Prediction of Resin and Fatty Acid Content of Biorefinery Feedstock by On-line Near-Infrared (NIR) Spectroscopy

2010 ◽  
Vol 24 (9) ◽  
pp. 5148-5152 ◽  
Author(s):  
Torbjörn A. Lestander ◽  
Robert Samuelsson
Keyword(s):  
Fatty Acid ◽  
Acid Content ◽  
Near Infrared ◽  
Fatty Acid Content ◽  
Nir Spectroscopy ◽  
On Line

scholarly journals On-Line Real-Time Monitoring of a Rapid Enzymatic Oil Degumming Process: A Feasibility Study Using Free-Run Near-Infrared Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 2368
Author(s):  
Jakob Forsberg ◽  
Per Munk Nielsen ◽  
Søren Balling Engelsen ◽  
Klavs Martin Sørensen
Keyword(s):  
Infrared Spectroscopy ◽  
Fatty Acid ◽  
Free Fatty Acid ◽  
Near Infrared Spectroscopy ◽  
Free Fatty Acid Content ◽  
Acid Content ◽  
Near Infrared ◽  
Fatty Acid Content ◽  
Enzymatic Degumming ◽  
On Line

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.

Prediction of Fatty Acid Content in Beef Neck Lean by near Infrared Reflectance Analysis

1998 ◽  
Vol 6 (1) ◽  
pp. 229-234 ◽  
Author(s):  
William R. Windham ◽  
W.H. Morrison
Keyword(s):  
Fatty Acids ◽  
Fatty Acid ◽  
Near Infrared ◽  
Fatty Acid Content ◽  
Reference Method ◽  
Nir Spectroscopy ◽  
Coefficient Of Determination ◽  
Longissimus Dorsi ◽  
Near Infrared Reflectance ◽  
Total Fatty Acids

Near infrared (NIR) spectroscopy in the prediction of individual and total fatty acids of bovine M. Longissimus dorsi neck muscles has been studied. Beef neck lean was collected from meat processing establishments using advanced meat recovery systems and hand-deboning. Samples ( n = 302) were analysed to determine fatty acid (FA) composition and scanned from 400 to 2498 nm. Total saturated and unsaturated FA values ranged from 43.2 to 62.0% and 38.3 to 56.2%, respectively. Results of partial least squares (PLS) modeling shown reasonably accurate models were attained for total saturate content [standard error of performance ( SEP = 1.10%); coefficient of determination on the validation set ( r2 = 0.77)], palmitic ( SEP = 0.94%; r2 = 0.69), unsaturate ( SEP = 1.13%; r2 = 0.77), and oleic ( SEP = 0.97; r2 = 0.78). Prediction of other individual saturated and unsaturated FAs was less accurate with an r2 range of 0.10 to 0.53. However, the sum of individual predicted saturated and unsaturated FA was acceptable compared with the reference method ( SEP = 1.10 and 1.12%, respectively). This study shows that NIR can be used to predict accurately total fatty acids in M. Longissimus dorsi muscle.

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 ◽  
...  
Keyword(s):  
Fatty Acid ◽  
Acid Content ◽  
Near Infrared ◽  
Fatty Acid Content ◽  
Infrared Instruments

scholarly journals 51 Feedstuff fatty acid content, variation, techniques and implications on practical animal nutrition

2019 ◽  
Vol 97 (Supplement_2) ◽  
pp. 25-26
Author(s):  
Cliff Ocker
Keyword(s):  
Fatty Acid ◽  
Acid Content ◽  
Near Infrared ◽  
Animal Health ◽  
Fatty Acid Content ◽  
Infrared Light ◽  
Near Infrared Reflectance ◽  
Wet Chemistry ◽  
Peak Identification ◽  
The Mean

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.

scholarly journals Determination of Fatty Acid Content of Rice during Storage Based on Feature Fusion of Olfactory Visualization Sensor Data and Near-Infrared Spectra

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3266
Author(s):  
Hongping Lu ◽  
Hui Jiang ◽  
Quansheng Chen
Keyword(s):  
Fatty Acid ◽  
Acid Content ◽  
Near Infrared ◽  
Feature Fusion ◽  
Fatty Acid Content ◽  
Sensor Data ◽  
Detection Accuracy ◽  
Model Based ◽  
Single Technique ◽  
Fusion Feature

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.

scholarly journals Functional approach and agro‐climatic information to improve the estimation of olive oil fatty acid content from near‐infrared data

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
Keyword(s):  
Fatty Acid ◽  
Olive Oil ◽  
Acid Content ◽  
Near Infrared ◽  
Fatty Acid Content ◽  
Functional Approach ◽  
Climatic Information ◽  
Infrared Data
Sign in / Sign up

Export Citation Format

Share Document