title: The use of the impedance measurements to distinguish between fresh and frozen-thawed chicken breast muscle.
abstract: An impedance system was built to differentiate fresh chicken breasts from those that had been frozen and thawed. Inserting needle electrode pairs of the detecting probe aligned with the longitudinal direction of muscle myofibers (PL) gave more satisfactory results. Learning vector quantization neural network (LVQNN) and partial least square-discriminant analysis (PLS-DA) were employed to acquire the prediction accuracy. The results demonstrated that the model using LVQNN achieved a satisfactory prediction accuracy, with a discrimination accuracy for fresh breasts of 100%. Additionally, the recognition results for a single frozen-thawed cycle were greater than 90%, and for two cycles were greater than 88%. The values obtained from PLS-DA were somewhat lower than for LVQNN, being 100% for fresh samples, in excess of 90% for single frozen-thawed cycle and more than 84% for those that had been multiple frozen-thawed. In conclusion, these results showed that the impedance system is a simple and effective application for the discrimination of fresh chicken breasts from frozen-thawed ones.
authors.affiliation: Key Lab of Meat Processing and Quality Control, MOE, Synergetic Innovation Center of Food Safety and Nutrition, Jiangsu Collaborative Innovation Center of Meat Production and Processing, Quality and Safety Control, National Center of Meat Quality and Safety Control, College of Food Science and Technology, Nanjing Agricultural University, China.
authors.forename: Tian-Hao
authors.initials: TH
authors.lastname: Chen
authors.affiliation: Key Lab of Meat Processing and Quality Control, MOE, Synergetic Innovation Center of Food Safety and Nutrition, Jiangsu Collaborative Innovation Center of Meat Production and Processing, Quality and Safety Control, National Center of Meat Quality and Safety Control, College of Food Science and Technology, Nanjing Agricultural University, China.
authors.forename: Ye-Pei
authors.initials: YP
authors.lastname: Zhu
authors.affiliation: Key Lab of Meat Processing and Quality Control, MOE, Synergetic Innovation Center of Food Safety and Nutrition, Jiangsu Collaborative Innovation Center of Meat Production and Processing, Quality and Safety Control, National Center of Meat Quality and Safety Control, College of Food Science and Technology, Nanjing Agricultural University, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Wang
authors.affiliation: Key Lab of Meat Processing and Quality Control, MOE, Synergetic Innovation Center of Food Safety and Nutrition, Jiangsu Collaborative Innovation Center of Meat Production and Processing, Quality and Safety Control, National Center of Meat Quality and Safety Control, College of Food Science and Technology, Nanjing Agricultural University, China.
authors.forename: Min-Yi
authors.initials: MY
authors.lastname: Han
authors.affiliation: Key Lab of Meat Processing and Quality Control, MOE, Synergetic Innovation Center of Food Safety and Nutrition, Jiangsu Collaborative Innovation Center of Meat Production and Processing, Quality and Safety Control, National Center of Meat Quality and Safety Control, College of Food Science and Technology, Nanjing Agricultural University, China.
authors.forename: Ran
authors.initials: R
authors.lastname: Wei
authors.affiliation: Key Lab of Meat Processing and Quality Control, MOE, Synergetic Innovation Center of Food Safety and Nutrition, Jiangsu Collaborative Innovation Center of Meat Production and Processing, Quality and Safety Control, National Center of Meat Quality and Safety Control, College of Food Science and Technology, Nanjing Agricultural University, China. Electronic address: xlxus@njau.edu.cn.
authors.forename: Xing-Lian
authors.initials: XL
authors.lastname: Xu
authors.affiliation: Key Lab of Meat Processing and Quality Control, MOE, Synergetic Innovation Center of Food Safety and Nutrition, Jiangsu Collaborative Innovation Center of Meat Production and Processing, Quality and Safety Control, National Center of Meat Quality and Safety Control, College of Food Science and Technology, Nanjing Agricultural University, China.
authors.forename: Guang-Hong
authors.initials: GH
authors.lastname: Zhou
mesh_terms: D000818:Animals; D002645:Chickens; D017097:Electric Impedance; D005504:Food Analysis / Q000379:methods*; D061353:Food Storage; D005615:Freezing; D008460:Meat / Q000032:analysis*; D018482:Muscle, Skeletal / Q000737:chemistry*
keywords: Electrical impedance; Frozen–thawed cycles; Learning vector quantization neural network; Partial least square-discriminate analysis; Prediction accuracy