此外,Fiberlyzer方法生成纤维形状特征，包括纤维分数，光纤面积,和纤维分支的数量，便于比较肉类似物样品和煮熟的鸡肉之间的结构相似性作为基准。通过简单的测量设置和友好的用户界面，Fiberlyzer方法可以成为集成到配方开发中的标准工具，质量控制,和基于植物的肉类类似物的生产程序。这种方法提供了快速，便宜,视觉纤维的标准化量化，在质量控制过程中最大限度地减少对专家知识的需求。
