A quasi-qualitative strategy for FT-NIR discriminant prediction: Case study on rapid detection of soil organic matter

Fourier transform near infrared (FT-NIR) is a technology to provide direct and rapid quantitative determinations of soil organic matter (SOM). In this paper, a new discriminant method is proposed for quasi-qualitative determination by combining the interval search principal component analysis algorithm with logistic regression (iPCALR). We firstly predict the SOM content of soil samples based on the partial least square (PLS) regression. To build up a quasi-qualitative analytical strategy, we design various fault-tolerant thresholds. Discriminate the sample marks as accurate or non-accurate according to the predicted values from priori PLS and the thresholds. The quantitative calibration model is thereby transformed into a quasi-qualitative discriminant model. We then leverage iPCA-LR to select informative FT-NIR wavebands with parameter optimization, according to the optimal discriminant accuracy. Results show that the FT-NIR quasi-qualitative discriminant predictive accuracy varies significantly with thresholds varying, but fortunately that the optimal accuracy climbed to above 74%. Furthermore, the test of different informative wavebands outputs the optimal calibration models with an accuracy above 88%. In the SOM content prediction of FT-NIR, iPCA-LR converts the quantitative problem into the quasi qualitative discriminant issue when combined with the threshold-transformed PLS results. The quasi-qualitative strategy helps to overcome the over-idealistic modeling in PLS quantitative analysis. It is more beneficial for the real-time application of spectroscopy technology.