NEW APPROACH FOR DISTANCE MEASUREMENT IN LOCALLY WEIGHTED REGRESSION

This paper presents a new approach for distance measurement in locally weighted regression (LWR2) by balancing the information in both chemical and spectral spaces. The new method (LWR2) is compared with the ordinary locally weighted regression method (LWR), another modified LWR method (LWR1), and the linear calibration methods, principal component regression (PCR) and partial least squares (PLS). A simulation was conducted to study how noise in chemical and spectral spaces affects the predictive ability and stability of the LWR2 method with respect to the original LWR. The simulation showed that the LWR2 method is more robust and maintains good predictive ability in the presence of noise in both chemical and spectral spaces. Three near-infrared transmittance (NIT) data sets for food products and one Taguchi gas sensor array data set were further used to test LWR2. The first three data sets were based on measurements of diffuse NIT for water concentrations in 103 meat samples, fat concentrations in 100 homogenized beef samples, and temperatures in 94 homogenized beef samples, respectively. The last data set was based on measurements of eight Taguchi gas sensors for two-component mixtures of toluene and benzene in 100 samples. LWR2 produced up to a 52 improvement in terms of prediction errors as compared to ordinary LWR.