Requirements-driven design of an imaging spectrometer system for characterization of the coastal environment

1997 ◽  
Author(s):  
Curtiss O. Davis ◽  
Kendall L. Carder
Keyword(s):  
Coastal Environment ◽  
Imaging Spectrometer ◽  
Spectrometer System

scholarly journals Laboratory Calibration of a Field Imaging Spectrometer System

Sensors ◽  
2011 ◽  
Vol 11 (3) ◽  
pp. 2408-2425 ◽  
Author(s):  
Lifu Zhang ◽  
Changping Huang ◽  
Taixia Wu ◽  
Feizhou Zhang ◽  
Qingxi Tong
Keyword(s):  
Imaging Spectrometer ◽  
Laboratory Calibration ◽  
Spectrometer System ◽  
Field Imaging

Design of Hyperspectral Resolution Ultraviolet Offner Imaging Spectrometer System

2018 ◽  
Vol 38 (2) ◽  
pp. 0222001
Author(s):  
朱雨霁 Zhu Yuji ◽  
尹达一 Yin Dayi ◽  
陈永和 Chen Yonghe ◽  
任百川 Ren Baichuan
Keyword(s):  
Imaging Spectrometer ◽  
Spectrometer System

scholarly journals Investigation of Volatile Compounds in Combination with Multivariate Analysis for the Characterization of Monofloral Honeys

2021 ◽  
Vol 12 (1) ◽  
pp. 264
Author(s):  
Chrysoula Tananaki ◽  
Vasilios Liolios ◽  
Dimitrios Kanelis ◽  
Maria Anna Rodopoulou
Keyword(s):  
Volatile Compounds ◽  
Volatile Profile ◽  
Gas Chromatograph ◽  
Aroma Profile ◽  
Extraction Separation ◽  
Mass Spectrometer System ◽  
Chemometric Approach ◽  
Spectrometer System ◽  
Thyme Honey

Lately there has been a growing demand for monofloral honeys with distinctive properties. Considering the limitations of pollen analysis, the volatile profile of honey has been proposed as a helpful supplementary tool for the confirmation of monoflorality; however, research remains regarding the volatile markers that may characterize the monofloral honey types. Therefore, in this study, we tried to expand the research by investigating the aroma profiles of five monofloral honey types (fir, pine, erica, thyme, cotton) and discriminate them through chemometric approach. A purge and trap–gas chromatograph–mass spectrometer system was used for the extraction, separation, and identification of volatile and semi-volatile compounds. Thyme honey had the richest quantitatively aroma profile, with 97 volatile compounds, whereas fir and cotton honeys had 65 and 60 volatile compounds, respectively. From a total of 124 compounds, the 38 were detected in all the studied honey types. Thyme honey was distinguished by the presence (or percentage participation) of benzeneacetaldehyde, benzealdehyde, and benzyl nitrile; erica honey of isophorone and furfural; cotton honey of 1-butanol, 2-methyl, 1-pentanol, and 4-methyl-; and honeydew honeys of α-pinene, octane, and nonanal. The discriminant analysis confirmed that the percentage participation of volatile compounds may lead to the discrimination of the studied monofloral honey types.

Use of an Imaging Spectrometer for Characterization of a Cesium Dosimeter Calibration Facility

2020 ◽  
Vol 118 (4) ◽  
pp. 462-469
Author(s):  
Roger J. Champion ◽  
Robert M. Golduber ◽  
Kimberlee J. Kearfott
Keyword(s):  
Imaging Spectrometer ◽  
Calibration Facility

Atmospheric Aerosol Multiband Synthesis Imaging Spectrometer

2018 ◽  
Vol 73 (2) ◽  
pp. 221-228
Author(s):  
Xiaoxu Wang ◽  
Zihui Zhang ◽  
Shurong Wang ◽  
Yu Huang ◽  
Guanyu Lin ◽  
...  
Keyword(s):  
Atmospheric Aerosol ◽  
Optical Design ◽  
Imaging Spectrometer ◽  
Spectral Bands ◽  
Good Imaging ◽  
Filter Array ◽  
Spectrum Distribution ◽  
Solid Foundation ◽  
Aerosol Remote Sensing ◽  
Spectrometer System

According to the characteristics of the spectrum distribution for atmospheric aerosol detection, a multiband synthesis imaging spectrometer system based on Czerny–Turner configuration is designed and proposed in this paper. Using a grating array instead of a traditional single grating, and together with a filter array, the proposed configuration can achieve hyperspectral imaging with the spectral resolution of 0.16 nm, 0.24 nm, 0.29 nm, and 2.05 nm in the spectral bands of 370–430 nm, 640–680 nm, 840–880 nm, and 1560–1660 nm, respectively. First, the system aberration caused by the spectral change was eliminated based on Rowland circle theory; then, Zemax software was used to optimize and analyze the optical design. The analysis results show that the root mean square (RMS) of the spot diagram is < 9 µm in all the working spectral bands, which demonstrates that the aberration has been corrected and a good imaging quality can be achieved. This design of multiband synthesis imaging spectrometer configuration proves to be not only feasible, but also simple and compact, which lays a solid foundation for the practical application in the field of atmospheric aerosol remote sensing spectroscopy.

