Kjell Ivar Hildrum

On-line NIR analysis of fat, water and protein in industrial scale ground meat batches

Fat, water and protein contents in industrial scale meat batches were determined on-line by near infrared (NIR) reflectance spectroscopy. The NIR instrument was mounted at the outlet of a large meat grinder, and the measurements were performed in an industrial environment. Beef and pork samples, with chemical compositions of 7-26% fat, 58-75% water and 15-21% protein, were processed with hole diameters of 13mm in the grinder plate. Calibrations were made both for a combined set of beef and pork samples, and for separate sets of beef and pork samples. Validations were either done by full cross validation of the calibration set, or by bias corrected prediction of a test set. Prediction errors for the two sample sets, expressed as root mean square errors of cross validation or standard error of prediction, were in the ranges 0.82-1.49% fat, 0.94-1.33% water and 0.35-0.70% protein, depending of sample set and species of animal. The presented application is an improvement to the existing manual meat standardisation procedure, and has been implemented for regular use in a Norwegian meat manufacturing plant.

Prediction of beef quality attributes from early post mortem near infrared reflectance spectra

Abstract The potential of predicting beef quality attributes after ageing from early post mortem near infrared (NIR) reflectance spectroscopy (1100–2500 nm) has been studied. Altogether, 127 hot boned, Longissimus dorsi muscles from both bulls and cows were investigated in two separate studies. 36 of these carcasses were low voltage electrically stimulated. NIR recordings were obtained 2–30 h post mortem on fresh, sliced loin, while the quality assessments were performed after 2 or 7 days ageing at 4°C on frozen/thawed beef. Spectral changes during rigor mortis were not related to the ageing potential of the individual loin samples. Predicting final tenderness from NIR spectra recorded at different post mortem times yielded predictive models. However, the multivariate correlation coefficients of the models were relatively low, for example, Warner–Bratzler (WB) shear press measurements ranged from 0.47 to 0.55. Making separate prediction models based on genders yielded models for WB shear press with correlation coefficients up to 0.68. Prediction from sensory tenderness gave prediction models with lower correlation coefficients. Intramuscular fat content in intact meat was predicted with correlation coefficients of 0.78-0.85, and prediction errors (RMSEP) of 1.2–1.4%. The results obtained in this study do not support that early post mortem NIR spectroscopy can be used as precise predictor of final tenderness.

Prediction of sensory characteristics of beef by near-infrared spectroscopy

Sensory hardness, tenderness and juiciness of M. Longissimus dorsi muscles from 10 beef carcasses at three ageing stages were predicted by near-infrared (NIR) spectroscopic analysis in the reflection (NIRR) and transmission modes (NIRT) during 14 days ageing at 2°C. Predicting the sensory variables hardness and tenderness from NIRR measurements using principal component regression (PCR), yielded correlation coefficients in the range 0·80-0·90. The root mean square errors of prediction for the predictions of hardness and tenderness were in the range 0·5-0·7, given in sensory assessment units. Juiciness was not well predicted. Prediction of sensory variables from NIRT measurements did not give satisfactory results. Including samples from all carcasses, cows and young bulls in the models resulted in good predictions from NIRR measurements of frozen and thawed samples. However, the best prediction results were generally obtained from separate calibrations of the samples from the bulls. The potential of NIR spectroscopy in the prediction of sensory variables in whole meat needs to be further investigated on a larger number of samples with different breeds, animals and process treatments included.

Application of proteomics to understand the molecular mechanisms behind meat quality.

The proteome is expressed from the genome, influenced by environmental and processing conditions, and can be seen as the molecular link between the genome and the functional quality characteristics of the meat. In contrast to traditional biochemical methods where one protein is studied at a time, several hundred proteins can be studied simultaneously. Proteomics is a promising and powerful tool in meat science and this is reflected by the increasing number of studies emerging in the literature using proteomics as the key tool to unleash the molecular mechanisms behind different genetic backgrounds or processing techniques of meat. Thus understanding the variations and different components of the proteome with regard to a certain meat quality or process parameter will lead to knowledge that can be used in optimising the conversion of muscles to meat. At present, there has been focus on development of techniques and mapping of proteomes according to genotypes and muscle types. In the future, focus should be more towards understanding and finding markers for meat quality traits. This review will focus on the methods used in the published proteome analyses of meat, with emphasis on the challenges related to statistical analysis of proteome data, and on the different topics of meat science that are investigated.

On-line prediction of chemical composition of semi-frozen ground beef by non-invasive NIR spectroscopy.

The chemical composition of industrial scale batches of frozen beef was measured on-line during grinding by near infrared (NIR) reflectance spectroscopy. The MM55E filter based non-contact NIR instrument was mounted at the outlet of a meat grinder, and the fat, moisture and protein contents determined from the average of each filter reading throughout the grinding of the batch. The filters were selected from full spectra measurements to be as insensitive to water crystallization as possible. For on-line calibration and prediction, 55 beef batches of 400-800 kg in the range of 7.66-22.91% fat, 59.36-71.48% moisture, and 17.04-20.76% protein, were ground through 4 or 13 mm hole plates. The regression results, presented as root mean square error of cross validation (RMSECV) were between 0.48 and 1.11% for fat, 0.43 and 0.97% for moisture and 0.41 and 0.47% for protein.

