Prediction of L-Ascorbic Acid using FTIR-ATR Terahertz Spectroscopy Combined with Interval Partial Least Squares (iPLS) Regression

Abstract

In this study iPLS regression was used to select the efficient spectral regions and variables to develop a calibration model for L-ascorbic acid (L-AA) determination using FTIR-ATR terahertz (THz) spectroscopy. The objectives of using iPLS were to improve the prediction performance of L-AA determination and to show mapping of contribution of high and low frequency in determining L-AA. The result obtained by iPLS model with 5 PLS factors was superior than that of full-spectrum PLS model with 10 PLS factors when 7 spectral regions and 70 variables were selected. Prediction performance of L-AA can be improved by using iPLS model with higher ratio prediction to deviation (RPD) value.