The application of Taguchi method to determine the optimum blend of unifloral honeys to most closely match thyme honey quality

Original article

The application of Taguchi method to determine the optimum blend

of unifloral honeys to most closely match thyme honey quality

Maria Dimou,1,2 Simos Marnasidis,2 Ioanna Antoniadou,2 Maria Pliatsika2 & George J. Besseris2*

1 Laboratory of Apiculture, Faculty of Agriculture, Artistotle University of Thessaloniki, Agroktima Panepistimiou, Thermi, Thessaloniki 57001,

Greece

2 Postgraduate Program (MSc) in Quality Assurance, School of Science and Technology, Hellenic Open University, Patras, 26335, Greece

(Received 26 June 2008; Accepted in revised form 11 November 2008)

Summary This study was conducted to investigate the effect of four blossom honey types (orange, chestnut, heather

and cotton) on a group of quality characteristics of thyme-type based mixture preparations. Mixture ratios

were prepared at 0%, 50% and 100% per blossom honey-type and then were blended with thyme honey in

parts 1:1. The melissopalynological, sensory and physicochemical quality characteristics for each blend were

monitored. A three-level, four-factor orthogonal array according to the Taguchi method was utilised to plan

the experiments maintaining the thyme component as a ‘slack-variable’ to contain the number of performed

trials. Subsequent anova treatment revealed that only a pure orange-type blend favours the simultaneous

maximisation of aroma (P < 0.05) and the minimisation of electrical conductivity (P < 0.05). Finally, there

was a significant effect of chestnut-type blend content on microscopical and physicochemical characteristics

(P < 0.05), nevertheless, their corresponding signal-to-noise ratios are maximised only at a concentration of

zero value.

Keywords anova, apples, food quality, honey, integrated pest management, plants, quality control.

Introduction

Honey is considered as one of the most appreciablenatural products and it has been used by humankindfrom ancient times. Its nutritional value has beenrecognised by scientists on international level and, alongwith other products, it has been established in theMediterranean diet.Honey is usually produced from several plant species

(multifloral), while honey that originates predominantlyfrom a single botanical source (unifloral) generallyimplies larger efforts by the beekeepers. However,market needs demand the standardisation of consum-able products. Consumer preference, and hence the priceof the product, depends mainly on its botanical origin.Consumers prefer unifloral honeys or honeys withunique characteristics of particular floral sources evenif they have higher price premiums (La-Serna Ramoset al., 1998; Unnevehr & Gouzou, 1998; Terrab et al.,2003a; b; Persano Oddo & Bogdanov, 2004; Dag et al.,2006). Thus, the producers may be able to further

promote honey products or develop new products onthe basis of floral source differentiations (Unnevehr &Gouzou, 1998). Already in several European countries,unifloral honeys concern a large market share. There-fore, it is very important that a certain honey type canbe always identified and the product meets the qualitystandards.Authentication of food products is important for both

consumers and industries. The authenticity of honey hastwo aspects: authenticity with respect to honey produc-tion and authenticity with respect to the geographicaland botanical origin (Ruoff & Bogdanov, 2004). Thedefinition of the botanical origin of honey is a complexprocedure and several quality characteristics should beexamined. In the last few years, novel evaluationtechniques based on analytical chemistry and statisticshave been tested but none of the methods proposed hasbeen accepted as a complementary technique anddefinitely not as a substitute to the traditional methods(Radovic et al., 2001; Bogdanov & Martin, 2002; Cotteet al., 2004; Arvanitoyannis et al., 2005; Ruoff et al.,2006; Mannas & Altug, 2007).At themoment, the evaluationof the botanical origin of

honey involves a combination of sensory, microscopical*Correspondent: Fax: 302108135453;

e-mail: [email protected]

International Journal of Food Science and Technology 2009, 44, 1877–1886 1877

doi:10.1111/j.1365-2621.2008.01899.x

� 2009 Institute of Food Science and Technology

and physicochemical parameters (Persano Oddo &Bogdanov, 2004; Piana et al., 2004). Electrical conduc-tivity and sum of glucose and fructose content are themain physicochemical characteristics that have beenproposed by the scientists and the European Council(Directive 2001 ⁄110 ⁄ EC) to determine the botanicalorigin of honeys (Bogdanov et al., 1997; Persano Oddo& Bogdanov, 2004). Generally, in blossom honeys thesum of glucose and fructose should be higher than 60%and the electrical conductivity lower than 0.8 mS cm)1

(Bogdanov et al., 1997; Persano Oddo & Bogdanov,2004).Sensory analysis is very important to determine the

botanical origin of unifloral honeys (Persano Oddo &Piro, 2004; Piana et al., 2004). A main advantage of thismethod is that it examines the same characteristics(colour, aroma, taste, texture) with the consumer.Melissopalynological analysis is the main method todefine the botanical origin of blossom honeys (Louve-aux et al., 1978; Mateo & Bosch-Reig, 1998; von derOhe et al., 2004). Pollen analysis can help the analyst toevaluate the nectar content of each source in a honeyaccording to the presence of the nectariferous pollengrains.Greece, where there are approximately 1 388 000

beehives, has the largest concentration of beehives perunit of territory worldwide. Annual honey productionamounts roughly to 14 000 tons which accounts forover 90% of the national honey consumption. Themajority of the production comes from honeydewhoney; however, several unifloral honeys such asthyme, orange, heather, chestnut and cotton honeyare also produced.Thyme honey is characterised by its light colour and

distinguished floral pungent aroma and flavour. It isproduced in several Mediterranean countries (Accortiet al., 1986; Thrasyvoulou & Manikis, 1995; Tsigouri& Passaloglou-Katrali, 2000; Perez-Arquillue et al.,1995; Mannas & Altug, 2007; Terrab et al., 2004a; b;Persano Oddo & Piro, 2004) and is considered as oneof the most delicious and preferable among the floralhoneys. In Greece, the price of thyme honey is rangingfrom two to three times higher than any other type ofhoney. Thymus capitatus (L.) Hoffmans & Link, themost widespread taxon of thyme in the country,blossoms at the beginning of summer for a few weeks.The climatic conditions at this period make the honeyproduction changeable, difficult and limited. Thus,often this type of honey is packaged and traded as ablend of thyme and other types of honey. Thedefinition of the optimum blend of thyme honey withother types of honey in packaging in order to meet theconsumers’ requests and the quality characteristics isnot easy to be achieved.Taguchi analysis is a methodology based on statistical

concepts and especially experimental design techniques

that has as its main objective to improve the product’squality, optimise manufacturing processes and help inthe new product’s design stage (Taguchi, 1986). Itprovides a systematic and efficient approach for con-ducting experimentation to determine near optimumsettings of design parameters for performance and cost.The classic way to understand the effect of a variable isto keep all other factors constant while varying factorsone at a time. At the advent of time, a full factorialstudy was adopted as the norm in carrying out scientificresearch which then turned out to be time-consumingwhile it required extensive resources. In contrast, theTaguchi method allows many variables to be studied,with the use of a minimum number of experimentalruns, through a systematic choice of combinations ofvariables. By using orthogonal arrays, it reduces thenumber of experimental trials to a practical andeffective size. Taguchi approach to design of experi-ments is easy to adopt and apply for users with limitedknowledge of statistics, hence gained wide popularity inthe engineering and scientific community (Antony,2003).While quality improvement methods have found a

permanent niche in manufacturing-related issues for thepast three decades, food engineering has begun toexploit such tools only in the recent years. Optimumconditions for microwave frying of food products wereachieved by resorting to a Taguchi-type L27 orthogonalarray for efficient experimental planning (Oztop et al.,2007). Microbiological safety was maximised through anin-depth investigation of critical variables influencingheating of intricate canned preparations such as spa-ghetti in sauce (May & Chappell, 2002). An illustratingexample of a successful utilisation of design of experi-ments in the beverage sector has been presented byBowles & Montgomery (1997).An attempt to describe in quantified terms the

quality levels of mixed-type of flora honeys has notbeen available in the current scientific literature. In thisstudy, we used the Taguchi method to optimise theanalogies of honeys from several botanical origins thatshould be mixed with thyme honey so that the finalblend will still have the quality characteristics of thymehoney.

Methodology

Honey samples

The blends were prepared using five common Greekunifloral types of honey: thyme (T. capitatus Hoffm. &Link.), orange (Citrus spp.), heather (Erica sp.), chestnut(Castanea sativa Miller) and cotton (Gossypium hirsutumL.). Each honey type was collected from a single harvestoriginating from different regions of Greece. All sampleswere stored at )18 �C until the analysis. The botanical

Taguchi method to determine the blend of unifloral honeys to thyme honey quality M. Dimou et al.1878

International Journal of Food Science and Technology 2009, 44, 1877–1886 � 2009 Institute of Food Science and Technology

origin of the samples was defined according to theirsensory, microscopical and physicochemical character-istics. The mixture of the samples was accomplishedaccording to the Taguchi experimental design, describedanalytically below. The samples were homogenised in38 �C for 24 h (Townsend, 1979).

Sensory analysis

The precise identification of the discrimination pointbetween multifloral and unifloral honeys are not easy tobe achieved (Persano Oddo & Bogdanov, 2004). Expertassessors are considered to be able to discriminate verysimilar products and perform better in repeating de-scriptors compared with trained assessors (Guerreroet al., 1997; Zamora & Guirao, 2004).The visual, olfactory and taste aspects of the samples

were examined by two quaternary groups of experttasters (Gonnet, 1996), with more than 3 years ofexperience in sensory analysis of honey. The sampleswere prepared with 100 g of honey placed into closedtransparent glass jars with air-tight seal lids. Threesessions were held using three samples in each, withthree-digit random numbers. The samples were pre-sented to the tasters at room temperature and a ten-grade scale was used for the evaluation. Slices of appleand mineral water were provided. Samples whosesensory characteristics corresponded to thyme honeycharacteristics were marked with ‘10’.

Melissopalynological analysis

For the qualitative analysis, the method described byLouveaux et al. (1978) was followed. Five grams ofhoney was diluted with 10 ml of distilled water. Thesamples were centrifuged for 10 min at 1550 · g in aCentra CL2 centrifuge (IEC, Needham Heights, MA,USA) (Pendleton, 2006). The slides were preparedwithout acetolysis. Frequency occurrence of pollenfrom nectariferous plants was calculated as a percent-age of totals of pollen of nectariferous plants. In eachsample, we counted at least 600 pollen grains. For thequantitative analysis the sediment distributed uniformlyon a 22 · 22 mm area on a slide and we examined tenfields at a magnification of 200·. Each honey samplewas analysed in duplicate.

Physicochemical parameters

Electrical conductivity (mS cm)1) was measured induplicate at 20 �C in a 20% (w ⁄v) solution of honey indeionised water using a conductimeter (Cond 315i ⁄ set,WTW Weilheim, Germany) (Bogdanov et al., 1997).Sugars (fructose and glucose) were determined using anHPLC chromatographic method (Bogdanov et al.,1997).

Design of experiments

In introducing DOE methods to quality quantificationit is necessary to establish a factor–response relation-ship between food preparation controls and producttrait observables early in the design. In addition, it isvery important to plan optimal measuring points thatmay allow accounting of possible deviations fromlinear phenomena thus averting the introduction ofserious errors in predicting product response. Fourcontrol factors were pre-selected based on the honeyfloral origin: (i) orange, (ii) heather, (iii) chestnut and(iv) cotton. Thyme honey forms the basis for thepredominant part of this new formulation because ofits availability and its known high quality characteris-tics. In this work, thyme honey technically plays therole of the slack-variable (Cornell, 2002). However,working with the Taguchi approach, it was determinedthat the slack variable should not receive any furtheranalysis because of its ‘equally-weighted’ presence to allpreparations. Furthermore, the experiments wereplanned in such a fashion that non-linear effects wouldbe able to be tracked down as long as they influencethe underlying response mechanisms. For this reason, afractional factorial design was employed that wouldrequire at least three points of examination for eachimplicated control factor. The orthogonal array judgedsuitable for such circumstances while expecting a multi-response regime of physical and sensory honey traitswas chosen to be an L9 (34) (Taguchi, 1986). As thefour control factors comprise constituents in a homol-ogous sense, the selected factor settings (or levels) wereadjusted in a similar manner. Each factor was exam-ined at settings located at 0%, 50%, and 100% of thefour-component blend. From Table 1, it is understoodthat the assigned percentages as declared may notmake much sense. Taguchi offers some insight on thisunique arrangement by satisfying all component spec-ifications by normalising (‘expanding–stretching’) theindicated factor settings to a sum total of 100% acrosseach trial run individually (Taguchi, 1987). Thenormalised settings of the L9 (34) orthogonal arraypertaining this mixture along with their denoted levelsare shown in Table 2. They represent nine experimentalruns that may capture the modulation the four factors(columns) on multiple quality characteristics for honeyin the specified testing range. The blend was mixedwith thyme honey in ratio 1:1. Thus, each samplecontained 50% of thyme honey and 50% of the blendas it was prepared according to L9 (34) orthogonalarray.The responses investigated in this research are attuned

to physicochemical, microscopical, and sensory evalua-tions. Electrical conductivity was the only physicochem-ical characteristic of sample honey blends that wasmonitored, as the sum of glucose and fructose content

Taguchi method to determine the blend of unifloral honeys to thyme honey quality M. Dimou et al. 1879

� 2009 Institute of Food Science and Technology International Journal of Food Science and Technology 2009, 44, 1877–1886

was in accordance with Greek and European legislationsin all cases. Microscopical properties were recorded interms of the responses: (i) concentration of thyme typepollen grains (%) and (ii) number of pollen grains per10 g of honeys. Sensory responses were gathered for: (i)colour, (ii) aroma, and (iii) flavour. As the experimentalwork was attempted for the first time, explorationobjectives sought an assessment of the factor activenessper investigated response as if it was a single-responseoptimisation case.Duplicate measurements obtained from conducting

the trial-run recipe as outlined in Table 1 were trans-formed according to the Signal-to-Noise ratio (S ⁄Nratio) concept recommended by Taguchi (Table 2). TheS ⁄N ratio equation depends on the criterion for thequality characteristic to be optimised. For all sensoryresponses the ‘larger-the-better’ optimisation rule wasadopted. If n-replicates are denoted by y1, y2…yn, thenthe appropriate transformation formula for signal-to-noise ratio is:

S=NRatio¼�10log10 1=y21þ1=y22þ��þ1=y2n� �

=n� �

ð1Þ

The same optimisation direction was expected for theconcentration of thyme-type pollen grains (%). How-ever, electrical conductivity and the number of pollengrains per 10 g of honeys were treated as ‘smaller-the-better’ characteristics subject to the following transfor-mation:

S=NRatio ¼ �10log10 y21 þ y22 þ � � � þ y2n� �

=n� �

ð2Þ

Regardless of the direction of optimisation of thecharacteristic that was used to calculate S ⁄N ratio, thisratio was always sought to be maximised. Statisticalanalysis was carried out using the commercial softwareminitab v.15.1 (MINITAB, PA, USA). The datareduction is effected by two methods: (i) a responsegraph and (ii) an anova treatment on pooled error. InTable 3, we have accumulated the results of all-factorialanova treatments for all six quality characteristics.

Table 2 The L9(34) orthogonal array in terms of Taguchi’s ‘expanding–stretching’ trial set-up and mixture data in signal-to-noise ratio form

Trial

No.

L9(34) ‘Expanding-Stretching’ OA Set-up Signal-to-noise ratios

Chestnut

(%)

Orange

(%)

Heather

(%)

Cotton

(%) Colour Aroma Flavour %TTPG NPG EC

1 0 100 0 0 19.20 18.32 17.64 32.57 )91.53 8.19

2 0 33 33 33 17.84 12.52 12.83 30.75 )89.08 5.80

3 0 0 50 50 19.20 12.81 12.72 30.32 )93.67 5.10

4 17 33 17 33 18.59 17.32 14.40 20.39 )102.30 7.32

5 25 25 50 0 18.96 12.30 10.16 19.52 )103.64 3.64

6 50 0 0 50 17.61 8.35 9.18 10.61 )108.97 1.95

7 29 29 29 14 19.89 17.35 17.64 17.44 )104.94 3.71

8 40 20 0 40 19.78 15.74 17.50 14.70 )106.37 2.94

9 67 0 33 0 18.19 13.22 9.71 9.54 )111.93 1.62

OA, orthogonal array; % TTPG, % thyme-type pollen grains; NPG, number of pollen grains per 10 g of honey; EC, electrical conductivity in mS cm)1.

