The most serious problem in multivariate calibration analysis is that the prediction samples may contain the interferents which are not modeled in the calibration step. Then how to detect the interferents and eliminate their influence is significantly important in order to obtain the correct compositional analysis results. The so-called residual spectra library search is supplied and an iterative loop regression algorithm to successfully correct the spectrum containing two interferents is developed in this paper. Three groups of which each sample contains no, one or two interferents are supplied to amplify the above method. The mean relative standard deviation (RSD) for the three groups are 0.163, 0.375, 0.355%, respectively. There are no comparable difference among these RSDs, which proves the validation of the method provided in this paper. The results specify that PLS with FTIR spectroscopy is a powerful tool to resolve both the multicomponent simultaneous determination and the identification of interferents when combined with additional diagnostic and corrected procedure.