Evaluation and predictive modeling of shelf life of minced beef stored in high-oxygen modified atmosphere packaging at different temperatures (original) (raw)

Color is one of the most important metrics of foodstuffs quality. It gives an indication of freshness, ingredient composition as well as about the presence or absence of falsification. Most often, the color is estimated visually, and thus, the evaluation is subjective. By automating the color analysis a wide application for this method could be found. The aim of this research is to study the principles of color analysis as applied to the task of evaluating the freshness of meat products using modern machine vision systems. From a scientific point of view, the color of meat depends on the proportion of myoglobin and its derivatives. It's the main pigment that characterizes the freshness of meat. Further color of meat can change due to oxidation of myoglobin during storage. Myoglobin exists in three forms. There are oxygenated form, oxidized form and form without oxygen. The meat color changes not only due to the conversion of one form into another. The content of amino acids and ammonia are another characteristics and constant signs of meat products spoilage. The paper presents the results of meat color computer simulation based on data on the content of various forms of myoglobin in different proportions. The spectral characteristic of the light source used to illuminate the meat sample is taken into account. Also the experimental studies were conducted using samples of beef. As a result the correlations between said biochemical indicators of the quality and color of the meat obtained with the help of machine vision system were found.

The objective of this study was to determine whether an electronic nose could be used for measuring and modelling sensory quality changes in a pizza topping product during storage. A method involving a minimum of sample preparation in combination with a short sampling cycle mimicking an on-line situation was developed. A multivariate data analysis strategy involving principal component analysis (PCA)

The applicability of an electronic nose for the quality control of modified atmosphere (MA) packaged broiler chicken cuts was evaluated in different temperature regimes. The electronic nose results were compared with those obtained by microbiological, sensory and headspace GC analyses. The electronic nose could clearly distinguish broiler chicken packages with deteriorated quality from fresh packages either earlier than or at

A beef strip loins (Musculus longissimus lumborum) freshness determination method utilizing electronic nose (e-nose) was investigated in this paper. Fresh beef strip loins samples were stored at 4°C continuously for 10 days. Total viable count (TVC) index, total volatile basic nitrogen (TVB-N) index, and e-nose responses to beef strip loins samples were measured every day. TVC and TVB-N index rose with the increase of storage time. Principal component analysis (PCA) only partially discriminated beef samples under different storage days. Stochastic resonance (SR) signal-to-noise ratio (SNR) spectrum discriminated all beef samples successfully. Beef strip loins freshness discrimination model was developed using SR SNR maximums (SNRmax) linear fitting regression. The proposed method forecasted beef freshness with high accuracy. It is holds promise in meat freshness determination applications.