Enzymatic Degumming Process Controlled by Near-infrared Spectroscopy

2021 ◽  
Author(s):  
Per Nielsen ◽  
Jakob Larsen ◽  
Soeren Engelsen ◽  
Klavs Sorensen
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Enzymatic Degumming ◽  
Degumming Process

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.

Near-infrared spectroscopy (NIRS) – a promising brain imaging method in Psychiatry?

2008 ◽  
Vol 39 (01) ◽  
Author(s):  
AJ Fallgatter ◽  
AC Ehlis ◽  
MM Richter ◽  
M Schecklmann ◽  
MM Plichta
Keyword(s):  
Infrared Spectroscopy ◽  
Brain Imaging ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Imaging Method

A Study of Frontal Signals in Brain Computer Interfaces: Interpretation of EEG & FNIRS

2020 ◽  
pp. 136-142
Author(s):  
S. Srilekha ◽  
B. Vanathi
Keyword(s):  
Infrared Spectroscopy ◽  
Healthy Subject ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Economic Conditions ◽  
Brain Computer Interfaces ◽  
Functional Near Infrared Spectroscopy ◽  
Computer Interfaces ◽  
Rehabilitation Devices

This paper focuses on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) comparison to help the rehabilitation patients. Both methods have unique techniques and placement of electrodes. Usage of signals are different in application based on the economic conditions. This study helps in choosing the signal for the betterment of analysis. Ten healthy subject datasets of EEG & FNIRS are taken and applied to plot topography separately. Accuracy, Sensitivity, peaks, integral areas, etc are compared and plotted. The main advantages of this study are to prompt their necessities in the analysis of rehabilitation devices to manage their life as a typical individual.

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