Optimization of a dynamic headspace technique for quantifying virgin olive oil volatiles. Relationships between sensory attributes and volatile peaks

Food Qualily and Preference5 (1994) 109-l 14 Q 1994 Elsetier Science Limited Printed in Great Britain. All rights reserved 095Ki293/94/$7.00

ELSEVIER

OPTIMIZATIONOF A DYNAMIC HEADSPACETECHNIQUE FOR QUANTIFYINGVIRGIN OLIVE OIL VOLATILES. RELATIONSHIPSBETWEENSENSORYATTRIBUTESAND VOLATILEPEAKS Ramon Aparicio & Maria Teresa Morales Instituto de la Grass y sus Derivados, Avda Padre Garcia Tejero, 4,410lZ Sevilla, Spain (Paper presented at ‘UnderstandingFtavour Quality: Relating Sensoryto Chemicaland PhysicalData ‘, 20-23 September1992, Bristol, UK) (Morales et al., 1992) though there is a trend towards the use of DHS rather than DI or SHS. The latter techniques have been progressively abandoned because they do not perform any concentration of the sample and so lack sensitivity, and are inadequate for trace analysis. Morales et al. (1992) review methods used to quantify virgin olive oil volatiles, mostly with SHS and DI, and summarize the studies carried out on the identification of volatiles, characterization of olive oil varieties and sensory attributes. This paper has two main objectives focused on virgin olive oil: (i) to optimize a DHS, and (ii) to design a procedure selecting the best volatile peaks characterizing the sensory attributes described by the International Olive Oil Council (COI, 1987). Figure 1 shows the process followed to attain these objectives.

ABSTRACT A dynamic headspace technique, using Tenax TA with therma1 desorption and cold trap injection, was used with Spanish virgin olive oil samples. The optimization of the procedure was carried out by a modzfied Simplex technique whilst the response surface was built by a regression algo rithm. A fuzzy overall &sirability estimatedfi-om three desirabilityfunctions was used as the response to be maximized. Finally, different statistical procedures were applied to explain 12 sensory attributes of virgin olive oil by volatile peak pro$les, before their identa$cation by mass spectrometry. Keywords: dynamic headspace; olive oil; volatile; optimiring; modelling; statistics.

OPTIMIZING AND THE DHS METHOD

ABBREVIATIONS APSR DHS DI FID GC Iv PCR SHS SMLR

All possible subsets regression Dynamic headspace Direct injection Flame ionization detector Gas chromatograph Independent variable Principal components regression Static headspace Step-wise multiple linear regression

Equipment

MODELLING

and reagents

Pre-concentration is required for DHS in order to trap the analytes in a medium that allows an ideal injection into the GC. Ntinez et al. (1984) have described many preconcentration methods that eliminate the disadvantages of direct injection. In the work described here the preconcentration has been carried out using the instrument designed by Cert & Bahima (1984)) in which a stream of gas (nitrogen) sweeps the surface of virgin olive oil and any volatiles are stripped in an open trap. The adsorption traps were filled with Tenax TA (Chrompack) which had been adequately conditioned prior to use. A Hewlett-Packard 5890 series II GC was used with a FID and a Hewlett-Packard 3396A integrator. Helium was used as the carrier and make-up gas. A fused silica Supelcowax 10 column (60 m, 0.32 mm i.d., 0.5 km FT) was employed. A Chrompack thermal desorption cold

INTRODUCTION Sensory analysis is the most widespread method used to measure food quality and it is well known that the volatile components of foods are often responsible for many flavour attributes evaluated by sensory trials. Several methods have been used to determine volatiles 109

110

R Aparicio & M. T. Morales

Dptimizing DWS method Inputs: Tesp., Flow-rate, Time Output: Desirebilities Fuzzy Implication Function

I Modified Simplex Techniqw

sentations were made using the FNP3D routine of the IMSL Exponent Graphics package (IMSL, 1991), version 1.1, after computing the coefficients of the secondorder regression equation. SMLR was run with the values of F-to-enter and Fto remove chosen in agreement with the Fstatistical table (F= O-95) and according to the number of samples and peaks.

