Geographical discrimination of honeys by usingmineral composition and common chemicalquality parametersAna M Gonzalez Paramas,1 J Alfonso Gomez Barez,1 Rafael J Garcia-Villanova,1*Teresa Rivas Pala,1 Ramon Ardanuy Albajar2 and Jose Sanchez Sanchez3

1Departamento de Quımica Analıtica, Nutricion y Bromatologıa, Facultad de Farmacia, Universidad de Salamanca, Campus ‘Miguel deUnamuno’, E-37007 Salamanca, Spain2Departamento de Estadıstica, Facultad de Ciencias, Universidad de Salamanca, Salamanca, Spain3Departamento de Botanica, Facultad de Biologıa, Universidad de Salamanca, Salamanca, Spain

Abstract: Sixty honey samples from six different production zones of the provinces of Salamanca,

Zamora and CaÂceres (western Spain) were analysed for 13 common legal physicochemical parameters

and 17 mineral elements (13 cations and four anions) in order to test for their geographical

classi®cation. Application of linear stepwise discriminant analysis to a number of variables made of a

selection of analytical results and their simple mathematical functions allowed, ®rstly, discrimination

between honeys from all six zones and, secondly, discrimination between honeys from the three zones

of the province of Salamanca. The most discriminant variables selected for the six zones were 10 in

number, a combination of three physicochemical parameters and nine elements, with a result of

91.38% of correctly classi®ed samples; for the three zones of the province of Salamanca, regarded with

special interest in our study, six variables were selected (made of eight elements) with a result of 97.07%

of correctly classi®ed samples.

# 2000 Society of Chemical Industry

Keywords: honey mineral composition; honey legal physicochemical parameters; honey geographical origin;linear discriminant analysis

INTRODUCTIONThe honey market currently shows a tendency to

production and hence characterisation of honeys as

either uni- or multi¯oral, thus providing the consumer

with a wide variety of choice. Furthermore, there is a

tendency to establish geographical limits of production

with the aim of protecting a production zone that has

developed and marketed a particular standard of

quality. Each of these circumstances has speeded up

the establishment of procedures during recent years

for the determination of either the botanical or the

geographical origin.

Regarding the botanical origin, microscopical

examination of the sediment (melissopalynological

analysis) was the ®rst technique to be used and,

currently complemented by sensory analysis, con-

tinues to be a reference tool for this purpose. However,

its known limitations have encouraged the search for

physicochemical parameters or, more directly, speci®c

chemical compounds that, as such or as a result of

transformation, could be used as unequivocal indica-

tors of botanical origin. Although no single or small

number of parameters has yet been reported to be

exclusive to each kind of uni¯oral honey, there are a

few compounds or parameters proposed as de®nitive

markers: methyl anthranilate1 or the ¯avonoid

hesperetin2 for citrus honeys and the norisoprenoid

S-dehydrovomifoliol3 for heather honeys. In the

absence of a single clear parameter, many authors

have tested statistical methods using a correlation

matrix made up of a number of parameters determined

over a consistent number of samples. Krauze and

Zalewski4 used principal component analysis for 18

physical and chemical parameters over 88 honey

samples divided into four groups (acacia, rape,

linden�multi¯oral and honeydew), obtaining the

strongest associations for the values of electrical con-

ductivity, ash, free acids and proline. Mateo et al5

employed colour, as measured by two methods, for the

characterisation of Spanish uni¯oral honeys rosemary,

orange blossom, lavender, sun¯ower, eucalyptus,

heather and honeydew); by using discriminant analy-

sis, they achieved levels of correctly classi®ed samples

of 70.1% and 76.0% for the two methods. The same

Journal of the Science of Food and Agriculture J Sci Food Agric 80:157±165 (2000)

* Correspondence to: Rafael J Garcia-Villanova, Departamento de Quımica Analıtica, Nutricion y Bromatologıa, Facultad de Farmacia,Universidad de Salamanca, Campus ‘Miguel de Unamuno’, E-37007 Salamanca, SpainContract/grant sponsor: Spanish Government (CICYT); contract/grant number: ALI95-0715Contract/grant sponsor: Junta de Castilla y Leon (Regional Government); contract/grant number: 54/08/92(Received 4 May 1999; revised version received 29 July 1999; accepted 26 August 1999)

# 2000 Society of Chemical Industry. J Sci Food Agric 0022±5142/2000/$17.50 157

honeys were later tested6 by discriminant analysis with

15 parameters, of which the following were selected by

the multivariate program as the most discriminantÐ

electrical conductivity, colour, water content, fructose

and sucroseÐwith an average level of correctly

classi®ed samples of 95.7%.

