scholarly journals Use of near infrared spectroscopy for assessment of beef quality traits

2007 ◽  
Vol 6 (sup1) ◽  
pp. 421-423 ◽  
Author(s):  
M. De Marchi ◽  
P. Berzaghi ◽  
A. Boukha ◽  
M. Mirisola ◽  
L. Galol
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Quality Traits ◽  
Beef Quality

scholarly journals Guaranteeing Canadian lamb meat quality using near-infrared spectroscopy on intact rack

2018 ◽  
Vol 98 (2) ◽  
pp. 390-393 ◽  
Author(s):  
M. Juárez ◽  
A. Horcada ◽  
N. Prieto ◽  
J.C. Roberts ◽  
M.E.R. Dugan ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Meat Quality ◽  
Near Infrared Spectroscopy ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Quality Traits ◽  
Meat Quality Traits ◽  
Lamb Meat ◽  
Predicted Values

Lamb racks from commercial carcasses were scanned using near-infrared spectroscopy. The prediction accuracies (R2) for meat quality traits were assessed. Prediction accuracy ranged between 0.40 and 0.94. When predicted values were used to classify meat based on quality, 88.7%–95.2% of samples were correctly classified as quality guaranteed.

Temporal, biochemical and structural factors that influence beef quality measurement using near infrared spectroscopy

Meat Science ◽  
2009 ◽  
Vol 82 (3) ◽  
pp. 379-388 ◽  
Author(s):  
Katja Rosenvold ◽  
Elisabeth Micklander ◽  
Per Waaben Hansen ◽  
Robert Burling-Claridge ◽  
Michelle Challies ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Quality Measurement ◽  
Structural Factors ◽  
Beef Quality

scholarly journals Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

2020 ◽  
Author(s):  
Mateus Teles Vital Gonçalves ◽  
Gota Morota ◽  
Paulo Mafra de Almeida Costa ◽  
Pedro Marcus Pereira Vidigal ◽  
Marcio Henrique Pereira Barbosa ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Pearson Correlation ◽  
Nir Spectroscopy ◽  
Quality Traits ◽  
Nucleotide Polymorphisms ◽  
Feedstock Quality ◽  
Regression Methods

AbstractThe main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data and whether the integration of genomic and NIR predictor variables can increase the prediction accuracy of two feedstock quality traits (fiber and sucrose content) in a sugarcane population (Saccharum spp.). The following three modeling strategies were compared: M1 (genome-based prediction), M2 (NIR-based prediction), and M3 (integration of genomics s and NIR wavenumbers). Data were collected from a commercial population comprised of three hundred and eighty-five individuals, genotyped for single nucleotide polymorphisms (SNPs) and screened using NIR spectroscopy. We compared partial least squares (PLS) and BayesB regression methods to estimate marker and wavenumber effects. In order to assess model performance, we employed random sub-sampling cross-validation to calculate the mean Pearson correlation coefficient between observed and predicted genotypic values. Our results showed that models fitted using BayesB were most predictive than PLS models. We found that NIR (M2) provided the highest prediction accuracy, whereas genomics (M1) presented the lowest predictive ability, regardless of the measured traits and regression methods used. The integration of predictors derived from NIR spectroscopy and genomics into a single model (M3) did not significantly improve the prediction accuracy for the two traits evaluated. These findings suggest that NIR-based prediction can be an effective strategy for predicting the genotypic value of sugarcane clones.

scholarly journals Near-infrared spectroscopy outperforms genomics for predicting sugarcane feedstock quality traits

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0236853
Author(s):  
Mateus Teles Vital Gonçalves ◽  
Gota Morota ◽  
Paulo Mafra de Almeida Costa ◽  
Pedro Marcus Pereira Vidigal ◽  
Marcio Henrique Pereira Barbosa ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Prediction Accuracy ◽  
Near Infrared ◽  
Pearson Correlation ◽  
Nir Spectroscopy ◽  
Quality Traits ◽  
Nucleotide Polymorphisms ◽  
Feedstock Quality ◽  
Regression Methods

The main objectives of this study were to evaluate the prediction performance of genomic and near-infrared spectroscopy (NIR) data and whether the integration of genomic and NIR predictor variables can increase the prediction accuracy of two feedstock quality traits (fiber and sucrose content) in a sugarcane population (Saccharum spp.). The following three modeling strategies were compared: M1 (genome-based prediction), M2 (NIR-based prediction), and M3 (integration of genomics and NIR wavenumbers). Data were collected from a commercial population comprised of three hundred and eighty-five individuals, genotyped for single nucleotide polymorphisms and screened using NIR spectroscopy. We compared partial least squares (PLS) and BayesB regression methods to estimate marker and wavenumber effects. In order to assess model performance, we employed random sub-sampling cross-validation to calculate the mean Pearson correlation coefficient between observed and predicted values. Our results showed that models fitted using BayesB were more predictive than PLS models. We found that NIR (M2) provided the highest prediction accuracy, whereas genomics (M1) presented the lowest predictive ability, regardless of the measured traits and regression methods used. The integration of predictors derived from NIR spectroscopy and genomics into a single model (M3) did not significantly improve the prediction accuracy for the two traits evaluated. These findings suggest that NIR-based prediction can be an effective strategy for predicting the genetic merit of sugarcane clones.

Sign in / Sign up

Export Citation Format

Share Document