diff --git a/src/molearn/analysis/analyser.py b/src/molearn/analysis/analyser.py
index e7d9e1b..c15e7ac 100644
--- a/src/molearn/analysis/analyser.py
+++ b/src/molearn/analysis/analyser.py
@@ -515,6 +515,25 @@ def _dope_score(self, frame, refine=True, **kwargs):
 
         return self.dope_score_class.get_score(f * self.stdval, refine=refine, **kwargs)
 
+    def _chirality_whole(
+        self,
+        n: np.ndarray[tuple[int,], np.dtype[np.float64]],
+        ca: np.ndarray[tuple[int,], np.dtype[np.float64]],
+        c: np.ndarray[tuple[int,], np.dtype[np.float64]],
+        cb: np.ndarray[tuple[int,], np.dtype[np.float64]],
+    ):
+        """
+        check chirality for a set o amino acid
+        """
+        ca_n = n - ca
+        ca_c = c - ca
+        cb_ca = cb - ca
+        normal = np.cross(ca_n, ca_c)
+        dot = np.einsum("ij,ij->i", normal, cb_ca)
+        # same but more calculations
+        # dot = np.diagonal(np.matmul(normal, cb_ca.T))
+        return dot
+
     @staticmethod
     def _ca_chirality(N, CA, C, CB):
         """
@@ -691,17 +710,19 @@ def scan_ca_chirality(self):
                     ].index[0]
                     indices[resname + str(resid)] = (N_id, CA_id, C_id, CB_id)
 
+            idx = np.asarray(list(indices.values()))
             results = []
             for j in decoded:
-                s = (j.view(1, 3, -1).permute(0, 2, 1) * self.stdval).numpy()
-                inversions = {}
-                for k, v in indices.items():
-                    dot_prod = self._ca_chirality(
-                        s[0, v[0], :], s[0, v[1], :], s[0, v[2], :], s[0, v[3], :]
-                    )
-                    if dot_prod < 0:
-                        inversions[k] = dot_prod
-                results.append(len(inversions))
+                s = (j.view(1, 3, -1).permute(0, 2, 1) * self.stdval).numpy().squeeze()
+                chir_test = self._chirality_whole(
+                    s[idx[:, 0], :],
+                    s[idx[:, 1], :],
+                    s[idx[:, 2], :],
+                    s[idx[:, 3], :],
+                )
+                wrong_chir = chir_test < 0
+                results.append(wrong_chir.sum())
+            results = np.asarray(results)
             self.surfaces[key] = np.array(results).reshape(
                 self.n_samples, self.n_samples
             )