Journal of Cereal Science 32 (2000) 73–87 doi:10.1006/jcrs.2000.0320, available online at http://www.idealibrary.com on The Effects of Bread Making Process and Wheat Quality on French Baguettes P. Baardseth, K. Kvaal, P. Lea, M. R. Ellekjær and E. M. Færgestad MATFORSK, Norwegian Food Research Institute, Osloveien 1, N-1430 Ås, Norway Received 10 June 1999 ABSTRACT The quality of baguettes can be evaluated by defined sensory attributes and image analyses. The eﬀect of flour quality, production process (traditional French and industrially modified), mixing and proofing time were studied. Process accounted for 40% of the variation in baguette quality whereas flour quality accounted for 16% of the variation when principal component analysis was applied on the sensory attributes. Baguettes produced using a soft dough and gentle treatment (traditional French process) had a higher sensory score for porosity, elasticity, crispness of crust, crackles on the crust, and porosity and volume as measured by image analysis, than baguettes produced using a stiﬀ dough and rough treatment (modified industrial process). Mixing and proofing time also aﬀected the porosity and area of the cut surface. Porosity, crackles on the crust, glossiness and volume were related to flour quality. 2000 Academic Press Keywords: sensory analysis, image analysis, French baguette quality. INTRODUCTION French bread (baguettes or ‘pain Parisian’) is typically characterised by a crisp eggshell crust 3–4 mm thick, an open and random crumb cell structure, a full-bodied flavour, a high specific volume (mL/g) and a relatively high crust:crumb ratio due to the diameter and the length of the loaves1,2. : ICC=International Association for Cereal Science and Technology; ISO=International Association for Standardisation; AACC= American Association for Cereal Chemists; SDSPAGE=sodium dodecyl sulphate polyacrylamide gel electrophoresis; HMW=high molecular weight; WA= water absorption; DU=dough development; DS= dough stability; BU=Brabender unit; ANOVA=analysis of variance; PCA=principal component analysis; PC=principal component; PCR=principal component regression; AMT=angle measure technique; P1=traditional French process; P2=modified industrially process. Corresponding author: P. Baardseth. 0733–5210/00/070073+15 $35.00/0 An important aspect of traditional French bread production is the adjustment of the flour:water ratio to give soft dough1,2. Mechanical dough handling in industrial processing, however, requires a stiﬀer dough to avoid adhesion of dough to the machinery. To obtain the characteristic pore structure of the crumb of a typical French bread, the dough should be fully expanded with numerous large, gas-filled bubbles at the end of the mixing and fermentation process1,2. In general, the mixing time necessary to obtain optimum dough development is dependent on flour quality and as well as the mixing equipment, mixing intensity, dough recipe, etc. Magnus3 found that the optimum fermentation time with respect to loaf volume, form ratio, loaf weight, pore size, firmness and colour of small hearth loaves also varied widely amongst wheats of diﬀerent qualities, and according to the mixing process used. In addition to producing a light dough capable of retaining gas produced by the yeast, the fermentation process also leads to the production of aromatic 2000 Academic Press 74 P. Baardseth et al. substances that contribute to the taste of the baguettes4,5. However, there are no published studies on the eﬀect of mixing and fermentation time on volume, pore structure and texture of French bread. Critical phases of French bread production follow fermentation. In this phase it is important to avoid degassing the dough if an open and random pore structure in the crumb is to be achieved1,2. This is unlike most other bread baking processes, which aim to obtain an even, fine pore structure in the crumb. A further increase in gas and consequent enhancement of bubble size occurs during the proofing stages which is the time from dough dividing to baking in the oven. Aromatic substances also build up during the proofing stage4–6. The cutting of the dough surface after the final