Volume 16
Issue 3
Food Science and Technology
JOURNAL OF
POLISH
AGRICULTURAL
UNIVERSITIES
Available Online: http://www.ejpau.media.pl/volume16/issue3/art-11.html
SOLVENT RETENTION CAPACITY (SRC’S) PROFILES AND TECHNOLOGICAL QUALITY PARAMETERS OF SELECTED CZECH WHEAT VARIETIES
Ivan Švec, Marie Hrušková, Jan Karas
Department of Carbohydrates Chemistry and Technology, Faculty of Food Technology and Biotechnology, Institute of Chemical Technology in Prague, Czech Republic
A collection of the 3 Czech winter and 3 spring wheat varieties (harvest years
2008–2010) was evaluated in terms of the solvent retention capacity (SRC)
method as well as common milling and baking quality parameters. Thousand kernel
weight values did correspond to varieties quality sorting, but it was (as usual)
significantly higher for the winter than spring wheat type the winter than the
spring type of wheat (44.3 and 38.6 g, respectively). According to PSI hardness,
samples belong to category very soft (‘Samanta’, ‘Sirael’
PSI 31–35%) and medium hard (‘Sultan’, ‘Sakura’, ‘SW
Kadrilj’, ‘Septima’; PSI 17–20%). The F-test proved
stronger impact of the harvest year on the variability of Solvent Retention Capacity
(SRC) profile. According the water SRC, all three harvest years (2008, 2009,
2010) were statistically different (averages 61.0, 56.4 and 51.0%, respectively).
According a new SRC characteristic, the gluten performance index (GPI), winter
and spring types of the Czech wheat were slightly discriminated (averages 0.70
and 0.77, respectively). Correlation analysis results confirmed some known relations
within the four SRC’s as well as to quality technological characteristic.
For lactic acid SRC, relationships to the thousand kernel weight, the falling
number and the one-stream flour yield (-0.60, -0.62, P = 0.01; and -0.49, P =
0.05, respectively) were determined. The strongest link was revealed between
the GPI and the thousand kernel weight (-0.67, P = 0.01).
Key words: SRC, GPI, technological quality, wheat variety, harvest year.
INTRODUCTION
Besides agronomical characteristics, wheat technological quality is traditionally described in terms of milling and baking one. The former combines kernel shape and mechanical properties (test weight, grain hardness), which affect disintegration progress and (fraction) flour yield. Also basic analytical parameters of protein content and quality (determined by sedimentation test) together with estimation of amylases activity (Falling number) are measured. In essential attitude, the latter includes the mentioned analytical characteristics, dough viscoelastic behaviour and baking test. Relations between these characteristics are known for many year tens and some of them are material and time-consuming, thus application in wheat breeding has limited utilization.
The solvent retention capacity method (SRC, AACC 56–11) was approved as a powerful diagnostic tool for wheat flour baking value assessment [5]. A seminal review of the SRC technology, its history, principles and applications published Kweon et al. [13]. Authors stated, widely used empirical rheology tests (farinography, mixography, extensography, alveography) determine only the combined, cumulative contribution of the major flour functional components, which include damaged starch, gluten proteins and arabinoxylans (“aka pentosans”), described in paper [13]. Compared, the SRC test is able to measure the individual functional contribution of each of those components separately. Its principle lie in a solvation assay of flours, i.e. swelling behaviour of listed polymer networks in single diagnostic solvents – distilled water, 50% weight/weight (w/w) saccharose in water (for pentosans), 5% w/w sodium carbonate in water (for damaged starch) and 5% lactic acid in water (for proteins).
Nowadays, the method application possibilities are spread from breeding to milling and baking laboratories, whose research purposes are related to genome, environment and harvest year influence evaluation [6, 7, 15] and wheat overall quality prediction [17] on one side to quality prediction of many types of final product – e.g. cookies [16], cakes [14], noodles [12] and also breads [18] on the other.
In the field of wheat milling quality, Hrušková et al. [10] published results of the SRC analysis of the selected fractions supplied from commercial wheat mill. A comparative study of the SRC quality of the 19 European wheat flour blends was written by Duyvejonck et al. [2]. Finally, how to optimise wheat blends for customer value was mentioned in the work goal of Haas [9]. There was documented, that customer’s demand could be met according targeted flour SRC profile (for example, 95.0–105.0% for lactic acid or 66.0–72.0% for sodium carbonate SRC’s). Within three trials, the largest deviation (nearly 2%) between targeted and actual SRC values occurred for lactic acid SRC. Unfortunately, the sodium carbonate SRC of blends overcame mentioned range in all three trials.
A scope of the presented paper was evaluation of technological quality as well as the SRC profiles of 6 Czech wheat varieties planted during 3 harvests year period. Statistical analysis was aimed on wheat variety and harvest year influence assessment, and on relation between milling-baking parameters and the SRC’s verification.
Table 1. Milling quality of the Czech wheat varieties in observed harvest years |
[g·L-1] |
[g] |
[%] |
[%] |
[%] |
||
[g·L-1] |
[g] |
[%] |
[%] |
[%] |
||
FQC – one-stream flour yield, CD1 – total flour yield (fraction milling) |
||||||
(P = 95%) |
||||||
MATERIALS AND METHODS
Materials
A tested wheat set included three winter varieties
(‘Sultan’, ‘Samanta’, ‘Sakura’)
and three spring ones (‘SW Kadrilj’, ‘Septima’, ‘Sirael’),
in both variety groups of a distinct quality. In the Czech Republic, four quality
classes of wheat varieties are registered. Tested varieties belong into three
of them – Sultan and SW Kadrilj into E one (elite baking quality, improving),
Samanta and Septima into A (high baking quality, separately workable), and Sakura
and Sirael into C one (others – unusable for yeasted bakery production).
Variety from B class (standard baking quality) was not included in this tested
set. Samples were grown in small field plot trials at the location in the Central
Bohemia region (breeding station Selgen – Stupice, altitude 300 m,
average annual temperature 8.3 C, annual sum of precipitation 588 mm,
soil texture clay loam, soil type haplic Luvisol). Field trials were performed
in three following years, in the harvests 2007–2008, 2008–2009 and
2009–2010.
Meteorological data of the harvests are presented graphically on Fig. 1, and differences between successive harvests were documented. Influence of meteorological conditions on quality parameters and their statistical variability depend on wheat variety type (winter, spring), so presumed relationships were not verified unequivocally. In spite of the highest precipitation level in 2009–2010 corresponds to grain hardness (PSI) decrease, and reversal trend was observed in year 2008–2009. Next example is related to temperature profile – the lowest average temperature during harvest 2009–2010 caused lowering of TW.
|
| Fig. 1. Weather course during three-year period: a) harvest 2007–2008, b) harvest 2008–2009, c) harvest 2009–2010 |
Description of field experiments
In each trial, varieties were sown in a randomized complete block design.
The plants were grown on 10 m2 plots with four replications. The replications
were harvested and bulked to provide seeds for analysis. Harvested grain samples
were cleaned prior to milling and analysis on the 2.5 mm sieves.
Winter wheat
Winter wheat plots were planted following
field pea. Winter varieties were sown at a sowing rate of 400 seeds×m-2.
Phosphorus-kalium fertilizers were applied in the autumn before ploughing and
sowing according to available nutrients in the soil. The nitrogen dose in total
amount 120 kg N ha-1 was
applied in as regenerative fertilization (BBCH 25–29) and as productive
fertilization (BBCH 31–32). Herbicide Glean in October 2007, 2008, and
2009, plant growth regulator Stabilan in April 2008, 2009 and 2010 as well as
the fungicide Tango Super in May 2008, 2009 and 2010 were applied.
Spring wheat
Spring wheat plots were planted following
field pea. Spring varieties were sown at a sowing rate of 450 seeds×m-2. Phosphorus-kalium fertilizers
were applied in the autumn before ploughing and sowing according to available
nutrients in the soil. The nitrogen dose for the spring wheat was divided into
two halves, the first was applied post-emergent (BBCH 11–13), the second
one as productive fertilization (BBCH 29–30). The content of nitrogen was
85 kg N ha-1. At the beginning of July 2008, 2009 and 2010, the fungicide
Tango Super at the rate of 1.0 L ha-1 was applied. Herbicides Sekator in May
2008, Agroxon in May 2009 and Glean in April 2010 were also applied.
Wheat quality determination
Technological quality testing
As the basic milling parameters,
test weight (TW), thousand kernels weight (TKW), grain hardness as Particle Size
Index (PSI, AACC 55–30) were determined
following the internal and international procedures. Small scale milling test
were performed twice – as a one-stream process at the FQC 109 mill (Hungary),
and as a fraction milling with at the CD1 AutoMill (France) (grain sample weights
of 100 and 530 g, respectively). In the former case, flour yield was measured
directly (trait FQC), in the latter total flour yield (trait CD1) was calculated
from fractions’ extraction rate. Flour wet gluten and protein contents
(WG and PRO, respectively) were evaluated with the employment of the “Inframatic
8600” (Sweden), calibrated according to the proper Czech norm. Further,
estimation of protein quality and amylases activity was assessed as the internationally
approved Zeleny’s sedimentation value (ČSN ISO 3093) and the Falling
Number (ČSN ISO 5529) (ZT and FN, respectively).
The SRC profile determination
Aimed on a verification
of the still new and progressive AACC 56–11
Method (Solvent Retention Capacity Profile) prediction possibilities, all six
Czech wheat varieties in the form of fine flour (mill CD1 Auto Chopin, flour
yields in Table 1) were subjected to the mentioned method. A standard procedure
is relatively simply and user-friendly one. A day before measurement alone, four
diagnostic solvents must be prepared – demineralised water and aqueous
dilutions of 50% saccharose, 5% sodium carbonate and 5% lactic acid (w/w). Flour
sample of twice 5.0 g (for two replicates) is weighted into a 50-mL conical-bottom
tube, and 25 g of a selected solvent is added to the flour. This flour-solvent
mixture is tapped on the bench and then mixed well with hand-shaking to disperse
flour without lumps. After a 20-min hydration/solvation time with 4 re-shaking
in 5 min intervals, the flour suspension is centrifuged at 1000 G for 15 min.
Then, the supernatant is discarded, and the tube is allowed to drain for 10 min.
The weight of the swollen pellet is then measured, and four SRC values are calculated
(SRC of water, saccharose, sodium carbonate and lactic acid – ‘WASRC’, ‘SASRC’, ‘SCSRC’ ‘LASRC’,
respectively), based on the 14% flour moisture content, and expressed as %. In
addition, variety grain samples were disintegrated to flour during the CD1 Auto
milling test, and the centrifuge Eppendorf 5702 (Germany) was employed in the
SRC test centrifugation step. Levine et al. [13] suggested new parameter ‘Gluten
Performance Index’ (GPI), which was reported as “an even better
predictor of the overall performance of flour glutenin in the environment of
other modulating networks of flour polymers”. The GPI is equal to ratio
of the LASRC / [SCSRC + SASCR] values.
Statistics
Correlation analysis of technological characteristics and the SRC profile
items was calculated by usage of Statistica 7.1 software (StatSoft Inc.,
USA). Also variance analysis (ANOVA) was appointed, aimed on strength estimation
of wheat variety (WV) and harvest year (HY) on one side and of wheat spring/wheat
type and harvest year interaction on the other.
RESULTS AND DISCUSSION
Milling quality
Influence of meteorological conditions
on quality parameters and their statistical variability depend on wheat variety
type (winter, spring), so presumed relationships were not verified unequivocally.
In spite of the highest precipitation level in 2009–2010 corresponds to grain hardness (PSI) decrease, and reversal
trend was observed in year 2008–2009. Next example is related to temperature
profile – the lowest average temperature during harvest 2009–2010
caused lowering of TW (Table 1).
Between three monitored grain characteristics, only test weight (TW) values of the wheat varieties as well as observed crop years demonstrated statistically insignificant differences (Table 1). Within winter varieties (Sultan, Samanta, Sakura) TW means were very closely together, while a range for the spring one was 57 g·L-1. Moreover, a decrease between 2008 and 2009 harvests around 40 g·L-1 could be negatively displayed in milling procedure.
TKW together with PSI were rather strongly affected by wheat variety than crop year (Table 1). Both quality parameters reflect a varietal heritability [12], and the TKW levels are usually higher for winter wheat like the TW ones. Oscillation of the TKW within winter cultivars subset were weaker compared to spring ones (Fig. 2) in three harvest years. Moreover, median values of 44.8 and 38.4 g statistically distinguished winter and spring wheat type (P = 95%), respectively. The PSI course indicated an extraordinary position of the varieties Samanta and Sirael due to three-crop means of 34.1 and 29.4%, respectively, characterising very soft wheat. Valuable differences of PSI means were evaluated between year’s pair 2008-2010 and the year 2009. From a viewpoint of the harvest year factor, significant difference was evaluated between years 2008 and 2009 (18.9 and 24.9%, respectively; Table 1). Moreover, slow increase of the harvest PSI means resulted in an insignificant decrease of the both flour yields at the FQC and the CD1 mills; relation between the PSI and the FQC was confirmed within the set of 80 samples of the Czech commercial wheat (r = 0.49, P = 99%; [11]). Flour yield at fraction milling test was recorded between 55.1 and 68.9% similarly to data published by Xiao et al. [18] (50.2–71.1%) [17]. Guttieri and Souza [8] cited flour extraction rates from 44.1 to 64.4%, dependently on wheat variety cross from three ones tested. Finally, week descending trend in the FQC values was observed for the tested varieties, corresponding to their sorting into quality classes. On the other hand, flour yields from a fraction milling were not statistically distinguishable.
|
| Fig. 2. Harvest year effect on the thousand kernel weight. Difference between winter (–––) and spring (– – –) variety means (44.3 and 38.6, respectively) provable at P = 95%. |
Observed factors affected the grain features in a different extent. The WV effect dominated in case of TKW and PSI (74 and 87%, respectively; Table 4). The HY had a significant role for FQC feature (71%), and for TW, TKW and CD1 characteristics it had comparable impact. Strong interaction of both factors was revealed for TW and CD1.
Technological characteristics
Within the Czech winter
and spring varieties set, analysis of flour chemical composition confirmed usually
higher wet gluten and protein contents in spring ones (12.5 vs. 13.2%, P = 95%,
respectively), for which a decrease in agreement to variety sorting was found
out (Table 2a). Also protein quality determined as the ZT was in average higher
for spring wheat type (39 ml vs. 36 ml, respectively), but that difference was
not significant also within three quality classes involved. For the FN as a typically
environment-dependent parameter [3], ANOVA did not qualify any diversity between
tested wheat varieties. However, some dissimilarity performed E-quality class
SW Kadrilj by medial FN value of 261 s (321 s,
382 s and 80 s in the individual observed year). It is a reflection
of a diverse sensitivity of single varieties to harvest environmental condition
(sprouting resistance). As demonstrated Faměra et al. [4] for 20 breeding
lines and 16 wheat varieties, also different local weather could seriously damage
technological quality by amylose activity multiplying for the one of variety
pair planted at two distant sites. For example, FN values of the cultivar Florett
were 478 s at the Ruzyně station and 73 s at the Humpolec one
(distance of 120 km). Authors summarised, such differences in a starch stage
resulted also into markedly diverse alveograph energy of water-salt dough (180·10-4–354·10-4 J
in Ruzyně, 20·10-4–269·10-4 J in Humpolec).
