Multidimensional Investigation of the Effect of Pre-treatment Solutions on Drying Characteristics and Raisin Quality

Year 2024, Volume: 30 Issue: 2, 229 - 242, 26.03.2024

Şahin Yılmaz Cuneyt Uyak

https://doi.org/10.15832/ankutbd.1306458

Abstract

This study aims to evaluate the relationships between pre-treatment solution, variety, drying characteristics, and raisin quality in raisin production, using multivariate analysis methods. The study was conducted on raisins obtained by dipping Bineteti and Zeyti local seed grape varieties in 13 different pre-treatment solutions which were obtained by mixing potassium carbonate and sodium bicarbonate with olive oil, hazelnut oil, and sesame oil at different concentrations. The dipped grapes were dried in the sun on a concrete drying platform. In the study, data of 15 numerical variables related to drying characteristics and raisin quality were reduced to four principal components (PC1, PC2, PC3 and PC4) using the principal component analysis (PCA), and their score values were numerically obtained. Then, two grape varieties, 13 pre-treatment solutions, and the four principal components were analyzed by non-linear principal component analysis (NLPCA). In addition, a cluster analysis was performed to determine the prominent pre-treatment solutions in terms of drying characteristics and raisin quality. It was determined that the pre-treatment solutions were effective on L*, a*, b*, chroma (C*), hue (h°), a/b values, antioxidant activity, total phenolic content, and drying time constituting PC1. It was remarkable that the colour parameters in prominent clusters in the cluster analysis also form PC1 in PCA analysis. The best pre-treatment solutions were found to be the "5% K2CO3 + 1% olive oil" solution for the Bineteti variety and the "5% K2CO3 + 2% hazelnut oil" solution for the Zeyti variety. It was determined that the pre-treatment solutions recommended for the varieties increased raisin quality and shortened the drying time, and had positive effects on the total phenolic content and antioxidant activity.

Keywords

dimension reduction, Principal component analysis (PCA), cluster analysis.

Supporting Institution

This study was supported by Van Yüzüncü Yil University Scientific Research Projects Department.

Project Number

FYL-2021-9416

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