Non-linear regression methods in NIRS quantitative analysis

Due to its speed and precision, near-infrared reflectance spectroscopy (NIRS) has become a widely used analytical technique in many im: It offers, moreover, a number of other advantages which make it ideal for meeting current demands in terms of control and traceability: low sample analysed; little or no need for sample preparation; ability to analyse a wide range of products and parameters; a high degree of reprod and repeatability. NIRS can be built into in-line processes, and - since no reagents are required - produces no waste. However, the major dr to the use of NIRS for its most traditional application (the generation of prediction equations) is that it is a secondary method, and as suc to be calibrated using a conventional reference method. For quantitative applications, calibration involves ascertaining the optimum mathe relationship between spectral data and data provided by the reference method. The model may be fairly complex, since the NIRS spectrum i variable and contains physical/chemical information for the sample which may be redundant. As a result, multivariate calibration is require on a set of absorption values from several wavelengths. Since the relationship to be modelled is often non-linear, classical regression med unsuitable, and more complex strategies and algorithms must be sought in order to model this non-linearity. This overview addresses t widely used non-linear algorithms in the management of NIRS data. (C) 2006 Elsevier B.V. All rights reserved.