• Optoelectronics Letters
  • Vol. 14, Issue 5, 380 (2018)
Wen-li YAN1, Shui-ying REN2, Xia-xia YUE3, Jun TANG3, Chen CHEN1, Xiao-yi Lü1、4、*, and Jia-qing MO1
Author Affiliations
  • 1School of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
  • 2Xinjiang Research Institute of Product Quality Supervision and Inspection, Urumqi 830046, China
  • 3Physics and Chemistry Detecting Center, Xinjiang University, Urumqi 830046, China
  • 4Institute of Health and Environmental Medicine, Academy of Military Medical Science, Tianjin 300050, China
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    In this paper, a new method for the rapid, economical and convenient detection of cyclic adenosine monophosphate (cAMP) in jujube is proposed and verified. Based on near-infrared (NIR) fiber spectroscopy combined with stoichio-metric analysis, the cAMP content in red jujube can be quickly detected. 68 red jujube samples were used for the NIR spectroscopy data acquisition and the corresponding chemical values were determined. The sample set was adjusted based on the joint XY distance (SPXY) to select the correction sample set. After different preprocessing on the spectra, the partial least squares (PLS) method was used to establish the model, and the smoothed and normalized PLS model result was obtained better. The model's correction correlation coefficient (Rc), correction set mean square error (RMSEC), prediction correlation coefficient (Rp), and prediction and mean square error (RMSEP) are 0.951 5, 25.793 7, 0.910 8 and 28.228 0, respectively. The results show that NIR combined with specific chemometric methods can achieve rapid de-tection of cAMP in red jujube.
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    YAN Wen-li, REN Shui-ying, YUE Xia-xia, TANG Jun, CHEN Chen, Lü Xiao-yi, MO Jia-qing. Rapid detection of cAMP content in red jujube using near-infrared spectroscopy[J]. Optoelectronics Letters, 2018, 14(5): 380
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    Category: Measurement Devices and Methods
    Received: Jul. 25, 2018
    Accepted: Aug. 6, 2018
    Posted: May. 1, 2018
    Published Online: Jul. 13, 2020
    The Author Email: Xiao-yi Lü (xiaoz813@163.com)