Automatic measurement of pores and porosity in pork ham and their correlations with processing time, water content and texture

Technological innovations or advancement in detecting frozen and thawed meat quality: A review

Frozen storage is one of the main storage methods for meat products. Freezing and thawing processes are important factors affecting the quality of stored foods. Deterioration of texture, denaturation of protein, decline of water holding capacity etc. are among the major quality issues during freezing that must be addressed. A number of advanced technologies are now available to detect the quality changes that can occur during freezing and/or thawing. This paper presents an overview of the techniques commonly used for the detection of meat product quality; these include: advanced microscopy, molecular sensory science and technology, nuclear magnetic resonance, hyperspectral technology, near infrared spectroscopy, Raman spectroscopy etc. These direct and indirect measurement techniques can characterize the quality of meat product from many different angles. The objective of this review is to provide an in-depth understanding of possible quality changes in meat products during freezing and thawing cycle so as to improve the quality of frozen and thawed meat products in industrial practice.

The use of computer-assisted image analysis in the evaluation of the effect of management systems on changes in the color, chemical composition and texture of m. longissimus dorsi in pigs

The effect of management systems on selected physical properties and chemical composition of m. longissimus dorsi was studied in pigs. Muscle texture parameters were determined by computer-assisted image analysis, and the color of muscle samples was evaluated using a spectrophotometer. Highly significant correlations were observed between chemical composition and selected texture variables in the analyzed images. Chemical composition was not correlated with color or spectral distribution. Subject to the applied classification methods and groups of variables included in the classification model, the experimental groups were identified correctly in 35-95%. No significant differences in the chemical composition of m. longissimus dorsi were observed between experimental groups. Significant differences were noted in color lightness (L*) and redness (a*).

Machine vision system: a tool for quality inspection of food and agricultural products

Quality inspection of food and agricultural produce are difficult and labor intensive. Simultaneously, with increased expectations for food products of high quality and safety standards, the need for accurate, fast and objective quality determination of these characteristics in food products continues to grow. However, these operations generally in India are manual which is costly as well as unreliable because human decision in identifying quality factors such as appearance, flavor, nutrient, texture, etc., is inconsistent, subjective and slow. Machine vision provides one alternative for an automated, non-destructive and cost-effective technique to accomplish these requirements. This inspection approach based on image analysis and processing has found a variety of different applications in the food industry. Considerable research has highlighted its potential for the inspection and grading of fruits and vegetables, grain quality and characteristic examination and quality evaluation of other food products like bakery products, pizza, cheese, and noodles etc. The objective of this paper is to provide in depth introduction of machine vision system, its components and recent work reported on food and agricultural produce.

Textural properties and their correlation to cell structure in porous food materials

This paper focuses on understanding the role of structural parameters and starch crystallization on the toughness of cake samples. Accurate mechanical measurements were performed to obtain toughness values, and these were related to structural parameters obtained from image analyses. Three-dimensional skeletons of food samples were generated by using X-ray tomography technique. The structural parameters (cell diameter, cell wall thickness, thickness to radius ratio (t/R), fragmentation index) were obtained after processing of the images with CTan software. The basic hypothesis of the paper is to show that the structural parameter t/R is a determinant for predicting toughness, which is a critical indicator of freshness. Freshness in cakes and other baked products is a leading factor in consumer perception. For this purpose three different cake formulations were stored at 37 and 50 °C. Cycling from these temperatures to lower storage temperatures of 25 and 4 °C was done to accelerate the starch retrogradation rate. Experimental results indicated that there was a strong interrelationship between morphological structure and the mechanical properties with regression coefficients of 0.68 and 0.95. Starch retrogradation, which was followed by X-ray diffractometry, was found to be directly proportional to toughness values, where the percent relative crystallinity increased with storage temperature.

New computational solution to quantify synthetic material porosity from optical microscopic images

This paper presents a new computational solution to quantify the porosity of synthetic materials from optical microscopic images. The solution is based on an artificial neuronal network of the multilayer perceptron type and a backpropagation algorithm is used for training. To evaluate this new solution, 40 sample images of a synthetic material were analysed and the quality of the results was confirmed by human visual analysis. In addition, these results were compared with ones obtained with a commonly used commercial system confirming their superior quality and the shorter time needed. The effect of images with noise was also studied and the new solution showed itself to be more reliable. The training phase of the new solution was analysed confirming that it can be performed in a very easy and straightforward manner. Thus, the new solution demonstrated that it is a valid and adequate option for researchers, engineers, specialists and other professionals to quantify the porosity of materials from microscopic images in an automatic, fast, efficient and reliable manner.

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.

Investigating the effects of ingredient levels on physical quality properties of cooked hamburger patties using response surface methodology and image processing technology

A three-factor central composite design was adopted to determine the interactive effects of fat (15-30%), water (10-20%) and textured soy protein (3-9%) content on the shrinkage, fat loss and moisture loss of hamburger patties after cooking. Image processing was used to estimate the shrinkage of hamburger patties. Textured soy protein (TSP) content was found to be the most important factor for minimizing fat and moisture loss. Both fat and water content were found to be significantly effective (P<0.05) in the model for shrinkage and moisture loss in linear form. The changes in shrinkage due to fat, water and TSP content were also in linear form. The model for fat loss was in linear and quadratic form, whereas the model for moisture loss was in full quadratic form. The models for shrinkage, fat loss and moisture loss had the R-square values of 0.954, 0.969 and 0.964, respectively.

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.

Adaptive watershed segmentation of binary particle image

Oversegmentation is a tough problem in the morphological watershed segmentation of irregular-shaped binary particles, which is usually caused by spurious minima in the inverse distance transform. The position relationship between two objects is clear, according to the value of overlap parameter defined in the paper, and an adaptive algorithm is presented to depress oversegmentation by building the criterion to merge the spurious local minima. Some particle images are provided to validate the performance of the proposed method.

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.