Assessment of Beef Quality Changes During Storage by Using Dynamic Stress Method Based on Contactless Airflow‐Optical Technique
Year: 2026
Type: article
Abstract: ABSTRACT In the present study, the airflow–optical detection technique was evaluated as a novel, contactless approach for assessing dynamic viscoelastic changes in beef during storage. Understanding the viscoelastic behavior of meat is essential for monitoring its quality, as changes in mechanical properties are closely linked to biochemical and structural alterations during storage. Beef apparent dynamic viscoelastic parameters, including apparent storage modulus and apparent loss modulus, were measured using the developed airflow–optical detection device. Concurrently, key quality attributes—total volatile basic nitrogen (TVB‐N) content and Warner‐Bratzler shear force (WBSF)—were determined through standard physicochemical analyses. Partial least squares regression (PLSR) models were constructed to predict beef freshness and tenderness based on the collected strain data. During storage, TVB‐N content of beef increased gradually, while WBSF values decreased, accompanied by a rise in apparent viscosity. This resulted in a significant increase in the apparent loss modulus (from 9.43 × 10 2 ± 267.45 to 1.95 × 10 3 ± 9662.65 Pa), whereas the apparent storage modulus remained largely unchanged. The PLSR models showed satisfactory predictive performance: for TVB‐N, the prediction set achieved a correlation coefficient ( R ) of 0.736 with a root mean square error (RMSE) of 1.262 mg/100 g; for WBSF, the prediction set yielded R = 0.741 with RMSE = 1.598 N. These findings demonstrate that the airflow–optical detection technique can effectively capture dynamic viscoelastic changes in beef and provide predictions of key quality parameters. The approach offers a promising, non‐destructive tool for real‐time monitoring of meat freshness and tenderness during storage.
DOI: 10.1111/jfpe.70756
Authors: Ke He, Xiuying Tang, Yuan Su
Institutions: Northwest A&F University, China Agricultural University
Language: English
Subfield: Animal Science and Zoology
Domain: Life Sciences
SDG: Zero hunger
Open Access status: bronze
OpenAlex ID: W7204232969