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1Academic Journal
Authors: D. A. Metlenkin, R. A. Platova, Yu. T. Platov, O. V. Fedoseenko, O. V. Sadkova, Д. А. Метленкин, Р. А. Платова, Ю. Т. Платов, О. В. Федосеенко, О. В. Садкова
Source: Food systems; Vol 6, No 1 (2023); 46-52 ; Пищевые системы; Vol 6, No 1 (2023); 46-52 ; 2618-7272 ; 2618-9771 ; 10.21323/2618-9771-2023-6-1
Subject Terms: PLS, quality, hyperspectral imaging, multivariate analysis, moisture, dry matter, качество, гиперспектральное изображение, многомерный анализ, влажность, сухой остаток
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Relation: https://www.fsjour.com/jour/article/view/228/216; Hurtado-Fernandez, E., Fernandez-Gutierrez, A., Carrasco-Pancorbo, A. (2018). Avocado fruit — Persea americana. Chapter in a book: Exotic Fruits. Academic Press, 2018. https://doi.org/10.1016/B978–0–12–803138–4.00001–0; Magwaza, L. S., Tesfay, S. Z. (2015). A review of destructive and non-destructive methods for determining avocado fruit maturity. Food and Bioprocess Technology, 8(10), 1995–2011. https://doi.org/10.1007/s11947–015–1568-y; UNECE STANDARD FFV-42. 2019. ‘Concerning the marketing and commercial quality control of Avocados’. Agricultural Quality Standards, Geneva, Switzerland.; Donetti, M., Terry, L. A. (2014). Biochemical markers defining growing area and ripening stage of imported avocado fruit cv. Hass. Journal of Food Composition and Analysis, 34(1), 90–98. https://doi.org/10.1016/j.jfca.2013.11.011; Ochoa-Ascencio, S., Hertog, M. L., Nicolaï, B. M. (2009). Modelling the transient effect of 1-MCP on ‘Hass’ avocado softening: A Mexican comparative study. Postharvest Biology and Technology, 51(1), 62–72. https://doi.org/10.1016/j.postharvbio.2008.06.002; Hussain, A., Pu, H., Sun, D. -W. (2018). Innovative nondestructive imaging techniques for ripening and maturity of fruits — A review of recent applications. Trends in Food Science and Technology, 72, 144–152. https://doi.org/10.1016/j.tifs.2017.12.010; Lohumi, S., Lee, S., Lee, H., Cho, B. -K. (2015). A review of vibrational spectroscopic techniques for the detection of food authenticity and adulteration. Trends in Food Science and Technology, 46(1), 85–98. https://doi.org/10.1016/j.tifs.2015.08.003; Elmasry, G., Kamruzzaman, M., Sun, D. -W., Allen, P. (2012). Principles and applications of hyperspectral imaging in quality evaluation of agrofood products: A review. Critical Reviews in Food Science and Nutrition, 52(11), 999–1023. https://doi.org/10.1080/10408398.2010.543495; Manley, M. (2014). Near-infrared spectroscopy and hyperspectral imaging: non-destructive analysis of biological materials. Chemical Society Reviews, 43(24), 8200–8214. https://doi.org/10.1039/c4cs00062e; Faltynkova, A., Johnsen, G., Wagner, M. (2021). Hyperspectral imaging as an emerging tool to analyze microplastics: a systematic review and recommendations for future development. Microplastics and Nanoplastics, 1(1), Article 13. https://doi.org/10.1186/s43591–021–00014-y; Rodionova, O. Ye., Pomerantsev, A.L. (2006). Chemometrics: Achievements and prospects. Russian Chemical Reviews, 75(4), 271–287. https://doi.org/10.1070/RC2006v075n04ABEH003599; Granato, D., Putnik, P., Kovačević, D. B., Santos, J. S., Calado, V., Rocha, R. S. et al. (2018). Trends in chemometrics: Food authentication, microbiology, and effects of processing. Comprehensive Reviews in Food Science and Food Safety, 17(3), 663–677. https://doi.org/10.1111/1541–4337.12341; Pinto, J., Rueda-Chacón, H., Arguello, H. (2019). Classification of Hass avocado (persea americana mill) in terms of its ripening via hyperspectral images. TecnoLógicas, 22(45), 111–130. https://doi.org/10.22430/22565337.1232; Vega Diaz, J. J., Sandoval Aldana, A. P., Reina Zuluaga, D. V. (2021). Prediction of dry matter content of recently harvested ‘Hass’ avocado fruits using hyperspectral imaging. Journal of the Science of Food and Agriculture, 101(3), 897–906. https://doi.org/10.1002/jsfa.10697; Behmann, J., Acebron, K., Emin, D., Bennertz, S., Matsubara, S., Thomas, S. et al. (2018). Specim IQ: Evaluation of a new, miniaturized handheld hyperspectral camera and its application for plant phenotyping and disease detection. Sensors (Switzerland), 18(2), Article 441. https://doi.org/10.3390/s18020441; Lyu, Y. (2019). Identify the ripening stage of avocado by multispectral camera using semi-supervised learning on small dataset. Thesis (M. Phil.)