Cui, C; Fearn, T; (2018) Modern practical convolutional neural networks for multivariate regression: Applications to NIR calibration. Chemometrics and Intelligent Laboratory Systems , 182 pp. 9-20. 10.1016/j.chemolab.2018.07.008.

Preview

Text Fearn_Modern practical convolutional neural networks for multivariate regression. Applications to NIR calibration_AAM.pdf - Accepted Version Download (3MB) | Preview

Abstract

In this study, we investigate the use of convolutional neural networks (CNN) for near infrared (NIR) calibration. We propose a unified CNN structure that can be used for general multivariate regression purpose. The comparison between the CNN method and the partial least squares regression (PLSR) method was done on three different NIR datasets of spectra and lab reference values. Datasets are from different sources and contain 6998, 1000 and 415 training and 618, 597 and 108 validation samples, respectively. Results indicated that compared to the PLSR models, the CNN models are more accurate and less noisy. The convolutional layer in the CNN model can automatically find the suitable spectral preprocessing filter on the dataset, which significantly saves efforts in training the model.

Download activity - last month

Export as JSONXMLCSV

Download activity - last 12 months

Export as XMLCSVJSON

Downloads by country - last 12 months

Export as CSVXMLJSON

Archive Staff Only

View Item