During three-year period, the statistically highest wet gluten and protein contents were determined in the harvest 2010, whereas protein quality (as ZT of 36 ml) was comparable to the previous one (Table 2b). Regardless to crop averages of the ZT, a range was the broadest in the first monitored year (24–48 ml), while in the both following harvests measured extents were narrower and similar together (30–41 ml and 31–39 ml in 2009 and 2010, respectively).
Table 2. Baking quality of the Czech wheat varieties in observed harvest years |
FN – Falling Number |
|||||
different (P = 95%) |
|||||
Amylases activity described by the year-mean FN was in fact more close together because of the mentioned accidental excess of the variety SW Kadrilj in 2010 (FN 80 s). Median of that year was determined on a level of 321 s, and with respect of the method accuracy, medial values 331 s, 355 s and 321 s could be considered as the same.
Wheat variety and harvest year factors provably affected WG and PRO characteristics only; considering both factor interactions, impacts of individual variance components were roughly comparable, around one-third (Table 4). Fluctuations of ZT and FN quality parameters did not allow precise statistical estimation of the factor, although their presumed dependence on WV and HY, respectively, was partially indicated.
The SRC testing
SRC’s profiles of tested wheat varieties showed their quality dependence
on weather of single observed harvest years. However, wheat varieties could be
partially discriminated by WASRC measurement (Table 3a). Significance of the
harvest year influence is supported by differentiating at least one year from
each other (Table 3b) for all four SRC’s, in the case of WASRC even all
three monitored years. Some additional information offers a comparison between
winter (Sultan, Samanta, Sakura) and spring varieties (Septima, Sirael, SW Kadrilj).
In this variety type groups, the LASRC as well as the GPI allowed a partial discrimination
of the harvest year influence on wheat quality, especially for the spring wheat
(Table 3c).
Table 3. Average SRC profiles of the Czech wheat varieties in observed harvest years |
[%] |
[%] |
[%] |
[%] |
[%] |
||
[%] |
[%] |
[%] |
[%] |
[%] |
||
[%] |
[%] |
[%] |
[%] |
[%] |
||
LASRC – lactic acid solvent retention capacities; GPI – gluten performance index, GPI = LASRC/(SASRC + SCSRC) |
||||||
(P = 95%) |
||||||
For other 6 varieties, planted in the Czech Republic, Dvořáček et al. [3] determined saccharose and sodium carbonate SRC’s in ranges 105.1–128.7% and 69.2–99.3%, respectively. Mentioned extends were higher and comparable, respectively, with values evaluated within the tested set.
Hrušková et al. [11] analysed 80 samples of Czech commercial wheat, for which the mean profile was 68.5, 96.7, 86.6, and 106.1% for water, sucrose, sodium carbonate and lactic acid SRC, respectively [11]. The SASRC only could be considered as comparable to the tested values determined. Absorbed amount of water (WASRC) was higher for the mentioned commercial samples of wheat, what corresponds to further flour usage in an industrial bakery. Within the same wheat group, the SCSRC reached similarly values higher level. Industrial milling process affected a portion of damaged starch and protein bakery potential – the SCSRC was higher and the LASRC reversely lower compared to the 6 Czech variety profiles. More intensive strain was applied during commercial milling, increasing so rate of thermo-mechanically treated starch. Furthermore, higher flour yield means an incorporation of non-gluten proteins from grain cover layers in to the final product.
Two American spring wheat cultivars, Centennial and Pomerelle, demonstrated significantly different SRC profiles [7]. Compared to data in Table 3, their scores were characterised by lower both SASRC (64.0 and 66.1%, respectively) and LASRC (99.9 and 99.5%, respectively), but at the same time higher SCSRC values (95.8 and 95.2%, respectively). Level of their WASRC’s was comparable to ones of Sirael variety (50.9 and 51.7% vs. 52.4%, respectively), for which the lowest WASRC was recorded within tested Czech wheat varieties set.
