A Deep Dive into Mango Variety Classification Using Convolutional Neural Network and Random Forest Model | IEEE Conference Publication | IEEE Xplore

A Deep Dive into Mango Variety Classification Using Convolutional Neural Network and Random Forest Model


Abstract:

This work’s goal was to separate pectins from the peels of two varieties of Cameroonian mangos, Amëliore´e and Mango. Ammonium oxalate, deionized water, and HCl were the ...Show More

Abstract:

This work’s goal was to separate pectins from the peels of two varieties of Cameroonian mangos, Amëliore´e and Mango. Ammonium oxalate, deionized water, and HCl were the three extraction conditions that were investigated. It was measured how much uronic acid, neutral sugar, and alcohol-insoluble residues (AIR) were present in mango peels. Next, these same characteristics, average molar mass, intrinsic viscosity, and the degrees of methylation and acetylation were assessed in the extracted pectins. The results showed that the extraction technique had a significant effect on uronic acid (262–709 mg/g dry weight), neutral sugar (160–480 mg/g), and pectin production (9–32% dry AIR). The mango seed kernels had a notable amount of total phenolic compounds, total lipid, unsaponifiable matter, and crude protein, but the quality of the was beneficial as it was full of all the essential amino acids. The two most prevalent phenolic compounds among the eight discovered were tannin and vanillin. While total lipid and neutral lipid had the same fatty acid compositions, phospholipid had a significantly higher quantity of palmitic, linoleic, and linolenic acids.
Date of Conference: 14-16 March 2024
Date Added to IEEE Xplore: 03 April 2024
ISBN Information:
Conference Location: Dehradun, India

References

References is not available for this document.