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《Spectroscopy and Spectral Analysis》 2013-08
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Low Carbon Number Fatty Acid Content Prediction Based on Near-Infrared Spectroscopy

SONG Zhi-qiang 1,SHEN Xiong 1,ZHENG Xiao 1,HE Dong-ping 2,QI Pei-shi 3,YANG Yong 4,FANG Hui-wen 4 1.College of Mechanical Engineering,Wuhan Polytechnic University,Wuhan 430023,China 2.College of Food Science and Engineering,Wuhan Polytechnic University,Wuhan 430023,China 3.PASHUN GROUP,Wuhan 430023,China 4.Wuhan Product Quality Supervision & Testing Institute,Wuhan 430023,China  
The rapid prediction of the low-carbon fatty acids (C≤14) content in grease samples was achieved by a mathematical model established by near infrared spectroscopy combined with support vector machine regression (SVR) .In the present project,near-infrared spectrometer SupNIR-5700was used to collect near-infrared spectra of 58samples ; partial least square (PLS) was applied to remove the strange samples,and principal component analysis (PCA) was conducted on the measurements ; radial basis function (RBF) kernel function was selected to establish a regression model supporting vector machine,and then detailed analysis and discussions were conducted concerning their spectral preprocessing and parameters optimization methods.Experimental results showed that by applying particle swarm optimization (PSO) the model demonstrated improved performance,stronger generalization ability,better prediction accuracy and robustness.In the second pretreatment method after PSO,when the optimization parameters are : C=2.085,γ=22.20,the prediction set and calibration set correlation coefficient (r) reached 0.998 0and 0.925 8,respectively ; and root mean square errors (MSE) were 0.000 4and 0.014 3,respectively.Research results proved that the method based on near infrared spectroscopy and PSO-SVR for accurate and fast prediction of the low-carbon fatty acid content in vegetable oil is feasible.
【Fund】: 国家“十一五”科技支撑计划项目(2009BADB9B08);; 武汉市科技攻关计划项目(2013010501010147);; 武汉工业学院食品营养与安全重大项目培育专项项目(2011Z06);; 武汉百信环保能源科技有限公司委托项目;; 武汉工业学院2012研究生创新基金项目(2012cx023);; 国家质检总局科技计划项目(2010QK139)资助
【CateGory Index】: O657.33;TS227
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