diff --git a/scripts/consolidated_data.py b/scripts/consolidated_data.py
index a4116a1..27dc63b 100644
--- a/scripts/consolidated_data.py
+++ b/scripts/consolidated_data.py
@@ -9,7 +9,7 @@ import sys
 import platform
 from datetime import datetime, timedelta
 
-environment = "af3cc"
+environment = "preprod"
 
 if __name__ == "__main__":
 
@@ -71,6 +71,7 @@ if __name__ == "__main__":
     print(df)
     print(df.size)
 
+
     # Save the DataFrame to a CSV file
     output_filename = f'data_{environment}_{datetime.today().strftime("%d_%m_%Y_%H_%M")}.xlsx'
     df.to_csv(output_filename, index=False)
@@ -81,9 +82,12 @@ if __name__ == "__main__":
     df = df[(df['testTime'] > start_date) & (df['testTime'] <= end_date)]
     df = df.sort_values(by=['testTime'], ascending=False)
     
-    df = df[["_id", "classificationResult",  "prdClassification", "predictedDenovixRatio", "calculatedRatio", "deviceRatio", "kitSerial", "abs1", "led1Average", "led1Buffer", "led1Sample", "abs2", "led2Average", "led2Buffer", "led2Sample", "abs3", "led3Average", "led3Buffer", "led3Sample", "abs4", "led4Average", "led4Buffer", "led4Sample", "batteryLevel", "batteryVoltage", "deviceId", "deviceSerialNumber", "name", "testTime"]]
+    df = df[["_id", "errorMessages", "classificationResult",  "prdClassification", "predictedDenovixRatio", "calculatedRatio", "deviceRatio", "kitSerial", "abs1", "led1Average", "led1Buffer", "led1Sample", "abs2", "led2Average", "led2Buffer", "led2Sample", "abs3", "led3Average", "led3Buffer", "led3Sample", "abs4", "led4Average", "led4Buffer", "led4Sample", "batteryLevel", "batteryVoltage", "deviceId", "deviceSerialNumber", "name", "testTime"]]
     # df.rename(columns={'deviceSerialNumber': "login_id", "calculatedRatio": "calibrated_ratio", "led1Buffer": "427_buffer_intensity", "led2Buffer": "555_buffer_intensity", "led1Sample": "427_sample_intensity", "led2Sample": "555_sample_intensity", "led1Average": "427_absorbance", "led2Average": "555_absorbance"}, inplace = True)
     # df = df.reindex(sorted(df.columns), axis=1)
+
+    df = df.drop_duplicates()
+
     df.to_excel(writer, sheet_name = 'data', index=False)
     writer.close()