diff --git a/R/plot_RLum.Results.R b/R/plot_RLum.Results.R
index df2fffd49..b142929f4 100644
--- a/R/plot_RLum.Results.R
+++ b/R/plot_RLum.Results.R
@@ -650,7 +650,7 @@ plot_RLum.Results<- function(
         poly.border<- FALSE
       }
       if(pdf.colors == "none") {
-        col.n<- NULL
+        col.n <- rgb(0,0,0,0)
         poly.border<- TRUE
       }
 
@@ -1028,10 +1028,9 @@ plot_RLum.Results<- function(
     ##get data
     df <- get_RLum(object = object, data.object = "dose.rate")
 
-    ##reduce the size for plotting, more than 100 points makes no sense
-    if(nrow(df)>100) {
+    ##reduce the size for plotting, more than 100 points make no sense
+    if(nrow(df)>100)
       df <- df[seq(1,nrow(df), length = 100),]
-    }
 
     ##plot settings
     plot.settings <- list(
diff --git a/tests/testthat/test_calc_FiniteMixture.R b/tests/testthat/test_calc_FiniteMixture.R
index b2cf03e35..5597d2cff 100644
--- a/tests/testthat/test_calc_FiniteMixture.R
+++ b/tests/testthat/test_calc_FiniteMixture.R
@@ -70,5 +70,19 @@ test_that("check class and length of output", {
     n.components = 3,
     verbose = TRUE),
     "The model produced NA values: either the input data are inapplicable")
+
   })
+
+  ## plot with plot_RLum.Results
+  t <- expect_s4_class(calc_FiniteMixture(
+    ExampleData.DeValues$CA1,
+    sigmab = 0.2,
+    n.components = 2:3,
+    grain.probability = FALSE,
+    trace = FALSE,
+    plot = FALSE,
+    main = "Plot title",
+    verbose = FALSE), "RLum.Results")
+  plot_RLum.Results(t, pdf.colors = "none")
+
 })
diff --git a/tests/testthat/test_calc_SourceDoseRate.R b/tests/testthat/test_calc_SourceDoseRate.R
index d13b1b92c..9f62775c4 100755
--- a/tests/testthat/test_calc_SourceDoseRate.R
+++ b/tests/testthat/test_calc_SourceDoseRate.R
@@ -39,6 +39,16 @@ test_that("General tests", {
     predict = 10
   ))
 
+  ## produce more than 100 rows
+  t <- expect_silent(calc_SourceDoseRate(
+    measurement.date = "2018-01-02",
+    calib.date = "2014-12-19",
+    calib.dose.rate = 0.0438,
+    calib.error = 0.0019,
+    predict = 150
+  ))
+  expect_silent(plot_RLum.Results(t))
+
   ##Gy/min as unit
   expect_silent(calc_SourceDoseRate(
     measurement.date = "2018-01-02",