Foliar insecticides in soybean: Implications for spectral detection of pest stress

2016 ◽  
Author(s):  
Tavvs M M. Alves
Keyword(s):  
Spectral Detection

scholarly journals A Compact Detection Platform Based on Gradient Guided-Mode Resonance for Colorimetric and Fluorescence Liquid Assay Detection

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2797
Author(s):  
Jing-Jhong Gao ◽  
Ching-Wei Chiu ◽  
Kuo-Hsing Wen ◽  
Cheng-Sheng Huang
Keyword(s):  
Limit Of Detection ◽  
Detection System ◽  
Fluorescence Emission ◽  
Assay Sensitivity ◽  
Detection Range ◽  
Current Form ◽  
Guided Mode ◽  
Spectral Detection ◽  
Guided Mode Resonance ◽  
Colorimetric Assays

This paper presents a compact spectral detection system for common fluorescent and colorimetric assays. This system includes a gradient grating period guided-mode resonance (GGP-GMR) filter and charge-coupled device. In its current form, the GGP-GMR filter, which has a size of less than 2.5 mm, can achieve a spectral detection range of 500–700 nm. Through the direct measurement of the fluorescence emission, the proposed system was demonstrated to detect both the peak wavelength and its corresponding intensity. One fluorescent assay (albumin) and two colorimetric assays (albumin and creatinine) were performed to demonstrate the practical application of the proposed system for quantifying common liquid assays. The results of our system exhibited suitable agreement with those of a commercial spectrometer in terms of the assay sensitivity and limit of detection (LOD). With the proposed system, the fluorescent albumin, colorimetric albumin, and colorimetric creatinine assays achieved LODs of 40.99 and 398 and 25.49 mg/L, respectively. For a wide selection of biomolecules in point-of-care applications, the spectral detection range achieved by the GGP-GMR filter can be further extended and the simple and compact optical path configuration can be integrated with a lab-on-a-chip system.

scholarly journals NEW EVIDENCE OF DISCRETE SCALE INVARIANCE IN THE ENERGY DISSIPATION OF THREE-DIMENSIONAL TURBULENCE: CORRELATION APPROACH AND DIRECT SPECTRAL DETECTION

2003 ◽  
Vol 14 (04) ◽  
pp. 459-470 ◽  
Author(s):  
WEI-XING ZHOU ◽  
DIDIER SORNETTE ◽  
VLADILEN PISARENKO
Keyword(s):  
Energy Dissipation ◽  
Three Dimensional ◽  
Energy Dissipation Rate ◽  
Corrections To Scaling ◽  
Developed Turbulence ◽  
Pairwise Correlations ◽  
Spectral Detection ◽  
Simple Variant ◽  
High Reynolds ◽  
New Evidence

We extend the analysis of Ref. 16 showing statistically significant log-periodic corrections to scaling in the moments of the energy dissipation rate in experiments at high Reynolds number (≈ 2500) of three-dimensional fully developed turbulence. First, we develop a simple variant of the canonical averaging method using a rephasing scheme between different samples based on pairwise correlations that confirms Zhou and Sornette's previous results. The second analysis uses a simpler local spectral approach and then performs averages over many local spectra. This yields stronger evidence of the existence of underlying log-periodic undulations, with the detection of more than 20 harmonics of a fundamental logarithmic frequency f = 1.434 ± 0.007 corresponding to the preferred scaling ratio γ = 2.008 ± 0.006.

Rapid spectral detection of sialic acid using phenylboronic acid functionalized rGO

2018 ◽  
Vol 227 ◽  
pp. 165-168 ◽  
Author(s):  
Xueying Hao ◽  
Qianqian Zhang ◽  
Ling Li ◽  
Limin Zhou
Keyword(s):  
Sialic Acid ◽  
Phenylboronic Acid ◽  
Spectral Detection

Spectral assessment of organic matter with different composition using reflectance spectroscopy 

2021 ◽  
Author(s):  
Nicolas Francos ◽  
Yaron Ogen ◽  
Eyal Ben-Dor
Keyword(s):  
Organic Matter ◽  
Sustainable Use ◽  
Environmental Parameters ◽  
Least Squares Regression ◽  
Dune Sand ◽  
Spectral Detection ◽  
Soil Surveys ◽  
Different Origins ◽  
Limited Accuracy ◽  
Spectral Assessment

<p>Soil surveys are critical for maintaining sustainable use of natural resources while minimizing harmful impacts to the ecosystem. A key soil attribute for many environmental parameters, such as CO2 budget, soil fertility and sustainability, is soil organic matter (SOM), and its sequestration. Soil spectroscopy is a popular method to assess SOM content rapidly in both field and laboratory domains. However, the SOM source composition differs from soil to soil and the use of spectral-based models for quantifying SOM may present limited accuracy when applying a generic approach for SOM assessment. We therefore examined the extent to which the generic approach can assess SOM contents of different origin using spectral-based models. We created an artificial big dataset composed of pure dune sand as a SOM-free background which was artificially mixed with increasing amounts of different organic matter (OM) sources obtained from commercial compost of different origins. Dune sand has high albedo and yields optimal conditions for SOM detection. This study combined two methods: partial least squares regression for the prediction of SOM content from reflectance values across the 400–2500 nm region, and soil spectral detection limit (SSDL) to judge the prediction accuracy. Spectral-based models to assess SOM content were evaluated with each OM source as well as with a merged dataset that contained all of the generated samples (generic approach). The latter was concluded to have limitations for assessing low amounts of SOM (<0.6%), even under controlled conditions. Moreover, some of the OM sources were more difficult to monitor than others; accordingly, caution is advised when different SOM sources are present in the examined population.</p>

Simulation and in vivo Experimental Study on Noninvasive Spectral Detection of Skin Cholesterol

2016 ◽  
Vol 43 (9) ◽  
pp. 0907001 ◽  
Author(s):  
候华毅 Hou Huayi ◽  
方朝晖 Fang Zhaohui ◽  
张元志 Zhang Yuanzhi ◽  
董美丽 Dong Meili ◽  
朱灵 Zhu Ling ◽  
...  
Keyword(s):  
Experimental Study ◽  
Spectral Detection

Terahertz spectral detection in human renal tissue

2016 ◽  
Vol 45 (5) ◽  
pp. 0525001
Author(s):  
李 晗 Li Han ◽  
余 晨 Yu Chen
Keyword(s):  
Renal Tissue ◽  
Spectral Detection
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