Estimating fatty acid content and related nutritional indexes in ewe milk using different near infrared instruments

2020 ◽  
Vol 88 ◽  
pp. 103427
Author(s):  
Nieves Núñez-Sánchez ◽  
Gabriele Acuti ◽  
Raffaella Branciari ◽  
David Ranucci ◽  
Naceur Mohamed Haouet ◽  
...  
2019 ◽  
Vol 97 (Supplement_2) ◽  
pp. 25-26
Author(s):  
Cliff Ocker

Abstract Fatty acid nutrition in ruminants, dairy cattle primarily, has increased as a point of emphasis with nutrition formulation in the past decade, as the diet fatty acid profile and metabolism has been found to impact milk fat concentration and animal health. Both the fatty acid supply and rumen degradation warrant further investigation in dairy diets for improved formulation strategies in the future. As supply and degradation are better understood, improved formulation approaches will be possible now that routine feed fatty acid measures have become more practical with the development of near-infrared reflectance spectroscopy (NIR) models at commercial feed analysis laboratories. NIR model development techniques vary; however, the general approach is to calibrate against a wide ranging database of feedstuff wet chemistry measures using a partial least squares approach. Models relate spectral observations (i.e. reflectance at a specific near-infrared light wavelength) to wet chemistry observations. NIR models should reflect both the mean and variation observed in the wet chemistry database. The NIR models developed by Rock River Laboratory, and resulting feed library database information presented in Table 1, were developed by calibrating against feedstuff chemistry performed at the Lock laboratory with Michigan State University. Wet chemistry fatty acid determination by analytical laboratories, using gas chromatography techniques against known fatty acid standards, deserves further discussion to agree upon peak identification schemes. Differences in peak identification from one laboratory to the another will result in different total fatty acid measures and NIR models. The fatty acid content and profile coefficients of variation, determined from the mean and standard deviations presented in Table 1, range from less than 10 to over 100% of the mean with an average CV of 27%. This suggests substantial variation is present in commercial feeds, and opportunities may exist to better account for variation and fatty acid supply in dairy diets.


Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3266
Author(s):  
Hongping Lu ◽  
Hui Jiang ◽  
Quansheng Chen

This study innovatively proposes a feature fusion technique to determine fatty acid content during rice storage. Firstly, a self-developed olfactory visualization sensor was used to capture the odor information of rice samples at different storage periods and a portable spectroscopy system was employed to collect the near-infrared (NIR) spectra during rice storage. Then, principal component analysis (PCA) was performed on the pre-processed olfactory visualization sensor data and the NIR spectra, and the number of the best principal components (PCs) based on the single technique model was optimized during the backpropagation neural network (BPNN) modeling. Finally, the optimal PCs were fused at the feature level, and a BPNN detection model based on the fusion feature was established to achieve rapid measurement of fatty acid content during rice storage. The experimental results showed that the best BPNN model based on the fusion feature had a good predictive performance where the correlation coefficient (RP) was 0.9265, and the root mean square error (RMSEP) was 1.1005 mg/100 g. The overall results demonstrate that the detection accuracy and generalization performance of the feature fusion model are an improvement on the single-technique data model; and the results of this study can provide a new technical method for high-precision monitoring of grain storage quality.


Foods ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2368
Author(s):  
Jakob Forsberg ◽  
Per Munk Nielsen ◽  
Søren Balling Engelsen ◽  
Klavs Martin Sørensen

Enzymatic degumming is a well established process in vegetable oil refinement, resulting in higher oil yield and a more stable downstream processing compared to traditional degumming methods using acid and water. During the reaction, phospholipids in the oil are hydrolyzed to free fatty acids and lyso-phospholipids. The process is typically monitored by off-line laboratory measurements of the free fatty acid content in the oil, and there is a demand for an automated on-line monitoring strategy to increase both yield and understanding of the process dynamics. This paper investigates the option of using Near-Infrared spectroscopy (NIRS) to monitor the enzymatic degumming reaction. A new method for balancing spectral noise and keeping the chemical information in the spectra obtained from a rapid changing chemical process is suggested. The effect of a varying measurement averaging window width (0 to 300 s), preprocessing method and variable selection algorithm is evaluated, aiming to obtain the most accurate and robust calibration model for prediction of the free fatty acid content (% (w/w)). The optimal Partial Least Squares (PLS) model includes eight wavelength variables, as found by rPLS (recursive PLS) calibration, and yields an RMSECV (Root Mean Square Error of Cross Validation) of 0.05% (w/w) free fatty acid using five latent variables.


2019 ◽  
Vol 8 (1) ◽  
pp. 351-360 ◽  
Author(s):  
María Isabel Sánchez‐Rodríguez ◽  
Elena M. Sánchez‐López ◽  
Alberto Marinas ◽  
Francisco José Urbano ◽  
José M. Caridad

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