proof is an integral part of processing of French bread and leads to a characteristic product. Cutting releases stresses in the dough during baking, i.e. increases crust area during dough expansion and in the bake out, gives an attractive appearance to the loaf and improves the flavour7. In determining the baking potential of flour, both protein content and protein quality are important. Although protein quality is complex, it is known that a major factor in determining the protein quality is the composite of the high molecular weight (HMW) glutenin subunits8,9. The present study focuses on how the baking process and flour quality aﬀect the characteristics of French baguettes produced by traditional and industrially modified methods. The following factors were investigated using a full factorial design: flour quality, a modified industrial process versus a traditional French process, mixing time and proofing time. Baguette quality was assessed by sensory and image analysis. MATERIAL AND METHODS Flour Four wheats of diﬀerent qualities were used: Tjalve (a Norwegian spring wheat), Folke (a Norwegian winter wheat) and two commercial baguette flours (A: 100% French soft wheat; B: 20% American wheat, 25% French wheat, 20% Norwegian spring wheat, 35% Norwegian winter wheat). The wheats were milled on a commercial mill (Simon, England with Miag rollers). Extraction rates were 75·7% for Tjalve and 77·5% for Folke. Ascorbic acid (30 ppm) was added to the flour immediately after milling. The two Norwegian flours were from the same wheats as used by Færgestad et al.10 The commercial baguette flours also contained 30 ppm ascorbic acid. Protein content was analysed by Kjeldahl N (ICC Standard no 105, N×5·7, presented on a dry weight basis). Water absorption and reological properties of the flours were determined using a Farinograph11 and Mixograph12 using water addition according to the ISO standard11. The Zeleny sedimentation test was performed according to the AACC procedure13. HMW glutenin subunits were determined by sodium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE)14. Tjalve flour and baguette flour B had similar protein contents, 12·7% and 12·1%, respectively, and both flours contained the HMW glutenin subunits 5+10 that are correlated with strong protein quality15 (Table I). Baguette flour B also contained the HMW glutenin subunits 2+12 and in addition subunits 5+10. Baguette flour A and Folke flour had low protein contents (10·2% and 9·0%, respectively), and both contained HMW glutenin subunits 2+12, associated with poor protein quality. The sedimentation volume and mixograph peak times were higher for the baguette flour B and Tjalve flour compared with the baguette flour A and Folke flour (Table I). Baguette flour A from soft wheat had a lower water binding capacity than the other flours, due to the lower damaged starch1,2. Experimental design A full factorial experimental design16 without replicates was set up and 32 baguette productions were performed in random order. The main experiment consisted of two factors, namely the four flour qualities and the industrial versus the traditional process. The two other factors, mixing and proofing time were nested within each process, both at two levels. Experimental baking Figure 1 shows scheme for the traditional French baking process (P1) and the modified industrial process (P2). The flour temperature (8 °C), water temperature (12 °C for 2+4 min mixing times, 0–4 °C for 2+8 min mixing times) and completion of mixing dough temperature (24 °C) were recorded together Baguette making and wheat quality Table I 75 Characteristics of the wheat flours Farinogram Mixograph Parameters Id. No 1 2 3 4 Wheat variety % flour source protein Tjalve Folke Baguette flour A Baguette flour B HMW glutenin subunits Zeleny WA DU sedimentation (%) (BU) DS Peak (BU) time (min) 12·7 9·0 10·2 2∗, 7+9, 5+10 2∗, 6+8, 2+12 2∗, 7+8, 2+12 48 28 12 62·4 60·9 56·0 2·5 1·7 1·7 60 5·36 110 2·62 70 2·80 12·1 2∗, 7+9, 6+8, 5+10, 2+12 39 60·6 2·0 80 5·64 WA=water absorption, DU=dough development, DS=dough stability, BU=Brabender unit. Process 1 Traditional French process Process 2 Industrial modified process