Evaluation of a new local modelling approach for large and heterogeneous NIRS data sets

• Published in: Chemometrics and intelligent laboratory systems Vol. 101; no. 2; pp. 87 - 94
• Main Authors: Zamora-Rojas, E., Garrido-Varo, A., Van den Berg, F., Guerrero-Ginel, J.E., Pérez-Marín, D.C.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.04.2010
• Subjects: Compound feed; Ingredient composition; Local Central Algorithm; NIRS; Non-linear algorithms; Robust calibrations; NIRS; Robust calibrations; Non-linear algorithms; Local Central Algorithm; Compound feed; Ingredient composition
• ISSN: 0169-7439; 1873-3239
• DOI: 10.1016/j.chemolab.2010.01.004

The industry is demanding quality control systems that enable not only certified safety of an end-product but also a secure and efficient production system. Due to this, fast and accurate technologies are required for developing real time decision systems. Sensors based on Near-Infrared Spectroscopy (NIRS), together with the use of chemometrics models, have been studied for on-line quality control as a Process Analytical Technology (PAT) tool in several industries. A critical issue is the development of robust and sufficiently accurate mathematical models that can contain hundreds of very heterogeneous samples representing the large natural variability of the process and product; this especially holds for the agro-food production. This paper evaluates the performance of different linear (PLS) and non-linear regression algorithms (LOCAL and Locally Weighted Regression — LWR) plus a new local approach for the prediction of ingredient composition in compound feeds (called, Local Central Algorithm — LCA). The comparison is based on complexity, accuracy and predicted percentages in test set samples. The new local modelling approach is based on the use of Principal Component Analysis (PCA) and the Mahalanobis Distance (MD) for selecting a training set and calculating the final prediction estimate using a central tendency statistics such as mean of the local neighbours for the unknown samples. The results show that the local strategy proposed in this work enables the prediction in seconds of all the unknown samples in the test set and performed comparable to LWR, although the RMSEP was somewhat higher than using LWR or LOCAL. However, it was found that this approach produced smaller prediction errors than the other methods for less commonly present ingredients that are not well represented by even a large number of training samples. This finding could be relevant for the start-up phase in the implementation of NIRS sensors in the feed industry at which stage the libraries build only on-line contain data of a limited production period.