Optimization of liquid chromatographic parameters for the separation of priority phenols by using mixed-level orthogonal array design

ANALmcA

CHIMICA ACTA ELSEVIER

Analytica

Chimica Acta 312 (1995) 271-280

Optimization of liquid chromatographic parameters for the separation of priority phenols by using mixed-level orthogonal array design K.K. Chee, W.G. Lan, M.K. Wong *, H.K. Lee Department

of Chemimy,

National University of Singapore,

Received 17 November

Kent Ridge. Singapore

1994; revised 11 April 1995; accepted

OS1 I, Singapow

16 April 1995

Abstract Mixed-level orthogonal array design (OAD) as a chemometric method has been employed to optimize liquid chromatographic (LC) conditions for the separation of environmental pollutants. Six parameters were examined by OAD, namely reversed-phase (C,,) LC columns from different manufacturers, content of methanol and acetonitrile in the mobile phase,

initial time-duration of a fixed mobile phase composition, time-duration of a gradient elution programme, and mobile phase flow rate. The optimization of these parameters for the determination of eleven priority substituted phenols was carried out to demonstrate the applicability of mixed-level OAD in environmental analytical chemistry in which the use of trial-and-error procedures is often unsatisfactory were discussed. Kewords:

Liquid chromatography;

due to sample complexity.

Mixed-level

orthogonal

The advantages

array design; Chemometrics;

1. Introduction Phenolic compounds are important environmental pollutants because of their toxic effects towards life in the aquatic environment [l]. They have been widely used in industry and agriculture, and this has led to the classification of phenol itself and ten other substituted phenols by the United States Environmental Protection Agency (USEPA) as priority pollutants [Z!]. Perhaps the most commonly used method for the determination of phenolic compounds, including

* Corresponding

author.

0003.2670/95/$09.50 0 1995 Elsevier Science B.V. All rights reserved 0003.2670(95)00214-6

SSDl

and the disadvantages

Substituted

of mixed-level

OAD

phenols; Phenols

those that are USEPA-listed, is reversed-phase liquid chromatography (LC) of which both gradient and isocratic elution modes have been employed [3-71. The optimization of conditions for better chromatographic separations is imperative for the analysis of environmental pollutants especially in cases where these pollutants are present in a wide range of concentrations (whereby the presence of one component at a low level may be masked by another at a higher concentration, both possessing similar retention times). Better separation is also necessary for pollutants occurring in multi-component complex mixtures in various environmental matrices. Appropriate procedures for the optimization of chromatographic conditions for analyzing environ-

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mental pollutants are available. In the last few years, there have been several such schemes that have been studied to get a better understanding of the various factors affecting chromatographic resolution for multicomponent separations [8-U]. Two general systematic optimization schemes are the sequential and simultaneous methods. The sequential simplex optimization scheme introduced by Spendley et al. [13] and modified by Nelder and Mead [14] is suitable for fine-tuning a separation [15,16]. A main disadvantage of this method is the requirement for the response surface to be sequentially tracked until an optimum has been located, which is not, however, an (a priori) global optimum [17]. Besides, it also results in a slow convergence on a highly complicated response surface with high dimensionality. Simultaneous optimization methods (e.g., factorial design [18,19] and mixture design [20]), do not suffer from these aforementioned problems. Factorial designs have an advantage over simplex optimization in the region preceding the optimum, in that a large quantity of quantitative information about the significance of various effects and interactions can be obtained [21,22]. Moreover, factorial designs can deal with both continuous and discrete factors, whereas simplex optimization can handle only continuous factors. However, one disadvantage of the factorial designs is that the number of experimental trials increases proportionally with more variables being examined. Fortunately, fractional factorial designs, such as Plackett-Burman schemes adopting two-level designs can minimize this problem [23-2.51. However, for multiple-level design, e.g., three-level design, it has been proven that this design can lead to inaccurate conclusions about the influence of the different factors. The reason may be due to the way the designs were constructed [26]. In the case of mixture designs, e.g., overlapping resolution mapping CORM), first introduced by Glajch et al. [27], they are useful for predicting optimum liquid chromatographic conditions by the selection of a mobile phase composition that provides the best resolution between the worst separated pair within a desired capacity factor (retention factor) range. This objective is achieved by manipulation of the selectivity of the mobile phase while maintaining an approximately constant solvent strength. The ORM approach has been employed for

