Next Article in Journal
An Analysis of Best Practice Patterns for Corporate Social Responsibility in Top IT Companies
Previous Article in Journal
Systematic Predictive Analysis of Personalized Life Expectancy Using Smart Devices
Previous Article in Special Issue
Model Selection and Quality Estimation of Time Series Models for Artificial Technical Surface Generation
Article Menu
Issue 3 (September) cover image

Export Article

Open AccessArticle
Technologies 2018, 6(3), 75; https://doi.org/10.3390/technologies6030075

Gathering and Analyzing Surface Parameters for Diet Identification Purposes

1
Institut Prime, CNRS, Université de Poitiers, ISAE-ENSMA, F-86962 Futuroscope Chasseneuil, France
2
PALEVOPRIM UMR 7262, CNRS, Université de Poitiers, 86073 Poitiers Cedex 9, France
*
Authors to whom correspondence should be addressed.
Received: 20 May 2018 / Revised: 27 July 2018 / Accepted: 9 August 2018 / Published: 11 August 2018
Full-Text   |   PDF [8376 KB, uploaded 11 August 2018]   |  

Abstract

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. View Full-Text
Keywords: dental microwear analysis; sampling method; statistical tests; surface parameters dental microwear analysis; sampling method; statistical tests; surface parameters
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Francisco, A.; Brunetière, N.; Merceron, G. Gathering and Analyzing Surface Parameters for Diet Identification Purposes. Technologies 2018, 6, 75.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Technologies EISSN 2227-7080 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top