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Pharmacol."},{"key":"10.1016\/j.saa.2024.124480_b0365","doi-asserted-by":"crossref","DOI":"10.1016\/j.saa.2023.123398","article-title":"Primary assessment of macronutrients in durian (CV Monthong) leaves using near infrared spectroscopy with wavelength selection, Spectrochim","volume":"304","author":"Phanomsophon","year":"2024","journal-title":"Acta Part A Mol. Biomol. Spectrosc."},{"key":"10.1016\/j.saa.2024.124480_b0370","article-title":"Practical NIR spectroscopy with applications in food and beverage analysis","author":"Osborne","year":"1993","journal-title":"Longman Scientific and Technical"},{"key":"10.1016\/j.saa.2024.124480_b0375","doi-asserted-by":"crossref","first-page":"43713","DOI":"10.1038\/srep43713","article-title":"The molecular structural features controlling stickiness in cooked rice, a major palatability determinant","volume":"7","author":"Li","year":"2017","journal-title":"Sci. Rep."},{"key":"10.1016\/j.saa.2024.124480_b0380","doi-asserted-by":"crossref","unstructured":"R. Pawar, W. Jadhav, S. Bhusare, R. Borade, S. Farber, D. Itzkowitz, A. Domb, Polysaccharides as carriers of bioactive agents for medical applications, in: Natural-based polymers for biomedical applications, Natural-Based Polymers for Biomedical Applications, 2008, pp. 3-53. doi: 10.1533\/9781845694814.1.3.","DOI":"10.1533\/9781845694814.1.3"},{"key":"10.1016\/j.saa.2024.124480_b0385","doi-asserted-by":"crossref","unstructured":"P. Williams, K. 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