摘要
为简化近红外光谱模型,提高对苹果可溶性固形物含量的预测精度,将移动窗口偏最小二乘法(MWPLS)与遗传算法、连续投影算法相结合优选特征变量,建立偏最小二乘回归校正模型。其中移动窗口偏最小二乘法和遗传算法相结合优选的36个光谱变量建立的校正模型预测结果最好,可以有效筛选近红外光谱特征波长,模型预测相关系数为0.90,模型的预测均方根误差为0.70°Brix。
In order to improve the accuracy of online detecting soluble solids content（SSC） of apples by the method of near infrared spectroscopy,the combination of moving window partial least squares（MWPLS） and genetic algorithm（GA）,successive projections algorithm（SPA） was used to select the characteristic variables,and then the partial least squares regression model was developed.The MW-GA model with the 36 selected characteristic variables obtained the best result with correlation coefficient of prediction（Rp） of 0.90 and root mean square error of prediction（RMSEP） of 0.70° Brix,which indicated that the combination of MWPLS and GA could select the characteristic variables of near infrared spectroscopy effectively.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2014年第4期220-225,共6页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家高技术研究发展计划(863计划)资助项目(2012AA101906)
科技部农业科技成果转化资金资助项目(2011GB2C500008)
江西省研究生创新基金资助项目(YC2012-S079)
关键词
苹果
可溶性固形物
近红外光谱
移动窗口偏最小二乘法
遗传算法
连续投影算法
Apple
Soluble solids
Near infrared spectoroscopy
Moving window partial least square
Genetic algorithm
Continuous projection algorithm