Regarding the determination of the geographical

origin of honeys, few references can be found in the

scienti®c literature. Two articles dealing with ¯avo-

noids in Portuguese heather honey7 and Tunisian

honeys8 reported different patterns of ¯avonoids with

respect to Spanish honeys, but without any further

systematic study. In contrast, three articles applied

multivariate analysis to a number of common chemical

parameters (legal quality indicators), sometimes in-

cluding a few sugars, with the aim of differentiating

their production areas in Spain: Sancho et al9 reported

differentiation of honeys from the three provinces of

the Basque Country with the help of a discriminant

mathematical function which selected ®ve variables

(total acidity, formol index, sucrose, fructose/glucose

and glucose±water/fructose ratios), with 77.4% of

correctly classi®ed samples; PenÄa and Herrero10

reported discrimination of honeys from two provinces

of Galicia (90% correct classi®cation) with the help of

only two common parameters (free acidity and

moisture content); ®nally, Sanz et al11 reported dis-

crimination of honeys from mountain and valley areas

of La Rioja (83% correct classi®cation) with four

parameters (ash content, conductivity, lactonic acidity

and fructose/glucose ratio). In most cases the authors

interpreted these results in terms of different ¯oral

origin.

Mineral composition has also been employed for the

purpose of geographical origin discrimination. The

mineral content of honey re¯ects the presence of

speci®c minerals within the forage area of the

hive.12±14 Varju15 determined the P, Ca, Mg, B, Cu,

Mn, Zn, Sn, Pb and total mineral contents of

Hungarian acacia honeys and reported a relationship

with those of plants and soils. An excess or insuf®-

ciency of certain chemical elements in soils, rocks or

water, as well as other phytosociological, environ-

mental or seasonal factors, is re¯ected in the mineral

composition of the plants and hence in the nectar,

pollen or honeydew.16,17 Taking this as a basis, Morse

and Lisk18 studied the elemental composition and

found signi®cant differences between honeys from the

United States, Mexico, El Salvador and China. Feller-

Demalsy et al19 undertook an ambitious trial to

differentiate honeys from the 10 Canadian provinces

by application of principal component analysis to the

data of 20mineral elements, achieving 92% correct

classi®cation; they found that these differences were

independent of the ¯oral origin of the honeys and

seemed to be very complexÐprobably they were

related to maritime in¯uence coupled with extent of

precipitation rather than being dependent upon soil

composition. Finally, Sauri and HernaÂndez20 applied

discriminant analysis to differentiate honeys of two

regions of Mexico by means of seven elements, with

100% correct classi®cation.

The contribution of contamination from the proces-

sing equipment and containers should also be con-

sidered. Acidic foods such as honey which had been in

contact with stainless steel surfaces during harvesting,

processing and/or preparation for market showed

higher values for Cr than those of comb honey, while

values for Ni did not re¯ect signi®cant variations.21

Honey contamination with Zn was also suggested to

occur via galvanised steel equipment.22

The present work is part of a wider study on the

quality of honeys from three zones of the province of

Salamanca (Fig 1) and of assays for their typi®cation

for a denomination of quality, as established in the

legislation of the European Union.23 These three

zones have an important production of honey from

wild vegetation almost without crops and of great

Figure 1. Geographical zones of honeyproduction studied.

158 J Sci Food Agric 80:157±165 (2000)

AM GonzaÂlez ParamaÂs et al

biodiversity. Close to them are found three other

zones, also honey producers, on the borders of

Salamanca and with which bulk commercial ex-

changes are frequent. The objectives of this study

were: ®rstly, to determine whether it would be possible

to differentiate between honeys of all six zones to

establish geographical limits to the production of each;

secondly, to determine whether it would be possible to

differentiate between the three zones of the province of

Salamanca, geographically and botanically different,

and thus establish within it three distinct types/

qualities. This has been attempted by applying dis-

criminant analysis to the values obtained for physico-

chemical parameters established in the Spanish

Norma de Calidad24 and for mineral content.

EXPERIMENTALHoney samplesSixty samples of fresh bulk honey were collected

between 1992 and 1994, from beekeepers or research

hives of our group, in six geographical zones of the

provinces of Salamanca, Zamora and Caceres (Fig 1).

All the data of interest (location, date, extraction

procedure, heat treatment if any, temperature and

storage location, as well as botanical and climatologi-

cal data of the location of the hives) were recorded.

Given that the intention was to study in greater depth

the honeys of the province of Salamanca, 12 samples

were taken from each of its zones and only eight

samples from those of the rest. Melissopalynological

analysis revealed the botanical origin, and the results

are summarised in Table 1. The number of samples of

honeys from nectar, honeydew and `forest' (naturally

mixed nectar and honeydew honey) re¯ects approxi-

mately the proportion of each type of them produced

in each zone.

