Free Fatty Acid Assessment of Crude Palm Oil Using a Non-Destructive Approach
Near infrared (NIR) spectroscopy has always been of
great interest in the food and agriculture industries. The development
of prediction models has facilitated the estimation process in recent
years. In this study, 110 crude palm oil (CPO) samples were used to
build a free fatty acid (FFA) prediction model. 60% of the collected
data were used for training purposes and the remaining 40% used for
testing. The visible peaks on the NIR spectrum were at 1725 nm and
1760 nm, indicating the existence of the first overtone of C-H bands.
Principal component regression (PCR) was applied to the data in
order to build this mathematical prediction model. The optimal
number of principal components was 10. The results showed
R2=0.7147 for the training set and R2=0.6404 for the testing set.
[1] K. Brodersen, “Exploration of the Use of NIR Reflectance Spectroscopy
to Distinguish and Measure Attributes of Conditioned and Cooked
Shrimp (Pandalus borealis),” Technol, vol. 34, pp. 533-541. 2001.
[2] K.S. Chia andH. Abdul Rahim, “Neural Network and principal
component regression in non-destructive soluble solids content
assessment : a comparison,” Biomed & Biotechnol, vol. 13, pp. 145-151.
2012.
[3] Y.B. Che Man and M.H. Moh , “ Determination of Free Fatty Acids in
Palm Oil by Near-Infrared Reflectance Spectroscopy,” Journal of the
America Oil Chemists’ Society (JAOCS), vol. 75. pp. 557 – 562. 1998.
[4] O.M.B. Saeed, S. Sankaran, A.R. Mohamed Shariff, H.Z. Mohd Shafri,
R. Ehsani, M. Salem Alfatni and M.H. Mohd Hazir, “Classification of
oil palm fresh fruit bunches based on their maturity using portable fourband
sensor system,” Computers and Electronics in Agriculture, vol. 82,
pp. 55-60.2012.
[5] Q. Yuan, W. Hanselmann and R.C. Anantheswaran, “Moisture and
sucrose transfer in fractionated palm kernel (PKO) based films
immersed in liquid solutions” Journal of Food and Engineering, vol.
108, pp. 13-21, 2012.
[6] T. Atinmo, “Palm Fruit in Traditional African Food Culture,” Asia
Pasific J Clin Nutr, vol. 12, pp. 350-354. 2003.
[7] K. Sundram, R. Sambanthamurthi and Y.-A. Tan, “Palm fruit chemistry
and nutrition,” Asia Pacific J Clin Nutr, vol. 12 , pp. 355-356. 2003.
[8] S. Kanagaratnam, M. Enamul Hoque, M. Mat Sahri and A. Spowage,
“Investigating the effect of deforming temperature on the oil-binding
capacity of palm oil based shortening,” Journal of Food Engineering,
vol. 118, pp. 90-99. 2013.
[9] M. Makky andP. Soni, “In situ quality assessment of intact oil palm
fresh fruit bunches using rapid portable non-contact and non-destructive
approach,”Journal of Food Engineering, vol. 120, pp. 248-259. 2014.
[10] F. Westad, A. Schmidt, and M. Kermit, “Incorporating chemical bandassignment
in near infrared spectroscopy regression models,”Journal of
Near Infrared Spectroscopy, vol. 16, pp. 265-273. 2008.
[11] J. GordonBellet. al “Substituting fish oil with crude palm oil in the diet
of Atlantic salmon (Salmo salar) affects muscle fatty acid composition
and hepatic fatty acid metabolism,”The Journal of nutrition, vol. 132,
pp. 222-230. 2002.
[12] A. Garrido-Varoet. al, “Near infrared spectroscopy for enforcement of
European legislation concerning the use of animal by-products in animal
feeds,” Biotechnol. Agron. Soc. Environ., vol. 9, pp. 3-9.2005.
[13] B. Stuart, “Infrared Spectroscopy: Fundamentals and Applications,”
John Wiley & Sons., 2004
[14] J. Stuth, A. Jama, D. Tolleson,“Direct and indirect means of predicting
forage quality through near infrared reflectance spectroscopy,” Field
Crops Research,vol. 84, pp. 45-56. 2003.
[15] M. Oubaha, P. Etienne and S. Calas, “Derived Organic and Inorganic
Hybrid Materials for Photonic Applications: Contribution to the
Correlation Between the Material Structure and the Transmission in the
Near Infrared Region,” Journal of Sol-Gel Science and Technology, vol.
33, pp. 241-248. 2005.
[16] A.A. El-Rahman and H.A.A. Badawy, “Ultraviolet, Visible and Infrared
Wavelengths for Determination of Palm Oil Quality,”New Trends in
Agricultural Engineering, pp. 777-793. 2006.
[1] K. Brodersen, “Exploration of the Use of NIR Reflectance Spectroscopy
to Distinguish and Measure Attributes of Conditioned and Cooked
Shrimp (Pandalus borealis),” Technol, vol. 34, pp. 533-541. 2001.
