Show source-provided references
2. Nutrition in the adult years
3. Phytochemicals: Biochemistry and physiology
4. Andersen, O.M., and Markham, K.R. (2005). Flavonoids: Chemistry, Biochemistry and Applications, CRC Press.
10.1201/9781420039443 ↗7. Solís-Oviedo, R.L., and De La Cruz Pech-Canul, Á (2019). The Role of UV-Visible Spectroscopy for Phenolic Compounds Quantification in Winemaking. Frontiers and New Trends in the Science of Fermented Food and Beverages, IntechOpen. Chapter 3.
10.5772/intechopen.73404 ↗8. Giusti, M.M., and Wrolstad, R.E. (2001). Characterization and Measurement of Anthocyanins by UV-Visible Spectroscopy. Curr. Protoc. Food Anal. Chem., F1.2.1–F1.2.13.
10.1002/0471142913.faf0102s00 ↗9. Phenolic acid contents in fruits of aubergine (Solanum melongena L.)
10. Quantitative analysis by ultraviolet spectrophotometry: The cis-trans-stilbene system
10.1021/ed034p496 ↗11. Spectrophotometric Quantification of Flavonoids in Herbal Material, Crude Extract, and Fractions from Leaves of Eugenia uniflora Linn
10.4103/pr.pr_143_16 ↗13. Ultrasound-assisted extraction (UAE) of bioactives from arecanut (Areca catechu L.) and optimization study using response surface methodology
10.1016/j.ifset.2012.10.001 ↗15. Gutiérrez, S., Tardaguila, J., Fernández-Novales, J., and Diago, M.P. (2016). Data Mining and NIR Spectroscopy in Viticulture: Applications for Plant Phenotyping under Field Conditions. Sensors, 16.
10.3390/s16020236 ↗16. Dey, U.K., Masud, A.H., and Uddin, M.N. (2017, January 16–18). Rice yield prediction model using data mining. Proceedings of the 2017 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox’s Bazar, Bangladesh.
10.1109/ECACE.2017.7912925 ↗17. Application and Analysis of the Folin Ciocalteu Method for the Determination of the Total Phenolic Content from Limonium Brasiliense L
10.3390/molecules18066852 ↗19. A reproducible, rapid and inexpensive Folin–Ciocalteu micro-method in determining phenolics of plant methanol extracts
10.1016/j.microc.2008.08.011 ↗22. Analysis of total phenolic, flavonoids, anthocyanins and tannins content in Romanian red wines: Prediction of antioxidant activities and classification of wines using artificial neural networks
10.1016/j.foodchem.2013.10.153 ↗23. Modelling through artificial neural networks of the phenolic compounds and antioxidant activity of blueberries
25. Watson, R.R. (2019). Chapter 7—Characterization and Quantification of Polyphenols in Fruits. Polyphenols in Plants, Academic Press. [2nd ed.].
26. Phytochemical screening and Identification of Polyphenols, Evaluation of Antioxidant activity and study of Biological properties of extract Silybum marianum (L.)
10.5958/0974-4150.2020.00037.1 ↗29. Folin-ciocalteau micro method for total phenol in wine
30. Záková, M., Podpecan, V., Zelezný, F., and Lavrac, N. (2009). Advancing data mining workflow construction: A framework and cases using the orange toolkit. Proc. 2nd Intl. Wshop. Third Gener. Data Min. Towar. Serv. Knowl. Discov., 39–52.
31. Vaishnav, D., and Rao, B.R. (2018, January 15–16). Comparison of Machine Learning Algorithms and Fruit Classification using Orange Data Mining Tool. Proceedings of the 2018 3rd International Conference on Inventive Computation Technologies (ICICT), Coimbatore, India.
10.1109/ICICT43934.2018.9034442 ↗33. Comparison of Model Prediction for Tile Production in Tabanan Regency with Orange Data Mining Tool