摘要
研究分析80个玉米实验光谱数据,经预处理后,分别进行区间偏最小二乘(iPLS)、组合区间偏最小二乘法(SiPLS)和连续投影算法(SPA)优选玉米水分组分的最佳波长,建立校正模型。结果表明,iPLS、SiPLS和SPA将建模变量从700个分别降低到70、140和2个,各占据全光谱的10%、20%和0.29%,而其建模精度比700个全谱变量建模精度甚至更好。其中SiPLS和SPA的建模精度相当,但是SPA方法将建模变量从700个降低到2个,计算复杂度得到最大程度的降低,并保持了建模精度,表明SPA是一种有效的特征波长提取方法,且这一研究方法可推广应用到对玉米中油脂、蛋白质和淀粉的含量检测中。
After pretreatment of 80 samples of maize, interval partial least square （ iPLS）, combination of interval partial least squares （SiPLS） and successive projections algorithm （SPA） is respectively used to optimize the best wavelength of moisture components, and the correction model is established. The results show that iPLS, SiPLS and SPA method reduces the modeling variables from 700 to 70, 140 and 2, respectively, which occupies 10% , 20% and 0.29% of the whole spectrum. And, the modeling accuracy is even better than that of the 700 full spectral variables. The modeling accuracy of SiPLS and SPA is matched. But the SPA method reduces variables from 700 to 2. The complexity is minimized, and the precision of the model is kept, which show that the SPA method is an effective feature extraction method of wavelength. This research method can be extended to the application of fat, protein and starch components detection of corn.
出处
《电子测量与仪器学报》
CSCD
北大核心
2017年第12期1960-1966,共7页
Journal of Electronic Measurement and Instrumentation
基金
国家重大科学仪器设备开发专项(2013YQ220643)资助
关键词
区间偏最小二乘
组合区间偏最小二乘
连续投影算法
定量分析
interval partial least squares
combined interval partial least squares
continuous projection algorithm
quantitative analysis
