Shi, J, Zhang, F, Wu, S et al. (5 more authors) (2019) Noise-free microbial colony counting method based on hyperspectral features of agar plates. Food Chemistry, 274. pp. 925-932. ISSN 0308-8146
Abstract
A noise-free bacterial colony counting method identifying noise (i.e., sausage, bacon, and millet fragments) with similar colors or shapes to those of colonies was developed for food quality assessment. First, spectral features corresponding to colony cluster regions and background regions (agar medium and food fragments) were extracted after collection of hyperspectral images. A cluster-segmenting calibration model that could identify colony clusters and background regions was developed. Second, spectral features of colony centers and borders were extracted, and a colony-separating calibration model that could separate single colonies from clusters (multiple colonies contacting each other) was developed. Third, each pixel of an agar plate hyperspectral image was identified using established calibration models, enabling the colonies on the agar plate to be counted successfully (R² = 0.9998). The results demonstrated that the proposed method could identify the noises caused by food fragments with similar colors or shapes to those of colonies.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Elsevier Ltd. All rights reserved. This is an author produced version of an article published in Food Chemistry. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Colony counting; Hyperspectral imaging technology; Noise-free; Spectral feature; Chemometrics |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Food Science and Nutrition (Leeds) > FSN Colloids and Food Processing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 Oct 2021 13:54 |
Last Modified: | 25 Jun 2023 22:47 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.foodchem.2018.09.058 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179319 |
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