RESUMEN
The detection of maize starch content is of great significance for maize processing industry and near-infrared spectroscopy (NIRS) is an ideal rapid detection technology. However, the interference of moisture in maize is a bottleneck problem that affects the accuracy of NIRS quantitative analysis. In this study, we proposed methods based on external parameter orthogonalization (EPO) combined with wavelength selection algorithms to bring more accurate analytical results. Two groups of maize starch samples with different moisture content distributions were investigated to compare the predictive performance of NIRS models. The results showed that the model built using EPO combined with the synergy interval partial least squares (EPO-siPLS) algorithm exhibited the superior prediction accuracy, whose RMSEP/RMSEPck is improved by 9.7 % compared with that of siPLS model, 25.3 % compared with that of EPO-PLS, and 45.8 % compared with that of the PLS model. This study provides a more accurate and robust new method for rapid detection of maize starch and offers new insights for its application.
RESUMEN
Chemical oxygen demand (COD), ammonia nitrogen (AN) and total nitrogen (TN) are the key parameters to reflect the degree of surface water pollution. Ultraviolet - visible (UV-Vis) spectroscopy and near - infrared (NIR) spectroscopy are ideal techniques for rapid monitoring of these indicators. In this study, a strategy based on the fusion of UV-Vis and NIR spectral data (UV-Vis-NIR) for water quality detection was proposed to further improve the quantitative analysis accuracy of spectroscopic methods. Seventy river samples with different levels of pollution were used for spectroscopic analysis. The UV-Vis-NIR fusion spectrum of each water sample was obtained by directly splicing sample's UV-Vis spectrum and NIR diffuse transmission spectrum. The UV-Vis-NIR fusion models were optimized through using different variable selection algorithms. The results show that the UV-Vis-NIR fusion models for surface water COD, AN and TN achieves better prediction results (the root mean square errors of prediction are 6.95, 0.195, and 0.466, respectively) than single-spectroscopic based models. Since better prediction performances were shown under different optimization conditions, the robustness of fusion models were also better than the single-spectroscopic based models. Therefore, the data fusion strategy proposed in this study has a promising application prospect for further accurate and rapid monitoring of surface water quality.