This study explored the application of near infrared spectroscopy for quantitative and qualitative prediction of sulfur content in diesel fuel in the range of 10.3–1038.0 mg kg−1. The original spectra were preprocessed through various methods such as decentralization, normalization, multivariate scattering correction, and a smoothing (15-point window with second order polynomial fit). The performances of models based on partial least squares (PLS) regression, the bootstrapping soft shrinkage (BOSS), competitive adaptive reweighted sampling and Monte Carlo uninformative variable elimination algorithms in quantitative analysis of diesel samples were compared. The model for quantitative prediction of sulfur content in diesel samples using the BOSS-PLS algorithm had the highest performance and accuracy with a RMSEP of 36.20 mg kg−1 and r2 of 0.98 using a Savitzky-Golay second derivative. Diesel fuel samples were classified into five groups according to the sulfur content for qualitative analysis. The interval PLS method was then used to determine the characteristic spectra of the diesel samples. The experimental results indicated that the discriminant partial least squares qualitative analysis model had the highest performance with the characteristic spectrum from 12,493 to 10,892 cm−1, with 92.04% accuracy.

Algorithmic mechanisms are used to improve terahertz (THz) image quality, which is critical to a biological sample analysis. A complete mechanism for the super-resolution reconstruction and evaluation of THz biological sample images was constructed in this study. With eucalyptus leaves as an example, the THz spectral region screening technique was adopted to select the characteristic frequencies for imaging, and the THz single-frequency images were reconstructed with the single-image super-resolution image reconstruction technique. The THz super-resolution reconstructed images without ideal reference were evaluated after the introduction of three no-reference image evaluation criteria considering the diversity and complexity of organisms. The results show that the THz image reconstruction mechanism proposed in this study led to an increase in resolution and a decrease in noise. At the same time, the imaging quality of biological samples was considerably improved, and the detailed information was enriched. These provide a reference for a THz imaging analysis of leaves and other biological samples.