Bryan Gilbert Murengami , Xudong Jing , Hanhui Jiang , Xiaojuan Liu , Wulan Mao , Yuedan Li , Xueyong Chen , Shaojin Wang , Rui Li , Longsheng Fu
{"title":"Monitor and classify dough based on color image with deep learning","authors":"Bryan Gilbert Murengami , Xudong Jing , Hanhui Jiang , Xiaojuan Liu , Wulan Mao , Yuedan Li , Xueyong Chen , Shaojin Wang , Rui Li , Longsheng Fu","doi":"10.1016/j.jfoodeng.2024.112299","DOIUrl":null,"url":null,"abstract":"<div><p>To address subjective, time-consuming, and labor-intensive manual methods for monitoring and classifying fermenting dough, an automated and non-destructive method is proposed. Deep learning model YOLOv8s and extracted features including dough surface area, contrast, and homogeneity from RGB color images were employed to monitor fermenting dough. The features were input to a stacked ensemble model (SEM) with base models SVM, AdaBoost, KNN, and RF, with AdaBoost as meta-learner to classify fermenting dough into under-fermented, fermented, and over-fermented. SEM demonstrated a high dough classification rate of 83%, with specific rates of 75% for under-fermented, 71% for fermented, and 90% for over-fermented dough. Results reviewed that combining dough surface area and texture features is effective for monitoring dough, and can be used in adjusting chamber conditions. Furthermore, SEM showed great ability in classifying fermenting dough. The proposed method offers a promising solution for improved bread quality and consistency in bread-making.</p></div>","PeriodicalId":359,"journal":{"name":"Journal of Food Engineering","volume":"386 ","pages":"Article 112299"},"PeriodicalIF":5.3000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Food Engineering","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0260877424003650","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
Abstract
To address subjective, time-consuming, and labor-intensive manual methods for monitoring and classifying fermenting dough, an automated and non-destructive method is proposed. Deep learning model YOLOv8s and extracted features including dough surface area, contrast, and homogeneity from RGB color images were employed to monitor fermenting dough. The features were input to a stacked ensemble model (SEM) with base models SVM, AdaBoost, KNN, and RF, with AdaBoost as meta-learner to classify fermenting dough into under-fermented, fermented, and over-fermented. SEM demonstrated a high dough classification rate of 83%, with specific rates of 75% for under-fermented, 71% for fermented, and 90% for over-fermented dough. Results reviewed that combining dough surface area and texture features is effective for monitoring dough, and can be used in adjusting chamber conditions. Furthermore, SEM showed great ability in classifying fermenting dough. The proposed method offers a promising solution for improved bread quality and consistency in bread-making.
期刊介绍:
The journal publishes original research and review papers on any subject at the interface between food and engineering, particularly those of relevance to industry, including:
Engineering properties of foods, food physics and physical chemistry; processing, measurement, control, packaging, storage and distribution; engineering aspects of the design and production of novel foods and of food service and catering; design and operation of food processes, plant and equipment; economics of food engineering, including the economics of alternative processes.
Accounts of food engineering achievements are of particular value.