Diffuse Reflectance Illumination Module Improvements in Near-Infrared Spectrometer for Heterogeneous Sample Analysis
This paper presents a portable and affordable prototype using a Digital Micro-mirror Device (DMD) based Near-Infrared Spectrometer and an improved diffuse reflectance illumination module (DRIM). The improved DRIM produced optical geometry parameters such as 3.5mm standoff distance (SD), 2mm depth of...
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| Auteurs principaux: | , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
IEEE
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/c5c199badc6b4f389d381e2b80b40cf4 |
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| Résumé: | This paper presents a portable and affordable prototype using a Digital Micro-mirror Device (DMD) based Near-Infrared Spectrometer and an improved diffuse reflectance illumination module (DRIM). The improved DRIM produced optical geometry parameters such as 3.5mm standoff distance (SD), 2mm depth of overlap illumination area (DOIR), and 4mm sample active illumination area (SAIA). It enables the single and multi-point scans to determine the crude content of various food quality parameters and homogeneity by averaging spatial inhomogeneities of raw material and heterogeneous sample mixtures placed at a standoff distance. The prototype outperformed the current portable NIRS by a factor of 2–3 in terms of optical throughput, signal-to-noise ratio, and baseline. The prototype’s repeatability was determined by assessing pure samples such as chalk powder, red chili powder, wheat, and groundnuts using scattering correction techniques and was computed <1% relative standard deviation (RSD). Partial least square regression (PLSR) was used to build a prediction model using around 100 randomly selected poultry feed samples with 10-20% moisture ranges-. Results of the experiments indicated values for the coefficient of determination as high as 0.991, and root mean square error was 0.32%, and a prediction accuracy with maximum deviation of <1%. The results indicated that the prototype was able to efficiently predict heterogeneous mixtures and food grains, provide new specifications for single and multi-point scan measurements, and this carries a lot of potential as a stand-alone or in-line food monitoring tool. |
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