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Good-quality cooked ham is a proud mainstay of French charcuterie. However, predicting the quality of cooked ham is a major challenge for the industry, as the main inprocessing defect of ham muscle is a PSE-like destructuration that can only be picked up after deboning, which means big economic losses at the slicer. We are steadily deciphering the mechanisms that trigger the defect, but detection upstream of process lines—or even from the farm gate—remains a challenge for the commodity chain. Spectral fingerprints of blood plasma are part of a battery of approaches widely used in medicine for biomarker discovery. These suitably practicable non-invasive approaches were used here to define markers that point to the PSE-like destructuration defect.

We developed a method for predicting the PSE-like defect in cooked ham based a combination of chemometrics and spectral fingerprinting of pig plasma. Blood samples were taken in vivo on 120 pigs. At 24h post mortem, the hams were screened against a reference read-off scale then split into two groups on the basis of whether or not they showed signs of the destructuration defect. Spectral fingerprints were acquired by MALDI-TOF (Matrix-Assisted Laser Desorption/Ionization–Time-Of-Flight) mass spectrometry and ATR-FTIR (Attenuated Total Reflectance–Fourier Transform InfraRed) spectroscopy on pig plasma samples, then analyzed using neural networks. The coupled methods proved to offer complementary predictive capabilities—combining the two spectral fingerprints by selecting the 30 most discriminatory peaks in the two approaches correctly predicted 100% of the data.

Full-scale deployment of this approach in real industry conditions would make it possible to run a predictive test of cooked ham quality without losing valuable carcass, as it is based on a simple blood sample. Ultimately, it would become possible to manage product technological quality upstream of slaughter, adapt products to technological processing routes based on the quality of the raw material, or even to select animals based on their fingerprinted traits. This research opens up new prospects for methodizing food quality and new applications surrounding food-animal selection.