Table 1 The L9(34) orthogonal array for a four-component honey blend and data collected

Trial

No.

Original L9(34) OA Set up Colour Aroma Flavour % TTPG NPG EC

Chestnut

(%)

Orange

(%)

Heather

(%)

Cotton

(%) Rep1 Rep2 Rep1 Rep2 Rep1 Rep2 Rep1 Rep2 Rep1 Rep2 Rep1 Rep2

1 0 100 0 0 9.25 9.00 8.50 8.00 7.75 7.50 42 43 39 636 35 722 0.392 0.387

2 0 50 50 50 8.50 7.25 4.50 4.00 5.50 3.75 35 34 29 066 27 832 0.514 0.512

3 0 0 100 100 9.25 9.00 4.25 4.50 4.00 4.75 31 35 49 148 47 386 0.558 0.554

4 50 100 50 100 8.50 8.50 7.00 7.75 5.25 5.25 10 11 136 170 124 204 0.429 0.432

5 50 50 100 0 8.75 9.00 4.00 4.25 3.50 3.00 10 9 145 758 158 232 0.673 0.642

6 50 0 0 50 7.25 8.00 2.75 2.50 3.50 2.50 3 4 289 800 271 360 0.815 0.783

7 100 100 100 50 9.75 10.0 7.25 7.50 7.50 7.75 8 7 173 518 179 506 0.656 0.648

8 100 50 0 100 9.75 9.75 6.00 6.25 7.50 7.50 6 5 215 620 200 294 0.708 0.718

9 100 0 50 0 8.00 8.25 4.25 5.00 3.50 2.75 3 3 404 112 385 356 0.835 0.824

OA, orthogonal array; % TTPG, % thyme-type pollen grains; NPG, number of pollen grains per 10 g of honey; EC, electrical conductivity in mS cm)1.



Analysis and results

Replications of measurements

The appropriateness of merely duplicating the initialreplicate of the L9 (34) orthogonal array (Table 1) isillustrated by taking a glimpse at the correlationcoefficients computed according to Spearman’s q non-parametric test (Table 4). It is obvious that all responsesexhibit a satisfying statistical significance at least at the

0.01 level for two-tailed comparisons. This provides aconfidence measure that Taguchi analysis may notnecessitate further trial runs and inferences drawn fromTable 3 should be firm.

Sensory analysis

Colour is the first sensory property perceived by theconsumers, which could determine if they will buy theproduct or not. All four-factor dependencies appear asnon-linear in the response graph for colour (Fig. 1a).Nevertheless, the mixture of thyme honey with the othertypes of honey did not influence significantly in astatistical fashion the original colour of thyme honey asit may be seen from pooled anova results (Table 3).Taguchi analysis showed that the addition of heatherand chestnut honey did not influence significantly theexamined responses.The delicate aroma of thyme honey can be easily

influenced by the odours of other honeys. The panellists’evaluation results showed significant differences amongthe tested samples for the orange-type honey constituent(Table 1). This difference is statistically significant to alevel of 0.05. It is noteworthy that the dependence of the

Table 3 Pooled analysis of variance results

of S ⁄N ratios for six quality characteristics

of honey blendsQuality

characteristics ANOVA values

Honey type

Chestnut Orange Heather Cotton

Colour Sum of squares 1.23 1.20 1.97 0.83

Mean sum of squares 0.615 0.60 0.99 0.41

F-value 1.49 1.45 2.38 a

p 0.40 0.41 0.30

Aroma Sum of squares 12.08 59.91 0.09 10.43


F-value a 7.95 a a

p 0.021

Flavour Sum of squares 23.95 54.45 9.07 8.89


F-value 2.67 6.06 a a

p 0.184 0.062

%TTPG Sum of squares 515.18 70.72 15.53 7.32


F-value 70.35 9.66 2.12 a

p 0.014 0.094 0.320

NPG Sum of squares 457.13 54.35 3.89 4.46


F-value 109.57 13.03 a a

p 0.000 0.018

EC Sum of squares 19.61 19.12 0.93 2.53


F-value 11.32 11.04 a a

p 0.023 0.024

Results were obtained before pooling.

