diff --git a/tests/test_transforms.py b/tests/test_transforms.py
index 3b76145e..067abad8 100644
--- a/tests/test_transforms.py
+++ b/tests/test_transforms.py
@@ -318,15 +318,12 @@ def test_pbc_wrap_batched_triclinic() -> None:
     )
     batch = torch.tensor([0, 1], device=DEVICE)
 
-    # Stack the cells for batched processing
-    cell = torch.stack([cell1, cell2])
-
     # Apply wrapping
     wrapped = ft.pbc_wrap_batched(positions, cell=cell, system_idx=batch)
 
-    # Calculate expected result for first atom (using original algorithm for verification)
-    expected1 = ft.pbc_wrap_general(positions[0:1], cell1)
-    expected2 = ft.pbc_wrap_general(positions[1:2], cell2)
+    # Calculate expected results by wrapping each system independently
+    expected1 = ft.wrap_positions(positions[0:1], cell1.T)
+    expected2 = ft.wrap_positions(positions[1:2], cell2.T)
 
     # Verify results match the expected values
     assert torch.allclose(wrapped[0:1], expected1, atol=1e-6)
diff --git a/torch_sim/transforms.py b/torch_sim/transforms.py
index 1b2c416b..53570a66 100644
--- a/torch_sim/transforms.py
+++ b/torch_sim/transforms.py
@@ -10,6 +10,7 @@
 
 import torch
 from torch.types import _dtype
+from typing_extensions import deprecated
 
 
 def get_fractional_coordinates(
@@ -110,6 +111,7 @@ def inverse_box(box: torch.Tensor) -> torch.Tensor:
     raise ValueError(f"Box must be either: a scalar, a vector, or a matrix. Found {box}.")
 
 
+@deprecated("Use wrap_positions instead")
 def pbc_wrap_general(
     positions: torch.Tensor, lattice_vectors: torch.Tensor
 ) -> torch.Tensor: