This tutorial demonstrates how to analyze consumer acceptability data obtained using hedonic scales, focusing on the appropriate use of ANOVA under different experimental designs. It covers the application of a Randomized Complete Block Design (RCBD) and Repeated Measures ANOVA using R, including post-hoc testing with the agricolae package and data visualization with ggplot2. The examples are based on simulated sensory data and are intended for students, researchers, and professionals in food science and sensory analysis.

This R Markdown document demonstrates how to apply the Friedman test to analyze data from a sensory ranking test. Sixty panelists evaluated four beverage formulations with different mango-to-passion-fruit ratios. The analysis includes data loading, Friedman test application, post-hoc comparison of mean ranks using the agricolae package, and result visualization. Ideal for those working in sensory science or food product development.

This document illustrates the application of sequential analysis in a triangle test using R. It explains how to calculate decision boundaries based on specified alpha and beta values, generate or input test data, and visualize the results. The approach allows for early decision-making, saving time and resources in sensory discrimination studies.

This document demonstrates the application of the Same/Different discrimination test using simulated sensory data. The analysis includes a confusion matrix, Chi-square and McNemar tests, and a signal detection model using the sensR package. The goal is to evaluate panelists' ability to discriminate between sample pairs and quantify sensory performance through d-prime estimation.

This report demonstrates the statistical analysis of the triangle test for sensory evaluation using R. It includes binomial, chi-squared, and z-tests, as well as the use of the sensR package and similarity testing methodology.

This article presents a Shiny-based application developed for the analysis of sensory data obtained through Quantitative Descriptive Analysis (QDA). It integrates statistical tools such as PCA, ANOVA, reproducibility graphs, and preference mapping into an intuitive interface designed to support researchers, students, and practitioners working in sensory science. A case study with yogurt samples illustrates the full workflow.