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1 LWT - Food Science and Technology 47 (2012) 56e63 Contents lists available at SciVerse ScienceDirect LWT - Food Science and Technology journal homepage: Suitability of solvent retention capacity tests to assess the cookie and bread making quality of European wheat flours Annelies E. Duyvejonck *, Bert Lagrain, Emmie Dornez, Jan A. Delcour, Christophe M. Courtin Laboratory of Food Chemistry and Biochemistry and Leuven Food Science and Nutrition Research Centre (LFoRCe), KU Leuven, Kasteelpark Arenberg 20, 3001 Leuven, Belgium article info abstract Article history: Received 29 July 2011 Received in revised form 6 December 2011 Accepted 3 January 2012 Keywords: Flour quality Farinograph Mixograph Alveograph Zeleny sedimentation test A solvent retention capacity (SRC) profile of flour consists of its water RC (WRC), sodium carbonate SRC (SCSRC), sucrose SRC (SuSRC) and lactic acid SRC (LASRC) values. SRC tests have been designed to assess flour quality of North American soft wheats, but the value of these tests for European flour which is generally from harder wheats is rather unclear. We here studied the ability of the SRC values to assess the cookie and bread quality of nineteen European commercial flours and compared their predictive value with that of some conventional flour quality parameters. WRC value was a better parameter to assess the cookie diameter than Farinograph or Mixograph water absorption capacities and Alveograph dough tenacity values. In contrast, Zeleny sedimentation values and some rheological data were better to assess bread volume than LASRC values. When LASRC values were corrected for the contribution of non-glutenin polymers, they could assess bread volume to a comparable extent as the Zeleny sedimentation readings. In conclusion, the SRC tests are good alternative tests to assess the cookie and bread quality of European wheat flours. Furthermore, it is not always necessary to determine the entire SRC profile to sufficiently assess flour quality for aspecific end-product. Ó 2012 Elsevier Ltd. All rights reserved. 1. Introduction Wheat flour is a major ingredient in a range of foods such as bread, pastry and breakfast cereals. The wide range of end-products results from different ingredient formulas and/or varying processing conditions. Not every flour type is equally suitable for the production of a specific end-product. Therefore, determination of flour quality is of great importance as it relates to the desired endproduct and its manufacturing process. Abbreviations: AM, approved method; AX, arabinoxylan; FDDT, Farinograph dough development time; FDS, Farinograph dough stability; FS, flour sample; FWA, Farinograph water absorption; Ie, dough elasticity index; L, dough extensibility; LASRC, lactic acid solvent retention capacity; MDDT, Mixograph dough development time; MDS, Mixograph dough strength; MWA, Mixograph water absorption; P, dough tenacity; PC, principal component; PCA, principal component analysis; PLS, partial least squares; SCSRC, sodium carbonate solvent retention capacity; SRC, solvent retention capacity; SuSRC, sucrose solvent retention capacity; W, dough deformation energy; WRC, water retention capacity. * Corresponding author. Tel.: þ ; fax: þ addresses: annelies.duyvejonck@biw.kuleuven.be (A.E. Duyvejonck), bert.lagrain@biw.kuleuven.be (B. Lagrain), emmie.dornez@biw.kuleuven.be (E. Dornez), jan.delcour@biw.kuleuven.be (J.A. Delcour), christophe.courtin@ biw.kuleuven.be (C.M. Courtin). Three groups of methods are available for evaluating wheat flour quality. The first group determines the level of flour constituents or properties thereof and are relatively straight-forward to interpret. Examples are protein determination [AACCI Approved Method (AM) and 39-11] (AACCI, 2000) and the Zeleny sedimentation method (Zeleny, 1947). The second group of methods are the rheological tests, such as the Brabender Farinograph (AACCI AM 54-21), Mixograph (AACCI AM 54-40A) and Chopin Alveograph (AACCI AM 54-30) analyses (AACCI, 2000), which are indicative for dough properties and, thus, flour quality. The last group of methods are baking tests such as the straightdough bread making (AACCI AM 10-10) and sugar-snap cookie making tests (AACCI AM 10-52) (AACCI, 2000). Those standardized baking tests reflect a typical bread and cookie making process. Indeed, the ideal test for flour quality determination in any given application is to examine the flour suitability in that specific process. All the aforementioned methods can be used for assessing flour quality to a certain degree. However, they do not always take into account the contribution or functionality of specific flour constituents in a given process. In practice, important differences in processing and/or end-product quality can arise, even if the flour specifications for a specific method are met /$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. doi: /j.lwt

