Optimizing Sensorial Quality of Iranian White Brine Cheese Using Response Surface Methodology MOHAMMAD ALIZADEH, MANOUCHEHR HAMEDI, and ASGHAR KHOSROSHAHI

JFS S: Sensory and Nutritive Qualities of Food

Optimizing Sensorial Quality of Iranian White Brine Cheese Using Response Surface Methodology MOHAMMAD ALIZADEH, MANOUCHEHR HAMEDI, AND ASGHAR KHOSROSHAHI

ABSTRACT: Response surface methodology was used to evaluate the effects of processing variables, such as ripening time (20 to 60 d), ripening temperature (6 to 10 °C), level of rennet added (1 to 2 g/100 kg milk), and brine concentration (8% to 14%, w/v), on the sensorial quality of Iranian pickled cheese (feta type). Optimiza- tion of sensorial quality was performed by canonical analysis to derive the stationary point. Based on contour plots and canonical analysis, optimum conditions were ripening time 32 d, ripening temperature 8.3 °C, level of rennet added 1.6 g/100 kg of milk, and brine concentration 11%. Predicted sensory score was 20.76 from maximum score of 25. Keywords: pickled cheese, sensorial quality, optimization, response surface methodology

Introduction Materials and methods n Iran, pickled cheese is a major item in diet, and consumption Iper capita per annum is about 5.4 kg. At the industrial level, the Cheese making ripening time is about 45 to 90 d (Azarnia 1997). However, there are The brine cheese was manufactured for this study according to trends to reduce this time period for economical reasons. the method used in Iranian cheese-making plants. White brined Pickled cheese, like other types of ripened cheese, requires cheese was prepared from cows’ milk. The milk was standardized to maturation to develop the required sensory properties. In warm cli- a fat content of 2.5%, pasteurized at 72 °C for 15 s, and cooled to 32 mates, it is necessary to preserve cheeses in brine. The specific to 35 °C. CaCl2 was added at a level of 15 g/100 kg of milk followed characteristics of brine cheese develop in the salted water and by the addition of 1% starter culture (Hansen’s Laboratory, Roskil- chemical, physical, and sensorial properties of this type of cheese de, Denmark) 30 min before renneting. Cultures of Streptococcus sal- are controlled by processing and environmental conditions (Abd ivarius subsp. thermophilus and Lactobacillus delbrueckii subsp. El-Salam 1987; Caric 1987; Abou-Donia 1991; Scott 1986). bulgaricus were used as starter. Commercial powdered microbial Pickled cheese–manufacturing plants in Iran work with different rennet (Meito, Sangyo Co., Nagoya, Japan) with milk-clotting activ- levels of processing variables. For example, brine concentration ity of 1 g/100 kg of milk were added at 3 experimental levels (1, 1.5, varies from 8% to 16% w/v, ripening temperature varies from 6 to and 2 g/100 kg milk) to coagulate milk samples. Following coagula- 16 °C, and so on. It is evident that with these conditions, the pro- tion, the curds were cut and then stirred. The curds were pressed duced cheeses will not have uniform quality. The number of factors using weights for 1 h (20 kg weight/30 kg final curd). The curds were that determine sensorial quality of pickled cheeses is so large that then cut to a suitable shape and size and soaked in a sterile brine it is impossible to study all of them simultaneously. (22%, w/v) for 16 h. The curd pieces were then placed in tin cans; Response surface methodology (RSM) is an effective tool regu- brines with 3 different concentrations (8%, 11%, and 14%, w/v) were larly used for studying the separate and interactive effects of sys- added to cover the curds completely and fill cans. The filled cans tem factors on a desired response variable (Hunter 1959). RSM were sealed immediately after brining. The sealed cans were stored currently is the most popular technique in food science for empir- at 3 different ripening temperatures (6, 10, and 14 °C) for 3 different ical optimization studies because of its comprehensive theory, rea- ripening times (20, 40, and 60 d). sonably high efficiency, and simplicity (Arteaga and others 1994). First- or second-order polynomials are the most common model Experimental design functions used to describe RSM (Khuri and Cornell 1987). A 3-level, 4-factor experimental design with 3 replicates at the In this study, we have evaluated the effects of 4 factors, ripen- center point was used (Box and Behnken 1960). The 4 factors (pro- ing temperature, ripening time, the level of rennet added, and cessing variables), levels, and experimental design in coded and brine concentration, on sensorial quality of pickled cheese and uncoded terms are given in Table 1. determine optimum conditions. Sensory evaluation Sensory evaluations of cheese samples were obtained for flavor, MS 20040700 Submitted 10/22/04, Revised 12/30/04, Accepted 2/15/05. Au- body and texture, and odor and appearance by Iran’s standard thors Alizadeh and Khosroshahi are with Food Science and Technology Dept., Faculty of Agriculture, Univ. of Urmia, Urmia, Iran. Author Hamedi 2344-1 (ISIRI 2002) for white pickled cheese. In this sensory system, is with Food Science and Technology Dept., Faculty of Agriculture, Univ. of the product is graded on a 25-point scale as follows: 10 points max- Tehran, Karaj, Iran. Direct inquiries to author Alizadeh imum for flavor (1 = intensive foreign flavors like acid, bitter, soapy, (E-mail: [email protected]). and so on to 10 = special flavor of the Iranian ripened white brine