Chemical analysisThe 13 physicochemical parameters presented in the

Spanish Norma de Calidad for honey destined for the

home market24 were determined using the methods of

analysis25 summarised in Table 2. Seventeen mineral

elements were determined (13 cations and four

anions) by heating 15g of honey at 550°C, dissolving

the resulting ash in 10ml of a mixture of equal parts of

HCl (2M) and HNO3 (2M) and ®nally making the

solution up to 100ml with doubly distilled water. The

methods of analysis of the mineral elements are

summarised in Table 3.

The precision of each method was determined in the

range of average concentration to decide upon its

acceptability; for the metallic elements the detection

limit was also determined according to the criteria of

Kaiser26 (Tables 2 and 3).

Multivariate analysisLinear discriminant analysis, which utilises functions

of linear classi®cation with the statistical variables

employed (functions of classi®cation of Fisher27), was

applied. A function of classi®cation exists for each of

the groups, and each sample was assigned to the group

for whose function of classi®cation it had maximum

value. The coef®cients of the functions of classi®cation

were estimated from samples previously assigned to

groups by us, and the selection of the statistical

variables most adequate for classi®cation was carried

out by a sequential process. The statistical criteria used

to introduce and eliminate variables were based on the

entry and output probabilities (PIN and POUT); only

Table 1. Zones of honey production studied and botanical origin

Number of samples

Zone Total Nectara Honeydew Forest

1. Arribes del Duero 12 11 (1) ± 1

2. Sierra de Francia 12 1 2 9

3. Sierra de Gata 12 7 (2) 3 2

4. Sanabria 8 5 (5) ± 3

5. La Vera 8 5 (2) 1 2

6. Plasencia 8 7 1 ±

a Number of mono¯orals in parentheses.

Table 2. Common physicochemical parameters determined, methods and repeatability

Parameter Method

Coef®cient of variation

(%)

Proline Reaction with ninhydrin and colorimetry 3.0

Reducing sugars Redox volumetry of Fehling reagent with methylene blue end-point detection 0.29

Apparent sucrose As above, acid hydrolysis and subtraction of reducing sugars value 4.6

pH Glass electrode potentiometry 0.41

Free acidity Acid±base titration to pH=8.5 1.38

Lactonic acidity Acid±base titration to pH=8.3 3.40

Total acidity Summation of above two 1.15

Ash Heating at 550°C 4.5

Moisture Refractometry at 20°C 0.55

Hydroxymethylfurfural (HMF) Spectrophotometry at 284 and 336nm 5.40

Diastase number Gothe index 2.5

Electrical conductivity Conductimetry of 200g lÿ1 (DM) honey solution at 20°C 0.80

Insoluble matter Gravimetry after 20±40mm sieve ®ltration 8.5

J Sci Food Agric 80:157±165 (2000) 159

Geographical discrimination of honeys

variables with P<0.05 (high discrimatory capacity)

were introduced in the equations. For this the program

SPSS Release 4.0, version for Macintosh, was used.

With regard to the size of the sample, the following

criterion was applied to ®x in the matrix of correlation

the number of variables (v) employed, as a function of

that of the samples (n) and the groups (g) into which

these are classi®ed:

v � �n=2� ÿ 2g ÿ 1

In turn, since in each group the employment of the

averages and variances of all the variables can be of

interest, the following condition should be ful®lled:

ngv � 4�v� v� � 8v

where ng is the number of samples per group, ie

ng � 8

RESULTS AND DISCUSSIONThe repeatability of each analytical method was ®rst

established in order to test the suitability of every

datum for this study; Tables 2 and 3 show that in

general the values of repeatability (% coef®cient of

variation) are acceptable, although some parameters

were ®nally rejected.

In Tables 4 and 5 the average values and ranges of

the common physicochemical parameters and mineral

elements respectively for each of the six geographical

zones studied are presented. Regarding mineral

composition, important differences can be observed,

especially within groups (ie within each of the six

zones) for some major elements such as Na and K,

which exceeded 100% of the coef®cient of variation

(CV) in the La Vera and Sierra de Francia zones

respectively. Among the minor elements, Al, Cd, Co,

Cr, Fe, Mn and Ni also exceeded 100% of the CV in at

least one of the six zones for each element. The

remainder of the elements presented less variability

within groups; in particular, the anions rarely exceeded

50% of the CV.

