|
|
|
|
|
|
Research of Mid-Infrared Time-Stretch Frequency Upconversion
Hyperspectral Imaging System |
PENG Bo1, WEN Zhao-yang1, WEN Qi1, LIU Ting-ting1, 2*, XING Shuai3, WU Teng-fei3, YAN Ming1, 2* |
1. State Key Laboratory of Precision Spectroscopy, East China Normal University, Shanghai 200062, China
2. Chongqing Key Laboratory of Precision Optics, Chongqing Institute of East China Normal University, Chongqing 401121, China
3. AVIC Changcheng Institute of Metrology & Measurement, Beijing 100095, China
|
|
|
Abstract Hyperspectral imaging is a non-contact, non-destructive detection method to analyze substances' chemical composition, physical properties, and morphology.Limited by the response speed and inherent noise of the detector,it is difficult for traditional hyperspectral imaging techniques to achieve high-speed and high signal-to-noise detection of molecular fingerprint spectra in the mid-infrared band. With the advantages of high measurement speed, high spectral resolution, and wide spectral coverage,the spectroscopy technology based on time-stretch frequency upconversion provides a reliable method for rapidlyanalyzing the type and morphology of the samples when combined with hyperspectral imaging technology. In thispaper, a mid-infrared time-stretch frequency upconversion hyperspectral imaging system was constructed. The average power of the 1 047 nm pump pulse and the 1 550 nm signal pulse generated by the same laser source is 2 W and 100 mW, respectively. Using synchronous pump technology, mid-infrared pulses were generated in one periodically poled lithium niobate crystal, and frequency upconverted into near-infrared pulses in another. This process transferred the mid-infrared molecular fingerprint spectra to the near-infrared band, which can effectively address the problem of lacking high-speed and low-noise detectors in the mid-infrared band. By tuning the operating temperature and working channels of the crystal,the detection range of the system can cover 2 700~3 900 nm, enabling the measurement of multiple samples. Combining the time-stretch method with hyperspectral imaging technology, the benzene solution's absorption spectra and spatial distribution information in a colorimetric dish were measured through point-by-point scanning. The spectral data obtained highly matched the results from a Fourier transform infrared spectrometer. Moreover, the system could perform hyperspectral imaging of a 600 μm×1 200 μm spatial region within 8 s. The acquisition time for a single pixel was 12.9 ns, and a spectral measurement speed of 77.6 MSpectra·s-1 and spectral resolution of 5.8 cm-1 was achieved. These results verified the systemhas the potential to measure the spectra and spatial distributionof liquid molecules within the spectral coverage range with highspeed and highresolution. This paper solves the problems of slow response speed, long integration time, and low signal-to-noise ratio of traditional hyperspectral methods in the mid-infrared band. It enables the spectraldetection and morphological measurement of multi-component samples with a spectral refresh rate of 107 frames per second. It could provide a new approach for imaging analysis in material and biological fields.
|
Received: 2023-08-17
Accepted: 2024-01-08
|
|
Corresponding Authors:
LIU Ting-ting, YAN Ming
E-mail: ttliu@lps.ecnu.edu.cn; myan@lps.ecnu.edu.cn
|
|
[1] Schliesser A,Picqué N,Hänsch T W. Nature Photonics,2012,6(7):440.
[2] YAN Ji-xiang,YAN Yan(阎吉祥,阎 研). Journal of Beijing Institute of Technology(北京理工大学学报),1998,18(1):110.
[3] Jacques S L. Physics in Medicine and Biology,2013,58(11):R37.
[4] WEI He-li,LIU Qing-hong,SONG Zheng-fang,et al(魏合理,刘庆红,宋正方,等). Journal of Infrared and Millimeter Waves(红外与毫米波学报),1997,16(6):19.
[5] Anirudh K,Xiao N,Adriana D B,et al. Scientific Reports,2021,11(1):24143.
[6] An D L,Sun F Y,Bian Y P,et al. Applied Spectroscopy Reviews,2023,58(10): 834.
[7] LIN Bai-yang, DANG Jing-min, ZHENG Chuan-tao, et al(蔺百杨,党敬民,郑传涛,等). Acta Photonica Sinica(光子学报),2018,47(4):73.
[8] YAN Ge, ZHANG Lei, YU Ling, et al(闫 格,张 磊,于 玲,等). Chinese Journal of Lasers(中国激光),2022,49(18):1810001.
[9] Hashimoto K,Badarla V R,Ideguchi T. Laser & Photonics Reviews,2021,15(1):2000374.
[10] CHEN Nan,WANG Yue,WANG Bo-yu,et al(陈 楠,王 玥,王博雨,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析),2021,41(4):1125.
[11] Porneala C,Willis D A. Applied Physics Letters,2006,89(21):211121.
[12] Rogalski A. Infrared Physics & Technology,2002,450(13):187.
[13] Solli D R,Ropers C,Koonath P,et al. Nature,2007,450(7172):1054.
[14] Runge A F J,Broderick N G R,Erkintalo M. Optica,2015,2(1):36.
[15] Herink G,Kurtz F,Jalali B,et al. Science,2017,356(6333):50.
[16] LÜ Lin-jie,WU Teng-fei,HAN Ji-bo,et al(吕林杰,武腾飞,韩继博,等). Infrared and Laser Engineering(红外与激光工程),2022,51(9):20210809.
[17] WANG Xiao-yue,WANG Zi-jian,PENG Bo,et al(王小月,王子健,彭 博,等). Laser & Optoelectronics Progress(激光与光电子学进展),2022,59(13):1336001.
[18] Hashimoto K,Nakamura T,Kageyama T,et al. Light: Science & Applications,2023,12(1):48.
[19] WANG Ye,ZHANG Song,ZHANG Bing(王 野,张 嵩,张 冰). Chinese Journal of Quantum Electronics(量子电子学报),2021,38(5):547.
[20] Murray R T,Runcorn T H,Kelleher E J R,et al. Optics Letters,2016,41(11):2446.
[21] Junaid S,Tomko J,Semtsiv M P,et al. Optics Express,2018,26(3):2203.
[22] Chen C,Lu Q M,Akhmadaliev S,et al. Optics and Laser Technology,2020,126:106128.
|
[1] |
YANG Cheng-en1, 2, GUO Rui-xue1, 3, XIN Ming-hao2, LI Meng4, LI Yu-ting2*, SU Ling1, 3*. Quantitative Determination of Polyphenols in Aronia Melanocarpa (Michx.) Elliott. by Mid-Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(11): 3075-3081. |
[2] |
WENG Shi-zhuang, PAN Mei-jing, TAN Yu-jian, ZHANG Qiao-qiao, ZHENG Ling*. Prediction of Soluble Solid Content in Apple Using Image Spectral Super-Resolution[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(11): 3095-3100. |
[3] |
SHI Rui1, 2, ZHANG Han2, WANG Cheng1, 2, KANG Kai2, LUO Bin1, 2*. Detection of Wheat Single Seed Vigor Using Hyperspectral Imaging and Spectrum Fusion Strategy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(11): 3206-3212. |
[4] |
KONG Li-qin1, 2, NIU Xiao-hu1, 2, WANG Cheng-lei1, 2, FENG Yao-ze1, 2, 3*, ZHU Ming1, 2. Application of Hyperspectral Imaging Technology in the Identification of Composite Adulteration Type in Beef Balls[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2183-2191. |
[5] |
WANG Hao-yu1, 2, 3, WEI Zi-yuan1, 2, 3, YANG Yong-xia1, 2, 3, HOU Jun-ying1, 2, 3, SUN Zhang-tong1, 2, 3, HU Jin1, 2, 3*. Estimation of Eggplant Leaf Nitrogen Content Based on Hyperspectral Imaging and Convolutional Auto-Encoders Networks[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(08): 2208-2215. |
[6] |
ZHAO Jia-le1, WANG Guang-long1, ZHOU Bing1*, YING Jia-ju1, LIU Jie1, LIN Chao2, CHEN Qi1, ZHAO Run-ze3. Target Detection Algorithm for Land-Based Hyperspectral Images
Associated With Geospatial Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(07): 2056-2065. |
[7] |
ZHANG Tian-liang1, 2, 3, 4, ZHANG Dong-xing1, 2, CUI Tao1, 2, YANG Li1, 2*, XIE Chun-ji1, 2, DU Zhao-hui1, 2, XIAO Tian-pu1, 2. Study on Nondestructive Testing of Corn Stalk Strength in Different
Periods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(06): 1703-1709. |
[8] |
WANG Zi-xuan1, YANG Liang2, 3, 4*, HUANG Ling-xia2, HE Yong4, ZHAO Li-hua3, ZHAN Peng-fei3. Nondestructive Determination of TSS Content in Postharvest Mulberry Fruits Using Hyperspectral Imaging and Deep Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(06): 1724-1730. |
[9] |
ZENG Hui, WEN Peng, YANG Guo-ming, ZHU Xing-ying, OU Dong-bin. Mid-IR Laser Absorption Diagnosis on Flow Characteristics for Mars
Entry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1277-1282. |
[10] |
SONG Shao-zhong1, LIU Yuan-yuan2, ZHOU Zi-yang3, TENG Xing3, LI Ji-hong3, LIU Jun-ling1, GAO Xun2*. Identification of Sorghum Breed by Hyperspectral Image Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1392-1397. |
[11] |
XIE Bai-heng1, MA Jin-fang1, ZHOU Yong-xin1, HAN Xue-qin1, CHEN Jia-ze1, ZHU Si-qi1, YANG Mao-xun2, 3*, HUANG Fu-rong1*. Identification of Citri Grandis Fructus Immaturus Based on Hyperspectral Combined With HHO-KELM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(05): 1494-1500. |
[12] |
ZHANG Fu1, 2, YU Huang1, XIONG Ying3, ZHANG Fang-yuan1, WANG Xin-yue1, LÜ Qing-feng4, WU Yi-ge4, ZHANG Ya-kun1, FU San-ling5*. Hyperspectral Non-Destructive Detection of Heat-Damaged Maize Seeds[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(04): 1165-1170. |
[13] |
JIANG Yue-peng, CAO Yun-hua*, WU Zhen-sen, CAO Yi-sen, HU Sui-jing. Measurement of Mid-Wave Infrared Hyperspectral Imaging
Characteristics of Ground Targets[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(04): 937-944. |
[14] |
XIA Yan-qiu1, XIE Pei-yuan1, NAY MIN AUNG1, ZHANG Tao1, FENG Xin1, 2*. The Improved Genetic Algorithm is Embedded Into the Classical
Classification Algorithm to Realize the Synchronous
Identification of Small Quantity and Multi Types of
Lubricating Oil Additives[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 744-750. |
[15] |
LI Guo-hou1, LI Ze-xu1, JIN Song-lin1, ZHAO Wen-yi2, PAN Xi-peng3, LIANG Zheng4, QIN Li5, ZHANG Wei-dong1*. Mix Convolutional Neural Networks for Hyperspectral Wheat Variety
Discrimination[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 807-813. |
|
|
|
|