diff --git a/scripts/device_qc_report.py b/scripts/device_qc_report.py
index 5dd4736..e1c354f 100644
--- a/scripts/device_qc_report.py
+++ b/scripts/device_qc_report.py
@@ -11,13 +11,9 @@ precision_tolerance = 0.009
 accuracy_tolerance = 0.02
 
 def perform_calculations(curdir, path_delim, report_file):
-    # print(curdir + path_delim + "hemocube_qc_09_08_2023_data.xlsx")
     df = pd.read_excel(curdir + path_delim + "hemocube_qc_09_08_2023_data.xlsx", sheet_name="qc_data")
     df_ref = pd.read_excel(curdir + path_delim + "hemocube_qc_09_08_2023_data.xlsx", sheet_name="reference_values")
     
-    # df_ref['device - con'] = ""
-    # for idx, row in df_ref.iterrows():
-    #     df_ref["device - con"][idx] = str(row["deviceId"]) + ", " + row["sol"] + ", " + row["wavelength"]
     print(df_ref)
     
     writer = pd.ExcelWriter(report_file, engine = 'xlsxwriter')
@@ -27,47 +23,40 @@ def perform_calculations(curdir, path_delim, report_file):
 
     # duplicate columns
     df["device"] = df["deviceId"]
-    df["device - con"] = ""
+    df["identifier"] = ""
     for idx, row in df.iterrows():
-        df['device - con'][idx] = row["deviceId"] + ", " + row["sol"] + ", " + row["wavelength"]
+        df['identifier'][idx] = row["deviceId"] + ", " + row["sol"] + ", " + row["wavelength"]
 
-    # print(df)
     df_max = df.groupby(['deviceId', "sol"]).max()
-    df_max['device - con'] = ""
-    # print(df_max.iloc[1])
+    df_max['identifier'] = ""
 
     df_min = df.groupby(['deviceId', "sol"]).min()
-    df_min['device - con'] = ""
+    df_min['identifier'] = ""
 
     for group, row in df_max.iterrows():
-        df_max["device - con"][group] = group[0] + ", " + group[1] + ", " + row["wavelength"]
+        df_max["identifier"][group] = group[0] + ", " + group[1] + ", " + row["wavelength"]
 
     df_max.to_excel(writer, sheet_name="max", index=False)
 
     for group, row in df_min.iterrows():
-        df_min["device - con"][group] = group[0] + ", " + group[1] + ", " + row["wavelength"]
+        df_min["identifier"][group] = group[0] + ", " + group[1] + ", " + row["wavelength"]
 
-    # TODO: combine 427 mean and 555 mean using df.groupby(['device - con'])[["Abs 427", "Abs555"]].mean()
-    df_427nm_mean = df.groupby(['device - con'])["Abs 427"].mean().to_frame()
+    # TODO: combine 427 mean and 555 mean using df.groupby(['identifier'])[["Abs 427", "Abs555"]].mean()
+    df_427nm_mean = df.groupby(['identifier'])["Abs 427"].mean().to_frame()
     df_427nm_mean.rename(columns={'Abs 427': "Abs427_mean"}, inplace = True)
 
-    df_555nm_mean = df.groupby(['device - con'])["Abs555"].mean().to_frame()
+    df_555nm_mean = df.groupby(['identifier'])["Abs555"].mean().to_frame()
     df_555nm_mean.rename(columns={'Abs555': "Abs555_mean"}, inplace = True)
 
     # df_mean = df.copy()
-    df_mean = df_427nm_mean.merge(df_555nm_mean, on="device - con")
+    df_mean = df_427nm_mean.merge(df_555nm_mean, on="identifier")
 
     df_describe = df.groupby(['deviceId', "sol"]).describe()
-    # print(df.groupby(['deviceId', "sol"]).describe())
     df_describe.to_excel(writer, sheet_name="describe")
 
-    # for group, row in df_describe.iterrows():
-    #     df_mean["Abs555_mean"] = row["Abs 427"]["mean"]
-    # print(df_mean)
-
-    df_max_min = df_max.merge(df_min, on="device - con")
-    df_max_min_mean = df_max_min.merge(df_mean, on="device - con")
-    df_max_min_mean_ref = df_max_min_mean.merge(df_ref, on="device - con")
+    df_max_min = df_max.merge(df_min, on="identifier")
+    df_max_min_mean = df_max_min.merge(df_mean, on="identifier")
+    df_max_min_mean_ref = df_max_min_mean.merge(df_ref, on="identifier")
 
     df_min.to_excel(writer, sheet_name="min", index=False)
     df_max_min_mean_ref.to_excel(writer, sheet_name="df_max_min_mean_ref", index=False)
@@ -79,12 +68,7 @@ def perform_calculations(curdir, path_delim, report_file):
     df_result["accuracy_555nm"] = df_max_min_mean_ref["ref_device_abs"] - df_max_min_mean_ref["Abs555_mean"]
 
     df_result = df_result.drop(["Abs 427_x", "Abs555_x", "wavelength_x", "Abs 427_y", "Abs555_y", "wavelength_y", "device_y"], axis=1)
-    df_result = df_result.groupby('device_x')[['device_x', "device - con", "precision_427nm", "precision_555nm", "accuracy_427nm", "accuracy_555nm"]].apply(lambda x: x)
-    # print(df_result)
-
-    # for idx, row in df_result.iterrows():
-    #     print(idx, row)
-    #     df_result["precision_427nm"][1] = df_max.loc["Abs 427"][1] + df_min["Abs 427"][1]
+    df_result = df_result.groupby('device_x')[['device_x', "identifier", "precision_427nm", "precision_555nm", "accuracy_427nm", "accuracy_555nm"]].apply(lambda x: x)
 
     df_result.to_excel(writer, sheet_name="precision", index=False)
     workbook = writer.book