Fish fillet authentication by image analysis

Visual Image Analysis for a new classification method of bovine carcasses according to EU legislation criteria

In the European Community, conformation and fat cover of bovine carcasses is assessed using the SEUROP grading system. In this study we pursued the development of an application software (App) based on Visual Image Analysis, useful for SEUROP and Fat Cover grading of bovine carcasses using a smartphone. The App was trained using 500 bovine carcasses. Carcass conformation and Fat Cover classes were assessed in parallel by expert evaluators and by App. Overall, a high correspondence was found between the measurements of carcasses parameters by operators and by the App, as high as 84.2% for SEUROP and 86.4% for the Fat Cover. In the 15.8% of samples with discordant SEUROP evaluation, and in the 13.6% of samples with discordant Fat Cover evaluation, the operators' and App measurements deviated by only one class. All values also aligned with the requirements expected by the current legislation for the use of automated and/or semi-automated systems able to determine the market value of carcasses.

Evaluation of gas holes in "Queijo de Nisa" PDO cheese using computer vision

"Queijo de Nisa" is a traditional Portuguese cheese, granted with PDO label, produced with raw ewe's milk in which the aqueous extract of cardoon flower Cynara cardunculus L. is the only coagulant allowed. As in similar cheeses with no use of starter cultures or pasteurisation, the quality and food safety are depending on prevention, high hygienic standards and a proper manufacturing process. This study investigated the use of computer vision as novel method for the evaluation of gas holes in Queijo de Nisa in three different ripening dates (0, 15 and 35 days). A total of 48 samples were produced using cardoon flower from three different origins (C1, C2 and C3) and a commercial vegetable coagulant (C4). The results presented a high correlation between image-dependent attributes and physical-chemical properties during ripening time, especially within the first 15 days of ripening time, where major structural changes were observed inside the Queijo de Nisa cheese. Principal component analysis presented a strong correlation (p < 0.05) between image parameters and the physical-chemical evolution until 15 days. From 15 to 35 days, the evolution of cheeses was mainly depending on structural parameters, like G'_1 Hz and hardness. No influence was observed due to the geographical origin of cardoon flower.

A cascade network for the classification of rice grain based on single rice kernel

This paper describes the classification of four different varieties of rice grain based on four sets of features, namely morphology, colour, texture and wavelet. The classification is carried out on single rice kernel using image pre-processing steps followed by a cascade network classifier. The performance of the classifiers based on the above feature sets is also compared. It is found that morphological feature is more suitable for the classification of rice kernels, as compared to other features. The number of input features is reduced by a feature selection process using statistical analysis system (SAS) software. The classification accuracy based on selected features is compared with that of original features using different classifiers. It is found that the selected features are able to provide classification accuracy very close to the original features. The performance of the proposed cascade classifier is also tested against standard datasets from the University of California, Irvine (UCI), and the results are compared with other classifiers. The results show that the proposed classifier provides better classification accuracy as compared to other classifiers.