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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zlknqk4ahbcsthbafxw55emm7a" style="color: black;">Journal of Sensors</a>
As portable spectrometers have been developed, the research of spectral analysis has evolved from a traditional laboratory-based closed environment to a network-connected open environment. Consequently, its application areas are expanding in combination with machine learning techniques. The device-to-device variation in the spectral response of portable spectrometers is a critical issue in a machine learning-based service scenario since the classification performance is highly dependent on the<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2019/8392583">doi:10.1155/2019/8392583</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ulcnlr6w6ncm7gadt2zpvi7e2u">fatcat:ulcnlr6w6ncm7gadt2zpvi7e2u</a> </span>
more »... onsistency of spectral responses from each spectrometer. To minimize device-to-device variation, a cuboid prism is employed instead of a combination of mirrors and prism to construct an optical system for the spectrometer. The spectral responses are calibrated to correct pixel shift on the image sensor. Experimental results show that the proposed method can minimize the device-to-device variation in spectral response of portable spectrometers.
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