Proportion Tests Applied in Assessors’ Selection Under Assumption Break: The Cases of Sensory Fatigue and Learning Process

Authors

  • Iasmine Queiroga de Paula Master of Science, Department of Applied Statistics and Biometry, Federal University of Alfenas, Alfenas (MG), Brazil http://orcid.org/0000-0001-9556-1449
  • Eric Batista Ferreira Professor, Department of Applied Statistics and Biometry, Federal University of Alfenas, Alfenas (MG), Poços de Caldas, Varginha, Brazil

Keywords:

fatiguing, sensory analysis, sensometrics, Monte Carlo, simulations

Abstract

Triangle trials are commonly employed in panelist selection, describing a Binomial distribution. However, assessors can fatigue or develop sensitivity after many trials, which violate constant proportion assumption. This violation could affect some tests obtained from normal distribution interval estimators (TN1, TN2 and TN3), F distribution (TF), Sequential Test (TS) and Poisson approximation with quantile (TP). To evaluate this tests performance, Monte Carlo simulations were performed for 1000 assessors through 5, 10, 15, ..., 100 triangle trials, fatiguing and learning in linear and nonlinear models. Violating this assumption can affect type I error and power. Power curves for linear and nonlinear fatigue models showed large type I error and low power with increasing p and small values of n. Power curves for linear and nonlinear learning models, increased simultaneous to p and n. Thus, n value has more influence on power curves than p. TS presented less power in many trials of learning functions with better behavior in few trials, while TP presented the smallest type I error rate in all situations. TN1, TN2 and TF presented small type I error and big power for linear and nonlinear learning functions, when n increases. However, it is not recommended using tests for sensory fatigue.

.

Author Biographies

  • Iasmine Queiroga de Paula, Master of Science, Department of Applied Statistics and Biometry, Federal University of Alfenas, Alfenas (MG), Brazil
    Master in Applied Statistics and Biometrics at Universidade Federal de Alfenas (UNIFAL - MG). Graduated in Food Engineering at University Federal de São João del-Rei (UFSJ). 
  • Eric Batista Ferreira, Professor, Department of Applied Statistics and Biometry, Federal University of Alfenas, Alfenas (MG), Poços de Caldas, Varginha, Brazil
    Adjunct Professor at the Institute of Mathematical Sciences, Federal University of Alfenas (Unifal-MG) and visiting professor at FAEPE (UFLA). Has training in Dairy Products (Cândido Tostes Institute, 1998), Agricultural Engineering (UFLA, 2002), Masters in Agricultural Statistics and Experimentation (UFLA, 2004), PhD in Agricultural Statistics and Experimentation (UFLA / Open University, 2007) and Postdoctoral in Multivariate Statistics (UFLA, 2009). Has experience in Sensometria, Multivariate Statistics, Probability and Statistics, Statistical Process Control, Statistics, Nonparametric Statistic and Statistical Education.

References

Zwimpfer T. Sensory Evaluation Lab Report. Nutrition 205, Section 3; 2013.

Lawless HT, Heymann H. Sensory Evaluation of Food, 2nd Edn. New York: Springer; 2010.

Chambers IV E, Wolf MB. Sensory testing methods, 2nd Edn. Lancaster: ASTM International; 2005.

Poste LM, Mackie DA, Butler G, Larmond E. Laboratory Methods for Sensory Analysis of Food. Canada: Agriculture Canada Publication; 1991.

Ferreira DF. Estatística Básica. Lavras: UFLA; 2005.

Mood AM, Gaybill DC, Boes C. Introduction of theory of statistics, 3rd Edn. United States of America: McGraw-Hill; 1974.

Looney MA, Spray JA. Effects of Violating Local Independence on IRT Parameter Estimation for the Binomial Trials Model. Res Q Exerc Sport. 1992; 63(4): 356–359p. doi: 10.1080/02701367.1992.10608756.

Bonate PL. A brief introduction to Monte Carlo simulation. Clin Pharmacakinet. 2001; 40(1): 15–22p.

Næs T, Brockhoff PB, Tomic O. Statistics for Sensory and Consumer Science. Chippenham, Wiltshire. United Kingdom: Wiley; 2010.

Emura T, Liao YT. Critical review and comparison of continuity correction methods: The normal approximation to the binomial distribution. Commun Stat Simul Comput. 2018; 47(8): 2266–2285p.

Leemis LM, Trivedi KS. A Comparison of Approximate Interval Estimators for the Bernoulli Parameter. Am Stat. 1996; 50(1): 63–68p.

Weiss JJ. Selection of Sensory Judge. J Food Qual. 1981; 4: 55–63p. doi: 10.1111/j.1745-4557.1981.tb00712.x

Minim VPR, Silva RCSN, Milagres MP, Martins EMF, Sampaio SCS, Vasconcelos CM. Análise descritiva: Comparação entre metodologias. Rev Inst Latic Cândido Tostes. 2010; 65(374): 41–48p.

Paula IQ, Ferreira EB. Análise sensorial de alimentos: uma comparação de testes para a seleção de potenciais provadores. Caderno de CiênciasAgrárias. 2019; 11: 01–08p.

Kunert J, Meyners M. On the Triangle Test with Replications. Food Qual Pref. 1999; 10: 477–482p. doi: 10.1016/S0950-3293(99)00047-6

VedoVatto T. Medidas de memória longa em séries temporais: Comparação de métodos de estimação do coeficiente de Hurst. Master’s thesis in Science, Universidade de Brasília, Brasília; 2014. 115p.

Downloads

Published

2021-04-14

Issue

Vol. 10 No. 1 (2021)

Section

Research Article

License

Declaration and Copyright Transfer Form

(to be completed by authors)

I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.

  • I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
  • I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
  • I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
  •  I/We also agree to the authorship of the article in the following order:

Author’s name                                    

 

1. ________________

2. ________________

3. ________________

4. ________________

 

We  Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal.