Experimental - Spectroscopy

® 

June 2011 Volume 26 Number 6 

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FT-IR Analysis of 

Algae for Biofuels 

Maxwell’s Equations 

Classical Least Squares 

and Spectral Results 

SERS Analysis of 

Methyl Yellow in 

Silver Colloid

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6 Spectroscopy 26(6) June 2011 

June 2011 

Volume 26 Number 6 

® 

CONTENTS 

Columns 

THE BASELINE 

Maxwell’s Equations, Part II 


Volume 26 Number 6 

JUNE 2011 

14 

In the realm of classical physics, Maxwell’s equations still rule, just as Newton’s equations of 

motion rule under normal conditions. 

David W. Ball 

CHEMOMETRICS IN SPECTROSCOPY 

22 

Classical Least Squares, Part VI: Spectral Results 

The detailed examination of the spectral behavior of three-component mixtures continues. 

Howard Mark and Jerome Workman, Jr. 

Cover image courtesy of 

Getty Images. 

ON THE WEB 

PITTCON THEATER 

Spectroscopy and LCGC presented LIVE 

video interviews at Pittcon 2011. Interviews 

with leading experts covered trends and 

applications in ICP/ICP–MS and hyphenated 

techniques. 

Specific topics include: 

ICP-MS in the Clinical Laboratory 

Deanna Jones, Research Chemist, 

Centers for Disease Control and Prevention 

Using LC–MS with Online Sample 

Preparation to Survey Metabolites 

Formed In Vitro 

Samuel Yang, University of Texas, Arlington 

Multidimensional Chromatography– 

MS in Polymer Analysis 

Hernan Cortes, Hernan J. Cortes Consulting 

Articles 

Optimizing FT-IR Sampling for a Method to 30 

Determine the Chemical Composition of Microbial Materials 

Algae and other aquatic species are promising sources for biomass that can be 

economically converted into fuel, and several infrared sampling techniques can be 

used to analyze these samples. 

Steve Lowry 

The pH Dependence of the SERS 38 

Spectra of Methyl Yellow in Silver Colloid 

Surface plasmon resonance, charge-transfer resonance, and their combination determine 

the enhancement of surface-enhanced Raman scattering signals, and the varying intensities 

of the signal at different pH levels may result from the change in contributions of the 

combined system. 

Zhen Long Zhang, Da Hu Chang, and Yu Jun Mo 

Watch the interviews online at: 


SPECTROSCOPY WAVELENGTH 

Subscribe to our monthly newsletter, which 

features interviews, technology roundtables, 

new products, and more. 

June Interview: Can LIBS help Japan 

with the nuclear crisis? 

Join the 

Spectroscopy Group 

on LinkedIn 

DEPARTMENTS 

News Spectrum . . . . . . 10 

Product Resources . . . . 44 

Calendar . . . . . . . . . . . . 48 

Short Courses . . . . . . . 49 

Ad Index . . . . . . . . . . . . 50 

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www.spectroscopyonline.com June 2011 Spectroscopy 26(6) 9 

Editorial Advisory Board 

Ramon M. Barnes University of Massachusetts 

Paul N. Bourassa Unity Home Medical 

Chris W. Brown University of Rhode Island 

Kenneth L. Busch Wyvern Associates 

Ashok L. Cholli University of Massachusetts at Lowell 

David M. Coleman Wayne State University 

Bruce Hudson Syracuse University 

David Lankin University of Illinois at Chicago, College of Pharmacy 

Barbara S. Larsen DuPont Central Research and Development 

Ian R. Lewis Kaiser Optical Systems 

Jeffrey Hirsch Thermo Fisher Scientific 

Howard Mark Mark Electronics 

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Gary McGeorge Bristol-Myers Squibb 

Linda Baine McGown Rensselaer Polytechnic Institute 

Robert G. Messerschmidt Rare Light, Inc. 

Francis M. Mirabella Jr. Mirabella Practical Consulting Solutions, Inc. 

John Monti Shimadzu Scientific Instruments 

Thomas M. Niemczyk University of New Mexico 

Anthony J. Nip CambridgeSoft Corp. 

John W. Olesik The Ohio State University 

Richard J. Saykally University of California, Berkeley 

Jerome Workman Jr. Unity Scientific 

Contributing Editors: 

Fran Adar Horiba Jobin Yvon 

David W. Ball Cleveland State University 

Kenneth L. Busch Wyvern Associates 

John Coates Coates Consulting 

Howard Mark Mark Electronics 

Volker Thomsen Consultant 

Jerome Workman Jr. Consultant 

Process Analysis Advisory Panel: 

James M. Brown Exxon Research and Engineering Company 

Bruce Buchanan Sensors-2-Information 

Lloyd W. Burgess CPAC, University of Washington 

James Rydzak Glaxo SmithKline 

Robert E. Sherman CIRCOR Instrumentation Technologies 

John Steichen DuPont Central Research and Development 

D. Warren Vidrine Vidrine Consulting 

European Regional Editors: 

John M. Chalmers VSConsulting, United Kingdom 

David A.C. Compton Industrial Chemicals Ltd. 

Spectroscopy’s Editorial Advisory Board is a group of distinguished individuals 

assembled to help the publication fulfill its editorial mission to promote the effective 

use of spectroscopic technology as a practical research and measurement tool. 

