Abstract
Precise control and accurate endpoint determination in the thawing of frozen beef are vital for maintaining its quality and safety in the food industry. Traditional thawing methods, which are time-controlled, often lead to inconsistencies like under or over-thawing, adversely affecting texture, color, nutritional value, and increasing the risk of microbial contamination. This study introduces a novel, non-destructive approach using ultrasonic signals and ultrasonic velocity for the quantification of beef thawing. It involves analyzing thermal images of beef cross-sections to assess thawing levels and gathering ultrasonic data at various thawing stages. Machine learning algorithms, including KNN, ANN, Extra-Trees, and LightGBM, were employed to develop prediction models that accurately identify the thawing endpoint. The models exhibit high accuracy, with R^2 values exceeding 0.9, some reaching as high as 0.986. This method represents a significant advancement in non-destructive quality control, enhancing safety and quality management in the beef processing sector.
Published Version
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