Open AccessDiffuse Reflectance Illumination Module Improvements in Near-Infrared Spectrometer for Heterogeneous Sample Analysis
Author(s) -
Umachandi Mantena,
Sourabh Roy,
Ramesh Datla
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3126393
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.