Modified Simplex: inputs and outputs variables Modelling DHS method Second-order equations by Stepuise Multiple Regression

Characterizing attributes through volatile peaks by: Step-wise Multiple Regression All Possible Subsets Regression Principd Coqwnents Regression

FIG. 1. Flow diagram for optimizing DHS of Virgin olive oil.

trap injector was coupled with the CC. The oven temperature was held at 40°C for four min and programmed to rise at 4”C/min to a final temperature of 240°C where it was held for 10 min. Isobutyl acetate, methyl nonanoate (PolyScience Corp.; analytical standards) and dioxane (Carlo Erba) were the internal standards added to each sample at a concentration of 2500 ppb. The solvent was CS, (Gr, Merck).

Procedure after optimizing the conditions Spanish virgin olive oil (0.5 g) was heated at 40°C and swept with nitrogen (200 ml/min) for 15 min in the concentrator designed by Zlatkis et al. (1973), as improved by Cert & Bahima (1984)) but of smaller dimensions (20 mm i.d., 40 mm o.d., 15 cm height; Ref. ANORSA X53233). Tenax TA was used as the trap. The volatiles were thermally desorbed at 220°C on to a fused silica trap cooled at -110°C for 5 min. When cold this trap was flushed by heating at 170°C for 5 min. The volatiles that had condensed within the trap were transferred onto a capillary column. The GC integrator was linked to an 80386 computer and the chromatograms were held in a SQL database.

Statistical procedures and software facilities All statistical analyses were carried out using the BMDP library (Dixon, 1983), version 1987, while for regression, the SMLR procedure was applied (Tabachnick & Fidell, 1983; Lebart et d, 1984). The graphical repre-

Modified simplex (Ryan et al, 1980) is an optimization technique that has become increasingly popular because it allows an easy implementation and a wide choice of methods. Simplex only needs the input variables, which are to be optimized, and an output variable that will be minimized or maximized. Numerous input variables had a possible influence upon the DHS response though most of them were insignificant or nonpertinent and were rejected; temperature, time and flow rate were identified as the most significant. The upper temperature was fixed at 90°C because beyond this there is a degradation of volatiles, whereas a temperature lower than 20°C did not permit the majority of the volatiles to be stripped. Technical reasons also limited the flow-rate boundaries. The upper flow-rate was fixed at 2500 ml/min and the lower at 50 ml/min, while the time limits were from 15 to 240 min. Optimizing also implies that the response to be maximized has been selected and that the chromatograms contain enough information for this purpose. Extensive operator experience in evaluating chromatograms has been used to decide which characteristics are useful in determining the quality of a preconcentration process. These characteristics - experts’ subjective opinions are called desirabilities (Derringer 8c Suich, 1980) and they will be the output variables to be maximized. Three desirabilities were evaluated: chromatographic balance, stripping quality and standard recovery. The chromatographic balance measures the ratio of the large and small peaks, taking into account the chromatographic resolution of these peaks. If the ratio is very high there will be many unresolved large peaks, but if it is small there will be too many unresolved small peaks, so that the perfect balance is attained when the number of unresolved peaks, either large or small, is minimum. The stripping quality evaluates the total number of peaks in the chromatogram. A small number of peaks means that the process conditions are inadequate for stripping volatiles; an excessive number indicates too much degradation during the process. The standard recovery describes how the standards were recovered after the process. The desirability functions are characterized by prob ability distributions and are graphically represented by different nonlinear equations, which are defined by

Dynamic Headspace Techniquefor Quantijjing Virgin Olive Oil Volutib

y is the result after applying the equation to x; /3is the bandwidth, in symmetrical functions, /3,= &; p is the peak-point.

two parameters: the peak point and the bandwidth. The peak-point is the point at which the desirability function is optimum (maximum). The bandwidths are defined as the distance between a crossover point, where the desirability function reaches its poorest values (minimum), and the peak point. The values of the desirability functions are computed as follows:

Figure 2 shows the parameters in detail. Since we have three desirability functions, we are analysing the same process from three different criteria. However, we are interested in building one unique response, therefore, we should join the three criteria in a single function (implication function) that generates the overall desirability. Although the peak-point and bandwidth of each one of these desirability functions both depend on experts, the subjective nature of the evaluations involves a degree of uncertain reasoning and fuzzyness. Thus, it seems reasonable that the implication function that links the desirability functions to calculate the overall desirability should derive from fuzzy logic rather than classical arithmetic. We have applied an iterative fuzzy algorithm, Luckasiewicz T,.,-conorm (Bandler 8c Kohout, 1987; Godo et aZ.,1987) formulated as:

