Abstract

This study evaluated the use of an E-nose using a piercing-assisted volatile extraction as a practical and non-destructive tool for distinguishing between three muscle types (biceps femoris—BF; semitendinosus—ST; and semimebranosus—SM) in 30-month ripened dry-cured Bísaro hams (n = 23). The muscles were analyzed for volatile organic compounds (VOC) using gas chromatography-mass spectrometry (GC-MS) and for signal profiles obtained from an E-nose system composed of metal oxide (SnO2) sensors. Sensor signals were standardized using Z-score normalization prior to chemometric modeling. Linear discriminant analysis (LDA) was used to evaluate the capability of the MOS-based E-nose to differentiate the VOC profiles of Bísaro ham across its main muscle types. The model trained on Z-score-standardized sensor signals achieved classification accuracies of 94.3% and 80.0% for the training and external test sets, respectively, demonstrating good predictive performance and robustness. When compared with the VOC-based LDA model (94.4% and 78.6% for the training and test sets, respectively), the E-nose showed comparable classification performance and slightly higher predictive capability in the external validation set. The first two discriminant functions explained 88.01% and 11.99% of the discriminant variance, respectively, indicating that most of the discrimination occurred along a single dominant axis. To chemically interpret the sensor-based discrimination, multiple linear regression models were established between the LDA scores and VOC concentrations. The first discriminant function was significantly associated with compounds related to lipid oxidation and aroma development, particularly 2-pentylfuran, butanoic acid, hexanoic acid, hexanal, and benzaldehyde (R2 = 0.617; p < 0.001), whereas the second discriminant function showed a weaker but significant relationship with hexanal, 3-methylbutanal, butanoic acid, and hexanoic acid (R2 = 0.202; p = 0.006). These findings demonstrate that the E-nose is capable of capturing meaningful chemical information associated with muscle-specific volatile profiles and can provide a rapid, non-destructive, and cost-effective alternative for the characterization and classification of dry-cured Bísaro ham.

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