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Chemical Industry & Chemical Engineering Quarterly www.ache.org.rs/CICEQ Chem. Ind. Chem. Eng. Q. 26 (1) 79−87 (2020) CI&CEQ

MARIJA JOKANOVIĆ1 TOWARDS REPRODUCIBILITY OF BOJANA IKONIĆ1 2 TRADITIONAL FERMENTED : PREDRAG IKONIĆ TEXTURE PROFILE ANALYSES AND VLADIMIR TOMOVIĆ1 TATJANA PEULIĆ2 MODELLING BRANISLAV ŠOJIĆ1 1 Article Highlights SNEŽANA ŠKALJAC • Texture profiles of three traditional dry-fermented sausages were investigated 1 MAJA IVIĆ • Regression analyses were used to correlate chemical composition and texture char- JELENA IVETIĆ3 acteristics • Differences were significant between sausages of the same type produced in different 1 University of Novi Sad, Faculty of facilities Technology, Novi Sad, Serbia • Regression analyses showed that moisture content was the most effective for texture 2University of Novi Sad, Institute of profile Food Technology, Novi Sad, • PCA revealed the best reproducibility of analysed characteristics for Petrovská klo- Serbia basá 3 University of Novi Sad, Faculty of Abstract Technical Sciences, Novi Sad, Serbia The aim of this study was to investigate textural characteristics of three tradit- ional dry fermented sausages (Sremski kulen, Lemeški kulen and Petrovská SCIENTIFIC PAPER klobása) manufactured in different small-scale facilities in northern Serbia, and to correlate them with physicochemical and sensory characteristics. The UDC sample sausages were supplied by different local traditional producers. The textural characteristics were correlated with physicochemical and sensory characteristics using multiple linear regression analysis and principal compo- nent analysis. Differences in physicochemical characteristics reflected even more notable differences in texture characteristics. Regarding regression equations, obtained results showed that moisture content was significant for hardness, springiness and cohesiveness. Hardness was also influenced by fat content, while chewiness was influenced by protein content. Principal compo- nent analysis separated samples of Petrovská klobása, as the group with the most reproducible analysed characteristics. Obtained results of statistical ana- lyses should provide knowledge for possible improvements of the traditional production, in a way that these sausages could be produced in different facil- ities with consistent textural characteristics. Keywords: dry fermented , MLR, PCA, texture analysis.

Almost all European countries have cultural redients, application of some manufacturing methods traditions linked with specific traditional foods, which passed on from generation to generation and region are recently attracting more interest from both res- specific environmental and climatic conditions. In earchers and consumers. Consumers are attracted by order to promote rural areas and support the local traditional products for their peculiar characteristics population, the European Union got involved in the due to the use of specific raw materials, authentic ing- protection of high-quality traditional foods from spe- cific regions or areas [1-4]. Correspondence: B. Ikonić, University of Novi Sad, Faculty of In the case of the Republic of Serbia, a number Technology, Bulevar cara Lazara 1, 21000 Novi Sad, Serbia. of traditional dry fermented sausages are produced in E-mail: [email protected] the northern part of the country (Autonomous Pro- Paper received: 24 December, 2018 Paper revised: 16 August, 2019 vince of ). Because of the specific and Paper accepted: 30 August, 2019 recognizable quality, three of them (Sremski kulen, https://doi.org/10.2298/CICEQ181224027J Lemeški kulen and Petrovská klobása) have been