© 2005 Institute of Food Technologists Vol. 70, Nr. 4, 2005—JOURNAL OF FOOD SCIENCE S299 Further reproduction without permission is prohibited Published on Web 4/28/2005 S: Sensory & Nutritive Qualities of Food www.ift.org Coded are constant are ij ␤ w/v) (X4) , and ii are ripening tempera- are ␤ 4 Uncoded , i Brine concentration ␤ (0.915). , 2 0 R ␤ , and x 3 x action action action action action 2, , x 1 Coded URLs and E-mail addresses are active links at is the uncoded independent variable. Pareto i Results and Discussion atic, and inter atic, and inter atic, and inter atic, and inter atic, and inter Rennet added (g/100 kg milk) (X3)(g/100 kg (% Uncoded , quadr , quadr , quadr , quadr , quadr = 0.271) and with a satisfactory fter statistical analysis of data, the second-order polynomial fter statistical analysis of data, the second-order equation is given below: P Because the relationship between factors and response was ei- With the lack of fit test, the analysis of variance (Table 2) showed With the lack of fit test, the analysis of variance (Table The length of each bar on a standardized pareto chart is propor- Linear Linear effects can be represented graphically effects can be represented graphically (Figure 1) on a standardized pareto chart (Figure 1) on a standardized pareto chart ther unknown or too complex to be useful, the simple empirical secondary order equation was assumed to be approximate (Floros and Chinnan 1987). ture, ripening time, rennet added, and brine concentration, respectively. that the model appeared to be adequate, with no significant lack of fit ( coefficients, and x where Y is response (sensory score), (sensoryY is response score), where A Y is sensory and x score where Linear effects can be represented graphically (Figure 1) on a standardized pareto chart chart and contour plots were created using Statistica version 6.0 chart and contour plots were created using Okla., U.S.A.). Tulsa, (Statsoft, Linear Linear effects can be represented graphically effects can be represented graphically (Figure 1) on a standardized pareto chart (Figure 1) on a standardized pareto chart Coded Time (d) (X2) Uncoded —Vol. 70, Nr. 4, 2005 70, Nr. —Vol. Coded Temperature (°C) (X1) Temperature Uncoded JOURNAL OF FOOD SCIENCE 1234567 68 69 6 6 6 –1 6 –1 10 –1 10 –1 10 –1 –1 0 20 0 40 0 40 40 40 –1 60 0 20 0 20 0 20 0 1 –1 1.5 –1 –1 1 1.5 1.5 2 0 1.5 1 –1 1.5 0 1.5 0 1 0 11 –1 0 11 0 8 14 0 11 11 11 0 –1 8 14 1 0 0 0 –1 1 10111213141516 1017 1018 1019 1020 1021 0 1022 0 1023 0 1024 0 1025 0 1026 0 1027 0 10 20 0 14 40 0 14 40 0 14 40 0 14 40 –1 0 14 40 1 0 14 40 1 0 40 1 0 60 1 0 60 1 0 60 1 0 2 60 0 20 1 1 40 1 1 40 1.5 1 40 1.5 1 40 –1 1.5 1 60 2 –1 0 2 –1 0 0 1 0 0 1.5 0 0 1.5 1 2 11 1.5 1 1 8 1 –1 14 0 1.5 11 0 1.5 11 2 11 0 0 1 1.5 –1 –1 1 8 14 0 0 11 0 0 8 0 14 1 0 11 –1 11 1 0 11 –1 8 1 14 0 0 11 11 0 –1 1 0 0 Run value value value value value value value value Experimental data were analyzed by response surface regres- The cheeses were evaluated organoleptically after 20, 40, and The total score was obtained by adding the scores for the 4 sen- for total to the mean value sensory refers score this study, In 300 Data analysis Data analysis S Data analysis 1989) to fit 6.12, SAS Inst. (Version of SAS software sion procedure the following second-order polynomial equation: cheese with no foreign flavor); 5 points maximum for body and tex- cheese with no foreign flavor); 5 points maximum not very or sticky to 5 = continuous body, (1 = very soft or hard ture = intensive foreign fla- soft or hard); 5 points maximum for odor (1 of the Iranian to 5 = special flavor or rancid vors like pungent, malty, foreign odors); and ripened white brine cheese with no perceptible very with large porous (1 = moldy, 5 points maximum for appearance nonporous with white pores, and yellow color to 5 = uniform and cut into pieces about color). Samples of white, brined cheese were white plates coded with 3 ×times/ 3 × 2 cm in size and placed on were tempered by three-digit random numbers. The cut samples and then presented to holding at ambient temperature (20 ± 2 °C) for was provided Water for testing. order the panelists in a random mouth washing between samples. 60 d of ripening by 5 experienced panelists according to a scoring card (Bodyfelt and others 1988). Panelists were familiar with feta cheese and were trained by exposure to the different commercial white brine cheese samples and a practice session was held 1 d before the regular evaluation sessions. Each of the panelists evaluated each experimental sample, and in each session, 3 samples were given to each of the panelists. sory attributes. An excellent cheese received a total score of 25. scores of each sample, and it was used in statistical analysis and modeling. Table 1—Box-Behnken design used to evaluate the effects of process variables on sensory score of cheese of on sensory variables process of score effects the to evaluate used design 1—Box-Behnken Table Data analysis Data analysis Optimizing sensorial quality of cheese . . . . of cheese quality sensorial Optimizing

