Radio Frequency (RF) wireless devices can be identified by the RF emissions they produce when transmitting. The reason is that such emissions contain intrinsic features originating from the physical structure and the materials used to build the wireless device itself. These features are usually called RF fingerprints in the literature, and they can be used to uniquely identify a wireless device through a process called radiometric identification. RF fingerprinting can support multifactor authentication of wireless devices in security applications. One of the main unresolved issues in radiometric identification is the lack of portability of the RF fingerprints. The RF emissions are collected by a RF receiver converting them into digital format, from which the fingerprints are extracted. The lack of portability issue is due to the fact that each RF receiver introduces a bias, which degrades the RF fingerprints of the emitting device. As a consequence, RF emissions of the same wireless device collected by different RF receivers will generate different fingerprints for the same wireless device. This issue strongly limits the applicability of RF fingerprinting for security purposes, since we are not afforded to use different RF receivers to perform identification, and the fingerprints are not portable from one receiver to another. In this paper, we propose a novel approach that helps mitigating this portability issue. Our approach is based on the removal of the bias introduced by RF receivers in the frequency domain through the use of one golden reference. The golden reference is used to generate a calibration function, which is then applied to the RF emissions collected by different RF receivers from any other wireless device. The specific approach is empirically validated against a set of ten Internet of Things (IoT) wireless devices (plus the golden reference), and three RF receivers. Our experimental evidence shows that our method is able to alleviate the portability issue at the cost of a minor degradation in identification accuracy.