Using near infrared spectroscopy to predict metabolizable energy of corn for pigs

Ferreira, S. L.; Vasconcellos, Ricardo Souza; Rossi, Robson Marcelo; Paula, Vinicius R C; Fachinello, Marcelise Regina; Huepa, Laura Marcela Díaz; Pozza, Paulo César

doi:10.1590/1678-992x-2016-0509

Cited by 5 publications

(2 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A wide range of references regarding the utilization of NIR spectroscopy in agriculture-related topics is available. For instance, studies employing NIR as an analytical tool include post-harvest quality monitoring [ 84 ], toxic compounds detection in seeds [ 35 ], and grain composition determination [ 85 , 86 ]. Further, Hayes et al (2017) [ 87 ] performed NIR predictions of 19 wheat end-use quality traits using multi-trait analysis and obtained improved accuracies of genomic predictions.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Similarly, Fan and coworkers [ 260 ] used n = 829 samples to assess protein content from several feed ingredients from China’s markets. Ferrerira and coworkers [ 261 ] determined dry matter, acid and neutral detergent fiber (ADF and NDF), gross energy, crude fat, ash, and protein to validate a mathematical model then to assess the metabolizable energy of corn ( n = 99 samples) used in swine feed. Crude ash, fat, and NDF were the variables with the most significant weight.…”

Section: Selected Instrumental Techniques and Their Applicationsmentioning

confidence: 99%

Selected Instrumental Techniques Applied in Food and Feed: Quality, Safety and Adulteration Analysis

Artavia

Cortés-Herrera

Granados-Chinchilla³

2021

Foods

View full text Add to dashboard Cite

This review presents an overall glance at selected instrumental analytical techniques and methods used in food analysis, focusing on their primary food science research applications. The methods described represent approaches that have already been developed or are currently being implemented in our laboratories. Some techniques are widespread and well known and hence we will focus only in very specific examples, whilst the relatively less common techniques applied in food science are covered in a wider fashion. We made a particular emphasis on the works published on this topic in the last five years. When appropriate, we referred the reader to specialized reports highlighting each technique’s principle and focused on said technologies’ applications in the food analysis field. Each example forwarded will consider the advantages and limitations of the application. Certain study cases will typify that several of the techniques mentioned are used simultaneously to resolve an issue, support novel data, or gather further information from the food sample.

show abstract

The use of near-infrared spectroscopy (NIRS) to determine the energy value of individual feedstuffs and mixed diets for pigs

Noel

Jørgensen

Knudsen

2022

Animal Feed Science and Technology

View full text Add to dashboard Cite

Using near infrared spectroscopy to predict metabolizable energy of corn for pigs

Cited by 5 publications

References 15 publications

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

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

Selected Instrumental Techniques Applied in Food and Feed: Quality, Safety and Adulteration Analysis

The use of near-infrared spectroscopy (NIRS) to determine the energy value of individual feedstuffs and mixed diets for pigs

Contact Info

Product

Resources

About