For the tested varieties, the GPI was compared to the independent WASRC graphically (pooled over three harvests; Fig. 3). An opposite course of the both parameters is noticeable as well as higher scatter for the GPI values. Considering that, partial discrimination between the Czech varieties was allowed by WASRC only (Table 3a), but independently to wheat type or quality class. According the GPI, winter and spring wheat type could be distinguished – the average for varieties Sultan, Samanta and Sakura was 0.70, while one for varieties SW Kadrilj, Septima and Sirael 0.77. Owing to limited number of wheat samples tested, that differentiating could not be considered as a general rule.
|
| Fig. 1. Weather course during three-year period: a) harvest 2007–2008, b) harvest 2008–2009, c) harvest 2009–2010 |
Exploring further factors acting the SRC profiles, interesting and significant interaction of the wheat winter/spring type and the harvest year was found out for the WASRC and the SCSRC. Beside differences significance within the whole Czech wheat varieties set, a descending tendency could be noticed both in winter and spring cultivar threesomes for those SRC’s.
The SRC profiles of Cetennial and Pomerelle spring wheat varieties, studied by Guttieri et al. [7], were predominantly influenced by the cultivar factor (over fertility one, equal to locality). Calculated F-values for fertility and cultivar factors were 0.91 vs. 6.74, 0.11 vs. 8.90, 2.12 vs. 17.4 and 0.03 vs. 0.69 for WASRC, SASRC, SCSRC and LASRC, respectively. In case of the Czech winter and spring varieties, differing in their baking quality, the SRC’s profiles were surprisingly affected by the harvest year only. Gained F-values were 31.9, 10.3, 11.7, 19.5 in the same order of the SRC parameters (Table 4, P = 99%). Wheat variety factor influenced just the WASRC, and the F-value 5.9 was significant at P = 95%. To be compared, the F-values for GPI were 3.5 (P = 95%) and 3.6 (non-significant) for the variety and harvest year factors, respectively.
For 6 commercial wheat varieties grown in three localities, Dvořáček et al. [3] evaluated influence of the variety and the locality factors for the saccharose and sodium carbonate SRC’s. The WV factor affected the SCSRC in a stronger rate (percentage effect of 65 vs. 29%), while the SASRC was subordinated to the planting locality one (31 vs. 60%). Within the tested variety set, both SASRC and SCSRC were affected strongly by HY than WV (90 vs. 10%, and 70 vs. 30%, respectively) (Table 4).
Table 4. F-test of wheat variety (WV) and harvest year (HY) effects |
portion [%] |
portion [%] |
|||
FQC – one-stream flour yield, CD1 – total flour yield (fraction milling) |
||||
FN – Falling Number |
||||
LASRC – lactic acid solvent retention capacities |
||||
The SASRC could alternate the FN feature, as well as the LASRC the Zeleny sedimentation value. In both mentioned cases, ANOVA quantification of the effects of tested factors did not shown similar tendencies in these feature pairs. As was mentioned above, both SRC’s allowed a partial distinguishing of harvest three years, while the ZT or FN were not statistically affected by any of the studied factors.
Statistical analysis of the SRC’s relation to technological
characteristics
For the set of the Czech wheat, a correlation matrix was established
firstly for the four SRC’s, and secondly between the SRC foursome on one
side and milling-baking parameters plus the GPI parameter on the other (Table
5a, 5b, respectively). Dependently to the set numerousness as well as measured
data oscillation discussed above, less verifiable correlations there were proved.
Correlation coefficients of the SRC foursome to PSI, WG, PRO and also ZT were
evaluated as insignificant (r between -0.40 and 0.43; rcrit = 0.48 for N = 18
and P = 95%).
