Formulation, Characterization, and Determination of the Rheological Profile of Loquat Compote Mespilus Germánica L. through Sustenance Artificial Intelligence. (1st April 2023)

• Record Type: Journal Article
• Authors: Ore Areche, Franklin; Flores, Denis Dante Corilla; Quispe-Solano, Miguel Angel; Nayik, Gulzar Ahmad; Cruz-Porta, Erika Amelia De La; Rodríguez, Alfonso Ruiz; Roman, Almer Ventura; Chweya, Ruth
• Other Names: Khan Rijwan Academic Editor.
• Is Part Of: Journal of food quality. Volume 2023(2023)
• Publication Date: 2023-04-01
• Subjects: Food industry and trade -- Quality control -- Periodicals; Food industry and trade -- Standards -- Periodicals; Food -- Periodicals; 664.07
• DOI: 10.1155/2023/3344539
• Languages: English
• ISSNs: 0146-9428

Abstract

Abstract : The theme of the presented study is to create a compote that is functional, inexpensive in cost, free of preservatives, and will have long shelf life, as well as to assess its rheological, sensory, and physicochemical properties. The objective was to construct a loquat compote ( Mespilus germánica L.) using agar from cochayuyo ( Chondracanthus chamissoi ) for infants, determining its rheological profile with the addition of agar extracted from cochayuyo in three concentrations (0.10, 0.15, and 0.20) % w/w, respectively, with help of artificial intelligence (AI) pathway. Agar was withdrawn from the cochayuyo by alkaline treatment with 0.04 M NaOH, obtaining a yield of 1%. Consequently, each compote was subjected to a sensory attributes using a 5-point hedonic scale with 60 panelists (30 undergraduate students and 30 infants between 3 and 5 years of age using a graphic hedonic scale). The sensory analysis using AI as a base is applied to both adult and infant panelists determined that the compote that had as input agar from cochayuyo at a concentration of 15% had greater acceptability due to the fact that significance was reported (p < 0.05 ) according to Friedman's test. The compote with the highest acceptability was subjected to proximal chemical characterization, reporting the following: moisture (64%), protein (1.68%), fat (1.01%), fiber (2.35%), ash (1.34%), and carbohydrates (29.62%). Its physicochemical characterization was also determined, reporting the following: pH (4.32), soluble solids (16° Brix), and total acidity (0.23 g malic acid/100 g compote). Finally, A Brookfield RV-DVIII ULTRA viscometer with Spindles N° 5 and 6 was used to integrate AI data gathering and use it for rheological profile assessment. The loquat compote was found to have a non-Newtonian, pseudoplastic behavior that was adjusted to the Ostwald–De Waele model with an R 2 = 0.987.