2 A.E. Duyvejonck et al. / LWT - Food Science and Technology 47 (2012) 56e63 57 Another relatively recent method, which can be classified in the first group of methods, for assessing the functionality of different flour constituents is that making use of the solvent retention capacity (SRC) tests (AACCI AM 56-11) (AACCI, 2000). The SRC methodology is based on quantifying the swelling behavior of flour polymer networks and, hence, their ability to retain a given solvent [water, 5.0 g/100 g sodium carbonate in water, 50.0 g/100 g sucrose in water and 5.0 g/100 g lactic acid in water]. By doing so, it relates flour quality to specific flour constituents (Duyvejonck, Lagrain, Pareyt, Courtin, & Delcour, 2011; Gaines, 2000; Kweon, Martin, & Souza, 2009; Slade & Levine, 1994). SRC tests are accessible and non-labor-intensive. Water retention capacity (WRC) has been associated with the overall water holding capacity of the different flour constituents, the sodium carbonate SRC (SCSRC) is related to the damaged starch levels of the flour, the sucrose SRC (SuSRC) is indicative for the arabinoxylan (AX) characteristics of the flour and the lactic acid SRC (LASRC) is associated with glutenin network formation and gluten strength (Kweon, Slade, & Levine, 2011; Slade & Levine, 1994). Different authors have described linear relations between the SRC parameters and conventional flour and dough specifications. For a set of Argentinian wheat cultivars, Colombo, Pérez, Ribotta, and León (2008) described a positive correlation between the LASRC of flour and the Zeleny sedimentation values. Also, relationships between SRC parameters and Farinograph (Ram, Dawar, Singh, & Shoran, 2005), Mixograph (Gaines, 2000; Gaines, Reid, Vander Kant, & Morris, 2006; Ram et al., 2005) and Alveograph (Gaines et al., 2006; Guttieri, Bowen, Gannon, O Brien, & Souza, 2001) values have been described. The evaluation of the ability of the SRC test methodologies for their ability to assess the cookie making quality of North American wheat flour from soft wheat cultivars has been subject of much research (Gaines, 2000; Guttieri et al., 2001; Guttieri & Souza, 2003; Guttieri, Souza, & Sneller, 2008). However, to the best of our knowledge, not much is known about their suitability to assess the cookie making quality of European wheat flour. Such flour generally originates from wheats that are harder than the typical North American soft wheats for which the SRC tests were initially developed, but which, at the same time are less hard than typical North American hard wheats (Delcour & Hoseney, 2010). Earlier, Duyvejonck et al. (2011) compared the SRC values of some European commercial wheat flours with those of some typical North American wheat flours. They found the SRC values of the European flour sample set to be in the range of those of North American hard winter wheat flours such as reported by Xiao, Park, Chung, Caley, and Seib (2006). WRC and SCSRC values exceed those of most North American soft wheat flours described by Bettge, Morris, DeMacon, and Kidwell (2002), Gaines (2000) and Guttieri et al. (2001, 2008). One of the implications of the above is that European cookie flour generally contains higher damaged starch levels and stronger gluten than typical North American cookie flour (Duyvejonck et al., 2011). These quality aspects are undesired for cookie flour (Pareyt & Delcour, 2008; Tanilli, 1976). Because of this, European cookie recipes typically contain relatively more sugar than standard North American cookie recipes in order obtain proper spread during baking (AACCI, 2000; Pareyt, Wilderjans, Goesaert, Brijs, & Delcour, 2008). Much less has been published on the value of SRC testing in the context of bread making than in that of cookie making. According to Pike and MacRitchie (2004), inthecaseofflour from hard North American wheats, a LASRC value exceeding 100% indicates good bread making quality. Also, Xiao et al. (2006) and Colombo et al. (2008) concluded that the LASRC value is predictive for the loaf volume of bread from North American hard and Argentinian wheat flours, respectively. However, to the best of our knowledge, no studies have appeared that investigated the ability of the SRC tests to assess the bread making quality of European wheat flour. Against this background, we here set out to study the predictive value of the SRC tests for the cookie and bread making quality of nineteen European commercial wheat flours. We also compared the analytical data with more conventional flour and dough parameters. We thus determined the chemical composition, the Zeleny sedimentation value, the SRC values and the Farinograph, Mixograph and Alveograph characteristics of these wheat flours. Furthermore, to assess the cookie and bread making quality of the flour samples, sugar-snap cookies and straight-dough breads were baked. Finally, we investigated whether the SRC tests are a good alternative for the evaluation of the flour quality of European commercial wheat flours. The latter was done by using multivariate statistical analysis. 