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All issues Volume 80 (2023) BIO Web Conf., 80 (2023) 01001 References
Open Access
Issue
BIO Web Conf.
Volume 80, 2023
4th International Conference on Smart and Innovative Agriculture (ICoSIA 2023)
Article Number 01001
Number of page(s) 8
Section Agricultural Big Data Analysis
DOI https://doi.org/10.1051/bioconf/20238001001
Published online 14 December 2023
  • FAO, “Rice Market Monitor, Food Agric. Organ. United Nations, ” Food Agric. Organ. United Nations, vol. XXI, no. 1, pp. 1–38, 2018. [Google Scholar]
  • R. Johnson, “Food fraud and ‘Economically motivated adulteration’ of food and food ingredients, ” Food Fraud Adulterated Ingredients Background, Issues, Fed. Action, pp. 1–56, 2014. [Google Scholar]
  • X. Feng, Q. Zhang, P. Cong, and Z. Zhu, “Preliminary study on classification of rice and detection of paraffin in the adulterated samples by Raman spectroscopy combined with multivariate analysis, ” Talanta, vol. 115, pp. 548–555, 2013, doi: 10.1016/j.talanta.2013.05.072. [CrossRef] [PubMed] [Google Scholar]
  • K. Renu et al., “Molecular mechanism of heavy metals (Lead, Chromium, Arsenic, Mercury, Nickel and Cadmium) induced hepatotoxicity – A review, ” Chemosphere, vol. 271, p. 129735, 2021, doi: 10.1016/j.chemosphere.2021.129735. [CrossRef] [PubMed] [Google Scholar]
  • J. F. X. Fen, L. Shiyao, C. Hui, and A. Di, “Determination of illegally added paraffin in rice by GC-MS/MS, ” Hubei Prov. Eng. Technol. Res. Cent. Food Qual. Saf. Test, Hubei Prov. Inst. Food Superv. Test, Wuhan 430075, China), 2019, doi: 10.11882/j.issn.0254-5071.2019.03.038. [Google Scholar]
  • D. Huang et al., “Concentrations of and risks posed by short-chain and medium-chain chlorinated paraffins in soil at a chemical industrial park on the southeast coast of China, ” Environ. Pollut., vol. 258, p. 113704, 2020, doi: 10.1016/j.envpol.2019.113704. [CrossRef] [Google Scholar]
  • U. Aline, T. Bhattacharya, M. A. Faqeerzada, M. S. Kim, I. Baek, and B. Cho, “Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables : a review, ” Front. Plant Sci., no. August, pp. 1–18, 2023, doi: 10.3389/fpls.2023.1240361. [Google Scholar]
  • M. Hussain Khan, Z. Saleem, M. Ahmad, A. Sohaib, H. Ayaz, and M. Mazzara, “Hyperspectral imaging for color adulteration detection in red chili, ” Appl. Sci., vol. 10, no. 17, 2020, doi: 10.3390/app10175955. [Google Scholar]
  • M. Kamruzzaman, “Fraud Detection in Meat Using Hyperspectral Imaging, ” Meat Muscle Biol., vol. 5, no. 3, 2021, doi: 10.22175/mmb.12946. [CrossRef] [Google Scholar]
  • M. A. Faqeerzada et al., “Hyperspectral shortwave infrared image analysis for detection of adulterants in almond powder with one-class classification method, ” Sensors (Switzerland), vol. 20, no. 20, p. 5855, 2020, doi: 10.3390/s20205855. [CrossRef] [Google Scholar]
  • R. E. Masithoh, L. M. Kandpal, S. Lohumi, W. S. Yoon, H. Z. Amanah, and B. K. Cho, “Shortwave Infrared Hyperspectral Imaging for the Determination and Visualization of Chemical Contents of Wheat and Tuber Flour, ” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 4, pp. 1574–1579, 2022, doi: 10.18517/ijaseit.12.4.14266. [CrossRef] [Google Scholar]
