ANALYSIS OF FATTY ACIDS USING HIGH-PERFORMANCE LIQUID
CHROMATOGRAPHY WITH CHARGED AEROSOL DETECTION
A Thesis
Presented to the faculty of the Department of Chemistry
California State University, Sacramento
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF SCIENCE
in
Chemistry
by
Lillian Fua Jaquinod
SPRING
2013
© 2013
Lillian Fua Jaquinod
ALL RIGHTS RESERVED
ii
ANALYSIS OF FATTY ACIDS USING HIGH-PERFORMANCE LIQUID
CHROMATOGRAPHY WITH CHARGED AEROSOL DETECTION
A Thesis
by
Lillian Fua Jaquinod
Approved by:
__________________________________, Committee Chair
Dr. Roy Dixon
__________________________________, Second Reader
Dr. Mary McCarthy-Hintz
__________________________________, Third Reader
Dr. Tom Savage
____________________________
Date
iii
Student: Lillian Fua Jaquinod
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
__________________________, Graduate Coordinator
Dr. Susan Crawford
Department of Chemistry
iv
___________________
Date
Abstract
of
ANALYSIS OF FATTY ACIDS USING HIGH-PERFORMANCE LIQUID
CHROMATOGRAPHY WITH CHARGED AEROSOL DETECTION
by
Lillian Fua Jaquinod
A study of parameters (organic content, additives and pH of the mobile phase) to
yield good separation and detection of a series of commercially available free fatty acids
ranging from C12:0 (lauric acid) to C18:3 (linolenic acid) using HPLC-CAD is
undertaken. Working methods using a C18 silica column were assessed by measuring the
experimental limit of detection (LOD) and sensitive ranges with consideration to effects
of the Charged Aerosol Detector (CAD) temperature and detector voltage. An isocratic
method with high content in acetonitrile and low pH was developed that allowed the
CAD detection and quantification of the less volatile fatty acids in the range of from
around 1 to 5 ng/L to over 200 ng/L. A power fit calibration curve was necessitated
since the response of the standards did not display a true linear relationship using linear
regression analysis. Conditions or mobile phase additives were not found to increase
detection of semi-volatile fatty acids such as lauric acid or myristic acid (C14:0). Column
bleed was potentially identified as an unexpected additive that resulted in enhanced peak
detection, attributed to the formation or stabilization of bigger aerosol particles. The
v
isocratic method was tested for an olive oil standard using an acetonitrile: 0.01M TFA
(96.5:3.5) mobile phase, ion voltage at -300 V, and CAD heater setting of 35°C. Using
those conditions, separation and detection of major C16 to C18 fatty acids were achieved
although palmitic and oleic acids were not completely resolved. The olive oil analysis
showed that relative recovery of the major fatty acid components is consistent and
supports the use of HPLC-CAD system for a rapid detection of fatty acids at trace levels.
_______________________, Committee Chair
Dr. Roy Dixon
_______________________
Date
vi
ACKNOWLEDGEMENTS
Dr. Roy Dixon
I would like to express my sincere thanks to Dr. Dixon for being my thesis
advisor and mentor. I am especially grateful for his expertise, time and dedication in
working with me through the years to succeed in the chemistry master’s program. I will
miss the bi-weekly discussions on experimental troubleshooting and data interpretation.
I gained insight into the importance of research.
In research, the interpretation of
unexpected or failed results is just as important as achieving successful research as each
outcome builds onto the next experiment. I would like to thank him for coaching me on
giving a stellar talk of my work with the HPLC-CAD system at the 2013 CSUS Student
Research Symposium. Lastly, I would like to thank him for the many valuable reviews
of my thesis draft revisions and for preparing me for my thesis presentation and defense.
Dr. Mary McCarthy-Hintz
I would like to thank Dr. McCarthy-Hintz for being on my graduate committee
and supporting my thesis work. It has been a privilege to have presented my thesis work
and to have defended my thesis to her.
vii
Dr. Tom Savage
I would like to thank Dr. Savage for being on my graduate committee and
supporting my thesis work. It has been an honor to have Dr. Savage present during my
thesis presentation and defense.
Dr. Cynthia Kellen-Yuen
I would like to thank Dr. Kellen-Yuen for providing advice on fatty acid research
throughout the graduate program and attending my thesis defense during her sabbatical.
My Family
I would like to give my special, heartfelt thanks to my husband, Laurent, and my
two sons, Jeremy and Corey, for supporting me in my pursuit of this master in chemistry.
I hope to have motivated my kids to value the importance of education and to persevere
in being the best that they can be. I am especially thankful to my husband for putting up
with me during many crunch times as I try to maintain a work/life balance and the life of
a student.
viii
TABLE OF CONTENTS
Page
Acknowledgements .................................................................................................... vii
List of Tables .............................................................................................................. xi
List of Figures ............................................................................................................ xii
OBJECTIVES …………………………………………………………………….….. 1
BACKGROUND ………………………………………………………….…...…….. 2
Importance of Fatty Acids ............................................................................... 2
Detection of Fatty Acids .................................................................................. 3
Structure of Fatty Acids ................................................................................... 6
Separation and Analysis of Fatty Acids ............................................................ 8
HPLC Separation of Fatty Acids ...................................................................... 9
Detection by HPLC Configured with Non-Aerosol Based Detectors ............. 9
Detection with Aerosol-Based Universal Detectors ....................................... 11
Detection with Evaporative Light Scattering Detectors (ESLD & CNLSD) . 12
Detection with Charged Aerosol Detector (CAD). ......................................... 14
MATERIALS AND METHODS ................................................................................ 16
HPLC-CAD System ....................................................................................... 16
Calibration Method ........................................................................................ 21
ix
Fatty Acid Standards Preparation ................................................................... 21
Isocratic Mobile Phase Preparations .............................................................. 22
Standards Preparation for Olive Oil Analysis ................................................ 23
Olive Oil Saponification without an Internal Standard .................................. 24
Olive Oil Saponification with Spiked Internal Standard ................................ 24
RESULTS AND DISCUSSIONS ................................................................................26
Methodology Development in CAD Detection of Fatty Acids ..................... 26
Optimization of Mobile Phase Parameters ..................................................... 28
A. Variation of acid modifiers .................................................................. 29
B. Variation of TFA concentrations.......................................................... 30
C. Analysis of mobile phase buffered with TFA and amines .................. 32
Methodology of CAD Parameters ................................................................. 34
Evaluation of Optimal Ion Voltage ................................................................ 34
Evaluation of Temperature Parameters for CAD Detection ........................... 38
Quantitative Analysis ..................................................................................... 39
Method Application to Olive Oil Analysis .................................................... 40
CONCLUSION .......................................................................................................... 50
FUTURE WORK ....................................................................................................... 52
References .................................................................................................................. 53
x
LIST OF TABLES
Tables
Page
Table 1. Detection of fatty acids using 0.01 M TFA or 0.01 M formic acid modified
ACN:H2O (88:12) mobile phase ..……….………………………..……..….29
Table 2. Detection of fatty acids using 0.01 M TFA or 0.01 M acetic acid
modified (88:12) ACN:H2O mobile phase .……………….………….…......30
Table 3. Area count comparison of myristic acid (500 ng/L) at -225 V using an
ACN:H2O (80:20) mobile phase set at various TFA concentrations ..…...... 31
Table 4. Oleic acid response associated with ion voltages of the EAA ...……....…... 35
Table 5. Baseline values at different ion voltage settings ………………...……..….. 38
Table 6a. Temperature effect on area count and baseline at ion voltage -225 V …… 39
Table 6b. Temperature effect on area count at -225 V and -300 V ….…..….…….... 39
Table 7. Fatty acid composition of olive oil by HPLC-CAD and Sigma GC/FID
analyses …………………………..…….………………….….......….……. 46
Table 8. Ratio of CAD:UV peak area count for oleic acid peak .…….….….…....… 48
Table 9. Oleic and palmitic acid absolute recovery based on Sigma-certified
percentages and CAD analyses ………….……………..………………...... 48
xi
LIST OF FIGURES
Figures
Page
Figure 1. Structural formulas of some saturated and cis- or trans-unsaturated C18
fatty acids ….……………….………………………..…….……………....…7
Figure 2. Diagram of HPLC-CAD process....……………………………….…………17
Figure 3. Nebulizer with custom spray and evaporation chambers ………..…....….... 18
Figure 4. Schematic of the EAA …………………………………….………...….….. 20
Figure 5. Chromatogram of fatty acids separated using 5 m column (Mobile phase:
88:12 acetonitrile: 0.01M trifluoroacetic acid (aq) …………….…………... 27
Figure 6. Proposed scheme for increased peak detection through the formation of less
volatile ion pairs of fatty acids ………….……………………….....…......... 33
Figure 7. Oleic acid standard curves from ion voltages of -150 V, -225 V and -300 V
using Agilent XDB-C18 (5m) column …...…….………………....…....…. 37
Figure 8. Saponification of triacylglycerol .………………………………….…….…. 40
Figure 9. Chromatogram of a saponified oil sample eluting in the following order:
linoleic (C18:2), oleic and palmitic (C16:0), margaric (C17 internal
standard), and stearic (C18:0) acids ……….………...……………….…….. 42
xii
Figure 10. Chromatogram of the 200 ng/L composite standard eluting in the
following order: linolenic (C18:3), linoleic, oleic, palmitic, margaric
and stearic acids ……................................………….………..………….. 43
Figure 11. Standard calibration curves of composite standards consisting of stearic,
heptadecanoic, oleic, linoleic, linolenic, and palmitic acids …..………… 44
Figure 12. Expansion of Figure 9 showing the oleic acid peak with palmitic acid
peak eluting at the tailing end ….………………………....……………... 46
xiii
1
OBJECTIVES
This master’s study explores the possibility of analyzing underivatized fatty acids
by direct analysis by HPLC with charged aerosol detection (CAD). A goal of this work
was to develop a simple method for the analysis of fatty acids at low level concentrations
which would show that HPLC-CAD can be used in the rapid detection or quantitative
analysis of any triglyceride containing oil for non-volatile fatty acids. Buffer composition
with consideration to pH and organic content was modified in order to create an efficient
separation and to lower the detection threshold. Our goal was to find a reproducible set of
parameters that yield conditions ensuring that there is sufficient resolution between fatty
acid peaks while allowing the detector to work with high sensitivity and over a useful
concentration range and without excessive run times.
2
BACKGROUND
Importance of Fatty Acids
Lipids are compounds of biological origin ranging from simple molecules such as
fatty acids to complex structures such as lipoproteins and biological membranes. They
are hydrophobic due to their large hydrocarbon content and are found in oils, vitamins
and hormones, or membrane components. They are classified as fatty acids,
acylglycerols, glycerophospholipids (the major lipid component of membranes),
sphingolipids, steroids (including hormones such as androgens, estrogens and progestins),
and 'others' such as waxes, terpenes, eicosanoids.
The important functions that lipids play in biology sustain the field of lipid
research. Determining fatty acids in food or in biological samples for health reasons
remain a challenge and a topic of current research as well. Fats often contain a complex
mixture of saturated, monounsaturated, and polyunsaturated fatty acids, each with a
variety of carbon chain lengths linked to form triglycerides.1 Lipids play also a crucial
role in food science due to their link to cardiovascular disease. For instance, given the
necessity of lipids for proper cellular function and energy storage, bioactivity of lipids
such as trans-fats, omega-3 fatty acids, low-density lipoprotein (LDL), high-density
lipoprotein (HDL) are studied. Other current interests remain focused on the development
of effective therapeutics for lipid disorders or defects in lipid metabolism which underlie
a number of human chronic diseases, obesity and diabetes. 2
Among the different lipid classes, fatty acids and glycerides are best known due to
their importance in human health and diet. Many fatty acids are found derived from
3
triglyceride (glycerol esters) or phospholipids and are rarely found in their “free” state.
Glycerol esters can contain identical fatty acids or a mixture of two or three different
fatty acids. When liquid at room temperature, they are called oils while the solid kinds,
which are often of animal origin such as butter and lard, are called fats.3 Fatty acids are
important sources of fuel: when metabolized, they yield large quantities of adenosine
triphosphates (ATP). Heart or muscle cells metabolize fatty acids for this purpose. A few
fatty acids cannot be synthesized by mammals and need to be ingested. For instance, two
human essential fatty acids (EFA) are -linolenic acid and linoleic acid (an 6-fatty
acid).3 EFA are precursors for the synthesis of eicosanoids such as prostaglandins, which
are intracellular hormone-like substances.
Cells also require fatty acids for the production of their membrane components.
For instance, phospholipids, which are major component of cell membranes, are
composed of two fatty acids, a glycerol unit, and a phosphate-containing polar head.
When placed in an aqueous environment such as cells, phospholipids assemble into a
lipid bilayer with their polar region oriented outward. The bilayer hydrophobic region,
consisting of the fatty acid groups, is oriented away from the cytosol and extracellular
aqueous fluid.3
Detection of Fatty Acids
Fatty acids (FAs) are challenging to analyze as they are made of various length
carbon chains that can be saturated, mono or polyunsaturated while containing at best
weak UV-absorbing chromophores and are somewhat prone to oxidation. Numerous
4
methods for their analysis have been developed,4 but often require derivatization prior to
analysis or require using expensive equipment or detectors. For example, a standard
method involves the esterification of fatty acids to their methyl esters (FAMEs)5 followed
by analysis using gas chromatography6 with flame ionization detection (GC-FID) or mass
spectrometric detection (GC-MS). Unlike the GC-FID method, which is the most
common chromatographic method, fatty acid detection by HPLC-UVD method is only
suitable provided that the lipids have chromophores detectable in UV wavelength range.
Fatty acids can be analyzed without derivatization using HPLC but detection methods
using either the UV-detector (for unsaturated fatty acids) or the RI detector are not very
sensitive. Aerosol-based detection methods for HPLC do not require fatty acid
derivatization and can accommodate a universal detector based on light scattering or on
particle charging.
Charged aerosol detectors (CAD), which are now commercially available through
ESA, a Dionex Corporation,7 were first developed and configured with an HPLC system
by Dixon and Peterson.8 (The first detector was named an aerosol charge detector but
worked in a very similar manner as the charged aerosol detector. Similarly, the detector
will be identified as the charged aerosol detector throughout this thesis.) Since its
introduction, its use is increasing, notably in industries where they have been used in
impurity profiling for the detection of compounds lacking UV-absorbing moieties.9-11
Charged aerosol detection uses nebulization and evaporation steps like in other aerosolbased detectors such as evaporative light scattering detectors (ELSDs) but rely on
charging the resulting aerosol for their detection. Charging the particles can be carried out
5
using different approaches with the initial and commercial versions using the generation
of gas phase ions from corona discharge.11 More recently, the Dixon research group has
used the spray electrification process that occurs during eluent nebulization as described
in detail by Abhyankar.12 While spray electrification produces both positively and
negatively charged droplets, this instrument uses an electric field to selectively remove
smaller positively and most negatively charged particles to create an aerosol of net
positive charge.
The universality of the CAD detector, in which response is independent of the
chemical properties for non-volatile analyses, has been shown, for instance with active
pharmaceutical ingredients having no chromophores such as bisphosphonates or bile
acids.13 These works showed that low limits of detection (LODs) were obtained using an
ESA CoronaTM CAD. In two publications published in 2006 and 2009, Moreau reports
using a charged aerosol detector to analyze a few different classes of lipid components
(phytosterol esters, triacylglycerols, free FAs, and free phytosterols in vegetable oils and
hexane extracts).14,15
In the following section, some structural information on lipids, notably fatty acids,
their nomenclature and some of their uses are reviewed. In a second section, the use of
various detectors and notably aerosol-based detectors (ABD) for the HPLC analysis of
lipids is introduced. Work in this thesis applies the charged aerosol detection to the
reversed-phase HPLC (RP-HPLC) separation of fatty acids. We aimed at testing the
modified CAD capability to detect low levels of fatty acids by finding HPLC buffers that
yield good separations while ultimately facilitating charged particle formation. Methods
6
for detection were tested for a given set of fatty acids under different mobile phase
compositions, different mobile phase pH values, and different electric field voltages.
Based on the evaluation of the system under different conditions, a method was selected
for analyzing samples of olive oil that were saponified to fatty acids. Feasibility of using
HPLC-CAD had an ultimate goal to detect and study of trace triglycerides and fatty acids
in barbecue smoke or in the environment, which require better selectivity and sensitivity
than available HPLC based methods.
Structure of Fatty Acids
A few important fatty acids are as follows:
lauric acid (CH3(CH2)10CO2H),
myristic acid (CH3(CH2)12CO2H), palmitic acid (CH3(CH2)14CO2H), stearic acid
(CH3(CH2)16CO2H), and oleic acid (CH3(CH2)14(CH)2CO2H).
Examples of some
saturated and unsaturated C18 fatty acids are shown in Figure 1. There are two main
groups of fatty acids, saturated and unsaturated (mono or poly-unsaturated). Triple bonds
are rare in fatty acids. The properties of unsaturated fatty acids largely depend on the
position of the first double bond relative to the end position. The first site of unsaturation
is commonly found between c-9 and c-10 while the second non-conjugated double bond
tends to begin with c-12 (as in linoleic acid and linolenic acid).3 When naming fatty acids,
number and position of double bonds are indicated. For example, ω-3 fatty acids indicate
that the first double bond starts at the third carbon-carbon bond from the terminal CH3 of
the chain or omega (ω) end of the chain.
7
CO2H
Stearic Acid (octadecanoic acid)
CO2H
Oleic Acid (cis-9-octadecenoic acid)
CO2H
Linoleic Acid (cis,cis-9,12-octadecatrienoic acid)
CO2H
Linolenic Acid (cis,cis,cis-9,12,15-octadecatrienoic acid)
CO2H
Elaidic Acid (trans-9-octadecenoic acid)
Figure 1: Structural formulas of some saturated and cis- or trans-unsaturated C18 fatty
acids.
In addition, unsaturated fatty acids differ by cis/trans configuration of their double
bonds; natural sources of unsaturated fatty acids such as liquid vegetable oils are rich in
cis-isomers. The cis-configuration of a double bond puts a rigid bend in the carbon chain
that interferes with crystal packing which result in lower melting points and oilier
physical states. This is needed to produce flexible, fluid membranes and aggregates. On
the contrary, a trans-double bond allows the fat molecules to assume a linear
conformation and resulting in a denser packing. The production of trans-fatty acids is
often the result of food processing. Partial oil hydrogenation converts some of cisisomers into trans-isomers in addition to partial conversion from unsaturated fatty acids
to saturated fatty acids. Unlike partially hydrogenated oil, most fully hydrogenated oil
8
does not contain trans-fatty acids. Fats containing saturated fatty acids and, to an even
greater extent, trans-fatty acids, have been shown to raise "bad" cholesterol (LDL) while
lowering "good" cholesterol (HDL), and, as a result, their use by food manufacturers has
greatly decreased. 16
Separation and Analysis of Fatty Acids
Work in this thesis describes the use of reversed-phase HPLC (RP-HPLC) for the
separation and detection of fatty acids when configured with a charged aerosol detector.
Gas Chromatography is a main analytical technique used to analyze fatty acids notably as
their fatty acid methyl esters (or FAME).2.17 In their free non-derivatized form, fatty acids
are somewhat difficult to analyze by GC due to their strong adsorption on the stationary
phase and to their high boiling point. Analysis of free fatty acids by GC often involves
polar stationary phase which need to be run at higher temperatures where faster column
degradation occurs. Their ester derivatives have reduced polarity and lower boiling point
temperatures and provide quick and quantitative samples for GC analysis. To achieve
high accuracy and nanomolar detection levels, their volatile methyl ester derivatives are
often prepared (using an alkylation derivatization reagent, such as methanol, in the
presence of a catalyst). Although gas chromatography is a predominant analysis
technique for fatty acids, the use of HPLC offers advantages when treating rare or
unstable samples as it operates at or near ambient temperature and thus minimizes sample
degradation. Another advantage from HPLC is that it does not necessitate derivatization
which can result in faster total analysis time and greater sample recovery.
9
I am aiming at finding the charged aerosol detector capability to detect low levels
of fatty acids and focus on HPLC buffer parameters that result in optimal performance in
the detection system we use that involves particle charging through the spray
electrification rather than corona discharge in the commercial CAD instrument.
HPLC Separation of Fatty Acids
High Performance Liquid Chromatography (HPLC) consists of a solvent delivery
system, a sample injection valve, a high pressure column and a detector. A
chromatographic run starts by injecting at the front of the column a mixture to analyze.
Elution of the mixture components occurs as the mobile phase is pumped under high
pressure through a stationary phase packed in the chromatographic column. Detection of
the eluting species can be either selective or universal, a choice that ultimately depends
on the response of the detector to the analytes being studied. The detector response of
each eluting component is saved and is displayed in a chromatogram showing retention
time and peak intensity. The choice and the availability of a detector ultimately dictate
the HPLC method requirements to consider when separating fatty acids; some of those
requirements are reviewed notably for aerosol based detectors.
Detection by HPLC Configured with Non-Aerosol Based Detectors
HPLC systems are associated with a broad range of detectors comprising UVdetector, refractive index, fluorescence and mass spectrometry. A method using HPLC to
separate underivatized fatty acids and using a refractive index detector has been
10
reported.18 Samples are detected upon measuring a change in refractive index of the
column effluent passing through the flow-cell (i.e. the deflection of a light beam is
changed when the composition in the sample flow-cell changes). To avoid rapid changes
of refractive index with time, elution is carried out under isocratic elution. A main
drawback comes from poor sensitivity when small difference in refractive index between
sample and mobile phase exist.
The UV-detector is useful to detect compounds having high extinction
coefficients. For those compounds, the detection limits are found within the 0.1 to 1 ng
range.19 However, fatty acids have poor to very weak absorptions. Their absorption is
generally attributed to the presence of double bonds, which limits UV-detection when
used as a non derivatization method to unsaturated lipids or those naturally containing
UV-absorbing chromophores. More sensitive UV-based methods require prior
derivatization of fatty acids through esterification with chromophore-containing reagents
such as 2-bromoacetophenone.20 Likewise, derivatization of fatty acids in esters bearing
groups such as 5-bromomethylfluorescein or 2-nitrophenylhydrazine enhances their
detection (to a sub-nanomolar range) when analyzed by UV-detection or laser-induced
fluorescence.21 Derivatization methods using HPLC-UV do not compare well to GC
methodology.
Both methods require a derivatization step prior analysis but the
convenience of preparing methyl ester derivatives and the better GC resolution
performance make the FAME method the favorable choice over fluorescence methods
despite their sensitivity and selectivity.
11
Detection with Aerosol-Based Universal Detectors
Non-derivatization methods are being sought and explain the need of universal
detectors such as HPLC-CAD. There are three types of aerosol-based detectors that can
be coupled to an HPLC system: the evaporative light scattering detector (ELSD),22,23 the
condensation nucleation light scattering detector (CNLSD),22,24-26 and the charged aerosol
detector (CAD).7,8,27 Those detectors are universal in their applicability as they are largely
independent of the type of solutes studied as long as they are not highly volatile. Such
universal detectors that can be sensitive to traces of fatty acids without the need for
derivatization are highly desired.
The three aerosol based detectors differ in their mode of detection but rely on
similar pre-detection steps: i) nebulization or spraying of the HPLC column effluent into
a fine mist of droplets and ii) evaporation of the mobile phase and additives at a
temperature that does not evaporate the analytes to yield solid or liquid particles made
from unevaporated residue to be detected. However, while related to the quantity of
analyte in the effluent and the subsequent solid particles generated, the three detector
responses rarely follow a linear calibration curve over their full detection range. For
instance, at low analyte concentrations, the resulting particle sizes are too small to scatter
light efficiently and lower the sensitivity of ELSD detectors.26 Custom-built or
commercial CAD instruments tend to be close to linear in detector response for polar
solvents at lower concentrations.8, 27
12
Detection with Evaporative Light Scattering Detectors (ESLD and CNLSD)
An evaporative light scattering detector detects analytes that are significantly less
volatile than the mobile phase.22-26 Buffers used in the analysis of lipids by HPLC-ELSD
are restricted to those that readily evaporate and can contain low concentration of acetic,
formic, or trifluoroacetic acid, ammonium acetate, ammonia, or triethylamine. The HPLC
column effluent is typically nebulized with nitrogen gas in a concentric nebulizer.
Solvent is evaporated from the resulting dispersion of droplets in the heated drift tube
yielding fine particles. ELSD works by detection of those particles through scattering of
light, typically at a forward angle, to a photodetector. The detector response is dependent
upon the size of the particles which is a function of the analyte concentration.26 As the
concentration of analytes decreases, particle sizes decrease until they reach a threshold
under which light scattering switches from efficient Mie scattering to inefficient Rayleigh
scattering. HPLC-ELSD was introduced by Christie23 for lipids detection and was also
used to analyze FAMEs.28 Results are dependent on volatility, molecular weight and
light-scattering properties. Limits of detections were generally above 1 g/mL, making it
difficult to quantify lipids at lower levels. Short chain FAMEs (lauric and myristic) were
detected but at higher concentrations than the long chain FAMEs such as oleic and
linolenic which had LOD of 1.5 and 2.4 g/mL, respectively.
Marcato and Cecchin simultaneously separated and analyzed glycerides and fatty
acids by high-performance liquid chromatography (HPLC) equipped with an evaporative
light-scattering detector.29 A C8 column and a mobile phase made of two consecutive
binary gradients consisting of acetonitrile-water plus acetic acid (0.1%, v/v) and
13
acetonitrile-methylene chloride were used. The addition of acetic acid did not impair
ELSD detection but eliminated peak broadening and tailing that the author attributed to
the simultaneous occurrence of both associated (HA) and dissociated (A-) forms of fatty
acids. Limits of detection (LOD) of the fatty acids were not discussed but chromatograms
were obtained with 1 to 2 g of injection of fatty acids such as lauric acid or palmitic
acid.
Gerard et al reported the separation and quantification of a series of mono-, di- or
tri-hydroxy and epoxy fatty acids by HPLC with an evaporative light scattering
detector.30 Separation was carried out using a normal phase Lichrosorb Si 60 silica
column using a gradient of hexane and isopropanol and 0.2% of acetic acid. They
reported a minimum limit of detection of 1 g and linearity for mass to ratio signal in the
10 to 200 g range. They applied their methodology to the quantitative analysis of
underivatized fatty acid monomers with different degree of unsaturation obtained by
saponifying Soxhlet extracts of cutins from various fruit seeds.31 Standards curves that
were constructed for several cutin monomers in the 0 to 50 g range, showed different
response for each monomer; this implies that a standard curve has to be constructed for
each monomer being analyzed. Hydroxy-fatty acids were identified upon analysis by GCMS which required a double derivatization prior to this analysis (i.e. methylation with
methanol in the presence of BF3 then silylation of the hydroxyl group with N,Obistrimethylsilylacetamide for 30 min at room temperature).
The condensation nucleation light scattering detection (CNLSD) utilizes light
scattering as well but relies on an additional step of condensing water vapor on the
14
aerosol particles to increase their size and ultimately their light scattering detection. 24-26
Lipid analysis has not been extensively studied with CNLSD. For analysis of neutral
lipids (such as triglycerides) and phospholipids (polar lipid) using microbore highpressure liquid chromatography, the LOD of phospholipid was found below 100 ng/mL
and LODs were even lower for neutral lipids at 12 ng/mL.32 LOD values attained with
CNLSD are better than with ELSD.
Detection with Charged Aerosol Detector (CAD)
Charged aerosol detectors rely on identical nebulizing techniques of the HPLC
column effluent, followed by evaporation of the solvent in the resulting droplets, but the
detection occurs by detecting the charge of the resulting particles with an electrometer.7,8
Particle charging is efficient for the small particles produced at lower concentrations.
Charging can be created by passing the particles through a region in which gas phase ions
have been produced from a corona discharge source. Particles can also be charged during
the process in which the HPLC column effluent is nebulized and sprayed out. In the
present work of fatty acid identification and analysis, the latter charging that does not
utilize a corona discharge is being explored.
The benefit of particle charging is that their detection is less influenced by the
droplet size; the CAD shows improved response linearity and lower LODs over that of
the ESLD. Linear or non-linear response usually depends on the solvent and the
concentration range of the analytes being studied. A prototype detector was first tested
using direct flow injection with water as the mobile phase and sodium sulfate as analyte.8
15
Linearity was observed over a concentration range of 0.2 to 100 ng/l. Linearity was also
observed for the direct quantitation of triacylglycerols from plant oils using gradient
compensation.34 Linearity was not achieved on a series of active pharmaceutical
separated by reversed-phase liquid chromatography with corona-CAD Detectors.35 The
authors further stress that compared to a UV calibration that theoretically only needs a
two point linear calibration curve, a main disadvantage of the CAD was its non-linear
response behavior forbids the use of linear regression for drawing calibration curves.
During the course of this thesis work, Moreau published studies on the CAD
detection of triacylglycerols, diacylglycerols, glycolipids, phospholipids, and sterols
when using reversed and normal phase HPLC analysis.14,15 Moreau showed that FAMEs
were not detected, probably because their higher volatility led to their complete
evaporation and did not result in charged aerosol particles. Lower molecular weight fatty
acids were detected but with low intensity peaks. The minimum limits of detection varied
with different mobile phase solvents, with the best detection being observed with hexane
(e.g. in normal phase HPLC) while reversed phase HPLC solvents such as methanol,
isopropanol, and acetonitrile contributed to higher levels of CAD background noise.
Mostly linear mass-to-peak area relationships were found for many types of lipids. The
LODs of triacylglycerols were found around 1 ng per injection and their mass-to-peak
area ratio was nearly linear from a range of about 1 ng to 20 ng per injection.
16
MATERIALS AND METHODS
In order to carry out the stated objectives, optimization of the analytical method
includes a review of the analytical hardware; the following being considered: (i) an inlaboratory built charged aerosol detector and (ii) separation parameters using HPLC. The
preparation of composite standards, procedure of olive oil saponification and analysis are
subsequently detailed.
HPLC-CAD System
The system is composed of an Agilent (Santa Clara, CA) 1100 HPLC system
equipped with a G1312A binary gradient pump, G1313A autosampler, G1316 Column
Heater, G1100 Degasser, and G1314A VWD UV detector and is configured to the
custom-made aerosol detector consisting of the Meinhard (Golden, CO) nebulizer with
Glass Expansion (West Melbourne, Australia) spray and evaporation (temperaturecontrolled) chambers and a TSI (Shoreview, MN) Electrical Aerosol Size Analyzer
(EAA), Model 3030. In addition, the nebulizer utilizes compressed nitrogen for the
nebulization, a vacuum pump to pull air through the EAA, a liquid waste bottle, and a
temperature controller for the evaporation chamber. See Figure 4 for a block diagram
and Figure 5 for a picture of the spray and evaporation chambers. The HPLC system is
controlled by Chemstation software. The HPLC-CAD system was used to detect certain
fatty acids of carbon chain lengths C:12 through C:18 and develop a method for
quantifying non-volatile or semi-volatile fatty acid. The Agilent Zorbax Eclipse XDBC18 column (150 x 4.6 mm - 5µm particle size) was used for the study of optimizing the
17
detection method. Due to the Eclipse XDB column deterioration and unavailability of an
exact replacement, a Grace column (150 x 4.6 mm - 3µm particle size) was used to
analyze the saponification of olive oil.
