Review Article
Year 2020,
Volume: 6 Issue: 3, 199 - 212, 22.06.2020
Abstract
Gıda bilimlerinde deneysel verilerin matematiksel modellerle tanımlanabilmesi için doğrusal olmayan regresyona sıklıkla ihtiyaç duyulmaktadır. Excel’in içerisinde yazılımda yüklü olan doğrusal olmayan birçok model olmasına karşın Excel bunları doğrusal hale dönüştürmekte ve verilere doğrusal olmayan regresyon yerine doğrusal regresyon uygulamaktadır. Oysa Excel’de yer alan “Çözücü” aracı kullanılarak doğrusal olmayan regresyon uygulamak mümkündür. Bu çalışmanın amacı Excel’deki Çözücü aracını kullanarak deneysel verilere doğrusal olmayan regresyonun nasıl uygulanacağını örnekler üzerinden göstermektir. Bu amaç doğrultusunda sırasıyla iki, üç ve dört parametreli doğrusal olmayan modeller, üç farklı veri setine Çözücü kullanılarak uygulanmıştır. İlk örnekte yeşil zeytinde bulunan violaksantin pigmentinin zamana bağlı olarak değişimi üstel model kullanılarak tanımlanmış, ikinci örnekte ise Escherichia coli bakterisinin sıcaklıkla inaktivasyonu üç parametreli doğrusal olmayan bir modelle açıklanmaya çalışılmıştır. Son örneğimizde Listeria monocytogenes bakterisinin büyümesi yine doğrusal olmayan bir modelle tanımlanırken model uyumunu gösteren değerler de model parametreleriyle birlikte hesaplanmıştır. Çözücü aracının tek olumsuz yanı parametre değerlerini standart hata veya güven aralıklarıyla birlikte hesaplayamamasıdır. Onun dışında doğrusal olmayan regresyon yapmak için kullanılan ücretli yazılımlardan herhangi bir farkı yoktur. Bu çalışmanın gıda mühendisliği veya gıda bilimi alanında çalışan ve bilgisayarlarında Excel yüklü olan ancak diğer ücretli yazılımlara sahip olmayan birçok araştırmacıya faydalı olacağı değerlendirilmektedir.
Keywords
References
- 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
- 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
- Kemmer, G., Keller, S. (2010). Nonlinear least-squares data fitting in Excel spreadsheets. Nature Protocols, 5, 267-281. https://doi:10.1038/nprot.2009.182
- Lambert, R.J.W., Mytilinaios, Maitland, L., Brown, A.M. (2012). Monte Carlo simulation of parameter confidence intervals for non-linear regression analysis of biological data using Microsoft Excel. Computer Methods and Programs in Biomedicine, 107, 155-163. https://doi:10.1016/j.cmpb.2011.05.009
- Leylak, C., Yurdakul, M., Buzrul, S. (2020). Gıda bilimlerinde Excel kullanımı 1: Doğrusal regresyon. Food and Health, 6(3), 186-198. https://doi.org/10.3153/FH20020
- Mínguez-Mosquera, M.I., Gandul-Rojas, B. (1994). Mechanism and kinetics of carotenoid degradation during the processing of green table olives. Journal of Agriculture and Food Chemistry, 42, 1551-1554. https://doi.org/10.1021/jf00043a030
- Valdramidis, V.P., Belaubre, N., Zuniga, R., Foster, A.M., Havet, M., Geeraerd, A.H., Swain, M.J., Bernaerts, K., Van Impe, J.F., Kondjoyan, A. (2005). Development of predictive modelling approaches for surface temperature and associated microbiological inactivation during hot air decontamination. International Journal of Food Microbiology, 100, 261-274. https://doi:10.1016/j.ijfoodmicro.2004.10.025
Year 2020,
Volume: 6 Issue: 3, 199 - 212, 22.06.2020
Abstract
Nonlinear regression is often required in order to define experimental data with mathematical models in food science. Although there are many non-linear models in Excel by default, Excel linearizes them and applies linear regression to the data instead of nonlinear regression. However, it is possible to apply non-linear regression by using the “Solver” tool in Excel. The objective of this study was to show how to apply non-linear regression to experimental data by using the Solver tool in Excel. For this purpose, non-linear models having two, three and four parameters, were applied to three different data sets using Solver, respectively. In the first example, the change of the violaxanthin pigment in green olives with respect to time was described using the exponential model, and in the second example, the heat inactivation of Escherichia coli was tried to be explained with a three-parameter non-linear model. In our last example, the growth of Listeria monocytogenes was again described by a non-linear model, while the goodness-of-fit indices were calculated together with the model parameters. The only disadvantage of the Solver tool was that it cannot calculate parameter values along with the standard errors or confidence intervals. Apart from that, there was no difference between shareware used for non-linear regression. It is considered that this study would be beneficial for many researchers having Excel installed in their computers but without other sharewares and working in the field of food engineering or food science.
Keywords
References
- 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
- 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
- Kemmer, G., Keller, S. (2010). Nonlinear least-squares data fitting in Excel spreadsheets. Nature Protocols, 5, 267-281. https://doi:10.1038/nprot.2009.182
- Lambert, R.J.W., Mytilinaios, Maitland, L., Brown, A.M. (2012). Monte Carlo simulation of parameter confidence intervals for non-linear regression analysis of biological data using Microsoft Excel. Computer Methods and Programs in Biomedicine, 107, 155-163. https://doi:10.1016/j.cmpb.2011.05.009
- Leylak, C., Yurdakul, M., Buzrul, S. (2020). Gıda bilimlerinde Excel kullanımı 1: Doğrusal regresyon. Food and Health, 6(3), 186-198. https://doi.org/10.3153/FH20020
- Mínguez-Mosquera, M.I., Gandul-Rojas, B. (1994). Mechanism and kinetics of carotenoid degradation during the processing of green table olives. Journal of Agriculture and Food Chemistry, 42, 1551-1554. https://doi.org/10.1021/jf00043a030
- Valdramidis, V.P., Belaubre, N., Zuniga, R., Foster, A.M., Havet, M., Geeraerd, A.H., Swain, M.J., Bernaerts, K., Van Impe, J.F., Kondjoyan, A. (2005). Development of predictive modelling approaches for surface temperature and associated microbiological inactivation during hot air decontamination. International Journal of Food Microbiology, 100, 261-274. https://doi:10.1016/j.ijfoodmicro.2004.10.025
There are 7 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Food Engineering |
| Journal Section | Review Articles |
| Authors | |
| Publication Date | June 22, 2020 |
| Submission Date | February 21, 2020 |
| Published in Issue | Year 2020Volume: 6 Issue: 3 |
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