the isocratic reversed-phase chromatographic separation of eleven priority phenols with a combination of different organic modifiers (methanol, acetonitrile and tetrahydrofuran) 128,291, nitrosamines [30], phthalates [31] and steroids [32]. However, in the ORM approach, the optimum is not necessarily identified especially for complex samples as all the contours may overlap completely. Besides, the number of experiments increases proportionally with the number of solutes, slowing down the process for complex mixtures of which the individual solutes need to be identified. Orthogonal array design (OAD), which is in fact a saturated fractional factorial design, keeps the merits of factorial design. On the other hand, the number of experiments performed by OAD increased arithmetically instead of geometrically thus keeping the merits of simplex optimization. In other words, the use of OAD can reduce the number of experiments without affecting the quality of results [l&33-44]. The theory and methodology of OAD as a chemometric method for the optimization of analytical procedures have been described in detail elsewhere [37-441. OAD is a sophisticated and cost-effective optimization strategy that is used to assign factors to a series of experimental combinations whose results can then be analyzed by analysis of variance (ANOVA). The main effects of the factors and interaction effects between factors can be considered separately as different factors and estimated by the design along with the corresponding linear graphs or triangular tables [33,44]. It has been mentioned previously that the use of ANOVA in OAD instead of the Student’s t-test in Plackett-Burman schemes and associated triangular tables for the assignment of variables and two-variable interaction were the two major advantages of OAD over fractional factorial design such as Plackett-Burman schemes although both designs have the same number of experimental trials [37]. In the present study, a mixed-level OAD with an OA,, (4l X 212) matrix was employed to examine and optimize the liquid chromatographic parameters needed to improve the separation and hence the determination of the eleven EPA-listed priority phenols. The construction of the mixed-level OA,, (4l X 2l*) matrix has been described in detail [43]. However, as described in Ref. [43], the use of a

K.K. Chee et al. /Analytica

273

Chimica Acta 312 (1995) 271-280

polynomial model to represent the response surface derived from using a mixed-level OAD is not suitable. Six parameters were examined and then optimized by the mixed-level OAD, namely reversedphase LC columns from various manufacturers, the content of mobile phase with respect to methanol and acetonitrile, initial time-duration of a fixed mobile phase composition, time-duration of a gradient elution programme and mobile phase flow rate. Lastly, a set of conditions for maximising the chromatographic separation of the compounds concerned is proposed.

the Waters 600E system controller, Waters 486 tunable absorbance detector and Waters 700 Satellite WISP’” autosampler. The Maxima’” 825/Baseline 815 Powerline LC software was used to control the LC system. The wavelength of the UV detector was set at 280 nm. The following columns were considered in this work: (i) Nova-Pak C,, (Waters) (150 mm X 3.9 mm i.d., 4 pm> (Column I), (ii) Partisil 10 ODS-3 (Whatman, Clifton, NJ) (250 mm X 4.6 mm i.d., 5 pm) (Column II), (iii) Maxsil 5 C,, (Phenomenex, Torrance, CA) (250 mm X 4.6 mm i.d., 5 pm) (Column III), and (iv> PBondapak’” C,, (Waters) (300 mm X 4.6 mm i.d., 10 pm) (Column IV).

2. Experimental 2.1. Optimization

Eleven priority substituted phenol standards (phenol, 4-nitrophenol, 2-chlorophenol, 2-nitrophenol, 2,4-dinitrophenol, 2,4_dimethylphenol, 4-chloro-3methylphenol, 2-methyl-4,6-dinitrophenol, 2,4-dichlorophenol, 2,4,6-trichlorophenol and pentachlorophenol) were purchased from Aldrich (Milwaukee, WI> and were of analytical grade. A standard mixture was prepared in methanol at a concentration of 1000 mg/dm3 for each compound. The working standard mixture was prepared by a 40-fold dilution of the stock solution. All organic solvents used were of LC-grade quality. Pure water was obtained from a Millipore (Milford, MA) Milli-Q system. LC analyses were performed on a Waters (Milford, MA) Powerline’” system, which comprised of Table

strategy

The six parameters selected for optimization of the analysis of the phenols were (i> the reversed-phase column (Factor A); (ii> content of methanol in mobile phase (Factor B); (iii) content of acetonitrile in the mobile phase (Factor C); (iv) time-duration of a fixed mobile phase composition (“initial time”) (Factor D); (v) time-duration of a gradient elution programme (“programme time”) (Factor E); and (vi) mobile phase flow rate (Factor F). One factor not considered was the percentage of water in the mobile phase since the proportions of methanol and acetonitrile would determine that of water. In this case, water is acidified with acetic acid to give a pH of 4. Because one four-level and five two-level

I

The assignment the responses

of factors and levels of experiment

X

using an OA,,(4’

212) matrix along with results of the effects ot selected variables

on

Column No. Kl Factor A

2

3

4

5

6

7

8

Y

10

11

17

13

(AXB),

(AxB),

(AXB),

C

(AXC),

(AXC),

(AX C),

BXC

D

E

(AxI)),

(AXE),

(AX

CXEb

(A x E),

I II III IV

13

--

“

30 50

20 10

a A = Reversed-phase LC columns; B = content of methanol (%); C = content of acetonitrile time (min); F = flow rate (ml/min). ’ The interactions in bold can be neglected according to experience.

0 5

20 IO

F D),

(AXE),

(AX D), 0.X I.2

(%o); D = initial time (min): E = programme

274

K.K. Chee et al. /Analytica

Chimica Acta 312 (1995) 271-280

variables were to be considered, the OA,, (4l X 2”) matrix was employed to assign the variables considered, and for the following two-variable interactions which might occur. Previous experience with LC and intuition were necessary to handle two-variable interactions. The following interactions were considered: A X B, A X C, A X D, A X E, and B X C. The assignment of the main-variables and two-variables interactions, and their levels are given in Table 1. Two response functions, namely the number of distinguishable peaks (NDP) and individual contributions (ICI, which have been used and described in detail in our previous paper [39], were chosen to assess the quality of the chromatograms. A distinguishable peak means that the peak apex is not concealed by other peaks. It can be used as a response function because it is convenient and timesaving especially when considering the effect of

Table 2 The OA,,(4l

X 2”)

changes in the variables on separation of multi-components in chromatographic experiments. However, one important point to note is that this response function is not a continuous response, and thus, no quadratic polynomial representing the response surface can be established. Individual contributions which is based on information theory can be interpreted as follows: if a mixture of n components is chromatographed and the resulting chromatogram is composed of k, singlets, k, doublets and k, p-multiplets with C(pk,) = n, the contribution of the p-multiplet to the quantity of information is given by I, = (pk,/n)log,(n/p) where pk,/n is the appearance frequency and log,( n /p) is the quantity of specific information brought by the identification of a component in a p-multiplet. The total information from the chromatogram is the sum of the individual con-

