Current alignment-based methods for classification in geometric morphometrics do not generally address the classification of new individuals that were not part of the study sample. However, in the context of infant and child nutritional assessment from body shape images this is a relevant problem. In this setting, classification rules obtained on the shape space from a reference sample cannot be used on out-of-sample individuals in a straightforward way. Indeed, a series of sample dependent processing steps, such as alignment (Procrustes analysis, for instance) or allometric regression, need to be conducted before the classification rule can be applied. This work proposes ways of obtaining shape coordinates for a new individual and analyzes the effect of using different template configurations on the sample of study as target for registration of the out-of-sample raw coordinates. Understanding sample characteristics and collinearity among shape variables is crucial for optimal classification results when evaluating children's nutritional status using arm shape analysis from photos. The SAM Photo Diagnosis App\c{opyright} Program's goal is to develop an offline smartphone tool, enabling updates of the training sample across different nutritional screening campaigns.

翻译：当前几何形态测量学中基于对齐的分类方法通常未涉及对未参与研究样本的新个体进行分类。然而，在通过体型图像评估婴幼儿营养状况的背景下，这是一个重要问题。在此情境下，从参考样本在形状空间获得的分类规则无法直接应用于样本外个体。实际上，在应用分类规则之前，需要进行一系列依赖样本的处理步骤，例如对齐（如普氏分析）或异速生长回归。本研究提出了获取新个体形状坐标的方法，并分析了使用研究样本中不同模板配置作为样本外原始坐标配准目标的效果。在使用照片臂形分析评估儿童营养状况时，理解样本特征及形状变量间的共线性对获得最佳分类结果至关重要。SAM照片诊断应用程序©计划旨在开发一款离线智能手机工具，实现在不同营养筛查活动中更新训练样本。