My main research topic is exploratory multivariate analysis, especially multiway data analysis. During my Ph.D. I’ve developed two multivariate techniques:

_ Hierarchical Multiple Factor Analysis

_ Dual Multiple Factor Analysis

I have also introduced some new validation techniques adapted to sensory data and exploratory multivariate techniques. I’m a specialist in sensory/consumer data but I also play with genomic data.

I like to supervise, in the sense that I like to work with junior researchers to help them grow. Actually, I should say that they help me grow…it’s very cliché but it’s so true.

Have a look at the Conferences section: you will find the slides of presentations I find particularly useful to understand the kind of workd I’m doing.

My Ph.D. students

I had the pleasure to co-supervise the following Ph.D. students:

_ Marine Cadoret

_ Marie Verbanck

_ Thierry Worch

_ Nguyen Ba Thanh

_ Tâm Minh Lê

_ Franck Paboeuf

_ Margot Brard

_ Alexiane Luc

_ Muriel Noël

Articles

  1. Husson, F., Le Dien, S., Pagès, J. (2001). Which value can be granted to sensory profiles given by consumers? Methodology and results. Food Quality and Preference, (5-7), 291-296

  2. Le Dien, S., Pagès, J. (2003). Analyse Factorielle Multiple Hiérarchique. Revue de Statistique Appliquée, LI (2), 47-73

  3. Le Dien, S., Pagès, J. (2003). Hierarchical Multiple Factor Analysis : application to the comparison of sensory profiles. Food Quality and Preference, (5-6), 397-403

  4. Husson, F., Le Dien, S., Pagès, J. (2005). Confidence ellipse for the sensory profiles obtained by Principal Component Analysis. Food Quality and Preference, (3), 245-250

  5. Pagès, J., Lê, S. & Husson, F. (2006). Une approche statistique de la performance en analyse sensorielle descriptive Sciences des aliments. 26 (5). pp. 116-169.

  6. Husson, F. & Lê, S. (2006). SensoMineR : un package pour le traitement de données sensorielles avec R Sciences des aliments. 26. pp. 355-356.

  7. Lê, S., Husson, F., Pagès, J. (2006). Another look at sensory data: How to “have your salmon and eat it, too!”. Food Quality and Preference, (7-8), 3-5.

  8. Lê, S., Ledauphin, S.(2006). “You like tomato, I like tomato: Segmentation of consumers with missing values”. Food Quality and Preference, (3-4), 228-233.

  9. Lê, S., Husson, F. & Pagès, J. (2006). Confidence ellipses applied to the comparison of sensory profiles. Journal of Sensory Studies, , 241-248.

  10. Pagès, J., Bertrand, C., Ali, R., Husson, F. & Lê, S. (2007). Sensory analysis comparison of eight biscuits by French and Pakistani panels. Journal of Sensory Studies, (6), 665-686.

  11. Husson, F., Lê, S. & Pagès, J. (2007). Variability of the representation of the variables resulting from PCA in the case of a conventional sensory profile. Food Quality and Preference, (7), 933-937.

  12. Lê, S., Josse, J., Husson, F. (2008). FactoMineR: an R package for multivariate analysis. Journal of Statistical Software, (1), 1-18.

  13. Lê, S., Pagès, J. & Husson, F. (2008). Methodology for the comparison of sensory profiles provided by several panels: application to a cross-cultural study. Food Quality and Preference, (2), 179-184.

  14. Lê, S., Husson, F. (2008). SensoMineR: a package for sensory data analysis. Journal of Sensory Studies, (1), 14-25.

  15. Cadoret, M., Lê, S. & Pagès, J. (2009). A Factorial Approach for Sorting Task data (FAST). Food Quality and Preference, (6), 410-417.

  16. de Tayrac, M., Lê, S., Aubry, M., Mosser, J., Husson, F. (2009). Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach. BMC Genomics, :32.

  17. Lê, S. & Pagès, J. (2009). DMFA: Dual Multiple Factor Analysis. Communication in Statistics - Theory and Methods.

  18. Worch, T., Lê, S. & Punter, P. (2010). How reliable are the consumers? Comparison of sensory profiles from consumers and experts. Food Quality and Preference, (3), 309-318.

  19. Ledauphin-Menard, S., Lê, S. (2010). Typologie des consommateurs et mesure de loyauté-fidélité. La Revue MODULAD, numéro 42.

  20. Pagès, J., Cadoret, M. & Lê, S. (2010). The Sorted Napping: a new holistic approach in sensory evaluation. Journal of Sensory Studies. (5), 637-658.

  21. Yoon, E. K., Hong, J. H., Lê, S. & Kim, K. O. (2011). Sensory characteristics and consumer acceptability of red ginseng extracts produced with different processing methods. Journal of food science, (5), S270-S279.

  22. Cadoret M., Lê S. & Pagès J. (2011). Multidimensional scaling versus multiple correspondence analysis when analyzing categorization data. Studies in Classification, Data Analysis, and Knowledge Organization (post-proceedings of first joint meeting of the SFC and the Cladag).

  23. Cadoret M., Lê S. & Pagès J. (2011). Statistical analysis of hierarchical sorting data. Journal of Sensory Studies. (2), 96-105.

  24. Bécue-Bertaut M., Lê S. (2011). Analysis of multilingual labeled sorting tasks: application to a cross-cultural study in wine industry. Journal of Sensory Studies. (5), 299-310.

  25. Worch, T., Lê, S. & Punter, P. (2012). Assessment of the consistency of ideal profiles according to non-ideal data for IPM. Food Quality and Preference, (1), 99-110.

