Fernando Mendoza

Colour calibration of a laboratory computer vision system for quality evaluation of pre-sliced hams.

Due to the high variability and complex colour distribution in meats and meat products, the colour signal calibration of any computer vision system used for colour quality evaluations, represents an essential condition for objective and consistent analyses. This paper compares two methods for CIE colour characterization using a computer vision system (CVS) based on digital photography; namely the polynomial transform procedure and the transform proposed by the sRGB standard. Also, it presents a procedure for evaluating the colour appearance and presence of pores and fat-connective tissue on pre-sliced hams made from pork, turkey and chicken. Our results showed high precision, in colour matching, for device characterization when the polynomial transform was used to match the CIE tristimulus values in comparison with the sRGB standard approach as indicated by their ΔE(ab)(∗) values. The [3×20] polynomial transfer matrix yielded a modelling accuracy averaging below 2.2 ΔE(ab)(∗) units. Using the sRGB transform, high variability was appreciated among the computed ΔE(ab)(∗) (8.8±4.2). The calibrated laboratory CVS, implemented with a low-cost digital camera, exhibited reproducible colour signals in a wide range of colours capable of pinpointing regions-of-interest and allowed the extraction of quantitative information from the overall ham slice surface with high accuracy. The extracted colour and morphological features showed potential for characterizing the appearance of ham slice surfaces. CVS is a tool that can objectively specify colour and appearance properties of non-uniformly coloured commercial ham slices.

Analysis and classification of commercial ham slice images using directional fractal dimension features

This paper presents a novel and non-destructive approach to the appearance characterization and classification of commercial pork, turkey and chicken ham slices. Ham slice images were modelled using directional fractal (DF(0°;45°;90°;135°)) dimensions and a minimum distance classifier was adopted to perform the classification task. Also, the role of different colour spaces and the resolution level of the images on DF analysis were investigated. This approach was applied to 480 wafer thin ham slices from four types of hams (120 slices per type): i.e., pork (cooked and smoked), turkey (smoked) and chicken (roasted). DF features were extracted from digitalized intensity images in greyscale, and R, G, B, L(∗), a(∗), b(∗), H, S, and V colour components for three image resolution levels (100%, 50%, and 25%). Simulation results show that in spite of the complexity and high variability in colour and texture appearance, the modelling of ham slice images with DF dimensions allows the capture of differentiating textural features between the four commercial ham types. Independent DF features entail better discrimination than that using the average of four directions. However, DF dimensions reveal a high sensitivity to colour channel, orientation and image resolution for the fractal analysis. The classification accuracy using six DF dimension features (a(90°)(∗),a(135°)(∗),H(0°),H(45°),S(0°),H(90°)) was 93.9% for training data and 82.2% for testing data.

Classification of pre-sliced pork and turkey ham qualities based on image colour and textural features and their relationships with consumer responses.

Images of three qualities of pre-sliced pork and Turkey hams were evaluated for colour and textural features to characterize and classify them, and to model the ham appearance grading and preference responses of a group of consumers. A total of 26 colour features and 40 textural features were extracted for analysis. Using Mahalanobis distance and feature inter-correlation analyses, two best colour [mean of S (saturation in HSV colour space), std. deviation of b*, which indicates blue to yellow in L*a*b* colour space] and three textural features [entropy of b*, contrast of H (hue of HSV colour space), entropy of R (red of RGB colour space)] for pork, and three colour (mean of R, mean of H, std. deviation of a*, which indicates green to red in L*a*b* colour space) and two textural features [contrast of B, contrast of L* (luminance or lightness in L*a*b* colour space)] for Turkey hams were selected as features with the highest discriminant power. High classification performances were reached for both types of hams (>99.5% for pork and >90.5% for Turkey) using the best selected features or combinations of them. In spite of the poor/fair agreement among ham consumers as determined by Kappa analysis (Kappa-value<0.4) for sensory grading (surface colour, colour uniformity, bitonality, texture appearance and acceptability), a dichotomous logistic regression model using the best image features was able to explain the variability of consumers responses for all sensorial attributes with accuracies higher than 74.1% for pork hams and 83.3% for Turkey hams.

Supervised neural network classification of pre-sliced cooked pork ham images using quaternionic singular values.

The quaternionic singular value decomposition is a technique to decompose a quaternion matrix (representation of a colour image) into quaternion singular vector and singular value component matrices exposing useful properties. The objective of this study was to use a small portion of uncorrelated singular values, as robust features for the classification of sliced pork ham images, using a supervised artificial neural network classifier. Images were acquired from four qualities of sliced cooked pork ham typically consumed in Ireland (90 slices per quality), having similar appearances. Mahalanobis distances and Pearson product moment correlations were used for feature selection. Six highly discriminating features were used as input to train the neural network. An adaptive feedforward multilayer perceptron classifier was employed to obtain a suitable mapping from the input dataset. The overall correct classification performance for the training, validation and test set were 90.3%, 94.4%, and 86.1%, respectively. The results confirm that the classification performance was satisfactory. Extracting the most informative features led to the recognition of a set of different but visually quite similar textural patterns based on quaternionic singular values.

Identification of important image features for pork and turkey ham classification using colour and wavelet texture features and genetic selection

A method to discriminate between various grades of pork and turkey ham was developed using colour and wavelet texture features. Image analysis methods originally developed for predicting the palatability of beef were applied to rapidly identify the ham grade. With high quality digital images of 50-94 slices per ham it was possible to identify the greyscale that best expressed the differences between the various ham grades. The best 10 discriminating image features were then found with a genetic algorithm. Using the best 10 image features, simple linear discriminant analysis models produced 100% correct classifications for both pork and turkey on both calibration and validation sets.

Characterization of fat-connective tissue size distribution in pre-sliced pork hams using multifractal analysis.

Fat-connective tissue size distribution (FSD) in hams is a fundamental physical property for its quality assessment. FSD is related to the sensory properties such as texture, taste, quality of raw meat and visual appearance. In this paper we present a tool to carry out the multifractal analysis (MFA) of two-dimensional binary images of pre-sliced pork hams through the calculation of the f(α)-spectra, Rényi (D(q)) dimensions, and associated statistical regressions and parameters. The application is presented for the structural characterization of FSD in three qualities of pork hams (high yield, medium yield and premium quality hams) using image sections of 512×512pixels(2) with a spatial resolution of 0.102mm/pixel. MFA was carried out using the method of moments in the optimized box size range of 32-512pixels for all the ham images using powers of 2, and estimating the probability distribution for moments ranging from -10<q<10 in steps of 0.5. The experimental results suggest that MFA has a discriminating effect among the three types of ham using the maximum entropy (H(max)(∗)) and correlation dimension D(2). This investigation revealed the usefulness of the MFA dimensions as quantitative descriptors of texture analysis and pattern distributions of FSD in pre-sliced ham images.

Multifractal Characterization of Apple Pore and Ham Fat-Connective Tissue Size Distributions Using Image Analysis

Pore size distribution (PSD) in apple tissue and fat-connective size distribution (FSD) in hams are the fundamental physical properties analyzed in assessing their quality. In apple tissue, PSD is related to the mass-transport phenomena characteristics and complexity of oxygen (O2) and carbon dioxide (CO2) diffusivity, and in the case of hams, FSD is related to sensory properties such as texture, taste, quality of raw meat, and visual appearance. In both food products, accurate representation of these microstructural properties is needed for an objective quality characterization and prediction during apple preservation and ham formulation.