(1) Choose x,,,,, and x,,,~,,as the cutoff points. Either side of these D(x) = 0. (2) Choose the position of maximum desirability p, not necessarily the midpoint of the cutoffs. D(p) = 1. (3) Calculate the midpoints (L and R in the diagram) between p and G, and %in. (4) The function is then defined by using a quadratic spline that goes through the points (x,,,~,,O), (-W5), (p, I), (KO.51, (a,,W, i.e. equations (2) and (5) are the piecewise quadratic functions that meet this criterion and also have continuous first derivatives at points L, p and R z*=D(x) =o i = 1,2,3

forxIp-p,orx>p+&

=i(x-p+p,)2 I

forp-P,IxIp-0.5&

= l-;

(x-p)’

forp-0.5P,IxIp

(x-p)’

for-p< xlpt

(1)

T,.5(Wi, Wj)=

and

O-5/3,

(4)

2

=J-(x-~-@~)~

forpt0.5P21xIp+P2

WiWj/[2

-

(u,

t Wj- WjW,)]

where w,, wj are the values associated with two desirabilities. It is an AND-function that fits the overall desirability meaning. We will get the best overall desirability when we obtain the best ‘chromatographic balance’ AND the best ‘stripping quality’ AND the best ‘standard recovery’. Once the response to be maximized was formulated, through the implication function, the vertices of initial Simplex were calculated by strictly applying the criteria given by Yarbro & Deming (1974). After successive expansions and contractions there were massive contractions in the vicinity of the optimum which was at 40°C 15 min and 200 ml/min. These values are in accordance with the thermal desorption technique because it does not need a high temperature or flow rate of the stripping

(2)

1 = 1-i

(5)

2

where D is the desirability function; x is the value of these functions; Dcx>

x xmrn.

-~-eFIG. 2. Calculation of desirability function D(x)

L

111

P

-52-~-~-

R

X-ox.

112

R Aparicio &M.

T. Morales

gas in the preconcentration procedure in order to strip 95% of the solute. The total number of cycles was 13.

Response equation

surface methods:

second-order

regression

The Simplex procedure does not provide a response surface, so different methods have been proposed to model results (Vuataz, 1986; Martens & Naes, 1989). A group of experiments (16), covering the whole space of variation, was added to the dataset of Simplex. The experiments were selected to get a fairly balanced dataset before applying the regression procedure. We have applied SMLR to a set of factors constituted by the combination of variables-temperature, flow rate and time-up to the second-order. These extra ‘artificial’ factors (?e, TX t,etc.) represent nonlinear transformations of the original variables and allow the modelling of nonlinear responses. A criterion, based on the Fdistribution, was used to select the best variable at each step of the regression process. At the end of the step wise process the following equation was obtained, which relates the overall desirability values (obtained from the implication function) to the flow rate, time and temperature parameters, D(x) = aTt

bT2

t cpF+ @T+

e7% + a

where, D(x) is the overall desirability, T is the temperature, F is the flow rate and t is the time. The values of the different coefficients were: a = 1.66E-02, b = -2.057E-04, c = 7.998E-08, d = -7*476E-09, e = 2.33E-08 and (Y = -0.397. The standard errors of coefficients were: a = 2*49E-03, b = 4.llE-05, c = 6.23E-08, d = 3.64E-09 and e= 9*08E-09. The analysis of variance gave O-516 for the mean square of the regression equation and 0.0452 for the residual, with an F-ratio of 11.42. The values of the coefficients indicate the great importance of the temperature factor. This conclusion is logical given that DHS sweeps the olive oil surface and works with volatile compounds, and it is in accordance with the conclusions of Bertsch et al. (1975). It is also interesting to note the small influence of the time variable in the equation. This is probably due to the fact that the flow and the product flow X time can be considered to be very similar. Figure 3 shows the response surface made by expo nent graphics (IMSL, 1991). It can be seen that the response improves as the temperature and flow rate increase from their minimum values and worsens as a result of significant degradation of volatiles when they reach high values. The response practically does not change at some flow rates when the temperature is low because too few volatiles are stripped, whilst above a certain maximum value, the response does not improve further and even worsens when the flow is increased due to the trap circuit being open (Cert & Bahima, 1984).

, 50

800

Flow-rate

80 TemPw3

@.

CQ

(mumin)

3. Surface response for the DHS method estimated by stepwise multiple linear regression.

FIG.

