Premium
Rapid and non‐destructive determination of rancidity levels in butter cookies by multi‐spectral imaging
Author(s) -
Xia Qing,
Liu Changhong,
Liu Jinxia,
Pan Wenjuan,
Lu Xuzhong,
Yang Jianbo,
Chen Wei,
Zheng Lei
Publication year - 2015
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.7292
Subject(s) - peroxide value , partial least squares regression , food science , chemistry , acid value , peroxide , moisture , water content , snack food , mathematics , statistics , biochemistry , organic chemistry , geotechnical engineering , engineering
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). CONCLUSION 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. © 2015 Society of Chemical Industry