diff --git a/ILP/Vanilla/nmrSimilarityVanillien.py b/ILP/Vanilla/nmrSimilarityVanillien.py
index da998eb..54a759c 100644
--- a/ILP/Vanilla/nmrSimilarityVanillien.py
+++ b/ILP/Vanilla/nmrSimilarityVanillien.py
@@ -157,14 +157,35 @@ CNMR2 = {
     5: ([191.0], [1]),
 }
 
-#Experimental 3,4-Dihydroxybenzaldehyd
+#Experimental Caffeicacid
 HNMR3 = {
+    1: ([6.99], [1]), #d 
+    2: ([6.84], [1]), #dd
+    3: ([6.73], [1]), #d
+    4: ([7.27], [1]), #d
+    5: ([6.28], [1]), #d
+}
+
+CNMR3 = {
+    1: ([147.9], [1]),
+    2: ([146.5], [1]),
+    3: ([129.3], [1]),
+    4: ([123.1], [1]),
+    5: ([121.7], [1]),
+    6: ([116.4], [1]),
+    7: ([141.5], [1]),
+    8: ([114.6], [1]),
+    9: ([176.2], [1]),
+}
+
+#Experimental 3,4-Dihydroxybenzaldehyd
+HNMR4 = {
     1: ([7.44], [1]),
     2: ([7.42], [1]),
     3: ([7.00], [1]),
 }
 
-CNMR3 = {
+CNMR4 = {
     1: ([124.59], [1]),
     2: ([115.21], [1]),
     3: ([145.44], [1]),
@@ -236,7 +257,7 @@ def main():
     for spectrumtrue in spectra:
         similaritybycorrection = []
         #Paper Chemical reviews Carbons bound to Heavy atoms (TMS) to high -> this could be reason for too high values.
-        correctionvalues = [2.63] #np.arange(0, 1.51, 0.01) #for C tested np.arange(-0.37, 7.64, 0.1) 0, 2.63, 1 (for CNMR3), 1.5 (true for all ref, 8.37 + 1.5 for the precision), 1 (good for first, ok for second, third because only 7 better/equal but for first much higher) np.arange(-1.5, 1.49, 0.01) (only for first), for H 0 (not good), np.arange(-0.26, 0.34, 0.01), -0.06 for first, second never first either 17 or caf higher np.arange(-0.15, 0.16, 0.01) good measure
+        correctionvalues = [2.5] #np.arange(0, 1.51, 0.01) #for C tested np.arange(-0.37, 7.64, 0.1) 0, 2.63, 1 (for CNMR3), 1.5 (true for all ref, 8.37 + 1.5 for the precision), 1 (good for first, ok for second, third because only 7 better/equal but for first much higher) np.arange(-1.5, 1.49, 0.01) (only for first), for H 0 (not good), np.arange(-0.26, 0.34, 0.01), -0.06 for first, second never first either 17 or caf higher np.arange(-0.15, 0.16, 0.01) good measure
         for correctionvalue in correctionvalues: 
             spectrumrefcorrected = correction(spectrumref, correctionvalue) #CCAFFEINE 11 (klappt hier sehr gut) CCAFFEINE2 12 CPARAXANTHINE 10 CNMR1 9, 10 o 11 (sehr gut) CNMR2 10 o 11
             similaritylist = []