多光谱活体荧光成像技术正逐渐成为生物医学研究的关键技术，但是荧光物质光谱之间的串扰和自发荧光现象严重影响了荧光影像的解译。混合光谱分解对于去除活体多光谱荧光影像自发荧光效应和进行多种荧光信号分离是非常有效的技术，但是光谱分解的前提是获得了各种荧光物质的光谱。基于多元曲线解析交替最小二乘法（MCR-ALS）计算框架，提出包括非负、等式、闭合性、单峰、波段范围及归一化的多约束条件的荧光纯光谱估计方法，利用估计的纯光谱和线性混合光谱模型得到不同荧光信号的分离，去除自发荧光背景对起标记目的的荧光物质信号的干扰。Dirichle分布随机混合构造的不同信噪比和纯净水平的荧光蛋白混合光谱数据分析结果反映出在混合问题严重、有噪声影响的情况下，该算法要比传统端元光谱分析方法的精度高10倍以上。活体鼠多光谱量子荧光影像的实验也证明了该算法在信号分离上的有效性。

One of the most important issues in biomedical research is in vivo fluorescence-based multispectral imaging, but quantitative image analysis has been generally perturbed by the cross talk problem of fluorescence signals and significant autofluorescence problem of tissues. Spectral unmixing is a useful technique in multispectral fluorescence imaging for reducing the effects of native tissue autofluorescence and separating multiple fluorescence probes in vivio. While spectral unmixing methods are well established in fluorescence microscopy, which typically rely on precharacterized spectra for each fluorophore. A spectral unmixing algorithm tailored for in vivo optical imaging that is able to find the signal distribution and the pure spectrum of each component is introduced. It is derived from multivariate curve resolution-alternative least square (MCR-ALS) method using multiple constraints such as nonnegative, equality, closure, unimodal, spectral range and normalization. The signal distribution maps help to separate autofluorescence from other probes in the raw images and hence provide better quantification and localization for each probe. The test of mixed spectral samples with abundance fractions generated by Dirichlet distribution demonstrates that the algorithm is robust with different noises and pure spectral. And a quantum dots mouse multispectral fluorescence image has convinced this method.