Figure 2 depicts the configuration of the HPLC-CAD system in which the effluent
passes through the 1100 Agilent system via standard HPLC tubing to the Meinhard
(Golden, CO) nebulizer.
evaporation
chamber
Figure 2: Diagram of HPLC-CAD process.
The effluent passes through the inner tube of the glass nebulizer with aid of
flowing nitrogen gas supplied by the nitrogen cylinder regulated between 58 to 60 psi,
flowing in the region between the inner tube and the outer tube of the nebulizer. The
outlet of the spray chamber is attached to tubing leading to the evaporation chamber. The
evaporation chamber was heated by an electrical heater (located near the fan in Figure 3)
that was controlled by an external Omega temperature control unit. These units are
18
designed to convert the naturally charged spray leaving the spray chamber into an
aerosol.
Figure 3: Nebulizer with custom spray and evaporation chambers.
Detection by CAD occurs as follows: a) nebulization of the sample-containing
eluent passing from the HPLC column at the concentric nebulizer into the spray chamber.
Previous research has shown that spontaneous charging of the liquid droplets formed
through nebulization occurs in a process known as spray electrification,12 b) evaporation
of the nebulized spray into an aerosol, c) modification to the charged aerosol in an
electrical field within the EAA and d) collection of the charged aerosol particles on a
filter connected to an electrometer for current measurement, also in the EAA.
A
significant difference of this study from earlier work with custom-built or commercial
CAD instruments is that it occurred during the nebulization and spray process and not
through the use of a corona discharge.
The components for the nebulization and
19
evaporation steps are shown in Figure 3. The charging of the aerosol in this study from
earlier work with custom-built or commercial CAD instruments7,8 is that it occurred
during the nebulization and spray process and not through use of a corona discharge.
Past research in our group has shown that this passive charging provides excellent
sensitivity in the analysis of carbohydrates using a variety of different mobile phases.36,37
After the spray and evaporation chambers, the aerosol flows into the EAA. Figure
4 depicts the EAA, which, it should be noted, is cylindrical in shape. The EAA is
designed to impart particle charging in the corona discharge region (top region of EAA in
Figure 4). However, the corona discharge voltage supply was disconnected for this work.
The lower part of the EAA consists of an ion filter, formed from a negative voltage
placed on the rod housing sheath air flow 2. This filter is designed to selectively remove
smaller positively charged particles formed in the corona discharge region from passing
to the aerosol filter. Under my use with spray electrification and without the corona
discharge, this region is believed to both remove smaller positively charged aerosol
particles and most negatively charged aerosol particles.
The greater efficiency for
removing negatively charged particles is due to the shorter travel distance to the outer
walls as the aerosol enters the cylinder close to the outer wall. The outer wall is
grounded and thus positive relative to the negatively charged rod and attracts the
negatively charged particles. Charged particles passing through the ion filter reach the
aerosol filter and then pass a current to the electrometer based on the net (positive minus
negative) charge flux from charged particles hitting the filter. The net effect of the
electric field region is to pass only the largest negatively charged particles and all but the
20
smallest positively charged particles. The size of the charged particles passing through
the ion filter and affecting the output signal is controlled by the adjustment settings of the
ion voltage on the EAA. The more negative or higher output of the electric field voltage,
the more particles will be removed in this section. Less negative values may be desired
for increasing sensitivity.
Figure 4: Schematic of the EAA.
21
Calibration Method
A non-linear calibration analysis was used since the response of the fatty acid
standards did not display a true linear relationship associated with the traditional linear
equation (y = mx + b). Therefore, response curves were fitted using the power-fit model
as is commonly used with aerosol based detectors using the equation as follows:
y = ACb
(1)
wherein y is the peak area, C is the concentration in µg/mL, and ‘A’ and ‘b’ values are
fixed parameters that are derived from the calibration power-fit equations. The power-fit
also represents a linear fit of response and concentration once those data are transformed
through use of logarithms. The need for the non-linear power-fit model was demonstrated
by ‘b’ terms (power variable) possessing values significantly greater than 1.0 (as will be
described in the results later). With this non-linear calibration scheme, concentrations of
components observed in olive oil samples were determined by rearranging the power fit
equations obtained for the fatty acid standard curves to solve for C, concentration, as
follows:
C = (y/A) 1/b (2)
Fatty Acid Standards Preparation
Linoleic acid (C18:2), linolenic acid (C18:3) and tristearin were manufactured by
Sigma (St. Louis, MO). The fatty acid standard obtained from Mallinckrodt was oleic
acid (C18:1). Fatty acids standards manufactured by Matheson, Coleman and Bell (East
Rutherford, NJ) included azelaic (C9:0), lauric (C12:0), myristic (C14:0), palmitic
22
(C16:0), stearic (C18:0) acids. The fatty acid standard obtained from MP Biomedicals
(Santa Ana, CA) was margaric acid (C17:0). HPLC grade solvents used are listed as
follows: acetonitrile (Fisher Scientific, Hanover Park, IL), methanol (Fisher Scientific,
Hanover Park, IL), concentrated ACS grade ammonium hydroxide (EM Science,
Gibbstown, NJ) and NaOH (EM Science, Gibbstown, NJ). Trifluoroacetic Acid was
obtained from Fluka (Milwaukee, WI). The fatty acid standards were solubilized in
100% ethanol (200 proof, ACS/USP grade, from Pharmco-Aaper, CT) at stock
concentrations of 2000 g mL-1. Composite standards, made from the stock solutions at
0.5, 2, 5, 20, 50, and 200 g mL-1, were pipetted into HPLC auto sampler vials and
loaded into the HPLC auto sampler. For the method optimization, all above standards
were used but linoleic and linolenic acids which are less stable overtime and very
expensive were only used in the olive oil experiments. All standard solutions were stored
at 4oC.
Isocratic Mobile Phase Preparations
For standard calibration curve analysis of standards, mobile phases consisted of
88% acetonitrile (from HPLC bottle C) and 12 % aqueous solution (from HPLC bottle
B), wherein bottle C consisted of pure acetonitrile (from Fisher Scientific) and Bottle B
consisted of water containing trifluoroacetic acid at 10-2, 10-3 or 10-4 M concentrations.
For instance, a 10-2 M TFA aqueous solution was prepared by diluting 0.383 mL of TFA
(MW = 114.02 g/mol, d = 1.489 g/mol) with nanopure water in a 500 mL volumetric
flask. After selecting the 88:12 acetonitrile:0.01M aq trifluoroacetic acid as the mobile
23
phase, EAA ion voltages were varied at -150 V, -225 V and – 300 V to find optimal ion
voltage for best peak sensitivity and linearity. The 12% aqueous solutions were also
prepared containing 0.01M acetic acid and 0.01M formic acid. Glacial acetic acid and
formic acid (ACS grade) were purchased from Sigma-Aldrich and from Chemical MFG,
respectively. Finally, two 12% aqueous solutions containing 5.5mM TFA:5.0 mM
NH4OH or 11 mM TFA:10 mM NH4OH) were prepared; pH readings were observed at
3.31 and 2.84, respectively. Working molarity of ammonium hydroxide solutions (28.0 to
30.0 % NH3 content) was first determined by titration with a 1 M HCl solution standard.
Standards Preparation for Olive Oil Analysis
Composite standards consisting of oleic, palmitic, stearic, C17, linoleic and
linolenic acid were prepared in the 0.5 ppm to 200 ppm range as above. Due to instability
of linoleic and linolenic acid standards, standards limited to a one-time use analysis after
which the standards were spent and/or discarded. Tristearin and margaric acid (C17)
were used as internal standards to test for recovery in the saponification. Margaric acid
tests the recovery of fatty acids released upon saponification of the olive oil and does not
interfere because essentially all natural fatty acids have even chain lengths. Tristearin,
which is a triglyceride containing only stearic acid, tests for the recovery of fatty acids
initially present as triglycerides. However, because stearic acid is present in olive oil, to
be effective, olive oil must be analyzed both with and without addition of tristearin.
24
Olive Oil Saponification without an Internal Standard
Certified Olive Oil Standard (Product O1514) was obtained from Sigma (St.
Louis, MO); it included a certificate of analysis released in September 2006 that reports
oil composition in percentage values obtained by GC-FID analysis. Three independent
saponification experiments were prepared in three 20 mL-vials by mixing a SigmaAldrich certified olive oil [100 mg (Sample #1 and #2) or 250 mg (Sample #3)] with a
prepared solution of 5% of NaOH (w/v) in ethanol/water (85:15). Two mL of the NaOH
solution were added to sample #1 and #2 and 5 mL were added to sample #3. The three
vials were capped and heated for 40 min at 40◦C then 5 min at 60◦C in a water bath. After
cooling, the unsaponified material was extracted with 2 washings of 5 ml hexane each.
The hexane washings were discarded. The aqueous layer was acidified with a 1M
solution of HCl. Three mL of HCl were added to samples #1 and #2, while six mL of HCl
were added to sample #3. The fatty acids were then extracted with two washings of
hexane (2 x 5 mL). The combined hexane solutions were rotary evaporated to dryness
and the resulting fatty acids were stored in a freezer prior analysis. Fatty acids were
resolubilized in 100% proof ethanol at the approximate concentrations of 200 g mL-1
concentrations prior HPLC analysis at 1 L injection.
Olive Oil Saponification with Spiked Internal Standard
HPLC analysis of the fatty acids from the olive oil, saponified in the presence of
added internal standards permits identification and quantification simultaneously. The
25
response factors for each fatty acid in the oil sample were determined in relation to (a
C17 fatty acid).
The saponification experiments were carried out by mixing 10 milligram weights
of margaric acid (C17) and a second standard tristearin with three samples of unfiltered
olive oil (100 mg or 250 mg) in a 20 mL vial. Solutions of 5% NaOH in ethanol/water
(85:15) were added (2 or 5 ml for 100 or 250 mg oil sample, respectively). The vial was
tightly capped and the reaction mixture was heated for 1 hr 30 min at 40oC and then for
an additional 1 hr 30 min at 60oC to ensure complete saponification of the tristearin
standard. Additional heating time vs. the non-spiked saponification experiments was
needed due to the low solubility of tristearin in the aqueous ethanol solution. During the
heating process, the sample vials were periodically vortexed and the cap loosened to
relieve pressure buildup. After cooling, the non-saponified materials were extracted with
two washings of 5 mL hexane, which were discarded. The aqueous layer was acidified
with a 1 M solution of HCl; specifically 3 mL were added to the 100 mg samples (#4 and
#5) and 6 mL was added to the 250 mg sample (#6). The acidified aqueous layers were
then extracted with hexane (2 x 5 mL). The combined hexane solutions were evaporated
to dryness and the resulting fatty acids were stored in a freezer prior to analysis. Fatty
acids were resolubilized in 100% proof ethanol at the approximate concentrations of 200
g mL-1 concentrations prior HPLC analysis at 1 L injection.