Recipe 0.9% yeast 1% salt soft dough conditions 500 BU–6% 0.8‰ ascorbic acid Recipe 0.3% yeast 1% salt stiff dough conditions 500 BU +5% 0.8‰ ascorbic acid Mixing time: 2 + 4 min (125 wh) 2 + 8 min (300 wh) Mixing time: 2 + 4 min (180 wh) 2 + 8 min (400 wh) 20 min fermentation Gentle dividing and moulding by hand Rough dividing treatment 15 min pre-proofing Figure 1 2). Gentle sheeting of the dough to final form Moulding and sheeting to final form Proofing time: 65 min 80 min Proofing time: 150 min 165 min Baking 270°C 220°C 10 min Baking 250°C 220°C 10 min Flow sheet of the two processes—traditional French process (process 1) and modified industrial process (process 76 P. Baardseth et al. Table II Average energy input (Wh) used for doughs from four flours and two processes Mixing time Tjalve Folke Baguette flour A Baguette flour B Process 1 (soft dough) 2+4 min 2+8 min 110 290 115 255 145 355 130 280 Process 2 (stiﬀ dough) 2+4 min 2+8 min 160 385 160 360 215 440 180 440 Table III Definition of the sensory attributes Sensory attributes Glossiness Crackles on the crust Porosity Area of cut surface Elasticity Odour intensity Fresh odour Flavour intensity Fresh flavour Salt flavour Firmness Moistness Crispness of the crust Definition Glossiness on the crust surface No intensity=non-glossy (matt) on the crust High intensity=high glossiness on the crust Crackle formation on the crust (after baking a fine network of cracks appears on the crust like in pottery) No intensity=no crackles, smooth, even crust High intensity=crackles and uneven crust Pore structure in the crumb measured using Dallmann’s pore table19 No intensity=dense pore structure High intensity=open and random pore structure Cuts on the crust so the dough can expand to increase the crust area of the bread No intensity=little expansion of the crumb High intensity=high expansion of the crumb Slices able to retain the shape after squeezing No intensity=no elasticity, slices do not retain shape after squeezing High intensity=high elasticity, slices retain shape after squeezing Total odour intensity of the sample No intensity=no odour High intensity=strong odour Fresh odour No intensity=no fresh odour High intensity=distinct fresh odour The strength of total flavour in the sample No intensity=no flavour High intensity=strong flavour Fresh flavour No intensity=no fresh flavour High intensity=distinct flavour Related to the flavour of sodium chloride No intensity=no salty flavour High intensity=distinct salty flavour Relates to the force needed to bite through the crust and the crumb No intensity=little force needed to bite through High intensity=high force needed to bite through Fluid feeling in the mouth after 3 to 4 bites No intensity=no moistness, no fluidity after 3 to 4 bites High intensity=distinct moistness, much fluidity after 3 to 4 bites Mechanical texture properties related to the ability of samples to retain the shape No intensity=no crispness, tough High intensity=distinctly crisp, fragile with the energy input (Table II). The doughs were mixed in a Bearmixer (Varimixer Programmable Control System MK III, Wodschow & Co, Copen- hagen, Denmark). The doughs were proofed at 25 °C and 70% RH in a proofing cabinet (Lillnord A/S, Odder Denmark). The baguettes were pre- Baguette making and wheat quality baked for 10 min in a rotating hearth oven equipped with a fan (Bago-line Type BEX 1.0, Fjellbroen A/S, Faaborg, Denmark). Live steam was injected during the first 35 s of baking, and the temperature was reduced from 270 °C (P1) or 250 °C (P2) to 220 °C, immediately after the baguettes were put in the oven. The baguettes were packed in a modified atmosphere (80% N2 and 20% CO2) and stored at room temperature (20 °C) for a maximum of 10 days prior to sensory and image analysis. Preparation of samples for sensory and image analyses Pre-baked baguettes were finally baked (220 °C, 11 min and live steam during the first 15 s of baking) and left for 30 min at room temperature prior to testing. Each panellist was given half a baguette on a white dish. The other half was used for textural analysis. Sensory evaluation Baguettes were analysed by conventional sensory profiling