|
|
|
| Development of an Evaluation Model for Freshness Grades of Refrigerated Salmon Based on Multivariate Statistical Analysis |
| ZHOU Bingwu, HU Qian, LI Guoping, XING Ranran, ZHANG Jiukai, DU Xinjun, CHEN Ying |
| 1. College of Food Science and Engineering, Tianjin University of Science and Technology, Tianjin 300457, China; 2. Chinese Academy of Inspection and Quarantine, Beijing 100176, China |
|
|
|
|
Abstract This study aimed to analyze the changes in the freshness of salmon during refrigerated storage and to develop an evaluation model for freshness grades. During storage at 4 ℃, the changes in sensory score, total volatile basic nitrogen content, pH value, thiobarbituric acid reactive substances (TBARS) value, K value, total viable count (TVC), color, shear force, drip loss rate, and water-holding capacity were measured. Multivariate statistical methods were then applied to divide salmon freshness into several intervals and establish a discriminant model to distinguish among different freshness grades. The results indicated that hierarchical cluster analysis divided the storage period at 4 ℃ into three intervals: days 0 to 6 (fresh), days 7 to 8 (semi-fresh) and day 9 to longer (spoiled). Principal component analysis (PCA) revealed that TBARS value, TVC, and sensory score were key indicators for evaluating salmon freshness. The developed Fisher linear discriminant model effectively distinguished among the three freshness grades of salmon meat. In conclusion, this study has developed an evaluation model capable of determining the freshness grade of refrigerated salmon, which can be used to accurately assess the freshness level of refrigerated salmon meat.
|
|
|
|
|
|
|
|
|
|
|
2025, Vol. 39 
