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Short communication: The preliminary research of identifying motor and
sensory fascicles using near infrared diffuse reflectance spectroscopy in combination with soft independent modelling of class analogy ShouShan Bu,a
Shaofei Xie,b Jun Lu,b XiaoJian Cao,a,* BingRen Xiangb,* and Ye Yeb aDepartment of
Orthopaedics, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, China. E-mail:
caoxiaoj001@yahoo.com.cn bCentre for Instrumental Analysis, China Pharmaceutical University, Nanjing 210009, China
ABSTRACT:
The aim of this study was to
determine the feasibility of using near infrared (NIR) spectroscopy to identify motor and sensory fascicles. Using 12 adult Beagle dogs which were anesthetised generally and
sacrificed, canalis vertebralis were opened and both anterior and posterior roots were harvested from the first lumbar vertebrae to the second sacral vertebrae
(L1–S2). A total of 126 anterior roots and 126 posterior roots were collected. NIR diffuse reflectance spectra were measured using a Fourier
transform near infrared (FT-NIR) spectrometer fitted with a fibre-optic probe. The average spectrum of each sample was used for further analysis. There were no clear differences
from the spectra between the anterior and posterior root samples. Original spectral data were transformed to second derivative spectral data. Soft independent modelling of class
analogy (SIMCA) was employed to identify the anterior and posterior roots based upon differences in their spectral features. SIMCA models correctly classified 79–100%
for the anterior and posterior roots and mean 83.7%. This study demonstrated that NIR spectroscopy in combination with pattern recognition methods (SIMCA) could be used to
classify the anterior and posterior roots. This result suggests that NIR spectroscopy may provide a rapid, correct, non-destructive, low-cost means to quickly differentiate motor and
sensory fascicles in mixed nerves.
Keywords: motor/sensory nerve identification, near-infrared spectroscopy, SIMCA
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