26
RESULTS AND DISCUSSION
Methodology Development in CAD Detection of Fatty Acids
Modifying the mobile phase composition, eluent flow rates, or nebulizer internal
pressure will alter the detector response of the fatty acids. In addition, shifts in retention
time and resolution, which are controlled by change in eluent strength, are also to be
expected. Peak area is used for calibration while peak height is important at looking at
sensitivity. HPLC mobile phase composition was first studied. Its content in acetonitrile
was varied in order to obtain good separations of fatty acids standards (consisting of
oleic, palmitic, stearic, margaric, linoleic and linolenic acid) with a minimal (1.5)
resolution for the two closest eluting peaks while minimizing run time to less than 15
minutes.
Mobile phase composition containing high organic percentages is required to
elute hydrophobic fatty acids on a reverse-phase column. Two types of column packed
with 3 mor 5 mC18-silica beads diameters were tested. Method development runs
were carried out with acetonitrile ranging from 80 and 95% using the Agilent Eclipse 5
m column. Azelaic (C9-dicarboxylic acid) was selected as the unretained peak marker
(See Figure 5). A run with 88% of ACN gave a resolution of 2 between the two closest
eluting peaks oleic (C18:1) and palmitic (C16:0) within a 15 minute time. This satisfied
our HPLC goal which led to keep this % of ACN for the subsequent studies of HPLC
parameters on CAD peak detection. When switching to a column from another
manufacturer, stearic acid, the last eluting compound, eluted at later retention time when
using the 88% ACN. To shorten the run time, ACN percentage was increased to 96.5%.
27
It was observed that palmitic acid (C16:0) eluted after oleic acid (C18:1). The two fatty
acids were not baseline resolved at low concentrations and eluted with less than ideal
resolution at 200 g/mL concentration as detailed in later section on olive oil analysis.
C9-di acid
C14:0
C16:0
C18:1
C18:0
Figure 5: Chromatogram of fatty acids separated using 5 m column (Mobile phase:
88:12 acetonitrile: 0.01M trifluoroacetic acid (aq).
Method development was then focused on finding modifier content and pH that
would maintain the fatty acids in the acid form and not in the anion form as well as favor
formation of solid charged particles to improve the aerosol detector response. The
addition of acid was shown in previous ELSD studies to eliminate peak broadening that is
attributed to the simultaneous occurrence of both associated (HA) and dissociated (A-)
forms of fatty acids. Acid and base modifiers (formic acid, acetic acid, trifluoroacetic
acid, and ammonium hydroxide) were used in various mobile phases in testing the given
set of fatty acids.
Secondly, the electric field voltage of the EAA with the corona discharge turned
off, was performed at the analyzer voltages of -150 V, -225 V and -300 V. The analyzer
voltage affects discrimination of residual particles based on size and charge with more
negative voltages reducing the signal but also noise. This allowed us to identify optimal
28
voltages for detector sensitivity, detector linearity, and detector range. In addition,
temperature settings of the evaporation chamber in the CAD detector were varied (30oC,
35oC and 45oC) at -225V to study their impact on the detection of oleic and palmitic acid.
Thirdly, once a working mobile phase composition using C18 columns was
established, composite standards were prepared and tested for their retention time and
detector response. Response curves were drawn using power fit regression.
Optimization of Mobile Phase Parameters
An isocratic method was sought, as it keeps the buffer composition reaching the
CAD detector constant. This is a preferred method in the absence of gradient mobile
phase compensation34 or in the absence of a known standard for the analyte of interest. In
the isocratic method, a mobile phase with a constant composition is pumped through the
column during the whole analysis. A gradient method can reduce the analysis time while
giving narrow peaks, but it usually requires a greater total time when including the longer
time required to stabilize the column before injection of next sample.
Most optimization parameters were carried out using an ACN:H2O (88:12)
isocratic mobile phase and were subsequently applied to the mobile phase used for the
olive oil analysis, although with a higher ACN content. Without the addition of a mobile
phase modifier, separation of fatty acids was achieved but did not yield good peak shapes
(data not shown). The addition of acidic modifiers improved peak shapes while
enhancing peak detection and lowering limit of detections. Variability in fatty acid
response is largely a function of the volatility of the fatty acids: large peak area is
29
observed for stearic acid, small peak area is observed for myristic acid and no peak area
for lauric acid is observed.
A. Variation of acid modifiers
The (88:12) acetonitrile:H2O mobile phase was modified with TFA, formic acid or
acetic acid which, by lowering the mobile phase pH, keep fatty acids uncharged. Table 1
and 2 compare peak areas and baselines observed for composite standards made of
palmitic, oleic and stearic acids run in each of the mobile phase at an ion voltage of -225
V. The acid modifier content was set at 10-2 M.
0.01 M TFA
Area Counts
0.01 M Formic Acid
Area Counts
Composite
Standard
(ng/L)
Baseline
(mV)
Palmitic
(6.75
min)
Oleic
7.20
min)
Stearic
(12.38
min)
Baseline
(mV)
Palmitic
(6.78
min)
Oleic
(7.23
min)
Stearic
(12.43
min)
200
50
20
5
2
0.5
25.3
25.2
25.0
24.9
24.8
24.7
2620
208
44.9
6.9
ND
ND
1390
173
48.8
5.8
ND
ND
7260
824
176
24.2
5
ND
27.0
26.5
25.3
25.1
25.3
25.5
3520
419
65.8
2.8
ND
ND
2080
233
52.6
4.9
ND
ND
8890
1380
242
34.4
13.4
ND
Table 1: Detection of fatty acids using 0.01 M TFA or 0.01 M formic acid modified
ACN:H2O (88:12) mobile phase. ND = not detected.
Tables 1 and 2 summarize data from runs on two different days and show day-today variability (e.g. in the TFA results, which were repeated), probably due to differences
in column equilibration or fluctuations in detector response. Non-volatile fatty acids
were detected as low as 2 ng/L, using either 0.01M TFA, formic acid, or acetic acid as
the 12% modified buffer component in the mobile phase. Since the signal to noise ratio
30
was generally better with TFA than with formic or acetic acid buffers at the lower fatty
acid concentrations, TFA as an acidic modifier was chosen. The higher baseline for TFA
in Table 2 vs. Table 1 may be attributed to insufficient time for column equilibration (as
shown by the decreasing baseline in a series of runs). However, the baseline difference
was minimal and did not impact integration of peak for area count quantitation.
Composite
Standard Baseline
(ng/L)
(mV)
200
50
20
5
2
0.5
28.2
27.5
26.1
25.7
25.6
25.5
0.01 M TFA
Area Counts
0.01 M Acetic Acid
Area Counts
Palmitic
(6.61
min)
Oleic
7.05
min)
Stearic
(12.12
min)
Baseline
(mV)
Palmitic
(6.77
min)
Oleic
(7.22
min)
Stearic
(12.39
min)
2830
478
121
22
4.7
ND
1900
377
95
16
4.4
ND
7440
1019
253
42
15
ND
25.9
25.1
23.5
23.1
23.1
23.0
2970
435
53
4.1
0.6
ND
820
273
39
5.1
1.6
ND
8496
1115
184
17.8
3.5
ND
Table 2: Detection of fatty acids using 0.01 M TFA or 0.01 M acetic acid modified
(88:12) ACN:H2O mobile phase.
B. Variation of TFA concentrations
A comparison of baseline changes and peak area counts of a volatile myristic acid
standard (500 ng/L) was analyzed at ion voltage -225 V using mobile phases containing
TFA at 10-2, 10-3 or 10-4 M concentrations. Table 3 indicates that increasing TFA
concentration from 10-4 to 10-3 M increased the myristic acid peak area count by a sevenfold factor.
31
TFA (mol/L)
Baseline (mv)
Area count
Peak Height
10-4
10-3
10-2
24.5
64.3
657
163
1162
Off scale
22.8
54.1
Off scale
Table 3: Area count comparison of myristic acid (500 ng/L) at -225 V using an
ACN:H2O (80:20) mobile phase set at various TFA concentrations.
Increasing TFA concentration significantly enhances the detection of volatile fatty
acids but led to an increase in baseline. Varying ion voltage output can help bring the
fatty acid on scale when higher baselines are recorded. In this set of runs, higher TFA
concentration led to higher baseline noise, which is inconsistent with the baselines
reported in Table 1 and 2. Higher baselines could stem from a larger column bleed,
particularly because in this instance, the eluent was not pumped through for an extensive
time period. Such column bleeding, which is the silica bonded phase breaking down and
leaving the column, is often associated with high acid concentration. High column bleed
often occurs when first switching to higher acid concentration, though, and it is expected
that this is the reason for higher baseline values. Interestingly, an unexpected increase of
the resulting detection signal of semi-volatile compounds could also be traced to this
column bleed that could help in forming or stabilizing aerosol. Increased sensitivity can
be expected due to the non-linear response in which the ‘b’ term in y = ACb (equation 1)
is larger than 1 (this is discussed further in future sections). Additionally, for more
volatile fatty acids, low response, particularly at low concentrations, is due to small
particles formed and the greater vapor pressure over curved than flat surfaces (known as
the Kelvin effect.38 As the non-volatile concentration increases, larger particles form,
32
resulting in a greater fraction of semi-volatile fatty acids existing in the aerosol particles
(as opposed to the gas phase). This can explain the greater sensitivity observed when
column bleed occurred. To determine whether this decreases LODs, you also would need
to look at noise levels (not just area counts).
C. Analysis of mobile phase buffered with TFA and amines
Solvent and modifier choice were also guided by the need of sufficient volatility
for evaporation in the detector under conditions that could favor formation of ion pairs
such as species 1 (see Figure 6), which would not be expected to evaporate and results in
increased peak detection.
As much lower sensitivities were observed for the more
volatile fatty acids, this could improve the detection of such acid. Experiments were
carried out using an ammonium-containing mobile phase [88ACN:12(5.5mM
TFA:5.0mM NH4OH)] and [88ACN:12(11mM TFA:10mM NH4OH)].