79 M. JOKANOVIĆ et al.: TOWARDS REPRODUCIBILITY OF TRADITIONAL... Chem. Ind. Chem. Eng. Q. 26 (1) 79−87 (2020) granted a protected designation of origin (PDO) label, − to explore the correlation of texture with according to Serbian legislation [5,6]. They are hand- physicochemical and sensory characteristics; crafted, usually in small-scale facilities during winter, − to obtain a deeper insight into the intercor- when atmospheric temperatures are around 0 °C or relation of investigated characteristics in order to lower. In the traditional production of these sausages, understand factors leading to quality reproducibility. curing salts and starter cultures are not used, and the required fermenting microorganisms originate from EXPERIMENTAL the meat itself or from the environment. Sausage bat- ter is stuffed in pig appendix (Sremski kulen, Lemeški Samples kulen) or pig large intestines (Petrovská klobasá), and Three Serbian traditional dry fermented sausage undergoes slow drying and ripening processes in types were considered in this study: Sremski kulen - traditional /drying rooms [6-8]. S (samples S1-S4), Lemeški kulen - L (samples L1- Traditional practices in the small scale facilities –L6), and Petrovská klobása - P (samples P1-P7). lead to great variability in products' properties (hetero- The manufacturing procedures for all three types of geneous quality), as there is no strict uniformity in the sausages are shown in Figure 1. The sausages of product manufacturing by different homemade pro- Sremski kulen and Lemeški kulen have a weight of cessors [1,3,9]. The texture is one of the most import- 700-800 g and the diameter varies from 80 to 95 mm, ant components of meat products’ quality. Many fac- whereas the weight of Petrovská klobása sausages is tors affect the final texture of fermented sausages, 500-600 g and the diameter ranges from 40 to 50 including ingredients used, processing parameters, mm. The sample sausages were supplied in triplicate acidification method, drying/ripening conditions, as (different batches) by different local traditional pro- well as interactions among these factors over an ext- ducers and were stored at 4 °C before textural testing ended period of time [10-13]. As it is one of the most and sensory evaluation. important components of the sausage quality, it is important to create a product of textural attributes accepted by the consumer, but it is also important to ensure the product's quality reproducibility, i.e., a low variability of product characteristics [14]. However, insufficient work has been conducted so far to assess the reproducibility of the texture quality of traditional dry fermented sausages [15]. Multiple linear regression (MLR) is a mathemat- ical tool that generates an equation to describe the statistical relationship between a dependent variable and one or more independent variables, and could be used to predict the textural parameters using the physicochemical parameters as independent vari- ables. Also, evaluation of products’ quality variability requires a collection of numerous different data [14]. Using principal component analysis (PCA), the total number of variables could be reduced, and original variables could be transformed into new factors or principal components [4]. Figure 1. Manufacturing procedures of three Serbian traditional Considering the high interest of consumers for dry fermented sausages: S – Sremski kulen; L - Lemeški kulen; traditional dry fermented sausages and the lack of P - Petrovská klobása. information about the texture characteristics of these products, which could contribute to their character- Chemical composition analyses ization, the aims of this work were: The pH was measured using the portable pH − to determine and compare physicochemical, meter Testo 205 (Testo AG, USA) equipped with a sensory and textural characteristics of three tradit- combined penetration tip with temperature probe [16]. ional dry fermented sausage types (Sremski kulen, The measurements were performed by direct penet- Lemeški kulen and Petrovská klobasá) produced in ration in different areas of the internal part of the sau- different small scale facilities; sage. The pH meter was calibrated before and during

