NIR Spectroscopy for Discriminating and Predicting the Sensory Profile of Dry-Cured Beef "Cecina"

For Protected Geographical Indication (PGI)-labeled products, such as the dry-cured beef meat "cecina de León", a sensory analysis is compulsory. However, this is a complex and time-consuming process. This study explores the viability of using near infrared spectroscopy (NIRS) together wit...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-12, Vol.20 (23), p.6892
Main Authors: Revilla, Isabel, Vivar-Quintana, Ana M, González-Martín, María Inmaculada, Hernández-Jiménez, Miriam, Martínez-Martín, Iván, Hernández-Ramos, Pedro
Format: Article
Language:English
Subjects:
Animals
artificial neural networks
Beef
Cattle
chemometry
Discriminant analysis
dry meat
Food Analysis
Humidity
Infrared spectroscopy
Meat
Meat - analysis
Meat products
Multilayer perceptrons
Near infrared radiation
near infrared spectra
Neural Networks, Computer
Neurons
organoleptic parameters
prediction
Principal components analysis
Regression coefficients
Spectroscopy, Near-Infrared
Spectrum analysis
Variance analysis
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Summary:For Protected Geographical Indication (PGI)-labeled products, such as the dry-cured beef meat "cecina de León", a sensory analysis is compulsory. However, this is a complex and time-consuming process. This study explores the viability of using near infrared spectroscopy (NIRS) together with artificial neural networks (ANN) for predicting sensory attributes. Spectra of 50 samples of cecina were recorded and 451 reflectance data were obtained. A feedforward multilayer perceptron ANN with 451 neurons in the input layer, a number of neurons varying between 1 and 30 in the hidden layer, and a single neuron in the output layer were optimized for each sensory parameter. The regression coefficient R squared (RSQ > 0.8 except for odor intensity) and mean squared error of prediction (MSEP) values obtained when comparing predicted and reference values showed that it is possible to predict accurately 23 out of 24 sensory parameters. Although only 3 sensory parameters showed significant differences between PGI and non-PGI samples, the optimized ANN architecture applied to NIR spectra achieved the correct classification of the 100% of the samples while the residual mean squares method (RMS-X) allowed 100% of non-PGI samples to be distinguished.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20236892