While the addition of trifluoroacetic acid (TFA) to the mobile phase improved
peak shape, the addition of ammonia (NH3) was intended to increase the equilibrium
toward forming presumably less volatile ammonium carboxylate salts and, thus, reach
lower detection thresholds. Ammonia choice was also guided by the need of for sufficient
volatility of the mobile phase for evaporation in the detector in order to minimize the
baseline and to provide conditions that could minimize the formation of gas phase analyte
molecules such as species 2 (see Figure 8), which do not contribute to the detected signal.
Mobile phases containing 11mM TFA: 10mM NH4OH or 5.5mM FTA: 5.0mM NH4OH
were used in order to favor the formation of ion pairs such as 1, which would not
33
evaporate, and result in increased peak detection. Both mobile phases were kept acidic
(pH at 2.84 and 3.31, respectively) in order to ensure column retention of the smaller
fatty acids during the HPLC step. An ammonia additive did not improve detection of
volatile lauric and myristic acids; other standard C16 and C18 fatty acids were observed.
Chromatograms show that lauric acid and myristic acid (at 400 ng L-1) may have been
detected as negative peaks at 2.45 minutes and 4.41 min, respectively.
(1)
(2)
CF3CO2H + NH3
H2O/ACN
[NH4+ CF3CO2-](s)
Detected (higher baseline):
Not desired
(3)
[NH4+CH3(CH2)nCO2-](s)
1
Detected: desired
Higher Peak
NH4+ + CF3CO2NH3(g) + CF3CO2H(g)
Not detected: desired
NH3(g) + CH3(CH2)nCO2H (g)
2
CH3(CH2)nCO2H (s)
Detected: desired
Figure 6: Proposed scheme for increased peak detection through the formation of less
volatile ion pairs of fatty acids.
Those negative peaks could be artifacts and had very small area count. Therefore,
this approach of charge contributions from charged mobile phase additives to enhance
semi-volatile fatty acid detection was discontinued.
34
When another base modifier, methylamine, was used to replace ammonia, the
baseline was observed off scale, exceeding the maximum recordable signal at 1000 mV.
The mobile phase may have formed non-volatile particles and had a detrimental effect on
the detector baseline. Another possibility is that the pH was greater than 8, resulting in
increased column bleed.
Methodology of CAD Parameters
After having optimized buffers that gave reproducible separations on the C18columns, the ion filter voltage of the EAA was adjusted in order to reduce the baseline or
increase the signal to noise ratio. This allowed us to determine the approximate limit of
detection of some common fatty acids (i.e. detection limit is the concentration of analyte
that gives a specified signal-to-noise ratio, typically greater than three). The fatty acids
could have been directly pumped into the detector to assess their individual LOD. In
order to take into account that bleeding from a silica based column can have a significant
impact on the CAD baseline and increase the limit of detection, a direct approach was not
pursued.
Evaluation of Optimal Ion Voltage
The ion voltage settings on the TSA Electrical Aerosol Analyzer (EAA) can be
adjusted such that the concentration ranges of sample analytes are detected on scale.
Calibration standards from 0.5 to 200 ng/L were analyzed for all FAs at ion voltages of
-150 V, -225 V and -300 V to find the best fit for quantifying samples in that calibration
35
range. All FAs have similar non-linear regression curves with b values greater than 1 and
yield the same conclusions. As an example, table 4 summarizes the observed peak height
and LOD values for oleic acid that were used in determining the dynamic range defined
as the log of the range of FA concentration over which the CAD-detector responds to
changes in FA concentration (from the minimum detectable concentration to the
concentration in which the maximum recordable signal is reached). LODs at each of the
ion voltages were determined using non-linear extrapolation to the lowest detected peak
distinguishable from noise.
Oleic Acid
200 ng/L
50 ng/L
20 ng/L
5 ng/L
2 ng/L
0.5 ng/L
S/N Ratio at 5 ng/L
Noise (6*Standard
Deviation)
3Noise ([6*SD]/2)
LOD (in ng)
Mass in ng
(maximum
quantified in Linear
Range)
Dynamic Range
Peak Height (mV) measured with the EAA at:
-150 V
287
54.3
21.5
4.7
1.6
0.67
8.0
-225 V
163
23.4
7.7
1.6
0.51
NQ
11.4
-300 V
80.1
7.4
2.1
0.51
ND
ND
1.08
1.18
0.28
0.80**
0.59
0.6
0.14
1
0.40
10
1300
5200
5200
3
4
3
Table 4: Oleic acid response associated with ion voltages of the EAA. NQ = non
quantifiable. ND = non detected. (** Higher noise due to enhanced baseline spikes.)
36
Comparison of results showed higher peak height for the same sample
concentration, and strong signal-to-noise ratios at the lower ion voltage scale of -150 V.
The signal to noise ratios at -300 V were less than optimal due to intermittent electronic
spikes in the baseline. Typically, noise is magnified at the lower ion voltage strengths at
standard concentrations near to the LOD; increasing signal to noise ratios with increasing
ion voltages is expected. Table 4 depicts signal to noise ratios for the 5 ng/L standard
since low level peaks at 0.5ng/L were not quantifiable at -225 V and -300 V. The LOD
value, determined by non-linear extrapolation to the lowest detectable concentration at
each ion voltage, ranged from 0.6 to 10 ng.
However, the maximum detectable
concentration, fit to the maximum detectable signal, was estimated at 1300 ng/L for the
-150 V ion voltage, resulting in more off-scale measurements for the more concentrated
samples.
At -300 V, results showed even smaller peaks for the same sample
concentration range and smaller signal-to-noise ratios. The calculated FA mass ranged
from 10 to 5200 ng at -300 V which is favorable for higher mass, but less favorable when
concerned about low concentrations.
For best sensitivity, the -150 V is the optimal ion voltage. Better linear response
was observed at ion voltage at -150 V, in which the calibration power fit was observed
with a “b” value close to 1 (Figure 7). Both calibration curves at -150 V and at -225 V
were well represented by power fit curves as shown by r2 values of > 0.99 (b values of 1
indicate linear response; r2 gives goodness of fit). The greatest span of detectable
concentrations was observed at -225 V with four orders of magnitude for the dynamic
range. The -300 V ion voltage was selected for testing the method on an olive oil analysis
37
since the major oil component peak (oleic acid) could be off scale if a lower ion voltage
was used. Based on the greatest dynamic range at -225 V, a lower ion voltage could have
been the best ion voltage to use.
Figure 7: Oleic acid standard curves from ion voltages of -150 V, -225 V and -300 V
using Agilent XDB-C18 (5m) column. Note dynamic range was observed at three
orders of magnitude for ion voltages of -150 V and -300 V.
Table 5 shows that baseline shifts were minimal at the different ion voltage scales.
In increasing the ion voltage of the EAA of CAD detector from -150 V to -300 V, the
baseline dropped from 31 mV to 23 mV. Overall, baseline shift is not significant since
baseline readings at 23 to 25 mV depict zero level of the detector output. However, such
a small change in baseline could be significant (from zero to non-zero) but would not be
expected to affect the noise. The higher noise seen at -300 V in Table 4 may be due to
greater “spikiness” (probably just due to random changes in nebulization characteristics).
38
Baseline (mV) measured at
Composite
Standard (ng/L)
-150 V
-225 V
-300 V
200
31.6
24.5
23.2
50
31.8
24.5
23.1
20
31.9
24.3
23.1
5
31.5
24.2
23.0
2
31.2
24.3
23.2
0.5
31.6
24. 3
23.1
Table 5: Baseline values at different ion voltage settings.
Evaluation of Temperature Parameters for CAD Detection
Temperature changes to the oven used to evaporate spray droplets were analyzed
at the baseline ion voltage setting of -225 V. Composite standards (made of palmitic,
oleic and stearic acids) were analyzed using (88:12) ACN:0.01M TFA at three different
CAD temperatures (30oC, 35oC, and 45oC) at ion voltage -225 V (see Table 6a). In
increasing the temperature, a small baseline shift from 25 mV to 24 mV was observed,
showing no significant impact. Area counts of the fatty acid components in the 50 ng/L
and 200 ng/L composite standards showed a decrease in area counts with increased
temperature conditions supporting the increased volatility and loss of detection of the
fatty acids by the CAD. At standard operating condition of 35oC, it was observed that the
39
area counts of the components in the composite 50 and 200 ng/L standards were higher
at the -225 V scale than at the -300 V scale as shown in Table 6b, which is due to the fact
that higher ion voltage outputs deflect a higher number of charged particles from entering
into the CAD detection stream. This is more pronounced for more volatile compounds
(C16 vs. C18) and at lower concentrations (50 vs. 200 ng/L), as is expected.
Area Counts
Composite
Standard
(ng/L)
CAD
Temperature
(oC)
200
50
200
50
200
50
30
30
35
35
45
45
Palmitic
(6.66
min)
Oleic
(7.11
min)
Stearic
(12.17
min)
3270
391
1920
155
482
71
1970
311
1190
141
538
86
7980
936
6600
682
3945
459
Baseline
(mV) at
-225 V
25.3
24.9
24.2
24.3
24.3
24.3
Table 6a: Temperature effect on area count and baseline at ion voltage -225 V.
Area Counts
Ion
Voltage
- 225 V
- 300 V
Composite
Standard
(ng/L)
CAD
Temperature
(oC)
200
Palmitic
(6.66
min)
Oleic
(7.11
min)
Stearic
(12.17
min)
Baseline
(mV) at
-225 V
35
1920
1190
6600
24.2
50
35
155
141
682
24.3
200
35
1340
77
3740
23.2
50
35
93
78
295
23.1
Table 6b: Temperature effect on area count at -225 V and -300 V.
Quantitative Analysis
Once the mobile phase gave satisfactory separations, the CAD was tested in
quantitative analysis by calibrating with a series of fatty acid standards using the
40
optimized operating parameters for the detector (ion voltage, temperature). With most
fatty acids, the response of the CAD using power fit represented the data well in the
range 5 to 200 ng/L of the calibration study for the olive oil analysis at -300 V (see next
section).
Method Application to Olive Oil Analysis
The saponification of an olive oil standard to determine its fatty acid content was
used to test the effectiveness of the HPLC-CAD methodology on a realistic sample.
Triacylglycerols contained in the olive oil yield upon saponification a mixture of
saturated and unsaturated fatty acids (see Fig 9) which were analyzed using HPLC-CAD.
Saponification was carried out at mild temperature in order to minimize double bond
isomerization and/or oxidation.18
Figure 8: Saponification of triacylglycerol.
