Machine Vision Requires Fewer Repeat Measurements than Colorimeters for Precise Seafood Colour Measurement
The colour of seafood flesh is often not homogenous, hence measurement of colour requires repeat measurements to obtain a representative average. The aim of this study was to determine the optimal number of repeat colour measurements required for three different devices [machine vision (digital imag...
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MDPI AG
2024
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Online Access: | https://doi.org/10.3390/foods13071110 https://doaj.org/article/93a502e22ccb48889bba6d074541ab60 |
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ftdoajarticles:oai:doaj.org/article:93a502e22ccb48889bba6d074541ab60 2024-09-15T17:56:28+00:00 Machine Vision Requires Fewer Repeat Measurements than Colorimeters for Precise Seafood Colour Measurement Kieren Watkins Melindee Hastie Minh Ha Graham Hepworth Robyn Warner 2024-04-01T00:00:00Z https://doi.org/10.3390/foods13071110 https://doaj.org/article/93a502e22ccb48889bba6d074541ab60 EN eng MDPI AG https://www.mdpi.com/2304-8158/13/7/1110 https://doaj.org/toc/2304-8158 doi:10.3390/foods13071110 2304-8158 https://doaj.org/article/93a502e22ccb48889bba6d074541ab60 Foods, Vol 13, Iss 7, p 1110 (2024) salmon rockling prawns fish Nix Minolta Chemical technology TP1-1185 article 2024 ftdoajarticles https://doi.org/10.3390/foods13071110 2024-08-05T17:49:37Z The colour of seafood flesh is often not homogenous, hence measurement of colour requires repeat measurements to obtain a representative average. The aim of this study was to determine the optimal number of repeat colour measurements required for three different devices [machine vision (digital image using camera, and computer processing); Nix Pro; Minolta CR400 colorimeter] when measuring three species of seafood (Atlantic salmon, Salmo salar , n = 8; rockling, Genypterus tigerinus , n = 8; banana prawns, Penaeus merguiensis , n = 105) for raw and cooked samples. Two methods of analysis for number of repeat measurements required were compared. Method 1 was based on minimising the standard error of the mean and Method 2 was based on minimising the difference in colour over repeat measurements. Across species, using Method 1, machine vision required an average of four repeat measurements, whereas Nix Pro and Minolta required 13 and 12, respectively. For Method 2, machine vision required an average of one repeat measurement compared to nine for Nix Pro and Minolta. Machine vision required fewer repeat measurements due to its lower residual variance: 0.51 compared to 3.2 and 2.5 for Nix Pro and Minolta, respectively. In conclusion, machine vision requires fewer repeat measurements than colorimeters to precisely measure the colour of salmon, prawns, and rockling. Article in Journal/Newspaper Atlantic salmon Salmo salar Directory of Open Access Journals: DOAJ Articles Foods 13 7 1110 |
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Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
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ftdoajarticles |
language |
English |
topic |
salmon rockling prawns fish Nix Minolta Chemical technology TP1-1185 |
spellingShingle |
salmon rockling prawns fish Nix Minolta Chemical technology TP1-1185 Kieren Watkins Melindee Hastie Minh Ha Graham Hepworth Robyn Warner Machine Vision Requires Fewer Repeat Measurements than Colorimeters for Precise Seafood Colour Measurement |
topic_facet |
salmon rockling prawns fish Nix Minolta Chemical technology TP1-1185 |
description |
The colour of seafood flesh is often not homogenous, hence measurement of colour requires repeat measurements to obtain a representative average. The aim of this study was to determine the optimal number of repeat colour measurements required for three different devices [machine vision (digital image using camera, and computer processing); Nix Pro; Minolta CR400 colorimeter] when measuring three species of seafood (Atlantic salmon, Salmo salar , n = 8; rockling, Genypterus tigerinus , n = 8; banana prawns, Penaeus merguiensis , n = 105) for raw and cooked samples. Two methods of analysis for number of repeat measurements required were compared. Method 1 was based on minimising the standard error of the mean and Method 2 was based on minimising the difference in colour over repeat measurements. Across species, using Method 1, machine vision required an average of four repeat measurements, whereas Nix Pro and Minolta required 13 and 12, respectively. For Method 2, machine vision required an average of one repeat measurement compared to nine for Nix Pro and Minolta. Machine vision required fewer repeat measurements due to its lower residual variance: 0.51 compared to 3.2 and 2.5 for Nix Pro and Minolta, respectively. In conclusion, machine vision requires fewer repeat measurements than colorimeters to precisely measure the colour of salmon, prawns, and rockling. |
format |
Article in Journal/Newspaper |
author |
Kieren Watkins Melindee Hastie Minh Ha Graham Hepworth Robyn Warner |
author_facet |
Kieren Watkins Melindee Hastie Minh Ha Graham Hepworth Robyn Warner |
author_sort |
Kieren Watkins |
title |
Machine Vision Requires Fewer Repeat Measurements than Colorimeters for Precise Seafood Colour Measurement |
title_short |
Machine Vision Requires Fewer Repeat Measurements than Colorimeters for Precise Seafood Colour Measurement |
title_full |
Machine Vision Requires Fewer Repeat Measurements than Colorimeters for Precise Seafood Colour Measurement |
title_fullStr |
Machine Vision Requires Fewer Repeat Measurements than Colorimeters for Precise Seafood Colour Measurement |
title_full_unstemmed |
Machine Vision Requires Fewer Repeat Measurements than Colorimeters for Precise Seafood Colour Measurement |
title_sort |
machine vision requires fewer repeat measurements than colorimeters for precise seafood colour measurement |
publisher |
MDPI AG |
publishDate |
2024 |
url |
https://doi.org/10.3390/foods13071110 https://doaj.org/article/93a502e22ccb48889bba6d074541ab60 |
genre |
Atlantic salmon Salmo salar |
genre_facet |
Atlantic salmon Salmo salar |
op_source |
Foods, Vol 13, Iss 7, p 1110 (2024) |
op_relation |
https://www.mdpi.com/2304-8158/13/7/1110 https://doaj.org/toc/2304-8158 doi:10.3390/foods13071110 2304-8158 https://doaj.org/article/93a502e22ccb48889bba6d074541ab60 |
op_doi |
https://doi.org/10.3390/foods13071110 |
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Foods |
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13 |
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7 |
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1110 |
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1810432667353612288 |