Gathering and Analyzing Surface Parameters for Diet Identification Purposes

• Main Authors: Arthur Francisco, Noël Brunetière, Gildas Merceron
• Format: Article
• Language: English
• Published: MDPI AG 2018-08-01
• Series: Technologies
• Subjects: dental microwear analysis; sampling method; statistical tests; surface parameters
• Online Access: http://www.mdpi.com/2227-7080/6/3/75

Modern surface acquisition devices, such as interferometers and confocal microscopes, make it possible to have accurate three-dimensional (3D) numerical representations of real surfaces. The numerical dental surfaces hold details that are related to the microwear that is caused by food processing. As there are numerous surface parameters that describe surface properties and knowing that a lot more can be built, is it possible to identify the ones that can separate taxa based on their diets? Until now, the candidates were chosen from among those provided by metrology software, which often implements International Organization for Standardization (ISO) parameters. Moreover, the way that a parameter is declared as diet-discriminative differs from one researcher to another. The aim of the present work is to propose a framework to broaden the investigation of relevant parameters and subsequently a procedure that is based on statistical tests to highlight the best of them. Many parameters were tested in a previous study. Here, some were dropped and others added to the classical ones. The resulting set is doubled while considering two derived surfaces: the initial one minus a second order and an eighth order polynomial. The resulting surfaces are then sampled—256 samples per surface—making it possible to build new derived parameters that are based on statistics. The studied dental surfaces belong to seven sets of three or more groups with known differences in diet. In almost all cases, the statistical procedure succeeds in identifying the most relevant parameters to reflect the group differences. Surprisingly, the widely used Area-scale fractal complexity (Asfc) parameter—despite some improvements—cannot differentiate the groups as accurately. The present work can be used as a standalone procedure, but it can also be seen as a first step towards machine learning where a lot of training data is necessary, thus making the human intervention prohibitive.