Development of NIR-HSI and chemometrics process analytical technology for drying of beef jerky

Beef jerky samples immersed in brine or water, both with and without ultrasound treatment were dried at 60 degrees C for 30, 60, 90, 120, 150, 180, 210 and 240 min. Drying behaviour was evaluated using ten drying kinetic models and hyperspectral imaging. All samples reached water activity values (Aw) < 0.85 during the first 90 min of drying. Moisture content (MC) of beef jerky was predicted using near infrared hyperspectral imaging and chemometrics. Partial least squares regression, band selection and spectral pre-treatments were applied to develop MC prediction models using beef jerky spectra. Most of the MC prediction models developed in this work had RPD values >4, indicating their suitability for process control applications. The best performing MC prediction models for the non-ultrasound and ultrasound treated samples were developed using the ensemble Monte Carlo variable selection (EMCVS) on second derivative of log(1/R) spectra and EMCVS-selectivity ratio on linear detrended log (1/R) spectra, respectively. This study demonstrated the potential of NIR-HSI and chemometrics as a PAT tool for drying of beef jerky.

Automated summary

In this study, beef jerky samples were dried under different conditions with and without ultrasound treatment, and their moisture content was predicted using near infrared hyperspectral imaging and chemometrics. The developed moisture content prediction models show potential for process control applications, with different optimal models for ultrasound treated and non-treated samples.