Abstract We used statistical correlation functions (CFs) to describe food microstructure and to reconstruct their 3D complexity by using limited information coming from single 2D microtomographic images. Apple fleshy parenchyma tissue and muffin crumb were chosen to test the ability of the reconstructions to mimic structural diversities. Several metrics based on morphological measures and cluster functions were utilized to analyze the fidelity of reconstructions. For the apple, reconstructions are accurate enough proving that lineal, L2, and two-point, S2, functions sufficiently describe the complexity of apple tissue. Muffin structure is isotropic but statistically inhomogeneous showing at least two different porosity domains which reduced the fidelity of reconstructions. Further improvement could be obtained by using more CFs as input data and by implementation of the techniques dealing with statistical non-stationarity. Novel stochastic reconstruction and CF-based characterization methods could improve the fidelity of reconstruction and future advances of this technology will allow estimating macroscopic food properties based on (limited) 2/3D input information.

中文翻译：

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使用来自 2D 横截面图像的有限统计信息模拟 3D 食品微观结构

摘要 我们使用统计相关函数 (CFs) 来描述食物微观结构并通过使用来自单个 2D 显微断层图像的有限信息来重建其 3D 复杂性。选择苹果肉质薄壁组织和松饼屑来测试重建模拟结构多样性的能力。基于形态学测量和聚类函数的几个指标被用来分析重建的保真度。对于苹果，重建足够准确，证明线性函数 L2 和两点 S2 函数足以描述苹果组织的复杂性。松饼结构是各向同性的，但在统计上是不均匀的，显示出至少两个不同的孔隙度域，这降低了重建的保真度。通过使用更多的 CF 作为输入数据和实施处理统计非平稳性的技术，可以获得进一步的改进。新的随机重建和基于 CF 的表征方法可以提高重建的保真度，该技术的未来进步将允许基于（有限的）2/3D 输入信息估计宏观食物特性。