Probabilistic shelf life assessment of white button mushrooms through sensorial properties analysis

LWT

Volumn 44, Issue 6, 2011, Pages 1443-1448

  Mohapatra, Debabandya ^a   Bira, Zuberi M ^a   Frias, Jesus M ^b   Kerry, Joe P ^a   Rodrigues, Fernanda A ^a  

^a UNIVERSITY COLLEGE CORK   (Ireland)

^b DUBLIN INSTITUTE OF TECHNOLOGY   (Ireland)

Author keywords

Batch variability; Mixed effect model; Mushroom; Sensory characteristics; Temperature

Indexed keywords

DIGITAL STORAGE; FASTENERS; FOOD STORAGE; HARDNESS; LOGISTIC REGRESSION; RANDOM PROCESSES; TEMPERATURE;

BATCH VARIABILITY; LOGISTIC REGRESSION MODELING; MIXED-EFFECT MODELS; MUSHROOM; OVERALL VISUAL QUALITIES; PROBABILISTIC ASSUMPTIONS; SENSORY CHARACTERISTICS; WHITE BUTTON MUSHROOMS;

FUNGI;

AGARICUS BISPORUS; BASIDIOMYCOTA;

EID: 79953027259     PISSN: 00236438     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.lwt.2011.01.014     Document Type: Article

References (32)

