EPO–PLS external parameter orthogonalisation of PLS application to temperature-independent measurement of sugar content of intact fruits

Abstract

Near Infrared (NIR) spectrometry would present a high potential for on-line measurement if the robustness of multivariate calibration was improved. The lack of robustness notably appears when an external parameter varies—e.g. the product temperature. This paper presents a preprocessing method which aims at removing from the X space the part mostly influenced by the external parameter variations. This method estimates this parasitic subspace by computing a PCA on a small set of spectra measured on the same objects, while the external parameter is varying. An application to the influence of the fruit temperature on the sugar content measurement of intact apples is presented. Without any preprocessing, the bias in the sugar content prediction was about 8° Brix for a temperature variation of 20 °C. After External Parameter Orthogonalisation (EPO) preprocessing, the bias is not more than 0.3° Brix, for the same temperature range. The parasitic subspace is studied by analysing the b-coefficient of a Partial Least Square Regression (PLS) between the temperature and the influence spectra. Further work will be achieved to apply this method to the case of multiple external parameters and to the calibration transfer issue.