Ultrasonic concentration measurement of citrus pectin aqueous solutions using PC and PLS regression
Abstract
(School of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China)
Abstract: This work demonstrated the use of multivariate statistical techniques called principal component (PC) and partial least squares (PLS) to extract the acoustic features of citrus pectin water solution. The concentration of citrus pectin water solution was predicted by PC and PLS regression method using the spectra of ultrasound pulse echoes travelling through mixtures. The values of root mean square error of validation (RMSEV) were 0.0675 g/100 g and 0.0662 g/100 g for PC and PLS regression model, respectively. Since the response variable was taken into account, PLS regression model was more accurate than PC regression model. Also, a method for temperature compensation was proposed to correct the impact of temperature variation on analyzed data. The proposed methods for pectin concentration measurement are easily adaptable to similar applications using existing hardware.
Keywords: Partial Least Square Regression, Principal Component Regression, concentration measurement, acoustic velocity
DOI: 10.3965/j.ijabe.20120502.010
Citation: Meng R F, Zhong J J, Zhang L F, Ye X Q, Liu D H. Ultrasonic concentration measurement of citrus pectin aqueous solutions using PC and PLS regression. Int J Agric & Biol Eng, 2012; 5(2): 76
Keywords
References
Yan Y L, Yu C H, Chen J, Li X X, Wang W, Li S Q. Ultrasonic-assisted extraction optimized by response surface methodology, chemical composition and antioxidant activity of polysaccharides from Tremella mesenterica. Carbohydrate Polymers, 2011; 83(1): 217-224.
Hou X J, Chen W. Optimization of extraction process of crude polysaccharides from wild edible BaChu mushroom by response surface methodology. Carbohydrate Polymers, 2008; 72 (1): 67-74.
Vilkhu K, Mawson R, Simons L, Bates, D. Applications and opportunities for ultrasound assisted extraction in the food industry-A review. Innovative Food Science and Emerging Technologies, 2008; 9(2): 161-169.
Xia T, Shi S Q, Wan X C. Impact of ultrasonic-assisted extraction on the chemical and sensory quality of tea infusion. Journal of Food Engineering, 2006; 74(4): 557–560.
Hauptmann P, Hoppe N, Püttmer A. Application of ultrasonic sensors in the process industry. Measurement Science and Technology, 2002; 13(8): R73-R83.
Resa P, Elvira L, Espinosa F M. Concentration control in alcoholic fermentation process from ultrasonic velocity measurements. Food Research International, 2004; 37(6): 587-594.
Schäfer R, Carlson J E, Hauptmann P. Ultrasonic concentration measurement of aqueous solutions using PLS regression. Ultrasonics, 2006; 44: e947-e950.
Kaatze U, Eggers F, Lautscham K. Ultrasonic velocity measurements in liquids with high resolution-techniques, selected applications and perspectives. Measurement Science and Technology, 2008; 19(6): 1-21.
Krause D, Schöck D, Hussein M A, Becker T. Ultrasonic characterization of aqueous solutions with varying sugar and ethanol content using multivariate regression methods. Journal of Chemometrics, 2011; 25(4): 216-223.
Schäfer R, Hauptmann P. Statistical modelling of utrasonic sensors in process industries-new prospects for conventional devices. Measurement Science and Technology, 2007; 18(5): 1627-1636
Joliffe I T. Principle Component Analysis. 2nd ed. New York: Springer. 2002; pp. 167-198.
Wold S, Sjöström M, Eriksson L. PLS regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 2001; 58(2): 109-130.
Williams P C. Implementation of Near-Infrared Technology. In: Williams P and Norris K, Editors, Near-Infrared Technology in the Agricultural and Food Industries. 2nd ed., St. Paul, MN. American Association of Cereal Chemists, 2001; pp. 145-169.
Copyright (c)