2. Experimental 2.1. Materials Nineteen European commercial flour samples (FS) were obtained from different suppliers as described previously (Duyvejonck et al., 2011). They were coded FSx with x varying from 1 to 19. Sugar and margarine were from Iscal Sugar (Moerbeke- Waas, Belgium) and Vandemoortele (Izegem, Belgium), respectively. Sodium bicarbonate (BICAR) was from Solvay Chemicals International (Brussels, Belgium). Compressed yeast was from Bruggeman (Brugge, Belgium). All chemicals, solvents and reagents were purchased from SigmaeAldrich (Bornem, Belgium) and of analytical grade unless specified otherwise Methods Moisture, protein and damaged starch levels of the flour samples were determined as described previously (Duyvejonck et al., 2011). The coefficients of variation on the results were less than 1.5%. Polysaccharide levels and compositions of flour samples were estimated using gas chromatographic analysis of alditol acetates obtained after acid hydrolysis of the samples and reduction and acetylation of the resulting monosaccharides as described previously (Duyvejonck et al., 2011). Starch level was calculated as 0.90 times the glucose content and AX levels were calculated as described previously (Duyvejonck et al., 2011). The coefficients of variation on the results were less than 2.0% SRC tests The SRC tests were conducted according to AACCI AM (AACCI, 2000) with some minor modifications as described by Duyvejonck et al. (2011). Flour samples (5.000 g) were weighed in 50 ml centrifuge tubes (height, 115 mm; internal diameter, 27 mm) with a conical bottom. Then, 25.0 ml of the respective media [deionized water, 5.0 g/100 g sodium carbonate in water, 50.0 g/ 100 g sucrose in water or 5.0 g/100 g lactic acid in water] were added and the mixtures were shaken vigorously for 5 s to suspend the flour. Samples were then horizontally shaken for 20 min (room temperature, 150 strokes per min) to allow the samples to solvate and swell. Samples were centrifuged (15 min, 1000g, room temperature, Beckman TJ-25, Fullerton, CA, USA). Supernatant was decanted from the tubes, the pellet drained for 15 min and weighed. The SRC values were calculated as follows (Haynes, Bettge, & Slade, 2009):

3 58 A.E. Duyvejonck et al. / LWT - Food Science and Technology 47 (2012) 56e63 wet pellet ðgþ SRC ðg=100 gþ ¼ 1 flour ðgþ flour moisture ðg=100 gþ 100 All SRC analyses were performed in triplicate and the coefficients of variation on the SRC values were less than 2.0% Zeleny sedimentation method Flour Zeleny sedimentation values were determined at least in duplicate according to ICC standard 116 (ICC, 1980). The coefficients of variation were less than 2.5% Rheological dough methods Farinograph parameters were determined according to the AACCI AM (AACCI, 2000) with a Farinograph (Brabender E330, Duisburg, Germany) fitted with a 50 g stainless steel mixing bowl using the constant flour weight procedure (50.00 g flour with 14.0 g/100 g moisture). Optimal water absorption for the Farinograph analyses was determined iteratively with different amounts of water. The following Farinograph parameters were recorded: water absorption (FWA), dough development time (FDDT) and dough stability (FDS). In the method, typical coefficients of variation for the FWA, FDDT and FDS of a measurement in triplicate were less than 0.5%, 10.0% and 10.0%, respectively. Mixograph parameters were assessed according to the AACCI AM 54-40A (AACCI, 2000) using a 10 g Mixograph (National Manufacturing, Lincoln, NE, USA). The constant flour weight procedure (10.00 g flour at 14.0 g/100 g moisture) was used. Optimal water absorption for the Mixograph analyses was determined iteratively with different amounts of water. The following Mixograph parameters were determined: water absorption (MWA), dough development time (MDDT) and dough strength (MDS, height of curve 3 min after MDDT). In the method, typical coefficients of variation for the MWA, MDDT and MDS of a single measurement were less than 2.0%, 10.0% and 5.0%, respectively. Chopin Alveograph parameters were determined according to AACCI AM (AACCI, 2000) using an Alveograph (Chopin, Villeneuve-La-Garenne, France). All flour samples were analyzed at a constant hydration of 50.0 g/100 g (15.0 g/100 g moisture basis). We assessed dough tenacity (P), extensibility (L), deformation energy (W) and elasticity index (Ie ¼ P200/P multiplied by hundred where P200 is the pressure 40 mm from the start of the curve). Five dough pieces were formed from each flour sample and the coefficients of variation for the P, L, W and Ie measurements were less than 5.0%, 10.0%, 10.0% and 3.0%, respectively Cookie making Cookies were prepared according to Pareyt et al. (2008). The ingredients were flour (200.0 g, 14.0 g/100 g moisture basis), sugar (144.0 g), margarine (90.0 g), deionized water (24.0 g) and sodium bicarbonate (4.0 g). Dough preparation consisted of a cream-up and then a dough-up phase. The final cookie dough contained 15.0 g/ 100 g moisture. Dough pieces were baked for 14 min at 185 Cin a rotary oven (National Manufacturing). Baked cookies were removed from the oven and cooled for 30 min. Their height and diameter was measured with a caliper. At least twelve cookies were baked from each flour sample. The coefficients of variation of the height and diameter of cookies baked from the same flour sample were less than 4.0% and 1.0%, respectively