Discriminant analysis could now use the data of

these 13 physicochemical parameters and 17 mineral

elements determined to attempt to differentiate the six

or three geographical zones which were the object of

study (60 or 36 samples respectively), which would

entail a total of 30 variables. To these could be added

mathematical functions of the variables in case they

might yield more information and thus increase the

discriminant power of the function of classi®cation. It

was decided to choose the simplest function: a

quotient of two physicochemical parameters (lactonic

acidity/free acidity quotient, previously used by

Crane28) and the quotients of the mineral elements

that were quanti®able in all the samples. The following

considerations were made to establish the ®nal

number of variables.

(a) The parameters of moisture content, hydroxy-

methylfurfural, diastatic activity and insoluble

solids should not be considered representative of

the nature of the honey, but are rather indicators

of its adequate manipulation and freshness.

Furthermore, the last three have very high values

of repeatability (Table 2).

(b) Cd was found to have values which were

extremely high in three samples (see Table 5),

giving a clear indication of contamination,

probably from the processing equipment.

(c) By convention, the 0.5 detection limit was

adopted for those samples in which Co was not

quanti®able.

(d) Only the ratios of the quanti®able heavy metals

in all the samples were used, rather than those of

all the elements, because the former presented

the greatest variability among all the samples, as

Table 3. Analytical methods for mineral elements

Parameter Method Coef®cient of variation (%) Detection limit (mgkgÿ1)

Sodium Flame emission photometry 4.13 33

Potassium Flame emission photometry 8.73 6.40

Zinc Atomic absorption spectroscopy (¯ame) 6.31 0.04

Aluminium Atomic absorption spectroscopy (electrothermal) 2.35 6.70

Iron Atomic absorption spectroscopy (electrothermal) 3.15 10.0

Manganese Atomic absorption spectroscopy (electrothermal) 1.95 3.33

Copper Atomic absorption spectroscopy (electrothermal) 1.40 0.87

Chromium Atomic absorption spectroscopy (electrothermal) 7.65 3.73

Nickel Atomic absorption spectroscopy (electrothermal) 7.35 10.0

Cobalt Atomic absorption spectroscopy (electrothermal) 5.60 6.33

Cadmium Atomic absorption spectroscopy (electrothermal) 10.2 0.33

Calcium Complexometric volumetry 3.55 Ð

Magnesium Complexometric volumetry 8.34 Ð

Chloride Mercurimetric volumetry 0.58 Ð

Phosphorus Vanadomolybdate colorimetry of phosphate 0.60 Ð

Silicon Molybdene blue colorimetry of silicate 3.63 Ð

Sulphur Turbidimetry of barium sulphate 3.50 Ð

a mgkgÿ1.

160 J Sci Food Agric 80:157±165 (2000)

AM GonzaÂlez ParamaÂs et al

could be expected, thereby yielding greater

discriminant information. To some extent this

would be attributable to the different composi-

tions of the soils of the various zones and their

vegetation, without ignoring the contribution of

contamination from the processing equipment

or containers, a matter, though more disputable,

which could also constitute a differentiating

element.

The resulting list of variables used for characterisa-

tion is given in Table 6.

With regard to the number of samples, it was

decided to reject two from Sierra de Francia because

they had abnormally high values of heavy metals,

probably also due to contamination from the equip-

ment or containers, the number of samples thus being

reduced to 58.

Therefore the result is a total of 9�1�16�21=47

variables, which multiplied by 58 cases or samples

gives a matrix of correlation of 2726 data. In

accordance with the criteria selected in the multi-

variate analysis, it can be seen that the maximum

number of variables (v), as a function of the number of

groups (g) into which the samples (n) were to be

classi®ed, was as follows.

. In the case of classi®cation into the six geogra-

phical zones:

v � �58=2� ÿ �2� 6� ÿ 1 � 16 variables

Table 4. Results of common physicochemical parameters

Parameter Arribes del Duero Sierra de Francia Sierra de Gata La Vera Plasencia Sanabria

Proline (mgkgÿ1)

Mean 533 1029 833 659 836 863

Range 850±280 1600±690 1030±610 870±440 1080±450 1420±520

Reducing sugars (mgkgÿ1)

Mean 717.1 683.7 736.0 722.0 716.5 712.9

Range 755±655 714±653 779±650 759±677 763±667 758±637

Apparent sucrose (mgkgÿ1)

Mean 35.8 19.3 24.3 30.1 20.8 26.5

Range 91±10 35±3 50±10 72±15 42±7 43±11

Free acidity (meqkgÿ1)

Mean 24.53 44.94 36.19 33.36 30.93 34.35

Range 36.2±13.6 62.5±30.6 50.9±23.7 45.0±22.8 43.1±21.4 47.2±22.5

Lactonic acidity (meqkgÿ1)