using 10 trained panellists. Members were selected and trained according to guidelines in ISO/DIS 8586–1:198917 and the method for sensory profiling according to ISO 6564:1985-E18. Panellists developed a lexicon by describing diﬀerences between extreme samples and developed a consensus list of 13 sensory attributes for profiling. These were glossiness, crackles on the crust, porosity (grain) scored according to Dallmann’s pore table19, area of cut surface, elasticity, odour intensity, fresh odour, flavour intensity, fresh flavour, salt flavour, firmness, moistness and crispness of crust. Panellists were trained in use of definitions of sensory attributes (Table III) and rating anchors by pre-testing extreme samples. A continuous non-structured scale was used for evaluation. The left side of the scale corresponded to the lowest intensity (value 1.0) and the right side corresponded to the highest intensity (value 9.0). Evaluations were performed in laboratory equipment as described in ISO 8589: 1988-E20. Each panellist evaluated the samples at their own pace using a computerised system for direct recording of data (CSA Compusense, version 4.2, Canada). Samples were served in a randomised order. Five to six samples were served each session (two sessions per day). No replicates 77 were performed. Between tasting, panellists cleaned their mouth with tap water (20 °C) and salt free crackers. Image analysis Images of the baguettes were produced using a Canon EX2 video camera. The images were recorded with the Screen Machine II frame grabber. The signal from the camera was composed of luminance and chrominance signals (Y/C or Hi8). This signal is common in semi-professional cameras and gives a good separation of crominance and intensity signals. The baguette slices were illuminated at a 45 ° angle from both sides using four tungsten lamps, two on each side. The recorded images were converted from true colour images (512 by 512 pixels) to 256 level grey scale. Images were centred using a thresholding technique together with calculations of the centre of gravity of the image. A central rectangular cutout and a resizing to obtain a 256×256 greyscale image was performed to ensure that no baguette was smaller than the quadrate. The images were enhanced with the 3×3 mask, unsharp, convolution, and the porosity calculated. The area of the baguette slices was calculated from images by counting the number of pixels covering the bread sample. Statistical analysis Several ANOVA models were employed to investigate the eﬀects of the diﬀerent factors. First, an overall model including: Process, Assessor and Flour was used. Of these, Assessor and all interaction terms involving the Assessor were regarded as random eﬀects, whereas the remaining eﬀects were regarded as fixed. Mixing time and Proofing time were nested within the Process eﬀect. Since these factors cannot be compared for the two processes. Thus a given mixing time will act diﬀerently on a soft dough vs a stiﬀer dough as seen by the diﬀerent mixing energies (Fig. 1). In addition, for proofing time there were diﬀerent time settings for the two processes. Separate models for each Process were employed using the following factors: Assessor, Flour, Mixing time, Proofing time. For the attributes with a significant Flour eﬀect, the diﬀerences were studied in more detail by Tukey’s Multiple Comparison Test. As the fixed eﬀects were on two levels only, it was not necessary to apply for these 78 P. Baardseth et al. eﬀects. The multivariate Principal component analysis (PCA) that treats all variables simultaneously21, was performed to obtain an overview of the sensory data using the Unscrambler software (CAMO A/S, Trondheim, Norway). In PCA, the information in the data is projected down to a small number of new variables called principal components (PCs), which are linear combinations of the original data. The diﬀerent PCs are orthogonal to each other, and are estimated to give, in decreasing order, the best description of the variability in the data. The first few PCs will contain most of the relevant information in the data. The PCs are described by loadings for the variables and scores