Meat Speciation by near Infrared Reflectance Spectroscopy on Dry Extract

1997 ◽  
Vol 5 (4) ◽  
pp. 195-208 ◽  
Author(s):  
Kari Thyholt ◽  
Ulf Geir Indahl ◽  
Kjell Ivar Hildrum ◽  
Marit Risberg Ellekjær ◽  
Tomas Isaksson
Keyword(s):  
Near Infrared ◽  
Screening Method ◽  
Poultry Meat ◽  
Rapid Screening ◽  
Prediction Errors ◽  
Near Infrared Reflectance ◽  
Dry Extract ◽  
Model Study ◽  
Minced Beef ◽  
Minced Meat

Professionals and consumers want to control the origin of meat, while producers can profit by mixing minced meat from low cost species into high value meat. Near infrared (NIR) spectroscopy on dry extracts was studied as a method for speciation of minced beef, pork, mutton and mechanically recovered poultry meat. This was divided into three levels: (1) classifying into “labelled species or not?”, (2) into one of four possible species or (3) predicting the degree of substitution of minced beef with other species in a model study. Minced meat from 68 carcasses from four animal species ranging from low to high fat contents were centrifuged. Extracted meat juices (0.5 mL) were dried on glass microfibre filters and the reflectance measured. Using the range 780–2500 nm and different methods of discriminant analysis by cross-validation, the 68 samples were classified 90–100% correctly according to speciation at levels 1) and 2). Level 3) included a Simplex design model study using 350 mixtures of the 68 centrifuged juices. Predicting the relative amounts from each species in samples containing 50–100% beef juice gave cross-validated prediction errors of 12% (w/w) for beef, 8% for pork and 7% for mutton and poultry. The results showed that NIR on dry extract might be used as a rapid screening method for classification and optimised to detect species adulteration.

scholarly journals Using Chemometrics and Near-Infrared Reflectance Spectroscopy for Discrimination of Poultry Meat and Prediction of Cholesterol Content

Krmiva ◽  
2018 ◽  
Vol 60 (1) ◽  
pp. 9-16
Author(s):  
Robert Gąsior ◽  
Katarzyna Połtowicz
Keyword(s):  
Classification Accuracy ◽  
Near Infrared ◽  
Predictive Ability ◽  
Cholesterol Content ◽  
Reflectance Spectroscopy ◽  
Poultry Meat ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

Four calibrations were made for cholesterol content in poultry meat (breasts and legs from chickens, cockerels, capons, and breasts and legs from geese). Standard uncertainties expressed as SECV (%, relative) for chickens, cockerels and capons were 9.2 for breasts and 7.8 for legs. These values for geese were 8.4 and 9.0, respectively. The discriminant method with the highest predictive ability, based on residuals RMSX Residents, was used to classify the samples. Classification accuracy values were good and ranged, on average, from 96.8% to 98%. The NIRS calibrations on cholesterol content in the breast and leg meat of chickens, capons, cockerels, as well as in the breast and leg meat of geese, are suitable for rapid routine analyses to use in practice.

scholarly journals Prediction of Texture of Raw Poultry Meat by Visible and Near–Infrared Reflectance Spetroscopy

2013 ◽  
Vol 64 (5) ◽  
Author(s):  
Herlina Abdul Rahim ◽  
Rashidah Ghazali ◽  
Shafishuhaza Sahlan ◽  
Mashitah Shikh Maidin
Keyword(s):  
Prediction Accuracy ◽  
Broiler Chickens ◽  
Near Infrared ◽  
Low Cost ◽  
Nir Spectroscopy ◽  
Food Composition ◽  
Poultry Meat ◽  
Near Infrared Reflectance ◽  
Village Chickens ◽  
Raw Poultry

Near-infrared (NIR) spectroscopy is a non-destructive, low cost and fast measurement technique that is required to improve the meat texture quality prediction. In this research, visible/NIR spectroscopy has been used for the prediction of raw chicken meat texture from different types of chickens by referring to the reference data obtained from destructive measurement using a Volodkevich Bite Jaws texture analyser. The Partial Least Squares analysis shows that the prediction accuracy is higher for the Az-Zain village organic chickens (85–95%) than for village chickens (42–68%) and broiler chickens (42–44%). The high prediction accuracy and low absorbance spectra of Az-Zain village organic chickens compared to broiler and village chickens could be correlated with the food composition of the chicken meal.