41
A first wash with hexane of the saponified solution extracts any unsaponified
organic material present in the oil sample. Then upon neutralization of the aqueous layer
with an HCl solution, a second wash with hexane extracts the fatty acids. For four
saponification experiments, hexane extractions and washing steps were directly carried
out in the saponification vials. Fractions of the hexane layer containing the fatty acids
were pipetted out and evaporated for analyses. Those experiments were used to determine
the relative oil composition. Two saponification experiments (Sample #3 and #6) were
worked up using separatory funnels. This allowed a complete recovery of the hexane
layer containing the fatty acids from the acidified aqueous layer. Those experiments were
used to calculate relative oil composition and total recovery yield based on the quantity of
weighed olive oil (see Table 9).
Of the six olive oil saponification experiments, three of them (samples #4 to #6)
were carried out with addition of two internal standards. Saponified samples #1 to #6
were separated using a 3m C18 column using an acetonitrile: 0.01M TFA (96.5:3.5)
mobile phase. The mobile phase had a higher ACN content than the mobile phase
optimized on a 5m C18 column; other HPLC-CAD parameters were identical to the
method developed using the 5m C18 column. Saponified olive oil samples showed
peaks at 3.83 (linoleic acid), 5.56 (oleic acid), 7.28 (heptadecanoic – an added internal
standard) and 9.29 (stearic acid) minutes in their CAD chromatograms. The oleic peak
was the major peak. Figure 9 is a representative chromatogram of a saponified olive oil
sample with C17 internal and tristearin standard.
42
C18:1 &C16:0)
C18:2
C18:2
C17:0
C18:0
C18:1
Figure 9: Chromatogram of a saponified oil sample eluting in the following order:
linoleic (C18:2), oleic and palmitic (C16:0), margaric (C17 internal standard), and stearic
(C18:0) acids. (upper trace CAD signal; lower trace UV signal).
Calibration curves were constructed for the fatty acids found in olive oil from
composite standards (ranging from 0.5 ng/L to 200 ng/L). The composite standard
contained also a C17 standard which allows us to further quantify using a recovery
normalization procedure, all fatty acid components being expected to be extracted and
recovered homogeneously. Figure 10 is a representative CAD-chromatogram from the
200 ng/L composite standard containing the C17 standard. The lower trace shows the
corresponding UV chromatogram. At high fatty acid concentrations (200 g/mL), fatty
acids containing one to three alkene bonds (C18:1 to C18:3) were detected by the UV-
43
detector. At 5 g/mL, oleic acid (C18:1) could not be detected. Only linolenic acid
(C18:3) could be detected in the composite standard prepared at the lowest concentration
with a LOD similar to those attained by the CAD-detector.
The mass of fatty acid components in the olive oil can be calculated using their
corresponding FA calibration curves normalized with the actual spiked standard C17
recovery. A recovery factor for C17 is calculated based on its known starting amount of
C17 vs. the calculated recovered C17 based on its calibration curves. Essentially, this
accounts for any losses (or gains) of fatty acids that can be attributed to the work-up
procedures.
C18:3
C18:2 C16:0
C18:1
C18:3
C17:0
C18:0
C18:2
Figure 10: Chromatogram of the 200 ng/L composite standard eluting in the following
order: linolenic (C18:3), linoleic, oleic, palmitic, margaric and stearic acids (Upper trace
CAD signal; lower trace UV signal).
44
Figure 11 shows the different standard curves of the five olive oil fatty acids and
the C17 standard plotted on the same graph. The strong divergence of the calibration
curves shows that a quantitative analysis based on the relative peak areas without the
knowledge of the response factor would be, at best, approximate. Calibration curves
required plotting using a power fit standard calibration curve analysis in an EXCEL sheet
with correlation coefficient of 0.995 and better found for all analyzed fatty acids (See
calibration method section). Linear fit calibrations yielded low correlation coefficients
(around 0.85) and were not considered further.
6000
5000
Peak Area
4000
3000
y = 3.3758x1.373
R² = 0.9957
y = 2.0726x1.3796
R² = 0.999
y = 1.9178x1.4316
R² = 0.9967
y = 2.2325x1.3712
R² = 0.9981
y = 1.1301x1.3659
R² = 0.9994
y = 0.4398x1.576
R² = 0.9965
Stearic Acid
(C18:0)
Heptadecanoic
Acid (C17:0)
Oleic Acid
(C18:1)
2000
Linoleic Acid
(C18:2)
1000
Linolenic Acid
(C18:3)
0
Palmitic Acid
0
50
100
150
200
250 (C16:0)
Concentration (ng/L)
Figure 11: Standard calibration curves of composite standards consisting of stearic,
heptadecanoic, oleic (with their corresponding regression equations in the first column)
and linoleic, linolenic, and palmitic acids (with their corresponding regression equations
in the second column).
45
Relative fatty acid composition of the six saponified samples with respect to the
sum of the components in each olive oil sample was consistent (see Table 7). This
showed that sample handling and recovery were consistent. HPLC separation of the
saponified Sigma Olive Oil standard shows the composition as 90% oleic, 8% linoleic,
and 1-2% stearic compared to the GC/FID analysis by Sigma with their composition
values of 67.3% oleic, 12% linoleic, and 2.9% stearic. Components expected but not
clearly observed were palmitic acid and linolenic acid. Linolenic was observable in some
of the HPLC-CAD plots but at levels close to the detection limit; lower CAD-measured
concentrations of linolenic acid could be due to the degradation of the olive oil standard
manufactured by Sigma in Sept. 2006. Stearic acid was lower in samples #1 though #3
than expected (composition percentage per the manufacturer is 2.9%). This is could be
attributed to quantitation with a wider standard concentration range for the calibration
curve analysis than the effective sample concentration range appropriate to the actual
stearic acid content.
A reason for the HPLC-CAD overestimate of oleic acid and missing of palmitic
acid is likely to be due to the overlapping of the two fatty acids in the HPLC method
used.
Chromatograms of the composite standards showed that oleic acid (RT of 5.54
minutes) is not baseline resolved from palmitic acid (RT of 5.71). Palmitic (expected at
an 11.8% percentage composition per Sigma) is probably co-integrated with the oleic
peak. An enlargement of this region of the chromatograms shows a tailing at the end of
the oleic acid peak while all of the other peaks showed only fronting.
46
Mass Percentage
Sample
C18:2
C18:1
C18:0
#
CAD
GC/FID
CAD
GC/FID
CAD
1
8.4
91.6
ND
2
7.8
89.6
1.6
3
8.1
91.1
0.8
4
7.6
90.1
11.5
GC/FID
2.2*
67.3
2.9
5
7.8
89.3
2.9*
6
7.1
89.8
2.9*
Average
7.8
90.3
N/A
SD
0.4
0.9
N/A
Table 7: Fatty acid composition of olive oil by HPLC-CAD and Sigma GC/FID
analyses. * Samples 4 to 6 were spiked with tristearin which is saponified to stearic acid
(C18:0).
Figure 12: Expansion of Figure 9 showing the oleic acid peak with palmitic acid peak
eluting at the tailing end.
47
The higher sensitivity of HPLC-CAD vs. HPLC-UV is clearly shown in
chromatogram figures 9 and 10, notably for saturated fatty acids. Oleic acid was detected
as a very small peak in the UV trace that could be integrated. Area counts were used to
calculate a (CAD area count)/(UV area count) ratio that allows the comparison of oleic
peaks in different sample analyses (A/B in Table 8). For example, oleic acid UV area
counts in sample #6 and in the 100 ng/mL composite are reported in Table 8. The ratio of
oleic acid in sample # 6 is 1.43 times bigger than that of the 100 ng/mL composite
standard. This indicates that roughly 1/1.43 or 70% of the oleic acid CAD response peak
is actually from oleic acid (with the rest from co-eluting palmitic acid). Such difference
in ratios presumably accounts for the differing oleic percentages between the
experimental and Sigma-certified values. Adjusting the average oleic experimental
content value (90.3%) by the 70% corrective factor gave a 63% actual oleic content. This
is close to the Sigma value supporting further that palmitic acid co-eluted with the oleic
acid peak. However, it would also indicate that palmitic acid is ~27% (i.e. the difference
between 90.3 and 63%) of the total mass, which is too high. The validity of this ratio
approach assumed the sensitivity for palmitic and oleic acids to be identical and that their
overlapping peaks had no effect on the calibration curves obtained from the injections of
the composite standards. These are very approximate assumptions since the two FA curve
shapes are different and overlapping peaks make prediction less reliable in the case of
non-linear calibration. An alternative method to comparing CAD:UV ratios in order to
confirm co-elution would be to inject a mixture a C18:1 and C16:0 prepared in a ~6:1
ratio and observe for the oleic peak with a tailing shoulder.
48
Sample
Oleic CAD
Retention
Time
Oleic CAD
Area Count
= (A)
Oleic UV
Retention
Time
Oleic UV
Area Count
= (B)
Ratio
(A)/(B)
100 ng/L
Composite
5.54
606
5.41
4.2
144
#6
5.57
639
5.44
3.1
206
Table 8: Ratio of CAD:UV peak area count for oleic acid peak.
Sample #
Olive Oil
Weight (in
mg)
CAD-Quantified
mass of C18:1
and C16:0
(in mg)
3
6
254.5
245.7
226
262
Expected mass of
C18:1 and C16:0
based on Sigma
found content
(in mg)
202
195
% Recovery
112
134
Table 9: Oleic and palmitic acid absolute recovery based on Sigma-certified percentages
and CAD analyses. % Recovery = mass of CAD-quantitated FA/expected amount of FA.
Table 9 displays the actual recoveries of oleic and palmitic acid for samples #3
and #6 based on the CAD quantified mass of C18:1 and C16:0. The assumption is made
that palmitic and oleic acids co-eluted and the expected mass calculated is based on their
combined 79.2% (67.3 +11.8%) manufacturer content. The CAD-quantified mass of
combined C18:1 and C16:0 were calculated using their respective calibration curve
according to their peak areas. The calculated combined mass were higher than the starting
olive oil weight (which is an artifact, the olive oil samples being weighted accurately) or
higher than the expected mass that was calculated based on the Sigma certified content
(Sample #3). The large mass deviation of the coeluted peaks (observed vs. expected) is an
49
indication that the standard calibration curves may require a better power fit in order to
provide more accurate quantitation.