With recognized expertise in a wide range of technique and application areas, board 

members perform a range of functions, such as reviewing manuscripts, suggesting 

authors and topics for coverage, and providing the editor with general direction and 

feedback. We are indebted to these scientists for their contributions to the publication 

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News Spectrum 

New Forensic Laser Technique 

Locks Hair in Time 

Jim Moran, a geochemist at the Pacific Northwest 

National Laboratory (Richland, Washington), led a team of 

researchers in the development of a new laser-powered 

chemical analysis technique that can take dozens of 

samples from a single strand of hair and distinguish the 

chemical signatures of each. 

Because existing methods destroy small samples, they 

don’t allow for exact time-based measurements. Using the 

new technique, which breaks up material instead of scorching 

it, carbon isotope measurements from human hair can be 

taken over time, revealing information about what people ate, 

whether they’ve traveled, and where they’ve been. 

The technique is described in detail in a paper published 

by Moran and his team in the April 12 issue of Rapid 

Communications in Mass Spectrometry. 

“The carbon you eat goes into your hair, so hair is a 

record of carbon ratios. If you’ve been traveling, I could 

guess which countries you’ve been to or what you 

ate,” Moran said in a Wired online article, indicating 

that forensic scientists should find the technique 

useful. Biologists exploring food pathways in microbes, 

and paleontologists using carbon-based data to look 

at ancient environments may also find value in the 

technique. 


In addition to carbon sampling, the laser-ablation 

system may also work with other chemical isotopes, 

including nitrogen, oxygen, and sulfur as Moran’s team is 

working on developing those applications. 

Research Funding Finds Friends 

in Gingrich, Bernanke 

In today’s difficult economic times, Americans may 

be surprised to hear former speaker of the House and 

potential Republican presidential candidate Newt Gingrich 

argue for increased spending on medical and scientific 

research. In doing so, he contradicts fellow GOP leader, 

Paul Ryan, chairman of the House Budget Committee, 

who would cut spending in these areas. 

According to a Wall Street Journal article, Gingrich, whose 

mother was bipolar, is a strong supporter of brain research. 

At a Brookings Institution conference on April 22, he said that 

while he applauds Ryan’s “courageous effort” to “right-size” 

government, he disagrees with some of the details. 

“One of them is cutting investment in science and research,” 

Gingrich said at the conference. “It’s essentially like saying 

I want to save money on your car (so) we’re not going to 

change the oil. And for about a year I can get away with it, then 

the engine will freeze, and we have to change the engine.” 

President Obama, in his Plan for Science and Innovation, 

issued in February, called for doubling the budgets of 

Market Profile: Process FT-NIR for PAT in Pharma and Biopharma 

Fourier-transform near-infrared (FT-NIR) 

spectroscopy continues to be a rapidly growing 

process analytical technique, particularly in 

the pharmaceutical and biopharmaceutical industry. 

The technique offers a number of advantages 

for online applications, and most of the major 

NIR instrument vendors now compete in this 

segment of the market. 

FT-NIR spectrometers make 

use of a simpler mechanical 

design than dispersive NIR 

instruments, and therefore 

provide a more rugged 

and reliable design that is 

advantageous in any industrial 

setting, including pharmaceutical 

and biopharmaceutical 

manufacturing. In addition, 

7% 

5% 5% 

7% 

14% 

20% 

FT-NIR provides simultaneous analysis 

of all frequencies in the spectrum range, rather 

than scanning individual wavelengths. FT-NIR is also 

capable of much higher resolution than dispersive 

instruments, which is important in the pharmaceutical 

and biopharmaceutical industry. 

43% 

FT-NIR is becoming increasingly popular for 

process analytical technology (PAT) and other 

online applications in the pharmaceutical and 

biopharmaceutical industry, such as monitoring 

drying and blending processes. The global market 

for process FT-NIR in the pharmaceutical and 

biopharmaceutical industry in 2010 was more than $21 

million, and it is expected to continue to see annual 

Bruker 

Thermo Scientific 

ABB 

AIT 

Buchi 

Yokogawa 

Other 

Biopharmaceutical and pharmaceutical process FT-NIR 

vendor share in 2010. 

growth in the mid-teens for the 

foreseeable future. At least a half 

dozen instrument vendors are 

significant competitors in the 

market. 

The foregoing data were 

extracted from SDi’s market 

analysis and perspectives 

report entitled Biotech & 

Pharmaceutical Process Analysis: 

PAT Instrumentation and More, March 2011. For more 

information, contact Stuart Press, Vice President, 

Strategic Directions International, Inc., 6242 

Westchester Parkway, Suite 100, Los Angeles, CA 

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three key science agencies in 2012. 

The National Science Foundation, 

the Department of Energy’s Office of 

Science, and the National Institute 

of Standards and Technology 

Laboratories would benefit from the 

proposed additional funding, which 

amounts to $13.9 billion in total 

funding for the three agencies. 

Federal Reserve Chairman Ben 

Bernanke, who said that government 

spending on research and development 

can help boost economic growth, 

recently backed Obama. 

“The primary economic rationale 

for a government role in R&D is that, 

absent such intervention, the private 

market would not adequately supply 

certain types of research,” Bernanke 

said at a Georgetown University 

conference in May. 