Bread making Breads were prepared according to the straight-dough bread making procedure of Finney (1984) for 100 g flour. Flour (100.0 g with 14.0 g/100 g moisture), sugar (6.0 g), salt (1.5 g), compressed yeast (5.3 g) and water (optimum water absorption) were mixed at 25 C with a 100 g pin-mixer (National Manufacturing). The optimum water absorption and mixing time (MDDT 1.25) were based on the Mixograph analyses. Dough was fermented at a temperature of 30 C and a relative humidity of 90%. During fermentation, the dough was punched after 52, 77 and 90 min. The fermented dough was molded and proofed for 36 min in a baking pan [internal dimension (width length height), 8.0 cm 14.5 cm 5.5 cm] at 30 C and 90% relative humidity and baked for 24 min at 215 Cinan electrically heated rotary oven (National Manufacturing). Baked breads were removed from the oven and cooled for 2 h. Their height and volume were determined, the latter with a Volscan Profiler (Stable Micro Systems Ltd., Surrey, UK). Six breads were baked from each flour sample. The coefficients of variation of the height and volume of the breads baked from the same flour sample were less than 2.0% Statistical analysis Pearson s correlation coefficients were calculated with the Statistical Analysis System software 9.2 (SAS Institute, Cary, NC, USA). Principal Component Analysis (PCA) and Partial Least Squares (PLS) multivariate analyses were conducted with the Unscrambler software (CAMO Technologies, Woodbridge, NJ, USA). In PCA, the high number of original variables is reduced to a smaller number of new variables called principal components (PCs) which are linear combinations of the original variables. These PCs are independent variables and describe in decreasing order the variability of the data. PLS is a linear regression technique that determines the greatest variation in the data correlated with the response (Y) that is investigated. In contrast to what is done in PCA analysis, the response variables (Y) are also taken in account in a PLS analysis. All variables were centered and scaled to unit variance prior to the multivariate analyses. 3. Results and discussion 3.1. Conventional flour and dough properties The flour sample set showed large variation in protein, damaged starch, total AX, water-extractable AX and water-unextractable AX levels (Table 1) (Duyvejonck et al., 2011). Based on the protein and damaged starch levels, the flour samples seemed to be more suitable for bread than for cookie making. Also, all flour samples had a Zeleny sedimentation value above 20 ml and thus a moderate to good bread making quality (Zeleny, 1947). FS6 even seemed to have superior bread making quality, as its Zeleny sedimentation value exceeded 55 ml (Zeleny, 1947). The European flour samples also showed a large variation in rheological parameters (Table 1). The Alveograph values were in the range of those of some typical North American hard and soft wheat flours as described by Bettge, Rubenthaler, and Pomeranz (1989) with mean values of P, L and W for the current set of European flour samples corresponding to those of North American hard wheat flours. According to a criterion postulated by García- Álvarez, Salazar, and Rosell (2011), the current flour sample set contained flours that are mainly suitable for bread making because they all had an Alveograph W value above 100. In general, the conventional analytical data of the samples show that the current flour samples are comparable to typical North American hard wheat flours SRC properties Table 1 lists the mean values and ranges of the SRC values of the 19 European wheat flour samples. As far as SRC values goes, the

4 A.E. Duyvejonck et al. / LWT - Food Science and Technology 47 (2012) 56e63 59 Table 1 Means and ranges of physico-chemical, rheological, solvent retention capacity (SRC) and baking data of nineteen European commercial wheat flours. Mean Range Physico-chemical parameters Starch (g/100 g) a,e e84.9 Damaged starch (g/100 g) a,d,e e8.0 Protein (g/100 g) a,d,e e16.1 Zeleny sedimentation [Zsed] value (ml) f e56.0 Arabinoxylan [AX] (g/100 g) a,d e2.32 Water-extractable AX [WE-AX] (g/100 g) a,d e0.67 Water-unextractable AX [WU-AX] (g/100 g) a,c,d e1.84 Rheological parameters Farinograph g Water absorption [FWA] (g/100 g) b e61.4 Dough development time [FDDT] (min) e8.4 Dough stability [FDS] (min) e11.4 Mixograph g Water absorption [MWA] (g/100 g) b e62.0 Dough development time [MDDT] (min) e3.6 Dough strength [MDS] (cm) e6.6 Alveograph Dough tenacity [P] (mm) h 77 58e99 Extensibility [L] (mm) h e135 Dough deformation energy [W] (10 4 J) h e349 Elasticity index [Ie] (%) h 50 43e60 SRC parameters Water retention capacity [WRC] (g/100 g) b,d e66.3 Sodium carbonate SRC [SCSRC] (g/100 g) b,d e88.1 Sucrose SRC [SuSRC] (g/100 g) b,d e102.1 Lactic acid SRC [LASRC] (g/100 g) b,d e147.1 Baking parameters Cookie height (mm) i e8.8 Cookie diameter (mm) i e88.6 Bread height (cm) j e10.0 Bread volume (cm 3 ) j e722 a Expressed on dry matter basis. b Expressed on 14.0 g/100 g moisture basis. c [WU-AX] ¼ [AX] [WE-AX]. d Duyvejonck et al. (2011). e Data are means of triplicate