@article{
 title = {A precise non-invasive blood glucose measurement system using NIR spectroscopy and Huber’s regression model},
 type = {article},
 year = {2019},
 keywords = {Blood glucose measurement,NIR spectroscopy,Non-invasive system,Regression model,Statistical analysis},
 volume = {51},
 id = {9dd55191-17dd-31b7-ae3e-82a60cf54fb6},
 created = {2019-02-17T23:59:00.000Z},
 file_attached = {false},
 profile_id = {11ae403c-c558-3358-87f9-dadc957bb57d},
 last_modified = {2020-11-20T06:15:33.826Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {© 2019, Springer Science+Business Media, LLC, part of Springer Nature.  Diabetes is one of the prominent diseases around the world. Presently, invasive techniques need a finger prick blood sample. A repetitively painful procedure that produces the chance of infection. To resolve this issue, non-invasive measurement approach is proposed. In this paper, an efficient NIR wave based optical detection system is proposed with optimized post-processing regression model. After real-time data analysis, it has been found that the coefficient of determination (R 2 ) is improved with the value of 0.9084 using proposed regression model. Mean absolute derivative is also increased with 3.87 mg/dl corresponding to predicted blood glucose concentration. Mean absolute relative difference has exceeded to 3.25%, and average error is improved with 3.77% using proposed regression model. Average accuaracy has been analyzed 94–95% for predicted blood glucose concentration.},
 bibtype = {article},
 author = {Jain, P. and Maddila, R. and Joshi, A.M.},
 doi = {10.1007/s11082-019-1766-3},
 journal = {Optical and Quantum Electronics},
 number = {2}
}

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