For past coverage of this topic, 

please see, “Ongoing Battle over 2011 

U.S. Budget May Affect President’s 

Plan for Additional Science 

Investment in 2012” at 

www.chromatographyonline.com 

Mass Spectrometry 

Provides New Insight in 

Stroke Research 

New research conducted using 

mass spectrometers has provided 

insight into key areas of stroke 

evaluation and treatment. Led by 

MingMing Ning, a clinical neurologist 

and researcher at the Clinical 

Proteomics Research Center at 

Massachusetts General Hospital 

(Boston, Massachusetts), the research 

provides potentially significant new insight 

into patent foramen ovale (PFO) and its 

connection with strokes. PFO refers to a 

congenital heart abnormality, which leaves 

open a passage between the left and right 

sides of the heart, enabling blood clots to 

travel from the leg to the brain. 

Strokes are the leading cause of 

serious long-term disability in the 

United States, and with PFO affecting 

25% of the worldwide population, 

the potential health impacts are 

significant. Identification of potential 

biomarkers in mass spectrometry 

data derived from the collaborative 

research provides scientists with 

new insights into how PFO can be 

related to strokes. If confirmed, 

these insights may be important in 

helping doctors to select the most 

appropriate treatment for individual 

PFO stroke patients. 

The research, conducted 

by Thermo Fisher Scientific’s 

Biomarker Research Initiatives 

in Mass Spectrometry Center in 

collaboration with Massachusetts 

General Hospital, Harvard University, 

has also led to potential insights 

in the understanding of tissue 

plasminogen activator (tPA) in 

stroke treatment. tPA is a drug 

that can be safely administered 

only within a very short window 

of time after stroke symptoms 

occur. The treatment, which 

works by dissolving blood clots, 

has proven highly effective, but 

involves significant risks. Only 5% 

of patients fit the timeframe criteria 

within which it is safe to administer 

tPA. Through the use of mass 

spectrometry–based proteomics 

workflows, data from the research 

may help scientists identify a wider 

scope of patients who might benefit 

from tPA. 

Raman Spectroscopy May 

Enable Rapid Diagnosis of 

Skin Cancers 

A new diagnostic device based on 

Raman spectroscopy could assist 

physicians with the early detection 

of skin cancers. The system is being 

studied by Harvey Lui, a professor 

of dermatology and chair of the 

department of dermatology and 

skin science at the University of 

British Columbia (Vancouver, British 

Colombia). Lui is the coinventor of 

the device, which he has licensed to 

Verisante (Vancouver). 

In preliminary data published in 

2008, Lui and colleagues observed 

289 skin cancers and benign lesions, 

and found that skin cancers could 

be distinguished from benign skin 

lesions with a sensitivity of 91% 

and specificity of 75%. Malignant 

melanoma could be distinguished 

from benign pigmented lesions with 

a sensitivity of 97% and a specificity 

of 78%. 

Lui says the new device overcomes 

one of the key limitations of using 

Raman spectroscopy in medicine, 

which is the amount of time needed 

to acquire data. 

“In the past, to collect Raman data 

from the skin, the patient would have 

to sit still for 20 minutes, and that 

is not practical,” Lui said in a May 1 

article in Dermatology Times. “But 

now it’s possible to acquire this signal 

within seconds, and that’s been the 

breakthrough.” 

Another advantage of the system, Lui 

says, is that it is user-friendly and can 

be used easily by technicians, and this 

does not require the extensive training 

that confocal microscopy does. 

The device, called the Verisante 

Aura, has been approved for use in 

Canada and Europe, but US approval 

is not expected until next year. 

X-ray and AFM Study 

of Volcanic Ash 

Particles Produces 

Risk-Assessment Protocol 

A paper published by a joint Icelandic 

and Danish team in the journal, 

Proceedings of the National Academy 

of Sciences (April 25, 2011), provides 

evidence that a move to ground 

European aircraft was justified 

after an April 14, 2010, volcanic 

event allowed meltwaters from the 

Eyjafjallaökull glacier to mix with 

hot magma from the volcanic site, 

sending fine ash into the jet stream. 

The team used X-ray photoelectron 

spectroscopy and atomic force 

microscopy to assess toxicity risk and 

to determine the composition of the 

individual ash particles. 

The study involved taking a unique 

set of dry ash samples collected 

immediately after the explosive event 

and comparing the samples with 

fresh ash from a later, more typical 

eruption. Using nanotechniques 

custom-designed for studying natural 

materials, the team explored the 

physical and chemical nature of the 

ash to determine if fears about health 

and safety were justified. Additionally, 

they developed a protocol that will 

serve for assessing risks during a 

future event. ◾



The Baseline 

Maxwell’s Equations, Part II 

This is the second part of a multipart series on Maxwell’s equations of electromagnetism. The 

ultimate goal is a definitive explanation of these four equations; readers will be left to judge 

how definitive it is. Please note that figures are being numbered sequentially throughout this 

series, which is why the first figure in this column is Figure 7. Another note: This is going to get 

a bit mathematical. It can’t be helped: models of the physical universe, like Newton’s second 

law F = ma, are based in math. So are Maxwell’s equations. 

David W. Ball 

James Clerk Maxwell (Figure 7) was born in 1831 

in Edinburgh, Scotland. His unusual middle name 

derives from his uncle, who was the 6th Baronet 

Clerk of Penicuik (pronounced “penny-cook”), a 

town not far from Edinburgh. Clerk was, in fact, the 

original family name; Maxwell’s father, John Clerk, 

adopted the surname Maxwell after receiving a substantial 

inheritance from a family named Maxwell. 

By most accounts, James Clerk Maxwell (hereafter 

referred to as simply “Maxwell”) was an intelligent but 

relatively unaccomplished student. 