80 M. JOKANOVIĆ et al.: TOWARDS REPRODUCIBILITY OF TRADITIONAL... Chem. Ind. Chem. Eng. Q. 26 (1) 79−87 (2020) the readings using standard phosphate buffers (pH were asked to score samples by using a 1–9 scale for value of calibration buffers was 7.00 and 4.01 at 25 each attribute to be evaluated, where 1 = very low °C. Measurements were performed in triplicate. Sau- and 9 = very high intensity. The descriptors con- sage samples were homogenized (Waring 8010ES sidered chewiness, cohesiveness, and juiciness. For Blender, USA; capacity 1 L, speed 18000 rpm, dur- the assessment casing was removed and the sau- ation of homogenization 10 s, temperature after sages were cut into slices of approximately 3 mm homogenization <10 °C), packaged in polyethylene thickness and served at room temperature on white bags and stored at –20 °C until the determination of plastic dishes. Water and unsalted toasts were pro- proximate chemical composition. Moisture content vided to cleanse the palate between samples. Assess- was determined by drying at 130 °C [17]; nitrogen ments were performed under natural light. concentration (N) was determined by the Kjeldahl Statistical analysis method; protein content was calculated as total N×6.25 [18] and fat content was obtained by Soxhlet The statistical analysis was carried out using extraction using petroleum ether [19]. All analyses software packages Statistica version 12 and Minitab were performed in duplicate. version 17. The analyses were conducted across all sausages types and the differences in investigated Texture profile analysis characteristics were analysed using ANOVA. Multiple Texture profile analysis (TPA) was performed as linear regression models were fitted to estimate tex- described by Bourne [20], at room temperature, using tural parameters such as hardness, springiness, TA.HDplus texture analyser (Stable Micro Systems, cohesiveness and chewiness using physicochemical Godalming, UK) equipped with a standard cylindrical parameters (moisture, protein, and fat content, as well plate of 75 mm in diameter. The samples (cylinders) 2 as pH) as independent variables. The best regression cm thick and 2.54 cm in diameter, after discarding the model for each dependent variable was based on the external layer of the sausage, were compressed twice value of the coefficient of determination (R2), F-value to 50% of their original thickness at a constant test and p-value. Coefficient of determination is a mea- speed of 1 mm/s. The following parameters were det- sure of the extent to which the total variation of the ermined: hardness (kg), springiness, cohesiveness dependent variable is explained by the regression and chewiness (kg). Hardness was defined by peak which means how close the data are to the fitted reg- force during the first compression cycle. Springiness ression line. The F-value and the p-value are used to was defined as the rate at which a deformed sample decide whether the model as a whole has the statis- goes back to its undeformed condition after the def- tically significant predictive capability (at the 95% orming force is removed. Cohesiveness was calcul- confidence level, the upper critical values of the F-dis- ated as the ratio of the area under the second curve tribution were given in the Statistica Handbook (NIST/ to the area under the first curve. Finally, chewiness /SEMATECH e-Handbook of Statistical Methods)). was obtained by multiplying hardness, cohesiveness Significance of each coefficient in the model was det- and springiness. ermined using a t-test, at the 5% significance level. The larger the magnitude of the t-value the more sig- Colour measurements nificant is the corresponding coefficient. PCA calcul- Colour measurements were performed on the ation was performed to obtain an insight into the rel- fresh cut of the sausage at room temperature using ationships of data obtained by TPA test, physico- MINOLTA Chroma Meter CR-400 (Minolta Co., Ltd., chemical and sensory analysis and to reduce the Osaka, Japan); D-65 lighting, 2° standard observer number of relevant variables. angle; 8-mm aperture in the measuring head. Before each set of measurements, the instrument was calib- RESULTS AND DISCUSSION rated using a white ceramic tile (CR-A43). Colour measurements were always performed in the center The results of physicochemical, textural and of the sausage sample. Sausage colour character- sensory analyses of all three groups of traditional dry istics were expressed by CIE L*a*b* system (L* – fermented sausages with basic statistics for charac- lightness, a* – redness, b* – yellowness) [21]. terization of the variability of the analysed samples, as well as ANOVA results, are given in Table 1. Sensory evaluation The sensory analyses were performed by the Physicochemical characteristics 9-member panel previously trained in descriptive ana- Based on the average moisture content, all three lysis for different meat products [22]. Panel tasters groups of samples were within the recommended values

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Table 1. Textural, physicochemical and sensory characteristics of traditional dry fermented sausages; HR - hardness [kg]; SP – springiness; CH – cohesiveness; CW - chewiness [kg]; W – water content (g/100 g); P – protein content (g/100 g); F – fat content (g/100 g); PH – pH value; D – diameter (mm); Chew - chewiness; Juic – juiciness; Cohe – cohesiveness; L* - lighteness; a* - redness, b* - yelowness; SD – standard deviation; * a–c: means within the same column with different superscript letters are different (p < 0.05)

