scholarly journals Feasibility of Application of Near Infrared Reflectance (NIR) Spectroscopy for the Prediction of the Chemical Composition of Traditional Sausages

2021 ◽  
Vol 11 (23) ◽  
pp. 11282
Author(s):  
Eleni Kasapidou ◽  
Vasileios Papadopoulos ◽  
Paraskevi Mitlianga
Keyword(s):  
Chemical Composition ◽  
Near Infrared ◽  
External Validation ◽  
Correlation Coefficients ◽  
Nir Spectroscopy ◽  
Standard Errors ◽  
Calibration Model ◽  
Good Precision ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance

In the present study, the potential of application of near infrared reflectance (NIR) spectroscopy for the estimation of the chemical composition of traditional (village style) sausages was examined. The chemical composition (moisture, ash, protein and, fat) was determined by standard reference methods. For the development of the calibration model, 39 samples of traditional fresh sausages were used, while for external validation, 10 samples of sausages were used. The correlation coefficients of calibration (RMSEC) and standard errors (SEC) were 0.92 and 1.58 (moisture), 0.77 and 0.18 (ash), 0.87 and 0.89 (protein) and 0.93 and 1.73 (fat). The cross-validation correlation coefficients (RMSECV) and standard errors (SECV) were 0.86 and 2.13 (moisture), 0.56 and 0.26 (ash), 0.78 and 1.17 (protein), and 0.88 and 2.17 (fat). The results of the calibration model showed that NIR spectroscopy can be applied to estimate with very good precision the fat content of traditional village-style sausages, whereas moisture and protein content can be estimated with good accuracy. The external validation confirmed the ability of NIR spectroscopy to predict the chemical composition of sausages.

Near Infrared Reflectance Spectroscopic Determination of Protein Nitrogen in Plant Tissue

1984 ◽  
Vol 67 (3) ◽  
pp. 506-509
Author(s):  
Robert A Isaac ◽  
William C Johnson
Keyword(s):  
Plant Tissue ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Leaf Tissue ◽  
Nir Spectroscopy ◽  
Nondestructive Method ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Protein Nitrogen

Abstract A rapid, nondestructive method is described for the determination of protein nitrogen in plant tissue, using near infrared reflectance (NIR) spectroscopy. Procedures for instrument calibration are discussed. Comparisons between Kjeldahl nitrogen and NIR nitrogen are made for corn leaf tissue from Georgia and Indiana. Multiple correlation coefficients for other plant tissues such as peanuts, soybean, wheat, pecan, bermuda grass, and bent grass are also shown.

Prediction of crude protein content in field peas using near infrared reflectance spectroscopy

2006 ◽  
Vol 86 (1) ◽  
pp. 157-159 ◽  
Author(s):  
G. C. Arganosa ◽  
T. D. Warkentin ◽  
V. J. Racz ◽  
S. Blade ◽  
C. Phillips ◽  
...  
Keyword(s):  
Crude Protein ◽  
Near Infrared ◽  
Spectroscopic Method ◽  
Correlation Coefficients ◽  
Reflectance Spectroscopy ◽  
Field Pea ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Infrared Reflectance Spectroscopy

A rapid, near-infrared spectroscopic method to predict the crude protein contents of 72 field pea lines grown in Saskatchewan, both whole seeds and ground samples, was established. Correlation coefficients between the laboratory and predicted values were 0.938 and 0.952 for whole seed and ground seed, respectively. Both methods developed are adequate to support our field pea breeding programme. Key words: Field pea, near-infrared reflectance spectroscopy, crude protein

Monitoring the performance of a broad-based calibration for measuring the nutritive value of two independent populations of pasture using near infrared reflectance (NIR) spectroscopy

1991 ◽  
Vol 31 (2) ◽  
pp. 205 ◽  
Author(s):  
KF Smith ◽  
PC Flinn
Keyword(s):  
Nutritive Value ◽  
Near Infrared ◽  
Linear Regression Analysis ◽  
Spectral Characteristics ◽  
Nir Spectroscopy ◽  
Cost Effective ◽  
Multiple Linear Regression Analysis ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance

Near infrared reflectance (NIR) spectroscopy is a rapid and cost-effective method for the measurement of organic constituents of agricultural products. NIR is widely used to measure feed quality around the world and is gaining acceptance in Australia. This study describes the development of an NIR calibration to measure crude protein (CP), predicted in vivo dry matter digestibility (IVDMD) and neutral detergent fibre (NDF) in temperate pasture species grown in south-western Victoria. A subset of 116 samples was selected on the basis of spectral characteristics from 461 pasture samples grown in 1987-89. Several grass and legume species were present in the population. Stepwise multiple linear regression analysis was used on the 116 samples to develop calibration equations with standard errors of 0.8,2.3 and 2.2% for CP, NDF and IVDMD, respectively. When these equations were tested on 2 independent pasture populations, a significant bias existed between NIR and reference values for 2 constituents in each population, indicating that the calibration samples did not adequately represent the new populations for these constituents. The results also showed that the H statistic alone was inadequate as an indicator of equation performance. It was confirmed that it was possible to develop a broad-based calibration to measure accurately the nutritive value of closed populations of temperate pasture species. For the resulting equations to be used for analysis of other populations, however, they must be monitored by comparing reference and NIR analyses on a small number of samples to check for the presence of bias or a significant increase in unexplained error.

