Preliminary Study for Raicilla Authentication by PCA and Cluster on Some Physicochemical Properties

Raicilla is a distinctive Mexican beverage produced in two central regions of Jalisco. This study aimed to analyze the physicochemical parameters of 25 raicilla alcoholic drinks originating from the Coast and Sierra regions. Each of the 25 raicilla brands underwent measurements of pH, conductivity, alcohol content, total solids, viscosity, sound velocity, density, and refractive index. Notably, these measurements are cost-effective and their analysis is straightforward. The results were analyzed using principal component analysis (PCA) and cluster analysis. According to the PCA, two main components were identified, explaining 81.77% of the total variability of the physicochemical measurements of the distinct Coast and Sierra regions. Furthermore, applying Fisher’s LSD to the Sierra raicilla cluster allowed for the identification of variations. Specifically, samples from the Sierra zone groups were identified through cluster analysis, demonstrating similarities in physicochemical parameters; both statistical methods indicated no significant differences in the physicochemical parameters between a more acidic pH, higher conductivity, and greater density than those from the Coast zone. After the analysis was carried out, it was possible to find similarities and differences between the raicilla produced in the two regions, so it is possible to assume that using these results could facilitate the authentication of raicilla.

Authors

Institutions

Publication Details

Journal
Beverages
Published
2025-07-24
DOI
https://doi.org/10.3390/beverages11040107
Primary Topic
Advanced Chemical Sensor Technologies
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Preliminary Study for Raicilla Authentication by PCA and Cluster on Some Physicochemical Properties

Clara Carreón‐Álvarez, Florentina Zurita, Marciano Sánchez-Tizapa, Alejandra Carreón-Álvarez et al.
Beverages
Advanced Chemical Sensor Technologies
article

Preliminary Study for Raicilla Authentication by PCA and Cluster on Some Physicochemical Properties

Clara Carreón‐Álvarez, Florentina Zurita, Marciano Sánchez-Tizapa, Alejandra Carreón-Álvarez, Héctor Huerta, Juan Pablo Morán-Lázaro, Nancy Tepale
article en

Abstract

Raicilla is a distinctive Mexican beverage produced in two central regions of Jalisco. This study aimed to analyze the physicochemical parameters of 25 raicilla alcoholic drinks originating from the Coast and Sierra regions. Each of the 25 raicilla brands underwent measurements of pH, conductivity, alcohol content, total solids, viscosity, sound velocity, density, and refractive index. Notably, these measurements are cost-effective and their analysis is straightforward. The results were analyzed using principal component analysis (PCA) and cluster analysis. According to the PCA, two main components were identified, explaining 81.77% of the total variability of the physicochemical measurements of the distinct Coast and Sierra regions. Furthermore, applying Fisher’s LSD to the Sierra raicilla cluster allowed for the identification of variations. Specifically, samples from the Sierra zone groups were identified through cluster analysis, demonstrating similarities in physicochemical parameters; both statistical methods indicated no significant differences in the physicochemical parameters between a more acidic pH, higher conductivity, and greater density than those from the Coast zone. After the analysis was carried out, it was possible to find similarities and differences between the raicilla produced in the two regions, so it is possible to assume that using these results could facilitate the authentication of raicilla.

BeveragesVol. 11(4)
Universidad de Guadalajara (MX), Benemérita Universidad Autónoma de Puebla (MX)
Consejo Nacional de Ciencia y Tecnología
Openalex Percentile: Top 83%
Advanced Chemical Sensor Technologies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.