CAJ | 학술논문

针对高维小样本光谱数据所显现的函数型数据(Functional data)特性、与性质参数的非线性关系及变量间存有的严重共线性,采用了样条变换集成罚函数偏最小二乘回归新技术.它首先以三次B基样条变换实现非线性光谱数据的线性化重构,随后将重构的新光谱矩阵交由罚函数偏最小二乘法(Penalized PLS)构建其与性质参变量间的校正模型,其中罚函数中的光滑因子由交叉验证优化确定以调控模型的拟合精度.最后,通过小麦样品水分含量的近红外光谱定量分析,结果显示该技术光谱数据重构稳健,去噪明显,并有效解决高维小样本的过拟合和变量间的共线性,而预测集的均方根误差(RMSEP)为0.1808%,方法的非线性校正模型预测能力得到了明显提高.
침대고유소양본광보수거소현현적함수형수거(Functional data)특성、여성질삼수적비선성관계급변량간존유적엄중공선성,채용료양조변환집성벌함수편최소이승회귀신기술.타수선이삼차B기양조변환실현비선성광보수거적선성화중구,수후장중구적신광보구진교유벌함수편최소이승법(Penalized PLS)구건기여성질삼변량간적교정모형,기중벌함수중적광활인자유교차험증우화학정이조공모형적의합정도.최후,통과소맥양품수분함량적근홍외광보정량분석,결과현시해기술광보수거중구은건,거조명현,병유효해결고유소양본적과의합화변량간적공선성,이예측집적균방근오차(RMSEP)위0.1808%,방법적비선성교정모형예측능력득도료명현제고.
Taking into account the near infrared spectra(NIR) on numerous predictor variables with serious collinearity and having nonlinear quantitative relationship with the chemical compositions, a novel nonlinear partial least squares(PLS) approach, termed as Spline-PPLS, was constructed by combining the penalized partial least squares(PPLS) regression with B-splines transformation. Firstly, the observed spectral predictors were considered as discrete observations of curves of the wavelength and were nonlinearly transformed using B-spline basis functions. The choice of the degree of the polynomial pieces and of the number of knots was performed using the cross-validation strategy. Then, the PPLS algorithm was performed on the high dimensional transformed data matrix to build the calibration model by imposing a penalty term to the optimization criterion of PLS. The roughness penalty term indeed controlled the curvature of the functions and its smoothing parameter could also be obtained by the cross-validation. Finally, the proposed Spline-PPLS approach was applied to the wheat NIR diffuse reflectance spectra reconstruction and quantitative analysis. The result indicates that the Spline-PPLS approach not only can yield high accuracy reconstructing spectrum, but also improves the model prediction accuracy in the case of nonlinear relationships.