Exploring the impacts of two heating methods on meat quality from generalized additive models

This study explored how boiling and microwave heating affect meat quality using Generalized Additive Models (GAM). Fresh longissimus dorsi muscles from pork and beef were analyzed to identify factors influencing meat shear force. Microwave heating achieved a core temperature of 75 ℃ in 110 s, significantly faster than boiling (525 s). Both methods reduced pork moisture, and boiling caused higher cooking loss. Microwave heating induced excessive myofibrillar protein denaturation, increasing shear force. Fat and protein content decreased in pork during heating, while free amino acids declined rapidly initially (0 to 1 min). Color parameters ( L* , b* ) increased and a* decreased with prolonged heating. The GAM identified meat type, moisture, fat, and protein content as key factors influencing shear force. Pork exhibited lower shear force than beef, with higher moisture and fat content. In the 30 %−32 % protein range, the shear force values were higher for both meats. The GAM model effectively predicted shear force with high predictive power ( R p = 0.92) and low root mean square error of prediction ( RMSE p = 9.97). This study demonstrated the efficacy of GAM in elucidating the complex relationships between meat quality attributes and processing parameters, providing a robust analytical framework for food-processing applications. • Boiling heating was compared to microwave heating on meat tenderness using GAMs. • Microwave heating led to higher shear forces due to rapid denaturation. • Meat type, moisture, fat, and protein content significantly affected shear force. • GAM model accurately predicted shear force ( R p = 0.92, RMSE p = 9.97). • The GAM model can help optimise food processing.

Institutions

Publication Details

Journal
Food Wellness
Published
2025-04-01
DOI
https://doi.org/10.1016/j.foodw.2025.100013
Citations
1
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
2.48
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploring the impacts of two heating methods on meat quality from generalized additive models

1 citations
Food Wellness
Geochemistry and Geologic Mapping
2.48
article

Exploring the impacts of two heating methods on meat quality from generalized additive models

article en
1 citations

Abstract

This study explored how boiling and microwave heating affect meat quality using Generalized Additive Models (GAM). Fresh longissimus dorsi muscles from pork and beef were analyzed to identify factors influencing meat shear force. Microwave heating achieved a core temperature of 75 ℃ in 110 s, significantly faster than boiling (525 s). Both methods reduced pork moisture, and boiling caused higher cooking loss. Microwave heating induced excessive myofibrillar protein denaturation, increasing shear force. Fat and protein content decreased in pork during heating, while free amino acids declined rapidly initially (0 to 1 min). Color parameters ( L* , b* ) increased and a* decreased with prolonged heating. The GAM identified meat type, moisture, fat, and protein content as key factors influencing shear force. Pork exhibited lower shear force than beef, with higher moisture and fat content. In the 30 %−32 % protein range, the shear force values were higher for both meats. The GAM model effectively predicted shear force with high predictive power ( R p = 0.92) and low root mean square error of prediction ( RMSE p = 9.97). This study demonstrated the efficacy of GAM in elucidating the complex relationships between meat quality attributes and processing parameters, providing a robust analytical framework for food-processing applications. • Boiling heating was compared to microwave heating on meat tenderness using GAMs. • Microwave heating led to higher shear forces due to rapid denaturation. • Meat type, moisture, fat, and protein content significantly affected shear force. • GAM model accurately predicted shear force ( R p = 0.92, RMSE p = 9.97). • The GAM model can help optimise food processing.

Food WellnessVol. 1(1)
Foshan University (CN), South China University of Technology (CN)
Zero hunger
Openalex Percentile: Top 7%
Geochemistry and Geologic Mapping
2.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Exploring the impacts of two heating methods on meat quality from generalized additive models · Food Wellness (2025) | TGRS Research Map | TGRS