Near infrared reflectance spectroscopy in the prediction of sensory properties of beef

Near infrared (NIR) spectroscopy in the prediction of sensory hardness, tenderness and juiciness of bovine M. Longissimus dorsi muscles has been studied. Principal component regressions (PCR) of sensory variables from NIR reflectance measurements on frozen/thawed beef of 120 heat treated samples yielded multivariate correlation coefficients of cross-validation of 0.74, 0.70 and 0.61 for hardness, tenderness and juiciness, respectively. The corresponding correlation coefficients for NIR measurements of fresh (non-frozen) samples were approximately 0.1 units lower for all sensory variables. Predicting Warner Bratzler (WB) shear press values from NIR measurements gave a correlation coefficient similar to that for prediction of sensory hardness. The univariate correlation coefficient between sensory hardness and WB shear press values was 0.90.

Muscle stretching techniques for improving meat tenderness

Abstract Methods for stretching or restraining pre-rigor single muscles or muscles in a carcass have been given increasing attention due to their ability to reliably improve tenderness and reduce the variation in tenderness of meat. The Tenderstretch method with aitch bone suspension of carcass sides has lately been successfully implemented in the beef industry in several countries. Tendercut, which implies cutting bones and connective tissue in the mid-loin and round/sirloin junction of carcass sides, is a promising method for increasing tension and tenderness of the muscles. For individual hot-boned muscles, the Pi-Vac packaging system with elastic film tubes is an efficient method for reducing contraction of these muscles. Combining stretching or restraining methods with other tenderising techniques like slow chilling or electrical stimulation usually yield little additional benefits in tenderness.

Influence of beef carcass stretching and chilling rate on tenderness of m. longissimus dorsi.

Sides of 31 non-stimulated carcasses of young bulls were subjected to the muscle stretching methods Tenderstretch (TS) by pelvic bone suspension or Tendercut (TC) with two skeletal cuts or served as controls by traditional Achilles tendon suspension. The sides were chilled at fast and medium rates, resulting in temperatures of 4-5 and 9°C in the m. longissimus dorsi (LD) at 10 h post mortem. The LDs were examined for sarcomere length, Warner-Bratzler peak shear force and sensory properties after 8 days of ageing at 4°C. At the fast chilling rate, TS and TC increased sarcomere lengths, reduced shear force and improved sensory tenderness of the LDs compared to the controls (P<0.05). At the medium chilling rate, sarcomere lengths increased (P<0.05), but no significant differences were found in shear force or sensory tenderness (P>0.05) of the muscles due to stretching. However, the medium chilling rate was efficient in producing tender LDs without applying muscle stretching methods. TS and TC are feasible alternatives for improving overall tenderness and reducing variation in tenderness of beef LD at cold shortening chilling conditions.

On-line, proximate analysis of ground beef directly at a meat grinder outlet.

The fat, moisture and protein contents of ground beef were determined on-line by a diffuse reflectance near infrared (NIR) spectroscopy instrument at the outlet of a meat grinder. Beef samples in the range of 6.2-21.7% fat, 59.6-72.9% moisture and 18.1-20.7% protein were studied. Calibrations from samples ground with hole diameters of 4, 8, 13 or 19 mm in the grinder plate were validated. In addition, calibrations of combinations of these samples from the different hole diameters were validated. Prediction errors, expressed as root mean square error of cross validation of the beef samples, were 0.73-1.50% for fat, 0.75-1.33% for moisture and 0.23-0.32% for protein, depending on the hole diameter of the grinder plate. Calibrations from samples ground with the smallest hole diameters gave lowest prediction errors. The present prediction error results are only slightly higher compared to reported prediction error results using conventional at- and off-line NIR instruments. It is concluded that the on-line NIR prediction results were acceptable for samples ground with grinder plates of 4, 8 or 13 mm hole diameter.

Prediction and classification of tenderness in beef from non-invasive diode array detected NIR spectra

NIR absorbance spectra of 48 beef samples were recorded 2, 9 and 21 days post mortem in the wavelength range 950–1700 nm with a Zeiss MCS 511 instrument equipped with diode array detector. These spectra were used to predict tenderness of the meat samples when Warner–Bratzler (WB) shear force was used as the reference method. Two types of prediction models were made. The models were either based on NIR spectra alone or NIR spectra in combination with information about post slaughter treatments. Prediction models from NIR spectra alone gave correlation coefficients in the range 0.52–0.83, but when variables for post slaughter treatments were included in the models the correlation coefficients were in the range 0.71–0.85. The additional variables had no effect on the prediction results when tenderness was predicted at the same time as NIR spectra were acquired, but improvements were found when tenderness was forecast later than the spectral acquisition. Based on these prediction models the beef samples were classified into two or three tenderness groups. When the beef samples were classified into two groups, 73–98% of the samples were correctly classified, while there were 63–75% correct classified samples when they were allocated into three groups.