He began blossoming in his early teens, however, 

becoming interested in mathematics (especially geometry). 

He eventually attended the University of 

Edinburgh and, later, Cambridge University, where 

he graduated in 1854 with a degree in mathematics. 

He stayed on for a few years as a fellow, then moved 

to Marischal College in Aberdeen. When Marischal 

merged with another college to form the University 

of Aberdeen in 1860, Maxwell was laid off (an action 

for which the university should still be kicking itself, 

but who can foretell the future?) and he found another 

position at King’s College London (later the University 

of London). He returned to Scotland in 1865, only to 

go back to Cambridge in 1871 as the first Cavendish 

Professor of Physics. He died of abdominal cancer in 

November 1879 at the relatively young age of 48; curiously, 

his mother died of the same ailment and at the 

same age, in 1839. 

Figure 7: James Clerk Maxwell as a young man (holding a color wheel 

he invented) and as an older man. 

Although he had a relatively short career, Maxwell 

was very productive. He made contributions to color 

theory and optics (indeed, the first photo in Figure 7 

shows Maxwell holding a color wheel of his own invention) 

and actually produced the first true color photograph 

as a composite of three images. He made major 

contributions to the development of the kinetic molecular 

theory of gases, for which the “Maxwell-Boltzmann 

distribution” is named partially after him. He also 

made major contributions to thermodynamics, deriving 

the relations that are named after him and devising a 

thought experiment about entropy that was eventually

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ing of electricity and magnetism, 

concisely summarizing their behaviors 

with four mathematical 

expressions known as Maxwell’s 

equations of electromagnetism. He 

was strongly influenced by Faraday’s 

experimental work, believing 

that any theoretical description of 

a phenomenon must be grounded 

in phenomenological observations. 

Maxwell’s equations essentially 

summarize everything about classical 

electrodynamics, magnetism, 

and optics, and were only supplanted 

when relativity and quantum 

mechanics revised our understanding 

of the natural universe at 

certain limits. Far away from those 

limits, in the realm of classical 

physics, Maxwell’s equations still 

rule just as Newton’s equations of 

motion rule under normal conditions. 

Figure 8: Maxwell proved mathematically that the rings of Saturn couldn’t be solid objects, but 

were likely an agglomeration of smaller bodies. This image of a back-lit Saturn is a composite of 

several images taken by the Cassini spacecraft in 2006. Depending on the reproduction, you may 

be able to make out a tiny bluish dot in the 10 o’clock position just inside the second outermost 

diffuse ring. That’s Earth. 

y axis (ordinate) 

x axis (abscissa) 

Figure 9: A plot of a straight line, which has a constant slope m, given by Δy/Δx. 

x 

y 

called “Maxwell’s demon.” He demonstrated 

mathematically that the 

rings of Saturn could not be solid, 

but must instead be composed of 

relatively tiny (relative to Saturn, 

of course) particles — a hypothesis 

that was supported spectroscopically 

in the late 1800s but finally directly 

observed the first time when the 

Pioneer 11 and Voyager 1 spacecraft 

passed through the Saturnian system 

in the early 1980s (Figure 8). 

Maxwell also made seminal 

contributions to the understand- 

A Calculus Primer 

Maxwell’s laws are written in the 

language of calculus. Before we 

move forward with an explicit discussion 

of the first equation, here 

we deviate to a review of calculus 

and its symbols. 

Calculus is the mathematical 

study of change. Its modern form 

was developed independently by 

Isaac Newton and the German 

mathematician Gottfried Leibnitz 

in the late 1600s. Although Newton’s 

version was used heavily in 

his influential Principia Mathematica 

(in which Newton used calculus 

to express a number of fundamental 

laws of nature), it is Leibnitz’s 

notations that are commonly used 

today. An understanding of calculus 

is fundamental to most scientific 

and engineering disciplines. 

Consider a car moving at constant 

velocity. Its distance from 

an initial point (arbitrarily set 

as a position of 0) can be plotted 

as a graph of distance from zero 

versus time elapsed. Commonly, 

the elapsed time is called the independent 

variable and is plotted on 

the x axis of a graph (called the abscissa) 

while distance traveled from 

the initial position is plotted on the 

y axis of the graph (called the ordinate). 

Such a graph is plotted in 

Figure 9. The slope of the line is a 

measure of how much the ordinate 

changes as the abscissa changes; 

that is, slope m is defined as 

m = ∆y 

∆x 

[1] 

For the straight line shown in 

Figure 9, the slope is constant, so 

m has a single value for the entire 

plot. This concept gives rise to the 

general formula for any straight

©2011 PerkinElmer, Inc. 400217_02. All rights reserved. PerkinElmer ® is a registered trademark of PerkinElmer, Inc. All other trademarks are the property of their respective owners. 

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y axis 

line in two dimensions, which is 

y = mx + b [2] 

where y is the value of the ordinate, 

x is the value of the abscissa, m is 

the slope, and b is the y-intercept, 

which is where the plot would 

intersect with the y axis. Figure 

x axis 

Figure 10: A plot of a curve, showing (with the thinner lines) the different slopes at two different 

points. Calculus helps us determine the slopes of curved lines. 

x 

f (x,y) 

δf 

δx 

Figure 11: For a function of several variables, a partial derivative is a derivative in only one 

variable. The line represents the slope in the x direction. 

9 shows a plot that has a positive 

value of m. In a plot with a negative 

value of m, it would be going down, 

not up, as you go from left to right. 

