Near infrared spectroscopy quantification based on Bi-LSTM and transfer learning for new scenarios

This study proposed a deep transfer learning methodology based on an improved Bi-directional Long Short-Term Memory (Bi-LSTM) network for the first time to address the near infrared spectroscopy (NIR) model transfer issue between samples. We tested its effectiveness on two datasets of manure and polyglutamic acid (gamma-PGA) solution, respectively. First, the optimal primary Bi-LSTM networks for cattle manure and the first batch of gamma-PGA were developed by ablation experiments and both proved to outperform one-dimensional convolutional neural network (1D-CNN), Partial Least Square (PLS) and Extreme Learning Machine (ELM) models. Then, two types of transfer learning approaches were carried out to determine model transferability to non-homologous samples. For poultry manure and the second batch of gamma-PGA, the obtained predicting results verified that the second approach of fine-tuning Bi-LSTM layers and re-training FC layers transcended the first approach of fixing Bi-LSTM layers and only re-training FC layers by reducing the RMSEPtarget of 23.4275% and 50.7343%, respectively. Finally, comparisons with fine-tuning 1D-CNN and other traditional model transfer methods further identified the superiority of the proposed methodology with exceeding accuracy and smaller variation, which decreased RMSEPtarget of poultry manure and the second batch of gamma-PGA of 7.2832% and 48.1256%, 67.1117% and 80.6924% when compared to that acquired by fine-tuning 1D-CNN, Tradaboost-ELM and CCA-PLS which were the best of five traditional methods, respectively. The study demonstrates the potential of the Fine-tuning-Bi-LSTM enabled NIR technology to be used as a simple, cost effective and reliable detection tool for a wide range of applications under various new scenarios.