Iranian Research Organization for Science and Technology (IROST) Innovative Food Technologies 2783-350X 10 4 2023 07 23 Non-destructive detection of bread staleness using hyperspectral images Non-destructive detection of bread staleness using hyperspectral images 299 317 1326 10.22104/ift.2023.6279.2142 FA Saman Abdanan Mehdizadeh Associate professor of Agricultural Sciences and Natural Resources University of Khuzestan Mohammad Noshad Department of Food Science & Technology, Faculty of Animal Science and Food Technology, Khuzestan Ramin University of Agricultural & Natural Resources, Mollasani, Iran 0000-0002-4060-9254 Fatemeh Nouri 2. MSc Student Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan Journal Article 2023 05 15 Hyperspectral imaging, a technology that combines imaging and spectroscopy, provides extensive spatial and spectral information, simultaneously. It is currently being developed as a non-destructive and rapid diagnostic tool for assessing food quality and safety. In this study, hyperspectral imaging was utilized to investigate the process of bread staleness and its effect on the behavior of the bread crumb within the wavelength range of 950-400 nm and with a resolution of 0.795 nm. Principal components were extracted and three modeling methods - PCR, PLSR, and GRNN - were employed to predict texture characteristics during six days of storage. Based on the findings of this study, it was observed that the General Regression Neural Network (GRNN) method demonstrated superior performance in terms of R2 values for both springiness and stiffness, with values of 0.96 and 0.94, respectively. Furthermore, the GRNN method also exhibited the lowest Root Mean Square Error (RMSE) values for cohesiveness and stiffness, with values of 0.11 and 0.32, respectively. This demonstrates the capability of the generalized regression neural network model to predict the textural characteristics of bread. Hyperspectral imaging, a technology that combines imaging and spectroscopy, provides extensive spatial and spectral information, simultaneously. It is currently being developed as a non-destructive and rapid diagnostic tool for assessing food quality and safety. In this study, hyperspectral imaging was utilized to investigate the process of bread staleness and its effect on the behavior of the bread crumb within the wavelength range of 950-400 nm and with a resolution of 0.795 nm. Principal components were extracted and three modeling methods - PCR, PLSR, and GRNN - were employed to predict texture characteristics during six days of storage. Based on the findings of this study, it was observed that the General Regression Neural Network (GRNN) method demonstrated superior performance in terms of R2 values for both springiness and stiffness, with values of 0.96 and 0.94, respectively. Furthermore, the GRNN method also exhibited the lowest Root Mean Square Error (RMSE) values for cohesiveness and stiffness, with values of 0.11 and 0.32, respectively. This demonstrates the capability of the generalized regression neural network model to predict the textural characteristics of bread. https://jift.irost.ir/article_1326_c70daf247944fe3add32218f914c75a6.pdf