scholarly journals Diagnostic ability of confocal near-infrared reflectance fundus imaging to detect retrograde microcystic maculopathy from chiasm compression. A comparative study with OCT findings

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253323
Author(s):  
Mário L. R. Monteiro ◽  
Rafael M. Sousa ◽  
Rafael B. Araújo ◽  
Daniel Ferraz ◽  
Mohammad A. Sadiq ◽  
...  
Keyword(s):  
Optic Atrophy ◽  
Near Infrared ◽  
Screening Method ◽  
Fundus Photography ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Chiasmal Compression ◽  
Fundus Imaging ◽  
Cell Layers ◽  
Level Of Agreement

Purpose To evaluate the ability of confocal near-infrared reflectance (NIR) to diagnose retrograde microcystic maculopathy (RMM) in eyes with temporal visual field (VF) loss and optic atrophy from chiasmal compression. To compare NIR findings with optical coherence tomography (OCT) findings in the same group of patients. Methods Thirty-four eyes (26 patients) with temporal VF loss from chiasmal compression and 41 healthy eyes (22 controls) underwent NIR fundus photography, and macular OCT scanning. VF loss was estimated and retinal layers thickness were measured on OCT. Two examiners blinded to the diagnosis randomly examined NIR images for the presence of hyporeflective abnormality (HA) and OCT scans for the presence of microcystic macular abnormalities (MMA). The total average and hemi-macular HA area and number of microcysts were determined. The groups were compared and the level of agreement was estimated. Results The OCT-measured macular retinal nerve fiber and ganglion cell layers were thinner and the inner nuclear layer was thicker in patients compared to controls. HA and MMA were detected in 22 and 12 patient eyes, respectively, and in 0 controls (p<0.001, both comparisons). HA was significantly more frequent than MMA in patients with optic atrophy, and agreement between HA and MMA (both total and hemi-macular) was fair (kappa range: 0.24–0.29). The mean HA area was significantly greater in the nasal than temporal hemi-macula. A re-analysis of the 14 eyes with discrepant findings allowed to confirm RMM in 20 eyes (20/34) indicating that OCT detected RMM in 12 and missed it in 8 eyes. On the other hand, NIR correctly detected 18 out of 20 eyes, overcalled 4 and missed 2. Conclusions RMM is a frequent finding in eyes with severe VF loss from long-standing chiasmal compression. NIR photography appears to be more sensitive than OCT for detecting RMM and may be useful as screening method for its presence.

scholarly journals Comparison of multivariate calibration methods for prediction of feeding value by near infrared reflectance spectroscopy.

1990 ◽  
Vol 38 (3B) ◽  
pp. 449-460
Author(s):  
P.W. Goedhart
Keyword(s):  
Near Infrared ◽  
Multivariate Calibration ◽  
In Vitro Digestibility ◽  
Calibration Model ◽  
Specific Information ◽  
Prediction Errors ◽  
Near Infrared Reflectance ◽  
Calibration Methods ◽  
Feeding Value

The spectrum of absorbance of near infrared spectroscopy (NIR) measurements contains indirect, non-specific information about the feeding value of the feed sample and can be used to predict this value. A linear calibration model was estimated from experimental data and a model was used to predict unknown in vitro values with measured spectra in future samples. Multicolinearity in the NIR measurements occurs frequently. Inclusion of all absorbances in a calibration model leads to complications and large prediction errors. To overcome multicollinearity several methods were proposed. The methods are described and applied to data in which in vitro digestibility of organic matter of maize for cattle was predicted by means of absorbances measured at 351 wavelengths. Comparison of methods showed that for these data Partial Least Squares was the best method. Multiplicative scatter correction of the spectra prior to estimation gave better predictions for all methods. (Abstract retrieved from CAB Abstracts by CABI’s permission)

Near-Infrared Reflectance Analysis of Iron Ores

1987 ◽  
Vol 41 (6) ◽  
pp. 1039-1042 ◽  
Author(s):  
Peter M. Fredericks ◽  
Alan Tattersall ◽  
Ralph Donaldson
Keyword(s):  
Near Infrared ◽  
Infrared Region ◽  
Prediction Errors ◽  
Iron Ores ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Element Analysis ◽  
Prediction Test ◽  
Reflectance Analysis ◽  
Rapid Characterization

Near-infrared reflectance analysis (NIRA) has been applied to the rapid characterization of mineral samples. A suite of 82 West Australian iron ores was used to carry out the work. Approximately half the samples, chosen randomly, were used as a calibration set, while the remaining samples formed a prediction test set. Correlations were sought for eight of the significant practical properties of the samples. These properties were major element analysis, combined water, relative density, and goethite concentration. Reasonably close correlations were obtained for most of the properties, except silicon, although the estimated prediction errors were worse than those obtained with conventional methods. Nevertheless, the speed and simplicity of the NIRA method should make it of use in many quality-control applications. The relatively poor result for silicon is probably due to the transparency of quartz in the near-infrared region.

