D?cs?iTii?E
?T1??D??(ciki)?s?Ei?k?ii??3、 旋转的不确定性
不同形状的浓度曲线和光谱可以以相同的拟合质量再生出原始数据。这是最相关的一种不确定性：
D?CS?ED??CT?TS其中T是任意一种转换矩阵。
T?-1T??E
减少或者压制多元曲线分析中的不确定性最主要的选择还是通过引入限制条件。有些限制条件可以保证对曲线中不确定性的压制，如trilinearity和hard-modeling。其他的限制条件，如local rank也能压制不确定性。有许多评估多元曲线分析中不确定性的方法。寻找出一特定函数最大值和最小值边界：
fi,min?mincsTiiCSTfi,max?maxcsTii
CST这组方程能够提供目标方程中的极值，这种方法可以应用于拥有几个
组分的系统，并可以很好的描述：
a) 通过计算fi,max?fi,min来确定其不确定性的程度。如果此值为0，那么目标解就是唯一的。此差值越大，那么目标函数的不确定性就越大。
b) 不确定性的位置。将一个特定组分中与fi,max和fi,min相关的浓度曲线和光谱作图，可以看到这种不确定性是否影响特定组分的浓度曲线和光谱。
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