Citation link:
Authors:
Baycheva, Stanka; Zlatev, Zlatin; Veleva, Petya
Title:
Influence of unregulated storage conditions on physicochemical, organoleptic and NIR spectral characteristics of yellow cheese
Date of Issue:
2023
Is Part of:
BIO Web of Conferences, 5810, Article number 01006
Identifiers:
10.1051/bioconf/20235801006 [other]
Type:
Article
Language:
eng
Subject:
yellow cheese; physicochemical characteristics; organoleptic characteristics
Abstract:
In the present work, software and hardware tools are proposed for determining the change in the main characteristics of Bulgarian yellow cheese during storage in conditions not regulated by the manufacturers. NIR images in the 800-1100 nm range of yellow cheese samples from 3 manufacturers were obtained using a GT-903 video camera with the IR-filter removed from the camera lens. Several physicochemical characteristics of the product were determined - active acidity, electrical conductivity and completely dissolved solids. Data from organoleptic evaluation of the product are presented. Using ABC-XYZ analysis, informative wavelengths are selected from the spectral features. Spectral indices calculated as ratios of the reflectance coefficients of selected wavelengths were defined and used to predict the storage characteristics of yellow cheese. It has been found that the shelf life of yellow cheese can be predicted with an accuracy of up to 95%, and the active acidity with an accuracy of up to 88%, depending on the manufacturer. The obtained results can be used for analyzes of yellow cheese during its storage and applied in automatic measurement and control systems, as well as in advisory systems for evaluating the quality of yellow cheese in the different stages of its production, transport and storage.
Sponsors:
This work was partially supported by the Bulgarian Ministry of Education and Science under the National Research Programme "Healthy Foods for a Strong BioEconomy and Quality of Life" approved by DCM #577/17.08.2018. Also, the work is supported by project 1.FTT/2021, on the topic: "Development and research of a methodology for automated processing and analysis of data from electrical sensors using artificial intelligence techniques”.
