Part IV. Selecting the Solvent, Temperature, and Microwave Power
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)A one-factor-at-a-time optimization is an effective and an efficient algorithm when the factors behave independently, and an effective, although not necessarily an efficient, algorithm when the factors are dependent. What does it mean to say that two factors are independent or dependent? What does it mean to say that an optimization is efficient or effective? Why do dependent factors generally require that we optimize each factor more than once? Although the choice of solvent, temperature, and microwave power are dependent factors, for this case study you will optimize each factor once only. Explain why. For the analysis in this case study, is the order in which these three factors are optimized important? Why or why not?
Two factors are independent if the effect on the response of a change in the level of one factor does not depend on the second factor’s level. In the table below, for example, factors A and B are independent because a change in factor A’s level from 10 to 20 increases the response by 40 both when factor B’s level is 10 (increasing from 40 to 80) and when its level is 40 (increasing from 50 to 90).
level of factor A |
level of factor B |
response |
---|---|---|
10 |
10 |
40 |
20 |
10 |
80 |
10 |
40 |
50 |
20 |
40 |
90 |
For dependent factors, the effect on the response of a change in the level of one factor is not independent of the other factor’s level. For example, in the table below factors A and B are dependent because a change in factor A’s level from 10 to 20 increases the response by 40 (from 40 to 8) when factor B’s level is 10, but it increases the response by 20 (from 50 to 70) when factor B’s level is 40.
level of factor A |
level of factor B |
response |
---|---|---|
10 |
10 |
40 |
20 |
10 |
80 |
10 |
40 |
50 |
20 |
40 |
70 |
An effective optimization is one that correctly finds the system’s global optimum. An optimization is not effective if it finds a local (or regional) optimum instead of the global optimum. An efficient optimization is one that finds the global optimum using as few experiments as possible. The most efficient optimization considers all factors at the same time, or optimizes each factor one time only; a less efficient optimization considers each factor separately and requires that we cycle through each factor multiple times.
When two factors are independent, the optimization of one factor does not depend on the level of the other factor; we can, therefore, find the global optimum by optimizing each factor once. Having optimized factor A, we can optimize factor B without changing the effect on the response of factor A. The optimization is efficient because we need only optimize each factor one time.
For dependent factors, however, the optimization of one factor depends on the other factor’s level. If we optimize factor A and then optimize factor B, the level for factor A is no longer at its optimum value (unless we are extraordinarily lucky!). As a result, to find the global optimum, we must repeat the process of optimizing each factor through additional cycles.
To optimize a factor we change its level along a continuous range of possible values with, perhaps, lower and upper limits. For example, we can set the microwave power to any value between a lower limit of 0 W (no power) to an upper limit equal to the microwave’s maximum power. The initial choice of solvent, however, is not continuous as it is limited to individual pure solvents, in this case pure water, methanol, or ethanol. Because we cannot vary the initial choice of solvent through a continuous range of values, we cannot reasonably cycle through the factors.
The order in which the factors are optimized is solvent, extraction temperature, and microwave power. This order is necessary because the maximum possible temperature depends on the solvent’s boiling point, and the choice of microwave power depends on the solvent’s temperature.
For the choice of solvent, consider ethanol, methanol, and water, as well as mixtures of water with ethanol or methanol, and predict how effective each is at extracting hydrophilic or lipophilic compounds. Why is a non-polar solvent, such as hexane, not a useful option for a microwave extraction? What limits, if any, might the choice of solvent place on the choice of temperature or microwave power?
Given the structures of the analytes it is reasonable to assume that each is soluble, to some extent, in methanol and ethanol. Although the hydrophilic compounds likely are soluble in water, the lithophilic compounds are insoluble in water. Because water has a greater solvent strength than methanol or ethanol, binary mixtures of methanol/water or of ethanol/water may be more effective solvents for the hydrophilic analytes; it is less clear if this is the case for the lithophilic analytes.
A non-polar solvent is not a useful option because it cannot absorb microwave energy and, therefore, cannot dissipate that energy to the sample in the form of heat.
The choice of solvent places an upper limit on temperature as it cannot exceed the solvent’s boiling point; the choice of solvent, on the other hand, places no limits on the microwave power.
Consider the data in Figures 3–5 and explain any trends you see in the relative extraction efficiencies of these three solvents. Are your results consistent with your predictions from Investigation 11? Why or why not? Which solvent is the best choice if you are interested in analyzing hydrophilic analytes only? Which solvent is the best choice if you are interested in analyzing lipophilic analytes only? Which solvent is the best choice if you are interested in analyzing both hydrophilic and lipophilic analytes?
The absorbance values for the analytes are summarized here:
absorbance in mAU using 100% |
|||
---|---|---|---|
analyte |
methanol |
ethanol |
water |
danshensu |
043.0 |
032.1 |
72.6 |
rosmarinic acid |
066.7 |
049.9 |
54.6 |
lithospermic acid |
047.2 |
025.2 |
56.3 |
salvianolic acid A |
037.3 |
023.2 |
23.3 |
dihydrotanshinone |
033.9 |
038.2 |
0.0 |
cryptotanshinone |
067.7 |
071.1 |
0.0 |
tanshinone I |
082.4 |
080.4 |
0.0 |
tanshinone IIA |
151.7 |
167.4 |
0.0 |
If we compare methanol to ethanol we see that extraction yields using methanol are greater than those using ethanol for danshensu, rosmarinic acid, lithospermic acid, and salvianolic acid; that the extraction yields using methanol and ethanol are similar for dihydrotanshinone, cryptotanshinone, and tanshinone I; and that the extraction yield using methanol is smaller than when using ethanol for tanshinone IIA. Water is a useful solvent for the hydrophilic compounds—indeed, it is the best solvent for danshensu and lithospermic acid—but, as expected, it does not extract the lipophilic compounds.
