On‐line real‐time monitoring of a rapid enzymatic oil degumming process: A feasibility study using free‐run near‐infrared spectroscopy
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On‐line real‐time monitoring of a rapid enzymatic oil degumming process : A feasibility study using free‐run near‐infrared spectroscopy. / Forsberg, Jakob; Nielsen, Per Munk; Engelsen, Søren Balling; Sørensen, Klavs Martin.
I: Foods, Bind 10, Nr. 10, 2368, 2021.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - On‐line real‐time monitoring of a rapid enzymatic oil degumming process
T2 - A feasibility study using free‐run near‐infrared spectroscopy
AU - Forsberg, Jakob
AU - Nielsen, Per Munk
AU - Engelsen, Søren Balling
AU - Sørensen, Klavs Martin
N1 - This article belongs to the Special Issue Advances in NIR Spectroscopy Analytical Technology in Food Industries.
PY - 2021
Y1 - 2021
N2 - 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 online 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.
AB - 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 online 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.
KW - Chemometrics
KW - Near‐Infrared spectroscopy
KW - Oil refinement
KW - Process analytical technology (PAT)
KW - Process control
KW - Processing technology
KW - Variable selection
KW - Vegetable oil
U2 - 10.3390/foods10102368
DO - 10.3390/foods10102368
M3 - Journal article
C2 - 34681417
AN - SCOPUS:85117243244
VL - 10
JO - Foods
JF - Foods
SN - 2304-8158
IS - 10
M1 - 2368
ER -
ID: 283017975