Digital image analysis to assess the texture of bread products

• MATLAB-based Bread Texture Analyser quantifies bread texture non-destructively. • Digital ‘texture-fingerprints’ objectively assess bread quality and uniformity. • Applied to five breads, capturing unique porosity, density, and cell distribution. • Digital analysis validated against sensory evaluation for perceived bread quality. • The quick, scalable, and cost-efficient method to monitor bread production. Texture is a key characteristic of bread, offering insights into its quality, ingredients, and baking methods. Digital image texture analysis involves computer analysis of the bread image taken with a digital camera or smartphone. We developed the Bread Texture Analyser, an application that facilitates bread texture digital image analysis and performs a fast and non-destructive assessment. The software generated a list of parameters unique to each bread texture, or ‘digital texture-fingerprints’. Additionally, the software produced an image where a colourmap technique was applied to visualise the connected regions by area size. The ‘fingerprint’ serves as a benchmark for monitoring baking process stability and product quality in production. We undertook research to examine the opportunities of using ‘digital texture-fingerprints’ and in the first case study, we applied image texture analysis to analyse the ‘fingerprints’ of the five distinctive types of bread products. In the second case study, we applied the digital image texture analysis to evaluate the artisan baguettes baked from the five sample flours. We used the digital texture-fingerprints as one of the tools to calibrate the baking performance of these flours. We found that digital image analysis can efficiently inspect bread texture, requiring much less effort and minimal running cost. We confirmed that the methodology works equally well on a diverse range of breads, where recipe-dependent distinctive textures are acquired as a result of using various ingredients and baking processes. The outcome of the study confirmed that digital image analysis is an acceptable method to inspect the bread texture. Furthermore, the methodology can be scaled up and deployed within the industry, as well as being applied to products other than breads.

Authors

Institutions

Publication Details

Journal
Applied Food Research
Published
2025-10-21
DOI
https://doi.org/10.1016/j.afres.2025.101447
Citations
1
Primary Topic
Spectroscopy and Chemometric Analyses
Type
article
Field-Weighted Citation Impact
0.75
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Digital image analysis to assess the texture of bread products

Kate S. Howell, Michael Ruderman, R. Appels
1 citations
Applied Food Research
Spectroscopy and Chemometric Analyses
0.75
article

Digital image analysis to assess the texture of bread products

Kate S. Howell, Michael Ruderman, R. Appels
article en
1 citations

Abstract

• MATLAB-based Bread Texture Analyser quantifies bread texture non-destructively. • Digital ‘texture-fingerprints’ objectively assess bread quality and uniformity. • Applied to five breads, capturing unique porosity, density, and cell distribution. • Digital analysis validated against sensory evaluation for perceived bread quality. • The quick, scalable, and cost-efficient method to monitor bread production. Texture is a key characteristic of bread, offering insights into its quality, ingredients, and baking methods. Digital image texture analysis involves computer analysis of the bread image taken with a digital camera or smartphone. We developed the Bread Texture Analyser, an application that facilitates bread texture digital image analysis and performs a fast and non-destructive assessment. The software generated a list of parameters unique to each bread texture, or ‘digital texture-fingerprints’. Additionally, the software produced an image where a colourmap technique was applied to visualise the connected regions by area size. The ‘fingerprint’ serves as a benchmark for monitoring baking process stability and product quality in production. We undertook research to examine the opportunities of using ‘digital texture-fingerprints’ and in the first case study, we applied image texture analysis to analyse the ‘fingerprints’ of the five distinctive types of bread products. In the second case study, we applied the digital image texture analysis to evaluate the artisan baguettes baked from the five sample flours. We used the digital texture-fingerprints as one of the tools to calibrate the baking performance of these flours. We found that digital image analysis can efficiently inspect bread texture, requiring much less effort and minimal running cost. We confirmed that the methodology works equally well on a diverse range of breads, where recipe-dependent distinctive textures are acquired as a result of using various ingredients and baking processes. The outcome of the study confirmed that digital image analysis is an acceptable method to inspect the bread texture. Furthermore, the methodology can be scaled up and deployed within the industry, as well as being applied to products other than breads.

Applied Food ResearchVol. 5(2)
Ecosystem Sciences (AU)
Openalex Percentile: Top 25%
Spectroscopy and Chemometric Analyses
0.75
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.