Review Article
Year 2020,
, 186 - 198, 22.06.2020
Abstract
Excel is usually included in the computer package as a part of Microsoft® Office. Almost everyone who deals with the experimental data is more or less familiar with the use of Excel. In food science, it is very common to use Excel to process, transform, sketch or define experimental data with any model. There is a wrong opinion as linear regression can only be used to fit a linear line to the data. However, a second order polynomial or a curved data could also be modelled by using the linear regression: if the model which is used to define the experimental data is linear according to its parameter(s), the parameter(s) of this model can be obtained by using linear regression. Regression application in data analysis tool in Excel could be used for linear models according to its parameters. The aim of this study was to show the application of models to the experimental data by using Excel with examples, where model parameters can be obtained by using linear regression. In the first example, using the linear model in Excel, the application of the data analysis tool on the microbiological data and the interpretation of the results were shown. In the second example, the application of a model that is not included in Excel but its equation is known by the user was shown to define the gas chromatography data. In the third example, the application of a model created by the user to define the microbial growth rate according to pH was shown. It is considered that this study would have important contributions for those working in the field of food engineering and food science.
References
- Baranyi, J., Roberts, T.A. (1995). Mathematics of predictive food microbiology. International Journal of Food Microbiology, 26, 199-218. https://doi.org/10.1016/0168-1605(94)00121-L
- Brown, A.M. (2001). A step-by-step guide to non-linear regression analysis of experimental data using a Microsoft Excel spreadsheet. Computer Methods and Programs in Biomedicine, 65, 191-200. https://doi.org/10.1016/S0169-2607(00)00124-3
- Davey, K.R., Amos, S.A. (2002). Letter to the editor. Journal of Applied Microbiology, 92, 583-584. https://doi.org/10.1046/j.1365-2672.2002.1617a.x
- Dolan, K.D., Mishra, D.K. (2013). Parameter estimation in food science. The Annual Review of Food Science and Technology, 4, 401-422. https://doi.org/10.1146/annurev-food-022811-101247
- Granato, D., Calado, V.M.A., Jarvis, B. (2014). Observations on the use of statistical methods in food science and technology. Food Research International, 55, 137-149. https://doi.org/10.1002/9781118434635
- Hassani, M., Álvarez, I., Raso, J., Condón, S., Pagán, R. (2005). Comparing predicting models for heat inactivation of Listeria monocytogenes and Pseudomonas aeruginosa at different pH. International Journal of Food Microbiology, 100, 213-222. https://doi.org/10.1016/j.ijfoodmicro.2004.10.017
- Jarvis, B. (1989). Statistical aspects of the microbiological analysis of foods. In: Progress in Industrial Microbiology, Vol. 21. Elsevier, Amsterdam. ISBN: 978-0128039748
- Montgomery, D.C., Runger, G.C. (2011). Applied statistics and probability for engineers (5th ed.) New York: Wiley. ISBN: 978-0470053041
- Moody, H.W. (1982). The evaluation of the parameters in the van Deemter equation. Journal of Chemical Education, 59, 290-291. https://doi.org/10.1021/ed059p290
- Mossel, D.A.A., Corry, J.E.L., Struijck, C.B., Baird, R.M. (1995). Essentials of the Microbiology of Foods: A Textbook for Advanced Studies. John Wiley & Sons, Chichester. ISBN: 978-0471930365 Ratkowsky, D.A. (2004). Model fitting and uncertainty, in RC McKellar, X Lu (Eds) Modeling Microbial Responses in Food, Boca Raton FL, CRC Press, pp. 151-196. ISBN: 978-0367394653, https://doi.org/10.1201/9780203503942.ch4
- Ray, B. (2014). Fundamental Food Microbiology. Boca Raton FL, CRC Press, pp. 346. ISBN: 978-1466564435
- van Boekel, M.A.J.S. (1996). Statistical aspects of kinetic modeling for food science problems. Journal of Food Science, 61, 477-86. https://doi.org/10.1111/j.1365-2621.1996.tb13138.x
- van Boekel, M.A.J.S. (2008). Kinetic modeling of food quality: A critical review. Comprehensive Review in Food Science and Food Safety, 7, 144-158. https://doi.org/10.1201/9781420017410
- van Boekel, M.A.J.S., Zwietering, M.H. (2007). Experimental design, data processing and model fitting in predictive microbiology. In: Modelling Microorganisms in Food, Brul, S., Van Gerwen, S., Zwietering, M.H. (Eds.), pp. 22-43. Woodhead Publishing Ltd: Cambridge, United Kingdom. ISBN: 978-1845690069, https://doi.org/10.1533/9781845692940.1.22
Year 2020,
, 186 - 198, 22.06.2020
Abstract
Excel genellikle kullandığımız bilgisayarlarda Microsoft® Office’in bir parçası olarak yüklü olarak gelmekte ve deneysel verilerle uğraşan hemen hemen herkes Excel’in basit de olsa kullanımına aşina olmaktadır. Gıda bilimlerinde de deneysel verileri işlemek, dönüştürmek, grafik haline getirmek ya da herhangi bir modelle tanımlamak için Excel’i kullanmak çok yaygındır. Doğrusal regresyon sadece düz bir çizgiyi veriye uydurmak için kullanılır gibi yanlış bir kanı vardır. Ancak, ikinci dereceden bir polinom da ya da bir eğri de doğrusal regresyon kullanılarak veriye uydurulabilir: eğer deneysel verileri tanımlamak için kullanılan model parametresine/parametrelerine göre doğrusalsa bu modelin parametresi/parametreleri doğrusal regresyon kullanılarak bulunabilir. Excel’deki veri çözümleme aracının içerisinde yer alan regresyon uygulaması parametrelerine göre doğrusal modeller için kullanılabilir. Bu çalışmanın amacı doğrusal regresyon kullanılarak parametrelerinin elde edilebileceği modellerin deneysel verilere Excel kullanılarak nasıl uygulanacağını örnekler üzerinde göstermektir. İlk örnekte Excel’in içinde yer alan doğrusal model kullanılarak mikrobiyolojik veriler üzerinde veri çözümleme aracının uygulaması ve sonuçların yorumlanması gösterilmiştir. İkinci örnekte gaz kromatografi verisini tanımlamak için Excel’in içinde yer almayan ancak kullanıcı tarafından denklemi bilinen bir modelin, üçüncü örnekte ise mikrobiyal büyüme hızını pH’a göre tanımlamak için kullanıcının kendi yarattığı bir modelin uygulamaları gösterilmiştir. Bu çalışmanın gıda mühendisliği ve gıda bilimleri alanında çalışanlar için önemli katkıları olacağı değerlendirilmektedir.
