Nitrogen is an important component to measure soil fertility. The traditional chemical method for detecting soil nitrogen content is complex and time-consuming. Fourier transform near infrared (FT-NIR) technology is utilized for direct and rapid quantitative determinationof soil nitrogen. Nevertheless, the calibration models always perform too ideally well to believe when established by the linear analytical methods, like partial least squares (PLS). That is not convinced for the practical application in on-line detection. In this paper, we proposed a fault-tolerant mechanism to be plug-into the quantitative analytical model, transforming the FT-NIR quantitative mode into a quasi-qualitative discriminant mode. In this way, the application ability of the calibration model can be enhanced. A new discriminant method was proposed for quasi-qualitative determination by combining the interval search principal component analysis algorithm with logistic regression (iPCA-LR). The nitrogen contents of soil samples were firstly predicted based on the common PLS regression. The fault-tolerant threshold was set as three different values of 0.05, 0.10 and 0.15, respectively. The samples were marked as accurately or non-accurately discriminated according to the priori predictive values and the thresholds, so that the original quantitative calibration method was transformed into a new quasi-qualitative discriminant method. The iPCA-LR method was applied for the FT-NIR quasi-qualitative discrimination of soil nitrogen. In the same process, we also discussed the latent variable extraction based on different wavebands that were generated by tuning the waveband division number as 5, 10, 15 and 20. Some informative FT-NIR wavebands were selected with optimal discriminant accuracy. And some combination of informative wavebands were also tested. Results showed that the FT-NIR quasi-qualitative discriminant predictive accuracy varied significantly for different thresholds, but fortunately the worst optimal accuracy climbed tothe level slightly above 75%. And the test of different informative wavebands or the combination of informative wavebands output optimal calibration models with the accuracy above 90%. These results were able to meet some practical cases of online detection. In the application of FT-NIR prediction of nitrogen content in soil samples, the proposed method of iPCA-LR manage to transform the common quantitative prediction problem into the quasi-qualitative discriminant problem when combined with the priori PLS prediction. The newly proposed method deals with the disadvantages of overfitting and overidealistic modeling that always appears in common PLS quantitative analysis. In comparison, the quasi-qualitative discriminant mode is more suitable for actual cases in field detection, more beneficial for real-time application of spectroscopy technology.