Mean 6.33 5.76 5.66 5.96 6.49 3.66

Range 14.4±1.1 9.3±2.5 8.7±1.2 13.4±2.2 13.6±3.4 7.5±1.1

Lactonic acidity/free acidity

Mean 0.3053 0.1338 0.1673 0.1916 0.2368 0.1180

Range 0.662±0.030 0.261±0.054 0.317±0.040 0.515±0.060 0.636±0.111 0.333±0.027

pH

Mean 3.768 4.483 3.993 3.955 4.028 4.485

Range 4.27±3.38 4.98±3.97 5.03±3.20 4.47±3.41 4.57±3.58 4.76±4.31

Total acidity (meqkgÿ1)

Mean 30.88 50.73 41.85 39.33 37.41 38.01

Range 43.6±21.8 69.6±34.7 55.3±30.3 52.4±26.0 47.9±29.0 50.8±27.8

Moisture (gkgÿ1)

Mean 150.4 154.7 160.7 155.3 157.5 158.6

Range 176±138 175±135 178±145 166±138 175±140 173±149

HMF (mgkgÿ1)

Mean 14.03 3.43 9.53 21.49 14.26 2.08

Range 55.5±0.3 11.1±<0.3 29.3±0.4 68.1±0.7 30.1±<0.3 5.3±<0.3

Diastase number

Mean 21.90 55.17 32.37 31.40 31.88 35.48

Range 38.0±8.0 81.4±38.2 46.5±15.1 46.7±14.3 46.9±12.3 61.9±5.2

Electrical conductivity (mScmÿ1)

Mean 0.43 1.10 0.55 0.51 0.54 0.94

Range 1.1±0.2 1.4±0.8 1.1±0.3 0.9±0.2 1.1±0.2 1.3±0.6

Insoluble matter (gkgÿ1)

Mean 0.294 0.288 0.260 0.201 0.168 0.770

Range 0.59±0.043 0.57±0.12 1.0±0.055 0.45±0.094 0.56±0.023 4.5±0.044

Ash (gkgÿ1)

Mean 2.03 5.76 3.48 2.60 2.32 4.88

Range 5.9±0.45 7.2±4.3 8.2±1.7 5.7±0.88 5.7±0.45 6.2±3.4

J Sci Food Agric 80:157±165 (2000) 161

Geographical discrimination of honeys

. In the case of classi®cation into the three zones of

the province of Salamanca:

v � �34=2� ÿ �2� 3� ÿ 1 � 10 variables

It can be seen that for both cases the number of

variables permitted was much smaller than the 47 in

the previous list. It was decided to reduce this number

as follows: ®rstly, by applying discriminant analysis

separately to the physicochemical parameters (10

variables) and then to the mineral elements and their

ratios (37 variables); secondly, by performing pre-

liminary exploratory analysis in case there was still an

excessive number of variables. Later, once a pre-

selection of the variables with greatest discriminant

power had been made, the maximum number permis-

Table 5. Results of mineral elements

Parameter Arribes del Duero Sierra de Francia Sierra de Gata La Vera Plasencia Sanabria

Na (mgkgÿ1)

Mean 55.8 118.9 84.9 121.1 74.1 70.1

Range 125±27 287±37 178±36 476±38 250±22 100±48

K (mgkgÿ1)

Mean 183.3 1483.8 440.5 736.8 453.3 779.0

Range 402±47 6785±514 677±180 1221±364 1172±90 1459±77

Ca (mgkgÿ1)

Mean 98.3 202.3 129.0 87.7 94.0 107.4

Range 172±60 420±107 236±66 125±59 212±51 165±62

Mg (mgkgÿ1)

Mean 23.9 78.1 56.7 54.1 54.7 63.8

Range 75±7 167±20 148±27 146±9 153±15 173±19

Al (mgkgÿ1)

Mean 3.04 5.30 5.31 6.04 3.11 8.31

Range 13±0.94 12±1.2 11±1.5 20±0.66 5.3±1.8 14±2.5

Fe (mgkgÿ1)

Mean 0.84 7.59 1.69 1.85 3.37 2.96

Range 1.5±0.60 60±0.71 2.8±1.0 2.7±1.2 5.3±0.92 4.2±0.80

Zn (mgkgÿ1)

Mean 1.25 1.99 1.66 2.57 2.76 1.19

Range 2.0±0.49 2.9±1.1 3.1±0.62 5.9±0.93 5.9±1.2 1.8±0.76

Mn (mgkgÿ1)