CHARACTERIZING SENSORY ATTRIBUTES BY VOLATILE PEAKS The described method obtains chromatographic profiles with more than 100 peaks, 63 of them being statistically significant (F< O-05)) these corresponding to different volatile compounds. On the other hand, these chemical compounds are responsible for the sensory attributes so that a relationship between them is to be expected. The paper published by Olias et al. (1978) suggests that the sensory attributes should be related to a profile rather than a single volatile, because of likely synergistic effects, and hence statistical procedures should be applied. However, the whole set of volatiles is not necessary and a subset that should predict the sensory attributes of virgin olive oil has been described in the International Olive Oil Council directive (COI, 1987). Consequently individual identification of the whole set of peaks is not necessary. This work proposes another way. Three regression procedures (SMLA, APSR and PCR) have been applied to formulate the sensory attributes by means of the volatile peaks. Thus, the peaks to be identified would be those explaining the attributes.

Samples Thirty samples of different virgin olive oils have been characterized by 63 volatile peaks and 12 attributes: bitter, pungent, harsh, green leaves, grass, greasy, earthy, heated, dry, muddy sediment, old and rancid. The attributes were quantified in triplicate using the procedure suggested by CO1 (1987).

Statistical procedures and software facilities The statistical analyses have been carried out using the following BMDP procedures (Dixon, 1983): SMLR, PCR and APSR (all possible subsets regression). APSR is a stepwise multiple regression procedure that implements the Furnival-Wilson (1974) algorithm. This algorithm estimates the adjusted-R* of all

Dynamic Headspace Techniquefbr Quantijjing Virgin Olive Oil Volutiles 113 possible profiles from only one peak to the whole set. This algorithm allows the determination not only of the best profile, but also of the second best, third best, etc., providing several good alternatives. The procedure was run on a mainframe vector computer Convex-220 due to the fact that the statistical procedure demanded a large memory and CPU time to perform all subsets of 63 volatile peaks. The other statistical procedures were run on an Inves 80386 computer.

RESULTS

AND

CONCLUSIONS

Table 1 shows the results of applying SMLR, and Fig. 4 displays a chromatogram where the peaks of Table 1 are numbered. Four attributes, pungent, rancid, green leaves and harsh, can be explained with R2 > 90% with only four peaks, their adjusted R2 being greater than 0.85, and TABLE 1. Results of

only four attributes, greasy, muddy sediment, earthy and heated, have adjusted-R2 values lower than 0.75. The cumulative normal plot of the residuals is satisfactory but the number of samples suggests that analysis of the residuals by the Durbin and Watson test (Peiia, 1987; Martens & Naes, 1989) is appropriate. The test values (Table 1) show that there is no correlation among the residuals of each regression though the test was not conclusive with ‘harsh’ and ‘old’ attributes at all. Despite the good results of adjusted-@ for each attribute, the doubt still existed as to whether there would be other profiles with better adjusted-R’. For this reason, APSR was applied to the datasets, and the best profiles for each one of the best attributes were in agreement with those attained by SMLR. The residual analysis show that the procedure gives precise predictions, but some researchers still think SMLR gives optimistic predictions. Therefore, we ap-

Regression Procedures. 3

Attributes

Adj-@

Peaks

SMLR

PCR

DurbiWatson

Pungent Harsh Rancid Green leaves Bitter Grass

0.88 0.87 0.83 0.79 0.78 0.75 0.72 0.70 0.68 0.70

0.77

-

0.76 0.76 0.75

I

;

o-90

0.87 0.83 0.83 0.80

Dry Old Greasy Muddy sediment Earthy Heated

;

10

1.54 2.58 1.75 2.31 2.33 2.16 2.37 2.51 1.69 2.20 2.15 2.05

0.92

0.94 0.93 0.92 0.90

-

,

15

R?

1

20

0.78 0.92 o-91 0.90 0.81 0.82 0.83 0.80 0.65 0.67 0.76 0.72

25,12,21,8 17,24,10,9 24,16,14,3 4,14,1,9 17,24,18,21 8,23,20 8,10,12 15,6,2 19,11,23 15,8,14 8,1,22,14 13,4,17

I

25

1

30

IT

35

F-vaue 6.98

5.30 9.05 9.03 21.61 62.05 6.82 6.97 25.47 8.49 8.65 36.16

min

FIG. 4. Chromatogram showing the 25 selected peaks. Sample quality level: extra-virgin olive oil; nationality: Spanish; variety: Arbequina; ripeness: medium; storaged time: <24 h; Extraction system: Centrifugation.

