diff --git a/NAMESPACE b/NAMESPACE
index 3bbf6aaa..a3a8f7e7 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -36,7 +36,6 @@ export(createRandomForestFeatureSelection)
 export(createRareFeatureRemover)
 export(createRestrictPlpDataSettings)
 export(createSampleSettings)
-export(createSklearnImputer)
 export(createSplineSettings)
 export(createStratifiedImputationSettings)
 export(createStudyPopulation)
diff --git a/R/PreprocessingData.R b/R/PreprocessingData.R
index fba55fc3..e5ff9546 100644
--- a/R/PreprocessingData.R
+++ b/R/PreprocessingData.R
@@ -114,6 +114,7 @@ minMaxNormalize <- function(trainData, featureEngineeringSettings, normalized =
         min = min(.data$covariateValue, na.rm = TRUE)
       ) %>%
       dplyr::collect()
+    on.exit(trainData$covariateData$minMaxs <- NULL, add = TRUE)
 
     # save the normalization
     attr(featureEngineeringSettings, "minMaxs") <-
@@ -193,6 +194,7 @@ robustNormalize <- function(trainData, featureEngineeringSettings, normalized =
       dplyr::mutate(iqr = .data$q75 - .data$q25) %>%
       dplyr::select(-c("q75", "q25")) %>%
       dplyr::collect()
+    on.exit(trainData$covariateData$quantiles <- NULL, add = TRUE)
 
     # save the normalization
     attr(featureEngineeringSettings, "quantiles") <-
@@ -213,7 +215,6 @@ robustNormalize <- function(trainData, featureEngineeringSettings, normalized =
         .data$covariateValue
       )) %>%
       dplyr::select(-c("median", "iqr"))
-    trainData$covariateData$quantiles <- NULL
     normalized <- TRUE
   } else {
     # apply the normalization to test data by using saved normalization values