-Hong Kong University of Science and Technology, 2019.; Albedo. Hyperspectral data processing software. Retrieved from https://geo.mipt.ru/albedo. Accessed October 20, 2022.; Ashton, O.B.O., Wong, M., McGhie, T. K., Vather, R., Wang, Y., RequejoJackman, C. et al. (2006). Pigments in avocado tissue and oil. Journal of Agricultural and Food Chemistry, 54(26), 10151–10158. https://doi.org/10.1021/jf061809j; Parodi, G., Sanchez, M., Daga, W. (November 12–16, 2007). Correlation of oil content, dry matter and pulp moisture as harvest indicators in Hass avo- cado fruit (Persea americana Mill) grown under two conditions of orchards in Chincha-Peru. Proceedings VI World Avocado Congress (Actas VI Congreso Mundial del Aguacate). Viña Del Mar, Chile, 2007.; Hofman, P. J., Jobin-Décor, M., Giles, J. (2000). Percentage of dry matter and oil content are not reliable indicators of fruit maturity or quality in late-harvested ‘Hass’ avocado. HortScience, 35(4), 694–695. https://doi.org/10.21273/HORTSCI.35.4.694; Posom, J., Klaprachan, J., Rattanasopa, K., Sirisomboon, P., Saengprachatanarug, K., Wongpichet, S. (2020). Predicting marian plum fruit quality without environmental condition impact by handheld visible – near-infrared spectroscopy. ACS Omega, 5(43), 27909–27921. https://doi.org/10.1021/acsomega.0c03203; Jamshidi, B., Minaei, S., Mohajerani, E., Ghassemian, H. (2014). Prediction of soluble solids in oranges using visible/near-infrared spectroscopy: Effect of peel. International Journal of Food Properties, 17(7), 1460–1468. https://doi.org/10.1080/10942912.2012.717332; Cen, H., He, Y. (2007). Theory and application of near infrared reflectance spectroscopy in determination of food quality. Trends in Food Science and Technology, 18(2), 72–83. https://doi.org/10.1016/j.tifs.2006.09.003; Croft, H., Chen, J. M. (2017). Leaf pigment content. Chapter in a book: Comprehensive Remote Sensing. Elsevier, 2017. https://doi.org/10.1016/B978–0–12–409548–9.10547–0; Saha, S., Singh, J., Paul, A., Sarkar, R., Khan, Z., Banerjee, K. (2020). Anthocyanin profiling using UV–VIS spectroscopy and liquid chromatography mass spectrometry. Journal of AOAC International, 103(1), 23–39. https://doi.org/10.5740/jaoacint.19–0201; Cox, K. A., McGhie, T. K., White, A., Woolf, A. B. (2004). Skin colour and pigment changes during ripening of ‘Hass’ avocado fruit. Postharvest Biology and Technology, 31(3), 287–294. https://doi.org/10.1016/j.postharvbio.2003.09.008; Anne Frank Joe, A. Gopal, A. (April 20–21, 2017). Identification of spectral regions of the key components in the near infrared spectrum of wheat grain. Proceedings of IEEE International Conference on Circuit, Power and Computing Technologies, ICCPCT. Kollam, 2017. https://doi.org/10.1109/ICCPCT.2017.8074207; Ollinger, S. V. (2011). Sources of variability in canopy reflectance and the convergent properties of plants. New Phytologist, 189(2), 375–394. https://doi.org/10.1111/j.1469–8137.2010.03536.x; https://www.fsjour.com/jour/article/view/228
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2Academic Journal
Authors: Sergey A. Rylov, Pavel V. Melnikov, Igor A. Pestunov
Source: Journal of Siberian Federal University. Engineering & Technologies. 10:805-811
Subject Terms: hyperspectral images, spectral-spatial classification, гиперспектральное изображение, 4. Education, 0103 physical sciences, локальный контекст, спектрально-текстурная классификация, local context, 01 natural sciences
Access URL: http://elib.sfu-kras.ru/bitstream/2311/35014/1/11_Melnikov.pdf
https://openrepository.ru/article?id=444687
http://elib.sfu-kras.ru/handle/2311/35014
https://cyberleninka.ru/article/n/comparison-of-spectral-spatial-classification-methods-for-hyperspectral-images-of-high-spatial-resolution
http://elib.sfu-kras.ru/bitstream/2311/35014/1/11_Melnikov.pdf -
3Academic Journal
Authors: КУЗНЕЦОВ А.Ю., СЕРГЕЕВ С.С.
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4Conference
Authors: Martyanova, A. V.
