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Principal component analysis reduces the dimensionality of data containing a large set of variables to small set without loosing the most important information in the original data set. Multiple correlated response variables are often included in experimental studies.  Principal component analysis can be used to generate one overall response variable that contains most of the information in the set of response variables. After obtaining one overall response variable of the experimental design model, response surface method can be used to optimize  the new response variable which is influenced by several independent variables. In this paper principal component analysis is applied to data of bread production process which contains correlated response variables such as phytic acid, phytate phosphor, total phosphor. Response surface method is conducted to optimize new response generated by principal component analysis and the optimum conditions are obtained.

Temel Bileşenler Analizi ve Yanıt Yüzey Yöntemi Kullanılarak Gıda Sektöründe Çoklu Yanıtların Optimizasyonu