50
CONCLUSION
To avoid base-line drift during a chromatographic run and CAD detection, an
isocratic RP-HPLC method that yields good separation of non-volatile, free fatty acids
was sought. Efforts were focused on finding aqueous modifier content and pH that favor
efficient separation and formation of solid particles in order to improve the aerosol
detector response. The proposed isocratic method was also designed with consideration
of the effects of CAD temperature and detector voltage. The actual system functionality
was assessed by measuring the experimental LOD and sensitive ranges. A preferred
method, using a 3 or 5 m C18 silica column and a mobile phase containing a few
percent of an acid modifier, allowed the CAD detection and quantification of the less
volatile fatty acids in the range of from around 1 to 5 ng/L to over 200 ng/L utilizing
power fit curve analysis. The isocratic method used for the olive oil standard utilized an
acetonitrile: 0.01M TFA (96.5:3.5) mobile phase, ion voltage at -300V, and CAD heater
setting of 35°C. Using those running conditions, separation and detection of major C16 to
C18 fatty acids were achieved although palmitic and oleic acids were not completely
resolved. Conditions or mobile phase additives were not found to increase detection of
the more volatile fatty acids such as lauric or myristic acids. However, column bleed was
tentatively identified as an unexpected source of peak enhancement, which was attributed
to the formation or stabilization of bigger aerosol particles.
Siloxane containing
chemicals that are known to bleed from columns could be at the origin of the higher peak
detection. Finding siloxane additives that could enhance peak detection could warrant
further studies.
51
Evaluation of the method applied to olive oil analysis showed that relative
recovery of the major fatty acid components is consistent and supports the use of HPLCCAD system for a rapid detection of fatty acids at trace levels. Reproducible data were
obtained when calculating the relative recoveries of major, nonvolatile fatty acid
components but in instances of co-eluting peaks, additional data from the HPLC UVdetector were required. This points to further improvements needed in the proposed
HPLC method. The HPLC-CAD system could still achieve rapid, qualitative detection of
non-volatile fatty acids at levels as low as 0.5 ng under ideal conditions.
52
FUTURE WORK
The proposed method shows that mobile phase organic to aqueous acid ratio is an
important factor in providing a natural charging of the analyte as well influencing shorter
elution times.
With further development of the method by varying the organic
constituent and mobile phase modifiers while still considering impact on the baseline, a
more robust method could be developed for the HPLC-CAD analysis, most notably of
volatile fatty acids. Notably, mimicking column bleed with the use of additives in the
mobile phase could confirm the hypothesis that column bleed is potentially responsible
for the increase in peak area. Such additives will have similar chemical structures to those
being released by the column when first switching to high TFA concentration. For
instance, addition of siloxane additives such as octamethylcyclotetrasiloxane or
polydimethylsiloxane to the mobile phase could enhance aerosol formation and yield
better FA or other analyte detection. Such studies will also need to examine effects on
baseline values and associated noise, which would be expected to increase. Besides
analysis of fatty acids, HPLC-CAD also has the potential to detect triacylglycerides,
allowing more complete analysis of oil samples. Such compounds are difficult to analyze
by GC because very high temperatures are required. However, for triacylglycerides, the
only sample pre-treatment step needed would be dilution.
53
References
(1) Information on the chemistry, biology, technology and analysis of lipids can be
retrieved online from The Lipid Library (http://lipidlibrary.aocs.org/index.html).
(2) Krishnan, A., Sanderson, S. O., Cazanave, S. C., McConico, A. L., Masuoka, H.,
Gores, G. J., Charlton, M., Effects of the fast food diet on hepatic and peripheral lipid
metabolism, Hepatology, 54, 1160A, (2011).
(3) Voet D., Voet, J. G., Biochemistry, John Wiley & Sons, Inc., Hoboken, NJ, 382-404
(2004).
(4) Brondz, I., Development of fatty acid analysis by high-performance liquid
chromatography, gas chromatography, and related techniques, Anal. Chim. Acta, 465, 137, (2002).
(5) Ruiz-Sala, P., Hierro, M. T. G., Martinez-Castro, I., Santa-Maria, G., Triglyceride
composition of ewe, cow and goat milk fat, JAOCS, 73, 283-293, (1996).
(6) Christie, W. W., A practical guide to the analysis of conjugated linoleic acid,
INFORM, 12, 147-152, (2001).
(7) Gamache, P. H., McCarthy, R. S., Freeto, S. M., Woodcock, D. J., Laws, M. J., Cole,
R. O., HPLC analysis of nonvolatile analytes using charged aerosol detection, LCGC
North America, 23, 150-161, (2005).
(8) Dixon, R. W., Peterson, D. S., Development and testing of a detection method for
liquid chromatography based on aerosol charging, Anal. Chem., 74, 2930-2937, (2002).
54
(9) Liu, X. K., Fang, J. B., Cauchon, N., Zhou, P., Direct stability-indicating method
development and validation for analysis of etidronate disodium using a mixed-mode
column and charged aerosol detector, J. Pharmaceut. Biomed., 46, 639-644, (2006).
(10) Novakova, L., Solichova, D., Solich, P., Hydrophilic interaction liquid
chromatography – charged aerosol detection as a straightforward solution for
simultaneous analysis of ascorbic acid and dehydroascorbic acid, J. Chromatogr. A,
1216, 4574-4581, (2009).
(11) Vehovec, T., Obreza, A., Review of operating principle and applications of the
charged aerosol detector, J. Chromatogr. A, 1217, 1549-1556, (2010).
(12) Abhyankar, M. S., Examination of Tracers of Forest Fire Smoke During August
2002 Smoke Episodes, Thesis, California State University, Sacramento (2007).
(13) Vertzoni, M., Archontaki, H., Reppas, C., Determination of intraluminal individual
bile acids by HPLC with charged aerosol detection, J. Lipid Res., 49, 2690-2695, (2008).
(14) Moreau, R. A., The Analysis of Lipids via HPLC with a Charged Aerosol Detector,
Lipids, 41, 727-734, (2006).
(15) Moreau, R. A., Lipid analysis via HPLC with charged aerosol detector, Lipid
Technology, 21, 191-194, (2009).
(16) McDonald, R. E., Mossoba, M. M., Methods for trans fatty acid analysis, in Food
Lipids: Chemistry, Nutrition, and Biotechnology, edited by Akoh, C., Min, D. B., Marcel
Dekker, Inc., New York, NY, 169-203, (2002).
(17) Eder, K., Gas chromatographic analysis of fatty acid methyl esters, J. Chromatogr.
B, 671, 113–131, (1995).
55
(18) Nishiyama-Naruke A., Souza, J. A., Carnelos, M., Curi, R., HPLC determination of
underivatized fatty acids saponified at 37oC. Analysis of fatty acids in oils and tissues,
Anal. Lett., 31, 2565-2576, (1998).
(19) Yeung, E. S., Synovec, R. E., Detectors for Liquid Chromatography, Anal. Chem.,
58, 1237A-1256A, (1986).
(20) Wood, R., Lee, T., High-performance liquid chromatography of fatty acids:
quantitative analysis of saturated, monoenoic, polyenoic, and geometrical isomers, J.
Chromatogr. A, 254, 237-246, (1983).
(21) Murphy, E. J., Rosenberg, T. A., Horrocks, L. A., Separation of neutral lipids by
high-performance liquid chromatography quantification by ultraviolet, light scattering
and fluorescence detection, J. Chromatogr. B, 685, 9-14, (1996).
(22) Koropchak, J. A., Magnusson, L. E., Heybroek, M., Fundamental aspects of aerosolbased light-scattering detectors for separations, Adv. Chromatogr., 40, 275-314, (2000).
(23) Christie, W. W., Rapid separation and quantification of lipid classes by high
performance liquid chromatography and mass (light-scattering) detection, J. Lipid Res.,
26, 507-512, (1985).
(24) Allen, L., Koropchak, J. A., Condensation nucleation light scattering: a new
approach to development of high sensitivity, universal detectors for separations, Anal.
Chem., 65, 841-844, (1993).
(25) Allen, L. B., Koropchak, J. A., Szostek, B., Condensation nucleation light scattering
detection for conventional reversed-phase liquid chromatography, Anal. Chem., 67, 659666, (1995).
56
(26) Koropchak, J. A., Heenan, C. L., Allen, L. B., Direct comparison of evaporative
light scattering and condensation nucleation light scattering detection for liquid
chromatography, J. Chromatogr. A, 736, 11-19, (1996).
(27) Dixon, R. W., Baltzell, G., Determination of levoglucosan in atmospheric aerosols
using high performance liquid chromatography with aerosol charge detection, J.
Chromatogr. A, 1109, 214-221, (2006).
(28) Bravi, E., Perretti, G., Montanari L., Fatty acids by high-performance liquid
chromatography and evaporative light-scattering detector, J. Chromatogr. A, 1134, 210214, (2006).
(29) Marcata, B., Cecchin, G., Analysis of mixtures containing free fatty acids and mono, di- and triglycerides by high-performance liquid chromatography coupled with
evaporative light-scattering detection, J. Chromatogr. A, 730, 83-90, (1996).
(30) Gerard, H. C., Moreau, R. A., Fett, W. F., Osman, S. F., Separation and quantitation
of hydroxy and epoxy fatty acid by high-performance liquid chromatography with an
evaporative light-scattering detector, JAOCS, 69, 301-304, (1992).
(31) Gerard, H. C., Osman, S. F., Fett, W. F., Moreau, R. A., Separation, identification
and quantification of monomers from cutin polymers by high performance liquid
chromatography and evaporative light scattering detection, Phytochem. Anal., 3, 139-144,
(1992).
(32) Yang, X., Koropchak, J. A., Condensation nucleation light scattering detection with
microbore liquid chromatography for lipid analysis, J. Microcolumn Sep., 12:4, 204-210,
(2000).
57
(33) Charlesworth, J. M., Evaporative analyzer as a mass detector for liquid
chromatography, Analytical Biochem., 50, 1414-1420, (1978).
(34) Lisa, M. Lynen F., Holcapek, M. Sandra, P., Quantitation of triacylglycerols from
plant oils using charged aerosol detection with gradient compensation, J. Chromatogr. A,
1176, 135-142, (2007).
(35) Vervoort, N., Daemen, D., Török, G., Performance evaluation of evaporative light
scattering detection and charged aerosol detection in reversed phase liquid
chromatography, J. Chromatogr. A, 1189, 92-100, (2008).
(36) Kiedrowski, N. P., Isolation and Quantification of the Cortical Granule Lectin
Ligand Oligosaccharides and Elucidation of Their Role in the Block to Polyspermic
Fertilization in Xenopus Laevis, Thesis, California State University, Sacramento (2010).
(37) Hu, K., The Analysis of Glycoprotein Oligosaccharides by High Performance Liquid
Chromatography – Charged Aerosol Detection, Thesis, California State University,
Sacramento (2012).
(38) Seinfeld, J., Pandis, S. N., Air Pollution to Climate: Atmospheric Chemistry and
Physics, John Wiley and Sons, Inc., New York, NY, 519-523 (1998).