Mean 0.98 10.9 3.80 1.35 1.69 4.60

Range 2.6±0.42 45±3.4 9.3±0.82 3.2±0.36 3.3±0.36 13±1.9

Cu (mgkgÿ1)

Mean 361.3 1361.2 696.8 485.3 669.5 1325.3

Range 723±226 2300±547 1603±240 1066±217 1922±209 1988±961

Cr (mgkgÿ1)

Mean 56.3 843.3 55.1 94.5 50.1 54.0

Range 153±16 4480±49 94±19 225±17 142±14 78±35

Ni (mgkgÿ1)

Mean 121.0 747.3 186.6 49.9 130.5 146.5

Range 212±28 3373±137 900±33 76±29 288±10 296±33

Co (mgkgÿ1)

Mean 10.5 136.2 33.8 <DLa 4.1 8.3

Range 31±DL 720±15 219±DL DL±DL 11±DL 36±DL

Cd (mgkgÿ1)

Mean 3.6 5.6 24.1 54.2 2.7 50.2

Range 15±DL 15±DL 247±DL 355±DL 18±DL 274±0.57

Cl (mgkgÿ1)

Mean 181.3 307.3 338.0 244.4 317.6 427.2

Range 393±77 460±78 467±200 404±112 455±121 579±290

P (mgkgÿ1)

Mean 67.7 205.0 103.6 111.3 102.0 121.5

Range 134±36 350±78 169±62 189±57 213±47 207±59

S (mgkgÿ1)

Mean 27.8 53.8 52.0 39.6 38.6 30.0

Range 31±24 130±27 71±25 46±19 89±27 37±25

Si (mgkgÿ1)

Mean 21.7 18.7 18.9 18.0 11.5 38.1

Range 46.7±5.3 23.4±10.4 30.7±9.1 38.1±3.4 21.5±5.0 46.0±15.1

a DL, detection limit.

162 J Sci Food Agric 80:157±165 (2000)

AM GonzaÂlez ParamaÂs et al

sible would be introduced in each case (16 or 10

variables according to the case) and the de®nitive assay

would be carried out.

Discrimination between the six geographical zones(P=0.05)A ®rst assay, performed only with the variables of

physicochemical parameters, yielded the selection of

ash, proline and conductivity, achieving only 51.67%

of samples well classi®ed.

With the variables of mineral elements and their

ratios, ie 16�21=37 variables (v>16), the program

®rst carried out preliminary exploratory analysis and

pre-selected nine variables, which were later intro-

duced for the de®nitive assay. It selected then in the

order Cl, Mn/Al, Ni/Mn, Si, Fe, S, K, Cu and Ca, with

86.21% of samples well classi®ed.

An attempt to improve this percentage was made by

including the variables of physicochemical parameters,

ie 10�37=47 variables (v>16). The preliminary

exploratory analysis selected 10 variables (v<16),

which were those ®nally introduced, and the program

selected then in the following order.

1. Ash 36.21%

2. Proline 44.83%

3. Fe/Ni 56.90%

4. Cl 58.62%

5. Conductivity 68.97%

6. Cu 75.86%

7. Si 84.48%

8. S 89.66%

9. Ni/Mn 89.66%

10. P 91.38%

11. Mg 91.38%

The number of samples well classi®ed now rose

somewhat (91.38%), with equal discriminant power of

the variables S and Ni/Mn on the one hand and P and

Mg on the other. In Fig 2 the samples are classi®ed in

each zone with its centroid as a function of the values

of the ®rst two canonical functions. The numbers of

correctly/incorrectly classi®ed samples for each group

were: Arribes del Duero, 11/1; Sierra de Francia, 10/0;

Sierra de Gata, 12/0; La Vera, 8/0; Plasencia, 6/2; and

Sanabria, 6/2.

Discrimination between the three zones of theprovince of Salamanca (P=0.05)The assay carried out with only the 10 variables of

Table 6. List of variables employed for multivariateanalysis

Physico chemical parameters and ratios (10)

Mineral elements and

ratios (37)

Proline Lactonic acidity/free acidity Sodium Fe/Ni

Reducing sugars Potassium Fe/Zn

Apparent sucrose Zinc Fe/Cr

pH Aluminium Fe/Mn

Free acidity Iron Fe/Cu

Lactonic acidity Manganese Fe/Al

Total acidity Copper Ni/Zn

Ash Chromium Ni/Cr

Electrical conductivity Nickel Ni/Mn

Cobalt Ni/Cu

Calcium Ni/Al

Magnesium Zn/Cr

Chloride Zn/Mn

Phosphorus Zn/Cu

Silicon Zn/Al

Sulphur Cr/Mn

Cr/Cu

Cr/Al

Mn/Cu

Mn/Al

Cu/Al

Figure 2. Plot of canonical function 1 versus canonical function 2 with thesix centroids (* ) for the 58 samples.

