ORIGINAL PAPER
Validation of new proprietary software (r-Algo) for predicting meat
chemical composition from ultrasound images of skeletal muscles
in live animals: Pectoralis major muscles of broiler chickens
1
University of Guelph, Ontario Veterinary College, Department of Biomedical Sciences, Guelph, Ontario, N1G 2W1, Canada
2
University of Agriculture in Kraków, Department of Animal Genetics, Breeding and Ethology; 30-059 Cracow, Poland
These authors had equal contribution to this work
Publication date: 2024-04-05
Corresponding author
P. M. Bartlewski
University of Guelph, Ontario Veterinary College, Department of Biomedical Sciences, Guelph, Ontario, N1G 2W1, Canada
University of Guelph, Ontario Veterinary College, Department of Biomedical Sciences, Guelph, Ontario, N1G 2W1, Canada
KEYWORDS
TOPICS
ABSTRACT
The objective of this study was to validate a novel computerized
method of ultrasound image analysis to determine chemical composition of
pectoralis major muscles in broiler chickens. Ultrasonograms of pectoral muscles
in the longitudinal and transverse planes were obtained from 40 birds just before
slaughter. All chemical constituents of muscle samples were determined with
the validated laboratory techniques, and the results served as a benchmark for
developing the present algorithmic estimates of chicken meat composition. An inhouse
developed algorithm (r-Algo) was used to normalize the ultrasonograms
and to identify pixel intensity ranges for which linear correlations between mean
numerical pixel values and the content of various chemical constituents were
the strongest (based on the values of correlation coefficients), using a stepwise
sequestration of ultrasound bitmaps. Percentages of chemical constituents were
the dependent (accepted) variables and the results of echotextural analyses
(luminance or pixel intensity), carried out with a commercially available image
analysis software (ImageProPlus®), were the explanatory variables. The
predictive regression equations were determined in 30 randomly selected
algorithm-training experimental units, and their accuracy was tested in a subset
of 10 birds allocated to the algorithm-validation group. Significant determination
coefficients were found for all chemical constituents studied, with the accuracy
ranging from 62.70% (linoleic acid, transverse plane, pixel range of 141–142)
to 96.65% (total hypocholesterolemic acids, longitudinal plane, pixel range of
136–150). The present validation results indicate that accurate prediction of
muscle chemical composition using echotextural image analyses is feasible
after identifying specific pixel intensity ranges.
ACKNOWLEDGEMENTS
We are indebted to Zachary Silverman
and Arvin Asgharian Rezaee (work-study
and summer students in the Department of
Biomedical Sciences, Ontario Veterinary
College) for their work on the Python version
of the r-Algo computer program and desktop
app, and Dr. David J. Hobson of the Research
Innovation Office, University of Guelph,
for his invaluable guidance with the r-Algo
patenting process. Parts of the current results
were presented, in a preliminary form, during
the Summer Career Opportunities and Research
Exploration Program Symposium (Ontario
Veterinary College, University of Guelph,
Guelph, ON, Canada; 13 August 2019), the
virtual Graduate Student Research Symposium
(Ontario Veterinary College, Universi-ty of
Guelph, Guelph, ON, Canada; 25 August 2020),
and the EAAP + WAAP + Interbull Congress in
Lyon, France (26 August- 1 September, 2023).
FUNDING
Canada Foundation for Innovation; Department
of Biomedical Sciences at the University of
Guelph, ON, Canada; University of Agriculture
in Kraków, Poland; and Ontario Pork co-funded
this study.
CONFLICT OF INTEREST
The Authors declare that there is no conflict of
interest.
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