A further problem connected with the use of the hazard index approach for the risk classification of mixtures is that a suitable reference has to be chosen.
According to this regulation, components identified as relevant should be considered in the classification of the mixture (i.e. the product).
It cannot be assumed that the general public can look up the classifications and labelling of the ingredients and derive the classification of the mixture themselves before they buy and use these products.
Substances classified as 'May cause respiratory irritation.' were present, but only the sum of these ingredients would have led to a classification of the mixture, not the single substances.
Mutagens (such as m-Phenylenediamine), carcinogens (such as Carbomer) or substances toxic for reproduction (such as Cyclomethicone) were part of the formulas scrutinized, but they usually stayed below the concentration thresholds for labelling and classification of the mixture.
This does not necessarily imply that they are listed as hazardous substances on the product SDS, as they may not have directly and ultimately triggered the classification of the mixture (see guidance on the application of the CLP criteria, [15]).
Mutagens (such as m-phenylenediamine), carcinogens (such as carbomer) or substances toxic for reproduction (such as cyclomethicone) were listed in formulations of PCPs [26], but they usually are below the concentration thresholds for labelling and classification of the mixture thus exposing uninformed consumers.
Detailed methods for interpreting toxicologic and epidemiologic research are provided, and concentration limits trigger classification of a mixture into each hazard category.
Topics of a more strategic nature include studies concerning the public health effects of large airports, and the development of criteria for a harmonized classification of chemical mixtures.
Currently, there is no practical solution to catalog all aberrations in all clones in a given sample, and we therefore used simulated data to test the accuracy of CNA classification of complex mixtures.
The data obtained were treated using advanced chemometric tools like Principal Component Analysis (PCA) and Soft Independent Modeling Class Analogy (SIMCA) for the classification of the wine mixtures and Partial Least Squares (PLS) regression for the quantification of the grape variety composition.