  26. Worch, T., Lê, S. & Punter, P. (2012). Extension of the consistency of the data obtained with the Ideal Profile Method: Would the ideal products be more liked than the tested products? Food Quality and Preference, (1), 74-80.

  27. Worch, T., Lê, S. & Punter, P. (2012). Construction of an Ideal Map (IdMap) based on the ideal profiles obtained directly from consumers. Food Quality and Preference, (1), 93-104.

  28. Worch, T., Lê, S. & Punter, P. (2012). Ideal Profile Method (IPM): the ins and outs. Food Quality and Preference.

  29. Verbanck, M., Lê, S. & Pagès, J. (2013). A new unsupervised gene clustering algorithm based on the integration of biological knowledge into expression data. BMC Bioinformatics, (1), 42.

  30. Worch, T., Crine, A., Gruel, A. & Lê, S. (2014). Analysis and validation of the Ideal Profile Method: Application to a skin cream study. Food Quality and Preference, , 132-144.

  31. Hong, J. H., Park, H. S., Chung, S. J., Chung, L., Cha, S. M., Lê, S. & Kim, K. O. (2014). Effect of Familiarity on a Cross-Cultural Acceptance of a Sweet Ethnic Food: A Case Study with Korean Traditional Cookie (Yackwa). Journal of Sensory Studies, (2), 110-125.

  32. Park, H. S., Lê, S., Hong, J. H. & Kim, K. O. (2014). Sensory Perception of Yackwa (Korean Traditional Fried Cookie) by Consumer Groups of Different Age Using the Sorted Napping Procedure. Journal of Sensory Studies, (6), 425-434.

  33. Lê, M.T., Lê, S. & Nguyen, H.D. (2014). Assessing consumer-perceived food quality using conjoint analysis. Journal of Science and Technology Development, , 17- 27.

  34. Lacou, L., Lê, S., Pezennec, S. & Gagnaire, V. (2015). An in silico approach to highlight relationships between a techno-functional property of a dairy matrix and a peptide profile. Colloids and Surfaces A: Physicochemical and Engineering Aspects, , 44-54.

  35. Tavares, G. M., Croguennec, T., Lê, S., Lerideau, O., Hamon, P., Carvalho, A. F. & Bouhallab, S. (2015). Binding of folic acid induces specific self-aggregation of lactoferrin: thermodynamic characterization. Langmuir, (45), 12481-12488.

  36. Lê, T. M., Husson, F. & Lê, S. (2016). Digit-tracking: Interpreting the evolution over time of sensory dimensions of an individual product space issued from Napping and sorted Napping. Food Quality and Preference, , 73-78.

  37. Brard, M., & Lê, S. (2016). The Ideal Pair Method, an Alternative to the Ideal Profile Method Based on Pairwise Comparisons: Application to a Panel of Children. Journal of Sensory Studies, (4), 306-313.

  38. Lê, T.M., Brard, M. & Lê, S. (2017). Holos: a collaborative environment for similarity-based holistic approaches. Behavior Research Methods, 1-8.

  39. Brard, M., & Lê, S. (2018). Adaptation of the Q-methodology for the characterization of a complex concept through a set of products: from the collection of the data to their analysis. Food Quality and Preference.

  40. Garnier, L., Mounier, J., Lê, S., et al. (2018). Development of antifungal ingredients for dairy products: From in vitro screening to pilot scale application. Food Microbiology.

  41. Leyva Salas, M., Thierry, A., Lemaître, M., Garric, G., Harel-Oger, M., Chatel, M., Lê, S.,… & Coton, E. (2018). Antifungal Activity of Lactic Acid Bacteria Combinations in Dairy Mimicking Models and Their Potential as Bioprotective Cultures in Pilot Scale Applications. Frontiers in microbiology, 9, 1787.

  42. Brard, M., & Lê, S. (2019). The Sequential Agglomerative Sorting task, a new methodology for the sensory characterization of large sets of products. Journal of Sensory Studies, 34(5), e12527.

  43. von Gastrow, L., Madec, M. N., Chuat, V., Lubac, S., Morinière, C., Lê, S., … & Valence, F. (2020). Microbial Diversity Associated with Gwell, a Traditional French Mesophilic Fermented Milk Inoculated with a Natural Starter. Microorganisms, 8(7), 982.

  44. Luc, A., Lê, S., & Philippe, M. (2020). Nudging consumers for relevant data using Free JAR profiling: An application to product development. Food Quality and Preference, 79, 103751.

  45. Von Gastrow, L., Madec, M. N., Chuat, V., Lubac, S., Morinière, C., Lê, S., … & Valence, F. (2020). Microbial diversity associated with gwell, a traditional french mesophilic fermented milk inoculated with a natural starter. Microorganisms, 8(7), 982.

  46. Noël, M., Noël, Y., Lucet, N., & Lê, S. (2022). Translating non-experts’ perception for expert engineers: A first step in co-designing automotive human–machine interfaces. Food Quality and Preference, 98, 104528.

  47. Luc, A., Lê, S., Philippe, M., Qannari, E. M., & Vigneau, E. (2022). Free JAR experiment: Data analysis and comparison with JAR task. Food Quality and Preference, 98, 104453.

  48. Luc, A., Lê, S., Philippe, M., Qannari, E. M., & Vigneau, E. (2022). A machine learning approach for analyzing Free JAR data. Food Quality and Preference, 99, 104581.

Conferences

This section is dedicated to the work that we’re doing with my students and that we’re presenting at conferences.

_ Pangborn Sensory Science Symposium 2019: Workshop on the Bradley-Terry model