matrix with the experimental results Response

Column No

1 2 3 4 5 6 7 8 9 10 11 12 13 14 1.5 16

1

2

3

4

5

6

7

8

9

10

11

12

13

NDP

IC

1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4

1 1 2 2 1 1 2 2 12 12 2 2 1 1 2 2

1 1 2 2 1 1 2 2

1 1 2 2 2 2 1 1

1 2 1 2 2 1 2 1 12

1 2 1 2 2 1 2 1

1 2 2 1 1 2 2 1 1

1 1 1 1 2 2

1 2 1 2 1 2 1 2 12 2 1 2 1 2 1 2

1 2 1 2 1 2 1 2

1 1 2 2 1 1

1 1 2 2 2 2 1 1 12 12 2 2 2 2 1 1

12 2 1 2 1 2 1

1 2 2 1 2 1

12 2 1 1 2 1 2

2 1 1 2 2 1

1 2 2 1 1 2 2 1 2 1 1 2 2 1 1 2

1 2 2 1 2 1 1 2 1 2 2 1 2 1 1 2

1 2 2 1 2 1 1 2 2 1 1 2 1 2 2 1

10 10 6 7 8 9 7 6 11 11 10 10 8 9 7 7

2.34 2.65 1.44 1.46 1.11 1.84 1.12 1.45 3.41 3.24 2.34 3.06 1.10 1.40 1.46 1.46

1.97 1.38 3.01 1.36

2.14 1.72

2.03 1.83

2.14 1.72

1.93 1.93

1.79 2.07

1.90 1.96

1.96 1.90

1.99 1.87

1.92 1.94

1.85 2.01

2.01 1.85

1.86 2.00

8.25 7.50 10.50 7.75

9.50 7.50

8.88 8.13

8.63 8.38

8.75 8.25

8.38 8.63

8.50 8.50

8.50 8.50

8.38 8.63

8.38 8.63

8.50 8.50

8.75 8.25

8.63 8.38

IC rl r2 r3

r4 NDP rl r2 r3

rA

215

K.K. Chee et al./Analytica Chimica Acta 312 (1995) 271-280

tribution (IC) of each peak group: ZC = X:( pk,/n)log,(n/p). The value of IC varies between zero, all the peaks eluting together, (p = II, k, = 1) and log,(n), with all the peaks separated (p= 1, k,=n). In order to estimate the factors’ effects after implementing the mixed-level OAD, the analysis of variance (ANOVA) technique is employed whereby both SS’ and PC(%) values for each factor can be computed. SS’ (purified sum of squares) is defined as the sum of squares minus the variance due to error, while PC(%) (percentage contribution) is the relative contribution of SS’ for each factor, or error to the total variance. The importance of a variable and/or an interaction can be estimated from the PC(%) values due to each significant factors. Furthermore, the PC(%) value due to error provides an estimate of the adequacy of the experiment [44].

3. Results and discussion Sixteen experimental trials were pre-designed according to the mixed-level OA,, (4l X 212) matrix and the corresponding chromatograms were obtained. For each chromatogram, both NDP (number of distinguishable peaks) and IC were calculated. The results obtained are listed in Table 2. As mentioned earlier, the NDP was considered as a discrete response function. Thus, in order to eliminate the rounding-off errors for ANOVA, this response is assumed to be a continuous function when calculating r, and r2 values. The r, and r2 values are the average of output responses (NDP and IC) of the

Table 3 ANOVA

table including

percent contribution

experimental trials for certain factors at level 1 and level 2, which help to facilitate ANOVA for the selection of the optimum level of the factors. Based on the method presented in our earlier publications [37,38], the results of the sum of squares for both NDP and IC for different variables and so-called variables interactions are first calculated Tables 3 and 4, respectively). From (as shown in the assignment of experiments given in Table 1, it is obvious that 4 columns (columns 7, 8, 9 and 10) can be treated as dummies and used for calculating error variance since their interactions, whether using either NDP or IC as response functions, are considered to be negligible. Thus, the ANOVA tables including the percent contributions can be constructed (as given in Table 3 (NDP) and Table 4 (IC)). Both responses tend to be quite consistent by providing similar conclusions: LC column (Factor A), content of methanol (Factor B) and interaction A X B for both NDP and IC, and content of acetonitrile (Factor C) for IC only are all statistically significant at p < 0.05. No statistical differences are observed for any other variables and interactions considered at p > 0.05 level of significance. However, there are some minor differences. Firstly, in the case of NDP, Factor B is significant at p < 0.001 while Factor A is significant at p < 0.005. On the other hand, Factor A is significant at p < 0.001 while Factor B is significant at p < 0.005 in the case of IC. Differences in the level of significance between two factors when NDP and IC are used as response functions have also been observed previously [42]. The latter seems to indicate that the effect of Factor B is more dominant than that of Factor A. However, one important point to note is

for the number of distinguishable

peak in the OA,,(4’

Source

ss

df

MS

F”

Column (A) Methanol (B) Acetonitrile (C) Initial time (D) Program time (E) Flow rate (F) AxB Error Total

22.50 16.00 0.25 0.00 1.00 0.25 3.50 0.50 44.00

3 1 1 1 1 1 3 4 15

7.50 16.00 0.25 _

57.69 123.08 1.92 _

1.00 0.25

7.69 1.92

1.17 0.13

9.00

* The critical

F value is 74.14 (* * *

X 212) matrix SS’

** ’ *’

22.00 15.50

0.5

*

P < 0.001). 31.33 (* * P < 0.005) and 7.71 (* P < 0.05)

3.00 3.00 44.00

PC (%) 50.00 35.23 _ 1.13 _ 6.82 6.X2 100.0

276

K.K. Chee et al. /Analytica

Chimica Acta 312 (1995) 271-280

that the significance values (F ratio) simply reflects the relative magnitudes of the incremental effects of both Factors A and B. Factor B is the result of only one increment (from level 1 to level 2) while Factor A has three increments (from level 1 to level 2, from level 2 to level 3 and finally from level 3 to level 4) contributing to the total effect of factor A. Besides, the availability of total effect for Factor A is consequence of the availability of the total span of levels 1 to 4 for Factor A. From the view point of total effect, it is clear that the effect of Factor A is more overwhelming than Factor B. Therefore, care should be taken when interpreting the significance (F ratio) in mixed-level OAD, in that the magnitude of the F ratio does not necessarily indicate the magnitude of the overall effect. Secondly, from the IC response, Factor C (content of acetonitrile in the mobile phase) is statistically significant at the p < 0.05 level whereas this factor is not statistically significant in the case of NDP. During the initial experiments for locating more exact experiment regions, NDP is suitable, however for the subsequent experiments which are within a region closer to the optimum, IC may be more suitable. An ANOVA table containing percentage contribution is presented in Table 3. It indicates that for both NDP and IC response functions, Factor A is the most important factor contributing to the output response, (50.00% for NDP and 75.10% for IC) and then followed by factor B (35.23% for NDP and 6.57% for IC). It is noted that for the NDP, the ranking of the significance (F ratio) is rectified by observing