S: Sensory & Nutritive Qualities of Food Optimizing sensorial quality of cheese . . . tional to the absolute value of its associated regression coefficient Table 2—Analysis of variance for processing variables or estimated effect. In this chart, the effects are standardized (each pertaining to the response sensory score effect is divided by its standard error). The order in which the bars Degrees of Sum of are displayed corresponds to the order of the size of the effects, with Source freedom squares F-ratio Prob > F the strongest effect on the top; this allows the most important ef- Model 14 423.13 9.25 0.0002 fects to be identified. The chart includes a vertical line, which cor- Linear 4 105.689 8.09 0.002 responds to the 95% confidence limit indicating statistical signifi- Quadratic 4 40.07 3.07 0.0590 Cross product 6 190.05 9.69 0.001 cance. An effect is therefore significant if it crosses this vertical line. Lack of fit 10 36.8 3.056 0.2716 This chart shows that temperature and interaction of time and tem- Pure error 2 2.41 perature are the most effective factors on sensory score. Added Total error 12 39.21 brine concentration and rennet were significantly effective in their R2 = 0.915Adjusted R2 = 0.812 quadratic forms but were not effective in linear form (Figure 1). Also, Figure 1 shows that, compared with other factors, brine concentration in the studied range (8% to 14%, w/v) had the least significant effect on sensory score. The relationship between factors and response can best be understood by examining the series of brine concentration and rennet were used (Figure 2A), maximum contour plots generated by holding 2 factors constant and plotting sensory score was 22 compared with 19, as shown in Figure 2C. This response as a function of 2 other factors. can be attributed to intensive proteolysis in cheese samples that High levels of added rennet and brine concentration had nega- originates from high levels of added rennet and NaCl. It is shown tive effect on sensory score of cheese (Figure 2). When low levels of that NaCl content of cheese (up to 6%) has a positive effect on ac-

Figure 1—Pareto chart showing the significance of processing variables on sensory score

Figure 2—Contour plots showing the effect of ripening time and temperature on sensory score under constant rennet content and brine concentration. The numbers inside the contours represent sensory scores of cheese samples from maximum score of 25.

