A Comparison Between Local and Global Models Among Different Near Infrared Spectroscopy Instruments for Corn Oils Prediction

Abstract

Near infrared (NIR) sensing technology has been widely implemented in various areas due to its noninvasive, green, and rapid measurement features. Artificial Neural Network (ANN) was used for the NIR calibration model. ANN can provide a reliable local calibration model using local data. However, a reliable local calibration model could be invalid or degraded when it is directly used for other instruments. This is because the instrumental variation causes the Local model's performance invalid. The instrumental variation is the difference among the spectra acquired by different instruments. The Global model identified the instrumental variation due to the Global model was developed with the calibration dataset acquired from two or more instruments. Thus, this study aims to compare the Local and Global models' performance among different NIR spectroscopy instruments in corn oil prediction. First, principle component analysis (PCA) was used to compress the NIR spectra. After that, Bayesian (BR) learning algorithm was applied to train an ANN with different initial conditions and hidden neurons to identify an optimal ANN for the primary instrument. The procedure of Global model development was similar to the Local model. The difference between Local and Global models is the global model used two or more calibration datasets to develop the model. Findings show that Global model was the best with the lowest root mean square error of prediction (RMSEP) of 0.1630%, followed by Local model of 0.4074% and 0.4330% for mp5 and mp6 as calibration datasets, respectively and the best correlation coefficient of 0.7514, 0.6532, and 0.7297, respectively, tested with m5 testing dataset in corn oils prediction applications. The same performance was found when the testing dataset were mp5 and mp6.