Batch accuracy of on-line fat determination

A review on the relation between grinding process and quality of ground meat

This review is providing an overview of the actual and past research in the field of ground meat. The forces that are acting in the meat grinder are well understood. The higher the forces that are acting on the meat while grinding, the stronger the disintegration of the meat cells after the process. These forces can be calculated as energy transfer in meat grinders using specific mechanical energy (SME). The amount of non-intact cells (ANIC) can be used to describe the extent of disintegrated cells. Different methods are available to rate the quality of ground meat, which is mainly influenced by the raw material and processing. Over the past decades of industrialization, the landscape of ground meat production has changed. However, the effects of the process adjustments on the quality of ground meat are not yet sufficiently described in the literature.

Estimation of Chemical Composition of Pork Trimmings by Use of Density Measurement-Hydrostatic Method

This study aims to determine the possibility of using density measurements by using the hydrostatic method for the estimation of the chemical composition of pork. The research material included 75 pork samples obtained during industrial butchering and cutting. The density measurements were performed using the hydrostatic method based on Archimedes’ principle. The meat samples were minced, and the content of the basic chemical components in them was determined. The usefulness of density measurement using the hydrostatic method in chemical composition estimation was determined by analyzing the correlation for the entire population, and after grouping the samples with a low (<15%), medium (15–25%), and high (>25%) fat content. High (in absolute value) coefficients of correlation between the meat density and the content of water (0.96), protein (0.94), and fat (−0.96) were found based on the results obtained. In order to achieve higher accuracy of the estimation, the applied regression equations should be adjusted to the presumed fat content in the meat. The standard error of prediction (SEP) values ranged from 0.67% to 2.82%, which indicates that the calculated estimation accuracy may be sufficient for proper planning of the production. Higher SEP values were found in fat content estimation and the lowest ones were found in protein content estimation.

A Potential Use of 3-D Scanning to Evaluate the Chemical Composition of Pork Meat

The aim of this study was to determine the possibility of 3-D scanning method in chemical composition evaluation of pork meat. The sampling material comprised neck muscles (1000 g each) obtained from 20 pork carcasses. The volumetric estimation process of the elements was conducted on the basis of point cloud collected using 3-D scanner. Knowing the weight of neck muscles, their density was calculated which was subsequently correlated with the content of basic chemical components of the pork meat (water, protein and fat content, determined by standard methods). The significant correlations (P ≤ 0.05) between meat density and water (r = 0.5213), protein (r = 0.5887), and fat (r = -0.6601) content were obtained. Based on the obtained results it seems likely to employ the 3-D scanning method to compute the meat chemical composition.

PRACTICAL APPLICATION

The use of the 3-D scanning method in industrial practice will allow to evaluate the chemical composition of meat in online mode on a dressing and fabrication line and in a rapid, noninvasive manner. The control of the raw material using the 3-D scanning will allow to make visual assessment more objective and will enable optimal standardization of meat batches prior to processing stage. It will ensure not only the repeatability of product quality characteristics, but also optimal use of raw material-lean and fat meat. The knowledge of chemical composition of meat is essential due to legal requirements associated with mandatory nutrition facts labels on food products.

Meat quality assessment using biophysical methods related to meat structure

This paper overviews the biophysical methods developed to gain access to meat structure information. The meat industry needs reliable meat quality information throughout the production process in order to guarantee high-quality meat products for consumers. Fast and non-invasive sensors will shortly be deployed, based on the development of biophysical methods for assessing meat structure. Reliable meat quality information (tenderness, flavour, juiciness, colour) can be provided by a number of different meat structure assessment either by means of mechanical (i.e., Warner-Bratzler shear force), optical (colour measurements, fluorescence) electrical probing or using ultrasonic measurements, electromagnetic waves, NMR, NIR, and so on. These measurements are often used to construct meat structure images that are fusioned and then processed via multi-image analysis, which needs appropriate processing methods. Quality traits related to mechanical properties are often better assessed by methods that take into account the natural anisotropy of meat due to its relatively linear myofibrillar structure. Biophysical methods of assessment can either measure meat component properties directly, or calculate them indirectly by using obvious correlations between one or several biophysical measurements and meat component properties. Taking these calculations and modelling the main relevant biophysical properties involved can help to improve our understanding of meat properties and thus of eating quality.

Control of lightness and firmness of cold and reheated frankfurter-type sausages using different spectroscopic methods applied to raw batter

Muscle types and collagen, fat, and muscle protein minus collagen were varied in cooked frankfurter-type sausages made from beef and pork meat as well as pork backfat. The content of collagen was fixed at preset levels with pork rind. The amount of total muscle protein in the sausages varied between 5.9% and 11.9% and the fat between 16.1% and 22.1%. The collagen content varied between 1.3% and 4%. Spectroscopic measurements (near-infrared reflectance spectra 1100 to 2500 nm; front-face autofluorescence emission spectra 360 to 640 nm) on raw batters were used to predict the amounts of total muscle protein minus collagen, collagen, myoglobin, and fat (biochemical components), L* values from a Minolta chromameter, and firmness of cold (22 degrees C) and reheated sausages (60 degrees C). Lightness of sausages was most accurately determined from the batter data with a Minolta chromameter or the autofluorescence measurement system. Firmness of cold sausages could be described by the amounts of biochemical components plus the type of muscle used in the sausage. The 2nd-best approach was to use the shape of the near-infrared spectra to determine firmness. This was possible as the shape of near-infrared spectra depended on total protein content, and total protein content largely determined the firmness of cold sausages. If the sausages were reheated to 60 degrees C, near-infrared spectroscopy alone determined firmness of the sausages with a lower accuracy than a combined solution of fluorescence and near-infrared spectroscopy. The 2 spectroscopic techniques could thus be used to estimate the amount of biochemical components in sausages. Once these components were known, firmness could be calculated from a model between the amounts of biochemical components and firmness. For reheated sausages, as opposed to cold ones, there was a need to differentiate between collagen and the other muscle proteins in order to determine firmness. This was optimally achieved by using both autofluorescence and near-infrared spectroscopy.