for the samples. The data are modelled in terms of significant factors, plus errors or residuals. Image analysis using the angle measure technique (AMT)22 has been shown to be feasible for modelling sensory porosity23. The images of the baguette slices from 32 doughs presented to three random panellists giving a total of 96 data sets were vectorized by AMT and modelled with sensory attributes and process variables (Y-variables) using principal component regression (PCR). The feature extraction method of AMT provides a data vector (X-variables) for each image. Principal component regression (PCR)24 was performed to predict the baking process and sensory property (Yvariables) from image analyses (X-variables). PCR combines PCA and traditional multiple regression analyses. First PCA is performed among the Xvariables (the image variables). Thereby, the few first significant PCs from the PCA analyses are used as X-variables in a traditional multiple regression analyses. In this way the multicollineary problem among the original X-variables is solved. The reference values were represented by the mean sensory attributes over all panellists. Each of the three data sets consisted of 32 images, and the mean sensory attributes (Y) were the same for each of the three data sets. Two of the data sets were used as calibration sets and one was the test set. Cross validation among the three data sets shows similar results. We also used a test set validation, and plotted the designed variable loading and scores together with the sensory reference values to graph the relationship between process and sensory variables22. RESULTS AND DISCUSSION PCA of sensory attributes Principal component analysis (PCA) of the sensory attributes of baguettes showed that 56% of the variations could be explained by the two first principal components (PCs), 40% by the first factor and 16% by the second factor. The loadings plot of the two firsts PCs [Fig. 2(a)] showed that texture attributes explained the main variation among the baguettes tested in this study. PC1 in the loadings plot separated firmness and elasticity i.e. internal characteristics. The corresponding scores plot [Fig. 2(b)] showed that PC1 separated the two processes—traditional French at the right and industrially modified at the left. Thus, baguettes made by the industrial adapted process with rough mixing and handling and stiﬀ dough (P2) were firmer, but less elastic than baguettes made by the traditional French process made with soft dough (P1) and gentle mixing and handling. Baguettes with high intensity of elasticity also had a high porosity, moistness and low firmness. Fresh odour and flavour were also associated with the traditional French process [Fig. 2(a)]. PC2 in the loadings plot separated the external characteristics, glossiness and area of cut surface. The cutting of the dough surface after final proof is an integral part of processing French bread. The two other external quality characteristics, crispness and crackles on the crust, were highly correlated with the area of cut surface. The eﬀects of crust crackles were more complex, but the scores plot shows flour quality to be important [Fig. 2(b)]. High glossiness was obtained on baguettes baked with Tjalve flour (1), whereas baguettes with a high area of cut surface were obtained with baguette flour A (3), from French soft wheat. Figure 2 Loading (a) and score plots (b) for the two first factors obtained by PCA for sensory attributes of baguettes formulated from wheat flours of four diﬀerent qualities and made by two diﬀerent processes. The first number is flour quality 1 (Tjalve), 2 (Folke), 3 (Baguette flour A), 4 (Baguette flour B), the second number indicates the mixing time 1 (2+4 min), 2 (2+8 min), the third number indicates the proofing time 1 (65/180 min), 2 (80/165 min) and the fourth number 1 (traditional process) and 2 (modified industrial process). Baguette making and wheat quality 79 (a) 0.6 glossiness 0.5 0.4 elasticity 0.3 odour intensity porosity PC2 0.2 flavour intensity moistness salt flavour 0.1 firmness 0 fresh flavour fresh odour –0.1 –0.2 crispness of the crust –0.3 crackles on the crust Area of cut surface –0.4 –0.4 –0.3 –0.2 –0.1 0 PC1 0.1 0.2 0.4 0.3 (b) 4 1121 3 1122 2 4121 1222 1111 1221 1212 1112 1 4122 PC2 –1 2122 2222 –2 3122 1211 4211 2111 4221 3111 4112 3221 2221 4222 3211 4212 3121 0 3112 2112 4111 2121 2211 3222 2212 –3 –4 3212 –5 –5 –4 –3 –2 –1 0 PC1 1 2 3 4 80 P. Baardseth et al. 1 2 3 4 Process 2 Process 1 Figure 3 Images of slices of baguettes produced with four diﬀerent flours (from top to bottom—Tjalve (1), Folke (2), baguette flour A (3) and baguette flour B (4)) and two diﬀerent processes (from left to right—process 2, mixing time 2+4 min and 2+8 min at proofing time 150 min, mixing time 2+4 min and 2+8 min at proofing time 165 min, process 1, mixing time 2+4 min and 2+8 min, fermentation time 20 min, pre-proofing time 15 min and proofing time 65 min, mixing time 2+4 min and 2+8 min, pre-proofing 20 min+15 min and proofing time 80 min, see Fig. 1) 0.10 odour intensity F2 F3 0.05 MT4 crackles on the crust P1 0 PC2 crispness of the crust elasticity porosity –0.05 area of cut surface salt flavour F1 PT2 PT1 firmness flavour intensity P2 glossiness moistness MT8 fresh odour fresh flavour area –0.10 F4 –0.15 50 150 PC1 250 Figure 4 The PCR-modelling of sensory attributes and process variables based on features extracted from images. The symbols in the loading plot are the four factors: Process P1 and P2; flour quality (F1=Tjalve, F2=Folke, F3=baguette flour A, F4=baguette flour B), mixing time (MT4=2+4 min, MT8=2+8 min), proofing time (PT1=65/150 min, PT2=80/ 165 min). Baguette making and wheat quality p = 0.0028 p = 0.0001 11 9 9 Porosity Crackles on the crust 11 81 7 5 3 7 5 3 1 1 P1 P2 P1 P2 p = 0.0099 11 9 9 Fresh flavour Elasticity p = 0.0224 11 7 5 3 5 3 1 1 P1 P2 P1 p = 0.0001 11 P2 p = 0.0001 5000 9 4000 Area mm2 Crispness of the crust 7 7 5 3 3000 2000 1000 1 P1 P2 P1 P2 Figure 5 Sensory attributes (crackles on the crust, porosity, elasticity, fresh flavour and crispness of the crust) and slice area (mm2) aﬀected significantly by process (P1 (traditional French) and P2 (modified industrial). Correlation coefficient between porosity measured by image analysis and by sensory analysis Figure 3 shows slices of 32 baguettes used in image analysis based on a multivariate feature extraction. The porosity was well predicted from image analysis using modelling with AMT extracted features. The correlation coeﬃcient between porosity predicted from images and porosity evaluated by the sensory panellist was 0.93 with a corresponding root mean square error of prediction (RMSEP) of 0.62. This indicates that image analysis may be used as a reference method for describing bread porosity. The correlation coeﬃcient between area of baguette slices and sensory porosity was 0.73. The prediction of all sensory attributes and process variables (Y-variables) based on AMT feature vectors from image analysis (X-variables) 82 P. Baardseth et al. Process 1 (traditional) (a) p = 0.0086 Area of cut surface Porosity 9 7 5 3 1 2+4 7 5 3 1 2+8 p = 0.0005 9 2+4 2+8 Mixing time (min) p = 0.0005 Area of cut surface Porosity 9 7 5 3 1 65 7 5 3 1 80 p = 0.0001 9 65 80 Proofing time (min) Figure 6 Sensory attributes aﬀected significantly by mixing time and proofing time within each process. (a) Process 1 mixing time 2+4 min (125 wh) and 2+8 min (300 wh), and 20 min fermentation time, 15 min pre-proofing, proofing time 65 min and 80 min. (b) Process 2 mixing time 2+4 min (180 wh) and 2+8 min (400 wh), and proofing time 150 min and 165 min (see Fig. 1). by using principal component regression (PCR)modelling is shown in Figure 4. The loading plot shows the relationship between texture attributes and process variables and flour qualities. Porosity, elasticity and juiciness correlated with the traditional French process and firmness correlated with the industrial process. Thus, the texture attributes could also be predicted by AMT from the image analysis. Effect of process on baguette quality Baguettes produced by the traditional French process had significantly higher porosity measured both by sensory analyses (Fig. 5) and image analyses (Fig. 4). This is also clearly seen in Figure 3. The area of the slices i.e. volume was also significantly