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