measurements. f Data are means of duplicate measurements. g Data were determined iteratively with different amounts of water. h Data are means of measurements on five dough pieces. i Data are means of measurements on at least 12 cookies. j Data are means of triplicate measurements on six breads. largest variation was in LASRC (106.4e147.1 g/100 g) and the lowest in SuSRC (90.2e102.1 g/100 g). In a previous paper (Duyvejonck et al., 2011), these SRC values were compared with those of some typical North American wheat flours. The SRC values of the European flour sample set were in the range of those of North American hard winter wheat flours (Xiao et al., 2006). Also, WRC and SCSRC values exceeded those of most North American soft wheat flours (Bettge et al., 2002; Gaines, 2000; Guttieri et al., 2001, 2008). According to Gaines (2000) flour with a WRC 51 g/100 g, a SCSRC 64 g/100 g, a SuSRC 89 g/100 g and a LASRC 87 g/ 100 g performs well in cookie making and flour with a WRC 57 g/ 100 g, a SCSRC 72 g/100 g, a SuSRC 96 g/100 g and a LASRC 100 g/100 g is suitable for sponge and dough systems. Based on this statement, the current flour samples are expected to perform well neither as cookie flour, nor in sponge and dough systems. However, according a criterion postulated by Pike and MacRitchie (2004), all flour samples appeared of good straightdough bread making quality because they all had a LASRC above 100 g/100 g Correlations between SRC parameters and conventional parameters To obtain an integrated view of the relationship between the SRC and more conventional flour and dough parameters (variables), PCA was executed (Fig. 1) and Pearson s correlation coefficients between the different parameters were calculated. In a PCA loading plot, variables close to each other are positively correlated, whereas variables found on the opposite sides of a diagonal are negatively correlated. Variables found in orthogonal direction are independent of each other. The loading plot of the first two PCs explained 71% of the total variance in the flour properties measured on nineteen European commercial flours (Fig. 1). The first PC, PC1, accounted for 43% of the explained variance. PC1 was strongly positively determined by the water-unextractable AX, the starch and the total AX levels of the flours and was strongly negatively determined by the protein level, W, FDS, MDS, FDDT and Ie. In addition, flour protein level was strongly positively correlated with FDDT, FDS, MDS, W and Ie (r > 0.80, p < ). Hence, in this PCA loading plot, PC1 was strongly determined by the variance in protein and (water-unextractable) AX levels in the flour sample set. For the flour samples, a strong negative linear relation was observed between flour protein level and flour waterunextractable AX (r ¼ 0.81, p < ), starch (r ¼ 0.74, p < 0.001) and total AX (r ¼ 0.66, p < 0.01) levels. The negative linear relation between flour starch and protein levels is logical, because wheat flour mainly consists of starch and proteins. Furthermore, total AX level in flour was positively related with the level of water-unextractable AX (r ¼ 0.80, p < ). The second PC, PC2, accounted for 28% of the explained variance and was strongly positively determined by the SCSRC value, the WRC value, the damaged starch level, the P value and the SuSRC value. WRC, SCSRC, SuSRC and P values were positively correlated with the damaged starch level (r > 0.60, p < 0.01) because they are all influenced by the water holding capacity of the flour. As the Alveograph analyses were conducted at constant hydration, the higher the flour damaged starch level, the more water the flour can bind, the stiffer the dough and the higher the resistance to deformation (P) (Dexter, Preston, Martin, & Gander, 1994; Khattak, D Appolonia, & Banasik, 1974). Hence, in this PCA loading plot, PC2 was strongly determined by the variance in damaged starch level in the flour sample set. In general, for the current flour sample set, most SRC and conventional variables can be divided into two groups (Fig. 1), i.e. variables more sensitive for flour protein level (FDDT, FDS, MDS, W and Ie) and variables more sensitive for damaged starch level (P, WRC, SCSRC and SuSRC). WRC, SCSRC and SuSRC were proximate to each other (Fig. 1), indicating a positive correlation between those three SRC variables. These results are in line with those of earlier studies (Colombo et al., 2008; Gaines, 2000), that also observed strong correlations between those three SRC values. Furthermore, flour FWA is positively correlated with WRC (r ¼ 0.72, p < 0.001) and MWA (r ¼ 0.72, p < 0.001). The underlying reason most probably being that all these variables give an indication for the flour water holding capacity. LASRC was orthogonal to the other SRC values (Fig. 1), showing that there is no strong correlation. The LASRC values were positively correlated with the W values (r ¼ 0.70, p < 0.001). Guttieri et al. (2001) earlier observed a linear relation between the LASRC values and the W values in a set of North American soft wheat flours. In addition, the PCA loading plot (Fig. 1) shows that, although the LASRC and the Zeleny sedimentation tests are based on the swelling capacity of especially glutenins in a lactic acid environment (Gaines, 2000; Zeleny, 1947), the LASRC variable does not

