diff --git a/skrf/tests/test_vectorfitting.py b/skrf/tests/test_vectorfitting.py
index 785654b13..637212d4d 100644
--- a/skrf/tests/test_vectorfitting.py
+++ b/skrf/tests/test_vectorfitting.py
@@ -153,6 +153,17 @@ def test_passivity_enforcement(self):
         # check if model is now passive
         self.assertTrue(vf.is_passive())
 
+    def test_autofit(self):
+        vf = skrf.VectorFitting(skrf.data.ring_slot)
+        vf.auto_fit()
+
+        assert vf.get_model_order(vf.poles) == 6
+        assert np.sum(vf.poles.imag == 0.0) == 0
+        assert np.sum(vf.poles.imag > 0.0) == 3
+
+        assert np.allclose(vf.get_rms_error(), 1.2748979815157275e-06)
+
+
 
 suite = unittest.TestLoader().loadTestsFromTestCase(VectorFittingTestCase)
 unittest.TextTestRunner(verbosity=2).run(suite)
diff --git a/skrf/vectorFitting.py b/skrf/vectorFitting.py
index 2829e7a7c..8a3cab472 100644
--- a/skrf/vectorFitting.py
+++ b/skrf/vectorFitting.py
@@ -7,6 +7,7 @@
 from typing import TYPE_CHECKING, Any
 
 import numpy as np
+from scipy.integrate import trapezoid
 
 try:
     from matplotlib.ticker import EngFormatter
@@ -140,7 +141,7 @@ def get_spurious(poles: np.ndarray, residues: np.ndarray, n_freqs: int = 101, ga
         omega_eval = np.linspace(np.min(poles.imag) / 3, np.max(poles.imag) * 3, n_freqs)
         h = (residues[:, None, :] / (1j * omega_eval[:, None] - poles)
              + np.conj(residues[:, None, :]) / (1j * omega_eval[:, None] - np.conj(poles)))
-        norm2 = np.sqrt(np.trapezoid(h.real ** 2 + h.imag ** 2, omega_eval, axis=1))
+        norm2 = np.sqrt(trapezoid(h.real ** 2 + h.imag ** 2, omega_eval, axis=1))
         spurious = np.all(norm2 / np.mean(norm2) < gamma, axis=0)
         return spurious