[2] K.S. Chia andH. Abdul Rahim, “Neural Network and principal
component regression in non-destructive soluble solids content
assessment : a comparison,” Biomed & Biotechnol, vol. 13, pp. 145-151.
2012.
[3] Y.B. Che Man and M.H. Moh , “ Determination of Free Fatty Acids in
Palm Oil by Near-Infrared Reflectance Spectroscopy,” Journal of the
America Oil Chemists’ Society (JAOCS), vol. 75. pp. 557 – 562. 1998.
[4] O.M.B. Saeed, S. Sankaran, A.R. Mohamed Shariff, H.Z. Mohd Shafri,
R. Ehsani, M. Salem Alfatni and M.H. Mohd Hazir, “Classification of
oil palm fresh fruit bunches based on their maturity using portable fourband
sensor system,” Computers and Electronics in Agriculture, vol. 82,
pp. 55-60.2012.
[5] Q. Yuan, W. Hanselmann and R.C. Anantheswaran, “Moisture and
sucrose transfer in fractionated palm kernel (PKO) based films
immersed in liquid solutions” Journal of Food and Engineering, vol.
108, pp. 13-21, 2012.
[6] T. Atinmo, “Palm Fruit in Traditional African Food Culture,” Asia
Pasific J Clin Nutr, vol. 12, pp. 350-354. 2003.
[7] K. Sundram, R. Sambanthamurthi and Y.-A. Tan, “Palm fruit chemistry
and nutrition,” Asia Pacific J Clin Nutr, vol. 12 , pp. 355-356. 2003.
[8] S. Kanagaratnam, M. Enamul Hoque, M. Mat Sahri and A. Spowage,
“Investigating the effect of deforming temperature on the oil-binding
capacity of palm oil based shortening,” Journal of Food Engineering,
vol. 118, pp. 90-99. 2013.
[9] M. Makky andP. Soni, “In situ quality assessment of intact oil palm
fresh fruit bunches using rapid portable non-contact and non-destructive
approach,”Journal of Food Engineering, vol. 120, pp. 248-259. 2014.
[10] F. Westad, A. Schmidt, and M. Kermit, “Incorporating chemical bandassignment
in near infrared spectroscopy regression models,”Journal of
Near Infrared Spectroscopy, vol. 16, pp. 265-273. 2008.
[11] J. GordonBellet. al “Substituting fish oil with crude palm oil in the diet
of Atlantic salmon (Salmo salar) affects muscle fatty acid composition
and hepatic fatty acid metabolism,”The Journal of nutrition, vol. 132,
pp. 222-230. 2002.
[12] A. Garrido-Varoet. al, “Near infrared spectroscopy for enforcement of
European legislation concerning the use of animal by-products in animal
feeds,” Biotechnol. Agron. Soc. Environ., vol. 9, pp. 3-9.2005.
[13] B. Stuart, “Infrared Spectroscopy: Fundamentals and Applications,”
John Wiley & Sons., 2004
[14] J. Stuth, A. Jama, D. Tolleson,“Direct and indirect means of predicting
forage quality through near infrared reflectance spectroscopy,” Field
Crops Research,vol. 84, pp. 45-56. 2003.
[15] M. Oubaha, P. Etienne and S. Calas, “Derived Organic and Inorganic
Hybrid Materials for Photonic Applications: Contribution to the
Correlation Between the Material Structure and the Transmission in the
Near Infrared Region,” Journal of Sol-Gel Science and Technology, vol.
33, pp. 241-248. 2005.
[16] A.A. El-Rahman and H.A.A. Badawy, “Ultraviolet, Visible and Infrared
Wavelengths for Determination of Palm Oil Quality,”New Trends in
Agricultural Engineering, pp. 777-793. 2006.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:68758", author = "Siti Nurhidayah Naqiah Abdull Rani and Herlina Abdul Rahim and Rashidah Ghazali and Noramli Abdul Razak", title = "Free Fatty Acid Assessment of Crude Palm Oil Using a Non-Destructive Approach", abstract = "Near infrared (NIR) spectroscopy has always been of
great interest in the food and agriculture industries. The development
of prediction models has facilitated the estimation process in recent
years. In this study, 110 crude palm oil (CPO) samples were used to
build a free fatty acid (FFA) prediction model. 60% of the collected
data were used for training purposes and the remaining 40% used for
testing. The visible peaks on the NIR spectrum were at 1725 nm and
1760 nm, indicating the existence of the first overtone of C-H bands.
Principal component regression (PCR) was applied to the data in
order to build this mathematical prediction model. The optimal
number of principal components was 10. The results showed
R2=0.7147 for the training set and R2=0.6404 for the testing set.
", keywords = "Palm oil, fatty acid, NIRS, regression.", volume = "9", number = "1", pages = "58-4", }