5 60 A.E. Duyvejonck et al. / LWT - Food Science and Technology 47 (2012) 56e63 Fig. 1. Principal component analysis (PCA) of measured variables: AX, arabinoxylan; corlasrc, corrected lactic acid solvent retention capacity; DS, damaged starch; FDDT, Farinograph dough development time; FDS, Farinograph dough stability; FWA, Farinograph water absorption; Ie, dough elasticity index; L, dough extensibility; LASRC, lactic acid solvent retention capacity; MDDT, mixograph dough development time; MDS, Mixograph dough strength; MWA, Mixograph water absorption; P, dough tenacity; PC, principle component; SCSRC, sodium carbonate solvent retention capacity; SuSRC, sucrose solvent retention capacity; W, dough deformation energy; WE-AX, water-extractable arabinoxylan; WRC, water retention capacity; WU-AX, water-unextractable arabinoxylan; Zsed value, Zeleny sedimentation value. Two groups of variables can be distinguished: variables sensitive for flour protein level (d) and variables that are more sensitive for flour damaged starch level (----). strongly correlate with the Zeleny sedimentation variable. Also no significant linear relation was observed between the LASRC values and the Zeleny sedimentation values (p > 0.01). It has previously been documented that the LASRC value is also influenced by other flour polymers such as AX and damaged starch (Barrera, Pérez, Ribotta, & León, 2007; Duyvejonck et al., 2011). When we divided the LASRC value by the sum of the SCSRC and SuSRC values (further referred as corrected LASRC value) to correct the LASRC value for the contribution of non-gluten polymers as described by Kweon, Slade, et al. (2009), a positive linear relation was observed between the corrected LASRC values and the Zeleny sedimentation values (r ¼ 0.69, p < 0.01). Fig. 1 shows the positive correlation between those two variables Suitable parameters to assess cookie and bread making quality of flour Suitable parameters to assess cookie making quality of flour In general, there are three important cookie quality parameters: size of the cookie, cookie top grain and cookie bite (Pareyt & Delcour, 2008). According to Finney and Andrews (1986), sugarsnap cookie diameter is an excellent indicator of soft wheat baking quality. The smaller the cookie diameter, the less the flour is suited for cookie making. According to Gaines and Finney (1989), cookie top grain is related with the cookie diameter, but there is no clear consensus about what makes up a good cookie bite. The present set of flour samples led to variation in cookie diameter (83.6e88.6 mm) and height (7.5e8.8 mm) (Table 1). As expected, the cookie diameter was negatively correlated with the cookie height (r ¼ 0.88, p < ). In what follows, the cookie diameter is taken as an indicator for the flour quality for cookie making. PLS regression analysis visualizes the predictive value of the SRC and conventional variables for cookie diameter. The physicochemical, rheological and SRC readings were used as X-variables and the cookie diameter as response variable (Y-variable) (Fig. 2). PC1 accounted for 31% of the overall explained variance and PC2 for 37%. More specifically, PC1 explained 68% of the cookie diameter variance and was strongly negatively determined by WRC, P, SCSRC, FWA, SuSRC and damaged starch levels (in decreasing order). The cookie diameter was negatively correlated with these variables, but especially with the WRC values (r ¼ 0.82, p < ) (Table 2). PC2 only accounted for 6% of the cookie diameter variance and was strongly positively determined by FDS, corrected LASRC, MDS, FDDT, protein level and L. All these variables are more orthogonal to the cookie diameter variable (Fig. 2) and unsuitable as parameters to assess cookie diameter. For the current sample set, it seems that especially flour damaged starch level determined the observed variability in cookie diameter (Fig. 2). Additionally, flour protein and AX levels also appeared to contribute to the variability in cookie diameter. Flour WRC, indicative for the overall water holding capacity of flour polymers, correlated even better with cookie diameter (r ¼ 0.82, p < ) than flour damaged starch level (r ¼ 0.70, p < ). Earlier studies already indicated the negative impact of high protein and AX levels on cookie diameter (Manley, 2000; Pareyt et al., 2008). In general, the PLS loading plot (Fig. 2) shows that WRC, P, SCSRC, SuSRC and damaged starch level can well assess cookie diameter. Flour damaged starch level, FWA, MWA and P were negatively correlated with the cookie diameter (Table 2). Thus, the higher the damaged starch level, the FWA and/or the MWA of the flour, the lesser water is available to dissolve the sugar during the cookie dough preparation. This leads to higher dough viscosity and a slower dough spread during the baking phase and finally results in a smaller