URLs and E-mail addresses are active links at www.ift.org Vol. 70, Nr. 4, 2005—JOURNAL OF FOOD SCIENCE S301 S: Sensory & Nutritive Qualities of Food www.ift.org are eigenvectors based on coded data and URLs and E-mail addresses are active links at 4 , and w 3 , w 2 , w 1 The method of ridge analysis computes the estimated ridge of op- The method of ridge analysis computes the Optimum processing conditions were determined by canonical were determined by processing conditions Optimum of the rennet added and brine concen- The response behavior based on the stationary point resulted in The canonical analysis Y is the sensory score. The mixed sign of eigenvalues indicated that Y is the sensory score. The mixed sign of eigenvalues point was shaped the predicted response surface of the stationary have unique optimum. like a saddle. That estimated surface did not the center of the original timum response for increasing radii from 4) indicated that maximum senso- (Table The ridge analysis design. ature and brine concentration (14 °C and 14%, w/v, respectively), and brine w/v, °C and 14%, (14 concentration ature likely that by was 19. It was of sensory score achieved maximum would be low score of cheese ripening time, the sensory increasing introduced for long time Use of elevated temperatures (Figure 4C). to the cheese samples. textural defects values of The stationary point, 1971; Draper 1963). analysis (Myers lo- 1st derivative of response was zero, were variables at which the of region with the predicted value cated exactly in the experimental 3). 20.76 (Table followed while holding ripening temperature tration (Figure 5) were point. The maximum value was predicted and time at the stationary of the rennet added = 1.6 g/100 kg of milk to be near a combination = 11%, w/v by contour plot. and brine concentration the following equation: where w 11.00 Uncoded Level —Vol. 70, Nr. 4, 2005 70, Nr. —Vol. Coded –0.43–0.38 8.29 32.38 w/v) –0.0059 JOURNAL OF FOOD SCIENCE Temperature and level of rennet added had significant and added had significant and level of rennet Temperature with high sen- Ripening time for production of cheese samples 302 S Figure 4—Contour plots showing the effect of rennet content and ripening time on sensory score under constant rip- Figure 4—Contour plots showing the effect of rennet content and ripening ening temperature and brine concentration Figure 3—Contour plots showing the effect of ripening temperature and rennet content on sensory score under con- Figure 3—Contour plots showing the effect of ripening temperature and stant brine concentration and ripening time tivity of proteolytic enzymes in cheese (Fox 1998; Visser 1977; Us- 1998; enzymes in cheese (Fox tivity of proteolytic tunol and Zeckler 1996). 3). sensorial quality of brine cheese (Figure determinant effect on when temperature and rennet added were As shown in Figure 3A, in- score of cheese samples increased by at low levels, the sensory By contrast, when temperature and rennet creasing ripening time. of cheese samples de- added were at high levels, sensory score 3C), and most of the creased with increasing ripening time (Figure had a bitter taste. It is cheese samples ripened in this condition leads to accumu- known that the addition of a high level of rennet: Visser and others 2000; lation of bitter peptides in cheese (Frister 1977; Lemieux and Simard 1991). temperature and sory score decreased with increasing ripening of temper- at higher levels 4). However, (Figure brine concentration Processing variables Processing Table 3—Predicted levels of processing variables at sta- variables of processing levels 3—Predicted Table tionary point Temperature (°C) Temperature Time (d) (g/100 kg milk)Rennet added Brine concentration (% 0.21 1.61 Optimizing sensorial quality of cheese . . . . of cheese quality sensorial Optimizing

S: Sensory & Nutritive Qualities of Food Optimizing sensorial quality of cheese . . .

Table 4—Estimated ridge of maximum response for variable Uncoded factor values Coded Estimated response Standard Temperature Time Rennet Brine concentration radius (sensory score) error (°C) (d) (g/100 kg milk) (% w/v) 0.0 19.7 1.04 10.00 40.00 1.50 11.00 0.1 20.0 1.04 9.67 38.92 1.50 10.97 0.2 20.3 1.03 9.29 38.24 1.50 10.95 0.3 20.6 1.01 8.84 38.66 1.49 10.93 0.4 20.8 0.99 8.42 40.20 1.47 10.92 0.5 21.0 0.96 8.08 41.85 1.45 10.91 0.6 21.3 0.93 7.78 43.45 1.43 10.91 0.7 21.7 0.91 7.49 45.00 1.41 10.90 0.8 22.1 0.90 7.21 46.53 1.39 10.89 0.9 22.5 0.91 6.95 48.03 1.37 10.88 1.0 23.0 0.94 6.69 49.50 1.35 10.88

