Time-resolved fluorescence spectroscopy ͑TRFS͒ is a powerful analytical tool for quantifying the biochemical composition of organic and inorganic materials. The potential of TRFS for tissue diagnosis has been recently demonstrated. To facilitate the translation of TRFS to the clinical arena, algorithms for online TRFS data analysis are essential. A fast model-free TRFS deconvolution algorithm based on the Laguerre expansion method has previously been introduced. One limitation of this method,

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... wever, is the need to heuristically select two parameters that are crucial for the accurate estimation of the fluorescence decay: the Laguerre parameter ␣ and the expansion order. Here, a new implementation of the Laguerre deconvolution method is introduced, in which a nonlinear least-square optimization of the Laguerre parameter ␣ is performed, and the optimal expansion order is selected based on a minimum description length criterion ͑MDL͒. In addition, estimation of the zero-time delay between the recorded instrument response and fluorescence decay is also performed based on normalized mean square error criterion ͑NMSE͒. The method is validated on experimental data from fluorescence lifetime standards, endogenous tissue fluorophores, and human tissue. The proposed automated Laguerre deconvolution method will facilitate online applications of TRFS, such as real-time clinical tissue diagnosis.