larger for baguettes produced by the traditional French process compared with baguettes baked with the industrial adapted process (Figs 3–5). Good French baguette quality is normally characterised by large volume and open porosity25,26, which were obtained by baking with the traditional French process. These results confirmed the importance of avoiding degassing of the dough (gentle treatment) to achieve the open and random pore structure of the crumb1,2. Baguettes produced by the traditional French process also had significantly higher elasticity, crust crispness and crackles on the crust compared with baguettes produced using the industrial adapted process with stiﬀ dough and rough treatment (Figs 4 and 5). Furthermore, baguettes produced by the traditional French process had significantly higher fresh flavour (Fig. 5) which is due to higher concentration of yeast used in the recipe (0·9% vs Baguette making and wheat quality Process 2 (industrial) Area of cut surface p = 0.0001 9 Porosity 7 5 3 7 5 3 2+4 1 2+8 p = 0.0001 9 7 5 3 1 p = 0.0001 9 2+4 Crispiness of the crust Crackles on the crust (b) 1 83 2+8 p = 0.0001 9 7 5 3 1 2+8 2+4 2+4 2+8 Crispness of the crust Mixing time (min) p = 0.0012 9 7 5 3 1 150 165 Proofing time (min) Figure 6(b) 0·3%) and longer fermentation time4,5. Mixing time aﬀected significantly the sensory attribute porosity and area of cut surface for both processes [Fig. 6(a,b)]. For the industrial process mixing time also aﬀected crackles on the crust and crispness of the crust [Fig. 6(b)]. Fermentation/proofing time aﬀected significantly porosity and area of cut surface of the baguettes baked with the traditional process [Fig. 6(a)], whereas crispness of the crust was aﬀected by proofing time when the industrial process was used [Fig. 6(b)]. Effects of flour quality on baguette quality Slice area i.e. volume was significantly larger for baguettes produced using baguette flour B (4) than baguettes produced using Tjalve flour (1) or baguette flour A (3), which again was significantly 84 P. Baardseth et al. Area mm 2 (a) P1 2000 P2 1000 0 5 Porosity (image) 3000 15 10 8 (b) P1 6 4 P2 2 0 5 15 10 Protein % Protein % (c) 3000 Area mm 2 2500 2000 1500 1000 500 0 P1 P2 Figure 7 (a) Eﬀect of flour quality on area of the baguette slices, measured in mm2 by image analysis, Tjalve (Χ), Folke (Φ), baguette flour A (Ε) and baguette flour B (Β), (b) Eﬀect of flour quality on porosity, measured by image analysis, and (c) interaction between flour quality and process on slice area. a 7 b a a 6 ab 5 4 ab ab a a b b a ab c b b b b a b ab a a a ab b b b b ab ab b a b a b bc c c bc 3 2 1 Glossiness Crackles Porosity Surface Elasticity Fresh odour Fresh flavour Firmness Juiciness Crispness Figure 8 Average values of each sensory attribute that were significantly diﬀerent between baguettes baked from flours from wheats of four diﬀerent qualities. (Φ) Tjalve, (C) Folke, (C) baguette flour A, (Ε) baguette flour B. attributes, with no significant level diﬀerence according to Tukey’s test are indicated by similar letter (a–c). larger than baguettes produced using Folke flour (2) [Figs 3, 4, 7(a)]. Baguettes produced using baguette flour B (4) also had a significantly more open crumb structure (higher crumb porosity) than Tjalve flour. Baguette flour A and Folke flour had the densest crumb structure [Figs 3, 4, 7(b) and 8]. Baguette flour B (4) and Tjalve flour (1) have both stronger protein quality and higher protein content than the remaining flours (Table I). The reason for the diﬀerent quality of baguettes produced with flour B compared with Tjalve is not known. Further experiments are needed to identify more precisely optimum level of protein and protein quality for baguettes. Baguette making and wheat quality 85 Table IV ANOVA for each sensory attribute aﬀected by the interaction between flour quality and process, flour quality and mixing time, flour quality and fermentation/proofing time. The p-value gives the lowest significance value at which the two groups are diﬀerent (i.e. if the p-value is less than 0·05, the two groups are significantly diﬀerent at the 0·05 level) Glossiness Crackles on the crust Porosity