Subject Terms: АГРЕГАЦИЯ, CHANNEL, ГИПЕРСПЕКТРАЛЬНОЕ ИЗОБРАЖЕНИЕ, HYPERSPECTRAL IMAGE, АГРЕГАЦИОННЫЙ ОПЕРАТОР, КАНАЛ, AGGREGATION, AGGREGATION OPERATOR
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Access URL: http://elar.urfu.ru/handle/10995/31682
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5Academic Journal
Source: Компьютерная оптика; Vol 38, No 3 ; Computer Optics; Vol 38, No 3 ; 2412-6179 ; 0134-2452
Subject Terms: гиперспектральное изображение, данные дистанционного зондирования Земли, авиационная съёмка, космическая съёмка, спектральный профиль, уравнение переноса, метод наименьших квадратов, MODTRAN, hyperspectral image, remote sensing data, airborne remote sensing, spaceborne remote sensing, spectral profile, transmittance equation, least square method
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Relation: http://www.computeroptics.smr.ru//article/view/1931/1951; http://www.computeroptics.smr.ru//article/view/1931
Availability: http://www.computeroptics.smr.ru//article/view/1931
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6Academic Journal
Authors: Кузнецов, Андрей, Мясников, Владислав
Subject Terms: ГИПЕРСПЕКТРАЛЬНОЕ ИЗОБРАЖЕНИЕ, ДЕРЕВО РЕШЕНИЙ, C5.0, БАЙЕС, ММП, СКО, КЛАССИФИКАЦИЯ ПО СОПРЯЖЁННОСТИ, КЛАССИФИКАЦИЯ ПО СПЕКТРАЛЬНОМУ УГЛУ
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7Academic Journal
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8Academic Journal
Source: Научно-технический вестник информационных технологий, механики и оптики.
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9Academic Journal
Authors: Колтырин, Артур
Subject Terms: ДИФФУЗНАЯ КАРТА, КЛАСТЕРИЗАЦИЯ, ГИПЕРСПЕКТРАЛЬНОЕ ИЗОБРАЖЕНИЕ
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10Academic Journal
Authors: Pestunov, Igor A., Melnikov, Pavel V.
Subject Terms: hyperspectral image, supervised classification, выделение информативных признаков, гиперспектральное изображение, метод главных компонент, principal component analysis, обучаемая классификация, support vector machine, informative feature extraction, метод опорных векторов
Access URL: https://openrepository.ru/article?id=420764
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11Academic Journal
Source: Интерэкспо Гео-Сибирь.
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12Academic Journal
Source: Интерэкспо Гео-Сибирь.
Subject Terms: КЛАССИФИКАЦИЯ, ГИПЕРСПЕКТРАЛЬНОЕ ИЗОБРАЖЕНИЕ, НЕЙРОННАЯ СЕТЬ, БИНАРНЫЕ НЕЙРОНЫ, МНОГОУРОВНЕВЫЕ НЕЙРОНЫ, ВЕРОЯТНОСТЬ ОШИБКИ
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13Academic Journal
Source: Компьютерная оптика.
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14Academic Journal
Source: Компьютерная оптика.
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15Academic Journal
Authors: Melnikov, Pavel V., Pestunov, Igor A., Rylov, S.A., Мельников, П.В., Пестунов, И.А., Рылов, С.А.
Subject Terms: hyperspectral images, local context, spectral-spatial classification, гиперспектральное изображение, локальный контекст, спектрально-текстурная классификация
Relation: Журнал Сибирского федерального университета. Техника и технологии. Journal of Siberian Federal University. Engineering & Technologies;2017 10 (6)
Availability: https://elib.sfu-kras.ru/handle/2311/35014
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16Academic Journal
Authors: Блинова, Н.К., Бибиков, С.А.
Relation: Dspace\SGAU\20170522\64096
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17Academic Journal
Authors: Е.В. Мясников
Subject Terms: гиперспектральное изображение, сегментация, кластеризация, преобразование водораздела, наращивание областей, слияние областей, мера качества сегментации, global consistency error, rand index
Relation: Dspace\SGAU\20170515\63754
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18Conference
Authors: Мартьянова, А. В., Martyanova, A. V.
Subject Terms: ГИПЕРСПЕКТРАЛЬНОЕ ИЗОБРАЖЕНИЕ, КАНАЛ, АГРЕГАЦИЯ, АГРЕГАЦИОННЫЙ ОПЕРАТОР, HYPERSPECTRAL IMAGE, CHANNEL, AGGREGATION, AGGREGATION OPERATOR
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Relation: Передача, обработка, восприятие текстовой и графической информации : материалы международной научно-практической конференции. — Екатеринбург, 2015.; http://elar.urfu.ru/handle/10995/31682; https://elibrary.ru/item.asp?id=23903179
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19Academic Journal
Authors: Гашников, М.В.
Subject Terms: интерполяция, компрессия, гиперспектральное изображение, максимальная погрешность, вариационный ряд
Relation: Dspace\SGAU\20161209\60723
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20Academic Journal
Subject Terms: гиперспектральное изображение, выделение информативных признаков, метод главных компонент, обучаемая классификация, метод опорных векторов, hyperspectral image, informative feature extraction, principal component analysis, supervised classification, support vector machine
Relation: Журнал Сибирского федерального университета. Техника и технологии. Journal of Siberian Federal University. Engineering & Technologies;2015 8 (6)
Availability: https://elib.sfu-kras.ru/handle/2311/19837
