应用近红外光谱对低碳数脂肪酸含量预测  

Low Carbon Number Fatty Acid Content Prediction Based on Near-Infrared Spectroscopy

作　　者：宋志强[1] 沈雄[1] 郑晓[1] 何东平[2] 亓培实 杨永 方慧文 

机构地区：[1]武汉轻工大学机械工程学院,湖北武汉430023 [2]武汉轻工大学食品科学与工程学院,湖北武汉430023 [3]武汉百信环保能源科技有限公司,湖北武汉430023 [4]武汉产品质量监督检验所,湖北武汉430023

出　　处：《光谱学与光谱分析》2013年第8期2079-2082,共4页Spectroscopy and Spectral Analysis

基　　金：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

摘　　要：应用近红外光谱技术结合支持向量机回归(support vector machine regression,SVR)方法测量食用植物油脂低碳数脂肪酸(C≤14)含量。使用SupNIR-5700近红外光谱仪采集58个样品的近红外光谱图,通过偏最小二乘(partial least square,PLS)算法剔除奇异样品。选择其中具有代表性的52个样品进行主成分分析(principal component analysis,PCA),选取径向基(radial basis function,RBF)核函数建立支持向量机回归模型,并对光谱预处理方法和参数寻优方法进行了详细的分析和讨论。实验表明,经过粒子群算法(parti-cle swarm optimization,PSO)优化后模型的性能都有所提高,泛化能力更强,预测的准确度和稳健性更好;其中预处理方法2经过PSO优化寻优后的参数C=2.085,γ=22.20时,预测集和校正集相关系数(correla-tion coefficient,r)分别达到了0.998 0和0.925 8,均方根误差(root mean square error,MSE)分别为0.000 4和0.014 3。研究结果表明,应用近红外光谱结合PSO-SVR方法进行食用植物油脂低碳数脂肪酸含量快速、准确的预测是可行的。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.

关 键 词：近红外光谱技术 支持向量机 低碳数脂肪酸 粒子群算法 参数优化 

分 类 号：O657.3[理学—分析化学]