Table 4 ANOVA

table including

percent contribution

Source

ss

Column (A) Methanol (B) Acetonitrile (C) Initial time (D) Program time (E) Flow rate (F)

7.18 0.71 0.31 0.10 0.10 0.08

AXB Error Total

0.87 0.09 9.44

a The critical

for the number of individual df

Table 5 Four-by-two

table for the analysis of the AX B interaction

NDP

B, B2

IC A,

A2

A,

A,

A,

A,

A,

A,

10 6.5

8.5 6.5

11 10

8.5 7.0

2.50 1.45

1.46 1.31

3.33 2.70

1.25 1.46

the percentage contribution. Obviously, the percentage contribution indicates that Factor A makes the greatest contribution towards variation observed in the experiments. This is in good agreement with the conclusion obtained from the total effect discussed above. The percentage contribution due to error (unknown and noncontributory factors) is quite low. This means that no important variables and/or interactions have been neglected in this experimental design. Hence, it is reasonable to ignore the twovariables interaction mentioned earlier. Taking into consideration the rl, r2, r3 and r4 values for Factor A, and the rl and r2 values of Factor B (as shown in Table 3), it is clear that for both responses, NDP and IC, the optimum level is A, and B,. However, because the interaction of A X B is statistically significant (p < O.OS), the choice of optimum experimental conditions of Factors A and B must depend on their interaction, which can be evaluated by means of a four-by-two table as shown in Table 5. The method of construction of a four-by-two table has been described in detail elsewhere [43]. As an illustration, let us consider the A X B interaction. The combination between A, and

contributions

(IC) in the OA,,(4]

X 2”)

matrix

MS

Fa

SS’

PC (%)

3 1 1 1 1 1

2.39 0.71 0.31 0.10 0.10 0.08

119.50 * * ’ 35.50 * * 15.50 * 5.00 5.00 3.50

7.09 0.62 0.22 0.01 0.01 _

75.10 6.57 2.33 0.10 0.10 _

3 4 15

0.29 0.02

0.78 0.72 9.44

8.26 7.63 100.00

14.50

*

F value is 74.14 (* * * P < O.OOl), 31.33 (’ * P < 0.005) and 7.71 (* P < 0.05).

K.K. Chee et al./Analytica

B, (A ,B,) represents the average response of the interaction A X B where A is at level 1 and B is at level 1. A,B, represents A at level 1 and B at level 2, and so forth. Table 4 shows that the optimum combination for A X B interaction which gives the maximum responses for both NDP and IC is A at level 3 and B at level 1. In summary, the optimum liquid chromatographic conditions selected for the complete resolution of the substituted phenols after the implementation of the mixed-level OAD are the Maxsil 5 C,, column (250 mm X 4.6 mm i.d., 5 pm), and 30% of methanol and 20% of acetonitrile in the mobile phase. Although the chromatographic separation of substituted phenols is not significantly influenced by the initial time (from 0 to 5 min), programme time (from 10 to 20

Chimica Acta 312 (19951 271-280

‘77

mini and flow rate (from 0.8 to 1.2 ml/min), it does not mean that they are not important. On the basis of chromatographic theory, all liquid chromatographic parameters considered in this optimization exercise are important parameters and they certainly have some influence on the separation efficiency. Although from the percentage contribution results the magnitude of effect of certain factors on the separation efficiency may be small or negligible as compared to other factors, it does not mean they have no effect at all. In the case of levels of significance within the predefined region, problems appear when the levels are set too close or too far apart, resulting in inaccurate significant differences. Thus, whether certain factors are significant or non-significant within a certain level of confidence is dependent on

:-

Fig. 1. Chromatogram for experimental trial No. 9. The retention times for the eleven priority substituted phenols arc as follows: (1) phenol (4.18 min); (2) p-nitrophenol (5.10 min); (3) o-chlorophenol (6.25 min); (4) o-nitrophenol(6.91 min); (5) 2.4.dinitrophenol (7.43 min); (6) 2,4_dimethylphenol (8.83 min); (7) 4-chloro-3-methylphenol (10.74 min); (8) 2-methyl-4.6-dinitrophenol (11.88 min); (9) 2,4-dichlorophenol (13.34 min); (10) 2,4$trichlorophenol (21.10 min); (11) pentachlorophenol (33.90 min).