114

R. Apakio

&’ M. T. Morales

plied PCR on the profile

of each attribute.

An F-value

has been used as a test of significance for entering prin-

cipal components, such that the entry of principal components stops when the corresponding F-value is <5*69 (F = O-975). Table 1 shows that the R* values computed by PCR are quite similar, only three attributes (pungent, bitter and greasy) having lower R* values in the latter procedure. In conclusion, at least four attributes have been characterized by volatile peaks, reducing the whole set of 63 peaks to 25 peaks, which are probably the only ones that need to be identified.

ACKNOWLEDGEMENTS The authors would like to acknowledge their indebtedness to Dr Cert for his contribution in solving many experimental problems. This work has been supported by CICYT-SPAIN ALJ-91-0786.

REFERENCES Bandler, W. & Kohout, L. J. (1987). Semantics of implication operators and fuzzy relational products in Fuzzy Reasoning and its implications. In FUZZY Reasoning and its A@ications, ed. Mandami, E. H. & Gaines, B. R. Academic Press, London. Bertsch, W., Anderson, E. & Holzer, G. (1975). Trace analysis of organic volatiles in water by gas chromatography-mass spectrometry with glass capillary columns. J. Chromatogr, 112,701-18. Cert, A. & Bahima, J. (1984). Headspace sampling and GC analysis of volatile urinary metabolites. J Chromatogr. Sci., 22,7-11. CO1 (1987),Organoleptic Assessment of Olive Oil. COI/ T.ZO/Doc 3. Resolution no. RES-5/56N/87. International Olive Oil Council, Madrid, Spain. Derringer, G. 8c Suich, R. (1980). Simultaneous optimization of several response variab1es.J. @al. Technol., 12, 214.

Dixon, W. J. (1983). BMDP Statistical Sojtwaw, University of California Press, Los Angeles, CA. Fumival, G. M. & Wilson, R. W. (1974). Regression by leaps and bounds. Technomdncs, 16,499-511. Godo, L. I., Lopez de Mantaras, R., Sierra, C. & Verdaguer, A. (1987). Managing linguistically expressed uncertainty in MILORD-application to medical diagnosis. In Proc. Expert System and its A@lications. Avignon, France, vol. EC2, pp. 571-96. IMSL (1991). Expmmt Oraphics Manual Version 1.1. Sugar Land, TX. Lebart, L., Morineau, A. & Fenelon, J. P. (1984). Traitement des donnees statistiques. Mithooks and Programmes. Dunod, Paris. Martens, H. & Naes, T. M. (1989). Multivariate Calibration. John Wiley, Chichester, UK. Morales, M. T., Aparicio, R. & Gutierrez, F. (1992). Techniques for the isolation and concentration of vegetables oils volatiles. Grasas y Awites, 43, 164-73. Nfiriez, A. J., Gonzglez, L. F. & JanPk, J. (1984). Preconcentration of headspace volatiles for trace organic analysis by gas chromat0graphy.J. Chromatogr., 300,127-62. Olias, J. M., Dobarganes, M. C., Gutiirrez, R. & Guti&rez, G. (1978). Componentes vol%les en el aroma de1 aceite de oliva virgen. II. Identificaci6n y andisis sensorial de 10s eluyentes cromatogrticos. Grasas y Ace&s. 29,21 l-18. Peria, D. (1987) . Estadistica Modelos y MCtodos. 2. Modelos lineales y Series Temporales. Alianza Universidad Textos, Madrid Ryan, P. B., Barr, L. B. & Todd, H. D. (1980). Simplex techniques for nonlinear optimization. Anal Gem., 52,1460-7. Tabachnick, B. G. & Fidell, L. S. (1983). Using Multivariate Statistics, Harper & Row, New York. Vuataz, L. (1986). Response surface methods in statistical procedures in food research. In Statistical Procedures in Food Research, ed. J. R. Piggot, Elsevier, London. Yarbro, L. A. & Deming, S. N. (1974) . Selection and preprocessing of factors for Simplex optimization. Anal. Chim. Acta, 73, 391-8. Zlatkis, A., Lichtenstein, H. A. & Tishbee, A. (1973) Concentration and analysis of trace volatile in gases and biological fluids with a new solid adsorbent. Chromalographia, 6, 67-70.