1. Aguirre L., Frias J.M., Barry-Ryan C., Grogan H. Assessing the effect of product variability on the management of the quality of mushrooms (Agaricus bisporus). Postharvest Biology and Technology 2008, 49(2):247-254.
2. Bates D.M., DebRoy S. Linear mixed models and penalized least squares. Journal of Multivariate Analysis 2004, 91(1):1-17.
3. Brennan M., Le Port G., Gormley R. Post-harvest treatment with citric acid or hydrogen peroxide to extend the shelf life of fresh sliced mushrooms. Lebensmittel-Wissenschaft und-Technologie 2000, 33(4):285-289.
4. Caporale G., Monteleone E. Influence of information about manufacturing process on beer acceptability. Food Quality and Preference 2004, 15(3):271-278.
5. Chabanet C., Pineau N. Using linear mixed models to handle variability of consumers' liking. Food Quality and Preference 2006, 17(7-8):665-666.
6. De Ketelaere B., Stulens J., Lammertyn J., Cuong N.V., De Baerdemaeker J. A methodological approach for the identification and quantification of sources of biological variance in postharvest research. Postharvest Biology and Technology 2006, 39(1):1-9.
7. Eastwood D., Burton K. Mushrooms - a matter of choice and spoiling oneself. Microbiology Today 2002, 29(1):18-19.
8. Erickson M.C., Bulgarelli M.A., Resurreccion A.V.A., Vendetti R.A., Gates K.A. Sensory differentiation of shrimp using a trained descriptive analysis panel. LWT - Food Science and Technology 2007, 40(10):1774-1783.
9. Fonseca S.C., Oliveira F.A.R., Frias J.M., Brecth J.K. Application of mathematical modelling and computer simulation to the design of modified atmosphere packages accounting for product variability. Computational techniques in food engineering 2002, 70-84. (Chapter 5), International Center for Numerical Methods in Engineering (CIMNE). E. Balsa-Canto, J. Mora, J.R. Banga, E. Onate (Eds.).
10. Guerra S., Lagazio C., Manzocco L., Barnabà M., Cappuccio R. Risks and pitfalls of sensory data analysis for shelf life prediction: data simulation applied to the case of coffee. LWT - Food Science and Technology 2008, 41(10):2070-2078.
11. Hertog M.L.A.T.M., Lammertyn J., De Ketelaere B., Scheerlinck N., Nicolaï B.M. Managing quality variance in the postharvest food chain. Trends in Food Science & Technology 2007, 18(6):320-332.
12. Hertog M.L.A.T.M., Scheerlinck N., Lammertyn J., Nicolaï B.M. The impact of biological variation on postharvest behaviour of Belgian endive: the case of multiple stochastic variables. Postharvest Biology and Technology 2007, 43(1):78-88.
13. Kreutzmann S., Thybo A.K., Bredie W.L.P. Training of a sensory panel and profiling of winter hardy and coloured carrot genotypes. Food Quality and Preference 2007, 18(3):482-489.
14. Latreille J., Mauger E., Ambroisine L., Tenenhaus M., Vincent M., Navarro S., et al. Measurement of the reliability of sensory panel performances. Food Quality and Preference 2006, 17(5):369-375.
15. Lukasse L.J.S., Polderdijk J.J. Predictive modelling of post-harvest quality evolution in perishables, applied to mushrooms. Journal of Food Engineering 2003, 59(2-3):191-198.
16. MacKay D., O'Mahony M. Sensory profiling with probabilistic multidimensional scaling. Journal of Sensory Studies 2002, 17(5):461-481.
17. Mohapatra D., Bira Z.M., Kerry J., Frias J.M., Oliveira F.A.R. Postharvest hardness and color evolution of white button mushrooms (Agaricus bisporus). Journal of Food Science 2010, 75(3):E146-E152.
18. Mohapatra D., Frias J.M., Oliveira F.A.R., Bira Z.M., Kerry J. Development and validation of a model to predict enzymatic activity during storage of cultivated mushrooms (Agaricus bisporus spp.). Journal of Food Engineering 2008, 86(1):39-48.
19. Montanez J.C., Frias J.M., Fonseca S.C., Luna R., Fernandez P., Oliveira F.A.R. Mathematical modelling accounting for food product variability using mixed effects models. Proceedings of international conference on simulation in food and bio-industry, Cork, Ireland 2002, 35-39.
20. Ortúzar J.de D. Estimating individual preferences with flexible discrete-choice-models. Food Quality and Preference 2010, 21(3):262-269.
21. Palazón M.A., Pérez-Conesa D., Abellan P., Ros G., Romero F., Vidal M.L. Determination of shelf-life of homogenized apple-based beikost storage at different temperatures using weibull hazard model. LWT - Food Science and Technology 2009, 42:319-326.
22. Piagentini A.M., Mendez J.C., Guemes D.R., Pirovani M.E. Modeling changes of sensory attributes for individual and mixed fresh-cut leafy vegetables. Postharvest Biology and Technology 2005, 38(3):202-212.
23. Piccolo D., D'Elia A. A new approach for modeling consumers' preferences. Food Quality and Preference 2008, 19(3):247-259.
24. Pineau N., Chabanet C., Schlich P. Modeling the evolution of the performance of a sensory panel: a mixed-model and control chart approach. Journal of Sensory Studies 2007, 22(2):212-241.
25. Pinheiro J.C., Bates D.M. Mixed-effects models in S and S-plus 2000, Springer-Verlag, New York.
26. R: A language and environment for statistical computing 2007, R Development Core Team, R Foundation for Statistical Computing. http://www.R-project.org.
27. Schouten R.E., Jongbloed G., Tijskens L.M.M., Kooten O.-V. Batch variability and cultivar keeping quality of cucumber. Postharvest Biology and Technology 2004, 32(3):299-310.
28. Smith A., Cullis B., Brockhoff P., Thompson R. Multiplicative mixed models for the analysis of sensory evaluation data. Food Quality and Preference 2003, 14(5-6):387-395.
29. Sundararajan N., Ndife M., Basel R., Green S. Comparison of sensory properties of hamburgers cooked by conventional and carcinogen reducing 'safegrill' equipment. Meat Science 1999, 51(4):289-295.
30. Vankerschaver K., Willocx F., Smout C., Hendrickx M., Tobback P. Modeling and prediction of visual shelf life of minimally processed endive. Journal of Food Science 1996, 61(5):1094-1098.
31. Villarino B.J., Marsha Dy L., Lizada M.C.C. Descriptive sensory evaluation of virgin coconut oil and refined, bleached and deodorized coconut oil. LWT - Food Science and Technology 2007, 40(2):193-199.
32. Vízhányó T., Felföldi J. Enhancing colour differences in images of diseased mushrooms. Computers and Electronics in Agriculture 2000, 26(2):187-198.