Toward the Digitalization of Sensory Analysis of Black Table Olives Using Electronic Nose, Tongue, and Eye

Trained sensory panels are the reference for evaluating odor, taste, and appearance, but routine use close to production is limited. This proof-of-concept study evaluated digital support for the sensory analysis of Californian-style black table olives using five electronic sensing blocks. The same 15 cans from three sensory-characterized batches were analyzed using the portable S3+ electronic nose and OECA electronic eye, together with the Integrated Electronic Sensory Analysis Platform (PIASE) at CICYTEX, which incorporates the ASTREE electronic tongue to provide the gustatory domain and, as reference systems, the HERACLES electronic nose and IRIS electronic eye. Can-level PLS-DA balanced accuracies were 1.000 for HERACLES, 0.933 for ASTREE, 0.867 for OECA, 0.867 for IRIS, and 0.533 for S3+. Because each condition was one production batch, these values measure discrimination among three batches, not validation of a general defect classifier. S3+ mean response followed the sensory ordering of off-odor intensity and OECA between-fruit color heterogeneity followed the panel ordering of color uniformity; both are descriptive three-batch concordances. OECA-calibrated color agreed with a CR-400 colorimeter within ΔE00 ≈ 1.2. Mid-level fusion did not outperform the best individual blocks. The results support complementary, domain-specific instruments to assist trained sensory panels rather than replace them.