A new classification method based on chemical image was established using ‘dynamic’ near infrared(NIR) spectroscopy with a deep learning based image recognition model GoogLeNet and transfer learning

with cashmere

cashmere/wool blends textiles

cotton and silk cotton textiles as the targets.Moisture perturbation was proposed to apply in this paper

collecting ‘dynamic’ spectra

expanding the spectral differences between samples of different types

and thus fusing the synchronous and asynchronous two dimensional map of dynamic spectra into a ‘chemical image’ which reflects the detailed differences between samples.A total of 234 textile samples were collected

and the samples with water contents of 0

5.4%

11.2% and 16.3% were prepared.Several preprocessing methods were employed before modeling.A total of 16 classification models were established

in which the best SIMCA and SVM models for cashmere vs cashmere/wool blends have the accuracies of 63.33% and 70.09%

while those of cotton and silk cotton textiles are 71.02% and 72.51%

respectively.Results demonstrated that the developed method is effective

the overall prediction correct rates of models are 92.59% for cashmere and blended and 94.74% for cotton and silk cotton.This contribution provides a successful demonstration for advanced identification techniques in the field of deep learning for solving chemical problems.