Maja Prevolnik

An attempt to predict pork drip loss from pH and colour measurements or near infrared spectra using artificial neural networks.

The ability to predict meat drip loss by using either near infrared spectra (SPECTRA) or different meat quality (MQ) measurements, such as pH(24), Minolta L(∗), a(∗), b(∗), along with different chemometric approach, was investigated. Back propagation (BP) and counter propagation (CP) artificial neural networks (ANN) were used and compared to PLS (partial least squares) regression. Prediction models were created either by using MQ measurements or by using NIR spectral data as independent predictive variables. The analysis consisted of 312 samples of longissimus dorsi muscle. Data were split into training and test set using 2D Kohonen map. The error of drip loss prediction was similar for ANN (2.2-2.6%) and PLS models (2.2-2.5%) and it was higher for SPECTRA (2.5-2.6%) than for MQ (2.2-2.3%) based models. Nevertheless, the SPECTRA based models gave reasonable prediction errors and due to their simplicity of data acquisition represent an acceptable alternative to classical meat quality based models.

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

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 16 months (n=32). Samples were minced and scanned in the wavelength range from 400 to 2500 nm 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.

Application of Artificial Neural Networks in Meat Production and Technology

The market of meat and meat products is growing continuously. In the sector of meat, there are many problems and challenges associated with the evaluation of meat quality at industrial level. The methods with the potential of industrial application should be accurate but also rapid, non-destructive, with no health or environment hazards, with benefits of automation and lower risk of human error. The lack of such methods represents a drawback for meat industry and the research focusing on the possible application of rapid methods is emerging. Many new promising techniques are being tested in meat science such as NIR (near infrared) and FT-IR (Fourier transformed infrared) spectroscopy, mass spectrometry, hyperand multispectral imaging techniques, machine/computer vision, biosensors, electronic noses (array of sensors), ultrasound techniques, etc. However, the enormous amount of information provided by these instruments demands an advanced data treatment approach. The artificial intelligent methods can be used for such purposes since their primary target is to distinguish objects or groups or populations. Artificial neural networks (ANN) are a well-known mathematical tool widely used and tested lately for the problems in meat production and technology. Its advantages are in the ability to handle with nonlinear data, highly correlated variables and the potential for identification of problems or classification. In particular promising applications of ANN in relation to meat sector is in carcass classification, quality control of raw material, meat processing, meat spoilage or freshness and shelf-life evaluation, detecting off-flavours, authenticity assessment, etc. In this chapter an overview of published studies dealing with the application of ANN in meat science is given. In the first part of the chapter basic concepts of artificial neural networks (ANN) are presented and described. The next part of the chapter summarizes the relevant publications on the use of ANN in case of meat production and technology issues and is divided in several paragraphs presenting the relevant research work with the most interesting applications of ANN.

An attempt to predict conformation and fatness in bulls by means of artificial neural networks using weight, age and breed composition information

The present study aimed to predict conformation and fatness grades in bulls based on data available at slaughter (carcass weight, age and breed proportions) by means of counter-propagation artificial neural networks (ANN). For chemometric analysis, 5893 bull carcasses (n=2948 and n=2945 for calibration and testing of models, respectively) were randomly selected from the initial data set (n≈27000; one abattoir, one classifier, three years period). Different ANN models were developed for conformation and fatness by varying the net size and the number of epochs. Tested net parameters did not have a notable effect on models’ quality. Respecting the tolerance of ±1 subclass between the actual and predicted value (as allowed by European Union legislation for on-spot checks), the matching between the classifier and ANN grading was 73.6 and 64.9% for conformation and fatness, respectively. Success rate of prediction was positively related to the frequency of carcasses in the class.