6 A.E. Duyvejonck et al. / LWT - Food Science and Technology 47 (2012) 56e63 61 Fig. 2. Partial least square (PLS) regression analysis of all measured variables with cookie diameter as response variable (full circle). Abbreviations as in Fig. 1. cookie diameter (Gaines, Donelson, & Finney, 1988; Hoseney & Rogers, 1994). As the Alveograph analyses were conducted at a constant hydration, the higher the flour water holding capacity, the more water the flour can bind, the stiffer the dough and the higher the dough resistance to deformation (P) (Dexter et al., 1994). Table 2 Pearson s correlation coefficients between flour and dough parameters [physicochemical, rheological and solvent retention capacity (SRC) readings] and endproduct parameters (cookie diameter and bread volume). Cookie diameter (mm) Bread volume (cm 3 ) Physico-chemical parameters Damaged starch (g/100 g) 0.70*** ns Protein (g/100 g) ns 0.73** Zeleny sedimentation [Zsed] value (ml) ns 0.77** Rheological parameters Farinograph Water absorption [FWA] (g/100 g) 0.69* ns Dough development time [FDDT] (min) ns 0.69* Dough stability [FDS] (min) ns 0.76** Mixograph Water absorption [MWA] (g/100 g) 0.60* ns Dough development time [MDDT] (min) ns ns Dough strength [MDS] (cm) ns 0.74** Alveograph Dough tenacity [P] (mm) 0.72** ns Extensibility [L] (mm) ns 0.49 Dough deformation energy [W] (10 4 J) ns 0.72** Elasticity index [Ie] (%) ns 0.80*** SRC parameters Water retention capacity [WRC] (g/100 g) 0.82*** ns Sodium carbonate SRC [SCRC] (g/100 g) 0.74** ns Sucrose SRC [SuSRC] (g/100 g) 0.71** ns Lactic acid SRC [LASRC] (g/100 g) ns 0.69* Corrected LASRC [corlasrc] ns 0.75** ns ¼ Not significant (p > 0.05). *p < 0.01, **p < and ***p < Furthermore, cookie diameters were negatively correlated with the WRC, as well as with the SCSRC and the SuSRC values, but no significant linear relation was observed with the LASRC values (Table 2). Earlier, Gaines (2000) and Zhang, Zhang, Zhang, He, and Pena (2007) also observed a negative relation between sugarsnap cookie diameters and WRC, SCSRC and SuSRC values of North American and Chinese wheat flours, respectively. In addition, Colombo et al. (2008) described a negative relation between the ratio of cookie diameter to cookie height and the WRC, SCSRC and SuSRC values of a set of Argentinian wheat flours. Although SRC tests were initially developed for assessing the quality of flour from North American soft wheats, which has lower water absorption, finer granulation and less starch damage than flour from hard wheats, the present and earlier studies (Colombo et al., 2008) show the suitability of three out of four SRC readings for assessing the quality of cookies from flour from hard(er) wheats. The WRC value was the best parameter to assess the cookie diameter (Table 2). For this particular set of samples, flours with higher WRC values resulted in cookies with smaller diameter. In general, all analytical results indicative for flour water holding capacity were suitable to assess the cookie diameter Suitable parameters to assess bread making quality of flour Bread height and volume varied between 8.3 and 10.0 cm and 579 and 722 cm 3, respectively (Table 1). As bread height was strongly correlated with bread volume (r ¼ 0.97, p < ), in the following discussion, bread volume is taken as indicator for flour quality for bread making. PLS regression analysis was used to visualize the predictive value of SRC and conventional parameters (variables) for bread volume (Fig. 3). PC1 accounted for 43% of the explained overall variance and PC2 for 19%. PC1 explained 77% of the bread volume variance and was strongly positively determined by FDS, protein level, MDS, W, FDDT, Ie, corrected LASRC, Zeleny sedimentation value and LASRC in decreasing order of importance. Hence, PC1 was mostly determined by all variables reflecting flour protein level and

7 62 A.E. Duyvejonck et al. / LWT - Food Science and Technology 47 (2012) 56e63 Fig. 3. Partial least square (PLS) regression analysis of all measured variables with bread volume as response variable (full circle). Abbreviations as in Fig. 1. quality. Furthermore, PC1 was negatively determined by waterunextractable and total AX levels, as well as by starch levels. PC2 only accounted for 9% of the bread volume variance and was strongly negatively determined by WRC, SCSRC and FWA. These three variables do not assess bread volume. Based on this loading plot (Fig. 3), protein level, Zeleny sedimentation value, Farinograph, Mixograph, Alveograph and SRC measurements can assess bread volume. A pronounced linear relation was observed between bread volumes and protein levels (Table 2). The higher the protein level, the better the flour s potential for bread making (Finney & Barmore, 1948). However, other flour characteristics also have an impact on flour quality for bread making. This is illustrated for example by FS9 and FS16. While they had similar protein levels (12.3 g/100 g on dry matter basis), results show a significantly different bread volume (618 cm 3 and 651 cm 3, respectively). Furthermore, PC1 was also strongly negatively correlated with flour water-unextractable AX level (Fig. 3). This points to a negative contribution of the flour waterunextractable AX level to