## 7 Conclusions  
This paper provides a comprehensive review of commonly used methods for processing grain size data ranging from elementary statistical approaches to more advanced techniques such as multivariate statistics and the innovative EMMAgeo method. The findings suggest that traditional statistical methods including statistic moments, sorting indices, and textural analysis have limited usefulness in interpreting low-contrasted alluvial records. While means of textural classification, such as ternary diagrams, can provide more informative results, the selection of an appropriate classification scheme is crucial and is often lacking in discussion. The application of multivariate statistics, specifically PCA pre-processing and HCA classification, shows that their effectiveness strongly depends on the choice of input variables. Contrary to common practice, using raw data appears to be more favorable than relying on elaborated statistical indices. However, it is important to note that multivariate analysis should be applied solely to non-zero values to avoid statistical bias, which can be a limitation in cases where there are contrasting grain size records. The EMMAgeo method shows promise as an innovative approach. However, it is necessary to establish clearer connections between the obtained end-member classes and sedimentological processes. Further research and refinement are needed to enhance the interpretation of EMMAgeo results in relation to sedimentological processes. Conceptualization, A.D. and J.-M.C. ; methodology, A.D. and J.-M.C. ; software, A.D. and J.-M.C. ; validation, A.D. and J.-M.C. ; formal analysis, A.D. and J.-M.C. ; investigation, A.D. and J.-M.C. ; resources, A.D. and J.-M.C. ; data curation, A.D. and J.-M.C. ;  
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