J Sci Food Agric 80:157±165 (2000) 163

Geographical discrimination of honeys

physicochemical parameters (v�10) achieved a high

number of samples well classi®ed (91.67%), selecting

four: total acidity, reducing sugars, conductivity and

ash.

With the variables of mineral elements and their

ratios, ie 16�21=37 variables (v>10), the program

®rst carried out a preliminary exploratory analysis and

pre-selected six variables, which were later introduced

for the de®nitive assay. It selected then in the order

Mn, Fe/Ni, Cr/Cu, Na, K and S, with 97.07% of

samples well classi®ed.

The conjunction of both types of variables did not

improve the percentage of classi®cation, though it

simpli®ed it, resulting in the following order.

1. Ash 61.78%

2. Total acidity 82.35%

3. Na 76.47%

4. Conductivity 91.18%

5. Cl 97.06%

In Fig 3 the samples are classi®ed in each zone with its

centroid as a function of the values of the ®rst two

canonical functions, and are separated into zones. The

numbers of correctly/incorrectly classi®ed samples for

each group were: Arribes del Duero, 12/0; Sierra de

Francia, 10/0; and Sierra de Gata, 11/1Ðie one

misclassi®ed sample out of 34.

CONCLUSIONSThe results of the study of the six honey-producing

zones demonstrate the poor discriminant power of the

physicochemical parameters (51.67% of samples well

classi®ed), while the variables elaborated with the

mineral elements considerably raise this value

(86.21%), and the conjunction of both classes of

variables raises it to 91.38%. Therefore the mineral

composition, presumably very dependent on the

soil15±17 and vegetation12±14Ðwithout neglecting con-

tamination from the processing equipment and con-

tainersÐhas a high power of differentiation of the

geographical origin in this case. The possible contam-

ination by heavy metals during harvesting, processing

and shipment was not studied, although the two

samples rejected because of very high values of Cd are

suspected of being subject to it. Should this occur, it

could be a contribution to the differentiation pattern of

the groups.

In the study of the three zones of the province of

Salamanca the discriminant power of the physico-

chemical parameters was much greater (91.67% of

samples correctly classi®ed), to which the smaller

number of groups of classi®cation undoubtedly con-

tributed. The variables made of mineral elements

achieve a greater value (97.07%, ie all the samples

except one), which also reveals a probable in¯uence of

the composition of the soil and its vegetation together

with possible contamination of the materials post-

harvest. The inclusion of all the parameters did not

improve the classi®cation (97.06%), but changed

notably the previously selected variables. This can be

explained in part by the distinct botanical origin of the

honeys in the three zones (Table 1): in Arribes del

Duero, those of nectar predominate, while in Sierra de

Gata and even more in Sierra de Francia, those of

forest or honeydew predominate. The pattern of

classi®cation is now rather that of the physicochemical

characteristics which, generally and a priori, are

attributed to one or the other: values of ash, con-

ductivity and macro-mineral content, greater in the

honeydew honeys than in those of nectar, which is

re¯ected in an unequivocal way by statistical analysis

as well.

ACKNOWLEDGEMENTSWe would like to thank Mr GH Jenkins for his

assistance with the English version of the MS. This

work was partly supported by the Spanish Govern-

ment (CICYT, ref ALI95-0715) and the Junta de

Castilla y LeoÂn (Regional Government, ref 54/08/92).

REFERENCES1 Serra J and Ventura F, Characterization of citrus honey (Citrus

spp.) produced in Spain. J Agric Food Chem 43:2053±2057

(1995).

2 Ferreres F, Giner JM and TomaÂs-BarberaÂn FA, A comparative

study of hesperetin and methyl anthranilate as markers of the

¯oral origin of citrus honey. J Sci Food Agric 65:371±372

(1994).

3 Tan ST, Wilkins AL, Holland PT and McGhie TK, Extractives

from New Zealand uni¯oral honeys. 2. Degraded carotenoids

and other substances from heather honey. J Agric Food Chem

37:1217±1222 (1989).

4 Krauze A and Zalewski RI, Classi®cation of honeys by principal

component analysis on the basis of chemical and physical

parameters. Z Lebensm Untersuch Forcsh 192:19±23 (1991).

5 Mateo R, Jimenez Escamilla M and Bosch-Reig F, Evaluation of

Figure 3. Plot of canonical function 1 versus canonical function 2 with thethree centroids (* ) for the 34 samples of the province of Salamanca.