scholarly journals Crop/Weed Discrimination Using a Field Imaging Spectrometer System

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5154 ◽  
Author(s):  
Bo Liu ◽  
Ru Li ◽  
Haidong Li ◽  
Guangyong You ◽  
Shouguang Yan ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Hyperspectral Image ◽  
Support Vector ◽  
Wavelet Coefficients ◽  
Weed Detection ◽  
Imaging Spectrometer ◽  
Spectral Bands ◽  
Spectrometer System ◽  
Field Imaging

Nowadays, sensors begin to play an essential role in smart-agriculture practices. Spectroscopy and the ground-based sensors have inspired widespread interest in the field of weed detection. Most studies focused on detection under ideal conditions, such as indoor or under artificial lighting, and more studies in the actual field environment are needed to test the applicability of this sensor technology. Meanwhile, hyperspectral image data collected by imaging spectrometer often has hundreds of channels and, thus, are large in size and highly redundant in information. Therefore, a key element in this application is to perform dimensionality reduction and feature extraction. However, the processing of highly dimensional spectral imaging data has not been given due attention in recent studies. In this study, a field imaging spectrometer system (FISS; 380–870 nm and 344 bands) was designed and used to discriminate carrot and three weed species (purslane, humifuse, and goosegrass) in the crop field. Dimensionality reduction was performed on the spectral data based on wavelet transform; the wavelet coefficients were extracted and used as the classification features in the weed detection model, and the results were compared with those obtained by using spectral bands as the classification feature. The classification features were selected using Wilks’ statistic-based stepwise selection, and the results of Fisher linear discriminant analysis (LDA) and the highly dimensional data processing-oriented support vector machine (SVM) were compared. The results indicated that multiclass discrimination among weeds or between crops and weeds can be achieved using a limited number of spectral bands (8 bands) with an overall classification accuracy of greater than 85%. When the number of spectral bands increased to 15, the classification accuracy was improved to greater than 90%; further increasing the number of bands did not significantly improve the accuracy. Bands in the red edge region of plant spectra had strong discriminant capability. In terms of classification features, wavelet coefficients outperformed raw spectral bands when there were a limited number of variables. However, the difference between the two was minimal when the number of variables increased to a certain level. Among different discrimination methods, SVM, which is capable of nonlinear classification, performed better.

scholarly journals Volatility measurement of atmospheric submicron aerosols in an urban atmosphere in southern China

2017 ◽  
Author(s):  
Li-Ming Cao ◽  
Xiao-Feng Huang ◽  
Yuan-Yuan Li ◽  
Min Hu ◽  
Ling-Yan He
Keyword(s):  
Southern China ◽  
Chemical Species ◽  
Urban Atmosphere ◽  
Chemical Components ◽  
Aerosol Mass Spectrometer ◽  
Aerosol Mass ◽  
Aerosol Pollution ◽  
Mass Spectrometer System ◽  
Spectrometer System

Abstract. Aerosol pollution has been a very serious environmental problem in China for many years. The volatility of aerosols can affect the distribution of compounds in the gas and aerosol phases, the atmospheric fates of the corresponding components and the measurement of the concentration of aerosols. Compared to the characterization of chemical composition, few studies have focused on the volatility of aerosols in China. In this study, a TD-AMS (Thermo-Denuder – Aerosol Mass Spectrometer) system was deployed to study the volatility of non-refractory PM1 species during winter in Shenzhen. To our knowledge, this paper is the first report of the volatilities of aerosol chemical components based on a TD-AMS system in China. The average PM1 mass concentration during the experiment was 42.7 ± 20.1 μg m−3, with organics being the most abundant component (43.2 % of the total mass). The volatility of chemical species measured by the AMS varied, with nitrate showing the highest volatility, with an MFR (mass fraction remaining) of 0.57 at 50 °C. Organics showed semi-volatile characteristics (the MFR was 0.88 at 50 °C), and the volatility had a relatively linear correlation with the TD temperature (from 50 to 200 °C), with an evaporation rate of 0.45 %·°C1. Five subtypes of OA were resolved from total OAs by PMF for data obtained under both ambient temperature and high temperatures through the TD, including a hydrocarbon-like OA (HOA, accounting for 13.5 %), a cooking OA (COA, 20.6 %), a biomass burning OA (BBOA, 8.9 %) and two oxygenated OAs (OOA): a less-oxidized OOA (LO-OOA, 39.1 %) and a more-oxidized OOA (MO-OOA, 17.9 %). Different OA species presented different volatilities; the volatility sequence of OA factors at 50 °C was HOA (MFR of 0.56) > LO-OOA (0.70) > COA (0.85) ≈ BBOA (0.87) > MO-OOA (0.99). The volatility sequence of OA components suggested that HOA, rather than BBOA or COA, could be a potentially important source of LO-OOA through the oxidizing process of Evaporation – Oxidation in gas phase – Condensation. The results above can contribute to the understanding of the formation and ageing of submicron aerosols in the atmosphere and will help to constrain aerosol modelling inputs.

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