Characteristics Sample Textural Physicochemical Sensory Colour HD SP CH CW W P F pH D Chew Juic Cohe L* a* b* S1 11.78 0.531 0.404 2.49 35.71 36.96 17.07 5.51 95.00 6.64 6.11 6.71 33.76 21.45 15.23 S2 8.02 0.583 0.469 2.19 37.21 35.85 18.95 5.70 80.67 5.71 8.74 6.68 36.08 20.17 14.95 S3 24.76 0.470 0.407 4.73 30.71 48.92 12.43 5.99 90.67 5.72 7.22 7.74 29.78 17.03 11.15 S4 13.24 0.525 0.471 3.27 35.41 39.95 14.98 5.55 93.33 5.00 9.00 8.91 30.04 16.26 11.27 Mean 14.45 0.53a* 0.44 3.1a 34.76 a 40.42 a 15.86 c 5.69 89.92 a 5.77 a 7.77 7.51 32.41 18.73 b 13.15 b SD 7.21 0.05 0.04 1.13 2.81 5.93 2.80 0.22 6.42 0.67 1.36 1.05 3.05 2.48 2.24 CV 43.23 7.65 7.41 31.02 7.01 12.70 15.30 3.34 6.18 10.11 15.14 12.16 8.14 11.47 14.76 L1 13.71 0.424 0.300 1.78 31.98 37.29 18.19 5.38 81.00 4.64 7.52 7.32 33.41 23.33 20.24 L2 10.56 0.417 0.380 1.66 36.36 32.94 20.68 5.10 86.00 5.00 8.87 7.29 39.18 29.61 25.98 L3 4.73 0.409 0.318 0.61 30.68 34.39 28.80 5.53 90.00 4.28 6.73 8.55 31.60 24.27 16.52 L4 18.72 0.395 0.363 2.64 27.00 44.08 20.35 5.57 88.00 4.55 7.42 7.14 29.85 21.38 15.11 L5 5.75 0.490 0.556 1.58 39.84 36.67 15.75 6.02 86.33 4.64 6.73 6.55 28.73 21.80 15.21 L6 10.29 0.588 0.432 2.59 31.85 34.46 25.52 6.04 95.33 5.30 5.00 4.00 29.59 21.36 15.96 Mean 10.63 0.45b 0.39 1.81 b 32.95 a 36.64 ab 21.55 b 5.60 87.78 a 4.74 b 7.05 5.90 32.06 23.63 a 18.17a SD 5.17 0.07 0.09 0.74 4.51 3.98 4.81 0.37 4.76 0.36 1.27 1.52 3.87 3.16 4.27 CV 44.43 14.75 21.75 37.73 12.50 9.92 20.36 6.00 4.95 6.94 16.47 20.41 11.01 12.20 21.45 P1 10.70 0.426 0.261 1.19 22.03 35.54 35.67 5.49 43.33 6.07 7.52 7.07 33.62 20.94 17.21 P2 5.48 0.410 0.536 1.23 24.02 34.65 34.39 5.17 47.33 6.36 6.88 3.00 33.74 24.01 18.63 P3 3.66 0.328 0.284 0.34 23.24 27.86 40.66 5.33 46.33 5.14 7.59 5.71 33.82 26.58 19.99 P4 2.93 0.377 0.272 0.30 22.70 28.54 38.95 5.52 43.67 4.96 9.19 5.79 31.47 24.21 17.56 P5 12.38 0.376 0.244 1.14 20.25 31.76 38.32 5.34 43.50 5.70 4.10 4.10 32.29 23.75 17.04 P6 9.82 0.399 0.265 1.03 18.78 34.81 38.68 5.48 44.25 6.62 6.08 7.40 32.70 24.87 18.20 P7 10.07 0.408 0.357 1.43 20.51 33.54 38.70 5.23 43.75 5.63 7.87 7.64 33.66 26.13 20.84 Mean 7.86 0.39b 0.32 0.96 b 21.65 b 32.39 b 37.91a 5.36 44.60 b 5.78 a 7.03 5.82 33.04 24.36 a 18.50a SD 3.76 0.03 0.10 0.45 1.87 3.11 2.14 0.14 1.58 0.61 1.60 1.75 0.91 1.85 1.44 CV 44.26 7.71 30.14 43.88 7.98 8.88 5.22 2.37 3.28 9.75 21.11 27.80 2.55 7.04 7.22

for dry fermented sausages [23], i.e., lower than 35%. fermentation and flavour development [24]. Average Sausages from group P had, on average, the lowest protein contents for all three groups of samples were (p < 0.05) moisture content (21.56%). Consequently, within the recommended values for dry fermented their average fat content was the highest (37.91%), sausages, i.e., minimally 24% [23]. Protein content od followed by L and S sausages, with significant S sausages (40.42%) was higher (p < 0.05) than that differences between all of them. The lower moisture of P sausages (32.39%). Further, samples of P group content in P sausages could be due to the difference showed the lowest (p > 0.05) pH value, with an aver- in diameter since ripening processes were in age value of 5.36, whereas S and L sausages had approximately the same environmental conditions for average pH value of 5.69 and 5.60, respectively. the same period of time. Determined fat contents These values are in accordance with the previous were in accordance with the fact that higher fat con- results for naturally fermented dry sausages [6,7,26], tent is a dominant characteristic of dry fermented sau- as well as with the fact that these sausages are gen- sages [15,24,25]. Fat contributes to the sensory char- erally characterised by low acidity [15]. Low process acteristics but also has a technological function in the (environmental) temperatures limit the intensity of fer- manufacture of dry fermented sausages as it helps mentation, and thus the pH does not decrease by the continuous release of moisture from the inner layer more than 0.2–0.4 units, as it was previously found of the sausage, a process necessary for undisturbed [27-29]. During the drying and maturation phases, the

