Development and validation of a classification model for boar taint detection in pork fat samples

Area of Science:

• Food Science
• Analytical Chemistry
• Animal Science

Background:

• Boar taint is an undesirable odor in pork from intact male pigs.
• Accurate prediction of boar taint is crucial for consumer acceptance and meat industry economics.

Purpose of the Study:

• To comprehensively profile volatile organic compounds (VOCs) in pork neck fat for boar taint prediction.
• To identify specific VOCs associated with boar taint beyond known compounds like androstenone and skatole.
• To develop and validate a statistical classification model for boar taint prediction.

Main Methods:

• Untargeted volatolomic analysis using headspace solid-phase microextraction coupled with gas chromatography-gas chromatography-time-of-flight mass spectrometry (HS-SPME-GC×GC-TOFMS) on 129 pork samples.
• Selection of odor-positive samples by combining human sensory evaluation with ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) for skatole and androstenone quantification.
• Development of a statistical model using 70 samples and validation on the remaining 59 samples.

Main Results:

• Identification of androstenone, skatole, and indole as key boar taint compounds.
• Discovery of 10 additional discriminant volatile organic compounds through untargeted analysis.
• Development of a classification model that successfully predicted boar taint in pork samples, with 7 samples ultimately classified as tainted.

Conclusions:

• A complete volatile organic compound profile of pork neck fat can be established for boar taint prediction.
• Beyond established markers, novel volatile compounds contribute significantly to boar taint characteristics.
• The developed statistical model offers a robust method for classifying boar taint in pork, enhancing quality control.