If we are interested in extracting hydrophilic compounds only, then methanol or water are appropriate options (or, perhaps, a mixture of the two); ethanol is not an unreasonable option, but it does not extract these compounds as efficiently as methanol or water. If we are interested in extracting lipophilic compounds only, then methanol or ethanol are suitable choices, although ethanol has a slight advantage over methanol for tanshinone IIA. Methanol is the best choice for extracting both hydrophilic and lipophilic compounds.
Note: The chromatograms in Figure 3 and Figure 4 are derived from data in the original paper. The chromatogram in Figure 5 uses data from the paper “Simultaneous quantification of six major phenolic acids in the roots of Salvia miltiorrhiza and four related traditional Chinese medicinal preparations by HPLC-DAD method,” the full reference for which is Liu, A; Li, L; Xu, M.; Lin, Y.; Guo, H.; Guo, D. J. Pharm. Biomed. Anal. 2006, 41, 48–56 (DOI:10.1016/j.jpba.2005.10.021). For reasons of simplicity, the chromatograms in this exercise are cleaned up by excluding peaks from other compounds in Danshen extracts and eliminating baseline noise.
Propose a set of experiments that will effectively and efficiently allow you to determine the optimum mixture of methanol and water to use for this extraction. What range of methanol/water mixtures will you explore? How many samples will you run? Explain the reasons for the range of mixtures and the number of samples you selected. In describing the solvent mixtures, report values as percent methanol by volume (e.g. 55% methanol by volume).
Because the lithophilic analytes are not soluble in water, there is little point in considering mixtures in which water is the predominate solvent; for this reason, it makes sense to limit the mixtures to a lower limit of 50% methanol by volume to an upper limit of 100% methanol by volume. Increasing the percent methanol in steps of 10%, a total of six treatments, provides sufficient information to determine the trend in each analyte’s solubility.
Consider the data in Figure 6 and explain any trends you see in the relative extraction efficiencies using different mixtures of methanol and water. What is the optimum mixture of methanol and water for extracting samples of Danshen? Are your results consistent with your predictions from Investigation 11 and the data from Investigation 12? Why or why not?
The optimum solvent is 80% methanol and 20% water (by volume). The effect of adding water is not surprising for the hydrophilic compounds, given our observations in Investigations 11 and 12; however, the increased extraction efficiency for lipophilic compounds in the presence of added water is unexpected.
Note: The data in Figure 6 are derived, in part, using data from the original paper and, in part, data from the paper “Simultaneous quantification of six major phenolic acids in the roots of Salvia miltiorrhiza and four related traditional Chinese medicinal preparations by HPLC-DAD method,” the full reference for which is Liu, A; Li, L; Xu, M.; Lin, Y.; Guo, H.; Guo, D. J. Pharm. Biomed. Anal. 2006, 41, 48–56 (DOI:10.1016/j.jpba.2005.10.021). Additional data was synthesized, based on trends in the original data, to extend the data set to a greater range of methanol–water mixtures.
Propose a set of experiments that will effectively and efficiently allow you to optimize the extraction temperature using the solvent selected in Investigation 14. What range of temperatures will you explore? How many samples will you run? Explain the reasons for the range of temperatures and the number of samples you selected.
The boiling point for a solvent that is 80% methanol and 20% water (by volume) is slightly greater than 70°C; thus, selecting 70°C for an upper limit is a reasonable choice. A lower limit of 50°C and intervals of 5°C will provide sufficient information to determine the trend in each analyte’s solubility.
Consider the data in Figure 7 and explain any trends you see in the relative extraction efficiencies as a function of temperature. What is the optimum temperature for extracting samples of Danshen? Are your results consistent with your expectations? Why or why not?
The optimum temperature is 70°C, which is consistent with the general expectation that higher temperatures increase extraction yields, assuming no thermal degradation. Interestingly, the effect is somewhat more pronounced for the lipophilic compounds than for the hydrophilic compounds.
Note: The data in Figure 7 are derived, in part, using data from the original paper for temperatures of 50°C, 60°C and 70°C. To extend the data set, additional data were synthesized for temperatures of 55°C and for 65°C based on trends in the original data.
Propose a set of experiments that will effectively and efficiently allow you to optimize the microwave power using the solvent and temperature selected in Investigation 16. What range of powers will you explore given that the microwave’s power is adjustable between the limits of 0 W and 1000 W? How many samples will you run? Explain the reasons for the range of microwave powers and the number of samples you selected.
Although the effect of microwave power on the extraction yield is not likely significant, it also is unpredictable. For this reason, we might opt for a large range, but with relatively few samples. If the resulting data suggest that extraction yields are particularly sensitive to microwave power, then we can run additional samples as needed. Setting a lower limit of 400 W and an upper limit of 1000 W, with steps of 200 W are reasonable choices and will provide sufficient information to determine the trend in each analyte’s solubility.
Consider the data in Figure 8 and explain any trends you see in the relative extraction efficiencies as a function of the microwave’s power. What is the optimum power for extracting samples of Danshen using a solvent that is 80% methanol and 20% water by volume and an extraction temperature of 70°C?
Although, as expected, microwave power does not affect significantly the extraction efficiency for most compounds, the extraction efficiency for some lipophilic compounds decreases at 1000 W; for this reason, the optimum microwave power is 800 W.
Note: The data in Figure 8 are derived using data from the original paper.