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 spectroscopy-a potentially useful method for rapid determination of fat and protein content in homogenized diets.

OBJECTIVE: To investigate the potential of near-infrared reflectance (NIR) spectroscopy as a rapid and non-destructive method to determine total fat and protein in mixed, homogenized and freeze-dried human diets. DESIGN: 29 students collected duplicate portions of their diet for four consecutive days. In addition, a detailed food diary was kept. The duplicate portions were analysed for total protein and fat content both by traditional chemical analysis (Kjeldahl and Folch methods) and through the recently developed NIR spectroscopy method. In addition, traditional computerized estimation of nutrient composition was performed. RESULTS: Plotting of the NIR-predicted fat content against the chemically analysed fat content gave a correlation coefficient of 0. 99. Plotting of the NIR-predicted protein content against the Kjeldahl-analysed protein gave a correlation coefficient of 0.81. CONCLUSION: NIR-spectroscopy seems to be able to determine fat content in mixed, homogenized diets to a high degree of accuracy. In surveys involving duplicate portion sampling this will save time and money. The prediction accuracy for protein was less convincing, but acceptable depending on the need for accurate individual data. SPONSORSHIP: Norwegian Food Research Institute, Institute for Nutrition Research at the University of Oslo and the Research Society of the Norwegian Edible Fat Producers and the food company Mills DA. European Journal of Clinical Nutrition (2000) 54, 20-23

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.

Combined effects of chilling rate, low voltage electrical stimulation and freezing on sensory properties of bovine M. longissimus dorsi.

The single and combined effects of low voltage electrical stimulation (LVES), chilling rate after slaughter, and freezing/thawing during ageing on the tenderness of bovine M. longissimus dorsi were studied. Of 27 young bulls, 14 were exposed to LVES (90 V, 32 s, 15 Hz) immediately after stunning. Four different treatments regarding chilling rate and freezing/thawing were randomly assigned to each of the 27 young bulls. Samples were aged for 7 days at 4°C and analyzed after freezing and thawing. The experimental design allowed direct comparison of different treatment effects and an assessment whether these were additive or not. As expected chilling rate after slaughter had highly significant effects on final tenderness, based on results from a trained sensory panel and Warner Bratzler (WB) shear press analysis. The effect of LVES on final tenderness was not statistically significant. The introduction of a freezing/thawing step during ageing did not significantly affect tenderness. The effects of individual treatments depended on the tenderness level and were relatively larger at high WB shear press values (7-8 kg/cm(2)) than at low values (4-5 kg/cm(2)). The results indicate that the treatment effects were not additive, but that the treatments are alternatives to tenderize beef.

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 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.