A horizontal line has a value of 0 

for m; a vertical line has a slope of 

infinity. 

Many lines are not straight. 

Rather, they are curves. Figure 10 

y 

gives an example of a plot that is 

curved. The slope of a curved line 

is more difficult to define than that 

of a straight line because the slope 

is changing. That is, the value of 

the slope depends on the point (x, 

y) of the curve you’re at. The slope 

of a curve is the same as the slope 

of the straight line that is tangent 

to the curve at that point (x, y). 

Figure 10 shows the slopes at two 

different points. Because the slopes 

of the straight lines tangent to the 

curve at different points are different, 

the slopes of the curve itself at 

those two points are different. 

Calculus provides ways of determining 

the slope of a curve, in any 

number of dimensions (Figure 10 is 

a two-dimensional plot, but we recognize 

that functions can be functions 

of more than one variable, so 

plots can have more dimensions, 

or variables, than two). We have 

already seen that the slope of a 

curve varies with position. That 

means that the slope of a curve is 

not a constant; rather, it is a function 

itself. We are not concerned 

about the methods of determining 

the functions for the slopes of 

curves here; that information can 

be found in a calculus text. Instead, 

we are concerned with how they 

are represented. 

The word that calculus uses for 

the slope of a function is derivative. 

The derivative of a straight line is 

simply m, its constant slope. Recall 

that we mathematically defined the 

slope m above using “Δ” symbols, 

where Δ is the Greek capital letter 

delta. Δ is used generally to represent 

“change”, as in ΔT (change 

in temperature) or Δy (change in y 

coordinate). For straight lines and 

other simple changes, the change 

is definite; in other words, it has a 

specific value. 

In a curve, the change Δy is different 

for any given Δx because the slope 

of the curve is constantly changing. 

Thus, it is not proper to refer to a definite 

change because — to overuse a 

word — the definite change changes 

during the course of the change. What 

we have to do is a thought experiment:


z 

F = 3i + 4j + 5k 

On-line 

NIR 

Spectrometers 

x 

i 

k 

j 

Figure 12: The definition of the unit vectors i, j, and k, and an example of how any vector can be 

expressed in terms of how many of each unit vector. 

y 

• 900 nm to 2550 nm 

• Faster integration times 

(higher sensitivity) 

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(flatter spectral response) 

y 

x 

Figure 13: An example of a vector function F = xi + yj. Each point in two dimensions defines 

a vector. Although only 12 individual values are illustrated here, in reality this vector function is a 

continuous, smooth function on both dimensions. 

We have to imagine that the change 

is infinitesimally small over both the 

x and y coordinates. This way, the 

actual change is confined to an infinitesimally 

small portion of the curve: 

a point, not a distance. The point 

involved is the point at which the 

straight line is tangent to the curve 

(Figure 10). 

Rather than using “Δ” to represent 

an infinitesimal change, calculus 

starts by using “d”. Rather than using 

m to represent the slope, calculus 

puts a prime on the dependent variable 

as a way to represent a slope 

(which, remember, is a function and 

not a constant). Thus, for a curve we 

have for the slope y′: 

For more info, visit 

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|a|cosθ 

[3] 

as our definition for the slope of 

that curve. 

We hinted earlier that functions 

may depend on more than one 

a 

θ 

b 

Figure 14: Graphical representation of the dot product of two vectors. The dot product gives 

the amount of one vector that contributes to the other vector. Understand that an equivalent 

graphical representation would have the b vector projected into the a vector. In both cases, the 

overall scalar results are the same. 

(a) 

(b) 

Force 

FRAGILE 

FRAGILE 

Force 

Displacement 

Displacement 

Figure 15: Work is defined as a dot product of a force vector and a displacement vector. (a) If 

the two vectors are parallel, they reinforce and work is performed. (b) If the two vectors are 

perpendicular, no work is performed. 

y = dy 

dx 

variable. If that is the case, how do 

we define the slope? First, we define 

a partial derivative as the derivative 

of a multivariable function 

with respect to only one of its variables. 

We assume that the other 

variables are held constant. Instead 

of using a “d” to indicate a partial 

derivative, we use the lowercase 

Greek delta “δ”. It is also common 

to explicitly list the variables being 

held constant as subscripts to the 

derivative, although this can be 

omitted because it is understood 

that a partial derivative is a onedimensional 

derivative. Thus we 

have 

∂f ∂f 

= = 

∂x ∂x 

' 

f 

x () 

y,z,... 

[4] 

spoken as “the partial derivative 

of the function f(x,y,z,…) with 

respect to x.” Graphically, this corresponds 

to the slope of the multivariable 

function f in the x dimension, 

as shown in Figure 11. 

The total derivative of a function, 

df, is the sum of the partial 

derivatives in each dimension; that 

is, with respect to each variable individually. 

For a function of three 

variables, f(x,y,z), the total derivative 

is written as 

∂f ∂f ∂f 

df = dx+ dy+ 

dz 

∂x ∂y ∂z 

[5] 

where each partial derivative is the 

slope with respect to each individual 

variable and dx, dy, and dz are the 

finite changes in the x, y, and z directions. 

The total derivative has as many 

terms as the overall function has variables. 

If a function is based in threedimensional 

space, as is commonly 

the case for physical observables, then 

there are three variables and so three 

terms in the total derivative. 

When a function typically generates 

a single numerical value 

that is dependent on all of its variables, 

it is called a scalar function. 

