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Article http://dx.doi.org/10.26855/ijfsa.2026.03.004

Predicting the Total Volatile Basic Nitrogen (TVB-N) Content of Wet-aged Beef and Lamb Meat Using Near Infrared Spectroscopy

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Kristy L. Bailes1,*, Richard G. Meyer1, Benjamin W. B. Holman2

1Wagga Wagga Agricultural Institute, NSW Department of Primary Industries and Regional Development, Wagga Wagga, New South Wales, 2650, Australia.

2Gulbali Institute, Charles Sturt University, Wagga Wagga, New South Wales, 2678, Australia.

*Corresponding author: Kristy L. Bailes

Published: March 18,2026

Abstract

This study aimed to validate the use of near-infrared spectroscopy (NIRS) to predict total volatile basic nitrogen (TVB-N) concentration for wet-aged samples of beef and lamb M. longissimus lumborum samples and to determine if a combined species calibration model could be developed. Partial least squares (PLS) regression models were developed for species-specific (beef and lamb) and combined-species datasets. Beef samples exhibited a TVB-N range of 4.0-17.0 mg/100 g, while lamb samples ranged from 5.4-7.2 mg/100 g. The combined-species model demonstrated strong predictive performance (R2 = 68.0), although residual prediction deviation values indicated that a larger and more diverse dataset would substantially improve model robustness and precision. Nonetheless, these preliminary findings do support the application of NIRS to rapidly and non-destructively predict TVB-N concentrations, using freeze-dried and ground samples of red meat.

Keywords

Near infrared spectroscopy (NIRS); total volatile basic nitrogen (TVB-N); partial least square (PLS) regression; freeze-dried samples; red meat; freshness; predictive calibration models

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How to cite this paper

Predicting the Total Volatile Basic Nitrogen (TVB-N) Content of Wet-aged Beef and Lamb Meat Using Near Infrared Spectroscopy

How to cite this paper: Kristy L. Bailes, Richard G. Meyer, Benjamin W. B. Holman. (2026) Predicting the Total Volatile Basic Nitrogen (TVB-N) Content of Wet-aged Beef and Lamb Meat Using Near Infrared Spectroscopy. International Journal of Food Science and Agriculture10(1), 25-30.

DOI: http://dx.doi.org/10.26855/ijfsa.2026.03.004