The multiple nuclides identification algorithm with low consumption and strong robustness is crucial for rapid radioactive source searching. This study investigates the design of a low-consumption multiple nuclides identification algorithm for portable gamma spectrometers. First, the gamma spectra of 12 target nuclides (including the background case) were measured to create training datasets. The characteristic energies, obtained through energy calibration and full-energy peak addresses, are utilized as input features for a neural network. A large number of single- and multiple-nuclide trainin...
