Abstract. This article discusses the influence of light source and band selection on prediction model performance. Two spectra acquisition systems for visible (Vis) and near-infrared (NIR) spectroscopy with a ring light source and a point light source were set up and compared based on the coefficient of variation (CV), signal-to-noise ratio (SNR), spectrum area change rate (ACR), and model results. Reflectance spectra of 61 pork samples were collected, and anomalous samples were eliminated by Monte Carlo method based on model cluster analysis. Partial least squares (PLS) models for total volatile basic nitrogen (TVB-N) based on a single spectral region (350-1100 nm or 1000-2500 nm) and a dual spectral region (350-2500 nm) were built to compare the influence of band choice. Based on the optimal chosen band, characteristic wavelengths were selected by competitive adaptive reweighted sampling (CARS), and a new PLS model was established. The results showed that spectra acquired with the ring light source had better stability and achieved optimal prediction models. The dual spectral region, which contained more comprehensive information on TVB-N, yielded better results than any single spectral region. Based on the dual-band spectra, a simplified PLS model using feature variables achieved a coefficient of determination in the prediction set (Rp2) of 0.8767 and standard error of prediction (SEP) of 2.8354 mg per 100 g. The results demonstrated that the choice of light source and modeling band had great influence on prediction results, and improvement of models would promote the application of Vis/NIR spectroscopy in on-line or portable detection. Keywords: Band selection, Light source, Nondestructive detection, Pork, TVB-N, Vis/NIR spectroscopy.
Application of Genetic Algorithm in the Modeling of Leaf Chlorophyll Level Based on Vis/Nir Reflection Spectroscopy