An example of a scalar function 

might be 

F(x,y) = 2x – y 2 [6] 

According to this definition, F(4,2) 

= 2∙4 – 2 2 = 8 – 4 = 4. The final 

value of F(x,y), 4, is a scalar: it has 

magnitude but no direction. 

A vector function is a function 

that determines a vector, which is


a quantity that has magnitude and 

direction. Vector functions can be 

easily expressed using unit vectors, 

which are vectors of length 1 

along each dimension of the space 

involved. It is customary to use 

the representations i, j, and k to 

represent the unit vectors in the x, 

y, and z dimensions, respectively 

(Figure 12). Vectors are typically 

represented in print as boldfaced 

letters. Any random vector can be 

expressed as, or decomposed into, 

a certain number of i vectors, j 

vectors, and k vectors as is demonstrated 

in Figure 12. A vector 

function might be as simple as 

F = xi + yj [7] 

in two dimensions, which is illustrated 

in Figure 13 for a few 

discrete points. Although only a 

few discrete points are shown in 

Figure 13, understand that the vector 

function is continuous. That is, 

it has a value at every point in the 

graph. 

One of the functions of a vector 

that we will have to evaluate is 

called a dot product. The dot product 

between two vectors a and b is 

represented and defined as 

a∙b = |a||b|cosϴ [8] 

Thus, if the two vectors are 

parallel (ϴ = 0° so cosϴ = 1) the 

work is maximized, but if the two 

vectors are perpendicular to each 

other (ϴ = 90° so cosϴ = 0), the 

object does not move and no work 

is done (Figure 15). 

. . . But We’ll Have to Wait 

I hope you’ve followed so far — 

but so far, it’s been easy. To truly 

understand Maxwell’s first equation, 

we need to do a bit more advanced 

stuff. Don’t worry — our 

job in “The Baseline” is to talk 

you through it. Unfortunately, 

we’re going to have to wait 

until the next installment to 

pursue the more advanced stuff 

and get to the heart of Maxwell’s 

first equation. 

For more information on this topic, please visit: 

www.spectroscopyonline.com/ball 

UV/Vis and NIR Fiber Optic 

Probes for Process and 

Laboratory applications 

David W. Ball is 

normally a professor of 

chemistry at Cleveland 

State University in Ohio. 

For a while, though, things 

will not be normal: starting 

in July 2011 and for the 

commencing academic 

year, David will be serving as Distinguished 

Visiting Professor at the United States Air 

Force Academy in Colorado Springs, Colorado, 

where he will be teaching chemistry to 

Air Force cadets. He still, however, has two 

books on spectroscopy available through 

SPIE Press, and just recently published two 

new textbooks with Flat World Knowledge. 

Despite his relocation, he still can be contacted 

at d.ball@csuohio.edu. And finally, 

while at USAFA he will still be working on 

this series, destined to become another 

book at an SPIE Press web page near you. 

where |a| represents the magnitude 

(that is, length) of a, |b| is the magnitude 

of b, and cosϴ is the cosine 

of the angle between the two vectors. 

The dot product is sometimes 

called the scalar product because 

the value is a scalar, not a vector. 

The dot product can be thought of 

physically as how much one vector 

contributes to the direction of the 

other vector, as shown in Figure 

14. A fundamental definition that 

uses the dot product is that for 

work, w, which is defined in terms 

of the force vector F and the displacement 

vector of a moving object, 

s, and the angle between these 

two vectors: 

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w = F∙s = |F||s|cosϴ [9] 

Transmission Reflection ATR Fluorescence Transflection


Chemometrics in Spectroscopy 

Classical Least Squares, Part VI: 

Spectral Results 

We continue to examine in detail the spectral behavior of three-component mixtures. 

Howard Mark and Jerome Workman, Jr. 

This column is the continuation of our discussion of 

the classical least squares approach to calibration 

(1–5). In this installment, as in the previous one (5), 

we will be dealing largely with figures, and the spectra 

therein. So far, we have looked at the spectra of the pure 

components comprising the ternary mixtures we are 

working with. We also have looked at the spectra of all 

the mixtures overlaid on each other. 

One important reason to perform this extensive exercise 

of examining spectra is to address, and hopefully 

clear up, a misconception some people have about nearinfrared 

(NIR) spectra. Because of its history, NIR spectroscopy 

sometimes is believed to rely on, and require, 

the “magic” of chemometric analysis to obtain meaningful 

scientific results. But that’s not always true. That perception 

has arisen because NIR measurements most often 

are made on samples that are powdered solids, part of a 

dynamic mechanism, or involve other difficult conditions 

that often make other types of spectral measurements 

impossible. 

This has created an impression that NIR exists in a 

Celebrating 25 Years 

The editors congratulate Howard Mark and 

Jerry Workman for 25 years of statistics and 

chemometrics columns in Spectroscopy. 

universe of its own, disconnected from the rest of science. 

Therefore, we take this opportunity to emphasize 

(and maybe overemphasize) the fact that NIR is not some 

“magical” technique that is different from the rest of the 

universe, but in fact is the same spectroscopy we are all 

used to seeing in other spectral regions. When samples 

are presented to a spectrometer, they behave the same 

way in the NIR as they do in the UV, visible, mid-IR, or 

any other region. 

In the current experiment, we have a situation where 

clear liquid samples are used, and measured in a cuvette 

having plane parallel windows — the near-ideal conditions 

that are normally used for any spectral region. In 

this way, we can demonstrate that NIR spectra, under 

those conditions, do indeed behave the same way as spectra 

in other spectral regions do. 

