摘要
为了简化苹果糖度预测模型和提高模型的精度,用遗传区间偏最小二乘法(GA-iPLS)建立苹果近红外光谱预测模型。应用结果表明,整个光谱划分为40个子区间,GA-iPLS选择其中的第4,6,8,11,18号共5个子区间联合建立苹果糖度模型。遗传区间偏最小二乘法所建的模型,其校正时的相关系数rc和交互验证均方根误差RMSECV分别为0.962和0.3346,预测时的相关系数rp和预测均方根误差RMSEP分别为0.932和0.3842。与全光谱模型相比,该方法建立的模型不论对校正集还是预测集,模型的预测能力都提高了许多,且模型得到了很大的简化:其实际采用的波数点个数比全光谱模型采用的波数点个数大大减少,主因子数也比全光谱少,由此建立的模型更加简洁、数据运算量也更少。
To improve and simplify the prediction model of sugar content, genetic algorithm interval partial least square （GA- iPLS） methods, the evolution of iPLS described by Lars N^rgaard, were proposed and used to establish the calibration models of sugar content against apple spectra. The applespectra data were divided into 40 intervals, among Which 5 subsets, i.e. No. 4, 6,-8, 11 and 18, containing 362 data points were selected by GA-iPLS. The optimum GA-iPLS calibration model was obtained with the correlation coefficient （re） of 0. 962, the root mean square error of cross-validation （RMSECV） of 0. 334 6 and the root mean square error of prediction （RMSEP） of 0. 384 6. Compared with the whole spectra data model, the data points and the factors in the GA-iPLS were decreased significantly. Consequently, the running time of the PLS model build by GA-iPLS was shorter than that of the whole spectra data model. Furthermore, the GA-iPLS model could not only improve precision, but also simplify the model.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2007年第10期2001-2004,共4页
Spectroscopy and Spectral Analysis
基金
教育部博士点基金项目(20040222009)
国家自然科学基金项目(30370813)
江苏省创新人才启动基金项目资助
关键词
近红外光谱
遗传算法
偏最小二乘法
糖度
苹果
NIR spectra
Genetic algorithm
Partial least square
Sugar content~ Apple
