## 1 Introduction  
Spectrofluorimetry has been widely implemented to predict the concentration of organics in samples for its advantages of sensitivity, selectivity, non-invasiveness, and fastness [1; 2]. To date, the emission fluorescence spectrum, which is excited by single excitation wavelength, is commonly used to predict the concentration of organics. Alternatively, an excitation-emission matrix (EEM) of the sample acquired by choosing individual excitation wavelengths is better to analyze the organics quantitatively, which consists of multiple emission spectra and includes more information of the samples. However, some of these emission fluorescence spectra are linearly correlated, by which lead to extra redundant information [3; 4], and Rayleigh scattering of the light also contributes to the EEM, which is known as noises. All this subordinate information is infaust for predicting the concentration of the organics [5]. To increase the accuracy of concentration prediction, the excitation and emission wavelengths in the EEM should be selected.  
To date most reported researches are focused on the selection of the emission wavelengths from the emission spectrum. The methods, for example, partial least squares regression (PLSR), moving window PLS, iterative predictor weighting-PLS, and uninformative variable elimination-PLS [6; 7; 8; 9; 10; 11], establish multi concentration prediction models with every emission wavelength in turn and the wavelengths corresponding to the lowest prediction concentration error are finally reserved. Comparing to these methods, the genetic algorithm (GA) and Monte Carlo method have more wavelengths selection throughput [12; 13; 14; 15], because emission wavelengths are selected by taking into account their contribution to the concentration prediction. The initial contribution to the concentration of each wavelength is presumably the same for GA, which is not consistent with the actuality; and the predetermined criteria is given for Monte Carlo to reserve the emission wavelengths which contribute most to the concentration. Considering the linear correlation between emission spectra excited by different wavelengths, all emission spectra are categorized into different classes based on their similarity, by aligning the similarity coefficients between different emission spectra in each class. Only the emission spectrum with the maximal similarity coefficient between it and the other emission spectra is reserved to eliminate the influence of the redundant information between different emission spectra.  
Overall, we developed a method to increase the accuracy of the prediction concentration based on the EEM, by which the excitation wavelengths and the emission wavelengths are selected respectively. This approach involves three discrete steps: (a) The clustering method is performed to categorize the emission spectra into different classes, only the emission spectrum with the maximal similarity coefficient between it and the other emission spectra in each class is reserved; then (b) all the reserved emission spectra are unfolded to one dimension emission spectrum by sorting their corresponding excitation wavelengths in ascending order, and the Monte Carlo method combined with PLSR is applied to refine the emission wavelengths; and finally (c) the prediction concentration model is established by the spectra corresponding to the selected wavelengths.