Discriminant analysis of asbestiform and non-asbestiform amphibole particles and its implications for toxicological studies

Context

Rock dusts often contain minerals called amphiboles. Elongate mineral particles produced by excavation, crushing, or grinding amphibole-containing rock can belong to different morphological groups, or habits: asbestiform or non-asbestiform. Some asbestiform particles are highly potent for causing mesothelioma, but non-asbestiform elongate structures have not been implicated in elevated cancer risk. Computational analysis and modelling of the dimensional characteristics of the elongate mineral particles is needed to develop efficient criteria for their differentiation, and also for determining the parameters driving their carcinogenic potential.

Objectives

To develop conceptual and quantitative models allowing reliable distinctions between asbestiform and non-asbestiform amphibole particles that are based on particle dimensions and are consistent with observed disease outcome following human exposure.

Methods

For modelling, the unique database including 56 datasets designated as dominantly asbestiform (67,876 amphibole particles), 37 designated as dominantly non-asbestiform (235,247 amphibole particles), and 12 as inhomogeneous or anomalous (35,277 amphibole particles) was utilized. The discriminant analysis was used to determine functions that separate elongate mineral particles by their habit based on length and width. Linear regression and cluster analysis were applied to determine the relationship between values of the selected discriminant function and relevant toxicological parameters.

Results

For particles longer than 5 µm, the function $Y=2.99{\log }_{10}Length-5.82{\log }_{10}Width-3.80$ was selected as the best discriminator of particles for their asbestiform and non-asbestiform habits, with a misclassification rate of about 15% total. The value of the discriminant function derived for each particle correlates with the particle’s calculated aerodynamic diameter (R = −0.859, p < 0.00001) and with its specific surface area (R = 0.857, p < 0.00001). The cluster analysis demonstrated that subdivision of particles by two groups according to their length and width closely reconstructs the pre-defined habits.

Conclusion

The proposed methodology of differentiating between asbestiform and non-asbestiform particles can be used for analytical, toxicological, and regulatory purposes.