# Sensory Panel Performance Evaluation--Comprehensive Review of Practical Approaches
## 5 Software  
In order to support the procedure of sensory analysis in research and in QC it is essential to rely on a specialized sensory software system. Generally, there are at least two possible solutions: commercially available platforms, or self-developed scripts. The former requires a considerable financial investment, the latter provides a larger degree of freedom for the end user. However, in case of a self-developed software one needs to be familiar with the programming languages and platforms (e.g., R-project, Python, etc.). Among the commercially available programmes, there are three major types: (a) supports the whole process of testing; (b) supports only the statistical analysis; (c) measuring panel performance or test performance parameters:  
* Panelists' recruitment, training, test design, scoresheet editing, test implementation, statistical analysis and reporting. Such programmes include the followings: Compusense (Compusense 20), Fizz, Redjade, EyeQuestion (V12021), SIMS (SIMS Sensory Software Cloud). * Specified statistical analysis of the test data and visualization. Some typical examples are: Senstools, XLSTAT (2021.5), SensoMineR (V3.1-5-1). * Measuring panel performance or test performance parameters: PanelCheck (V1.4.2), V-Power, SensCheck. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Panelist or Panel** & **Panel Performance Parameter** & **Statistical Method/Plot** & **Software** & **References** \\ \hline \multirow{3}{*}{Panelist and panel} & Agreement (Consonance) & \multirow{3}{*}{_[PERSON]_’s_} & \multirow{3}{*}{SPSS} & [21] \\ \cline{2-2} \cline{4-5}  & Repeatability & & & \\ \cline{2-2} \cline{4-5}  & Reproducibility & & & \\ \hline \end{tabular}  
* ANOVA: Analysis of variance; \({}^{2}\) CPCA: Consensus Principal Component Analysis; \({}^{3}\) PCA: Principal Component Analysis; \({}^{4}\) MSE plot: Mean Squared Error plot; \({}^{5}\) RMSE: Root Mean Square Error; \({}^{6}\) H: [PERSON]’s repeatability measure; \({}^{7}\) K: [PERSON]’s reproducibility measure; \({}^{8}\) MFA: Multiple factor analysis; \({}^{9}\) DT: distance from target; \({}^{10}\) ADT: adjusted distance from target; \({}^{11}\) ICC: Intraclass correlation coefficient; \({}^{12}\) CSM: Complex Self-similarity Measure; \({}^{13}\) CCAP: Gravity Centre and Area/Printerter; \({}^{14}\) CVA: Canonical Variate Analysis; \({}^{15}\) CLV3W: Clustering latent variables (three-way data); \({}^{16}\) SRD: Sum of ranking differences; \({}^{17}\) CVV: Robust Coefficient of Variation, \({}^{16}\) RV: Regression Vector, \({}^{19}\) NRV: Normalized regression vector; \({}^{20}\) PARAFAC: Parallel factor analysis; \({}^{21}\) N-PLS: N-way Partial Least Squares; \({}^{22}\) PANACA: Panel concordance analysis; \({}^{23}\) CPA: Generalized Procrustes analysis; \({}^{24}\) FCM: Feedback Calibration Method; \({}^{25}\) VAF: Variance: Accounted For; \({}^{26}\) C: Consonance; \({}^{27}\) G: reliability (generalizability) coefficient, univariate quality index; \({}^{28}\)\(\phi\): coefficient, focus on the absolute scores produced by the panel; \({}^{29}\)_RV2: modified_ Regression Vector _coefficient_; \({}^{*}\) open-source software.  
\end{table} 
Table 1: Cont. In the last decade, there has been a shift from purchasing software towards subscribing to its use. The name of the technology is SaaS (Software as a Service); it provides a cloud technology, and users access the servers through a lean client. Some companies offer a larger SaaS package with annual subscription, whereas there are other solutions with a one-time purchase (this is quite popular among the academic sector). For users who prefer the self-developed platforms, one of the most popular languages is the R-project ((R Core Team, a Language and Environment for Statistical Computing, Austria: R Foundation for Statistical Computing Vienna [[https://www.R-project.org/](https://www.R-project.org/)]([https://www.R-project.org/](https://www.R-project.org/)), accessed on 14 December 2021)).