Area of cut surface Elasticity Odour intensity Fresh odour Flavour intensity Fresh flavour Salt flavour Firmness Moistness Crispness of crust Flour quality×process Flour quality×mixing time Flour quality×fermentation /proofing time 0·0020 0·0003 0·0001 0·0002 0·0074 0·3921 0·0679 0·1662 0·0369 0·1183 0·1414 0·4389 0·0003 0·0782 0·0112 0·0003 0·0002 0·2113 0·1585 0·1416 0·1974 0·0538 0·3129 0·2544 0·0243 0·0001 0·4600 0·1142 0·0323 0·2754 0·0642 0·8692 0·5884 0·6362 0·2198 0·5907 0·0036 0·7071 0·0001 The area of cut surface was significantly higher for baguettes produced using baguette flour A (3) than with other flours (Figs 2 and 8). Dough made from this flour was stable during mixing (70), but had short development time (1.7) (Table I). On the other hand, higher energy input during mixing was recorded for these doughs compared to the others (Table II). The moistness of baguettes produced with baguette flour A was also low (Fig. 8). It is possible that this property relates to the quality of the soft wheat, although water was added, based on the water uptake determined using the Farinograph. Interaction between process and flour qualities on baguette quality The eﬀect of flour depends on the process (Table IV). Baguettes made from flours of all qualities had a denser crumb structure when produced by the modified industrial process compared with the more traditional process, but there were small diﬀerences among the flours used in the modified industrial process (Fig. 9). Despite these small diﬀerences, baguette flour B still gave the most open crumb structure (Figs 3 and 8). Slice area of the baguettes produced using flours of all qualities also decreased significantly in the modified industrial process compared with the traditional French process, but baguettes from baguette flour B retained a relatively high slice area [Fig. 7(c)]. Crackles on the crust, crispness of the crust and elasticity were significantly higher in baguettes produced using traditional process compared with industrial process for all flours. The diﬀerences between these attributes from process 1 to process 2 was, however, somewhat diﬀerent for the various flours (Fig. 7). For the area of cut surface the interaction plots (Fig. 9) were complex. This study also demonstrated (Table II) that the mixing time necessary to obtain optimum dough development varies among diﬀerent flours and varies according to mixing equipment, mixing intensity and recipe3. When making the dough, the four flour qualities behaved diﬀerently using a fixed mixing time (Table II). Interactions between flour quality and mixing time was also found for the following sensory attributes crackles on the crust, porosity, area of cut surface, moistness and crispness of the crust (Table IV). Porosity, firmness and crispness of the crust were aﬀected by interactions between flour quality and fermentation/proofing time (Table IV). Acknowledgements Technical assistance from the bakers Alf O. Nielsen and Leif A. Fardal is greatly appreciated. We also wish to thank Grethe Enersen and Bjørg Narum Nilsen for skilful technical assistance during image recording. 86 P. Baardseth et al. 8 7 7 Crackles on the crust 8 Glossiness 6 5 4 3 2 1 P1 4 3 2 8 8 7 7 Area of cut surface Porosity 5 1 P2 6 5 4 3 2 1 P2 P1 P2 P1 P2 6 5 4 3 P1 1 P2 8 8 7 7 6 6 5 4 5 4 3 3 2 2 1 P1 2 Elasticity Crispness of the crust 6 P1 P2 P1 P2 1 Fresh flavour 11 9 7 5 3 Figure 9 Average values of the sensory attributes showing significant interaction (Table IV) between flour quality ((Χ) Tjalve, (Φ) Folke, (Ε) baguette flour A, (Β) baguette flour B) and process (P1 (traditional French) and P2 (modified industrial). Baguette making and wheat quality REFERENCES 1. Collins, T.H. Chorleywood process for French bread. British Baker 16 (1978) 11–18. 2. Tweed, A.R. A look at French “Baguettes”. Cereal Foods World 28 (1983) 397–399. 3. Magnus, E.M., Bråthen, E., Sahlstöm, S., Færgestad, E.M. and Ellekjær, M.R. Eﬀects of wheat variety and processing conditions in experimental bread baking studied by univariate and multivariate analyses. Journal of Cereal Science 25 (1997) 289–301. 4. 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