%TTPG, % thyme-type pollen grains; NPG, number of pollen grains per 10 g of honey; EC,

electrical conductivity (mS cm)1).aDenominator of F-test is zero due to zero-degrees of freedom.

Table 4 Correlation coefficients for testing statistical significance of

replicates

Correlation

test ⁄ responses Colour Aroma Flavour %TTPG NPG EC

Spearman’s q

test statistic

0.910 0.879 0.893 0.975 1.000 0.983

Significance

(2-tailed)

0.001 0.002 0.001 0.000 0.000 0.000

%TTPG, % thyme-type pollen grains; NPG, number of pollen grains per

10 g of honey; EC, electrical conductivity (mS cm)1).



amount that maximises the S ⁄R ratio is for themaximum content (100%) of this type of honey(Fig. 1b). From Fig. 1a, it is observed that the near

linear dependence of the S ⁄R ratio for the colourresponse is with respect to the percentage contributionof orange-type honey.

(a) (b)

(c) (d)

(e) (f)

Figure 1 Response graphs (a–f): S ⁄N ratios for main effects for all six investigated quality characteristics of honey blends.



A wide variation in the results concerning the tastewas also noticed among the samples even though allsamples contained the same quantity of thyme honeyand in all cases the taste was influenced significantly(Table 1). Statistical analysis showed no differenceamong the types and or quantities of honey that wereadded to the blends for a level of 0.05 (Table 3).

Melissopalynological analysis

According to Greek legislation, a honey can be labelledas thyme honey only when the percentage of thyme typepollen grains is ‡18% of the total nectariferous pollengrains and the total number of the pollen grains per 10 gof honey is <90 000.The melissopalynological analysis showed that several

samples did not meet these microscopical characteristics.Qualitative analysis revealed a great variety with regardsto the presence of thyme pollen in the samples. Only inthree samples the presence of thyme pollen was over18%. In most cases, the thyme pollen ranged from 3%to 10% (Table 1). Similar results were also found fromthe quantitative analysis. Only the three samples thatwere characterised by the high presence of thyme pollenduring qualitative analyses were also containing<90 000 pollen grains per 10 g of honey (Table 1).Pooled anova results showed that the addition of

chestnut and orange type of honey induces statisticallysignificant modulation on the S ⁄R ratios of the percent-age of thyme-type pollen grains which are comparableto levels of the 0.05 and 0.1 respectively (Table 3). S ⁄Rratios are maximised when both factor setting arelocated at level 1 (Table 1) which suggests that thischaracteristic is best behaving when addition of chestnutoriginated honey is avoided (0%) and orange is added tomaximum quantities (100%).Moreover, pooled anova results reveal that again

chestnut and orange type of honey are active effects onthe influence exerted on the number of pollen grains per10 g of honeys (Table 3). The statistical significance thistime is to be taken at levels of 0.001 and 0.05correspondingly. Likewise, the factor settings for botheffects are maximising the S ⁄N ratio for the number ofpollen grains per 10 g of honeys when they are located atlevel 1 which is translated to addition of orange honeyand no addition of chestnut honey (Table 3).

Physicochemical analysis

Electrical conductivity was influenced significantly bytwo of the four constituents. In half of the samples,electrical conductivity was higher than 0.6 mS cm)1 –the maximum value allowed by the Greek legislation forthyme honey.Taguchi analysis showed that the addition of chestnut

and orange honey affected the product response in a

linear fashion (Fig. 1f). The statistical significances ofboth effects were noted at levels of 0.05 respectively(Table 3). It is observed that maximisation of the S ⁄Rratio values is achieved when orange honey is blended atmaximum content (100%) while chestnut honey doesnot participate in the mixture.

Discussion

The results of the sensory, microscopical, physicochem-ical and statistical analyses showed that the botanicalorigin of the honey which is used in a blend with thymehoney influences significantly the final product. Inaddition, its influence is not always analogue to theadded quantity nor does it affect in the same ratio eachquality characteristic. More ‘sensitive’ parameters wereconsidered to be the aroma, taste and pollen graincontent while less sensitive was the colour.Thyme honey is characterised by its light colour and

distinguished floral pungent aroma and flavour. Theelectrical conductivity in Greek thyme honeys has anaverage value of 0.42 mS cm)1 and usually <50 000pollen grains per 10 g of honey (Thrasyvoulou &Manikis, 1995; Tsigouri et al., 2004). Similar resultshave also been reported for Italian and Spanish thymehoneys, although, in the latter case the pollen richnesswas high (around 140 000 pollen grains per 10 g ofhoney) (Persano Oddo et al., 1995; Terrab et al.,2004a,b).On the contrary, chestnut honey is characterised from

a significantly large number of pollen grains per gram ofhoney (over-represented) and high electrical conductiv-ity. Previous studies concerning Greek chestnut honeyhave reported that the value of the electrical conductiv-ity was over 1.110 mS cm)1 and the number of pollengrains per 10 g of honey was over 245 000 in all samples(Thrasyvoulou & Manikis, 1995; Tsigouri et al., 2004).Similar results have also been reported in other Euro-pean countries. By examining several French chestnuthoneys, Devillers et al. (2004) demonstrated that theiraverage electrical conductivity was 1.308 mS cm)1.Respectively, the electrical conductivity in Italian chest-nut honeys ranged between 1.01 and 2.09 mS cm)1 andthe number of pollen grains per 10 g of honey was over100 000 (Persano Oddo et al., 1995; Marini et al., 2004).Guyot et al. (1998) also showed that the number ofpollen grains per 10 g of French and Italian chestnuthoneys was over 125 000, while the pollen density inSpanish chestnut honey samples was even higher (onaverage 268 000 pollen grains per 10 g of honey) (Seijoet al., 1997).Chestnut honey affects particularly the microscopical

and physicochemical characteristics. This study showedthat adding chestnut honey to the blend could lead to anon-legal product considering the thyme honey qualitycharacteristics. Thus, adding even small amounts of



chestnut honey in the blends with thyme honey shouldbe avoided.In contrast, the addition of orange honey either did

not affect or positively influenced the sensory, micro-scopical and physicochemical characteristics of the finalproduct. Citrus honey has light colour, delicate floralodour and flavour, low electrical conductivity and lowpollen density. Electrical conductivity in Greek orangehoneys ranges from 0.15 to 0.32 mS cm)1 and thenumber of pollen grains per 10 g of honey from 20 000to 71 000 (Thrasyvoulou & Manikis, 1995; Tsigouriet al., 2004). Electrical conductivity up to 0.37 mS cm)1

and <20 000 pollen grains per 10 g of honey characte-rise Spanish citrus honeys (Bonvehi & Coll, 1995;Serrano et al., 2004). Persano Oddo et al. (1995) alsodemonstrated a low value of electrical conductivity andpollen richness for Italian citrus honeys. Similar resultshave also been reported for Moroccan orange honeyswhere the average electrical conductivity was0.24 mS cm)1 and the number of pollen grains per10 g of honey was <20 000 in most samples (Terrabet al., 2003a). These characteristics of citrus honey aregenerally similar to these of thyme honey (PersanoOddo et al., 1995; Thrasyvoulou & Manikis, 1995;Terrab et al., 2004b; Tsigouri et al., 2004).Cotton honey, which is characterised by a light

colour, taste and aroma and a low number of pollengrains per gram of honey (Tsigouri et al., 2004) also didnot influence significantly the quality characteristics ofthe final product.Heather honey has a dark colour and strong flavour.