82 M. JOKANOVIĆ et al.: TOWARDS REPRODUCIBILITY OF TRADITIONAL... Chem. Ind. Chem. Eng. Q. 26 (1) 79−87 (2020) pH may return to similar values to those of the rip- Multiple linear regression ened meat due to the liberation of peptides, amino In order to better understand the effect of phys- acids and ammonia from proteolytic reactions [29]. icochemical characteristics (independent variables) Textural characteristics on the textural parameter of sausages (dependent variables), a series of MLR analyses were performed. The highest mean hardness value was deter- Firstly, models for each of the four textural outcome mined for sausages S (14.45 kg), followed by L (10.63 variables included a linear combination of all physico- kg) and P (7.86 kg) sausages. The differences among chemical predictor variables. Considering the obtained mean hardness values were not significant (p > 0.05), results (not reported), it was observed that regression probably because of high variability among measure- models for springiness and chewiness were statis- ments within each sausage type, with almost the tically significant (significant overall F-statistics), same coefficients of variability for all three experimen- although each individual coefficient in the model was tal groups. Hardness values obtained in the present not (not-significant t-tests for the individual coef- study for P sausages, were in agreement with results ficients). This often indicates possible multicollinearity reported for sucuk [30], while the ones obtained for S among the predictor variables [32]. Multicollinearity and L sausages were higher than those for sucuk occurs when the model includes multiple factors that [30], de Pamplona [25], and Italian low-acid are correlated not just to the response variable, but sausage [31]. Cohesiveness mean values, ranging also to each other. from 0.32 to 0.44 did not differ significantly among The variance inflation factor (VIF), measures samples. According to Spaziani et al. [31], the lack of how much the variance of the estimated regression variability in cohesiveness could be a consequence of coefficient is “inflated” by the existence of correlation pH value being close to or over the isoelectric point. among the predictor variables in the model, and help Springiness was the highest (p < 0.05) for S (0.53) to detect multicollinearity. The literature data indicates sausages followed by L (0.45) and P (0.39) sausages, VIF exceeding 10 is a sign of serious multicollinearity which did not differ significantly. Chewiness value had requiring correction [33]. Multicollinearity does not the same trend as hardness and springiness, being affect the goodness of fit and the goodness of predict- highest (p < 0.05) for S sausages (3.17 kg), and fol- ion, but it can be a problem if the purpose of the study lowed by L (1.81 kg) and P (0.96 kg) sausages, with- is to estimate the contributions of individual pred- out significant difference between the last two. The ictors. For example, the VIF values were 28.04, major changes in texture take place in the ferment- 13.78, 49.78 and 1.60 for moisture, protein and fat ation process when the pH declines and the solubil- content and pH value, respectively, and indicate a ized miofibrillar proteins aggregate to form a gel, thus serious multicollinearity problem. In order to over- transforming the meat mixture into a ripened sau- come this issue, a stepwise regression was per- sage. In addition to fermentation, drying is an impor- formed. Results of stepwise MLR analysis, using tant factor affecting texture properties [11,30,31]. Minitab v17, are given in Table 2. Colour and sensory characteristics Regarding the regression equation for hardness, 2 The quality of meat and meat products is often R indicated that the model explained 87.4% of the estimated based on colour and appearance [30]. variability, and both predictors in the model (moisture Average L* values of analysed three groups of sau- and fat content) are statistically significant according sages were in the range of 32.06-33.04, with no sig- to their t-values. Also, a negative correlation between nificant differences (p > 0.05) between them. Regard- dependent and independent variables (negative values ing a* and b* values S sausages were significantly of regression coefficients) was observed, meaning the lowest (p > 0.05), while L and P did not differ sig- that hardness decreases when moisture and fat con- nificantly. Colour characteristics values were lower tents increase. Olivares et al. [24], analysing texture than determined for [25] but parameters based on fat content, found a significant higher than the ones reported for sucuk [30]. Results increase in hardness due to fat reduction only at of sensory analyses revealed no significant differ- longer ripening times due to the loss of moisture. ences among analysed groups of sausage samples Contrary to our findings, authors [11,25] found a sig- for juiciness and cohesiveness, while chewiness was nificant correlation between hardness and pH value. found to be the lowest (p < 0.05) for L sausages. Additionally, the significant positive correlation between protein content and hardness was found (Pearson r = 0.86), but this predictor was omitted from

