Introducing an Automatic Bread Quality Assessment Algorithm using Image Processing Techniques

Archandani, Roya Biglari; Mohanna, Farahnaz; Ahsani, Mohammad Javad

doi:10.24018/ejece.2022.6.6.470

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…This study affirms the application of NIR-HIS as a non-destructive technique to inspect and sort cakes and other related products. Archandani et al (2022) also used a multilevel SVM classifier to classify bread samples into five different defect subgroups which include samples with fractures, cuts, folds, non-uniformity, black and burnt areas in baking, deformity, color, and size. The authors extracted only 15 features per image containing a database of 1250 bread images with different quality attributes.…”

Section: Quality Classificationmentioning

confidence: 99%

Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review

Olakanmi

Jayas

Paliwal

2023

Comp Rev Food Sci Food Safe

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One of the most widely researched topics in the food industry is bread quality analysis. Different techniques have been developed to assess the quality characteristics of bakery products. However, in the last few decades, the advancement in sensor and computational technologies has increased the use of computer vision to analyze food quality (e.g., bakery products). Despite a large number of publications on the application of imaging methods in the bakery industry, comprehensive reviews detailing the use of conventional analytical techniques and imaging methods for the quality analysis of baked goods are limited. Therefore, this review aims to critically analyze the conventional methods and explore the potential of imaging techniques for the quality assessment of baked products. This review provides an in-depth assessment of the different conventional techniques used for the quality analysis of baked goods which include methods to record the physical characteristics of bread and analyze its quality, sensory-based methods, nutritional-based methods, and the use of dough rheological data for end-product quality prediction. Furthermore, an overview of the image processing stages is presented herein. We also discuss, comprehensively, the applications of imaging techniques for assessing the quality of bread and other baked goods.These applications include studying and predicting baked goods' quality characteristics (color, texture, size, and shape) and classifying them based on these features. The limitations of both conventional techniques (e.g., destructive, laborious, error-prone, and expensive) and imaging methods (e.g., illumination, humidity, and noise) and the future direction of the use of imaging methods for quality analysis of bakery products are discussed.

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Section: Quality Classificationmentioning

confidence: 99%

Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review

Olakanmi

Jayas

Paliwal

2023

Comp Rev Food Sci Food Safe

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Impact of fat types on the visual, textural, and sensory properties of Nanberenji (a traditional gluten-free cookie)

Heydari

Mohebbi

Ghaitaranpour

2023

International Journal of Food Engineering

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The objective of this research was to evaluate the effects of various fat types (Bakery fat, Ghee, and Olive oil) on visual characteristics, baking behavior, textural, and sensory properties of a traditional gluten-free rice-based cookie (Nanberenji). The ability of image analysis for measuring some quality properties of prepared samples were also assessed. The obtained data demonstrated that the dough prepared with bakery and olive oil had maximum and minimum hardness values, respectively. However, after baking, the samples prepared with olive oil showed the highest hardness, meaningfully. The given outputs from image processing depicted that cookie prepared with bakery fat (NBF) and samples prepared with olive oil (NOO) had the highest and lowest surface color uniformity, respectively. Over baking time, the intensity and changes in the behavior of image textural features of NG and NBF samples were the same, while NOO samples showed a different behavior. Based on sensory analysis results, olive oil, as a healthy unsaturated fat, is advised to be used in the formulation of Nanberenji to produce a gluten-free cookie with a desirable texture, color, and flavor.

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Non-Destructive Prediction of Bread Staling Using Artificial Intelligence Methods

ARGUN,

TÜRK,

KURT

2023

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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In foods with limited shelf life and in new product development studies, it is important for producers and consumers to estimate the degree of staling with easy methods. Staling of bread, which has an essential role in human nutrition, is an important physicochemical phenomenon that affects consumer preference. Costly technologies, such as rheological, thermal, and spectroscopic approaches, are used to determine the degree of staling. This research suggests that an artificial intelligence-based method is more practical and less expensive than these methods. Using machine learning and deep learning algorithms, it was attempted to predict how many days old breads are, which provides information on the freshness status and degree of staling, from photos of whole bread and pieces of bread. Among the machine learning algorithms, the highest accuracy rate for slices of bread was calculated as 62.84% with Random Forest, while the prediction accuracy was lower for all bread images. The training accuracy rate for both slice and whole bread was determined to be 99% when using the convolutional neural network (CNN) architecture. While the test results for whole breads were around 56.6%, those for sliced breads were 92.3%. The results of deep learning algorithms were superior to those of machine learning algorithms. The results indicate that crumb images reflect staling more accurately than whole bread images.

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Introducing an Automatic Bread Quality Assessment Algorithm using Image Processing Techniques

Cited by 4 publications

References 23 publications

Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review

Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review

Impact of fat types on the visual, textural, and sensory properties of Nanberenji (a traditional gluten-free cookie)

Non-Destructive Prediction of Bread Staling Using Artificial Intelligence Methods

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