Table 5. Correlation analysis of the wheat quality characteristics |
to the technological characteristics |
||||
LASRC – lactic acid solvent retention capacities |
||||
FQC – one-stream flour yield; GPI – glutenin performance index; GPI = LASRC/(SASRC+SCSRC), FN – Falling Number |
||||
respectively |
||||
Features of the SRC test confirmed their mutual relations, and with exception of the SASRC, water, sodium carbonate and lactic acid retention capacities were correlated to three others. In the correlation matrix, the strongest interaction was revealed between the water and the sodium carbonate SRC’s (0.91, P = 99%).
Further, TW was correlated to SCSRC and WASRC with similar values of Pearson’s coefficient at P = 99% (0.63 and 0.62, respectively). A negative relation of TKW and FN to LASRC (r = -0.60 and -0.62, respectively) was found on the same probability level. Connection between TKW and LASRC mentioned Xiao et al. [18], which was of better fitting than for analysed set (r = 0.66, P = 99.9%; N = 116). Related to WASRC, GPI and LASCR as dependent parameters correlated with similar strength (-0.70 and -0.73, P = 99%, respectively). Considering the SCSRC in the same comparison, correlation was found as weaker.
Within the tested set of varieties, its lower numerousness affected relations among the four SRC’s, GPI and determined baking quality parameters. Only mentioned links between the FN vs. GPI and LASRC were verifiable (Table 5). Their relations to WG or PRO (identical r ranges from -0.39 to 0.43) and ZT (r ranges from -0.20 to 0.18) were evaluated as insignificant. Hrušková et al. [11] mentioned a verifiable relationships among water or lactic acid SCR and wet gluten/protein content (both 0.24, P = 95%, N = 80).
CONCLUSIONS
Milling and baking quality of 3 winter and 3 spring Czech wheat varieties was monitored in years 2008–2010, and prediction capacity of the Solvent Retention Capacity method was validated. Besides confirmation of known technological features relationships and dependencies (e.g. differentiating of winter and spring varieties by thousand kernel weight or flour protein content), stronger relevance of harvest year than wheat variety for tested variety SRC profiles was revealed. According the water SRC, three observed harvests were statistically different (61.0, 56.4, and 51.0% in order of years 2008–2010). Further, at least one from the other harvest years could be differentiated by saccharose, sodium carbonate or lactic acid SRC. The highest difference occurred between years 2009 and 2010 in the lactic acid SRC (means 110.9 and 140.6%, respectively). A new predictive parameter suggested by the method authors, gluten performance index (GPI), combining lactic acid, saccharose and sodium carbonate SRC, seems to be used for a partial discrimination of winter and spring type of wheat.
Results of correlation analysis were affected by lower numerousness of the tested set, both within the SRC foursome and between the SRC items and technological parameters. In the former case, 7 relations from 10 possible were provable; the strongest link was identified between water and sodium carbonate SRC’s (r = 0.91, P = 99%). In the latter case, lactic acid SRC and GPI correlated with 3 of 8 determined milling and baking quality characteristics. A negative correlation of thousand kernel weight and Falling Number (r = -0.60 and -0.62, respectively, P = 99%) and one-stream flour (r = -0.49, P = 95%) signalised tighter interactions to lactic acid SRC than to GPI.
ACKNOWLEDGEMENTS
I thank M.Sc. Jyrki Ollikainen of the School of Information Sciences of the University of Tampere for helpful comments on statistical treatment of the results.
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Accepted for print: 12.09.2013
Ivan Švec
Department of Carbohydrates Chemistry and Technology, Faculty of Food Technology and Biotechnology, Institute of Chemical Technology in Prague, Czech Republic
Technická 5
166 28 Prague
Czech Republic
email: Ivan.Svec@vscht.cz
Marie Hrušková
Department of Carbohydrates Chemistry and Technology, Faculty of Food Technology and Biotechnology, Institute of Chemical Technology in Prague, Czech Republic
Technická 5
166 28 Prague
Czech Republic
Jan Karas
Department of Carbohydrates Chemistry and Technology, Faculty of Food Technology and Biotechnology, Institute of Chemical Technology in Prague, Czech Republic
Technická 5
166 28 Prague
Czech Republic
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