References
- Baranyi, J., Roberts, T.A. (1995). Mathematics of predictive food microbiology. International Journal of Food Microbiology, 26, 199-218. https://doi.org/10.1016/0168-1605(94)00121-L
- Brown, A.M. (2001). A step-by-step guide to non-linear regression analysis of experimental data using a Microsoft Excel spreadsheet. Computer Methods and Programs in Biomedicine, 65, 191-200. https://doi.org/10.1016/S0169-2607(00)00124-3
- Davey, K.R., Amos, S.A. (2002). Letter to the editor. Journal of Applied Microbiology, 92, 583-584. https://doi.org/10.1046/j.1365-2672.2002.1617a.x
- Dolan, K.D., Mishra, D.K. (2013). Parameter estimation in food science. The Annual Review of Food Science and Technology, 4, 401-422. https://doi.org/10.1146/annurev-food-022811-101247
- Granato, D., Calado, V.M.A., Jarvis, B. (2014). Observations on the use of statistical methods in food science and technology. Food Research International, 55, 137-149. https://doi.org/10.1002/9781118434635
- Hassani, M., Álvarez, I., Raso, J., Condón, S., Pagán, R. (2005). Comparing predicting models for heat inactivation of Listeria monocytogenes and Pseudomonas aeruginosa at different pH. International Journal of Food Microbiology, 100, 213-222. https://doi.org/10.1016/j.ijfoodmicro.2004.10.017
- Jarvis, B. (1989). Statistical aspects of the microbiological analysis of foods. In: Progress in Industrial Microbiology, Vol. 21. Elsevier, Amsterdam. ISBN: 978-0128039748
- Montgomery, D.C., Runger, G.C. (2011). Applied statistics and probability for engineers (5th ed.) New York: Wiley. ISBN: 978-0470053041
- Moody, H.W. (1982). The evaluation of the parameters in the van Deemter equation. Journal of Chemical Education, 59, 290-291. https://doi.org/10.1021/ed059p290
- Mossel, D.A.A., Corry, J.E.L., Struijck, C.B., Baird, R.M. (1995). Essentials of the Microbiology of Foods: A Textbook for Advanced Studies. John Wiley & Sons, Chichester. ISBN: 978-0471930365 Ratkowsky, D.A. (2004). Model fitting and uncertainty, in RC McKellar, X Lu (Eds) Modeling Microbial Responses in Food, Boca Raton FL, CRC Press, pp. 151-196. ISBN: 978-0367394653, https://doi.org/10.1201/9780203503942.ch4
- Ray, B. (2014). Fundamental Food Microbiology. Boca Raton FL, CRC Press, pp. 346. ISBN: 978-1466564435
- van Boekel, M.A.J.S. (1996). Statistical aspects of kinetic modeling for food science problems. Journal of Food Science, 61, 477-86. https://doi.org/10.1111/j.1365-2621.1996.tb13138.x
- van Boekel, M.A.J.S. (2008). Kinetic modeling of food quality: A critical review. Comprehensive Review in Food Science and Food Safety, 7, 144-158. https://doi.org/10.1201/9781420017410
- van Boekel, M.A.J.S., Zwietering, M.H. (2007). Experimental design, data processing and model fitting in predictive microbiology. In: Modelling Microorganisms in Food, Brul, S., Van Gerwen, S., Zwietering, M.H. (Eds.), pp. 22-43. Woodhead Publishing Ltd: Cambridge, United Kingdom. ISBN: 978-1845690069, https://doi.org/10.1533/9781845692940.1.22
There are 14 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Food Engineering |
| Journal Section | Review Articles |
| Authors | |
| Publication Date | June 22, 2020 |
| Submission Date | February 14, 2020 |
| Published in Issue | Year 2020 |
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