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An Ultra-Portable Vis-NIR Spectrometer with an Integrated Light Source for Chemometric Applications

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On-site material inspection and quality analysis of food and agricultural produce require portable sensing systems. We report the development of a miniaturized spectrometer with an integrated light source operating in the visible and near-infrared range, for chemometrics based material-sensing applications. The proposed system uses off-the-shelf light source and detector. The electronic circuit is designed, developed, and tested in-house. To validate the system’s usability, a set of classification experiments are carried out with measured spectra from culinary white powders and medicinal pills. Several classification algorithms are used to build predictive models and the best-suited ones give prediction accuracies of 80% and 92.6% respectively. A regression model built to estimate the curcumin content in turmeric shows a coefficient-of-determination of 0.97 for prediction. With more than 90% repeatability in the measured reflectance spectra, robustness of the device is demonstrated. Realization of a portable spectrometer, along with a framework for building appropriate prediction models, is expected to spur the development of point-of-use material sensing in the Vis-NIR range.
Title: An Ultra-Portable Vis-NIR Spectrometer with an Integrated Light Source for Chemometric Applications
Description:
On-site material inspection and quality analysis of food and agricultural produce require portable sensing systems.
We report the development of a miniaturized spectrometer with an integrated light source operating in the visible and near-infrared range, for chemometrics based material-sensing applications.
The proposed system uses off-the-shelf light source and detector.
The electronic circuit is designed, developed, and tested in-house.
To validate the system’s usability, a set of classification experiments are carried out with measured spectra from culinary white powders and medicinal pills.
Several classification algorithms are used to build predictive models and the best-suited ones give prediction accuracies of 80% and 92.
6% respectively.
A regression model built to estimate the curcumin content in turmeric shows a coefficient-of-determination of 0.
97 for prediction.
With more than 90% repeatability in the measured reflectance spectra, robustness of the device is demonstrated.
Realization of a portable spectrometer, along with a framework for building appropriate prediction models, is expected to spur the development of point-of-use material sensing in the Vis-NIR range.

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