Interval partial least-squares regression (iPLS):: A comparative chemometric study with an example from near-infrared spectroscopy
Interval partial least-squares regression (iPLS):: A comparative chemometric study with an example from near-infrared spectroscopy
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DOI：
10.1366/0003702001949500
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发表时间：
2000-03-01
影响因子：
3.5
通讯作者：
Engelsen, SB
中科院分区：
文献类型：
--
作者：
Norgaard, L;Saudland, A;Engelsen, SB
提出了一种新的面向图形的局部建模方法，称为区间偏最小二乘（iPLS）用于光谱数据。将iPLS方法与全谱偏最小二乘法和变量选择方法主变量（PV）、前向逐步选择（FSS）和递归加权回归（RWR）进行了比较。近红外（NIR）光谱数据集上记录的60个啤酒样品与原始提取物浓度的方法进行了测试。利用iPLS将近红外光谱与萃取物浓度的全光谱相关模型的误差降低了4倍（r = 0.998，预测均方根误差为0.17%板），图形输出有助于解释所观察的化学体系。测试的其他方法给出了一个相当的减少预测误差，但遭受的解释优势的图形界面。iPLS选择的区间既涵盖了FSS发现的变量和所有可能的组合，也涵盖了PV和RWR发现的变量，iPLS仍然能够利用一阶优势。
A new graphically oriented local modeling procedure called interval partial least-squares (iPLS) is presented for use on spectral data. The iPLS method is compared to full-spectrum partial least-squares and the variable selection methods principal variables (PV), forward stepwise selection (FSS), and recursively weighted regression (RWR). The methods are tested on a near-infrared (NIR) spectral data set recorded on 60 beer samples correlated to original extract concentration. The error of the full-spectrum correlation model between NIR and original extract concentration was reduced by a factor of 4 with the use of iPLS (r = 0.998, and root mean square error of prediction equal to 0.17% plate), and the graphic output contributed to the interpretation of the chemical system under observation. The other methods tested gave a comparable reduction in the prediction error but suffered from the interpretation advantage of the graphic interface. The intervals chosen by iPLS cover both the variables found by FSS and all possible combinations as well as the variables found by PV and RWR, and iPLS is still able to utilize the first-order advantage.