Let us look at the three sets of two-component mixtures 

that the experimental design includes. These are 

presented in Figures 1–3. In these figures, we first present 

the full spectrum, and then for each mixture we present 

the spectra of the mixtures in the several subranges that 

contain the useful absorbance bands. 

Examining these spectra, we see some features that 

were partially obscured when the spectra from all the 

mixtures were plotted together. One effect that was 

hidden by the overlapping spectra was the presence of 

isosbestic points. Although Figure 5e from part V of this 

column series (5) shows a ternary isosbestic point, that 

is rare; for the most part isosbestic points are not seen


June 2011 Spectroscopy 26(6) 23 

(b) 

(a) 1 

0.9 

-0.2 -0.1 

4000 5000 6000 7000 8000 9000 10,000 4000 4100 4200 4300 4400 4500 4600 4700 4800 4900 5000 

0.8 

0.8 

0.7 

0.6 

0.6 

0.5 

0.4 

0.4 

0.3 

0.2 

0.2 

0.1 

0 

0 

Wavelength 

Transmittance 

(c) 1 

(d) 

1 

Transmittance 

0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

5000 5500 6000 6500 

Wavelength 

Transmittance 

0.95 

0.9 

0.85 

0.8 

0.75 

0.7 

0.65 

6500 6600 6700 6800 6900 7000 7100 7200 7300 7400 7500 

Wavelength 

(e) 

1 

0.95 

0.9 

Transmittance 

0.85 

0.8 

0.75 

0.7 

0.65 

7500 8000 8500 9000 

Wavelength 

Figure 1: Transmission spectra of mixtures of toluene and dichloromethane: (a) full spectrum from 4000 to 10,000 cm -1 ; (b) 4000–5000 cm -1 ; (c) 

5000–6500 cm -1 ; (d) 6500–7500 cm -1 ; (e) 7500–9000 cm -1 .


(a) 

1 

0.8 

0.6 

(b) 0.8 

0.7 

0.6 

0.5 

Transmittance 

0.4 

0.2 

Transmittance 

0.4 

0.3 

0.2 

0.1 

0 

0 

-0.2 

4000 5000 6000 7000 8000 9000 10,000 

Wavelength 

-0.1 

4000 4100 4200 4300 4400 4500 4600 4700 4800 4900 5000 

Wavelength 

(c) 

1 

(d) 

1 

0.9 

0.8 

0.95 

0.7 

0.9 

Transmittance 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

5000 5500 6000 6500 

Wavelength 

Transmittance 

0.85 

0.8 

0.75 

0.7 

0.65 

6500 6600 6700 6800 6900 7000 7100 7200 7300 7400 7500 

Wavelength 

(e) 

1 

0.95 

0.9 

Transmittance 

0.85 

0.8 

0.75 

0.7 

0.65 

7500 8000 8500 9000 

Wavelength 

Figure 2: Transmission spectra of mixtures of toluene and n-heptane: (a) 4500–10,000 cm -1 ; (b) 4000–5000 cm -1 ; (c) 5000–6500 cm -1 ; (d) 6500– 

7500 cm -1 ; (e) 7700–9000 cm -1 .



(a) 

Transmittance 

1 

0.8 

0.6 

0.4 

0.2 

(b) 0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

Transmittance 

0 

-0.2 

4000 5000 6000 7000 8000 9000 10,000 

Wavelength 

0.1 

0 

-0.1 

4000 4100 4200 4300 4400 4500 4600 4700 4800 4900 5000 

Wavelength 

(c) 0.1 

0.9 

(d) 

1 

0.95 

Transmittance 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

Transmittance 

0.9 

0.85 

0.8 

0.75 

0.7 

0.65 

5000 5500 6000 6500 

Wavelength 

(e) 1 

6500 6600 6700 6800 6900 7000 7100 7200 7300 7400 7500 

Wavelength 

0.95 

0.9 

Transmittance 

0.85 

0.8 

0.75 

0.7 

0.65 

7500 8000 8500 9000 

Wavelength 

Figure 3: Transmission spectra of mixtures of dichloromethane and n-heptane: (a) 4000 to 10,000 cm -1 ; (b) 4000–5000 cm -1 ; (c) 5000–6500 cm -1 ; 

(d) 6500–7500 cm -1 ; (e) 7600–9000 cm -1 . 

in the previous presentations of the 

spectra because the other spectra in 

the set obscure the isosbestic points. 

Examining the spectra of the 

two-component mixtures, however, 

reveals a plethora of isosbestic points. 

In fact, when we look at the expansions 

of the wavelength ranges, we 

find that all three sets of two-component 

mixtures contain multiple 

isosbestic points. 