Short Communication: Near infrared reflectance spectroscopy accurately predicts the digestible energy content of barley for pigs

2011 ◽  
Vol 91 (2) ◽  
pp. 301-304 ◽  
Author(s):  
R. T. Zijlstra ◽  
M. L. Swift ◽  
L. F. Wang ◽  
T. A. Scott ◽  
M. J. Edney
Keyword(s):  
Near Infrared ◽  
Energy Content ◽  
External Validation ◽  
Reflectance Spectroscopy ◽  
Commercial Application ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Digestible Energy ◽  
Infrared Reflectance Spectroscopy

Zijlstra, R. T., Swift, M. L., Wang, L. F., Scott, T. A. and Edney, M. J. 2011. Short Communication:Near infrared reflectance spectroscopy accurately predicts the digestible energy content of barley for pigs. Can. J. Anim. Sci. 91: 301–304. Density, chicken apparent metabolizable energy (AME), and near infrared reflectance spectroscopy (NIRS) were tested to predict the widely varying swine digestible energy (DE) content of barley. The DE content of 39 barley samples ranged from 2686 to 3163 kcal kg−1 (90% DM) in grower pigs. The R2 between DE content and density (0.14) and broiler chicken AME content (0.18 and 0.56, without and with enzyme, respectively) was low. In contrast, the coefficient of determination to predict swine DE content for ground barley samples using NIRS was respectable for external validation (R2=0.74) and internal cross validation (1-VR=0.79), but more robust calibrations should be developed for commercial application.

On-line application of visible and near infrared reflectance spectroscopy to predict physical and sensory characteristics of beef quality

2009 ◽  
Vol 2009 ◽  
pp. 135-135
Author(s):  
N Prieto ◽  
D W Ross ◽  
E A Navajas ◽  
G Nute ◽  
R I Richardson ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Reflectance Spectroscopy ◽  
Sensory Characteristics ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Beef Quality ◽  
Near Infrared Reflectance ◽  
On Line ◽  
Meat Samples

Visible and near infrared reflectance spectroscopy (Vis-NIR) has been widely used by the industry research-base for large-scale meat quality evaluation to predict the chemical composition of meat quickly and accurately. Meat tenderness is measured by means of slow and destructive methods (e.g. Warner-Bratzler shear force). Similarly, sensory analysis, using trained panellists, requires large meat samples and is a complex, expensive and time-consuming technique. Nevertheless, these characteristics are important criteria that affect consumers’ evaluation of beef quality. Vis-NIR technique provides information about the molecular bonds (chemical constituents) and tissue ultra-structure in a scanned sample and thus can indirectly predict physical or sensory parameters of meat samples. Applications of Vis-NIR spectroscopy in an abattoir for prediction of physical and sensory characteristics have been less developed than in other fields. Therefore, the aim of this study was to test the on-line Vis-NIR spectroscopy for the prediction of beef quality characteristics such as colour, instrumental texture, water holding capacity (WHC) and sensory traits, by direct application of a fibre-optic probe to the M. longissimus thoracis with no prior sample treatment.

scholarly journals Analysis of Fermented Milk Products by Near-Infrared Reflectance Spectroscopy

1996 ◽  
Vol 79 (3) ◽  
pp. 817-821 ◽  
Author(s):  
José L Rodríguez-Oxero ◽  
Marià Hermtoa
Keyword(s):  
Near Infrared ◽  
Fermented Milk ◽  
Reflectance Spectroscopy ◽  
Standard Errors ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Total Solids ◽  
Infrared Reflectance Spectroscopy ◽  
Reference Methods

Abstract Fat, protein, and total solids in fermented milk were determined by near-infrared reflectance spectroscopy (NIRS). A set of 107 samples from diverse types of fermented milk (whole, skimmed, with added flavors, and without added flavors) were used to calibrate the instrument by modified partial least-square regression. Global calibration, using all samples, was more effective than specific calibrations using fewer samples of each type. Standard errors of a calibration were 0.071 for fat, 0.075 for protein, and 0.083 for total solids. Values of the correlation coefficient square (R2) were 0.997 for fat, 0.981 for protein, and 1.000 for total solids. Calibration was validated with an independent set of 34 samples of the same types. Standard errors of validation were 0.08,0.14, and 0.25 for fat, protein, and total solids, respectively, and values of R2 for the regression of measurements by NIRS versus reference methods were 1.00 for fat and total solids and 0.94 for protein. When Standard 128 of the International Dairy Federation was used to compare NIRS results with those from reference methods, no significant differences were found (p = 0.05).

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