A Vision System for Surface Homogeneity Analysis of Dough Based on the Grey Level Co‐occurrence Matrix (GLCM) for Optimum Kneading Time Prediction

Abstract

This paper presents an investigation of a methodology for predicting the optimum time to decide dough readiness by using two-dimensional imaging and statistical texture analysis. Based on the master baker experience and the torque curve generated by the dough mixer, the system will be correlated to stop the kneading process with the minimum error on the optimum time or point of maximum dough development (peak consistency). This development takes advantage of the gray level co-occurrence matrix (GLCM) texture analysis for the development of an algorithm to emulate the visual inspection performed by the master bakers, where the homogeneity feature of dough is analyzed during the kneading time. An automatic graphical user interface software for offline and inline analysis was developed for the homogeneity analysis. The programmed algorithm was implemented in many experiments (online and offline) for testing, producing an average error of 33.9 s with respect to average optimal mixing time. PRACTICAL APPLICATIONSThe development of a dough analyzing method, on the basis of an optical surface evaluation, represents an entirely new way to look at the dough properties. The possibility of applying an online dough judgment method without any losses will be given. Simple upgrading of existing production lines can help to prevent erroneous processing and to increase the quality of the end product. Instantaneous state of the art for determining the optimum dough development or dough readiness is the visual and tactile evaluation of the technical staff and laboratory investigations. The expansion of the indirect method by a digital interface-based analytic approach provides a significant improvement in industrial bakery production and a potential direct method for dough analysis.

Expert System for Monitoring the Malaxing State of the Olive Paste Based on Computer Vision †

“…This could be due to the lumps over the olive paste surface tending to be larger at the end of the malaxing time when olive paste can be considered as prepared. Similar results were obtained in [ 13 ] where authors employed computer vison and the GLCM matrix to assess when the dough for the bakery was ready. They reached linear dependency between the homogeneity parameter and the specialist knowledge.…”

Nondestructive evaluation of baking parameters on pogácsa texture

“…The GLCM, which is a well-known statistical method for texture characterization, provides information on the distribution of gray-level intensity differences in sample images (Mohanaiah, Sathyanarayana, & Gurukumar, 2013;Sun, 2012). Recently, different GLCM-based analysis methods have been introduced for the extraction of textural features from the digital images of food samples (Andino, Pieniazek, & Messina, 2019;Perez Alvarado, Hussein, & Becker, 2016). Nouri, Nasehi, Goudarzi, and Abdanan Mehdizadeh (2018) proposed a texture-based image analysis method to successfully evaluate the staling rate of baguette bread during 5 days of storage.…”

Monitoring Mixing Processes Using Ultrasonic Sensors and Machine Learning

“…NIRS has previously been used to monitor the chemical and surface structure changes occurring during dough mixing [51][52][53] and image analysis of the dough surface has been used to determine optimal mixing time [54]. Measuring the power or torque supplied to the impeller is a common method of monitoring dough mixing.…”

“…Measuring the power or torque supplied to the impeller is a common method of monitoring dough mixing. Mixing should be stopped at the maximum power input for optimal bread properties [54]. Beyond this point of maximum resistance to extension, the gluten network begins to breakdown.…”