diff --git a/cloud-functions/python/functions/performance/main.py b/cloud-functions/python/functions/performance/main.py
index e1cbfd7..8f88cf9 100644
--- a/cloud-functions/python/functions/performance/main.py
+++ b/cloud-functions/python/functions/performance/main.py
@@ -174,11 +174,11 @@ def performance(request):
     df_reference_device.columns.name = 'Wavelength'
 
     if not df.empty:
-        conditions = [df['name'].str.contains('Tar', case=False, na=False),
-                  df['name'].str.contains('KM', case=False, na=False),
+        conditions = [df['name'].str.contains('Tartrazine', case=False, na=False),
+                  df['name'].str.contains('Acid Red', case=False, na=False),
                   df['name'].str.contains('HB', case=False, na=False)]
     
-        choices = ['Tartrazine', 'KMnO4', "HB"]
+        choices = ['Tartrazine', 'Acid Red', "HB"]
         
         df['solution'] = np.select(conditions, choices, default=None)
 
@@ -289,7 +289,7 @@ def performance(request):
 
         fig, ax = plt.subplots()
 
-        filtered_df = df[df['solution'].isin(['KMnO4', 'Tartrazine', 'HB'])]
+        filtered_df = df[df['solution'].isin(['Acid Red', 'Tartrazine', 'HB'])]
 
         for solution in filtered_df['solution'].unique():
             if pd.notna(solution):