Adaptive Artificial Neural Network in near infrared spectroscopy for standard-free calibration transfer

Near infrared spectroscopy (NIRS) is a secondary analytical method that could use machine learning to predict components of interest for various applications. Calibration transfer (CT) algorithms have been used to transfer the existing NIRS calibration model to another NIRS instruments to reduce the involvement of expensive and tedious conventional chemical analytical methods. However, most of CT algorithms involve both primary and secondary instruments to acquire NIRS signals from standard samples for spectral standardization. On the other hand, artificial neural network (ANN) that can adapt to new environmental conditions may be an alternative standard-free CT algorithm in transferring a trained ANN to secondary instruments without the involvement of the primary instrument. Although ANN has been widely implemented in NIRS research as a calibration model, its feasibility as an alternative CT method has not been evaluated. Thus, this study evaluates the feasibility of an adaptive ANN (AANN) in transferring ANN model from primary to secondary instruments using two traceable NIRS datasets. First, ANN and ANN with principal components (PCs-ANN) were developed and optimized using Bayesian learning algorithm. After that, these ANNs were adapted to secondary instruments using respective transfer data, in which, only the weights and biases of the ANNs were updated. Findings show that the lowest averaged RMSEP was achieved by the proposed PCs-AANN and AANN in the corn and wheat datasets, respec-tively. Particularly, the computation cost of having a calibration model in secondary instruments has been substantially reduced by means of the proposed AANN algorithm.

Automated summary

The study evaluated the feasibility of an adaptive ANN in transferring the ANN model from primary to secondary instruments, showing that the AANN significantly reduces the computation cost of calibration models.