Nondestructive Testing of Lettuce Nitrogen Stress Based on Multidimensional Image

Visible light near infrared (VS-NIR) hyperspectral combined with three-dimensional laser scanning was applied to extract the VS-NIR features of lettuce nitrogen between 400-1700 nm and 3D morphological features of the plants. Such combination realizes the rapid quantitative detection of lettuce nitrogen. This study is based on the hyperspectral image data cube achieved from lettuce leaves with different nitrogen levels. Stepwise regression sensitive area was used and adaptive band selection method was combined to extract the characteristic spectrum and feature image of lettuce nitrogen and characterize the average image intensity. Also; the error caused by moisture variation content in lettuce nitrogen image features was compensated. Then a model of lettuce nitrogen hyperspectral image diagnosis was built. The reverse engineering software Geomagic Qualify was used to repair and smooth interference noise and discontinuous range which are based on the 3D laser scanning data of lettuce. Accordingly, the stem diameter, plant height, leaf area, and biomass features of different nitrogen levels of lettuce are obtained and the model of nitrogen detection about lettuce growth features was built based on reverse engineering and integral method. Multi-scale fusion lettuce nitrogen detection model is built by using the acquired hyperspectral images with growing features of lettuce nitrogen and adopting genetic algorithm combined with partial least squares regression. Results show the correlation coefficient R of the built model is 0.95; the model precision is much better than single feature of hyperspectral images and 3D laser scanning model. The feature extraction algorithm and the eigenvectors provide the reference for development of facilities for online monitoring system of crop growth information.

Isolation and Characterization of [D-Leu1]microcystin-LY from Microcystis aeruginosa CPCC-464

[D-Leu1]MC-LY (1) ([M + H]+ m/z 1044.5673, Δ 2.0 ppm), a new microcystin, was isolated from Microcystis aeruginosa strain CPCC-464. The compound was characterized by 1H and 13C NMR spectroscopy, liquid chromatography–high resolution tandem mass spectrometry (LC–HRMS/MS) and UV spectroscopy. A calibration reference material was produced after quantitation by 1H NMR spectroscopy and LC with chemiluminescence nitrogen detection. The potency of 1 in a protein phosphatase 2A inhibition assay was essentially the same as for MC-LR (2). Related microcystins, [D-Leu1]MC-LR (3) ([M + H]+ m/z 1037.6041, Δ 1.0 ppm), [D-Leu1]MC-M(O)R (6) ([M + H]+ m/z 1071.5565, Δ 2.0 ppm) and [D-Leu1]MC-MR (7) ([M + H]+ m/z 1055.5617, Δ 2.2 ppm), were also identified in culture extracts, along with traces of [D-Leu1]MC-M(O2)R (8) ([M + H]+ m/z 1087.5510, Δ 1.6 ppm), by a combination of chemical derivatization and LC–HRMS/MS experiments. The relative abundances of 1, 3, 6, 7 and 8 in a freshly extracted culture in the positive ionization mode LC–HRMS were ca. 84, 100, 3.0, 11 and 0.05, respectively. These and other results indicate that [D-Leu1]-containing MCs may be more common in cyanobacterial blooms than is generally appreciated but are easily overlooked with standard targeted LC–MS/MS screening methods.

Application of Near-infrared Spectroscopy and Multiple Spectral Algorithms to Explore the Effect of Soil Particle Sizes on Soil Nitrogen Detection

Soil nitrogen is the key parameter supporting plant growth and development; it is also the material basis of plant growth. An accurate grasp of soil nitrogen information is the premise of scientific fertilization in precision agriculture, where near-infrared (NIR) spectroscopy is widely used for rapid detection of soil nutrients. In this study, the variation law of soil NIR reflectivity spectra with soil particle sizes was studied. Moreover, in order to precisely study the effect of particle size on soil nitrogen detection by NIR, four different spectra preprocessing methods and five different chemometric modeling methods were used to analyze the soil NIR spectra. The results showed that the smaller the soil particle sizes, the stronger the soil NIR reflectivity spectra. Besides, when the soil particle sizes ranged 0.18–0.28 mm, the soil nitrogen prediction accuracy was the best based on the partial least squares (PLS) model with the highest Rp2 of 0.983, the residual predictive deviation (RPD) of 6.706. The detection accuracy was not ideal when the soil particle sizes were too big (1–2 mm) or too small (0–0.18 mm). In addition, the relationship between the mixing spectra of six different soil particle sizes and the soil nitrogen detection accuracy was studied. It was indicated that the larger the gap between soil particle sizes, the worse the accuracy of soil nitrogen detection. In conclusion, soil nitrogen detection precision was affected by soil particle sizes to a large extent. It is of great significance to optimize the pre-treatments of soil samples to realize rapid and accurate detection by NIR spectroscopy.

Using Advanced Spectroscopy and Organic Matter Characterization to Evaluate the Impact of Oxidation on Cyanobacteria

Drinking water treatment plants throughout the world are increasingly facing the presence of toxic cyanobacteria in their source waters. During treatment, the oxidation of cyanobacteria changes cell morphology and can potentially lyse cells, releasing intracellular metabolites. In this study, a combination of techniques was applied to better understand the effect of oxidation with chlorine, ozone, potassium permanganate, and hydrogen peroxide on two cell cultures (Microcystis, Dolichospermum) in Lake Champlain water. The discrepancy observed between flow cytometry cell viability and cell count numbers was more pronounced for hydrogen peroxide and potassium permanganate than ozone and chlorine. Liquid chromatography with organic carbon and nitrogen detection was applied to monitor the changes in dissolved organic matter fractions following oxidation. Increases in the biopolymer fraction after oxidation with chlorine and ozone were attributed to the release of intracellular algal organic matter and/or fragmentation of the cell membrane. A novel technique, Enhanced Darkfield Microscopy with Hyperspectral Imaging, was applied to chlorinated and ozonated samples. Significant changes in the peak maxima and number of peaks were observed for the cell walls post-oxidation, but this effect was muted for the cell-bound material, which remained relatively unaltered.

Research on Tomato Nitrogen Content Nondestructive Testing Method Based on Multidimensional Image Processing Technology

This paper is aimed at greenhouse tomato nitrogen detection using hyperspectral imaging combined with three dimensional laser scanning technology. This technology extracts the nitrogen hyperspectral feature image and the plant three dimensional morphological characters, to achieve the rapid quantitative analysis of nitrogen in tomato. The characteristic spectrum of nitrogen was extracted, and the mean intensity characteristic of the image feature was obtained. Then based on the acquisition of the tomato hyperspectral image data cube at different nitrogen levels, the sensitive region stepwise regression combined with correlation analysis was performed. Based on the acquired three dimensional laser scanning data of tomatoes, the stem diameter, the plant height and other biomass characteristics of different nitrogen levels were obtained by establishing the spatial geometric model of tomato three dimensional point cloud. A multi-feature fusion model for tomato nitrogen detection was established by partial least square regression. The results showed that the R2 in the constructed model was 0.94, with the accuracy significantly better than that of the single feature model established by using hyperspectral image and three dimensional laser scanning.