摘要
应用近红外光谱技术可以实现整粒带壳作物种子中脂肪酸含量的快速、无损分析。以385份棉花种子为实验材料,应用线性的偏最小二乘(PLS)和非线性的最小二乘支持向量机(LS-SVM)方法,结合蒙特卡罗无信息变量消除法(MC-UVE),构建整粒棉籽中脂肪酸含量的近红外校正模型。结果表明,基于变量选择的LS-SVM模型具有最佳的预测性能,其棕榈酸、硬脂酸、油酸、亚油酸、饱和脂肪酸和不饱和脂肪酸含量的近红外校正模型的相关系数R2分别为0.863,0.881,0.843,0.806,0.894和0.917,剩余预测偏差RPD分别为2.669,2.880,2.508,2.202,3.023和3.473。本方法省略了种子的粉碎过程,MC-UVE方法有助于提高校正模型的稳健性和精确度。
Near infrared（NIR） spectroscopy was applied for the rapid and nondestructive determination of fatty acid contents in shell-intact crop seed.Total 385 samples of cottonseed were used in this experiment.Linear partial least squares（PLS） and nonlinear least-squares support vector machine（LS-SVM） methods with the variables selected by Monte Cario uninformative variables elimination（MC-UVE） method were used to develop the calibration models to predict the content of fatty acid contents.A best prediction performance was obtained by using the MC-UVE-LS-SVM models.The correlation coefficients（R2） of calibration models for palmitic acid,stearic acid,oleic acid,linoleic acid,saturate fatty acids,unsaturated fatty acids were 0.863,0.881,0.843,0.806,0.894 and 0.917,respectively;the residual predictive deviations（RPD） were 2.669,2.880,2.508,2.202,3.023 and 3.473,respectively.The results indicated that NIR technology could be usedl for the rapid quality analysis of shell-intact cottonseed avoiding the need of grinding.Furthermore,the variable selection of MC-UVE could provide more robust and accurate calibration models.
出处
《分析化学》
SCIE
EI
CAS
CSCD
北大核心
2013年第6期922-926,共5页
Chinese Journal of Analytical Chemistry
基金
国家973计划项目(No.2010CB126006)
转基因生物新品种培育重大专项(No.2013ZX08005-005)
国家863项目(No.2011AA10A102
2013AA102601)资助
关键词
整粒棉籽
近红外
脂肪酸
变量选择
最小二乘支持向量机
Intact cottonseed
Near infrared spectroscopy
Fatty acid
Variable selection
Least-squares support vector machine
