scholarly journals Using near-infrared spectroscopy to determine intramuscular fat and fatty acids of beef applying different prediction approaches

2020 ◽  
Vol 98 (11) ◽  
Author(s):  
Wilson Barragán-Hernández ◽  
Liliana Mahecha-Ledesma ◽  
William Burgos-Paz ◽  
Martha Olivera-Angel ◽  
Joaquín Angulo-Arizala
Keyword(s):  
Fatty Acids ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Prediction Models ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Ground Meat ◽  
Prediction Ability

Abstract This study aimed to predict fat and fatty acids (FA) contents in beef using near-infrared spectroscopy and prediction models based on partial least squares (PLS) and support vector machine regression in radial kernel (R-SVR). Fat and FA were assessed in 200 longissimus thoracis samples, and spectra were collected in reflectance mode from ground meat. The analyses were performed for PLS and R-SVR with and without wavelength selection based on genetic algorithms (GAs). The GA application improved the error prediction by 15% and 68% for PLS and R-SVR, respectively. Models based on GA plus R-SMV showed a prediction ability for fat and FA with an average coefficient of determination of 0.92 and ratio performance deviation of 4.8.

scholarly journals Determination of fatty acids and lipid classes in salmon oil by near infrared spectroscopy

2018 ◽  
Vol 239 ◽  
pp. 865-871 ◽  
Author(s):  
Mari Merce Cascant ◽  
Cassandra Breil ◽  
Anne Silvie Fabiano-Tixier ◽  
Farid Chemat ◽  
Salvador Garrigues ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Lipid Classes

scholarly journals Estimate of the density of Eucalyptus grandis W. Hill ex Maiden using near infrared spectroscopy

CERNE ◽  
2013 ◽  
Vol 19 (4) ◽  
pp. 647-652 ◽  
Author(s):  
Silviana Rosso ◽  
Graciela Ines Bolzon de Muniz ◽  
Jorge Luis Monteiro de Matos ◽  
Clóvis Roberto Haselein ◽  
Paulo Ricardo Gherardi Hein ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Wood Density ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Eucalyptus Grandis ◽  
Basic Density ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Old Trees

This study aimed to analyze use of near infrared spectroscopy (NIRS) to estimate wood density of Eucalyptus grandis. For that, 66 27-year-old trees were logged and central planks were removed from each log. Test pieces 2.5 x 2.5 x 5.0 cm in size were removed from the base of each plank, in the pith-bark direction, and subjected to determination of bulk and basic density at 12% moisture (dry basis), followed by spectral readings in the radial, tangential and transverse directions using a Bruker Tensor 37 infrared spectrophotometer. The calibration to estimate wood density was developed based on the matrix of spectra obtained from the radial face, containing 216 samples. The partial least squares regression to estimate bulk wood density of Eucalyptus grandis provided a coefficient of determination of validation of 0.74 and a ratio performance deviation of 2.29. Statistics relating to the predictive models had adequate magnitudes for estimating wood density from unknown samples, indicating that the above technique has potential for use in replacement of conventional testing.

scholarly journals Quantitative Determination of Germinability ofPuccinia striiformisf. sp.triticiUrediospores Using Near Infrared Spectroscopy Technology

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yaqiong Zhao ◽  
Feng Qin ◽  
Pei Cheng ◽  
Xiaolong Li ◽  
Zhanhong Ma ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Quantitative Determination ◽  
Near Infrared Spectroscopy ◽  
Spectral Region ◽  
Near Infrared ◽  
Puccinia Striiformis ◽  
Support Vector ◽  
Near Infrared Reflectance ◽  
Preprocessing Method

Stripe rust caused byPuccinia striiformisf. sp.tritici(Pst) is an important disease on wheat. In this study, quantitative determination of germinability ofPsturediospores was investigated by using near infrared reflectance spectroscopy (NIRS) combined with quantitative partial least squares (QPLS) and support vector regression (SVR). The near infrared spectra of the urediospore samples were acquired using FT-NIR MPA spectrometer and the germination rate of each sample was measured using traditional spore germination method. The best QPLS model was obtained with vector correction as the preprocessing method of the original spectra and 4000–12000 cm−1as the modeling spectral region while the modeling ratio of the training set to the testing set was 4 : 1. The best SVR model was built when vector normalization was used as the preprocessing method, the modeling ratio was 5 : 1 and the modeling spectral region was 8000–11000 cm−1. The results showed that the effect of the best model built using QPLS or SVR was satisfactory. This indicated that quantitative determination of germinability ofPsturediospores using near infrared spectroscopy technology is feasible. A new method based on NIRS was provided for rapid, automatic, and nondestructive determination of germinability ofPsturediospores.

Modeling Wood Density of Larch by Near-Infrared Spectrometry and Support Vector Machine

2011 ◽  
Vol 55-57 ◽  
pp. 433-438
Author(s):  
Ya Zhao Zhang ◽  
Yao Xiang Li ◽  
Hong Fu Zhang ◽  
Hui Juan Zhang ◽  
Pai Li
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Wood Density ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Model Building ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Infrared Spectrometry ◽  
Support Vector

Model for predicting wood density of Larch was established using near-infrared spectroscopy (NIR) combined with support vector machine (SVM). A hundred and seventeen Larch samples were used in the study. Wood density of samples was measured according to standard test methods for physical and mechanical properties of wood. Support vector machines for regression (SVR) was used for model building. Radial basis function (RBF) was used as kernel function to establish a model for predicting wood density. For the train set, the coefficient of determination (R2) and the mean square error (MSE) were 0.8504 and 0.6460×10-3, while the R2 and MSE was 0.8520 and 0.4451×10-3, respectively, for the test set. Results showed that using SVM in near-infrared spectroscopy calibration could significantly improve the model performance in order to rapidly and accurately predict wood density.

The determination of fatty acids in cheeses of variable composition (cow, ewe's, and goat) by means of near infrared spectroscopy

2020 ◽  
Vol 156 ◽  
pp. 104854 ◽  
Author(s):  
M. Inmaculada González-Martín ◽  
Ana M. Vivar-Quintana ◽  
Isabel Revilla ◽  
Javier Salvador-Esteban
Keyword(s):  
Fatty Acids ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Variable Composition
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