  • H. Z. Amanah et al., “Near-infrared hyperspectral imaging (Nir-hsi) for nondestructive prediction of anthocyanins content in black rice seeds, ” Appl. Sci., vol. 11, no. 11, 2021, doi: 10.3390/app11114841. [CrossRef] [Google Scholar]
  • A. Rahman, Q. Wu, H. Chang, S. Wang, J. Yan, and H. Xu, “Assessment of Intact Macadamia Nut Internal Defects Using NearInfrared Spectroscopy, ” ASABE Annu. Int. Meet., pp. 3–8, 2020. [Google Scholar]
  • A. Rahman et al., “Quality analysis of stored bell peppers using near-infrared hyperspectral imaging, ” Trans. ASABE, vol. 61, no. 4, pp. 1199–1207, 2018, doi: 10.13031/trans.12482. [CrossRef] [Google Scholar]
  • Å. Rinnan, F. van den Berg, and S. B. Engelsen, “Review of the most common preprocessing techniques for near-infrared spectra, ” TrAC Trends in Analytical Chemistry. 2009, doi: 10.1016/j.trac.2009.07.007. [Google Scholar]
  • J. Yasmin, M. R. Ahmed, S. Lohumi, C. Wakholi, M. S. Kim, and B. K. Cho, “Classification method for viability screening of naturally aged watermelon seeds using FTNIR spectroscopy, ” Sensors (Switzerland), vol. 19, no. 5, 2019, doi: 10.3390/s19051190. [CrossRef] [Google Scholar]
  • H. Chung, “Applications of near-infrared spectroscopy in refineries and important issues to address, ” Applied Spectroscopy Reviews, vol. 42, no. 3. pp. 251–285, 2007, doi: 10.1080/05704920701293778. [CrossRef] [Google Scholar]
  • S. Serranti, D. Cesare, and G. Bonifazi, “The development of a hyperspectral imaging method for the detection of Fusariumdamaged, yellow berry and vitreous Italian durum wheat kernels, ” Biosyst. Eng., vol. 115, no. 1, pp. 20–30, 2013, doi: 10.1016/j.biosystemseng.2013.01.011. [CrossRef] [Google Scholar]
  • Z. Wu, G. Ouyang, X. Shi, Q. Ma, G. Wan, and Y. Qiao, “Absorption and quantitative characteristics of C-H bond and O-H bond of NIR, ” Opt. Spectrosc. (English Transl. Opt. i Spektrosk., vol. 117, no. 5, pp. 703–709, 2014, doi: 10.1134/S0030400X1411023X. [Google Scholar]
  • S. Weng et al., “Hyperspectral imaging for accurate determination of rice variety using a deep learning network with multi-feature fusion, ” Spectrochim. Acta Part A Mol. Biomol. Spectrosc., vol. 234, p. 118237, 2020, doi: 10.1016/j.saa.2020.118237. [CrossRef] [Google Scholar]
  • L. Ma et al., “Systematic discovery about NIR spectral assignment from chemical structural property to natural chemical compounds, ” Sci. Rep., vol. 9, no. 1, pp. 1–17, 2019, doi: 10.1038/s41598-019-45945-y. [Google Scholar]
  • J. Hang, D. Shi, J. Neufeld, K. E. Bett, and J. D. House, “Prediction of protein and amino acid contents in whole and ground lentils using near-infrared reflectance spectroscopy, ” Lwt, vol. 165, no. June, p. 113669, 2022, doi: 10.1016/j.lwt.2022.113669. [CrossRef] [Google Scholar]
  • R. Pourdarbani, S. Sabzi, S. Jarolmasjed, and T. Panagopoulos, “Determination of the most effective wavelengths for prediction of fuji apple starch and total soluble solids properties, ” Appl. Sci., vol. 10, no. 22, pp. 1– 16, 2020, doi: 10.3390/app10228145. [Google Scholar]
  • J. H. Lam, K. J. Tu, and S. Kim, “Narrowband diffuse reflectance spectroscopy in the 900– 1000 nm wavelength region to quantify water and lipid content of turbid media, ” Biomed. Opt. Express, vol. 12, no. 6, p. 3091, 2021, doi: 10.1364/boe.425451. [CrossRef] [Google Scholar]

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