title: Rapid and non-destructive determination of rancidity levels in butter cookies by multi-spectral imaging.
abstract: BACKGROUND
  Rancidity is an important attribute for quality assessment of butter cookies, while traditional methods for rancidity measurement are usually laborious, destructive and prone to operational error. In the present paper, the potential of applying multi-spectral imaging (MSI) technology with 19 wavelengths in the range of 405-970 nm to evaluate the rancidity in butter cookies was investigated.
  
  
  RESULTS
  Moisture content, acid value and peroxide value were determined by traditional methods and then related with the spectral information by partial least squares regression (PLSR) and back-propagation artificial neural network (BP-ANN). The optimal models for predicting moisture content, acid value and peroxide value were obtained by PLSR. The correlation coefficient (r) obtained by PLSR models revealed that MSI had a perfect ability to predict moisture content (r = 0.909), acid value (r = 0.944) and peroxide value (r = 0.971).
  
  
  CONCLUSIONS
  The study demonstrated that the rancidity level of butter cookies can be continuously monitored and evaluated in real-time by the multi-spectral imaging, which is of great significance for developing online food safety monitoring solutions.
authors.affiliation: School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei, 230009, China.
authors.forename: Qing
authors.initials: Q
authors.lastname: Xia
authors.affiliation: School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei, 230009, China.
authors.forename: Changhong
authors.initials: C
authors.lastname: Liu
authors.affiliation: School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei, 230009, China.
authors.forename: Jinxia
authors.initials: J
authors.lastname: Liu
authors.affiliation: School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei, 230009, China.
authors.forename: Wenjuan
authors.initials: W
authors.lastname: Pan
authors.affiliation: Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei, 230031, China.
authors.forename: Xuzhong
authors.initials: X
authors.lastname: Lu
authors.affiliation: Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei, 230031, China.
authors.forename: Jianbo
authors.initials: J
authors.lastname: Yang
authors.affiliation: School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei, 230009, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
authors.affiliation: School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei, 230009, China.|School of Medical Engineering, Hefei University of Technology, Hefei, 230009, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Zheng
mesh_terms: D002079:Butter; D005504:Food Analysis / Q000379:methods*; D063427:Food Quality; D006863:Hydrogen-Ion Concentration; D010545:Peroxides / Q000737:chemistry*; D013057:Spectrum Analysis / Q000379:methods*; D014867:Water / Q000737:chemistry*
chemical_list: D010545:Peroxides; D014867:Water; D002079:Butter
keywords: butter cookies; multi-spectral imaging; non-destructive; rancidity monitoring