Assessing the influence of temperature on NIRS prediction models for the determination of sodium content in dry-cured ham slices

Temperature fluctuations are a key factor in the development of prediction models using near infrared spectroscopy (NIRS). In the present study, this influence has been investigated and a methodology has been proposed to reduce the effect of sample temperature on NIRS model prediction of the sodium content in dry-cured ham slices. Spectra were taken directly from the slices using a remote measurement probe (for non-contact analysis) at three different temperature ranges: −12 °C to −5°C, −5°C to 10 °C and 10 °C to 20 °C. Local and global temperature compensation methods were established. Partial-least squares (PLS) regression was used as a chemometrics tool to perform the calibrations. The results showed that local models were sensitive to changes in temperature, while a global temperature model using sample spectra over the entire temperature range showed good prediction ability, reducing the error caused by temperature fluctuations to acceptable levels for practical applications.