Exploring the Spectral Database of Food Samples Using ASD Field Spec 4 Spectroradiometer
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Abstract
Accurate and precise spectral measurements reflect the inherent properties of the material being analyzed. However, certain factors contributing to changes in spectral signatures can adversely affect the quality of spectral measurements. In this study, we utilized the ASD FieldSpec4 Spectroradiometer, which offers an extensive spectral range spanning from 350 to 2500 nanometers, along with its associated software, to construct a spectral library of food samples. This paper defines a method for standard spectral reflectance measurements and the process for data collection.
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Vinod, P., Ratnadeep, J., & Deshmukh, R. (2017). Hyperspectal remote sensing for agriculture: A review. Int. J. Comput. Appl, 172(7), 30-34. https://doi.org/10.5120/ijca2017915185
Dhangar, V. D., Dhole, P. V., Shejul, S. D., & Gawali, B. W. (2023, April). Assessment of Adulteration from Food Products using ASD Field Spec4. In 2023 IEEE International Conference on Contemporary Computing and Communications (InC4) (Vol. 1, pp. 1-6). IEEE. https://doi.org/10.1109/InC457730.2023.10262925
Danner, M., Locherer, M., Hank, T., & Richter, K. (2015). Spectral Sampling with the ASD FIELDSPEC
Janse, P. V., Kayte, J. N., Agrawal, R. V., & Deshmukh, R. R. (2018, December). Standard spectral reflectance measurements for ASD FieldSpec Spectroradiometer. In 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) (pp. 729-733). IEEE. https://doi.org/10.1109/PDGC.2018.8745808
Bhogilal, V. H. (2019). Spectral Reflectance of Bulk Soil Samples and Their Aggregate Size Fractions for Estimating Soil Properties (Doctoral dissertation, IIT Kharagpur).
Yang, H., Kuang, B., & Mouazen, A. M. (2011, January). Affect of different preprocessing methods on principal component analysis for soil classification. In 2011 Third International Conference on Measuring Technology and Mechatronics Automation (Vol. 1, pp. 355-358). IEEE. https://doi.org/10.1109/ICMTMA.2011.90
Kavitha, A. V., Srikrishna, Dr. A., & Satyanarayana, Dr. Ch. (2019). Classification of Land Cover from Remote Sensing Images using Morphological Linear Contact Distributions and Rough Sets. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 3, pp. 676–688). https://doi.org/10.35940/ijrte.b2822.098319
Nagaraju, T., & Suneetha, Dr. Ch. (2019). Distributed Framework for Processing High Resolution Remote Sensing Images. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 1, pp. 4287–4292). https://doi.org/10.35940/ijeat.j9976.109119
V E, S., Moorthy, U., Park, J., Shin, C., & Cho, Y. (2019). Internet Role in Remote Sensing and Geo Informatics System. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 2, pp. 57–64). https://doi.org/10.35940/ijitee.a4859.129219
Dutta, D., Halder, T., Penchala, A., Krishna, K. V., Prashnath, G., & Chakravarty, D. (2024). A Case Study on Image Co-Registration of Hyper Spectral and Dual (L & S) Band SAR Data and Ore Findings Over Zewar Mines, India. In International Journal of Emerging Science and Engineering (Vol. 12, Issue 6, pp. 17–25). https://doi.org/10.35940/ijese.a8055.12060524
Proença, M. da C. (2022). On the Need of Quick Monitoring for Wildfire Response from City Halls. In Indian Journal of Image Processing and Recognition (Vol. 2, Issue 3, pp. 1–4). https://doi.org/10.54105/ijipr.c1014.042322