Ananas ve Kivi Sularında Protein Parametrelerinin Optimizasyonu için YYY Uygulaması

Year 2025, Volume: 8 Issue: 2, 87 - 92, 29.12.2025

Gül Ozyılmaz , Aykut Tayfur Çağlik

https://doi.org/10.46239/ejbcs.1827652

Abstract

Bu çalışmanın amacı, enzim kokteyli (proteaz, selülaz, amilaz ve pektinaz) hacmi, kitosan adsorbent miktarı, sıcaklık ve işlem süresi gibi parametrelerin ananas ve kivi sularının protein konsantrasyonu üzerindeki karmaşık ve sinerjistik etkilerini araştırmaktır. Temel hedef, bu parametrelerin eşzamanlı ve birbirini etkileyen (interaktif) etkilerini incelemektir. Çok faktörlü bu ilişkileri modellemek için verimli bir istatistiksel araç olan Yanıt yüzey yöntemi (YYY) kullanılmış ve temel yanıt değişkeni olarak protein konsantrasyonu ele alınmıştır. Her iki meyve suyu için oluşturulan kuadratik modeller istatistiksel olarak anlamlı (p < 0,05) bulunmuş ve farklı etkileşim profilleri ortaya koymuştur. Hem ananas hem de kivi suyunda protein konsantrasyonu üzerinde en büyük etkiye enzim kokteyli hacminin sahip olduğu gözlemlenmiştir. Ayrıca, meyve sularının protein konsantrasyonunun, enzim hacmi ve süre kombinasyonundan ve aynı zamanda adsorbent miktarı ve sıcaklık kombinasyonundan etkilendiği tespit edilmiştir. Bu çalışma, RSM'nin parametre sinerjilerini ortaya çıkarmada son derece etkili bir yöntem olduğunu ve meyve suyu işleme sistemlerinde pratik bir uygulama potansiyeli taşıdığını göstermektedir.

Keywords

Yanıt yüzey yöntemi , Sinerjistik Etki , Enzim , Kitosan , Ananas Suyu , Kivi Suyu

Ethical Statement

Bu çalışma, herhangi bir insan veya hayvan denek içermemektedir. Bu nedenle etik onay gerekmemektedir.

Supporting Institution

Mustafa Kemal Üniversitesi

Project Number

21YL002

Application of RSM to optimize protein parameters in pineapple and kiwi juices

Abstract

The aim of this study was to investigate the complex, synergistic effects of enzyme mixture (protease, cellulase, amylase, and pectinase) volume, chitosan adsorbent amount, temperature and processing time on the protein concentration of pineapple and kiwi juices. The primary objective was to examine the simultaneous and interactive effects of these parameters. Response Surface Methodology (RSM) was employed as a efficient statistical tool to model these multifactorial relationships, with protein concentration as a key response variable. The quadratic models generated for both juices were statistically significant (p < 0.05) and revealed distinct interaction profiles. It was observed that the enzyme mixture volume had the greatest effect on protein concentration in both pineapple and kiwi juices. Additionally, the protein concentration of fruit juices were influenced by the combined changes in enzyme volume and processing time, as well as the adsorbent amount and temperature. This study demonstrates that RSM is an effective method for revealing parameter synergies and is practical for application in fruit juice processing systems.

Keywords

Response Surface Methodology , Synergistic Effect , Enzyme , Chitosan , Pineapple Juice , Kiwi juice

Ethical Statement

This study did not involve any human or animal subjects. Therefore, no ethical approval was required.

Supporting Institution

Mustafa Kemal University

Project Number

21YL002

References

• Chen MJ, Chen KN, Lin CW (2005). Optimization on response surface models for the optimal manufacturing conditions of dairy tofu. J Food Eng, 68(4): 471–480.
• Erbay Z, Icier F (2009). Optimization of hot air drying of olive leaves using response surface methodology. J Food Eng, 91(4): 533–541.
• Göncü Y (2008). Optimization of boron nitride purification using response surface methodology (RSM). In Proceedings of the 2nd Boron Workshop (pp. 161–167). Eskişehir, Türkiye.
• Lowry OH, Rosebrough NJ, Farr AL, Randall RJ (1951). Protein measurement with the Folin phenol reagent. J Biol Chem 193(1): 265–275.
• Myers R, Montgomery D (1995). Response surface methodology: process and product optimization using designed experiments. John Wiley.
• Ozyilmaz G, Günay E (2023) Clarification of apple, grape and pear juices by co-immobilized amylase, pectinase and cellulase. Food Chem 398: 133900
• Ribeiro DS, Henrique SMB, Oliveira LS, Macedo GA, Fleuri LF (2010). Enzymes in juice processing: a review. International J Food Sci Technol 45(4): 635–641.
• Rungsardthong V, Wongvuttanakul N, Kongpien N, Chotiwaranon P (2006). Application of fungal chitosan for clarification of apple juice. Process Biochem 41(3): 589–593.
• Sin HN, Yusof S, Sheikh Abdul Hamid N, Rahman RA (2006). Optimization of enzymatic clarification of sapodilla juice using response surface methodology. J Food Eng 73(4): 313–319.
• Siebert KJ (2006). Haze formation in beverages. LWT - Food Sci Technol 39(9): 987–994.
• Turan MD, Altundoğan HS (2011) Application of response surface methodology in hydrometallurgical research. Madencilik, 50: 11–23.
• Yang B, Liu X, Gao Y (2009). Extraction optimization of bioactive compounds (crocin, geniposide and total phenolic compounds) from Gardenia (Gardenia jasminoides Ellis) fruits with response surface methodology. Innovative Food Sci Emerging Technol 10(4): 610–615.
• Yagiz E, Ozyilmaz G, Ozyilmaz AT (2024). Response Surface Methodology use in construction of polyaniline-coated carbon paste electrode–based biosensor: Modification and characterization. Biotechnol Appl Biochem 71(1):147-161