title: Rapid detection of frozen pork quality without thawing by Vis-NIR hyperspectral imaging technique.
abstract: Quality determination of frozen food is a time-consuming and laborious work as it normally takes a long time to thaw the frozen samples before measurements can be carried out. In this research, a rapid and non-destructive determination technique for frozen pork quality was tested with a hyperspectral imaging (HSI) system. In this study, 120 pieces of pork meat were frozen by four kinds of methods with various freezing temperatures from -20 to -120°C. The hyperspectral images of the samples were acquired at the frozen state. Quality indicators including drip loss, pH value, color, cooking loss and Warner-Bratzler shear force (WBSF) of the samples were measured after thawing. The spectral characteristics of the frozen meat samples were studied and it was revealed that the reflectance at 1100nm had a close relationship with the freezing temperature (R=-0.832, p<0.01). Partial least squares regression (PLSR) was applied to establish the spectral models, and the models were then optimized. Results showed that the improved region of interest (ROI) method could be used to extract effective spectral information to withstand the interference of freezing, and choosing appropriate spectral bands and spectral pretreatment techniques were crucial to develop robust mathematical model. The performances of the models established were diverse based on different quality indicators. The coefficients of determination for prediction (Rp(2)) for L*, cooking loss, b*, drip loss and a* were 0.907, 0.845, 0.814, 0.762, and 0.716, respectively. However there were low correlations (Rp(2)) for pH and WBSF measurements. The current study indicated that HSI had the potential for non-destructive determination of frozen meat quality without thawing.
authors.affiliation: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, China.
authors.forename: Anguo
authors.initials: A
authors.lastname: Xie
authors.affiliation: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, China; Food Refrigeration and Computerised Food Technology, University College Dublin, National University of Ireland, Agriculture and Food Science Centre, Belfield, Dublin 4, Ireland. Electronic address: dawen.sun@ucd.ie.
authors.forename: Da-Wen
authors.initials: DW
authors.lastname: Sun
authors.affiliation: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, China.
authors.forename: Zhongyue
authors.initials: Z
authors.lastname: Xu
authors.affiliation: College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, China.
authors.forename: Zhiwei
authors.initials: Z
authors.lastname: Zhu
mesh_terms: D000818:Animals; D003296:Cooking; D005615:Freezing; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016018:Least-Squares Analysis; D008460:Meat / Q000032:analysis*; D008962:Models, Theoretical; D011786:Quality Control; D019265:Spectroscopy, Near-Infrared / Q000379:methods*; D013552:Swine
keywords: Drip loss; Freezing; Frozen food; Hyperspectral imaging; Meat color; NIR spectroscopy