bread volume. Water-unextractable AX can destabilize the dough structure, because they form physical barriers for optimal dough development and gas cell stabilization during bread making (Courtin & Delcour, 2002). In general, for the present flour sample set with a large variation in protein levels (Table 1), all parameters related to flour protein level and/or quality were predictive for the bread volume. Farinograph, Mixograph, Alveograph, and Zeleny sedimentation, as well as LASRC readings were suitable parameters to assess bread volume. When the LASRC values were corrected for the contribution of non-gluten polymers (Kweon, Slade, et al., 2009), they correlated even better with bread volumes (Table 2). Earlier, Xiao et al. (2006) and Colombo et al. (2008) also observed a positive correlation between the LASRC values and the loaf volumes in a set of North American hard wheat flours and in a set of Argentinian wheat flours, respectively. The latter studies also demonstrate the suitability of the SRC tests, more specific the LASRC test, as assessors for bread making quality of flour from hard(er) wheats. In addition, it is often desired to have a flour with high FWA or MWA and a workable dough with an acceptable dough stability. The former is important, because bread is typically sold on weight basis. Furthermore, bread volume increases when the percentage of water, necessary to yield dough of a desired consistency after mixing, increases (Roels, Cleemput, Vandewalle, Nys, & Delcour, 1993). As such, flour WRC also has its value in assessing the bread making quality of flour, because WRC is indicative for the overall water holding capacity of the flour polymers and correlates positively with FWA (r ¼ 0.72, p < ) and to a lesser extent with MWA (r ¼ 0.54, p < 0.05). Dough stability is indicative for capacity of dough to keep up with mixing and machining operations in commercial baking. Fig. 1 shows that FDS, a measure for dough stability, is strongly positively related with all parameters indicative for flour protein level and/or quality, but negatively related with flour water-unextractable AX level. 4. Conclusions The present work shows that it is impossible to assess the quality of flour for cookie or bread making with just one analytical procedure. However, a good selection of analyses is, in most cases, sufficient to adequately assess flour quality. SRC tests are suitable for assessing cookie quality of European wheat flours, and are valuable alternative tests for commonly applied methods such as the Farinograph, Mixograph and Alveograph methods. The WRC and, to a lesser extent, also the SCSRC and the SuSRC tests values are good tests to assess the diameter of cookies from European wheat flour. WRC values even correlated better with the cookie diameter than commonly applied analytical readings such as FWA, MWA or Alveograph dough tenacity values. Methods that measure water holding capacity based on the development of visco-elastic dough (Farinograph, Mixograph and Alveograph) were less predictive for the cookie diameter than the WRC results. Instead of determining the entire SRC profile, flour WRC seemed to be sufficient for

8 A.E. Duyvejonck et al. / LWT - Food Science and Technology 47 (2012) 56e63 63 assessing cookie making quality of the flour in the present study. In addition, SCSRC and SuSRC values are useful indicators for the flour polymers responsible for the WRC of flour and, hence, cookie diameter. To a lesser extent, the SRC tests can be useful for assessing straight-dough bread making quality of European wheat flours. The LASRC test can assess the volume of bread, especially when the obtained LASRC value is corrected for the contribution of nonglutenin polymers by dividing the LASRC value by the sum of the SCSRC and SuSRC values. Furthermore, WRC value can also have predictive value for the bread making quality, because bread volume typically increases when the percentage of water necessary to yield dough of desired consistency after mixing, increases. A general advantage of the SRC tests is that they are very accessible and not very time consuming. Furthermore, they do not require skilled labor to reveal information about the contribution of different flour constituents to the flour quality. In addition, our study shows that is not always necessary to determine the entire SRC profile to evaluate flour quality for a certain end-product. Acknowledgments Part of this work was carried out in the framework of a Flanders FOOD (Brussels, Belgium) project. It is also part of the Methusalem programme Food for the future (2007e2014) at the KU Leuven (Leuven, Belgium). B. Lagrain and E. Dornez wish to acknowledge the Research Foundation e Flanders (FWO, Brussels, Belgium) for their position as postdoctoral researcher. The authors thank H.X. Zhu for her help with conducting the cookie making experiments and Mr. M. Soubry (Soubry, Roeselare, Belgium) for making an Alveograph available. Dr. J. Lammertyn (Division of Mechatronics, Biostatistics and Sensors, this University) is thanked for advice on multivariate analyses. 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