Nuclear Quadrupole Resonance (NQR) is a radio frequency (RF) technique that can be used to detect the presence of quadrupolar nuclei, such as the 14N nucleus prevalent in many explosives and narcotics. The technique has been hampered by low signal-to-noise ratios and is further aggravated by the presence of RF interference (RFI). To ensure accurate detection, proposed detectors should exploit the rich form of the NQR signal. Furthermore, the detectors should also be robust to any remaining residual interference, left after suitable RFI mitigation has been employed. In this paper, we propose a new NQR data model, particularly for the realistic case where multiple pulse sequences are used to generate trains of spin echoes. Furthermore, we refine two recently proposed approximative maximum likelihood (AML) detectors, enabling the algorithm to optimally exploit the data model of the entire echo train and also incorporate knowledge of the temperature dependent spin-echo decay time. The AML-based detectors ensure accurate detection and robustness against residual RFI, even when the temperature of the sample is not precisely known, by exploiting the dependencies of the NQR resonant lines on temperature. Further robustness against residual interference is gained as the proposed detector is frequency selective; exploiting only those regions of the spectrum where the NQR signal is expected. Extensive numerical evaluations based on both simulated and measured NQR data indicate that the proposed Frequency selective Echo Train AML (FETAML) detector offers a significant improvement as compared to other existing detectors.