Iranian Research Organization for Science and Technology (IROST) Innovative Food Technologies 2783-350X 7 4 2020 07 22 Intelligent Modeling of Bread texture Acoustic Measurement Method and Artificial Neural Network (Case Study: Enriched Bread with Chia) Intelligent Modeling of Bread texture Acoustic Measurement Method and Artificial Neural Network (Case Study: Enriched Bread with Chia) 517 534 929 10.22104/jift.2020.3734.1888 FA Mahrokh Hatamian Department of Food Science & Technology, Agricultural Sciences and Natural Resources University of Khuzestan, Iran Mohammad Noshad Department of Food Science & Technology, Faculty of Animal Science and Food Technology, Khuzestan Ramin University of Agricultural & Natural Resources, Mollasani, Iran Saman Abdanan Mehdizadeh Assistant professor of Khuzestan Agricultural Sciences and Natural Resources University Hassan Barzegar Food Science and Technology, Agricultural Sciences and Natural Resources University of Khuzestan Journal Article 2019 07 28 The purpose of this study is to model the bread texture by acoustical- mechanical method, non- destructively. To do so, bread texture enriched with 3 different levels of modified chia (2.5%, 5% and 7.5%) was evaluated by texture analyzer at a test speed of 3 mm. s-1 to 30% compression, while the microphone was located at 5 cm from the samples at a 45° angle to the horizon. From the sound recorded during loading mean sound intensity, maximum of sound, variance, standard deviation, root mean absolute value, root mean square value, skewness, kurtosis, fifth moment, sixth moment, energy, entropy in time domain, and spectral entropy and natural frequency in the frequency domain were extracted. After selecting the most suitable features (maximum sound, variance, standard deviation, root mean absolute value, energy, entropy and natural frequency), based on statistical analysis, artificial neural network with 3 algorithms ( Marquardt, scaled conjugate gradient, gradient descent) was trained and tested with 7 neurons in the input layer (in accordance with the selected features) and 3 neurons in the output layer (hardness, gumminess, chewiness). Based on the results, the training error in the Levenberg - Marquardt algorithm was lower than the other algorithms, and the root mean squared error of the test stage of this algorithm to predict hardness, gumminess and chewiness were 0.14, 0.23, and 0.33, respectively. This shows the ability of the proposed method in predicting the quality of bread. The purpose of this study is to model the bread texture by acoustical- mechanical method, non- destructively. To do so, bread texture enriched with 3 different levels of modified chia (2.5%, 5% and 7.5%) was evaluated by texture analyzer at a test speed of 3 mm. s-1 to 30% compression, while the microphone was located at 5 cm from the samples at a 45° angle to the horizon. From the sound recorded during loading mean sound intensity, maximum of sound, variance, standard deviation, root mean absolute value, root mean square value, skewness, kurtosis, fifth moment, sixth moment, energy, entropy in time domain, and spectral entropy and natural frequency in the frequency domain were extracted. After selecting the most suitable features (maximum sound, variance, standard deviation, root mean absolute value, energy, entropy and natural frequency), based on statistical analysis, artificial neural network with 3 algorithms ( Marquardt, scaled conjugate gradient, gradient descent) was trained and tested with 7 neurons in the input layer (in accordance with the selected features) and 3 neurons in the output layer (hardness, gumminess, chewiness). Based on the results, the training error in the Levenberg - Marquardt algorithm was lower than the other algorithms, and the root mean squared error of the test stage of this algorithm to predict hardness, gumminess and chewiness were 0.14, 0.23, and 0.33, respectively. This shows the ability of the proposed method in predicting the quality of bread. https://jift.irost.ir/article_929_113ad7c5af022a3e20e95ef1f16a65ab.pdf