Classification of dry-cured hams according to the maturation time using near infrared spectra and artificial neural networks

• Published in: Meat science Vol. 96; no. 1; pp. 14 - 20
• Main Authors: Prevolnik, M., Andronikov, D., Žlender, B., Font-i-Furnols, M., Novič, M., Škorjanc, D., Čandek-Potokar, M.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.01.2014
• Subjects: Animals; Artificial neural networks; Classification; Desiccation; Dry-cured ham; Food Handling - methods; ham; Meat Products - classification; Models, Biological; Muscle, Skeletal - chemistry; Near infrared spectroscopy; neural networks; Neural Networks (Computer); Reproducibility of Results; sampling; spectral analysis; Spectroscopy, Near-Infrared; Swine; wavelengths; United States; Near infrared spectroscopy; Dry-cured ham; Classification; Artificial neural networks
• ISSN: 0309-1740; 1873-4138
• DOI: 10.1016/j.meatsci.2013.06.013

An attempt to classify dry-cured hams according to the maturation time on the basis of near infrared (NIR) spectra was studied. The study comprised 128 samples of biceps femoris (BF) muscle from dry-cured hams matured for 10 (n=32), 12 (n=32), 14 (n=32) or 16months (n=32). Samples were minced and scanned in the wavelength range from 400 to 2500nm using spectrometer NIR System model 6500 (Silver Spring, MD, USA). Spectral data were used for i) splitting of samples into the training and test set using 2D Kohonen artificial neural networks (ANN) and for ii) construction of classification models using counter-propagation ANN (CP-ANN). Different models were tested, and the one selected was based on the lowest percentage of misclassified test samples (external validation). Overall correctness of the classification was 79.7%, which demonstrates practical relevance of using NIR spectroscopy and ANN for dry-cured ham processing control. •NIR spectral data and ANN were used to predict the maturity of dry-cured hams.•By varying net size and number of epochs different (n=24) models were tested.•Net 12×12 with 200 epochs produced highest (79.7%) accuracy of prediction.•Results represent practical relevance for control purposes in dry ham processing.•Results apply for Kraški pršut, for other products further verification is needed.