Optimal partner wavelength combination method applied to NIR spectroscopic analysis of human serum globulin

Rapid determination of triglyceride and glucose levels in Drosophila melanogaster induced by high-sugar or high-fat diets based on near-infrared spectroscopy

Triglyceride and glucose levels are important indicators for determining metabolic syndrome, one of the leading public-health burdens worldwide. Drosophila melanogaster is an ideal model for investigating metabolic diseases because it has 70% homology to human genes and its regulatory mechanism of energy metabolism homeostasis is highly similar to that of mammals. However, traditional analytical methods of triglyceride and glucose are time-consuming, laborious, and costly. In this study, a simple, practical, and reliable near-infrared (NIR) spectroscopic analysis method was developed for the rapid determination of glucose and triglyceride levels in an in vivo model of metabolic disorders using Drosophila induced by high-sugar or high-fat diets. The partial least squares (PLS) model was constructed and optimized using different spectral regions and spectral pretreatment methods. The overall results had satisfactory prediction performance. For Drosophila induced by high-sugar diets, the correlation coefficient (R_P) and root mean square error of prediction (RMSEP) were 0.919 and 0.228 mmoL gprot^-1 for triglyceride and 0.913 and 0.143 mmoL gprot^-1 for glucose respectively; for Drosophila induced by high-fat diets, the R_P and RMSEP were 0.871 and 0.097 mmoL gprot^-1 for triglyceride and 0.853 and 0.154 mmoL gprot^-1 for glucose, respectively. This study demonstrated the potential of using NIR spectroscopy combined with PLS in the determination of triglyceride and glucose levels in Drosophila, providing a rapid and effective method for monitoring metabolite levels during disease development and a possibility for evaluating metabolic diseases in humans in clinical practice.

Rapid detection of hepatitis B virus DNA level based on interval-point data fusion of infrared spectra

Hepatitis B is an infectious disease cause by the hepatitis B virus (HBV). In recent years, HBV-DNA level clinically gets more attention for its detailed information than other serological markers. Unfortunately, common clinical method for HBV-DNA level detection is limited for its hours consuming. This study combined infrared spectroscopy with machine learning to investigate the feasibility of near-infrared (NIR) and mid-infrared (MIR) spectra for rapid detection of HBV-DNA level. Based on partial least squares-discriminant analysis (PLS-DA) modeling method, the optimal NIR and MIR models and traditional data fusion models were constructed, respectively. Considering inequal weight between interval and point data in machine learning, interval-point data fusion method was used to compare with other traditional date fusion methods. The results of the study illustrate that interval-point data fusion of NIR and MIR spectra combined with PLS-DA modeling can provide a rapid method for HBV-DNA level detection.