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Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos

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Developing an alternative classification method for predicting ham composition using linear measurements from the

X Wei¹, B Bohrer², B Uttaro³

¹Lacombe Research and Development Centre, Agriculture and Agri-Food Canada, Lacombe, AB T4L 1W1, Canada; University of Guelph, Guelph, ON N1G 2W1, Canada.

|June 10, 2023

Summary

This summary is machine-generated.

Digital image analysis of pork hams accurately predicts lean and fat percentages using linear measurements. This method can classify hams for commercial processing, offering practical applications for pork quality assessment.

Keywords:

Classification Composition DXA Ham Linear measurements

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Area of Science:

Animal Science
Agricultural Engineering
Food Science

Background:

Accurate assessment of lean and fat content in pork is crucial for grading and processing.
Traditional methods for determining meat composition can be time-consuming or require specialized equipment.

Purpose of the Study:

To develop and validate a digital image analysis method for predicting lean and fat percentages in bone-in pork hams.
To establish a classification system for identifying extreme lean or fat hams based on image analysis predictions.

Main Methods:

Digital image analysis of ham cross-sections was employed to measure specific lean muscle and subcutaneous fat locations.
Linear measurements from two fat locations were used in stepwise regression to predict dual-energy X-ray (DXA) fat and lean percentages.
A classification system was developed using prediction equations to identify hams at the 10th percentile thresholds for DXA fat and lean percentages.

Main Results:

Linear measurements predicted DXA fat or lean percentages with a prediction accuracy (R²) of 0.7.
The classification system successfully identified extreme lean (< 60.2%) and fat (> 32.0%) hams at the 10th percentile threshold.
Adjusting the classification threshold to the 30th percentile improved fat ham prediction accuracy by 60% while decreasing lean ham accuracy by 18%.

Conclusions:

Digital image analysis of ham cross-sections provides a reliable method for predicting pork composition.
The developed classification system has potential for conversion into a practical tool for the commercial pork industry.
This approach offers a non-destructive and efficient means for assessing pork quality and facilitating commercial processing decisions.