ry score (23 ± 0.94) would be at ripening temperature = 6.69 °C, ripen- temperature (°C) and ripening time (days) were the most effective ing time = 49.5 d, level of rennet added = 1.35 g/100 kg of milk, and factor affecting the sensory score of Iranian white brine cheese and brine concentration = 10.88%, w/v, at the distance of coded radius 1.0. it is possible to reduce ripening period by optimal combination of Compared with stationary point and maximum point in the ex- processing variables. perimental region, the brine concentration, level of rennet added, and ripening temperature were very close, but increasing ripening References time from 32.38 d to 49.5 d increased the sensory score by only 2.24. Abd El-Salam MH. 1987. Domiati and Feta type cheeses. In: Fox PF, editor. Cheese: chemistry, physics and microbiology. London: Elsevier Applied Science Pub- Ripening time at the stationary point was about 17 d shorter lishers. p 227–309. than ripening time at the maximum point. Therefore, the stationary Abou-Donia SA. 1991. Manufacture of Egyptian soft and pickled cheeses. In: Robinson RK, Tamime AY, editors. Feta and related cheese. London: Ellis Hor- point was recommended as the optimal processing condition (Fig- wood Limited. p 160–80. ure 5). It should be noted that our objective was to find optimum Arteaga GE, Li-Chan E, Vazquez MC, Nakai S.1994. Systematic experimental designs for product formula optimisation. Trends Food Sci Technol 5:243–54. processing conditions, not necessarily maximum sensory score. Azarnia S, Ehsani MR, Mirhadi SA. 1997. Evaluation of the physico-chemical characteristics of the curd during the ripening of Iranian brine cheese. Int Conclusions Dairy J 7:471–8. Bodyfelt FW, Tobias J, Trout GM. 1988. The sensory evaluation of dairy products. his paper has presented the results of modeling the sensory London: Van Nostrand Reinhold. score of Iranian white brine cheese by response surface meth- Box GEP, Behnken DW. 1960. Some new three level designs for the study of quan- T titative variables. Technometrics 2(4):455–75. odology. Analysis of response surface model revealed that ripening Caric M. 1987. Mediterranean cheese varieties: Ripened cheese varieties na- tive to the Balkan countries. In: Fox PF, editor. Cheese: chemistry, physics and microbiology. London: Elsevier Applied Science Publishers. p 257–79. Draper NR. 1963. Ridge analysis of response surfaces. Technometrics 5(4):469– 79. Floros JD, Chinnan MS. 1987. Optimization of Pimiento pepper lyepeeling process using response surface methodology. Trans ASAE 30: 560–5. Fox PF. 1998. Cheese: chemistry, physics and microbiology, vol. 2. London: Chap- man & Hall. Frister H, Michaelis M, Schwerdtfeger T, Folkenberg DM, Sorensen NK. 2000. Evaluation of bitterness in Cheddar cheese. Milch 55(12):691–5. Hunter JS. 1959. Determination of optimum condition by experimental methods. Industr Qual Cont 15:6-14. [ISIRI] Inst. of Standards and Industrial Research of Iran. 2002. Cheese in brine: specifications and test methods. Iranian national standard 2344–1. Karaj: Inst. of Standards and Industrial Research of Iran Khuri AL, Cornell JA. 1987. Response surfaces: design and analyses. In: Owen DB, Cornell RG, Kennedy WJ, Kshirsagar AM, Schilling EG, editors. Statistics: Textbooks and monographs, vol. 81. New York: Marcel Dekker Inc. Lemieux L, Simard RE. 1991. Bitter flavor in dairy products. I. A review of the factors likely to influence its development, mainly in cheese manufacture. Lait 71(6):599–636. Myers RH. 1971. Response surface methodology. Boston, Mass.: Allyn & Bacon. Scott R. 1986. Cheesemaking practice, 2nd ed. London: Elsevier Applied Science. SAS Inst. 1989. SAS user’s guide: statistics. Ver. 6.12. Cary, N.C.: SAS Inst. Ustunol Z, Zeckler T. 1996. Relative proteolytic action of milk-clotting enzymes preparation on bovine ␣-, ␤-, and ␬-casein. J Food Sci 61(6):1136–59. Visser F. 1977. Contribution of enzymes from rennet, starter bacteria and milk Figure 5—Contour plot of optimum processing condition to proteolysis and flavor development in Gouda cheese. II. Development of at the stationary point: ripening temperature (8.29 °C) and bitterness and cheese flavor. Neth. Milk Dairy J 31(3):188–20. ripening time (32.4 d).

URLs and E-mail addresses are active links at www.ift.org Vol. 70, Nr. 4, 2005—JOURNAL OF FOOD SCIENCE S303 S: Sensory & Nutritive Qualities of Food