164 J Sci Food Agric 80:157±165 (2000)

AM GonzaÂlez ParamaÂs et al

the colour of some Spanish uni¯oral honey types as character-

ization parameter. J AOAC Int 75:537±542 (1992).

6 Mateo R and Bosch-Reig F, Classi®cation of Spanish uni¯oral

honeys by discriminant analysis of electrical conductivity,

color, water content, sugars, and pH. J Agric Food Chem

46:393±400 (1998).

7 Ferreres F, Andrade P and TomaÂs-BarberaÂn FA, Flavonoids

from Portuguese heather honey. Z Lebensm Untersuch Forsch

199:32±37 (1994).

8 Martos I, Cossentini M, Ferreres F and TomaÂs-BarberaÂn FA,

Flavonoid composition of Tunisian honeys and propolis. J

Agric Food Chem 45:2824±2829 (1997).

9 Sancho MT, Muniategui S, Huidobro JF and Simal J, Provincial

classi®cation of Basque Country (northern Spain) honeys by

their chemical composition. J Apic Res 30:168±172 (1991).

10 PenÄa R and Herrero C, Pattern recognition analysis applied to

classi®cation of honeys from two geographic origins. J Agric

Food Chem 41:560±564 (1993).

11 Sanz S, PeÂrez C, Herrera A, Sanz M and Juan T, Application of a

statistical approach to the classi®cation of honey by geographic

origin. J Sci Food Agric 69:135±140 (1995).

12 Pratt CR and Sikorski RS, Lead content of wild¯owers and

honey bees (Apis mellifera) along a roadway: possible contam-

ination of a simple food chain. Proc Pa Acad Sci 45:151±152

(1982).

13 Free JB, Williams IH, Pinsent RJFH, Townshend A, Basi MS

and Graham CL, Using foraging honey bees to sample an area

for trace metals. Environ Entomol 9:9±12 (1983).

14 Crane E, Bees, honey and pollen as indicators of metals in the

environment. Bee Wld 65:47±49 (1984).

15 Varju M, Mineral content of Hungarian acacia honeys and its

relationship with the plant and soil. Z Lebensm Untersuch Forcsh

144:308±312 (1970).

16 Ghizelan G and Ghizelan R, DonneÂes concernent les eÂleÂments

mineraux dans quelques sortes de miel. Proc 23rd Int Congr on

Apiculture, Moscow (1971).

17 Petrovic ZT, Mandic ML, Grgic J, Grgic Z and Besic J, Selenium

levels in some species of honey in Eastern Croatia. Dt Lebensm-

Rundsch 89:46±49 (1993).

18 Morse RA and Lisk DJ, Elemental analysis of honeys from

several nations. Am Bee J 120:522±523 (1980).

19 Feller-Demalsy MJ, Vincent B and Beaulieu F, Teneur en

mineÂraux et origine geÂographique des miels du Canada.

Apidology 20:77±91 (1989).

20 Sauri E and HernaÂndez JC, Characterization of honeys gathered

from Tahonal (Viguiera dentata, var. heliantoides) and

Tzi'tzil'che (Gimnopodium antigonoides, Blake) ¯owers by their

mineral content utilizing discriminant analysis. Rev Esp Cienc

Tecnol Alim 34:450±457 (1994).

21 Stoewsand G, Stamer JR, Kosikowski FV, Morse RA, Bache CA

and Lisk DJ, Chromium and nickel in acidic foods and by-

products contacting stainless steel during processing. Bull

Environ Contam Toxicol 21:600±603 (1979).

22 Darmati D, Boskovic L and Darmati S, Trace elements in honey

from Sumadija region. Hrana-i-Ishrana 26:129±131 (1985).

23 Council Regulation (EEC) No 2081/92 of 14 July 1992 on the

protection of geographical indications and designations of

origin for agricultural products and foodstuffs. Off Eur

Commun L208 (24 July):1±8 (1992).

24 Presidencia del Gobierno, Norma de Calidad para la miel

destinada al mercado interior. BOE de 13 de Agosto de 1983,

Madrid (1983).

25 Presidencia del Gobierno, MeÂtodos o®ciales de anaÂlisis para la

miel. BOE de 18 de Junio de 1986, Madrid (1986).

26 Kaiser H, De®nition of detection limit and guaranteed (impurity)

and their application: question and results of discussion. Z

Analyt Chem 216:80±94 (1966).

27 Fisher RA, The use of multiple measurements in taxonomic

problems. Ann Eugen 7:179±188 (1936).

28 Crane E, Bees and Beekeeping: Science, Practice and World

Resources, Heinemann Newnes, Oxford (1990).

J Sci Food Agric 80:157±165 (2000) 165

Geographical discrimination of honeys