Multivariate Optical Computing and Next-generation Spectrometer

被引:1
|
作者
Duan Chao-Shu [1 ]
Cai Wen-Sheng [1 ,2 ]
Shao Xue-Guang [1 ,2 ,3 ]
机构
[1] Nankai Univ, Coll Chem, Res Ctr Analyt Sci, Tianjin 300071, Peoples R China
[2] Tianjin Key Lab Biosensing & Mol Recognit, Tianjin 300071, Peoples R China
[3] State Key Lab Med Chem Biol, Tianjin 300071, Peoples R China
基金
中国国家自然科学基金;
关键词
Multivariate optical computing; Multivariate optical elements; Next-generation spectrometer; Chemometrics; Filter design; Review; DIGITAL MICROMIRROR DEVICE; TAXONOMIC CLASSIFICATION; ELEMENT; DESIGN; FLUORESCENCE; PHYTOPLANKTON; TECHNOLOGY; PRECISION;
D O I
10.1016/S1872-2040(21)60093-2
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Multivariate optical computing (MOC) is a technique that uses optical filters to modulate the light to achieve calculation and obtain qualitative or quantitative results directly by detection of a single-point detector. Therefore, the complicated instrument design and cumbersome data processing are no longer needed for the MOC-based analytical instrument. The key technique for MOC is the design of the filter to achieve the detection that directly reflects the result for different purposes. In recent years, more efforts have been made for designation and optimization of the filters to enhance the performance. MOC-based spectrometer is a perfect combination of chemometrics and instrument design, and thus it is praised as the next-generation spectrometer. In this paper, the history, principle and the recent advances of MOC related techniques were summarized, with an emphasis on the filter design, filter optics and applications, and the challenges in the design and the optical implementation of the filters, as well as the integration of the instrument system.
引用
收藏
页码:593 / 601
页数:9
相关论文
共 52 条
  • [1] Programmable single-pixel-based broadband stimulated Raman scattering
    Berto, Pascal
    Scotte, Camille
    Galland, Frederic
    Rigneault, Herve
    de Aguiar, Hilton B.
    [J]. OPTICS LETTERS, 2017, 42 (09) : 1696 - 1699
  • [3] Multimode Imaging in the Thermal Infrared for Chemical Contrast Enhancement. Part 3: Visualizing Blood on Fabrics
    Brooke, Heather
    Baranowski, Megan R.
    McCutcheon, Jessica N.
    Morgan, Stephen L.
    Myrick, Michael L.
    [J]. ANALYTICAL CHEMISTRY, 2010, 82 (20) : 8427 - 8431
  • [4] Multimode Imaging in the Thermal Infrared for Chemical Contrast Enhancement. Part 1: Methodology
    Brooke, Heather
    Baranowski, Megan R.
    McCutcheon, Jessica N.
    Morgan, Stephen L.
    Myrick, Michael L.
    [J]. ANALYTICAL CHEMISTRY, 2010, 82 (20) : 8412 - 8420
  • [5] Multimode Imaging in the Thermal Infrared for Chemical Contrast Enhancement. Part 2: Simulation Driven Design
    Brooke, Heather
    Baranowski, Megan R.
    McCutcheon, Jessica N.
    Morgan, Stephen L.
    Myrick, Michael L.
    [J]. ANALYTICAL CHEMISTRY, 2010, 82 (20) : 8421 - 8426
  • [6] Hyperspectral integrated computational imaging
    Cassis, LA
    Urbas, A
    Lodder, RA
    [J]. ANALYTICAL AND BIOANALYTICAL CHEMISTRY, 2005, 382 (04) : 868 - 872
  • [7] In vivo applications of a molecular computing-based high-throughput NIR spectrometer
    Cassis, LA
    Dai, B
    Urbas, A
    Lodder, RA
    [J]. GENETICALLY ENGINEERED AND OPTICAL PROBES FOR BIOMEDICAL APPLICATIONS II, 2004, 5329 : 239 - 253
  • [8] Recent Trends in Compressive Raman Spectroscopy Using DMD-Based Binary Detection
    Cebeci, Derya
    Mankani, Bharat R.
    Ben-Amotz, Dor
    [J]. JOURNAL OF IMAGING, 2019, 5 (01):
  • [9] Pharmaceutical Application of Fast Raman Hyperspectral Imaging with Compressive Detection Strategy
    Cebeci-Maltas, Derya
    McCann, Ryan
    Wang, Ping
    Pinal, Rodolfo
    Romanach, Rodolfo
    Ben-Amotz, Dor
    [J]. JOURNAL OF PHARMACEUTICAL INNOVATION, 2014, 9 (01) : 1 - 4
  • [10] Chiou PY, 2005, NATURE, V436, P370, DOI [10.1038/nature03831, 10.1038/nature0383l]