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Table 2. Results of stepwise MLR analysis; R2 - Coefficient of determination; *p < 0.05; VIF - variance inflation fact

Dependent variable R2 F-value p-value Independent variable Regression coefficient t-value VIF Hardness 0.874 48.64 <0.0005 Constant 76074 10.39* Moisture content, % -1298 -8.07* 4.44 Fat content, % -1054 -9.79* 4.44 Springiness 0.614 11.14 0.001 Constant -0.293 -1.17 Moisture content, % 0.0058 2.90* 1.23 pH 0.1032 2.11 1.23 Cohesiveness 0.439 11.75 0.004 Constant 0.1003 1.23 Moisture content, % 0.00945 3.43* 1.00 Chewiness 0.811 30.06 <0.0005 Constant -2603 -1.50 Protein content, % 147.1 3.93* 2.11 Fat content, % -32.6 -1.75 2.11

the stepwise MLR due to the high negative correlation Comparing the four models obtained by MLR, it with fat content (Pearson r = -0.72). could be concluded that some textural characteristics, The regression analysis for the springiness namely hardness and chewiness, were more influ- showed that only moisture content played an import- enced by physicochemical characteristics than others. ant role in the model (positive correlation), and the This implies that special attention should be paid to model explained 61.4% of the variability in the sprin- raw materials used in the production. Particularly, the giness. The same correlation was determined by Boz- fat content should be tightly controlled, because it kurt and Bayram [30], while Gonzalez-Fernandez et was significantly correlated with all analysed textural al. [11] found a significant correlation between sprin- characteristics either by MLR or Pearson correlation. giness and pH value. Pearson correlation between fat Principal component analysis content and springiness was significant (-0.65) in this study. For a global view, the principal component The third model positively correlated cohesive- analysis was performed using the data of instrumental ness with moisture content, but the value of the coef- (textural and colour measurements), physicochem- ficient of determination was only 0.439, meaning that ical, and sensory characteristics of the fermented the model can explain less than 50% of the variability. sausages samples (Figures 2 and 3). Bozkurt and Bayram [30] also found a correlation Three main components (F1, F2 and F3) were between cohesiveness and moisture content, whereas used, which accounted for 73.77% of the total vari- other authors found a significant correlation between ance data, and the individual contributions of the cohesiveness and fat content [24] and between coh- components were 45.90, 15.87 and 12.00%, respect- esiveness and pH value [11,25]. The significant neg- ively. The fact that three dimensions were used, and ative correlation between fat content and cohesive- explained only 73.77%, suggested the wide variability ness was also found (Pearson r = -0.59) in this study, of the sample regarding the analysed parameters but this predictor was omitted from the stepwise MLR what was linked to different raw material/formulation due to the high negative correlation with moisture and processing parameters. First PC (F1) was asso- content (Pearson r = -0.88). ciated with most of the physicochemical parameters Finally, highly significant MLR (R2 = 0.811) was and redness. The second PC (F2) was mostly loaded found between chewiness and chemical composition by sensory and colour parameters, and the third one (protein and fat content), but only protein content had was mostly associated with textural characteristics. a significant influence on chewiness, according to its Through first two components, it was only pos- t-value, and these two variables were positively cor- sible to separate samples of P sausages, which were related. However, the Pearson correlation between fat allocated on the negative quadrant of F1, highly influ- content and chewiness was significant (-0.78). In dif- enced by the fat content of the samples, and negat- ferent studies, authors determined correlations between ively correlated with moisture content and diameter chewiness and fat content [24], between chewiness size. As it was previously shown these samples had and moisture content [30], and between chewiness the highest fat content, with the lowest coefficient of and pH value [11,25]. variation. Data for other samples were scattered mostly in the positive quadrant of F1.

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Figure 2. PCA loading plots in the plane of factors 2 and 3 vs. factor 1 – the labels correspond to variables listed in Table 1.