In the case of honey mixtures, even small quantities of ahighly aromatic honey can considerably change theorganoleptic characteristics of a unifloral honey (Pianaet al., 2004). Heather honey influenced more the orga-noleptic characteristics while its presence in the samplesin all levels was neutral considering the microscopical orphysicochemical parameters. The pollen density ofheather honey is similar to that of thyme honeys andthe electrical conductivity is relatively higher than thyme– but usually lower than 0.8 mS cm)1. The mean valueof the electrical conductivity of Greek and Italianheather honey is 0.67 mS cm)1 (Persano Oddo et al.,1995; Thrasyvoulou & Manikis, 1995). Slightly lower isthe average value of the electrical conductivity in Frenchand Portuguese heather honeys, 0.604 mS cm)1 and0.523 mS cm)1 respectively (Andrade et al., 1999; Dev-illers et al., 2004). On the contrary, the electricalconductivity of Moroccan heather honeys was higherand its mean value was 0.78 mS cm)1 (Terrab et al.,2003b). The average number of pollen grains per 10 g ofhoney in Spanish heather honeys was 70 890, while itranged from 50 000 to 150 000 in Italian heather honeysamples (Persano Oddo et al., 1995; Seijo et al., 1997).To conclude, any addition of chestnut honey to a

blend with thyme honey influenced negatively the

quality characteristics that a thyme honey had, unlikeorange honey, which influenced the quality characteris-tics positively. The determination of the optimum blendof thyme and other flora honeys needs special attentionfrom the honey packagers so that the final product willmeet all the quality characteristics.As the characteristics of honeys can vary according to

their botanical and geographical origin (Anklam, 1998;Mateo & Bosch-Reig, 1998; Gomez-Barez et al., 2000;Popek, 2002; Seijo et al., 2003; Terrab et al., 2003b;Marini et al., 2004), the application of an accurate, fastand low cost method is necessary to insure the quality ofthe products prior to labelling and marketing. TheTaguchi method can be used to improve the product’squality, optimise manufacturing processes and help in anew product’s design stage (Taguchi, 1986). It providesa systematic and efficient approach for conductingexperimentation to determine near optimum settings ofdesign parameters for performance and cost. Rescalingthe factors as tabulated in Table 2 is a well-knownmethod of ‘Expanding–Shrinking’ proposed by Taguchi(Taguchi, 1987; Logothetis & Wynn, 1990). It isobserved that under this scaling, orthogonality is notlost because the law of proportional frequencies is stillmaintained. Moreover, for experimentation on foodpreparations that may be susceptible to time degrada-tion executing a minimum schedule of experiments maybe desirable. It is true that instead of executing a trialplan of nine runs according to Taguchi’s L9 orthogonalarray as was presented in this work, an alternative five-constituent scheme may have been employed by theclassic method of Simplex Centroid Design (MixtureDesign) that would otherwise necessitate as many as 31runs; or 36 runs if augmented (Cornell, 2002). Of course,great savings in experimental effort over the lattermethod would be anticipated if the method of SimplexLattice Design (Mixture Design) was employed whichwould further require a range of 15 to 21 runs to beconducted depending on the placement of the latticepoints. Either way, it is clear that the Taguchi method ispreferable in terms of the pronounced economyachieved along with the short experimental work itdemands in contrast to the other two mixture methodswe just mentioned. The main statistical processor inTaguchi’s methodology is anova which is comparable tothe regression practices associated with the two simplexdesign models referred to above. Thus, no essential gainis to be found in the analysis part of the generated datawith either methodologies.As the work presented here was carried out in a

research laboratory in a state institution (AristotelianUniversity of Thessaloniki, Greece) and repeatabilityconcerns were seen to be subdued, no noise-controlvariables were assigned. In the future, it would beinteresting to repeat this study in a production settingwhere several environmental (noise) factors along



variation issues because of personnel, methods andtechniques would be included.In conclusion, considering the market needs demand

for the standardisation of the marketable honey, theresults of this study demonstrated that design experi-ments and statistical analysis such as the Taguchimethod can play an important role in the honeyindustry.

Acknowledgments

The authors are greatly indebted to the Editor in Chief,Associate Editor and the two reviewers for theirconstructive comments. Also, we are thankful to DrN. Logothetis for elucidating on Taguchi’s ‘expanding–shrinking’ technique for mixtures.

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The application of Taguchi method to determine the optimum blend of unifloral honeys to most closely match thyme honey quality