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There is a pressing market demand to minimize the test time of Prompt GammaNeutron Activation Analysis (PGNAA) spectra measurement machine, so that itcould function as an instant material analyzer, e.g. to classify waste samplesinstantaneously and determine the best recycling method based on the detectedcompositions of the testing sample.  This article introduces a new development of the deep learning classificationand contrive to reduce the test time for PGNAA machine. We propose both RandomSampling Methods and Class Activation Map (CAM) to generate "downsized" samplesand train the CNN model continuously. Random Sampling Methods (RSM) aims toreduce the measuring time within a sample, and Class Activation Map (CAM) isfor filtering out the less important energy range of the downsized samples.  We shorten the overall PGNAA measuring time down to 2.5 seconds whileensuring the accuracy is around 96.88 % for our dataset with 12 differentspecies of substances. Compared with classifying different species ofmaterials, it requires more test time (sample count rate) for substances havingthe same elements to archive good accuracy. For example, the classification ofcopper alloys requires nearly 24 seconds test time to reach 98 % accuracy.

"Prompt-Gamma Neutron Activation Analysis (PGNAA)" Metal Spectral Classification using Deep Learning Method