Regarding instrumental colour characteristics, terisation of samples, and no significant correlations F2 cooperated with separating sample L2, which had (p > 0.05, data not shown) were determined between highest L*, a* and b* values, comparing with all other instrumentally and sensory determined texture para- samples, i.e., it was the lightest and with the highest meters. share of red colour. Lightness (L*) seems to be the The results of PCA lead to the following conclus- most informative parameter for colour changes, but ions. Firstly, it could be observed that Petrovská klo- the importance of red (a*) should not be ignored [25]. basá sausages are clearly distinct from the other two Further, the third PC (F3) was associated with hard- types of sausages. Secondly, all three types of sau- ness and protein content what was important in the sages exhibited high variation which is evident from characterisation of S3, S4 and L4 samples, since the cluster sizes (Figure 3). Considering the analysed these samples were the highest in these character- parameters, a grouping of likewise variables into fac- istics compared to the others. Also, springiness and tors was evident, to some extent, although with sev- cohesiveness position on the positive quadrant of F3 eral exceptions. More comprehensive and informative was important for the characterisation of samples S1, results regarding the relationship between the type of S2, L5 and L6 being the highest in these values com- sausage and their quality characteristics could be pared to other samples, and also being the highest in obtained by using canonical correlation analysis moisture content which was positively correlated with (CCA), however, due to the very limited sample size, these two textural parameters. Analysed sensory pro- it was not possible to perform this type of analysis in perties of sausages (chewiness, cohesiveness and this study. juiciness) did not make any difference for the charac-

Figure 3. Results of the PCA – score plots in the plane of factors 2 and 3 vs. factor 1 – the labels correspond to samples given in Table 1.

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CONCLUSION Sebranek, E.T.F. Silveira, L.H. Stahnke, R. Talon (Eds.), Blackwell Publishing, Ames, IA, 2007, p. 217 Although the recipes and procedures in the trad- [13] A. Bagdatli, A. Kundakci, J Food Sci Technol. 53 (2016) itional production of Sremski kulen, Lemeški kulen 1679-1689 and Petrovská klobasá are well known, obtained [14] G. Probola, L. Zander, J Food Eng. 83 (2007) 93-98 results showed differences in physicochemical and [15] J. Rason, A. Laguet, P. Berge, E. Dufour, A. Lebecque, sensory characteristics even between sausages of Meat Sci. 75 (2007) 359-370 the same type produced in different small scale facil- [16] ISO 2917, International Organisation for Standardisation ities. These differences reflected to even more not- (1999) able differences in texture characteristics. Obtained [17] ISO 1442, International Organisation for Standardisation results of statistical analyses should provide know- (1997) ledge for possible improvements of the traditional pro- [18] ISO 937, International Organisation for Standardisation duction, in a way that these sausages could be pro- (1978) duced in different facilities with consistent textural [19] ISO 1443, International Organisation for Standardisation (1973) characteristics. [20] M.C. Bourne, Food Technol. 32 (1978) 62-66 Acknowledgements [21] CIE, International Commission on Illumination, Color- Research was financially supported by the Min- imetry: Official Recommendation of the International istry of Education, Science and Technological Dev- Commission on Illumination, Publication CIE No. (E- -1.31), Bureau Central de la CIE Paris, 1976 elopment, the Republic of Serbia, projects TR31032 [22] ISO 8586–1, International Organisation for Standard- and III44006. isation (1993) [23] Serbian Legislation 94- 2015, Official Gazette of the SCG REFERENCES (2015) [24] A. Olivares, J. L. Navarro, A. Salvador, M. Flores, Meat [1] L. Aquilanti, S. Santarelli, G. Silvestri, A.Osimani, A. Sci. 86 (2010) 251-257 Petruzzelli, F.Clementi, Food Microbiol. 120 (2007) 136- –145 [25] O. Gimeno, D. Ansorena, I. Astiasaran, J. Bello, Food [2] J. Jordana, Food Res Int. 33 (2000) 147-152 Chem. 69 (2000) 195-200 [26] P. Ikonić, Lj. Petrović, T. Tasić, M. Jokanović, S. Savatić, [3] R. Talon, S. Leroy, I. Lebert, Meat Sci. 77 (2007) 55-62 B. Ikonić, N. Džinić, Chem. Ind. Chem. Eng. Q. 18 (2012) [4] P. Wang, M.Y. Han, Q.L. Dong, X.L. Xu, G.H. Zhou, S.L. 163-169 Lu, J. Texture Stud. 41 (2010) 757-773 [27] S. Bover-Cid, M. Izquierdo-Pulido, C. M. Vidal-Carou, [5] Lj. Petrović, N. Džinić, V. Tomović, P. Ikonić, T. Tasić, Meat Sci. 57 (2001) 215-221 Republic of Serbia: Intellectual Property Office Decision [28] P. Ikonić, M. Jokanović, Lj. Petrović, T. Tasić, S. Škaljac, No. 9652/06 G-03/06, 2007 B. Šojić, N. Džinić, V. Tomović, J. Tomić, B. Danilović, B. [6] I. Vuković, S. Saičić, D.Vasilev, Tehnologija mesa 52 Ikonić, Int J Food Prop. 19 (2016) 1924-1937 (2011) 134-140 [29] G. Kaban, M. Kaya, J Food Sci. 74 (2009) 58-63 [7] P. Ikonić, T.Tasić, Lj. Petrović, S. Škaljac, M. Jokanović, [30] H. Bozkurt, M. Bayram, Meat Sci. 73 (2006) 344-350 A. Mandić, B. Ikonić, Food Control 30 (2013) 69-75 [31] M. Spaziani, M. Del Torre, L. Stecchini, Meat Sci. 81 [8] T. Tasić, P. Ikonić, A. Mandić, M. Jokanović, V. Tomović, (2009) 77-85 S. Savatić, Lj. Petrović, Food Control 23 (2012) 107-112 [32] L. Simon, Penn State lectures, https://onlinecourses.sci- [9] A. Salgado, M.C. Garcia Fontan, I. Franco, M. Lopez, J. ence.psu.edu/stat501/node/347 (accessed 11 May 2018) Carballo, Food Control 17 (2006) 213-221 [33] Mathworks Support, http://www.mathworks.com/help/ [10] A.S. Szczesniak, Food Qual. Prefer. 13 (2002) 215-225 stats/f-statistic-and-t-statistic.html (accessed 24 May [11] C. Gonzalez-Fernandez, E.M. Santos, J. Rovira, I. Jaime, 2018). Meat Sci. 74 (2006) 467-475 [12] S. Barbut, in Handbook of Fermented Meat and Poultry, F. Toldra, Y.H. Hui, I. Astiasarán, W.K. Nip, J.G.

