Qing Xia1, Changhong Liu1, Jinxia Liu1, Wenjuan Pan1, Xuzhong Lu2, Jianbo Yang2, Wei Chen1, Lei Zheng1,3. 1. School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei, 230009, China. 2. Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei, 230031, China. 3. School of Medical Engineering, Hefei University of Technology, Hefei, 230009, China.
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). 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.
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.
Authors: Marcellus Arnold; Yolanda Victoria Rajagukguk; Andrzej Sidor; Bartosz Kulczyński; Anna Brzozowska; Joanna Suliburska; Natalia Wawrzyniak; Anna Gramza-Michałowska Journal: Int J Environ Res Public Health Date: 2022-04-01 Impact factor: 3.390