278

K.K. Chee et al. /Analytica

the initial values of the levels. Finally, the interpretation of the influence of certain factors must result from a combination of their statistical significance and percentage contribution data. When the retention time is considered, those factors which do not affect the separation efficiency should be taken into account. It is affirmed that the above conclusion from ANOVA is the same as that from the direct observation method, which indicates that optimum separation efficiency is obtained in experimental trials 9 and 10 (Table 2). The optimum LC gradient conditions for experimental trial No. 9 used an initial eluent of methanol-acetonitrile-buffer (30:20:50), maintained for 5 min, then linearly programmed to methanolbuffer (80:20) over 20 min and maintained isocrati-

Chimica Acta 312 (1995) 271-280

tally for another 15 min. The mobile phase flow rate was maintained at 1.2 ml/min throughout the run. In the case of experimental trial No. 10, an initial eluent of methanol-acetonitrile-buffer (30:10:60) was linearly programmed to 80:20 over 10 min and then maintained isocratically for another 25 min. The mobile phase flow rate in this experimental trial was set at 0.8 ml/min. The chromatograms for both experimental trials No. 9 and 10 are shown in Figs. 1 and 2, respectively. The former conditions gave complete baseline separation whereas one pair of components was not completely resolved in trial No. 10. Besides the above-mentioned optimum LC gradient conditions, the use of a higher flow rate, especially after the elution of 2,4,6-trichlorophenol (corresponding to peak 10 in Figs. 1 and 2) from

”

m

”

,7: _.

Fig. 2. Chromatogram for experimental trial No. 10. The retention times for the eleven priority substituted phenols are as follows: (1) phenol (9.24 mink (2) p-nitrophenol (12.98 min); (3) o-chlorophenol (15.14 min); (4) o-nitrophenol (15.77 min); (5) 2,4-dinitrophenol (16.29 min); (6) 2,4_dimethylphenol (17.99 min); (7) 4-chloro-3-methylphenol (19.28 min); (8) 2-methyl-4,6_dinitrophenol (19.38 min); (9) 2,4-dichlorophenol (20.13 min); (10) 2,4,6-trichlorophenol (22.88 min); (11) pentachlorophenol (32.93 min).

K.K. Chee et al./Analytica

the column, permitted pentachlorophenol (peak 11) to be eluted earlier without affecting the peak symmetry; in this way, the chromatographic run could be accomplished with satisfactory results in less than 30 min (chromatogram not shown).

4. Conclusion This paper is concerned with the optimization of LC gradient conditions for the separation of eleven priority phenols using mixed-level orthogonal array design (OAD). This design used one 4-level setting with five 2-level settings to optimize the separation conditions. With the application of such a mixed-level OAD. the feasibility and suitability of the technique as a chemometric method for the optimization of analytical procedures in general and chromatographic conditions in particular is successfully demonstrated. For OAD involving multiple-level mixed-level design, the number of experiments to be performed became very large even for a modest amount of factors. Besides, many complex interactions must be neglected or many assumptions must be made, thus making these larger mixed-level OAD less effective. Although this is a shortcoming for the mixed-level OAD, this design with six main variables is still effective for the LC separation of phenols in this study with a reasonable amount of experimental trials. The present study considers the chromatographic column, a discrete or qualitative variable, as a factor. The experimental data obtained show that this factor has a significant influence on the optimization of the conditions for satisfactory separation of the phenols. Although all eleven priority substituted phenols can be satisfactorily separated using the above conditions. those factors which are not statistically significant or have lesser importance in terms of magnitude of effect owing to the selection of levels in this optimization exercise to influence the separation efficiency should also be taken into consideration. It should be emphasized that the optimization of liquid chromatographic parameters can be affected when the levels are set too close together or are too far apart, giving meaningless significant differences. Therefore, it is necessary to rely on past experiences, previous knowledge of the system and intuition when the levels of the variables are selected [45].

‘7’1

Chimica Actu 312 (19%) 271-280

Acknowledgements Kok Kay Chee and Wei Guang Lan wish to thank the National University of Singapore for their research scholarships. The authors are grateful to the Government of Canada for providing financial assistance under the ASEAN-Canada Cooperative Programme on Marine Science Phase II project.

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