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MARIJA JOKANOVIĆ1 POSTIZANJE REPRODUCIBILNOSTI U PROIZVOD- BOJANA IKONIĆ1 NJI TRADICIONALNIH FERMENTISANIH 2 PREDRAG IKONIĆ KOBASICA: ANALIZA TEKSTURE I VLADIMIR TOMOVIĆ1 2 MODELOVANJE TATJANA PEULIĆ BRANISLAV ŠOJIĆ1 Cilj ovog rada bio je da se ispitaju karakteristike teksture tri tradicionalne fermentisane SNEŽANA ŠKALJAC1 suve kobasice (Sremski kulen, Lemeški kulen i Petrovská klobása) proizvedene u razli- 1 MAJA IVIĆ čitim malim objektima za preradu mesa na severu Srbije, i da se dobijeni rezultati 3 JELENA IVETIĆ povežu sa fizičko-hemijskim i senzorskim karakteristikama. Uzorci za analizu su nabav- 1Univerzitet u Novom Sadu, Tehnološki ljeni od različitih lokalnih proizvođača tradicionalnih kobasica. Karakteristike teksture su fakultet Novi Sad, Bulevar cara Lazara korelirane sa fizičko-hemijskim i senzorskim karakteristikama korišćenjem višestruke 1, 21000 Novi Sad, Srbija linearne regresione analize i analize glavnih komponenti. Razlike u fizičko-hemijskim 2Univerzitet u Novom Sadu, Naučni karakteristikama odražavale su se još značajnije na razlike u karakteristikama teksture. institut za prehrambene tehnologije u Rezultati regresione analize pokazali su da je sadržaj vlage značajan za tvrdoću, elas- Novom Sadu, Bulevar cara Lazara 1, tičnost i kohezivnost kobasica. Na tvrdoću je značajno uticao i sadržaj masti, dok je na 21000 Novi Sad, Srbija žvakljivost uticaj imao i sadržaj proteina. Analiza glavnih komponenti izdvojila je uzorke 3 Univerzitet u Novom Sadu, Fakultet Petrovská klobása kao grupe sa najvećom ponovljivošću analiziranih karakteristika. tehničkih nauka, Trg Dositeja Dobijeni rezultati statističkih analiza treba da ukažu na mogućnost poboljšanja tradicio- Obradovića 6, 21000 Novi Sad, Srbija nalne proizvodnje na način da kobasice proizvedene u različitim tradicionalnim objek- tima imaju konzistentne karakteristike teksture. NAUČNI RAD Ključne reči: fermentisane suve kobasice, MLR, PCA, analiza teksture.

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