Liquid Analysis by Dry-Extract Near-Infrared Reflectance on Fiberglass

1990 ◽  
Vol 44 (6) ◽  
pp. 979-986 ◽  
Author(s):  
G. Alfaro ◽  
M. Meurens ◽  
G. S. Birth
Keyword(s):  
Near Infrared ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Dry Extract

Application of Near Infrared Reflectance Spectroscopy to Compositional Analysis of Commercial Pig Feed Mixes

1987 ◽  
Vol 70 (3) ◽  
pp. 420-423
Author(s):  
Shih-Ling Y Chen ◽  
Ali Hsu ◽  
Mane-Lane Lee
Keyword(s):  
Near Infrared ◽  
Screening Method ◽  
Compositional Analysis ◽  
Correlation Coefficients ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy ◽  
Pig Feed

Abstract Official samples of commercial pig feed mixes taken for routine inspection were analyzed by near infrared reflectance spectroscopy (NIRS). Separate calibrations were established for pre-starting pig, starting pig, growing pig, finishing pig, and lactating sow feeds. Mean correlation coefficients and standard errors of calibration, respectively, obtained by comparing NIRS values with conventional chemical analysis values, were as follows: calibration set (about 35 samples per category)-moisture 0.87,0.44%; protein 0.90,0.75%; fiber 0.8b, 0.49%; analytical set (about 15 samples per category)—moisture 0.73, 0.42%; protein 0.90, 0.72%; fiber 0.83, 0.40%. Mean coefficients of variation for NIRS were moisture 3.9%, protein 4.2%, and fiber 14.8%; those for conventional analyses were moisture 2.7%, protein 1.1%, and fiber 11.7%. The results indicated that for moisture, protein, and fiber determinations in commercial pig feed mix products with various and unknown formulations, successful rapid NIRS analysis could be achieved by using a filter-type spectrometer and advanced mathematical data treatments. Among 246 samples inspected, protein content in 70% of them exceeded by 1-8% the legal minimum protein level. Application of NIRS provides an accurate and prompt on-line technique for feed mix analysis during the formulation process and can be beneficial to the operation of feed mills. For official inspection, the technique might serve as a preliminary screening method.

The estimation of chemical, physical and sensory parameters of homogenized fresh pork eye muscle by near infrared reflectance spectroscopy (NIRS)

2002 ◽  
Vol 2002 ◽  
pp. 187-187
Author(s):  
R. S. Park ◽  
R. E. Agnew ◽  
E. Beattie ◽  
B. W. Moss
Keyword(s):  
Near Infrared ◽  
Eating Quality ◽  
Prediction Errors ◽  
Near Infrared Reflectance ◽  
Large White ◽  
Eye Muscle ◽  
Sensory Parameters ◽  
Pork Samples ◽  
Imf Content

This study was based on research to identify particular pig breeds, which produce high quality eating pork. Duroc in particular is thought to improve meat quality of progeny when crossed with Large White/Landrace (LW/Lr) hybrid dams by altering the intra-muscular fat (IMF) content of the lean, which is positively related to eating quality (McGloughlin et al., 1988). The extraction of IMF is slow and laborious with harmful solvents involved. Eichinger and Beck (1991) have successfully used NIRS to measure IMF ranging from 1-11% in 39 beef carcases. Ground beef and pork samples have also been predicted for fat, water and protein by NIRS, with prediction errors of 0.82-1.49%, 0.94-1.33% and 0.35-0.70% respectively (Togersen et al.,1999). Instrumental measurements of pork are accepted as indicators of tenderness. Sensory variables such as juiciness, tenderness and flavour are important characteristics for the consumer. Therefore the objective of this study was to explore the potential of NIRS to estimate the chemical, physical and sensory parameters of homogenized fresh pork eye muscle.

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