Prediction of Sensory Parameters of Cured Ham: A Study of the Viability of the Use of NIR Spectroscopy and Artificial Neural Networks

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 19; p. 5624
• Main Authors: Hernández-Ramos, Pedro, Vivar-Quintana, Ana María, Revilla, Isabel, González-Martín, María Inmaculada, Hernández-Jiménez, Miriam, Martínez-Martín, Iván
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2020; MDPI
• Subjects: artificial neural network (ANN); cured ham quality; Food; Hogs; near-infrared spectroscopy (NIR); Neural networks; Parameters; Quality assessment; sensory analysis; Sensory perception; Spectrum analysis; Statistical analysis
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20195624

Dry-cured ham is a high-quality product owing to its organoleptic characteristics. Sensory analysis is an essential part of assessing its quality. However, sensory assessment is a laborious process which implies the availability of a trained tasting panel. The aim of this study was the prediction of dry-ham sensory characteristics by means of an instrumental technique. To do so, an artificial neural network (ANN) model for the prediction of sensory parameters of dry-cured hams based on NIR spectral information was developed and optimized. The NIR spectra were obtained with a fiber-optic probe applied directly to the ham sample. In order to achieve this objective, the neural network was designed using 28 sensory parameters analyzed by a trained panel for sensory profile analysis as output data. A total of 91 samples of dry-cured ham matured for 24 months were analyzed. The hams corresponded to two different breeds (Iberian and Iberian x Duroc) and two different feeding systems (feeding outdoors with acorns or feeding with concentrates). The training algorithm and ANN architecture (the number of neurons in the hidden layer) used for the training were optimized. The parameters of ANN architecture analyzed have been shown to have an effect on the prediction capacity of the network. The Levenberg–Marquardt training algorithm has been shown to be the most suitable for the application of an ANN to sensory parameters