Correction of the Temperature Effect on near Infrared Calibration—Application to Soluble Solid Content Prediction

Abstract

Non-destructive fruit quality assessment in packing houses can be carried out using near infrared spectroscopy. However, the prediction performance can be affected by measurement conditions (“external parameters”), such as temperature or variation in stray light. In this work we propose a methodology to reduce the effect of the fruit temperature on sugar content prediction. Two approaches were used to correct for the temperature effect depending on whether the temperature is measurable or not. In the case of measurable temperature, three methods were tested. The first uses a spectrum correction while the second and third are based on regression coefficients which vary with temperature. In case of non-measurable temperature, the studies have led to robust calibration models and to a self-correcting model (where fruit temperature is estimated using spectral data). All techniques were tested with “Golden Delicious” apples. Our study has shown that the most efficient models remove the temperature from the spectral space. These methodologies can be used to minimise other external parameters in NIR calibration.