Abstract：
Despite the growing demand and interest in 3D printing for food manufacturing, predicting printability of food-grade materials based on biopolymer composition and rheological properties is a significant challenge. This study developed two image-based printability assessment metrics: printed filaments\' width and roughness and used these metrics to evaluate the printability of hydrogel-based food inks using response surface methodology (RSM) with regression analysis and machine learning. Rheological and compositional properties of food grade inks formulated using low-methoxyl pectin (LMP) and cellulose nanocrystals (CNC) with different ionic crosslinking densities were used as predictors of printability. RSM and linear regression showed good predictability of rheological properties based on formulation parameters but could not predict the printability metrics. For a machine learning based prediction model, the printability metrics were binarized with pre-specified thresholds and random forest classifiers were trained to predict the filament width and roughness labels, as well as the overall printability of the inks using formulation and rheological parameters. Without including formulation parameters, the models trained on rheological measurements alone were able to achieve high prediction accuracy: 82% for the width and roughness labels and 88% for the overall printability label, demonstrating the potential to predict printability of the polysaccharide inks developed in this study and to possibly generalize the models to food inks with different compositions.
摘要：
尽管食品制造业对3D打印的需求和兴趣不断增长，基于生物聚合物组成和流变性能预测食品级材料的适印性是一个重大挑战。这项研究开发了两个基于图像的可印刷性评估指标：印刷长丝的宽度和粗糙度，并使用这些指标使用响应面方法（RSM）和回归分析和机器学习来评估基于水凝胶的食品油墨的可印刷性。使用具有不同离子交联密度的低甲氧基果胶（LMP）和纤维素纳米晶体（CNC）配制的食品级油墨的流变和组成特性被用作可印刷性的预测指标。RSM和线性回归显示基于配方参数的流变性能的良好可预测性，但无法预测可印刷性指标。对于基于机器学习的预测模型，打印性指标用预先指定的阈值进行二值化，并训练随机森林分类器来预测细丝宽度和粗糙度标签，以及使用配方和流变参数的油墨的整体可印刷性。不包括配方参数，仅在流变测量上训练的模型就能够实现高预测精度：宽度和粗糙度标签为82%，整体印刷适性标签为88%，证明了预测本研究中开发的多糖油墨的可印刷性的潜力，并可能将模型推广到具有不同成分的食品油墨。
