Kim Hyun-Jun, Kim Hye-Jin, Hong Heesang, Choi Minwoo, Ismail Azfar, Mun Daye, Kim Younghoon, Kim Gap-Don, Jo Cheorun
Institutes of Green Bio Science and Technology, Seoul National University, Pyeongchang, 25354, Republic of Korea.
Department of Agricultural Biotechnology, Center for Food and Bioconvergence, and Research Institute of Agriculture and Life Science, Seoul National University, Seoul, 08826, Republic of Korea.
NPJ Sci Food. 2025 Apr 23;9(1):55. doi: 10.1038/s41538-025-00421-y.
This study validated the use of pork drip metabolites for non-destructive freshness prediction. The pork loin was vacuum-packaged and stored for 27 days at 4 °C. The pH, drip loss, total aerobic bacterial counts (TAB), microbial composition and drip metabolites were examined. LASSO and Random Forest (RF) were selected and used for variable selection, while Ridge regression and Support Vector Regression were utilized to develop predictive models. Validation was performed using leave-one-out cross-validation. LASSO and RF selected 13 and 10 metabolites, respectively. The metabolites selected by each method were trained using Ridge regression and SVR. Each of the four trained models achieved R values of over 0.9. In the validation step, the model trained by Ridge regression using drip metabolites selected through LASSO showed the lowest RMSE value of 0.283 log CFU/g. Therefore, selected drip metabolites can be used to predict TAB and microbial composition of pork loin through mathematical modeling.
本研究验证了利用猪肉滴液代谢物进行无损新鲜度预测的方法。猪里脊肉进行真空包装后在4℃下储存27天。检测了pH值、滴水损失、需氧细菌总数（TAB）、微生物组成和滴液代谢物。选择套索回归（LASSO）和随机森林（RF）进行变量选择，同时利用岭回归和支持向量回归建立预测模型。采用留一法交叉验证进行模型验证。LASSO和RF分别选择了13种和10种代谢物。用岭回归和支持向量回归对每种方法选择的代谢物进行训练。四个训练模型的R值均超过0.9。在验证步骤中，通过LASSO选择的滴液代谢物经岭回归训练的模型显示最低均方根误差值为0.283 log CFU/g。因此，选择的滴液代谢物可通过数学建模用于预测猪里脊肉的TAB和微生物组成。