Another phenomenon more easily 

seen in the spectra of the twocomponent 

mixtures is the presence 

of the expected nonlinearity of the 

transmission spectra with respect 

to composition, especially for the 

stronger absorbance bands. Because 

the strongest absorbance bands in 

the spectra generally fall in the range 

of 5000–6500 cm -1 , this nonlinearity 

is very visible in the bands of the 

two-component mixtures falling in 

this range. Examples where this ef-


(a) 

2 

(b) 

1.6 

Wavelength 

1.8 

1.6 

1.4 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

Wavelength 

1.4 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

0 

4000 5000 6000 7000 8000 9000 10,000 

Absorbance 

0 

4500 4550 4600 4650 4700 4750 4800 4850 4900 4950 5000 

Absorbance 

(c) 

2 

(d) 

0.2 

1.8 

0.18 

Wavelength 

1.6 

1.4 

1.2 

1 

0.8 

0.6 

0.4 

Wavelength 

0.16 

0.14 

0.12 

0.1 

0.08 

0.06 

0.2 

0 

5000 5500 6000 6500 

Absorbance 

0.04 

0.02 

6500 6600 6700 6800 6900 7000 7100 7200 7300 7400 7500 

Absorbance 

(e) 0.18 

0.16 

0.14 

Wavelength 

0.12 

0.1 

0.08 

0.06 

0.04 

0.02 

7500 8500 9000 

8000 

Absorbance 

Figure 4: Absorption spectra of mixtures of toluene and dichloromethane: (a) from 4500 to 10,000 cm -1 ; (b) 4500–5000 cm -1 ; (c) 4800–6300 cm -1 ; 

(d) 6500–7500 cm -1 ; (e) 8000–9000 cm -1 . 

fect is prominent include the band 

at 5700 cm -1 in Figure 1c, the band 

at 8400 cm -1 in Figure 1e, and the 

band at 5700 cm -1 in Figure 3c. In 

fact, the “clipping” that we previously 

observed in the 4000–5000 cm -1 region 

now can be seen as simply an 

exaggerated version of this effect; the 

absorbance bands in that region are 

so strong that effectively no energy is 

left to come through the sample. 

The nonlinearity is not necessarily 

visible in all spectra, of course. 

For the compression of the spectra 

to become easily visible, simply having 

a part of the spectrum with high 

absorbance does not suffice. It is also 

necessary for the transmittance at 

the given wavelength to vary over 

an appreciable range in the different 

samples. If the transmittance is 

uniformly high, as is the case for the 

band around 7270 cm -1 in Figure 1d, 

then the nonlinearity does not com-



(a) 

1.6 

1.4 

1.2 

(a) 2 

1.8 

1.6 

1.4 

wavelength 

1 

0.8 

0.6 

0.4 

Wavelength 

1.2 

1 

0.8 

0.6 

0.4 

(b) 

0.2 

0 

4000 5000 6000 7000 

Absorbance 

1.6 

1.4 

1.2 

8000 9000 10,000 

0.2 

(b) 0.7 

0 

4000 5000 6000 7000 8000 9000 10,000 

Absorbance 

0.6 

0.5 

wavelength 

1 

0.8 

0.6 

Wavelength 

0.4 

0.3 

0.4 

0.2 

0.2 

0.1 

0 

4500 

(c) 1.4 

4550 

4600 4650 4700 4750 4800 4850 4900 4950 5000 

Absorbance 

(c) 2 

0 

4500 4550 4600 4650 4700 4750 4800 4850 

Absorbance 

4900 4950 5000 

1.2 

1.8 

1.6 

1 

1.4 

wavelength 

0.8 

0.6 

0.4 

0.2 

Wavelength 

1.2 

1 

0.8 

0.6 

0.4 

0.2 

(d) 0.18 

0 

5000 5500 6000 6500 

Absorbance 

0.16 

0.14 

(d) 0.2 

0 

5000 5500 6000 6500 

Absorbance 

0.18 

0.16 

wavelength 

0.12 

0.1 

0.08 

0.06 

Wavelength 

0.14 

0.12 

0.1 

0.08 

0.06 

0.04 

(e) 

0.04 

6000 6600 6700 6800 6900 7000 7100 7200 7300 7400 7500 

Absorbance 

0.2 

0.18 

0.16 

0.14 

(e) 0.2 

0.02 6500 6600 6700 6800 6900 7000 7100 7200 7300 7400 7500 

Absorbance 

0.18 

0.16 

0.14 

wavelength 

0.12 

0.1 

Wavelenght 

0.12 

0.1 

0.08 

0.08 

0.06 

0.06 

0.04 

0.04 

0.02 

7500 8000 8500 9000 

Absorbance 

0.02 

7500 8000 8500 9000 

Absorbance 

Figure 5: Absorption spectra of mixtures of toluene and n-heptane: (a) 

from 4500 to 10,000 cm -1 ; (b) 4500–5000 cm -1 ; (c) 5000–6500 cm -1 ; (d) 

6500–7500 cm -1 ; (e) 7700–9000 cm -1 . 

Figure 6: Absorption spectra of mixtures of dichloromethane and 

n-heptane: (a) from 4500 to 10,000 cm -1 ; (b) 4500–5000 cm -1 ; (c) 5000– 

6500 cm -1 ; (d) 6500–7500 cm -1 ; (e) 7600–9000 cm -1 .


press the spectra to the point where it 

is readily visible to the eye. Similarly, 

if the transmittance is low, but is uniformly 

low for all the samples of the 

set, we do not observe the nonlinearity 

because it would not be sufficient 

over a small range of transmission 

to be observed. An example of this 

is the band at 5700 cm -1 in Figure 

3c. This behavior of the absorbance 

bands, to not show the nonlinearity, 

is often associated with an isosbestic 

point, as in the case of the band in 

Figure 3c. 

It is well known this nonlinearity 

of transmittance versus composition 

in the 5000–6500 cm -1 range, however, 

occurs because the transmission 

of energy through a clear but 

absorbing liquid decreases asymptotically, 

but exponentially, to zero. 

Therefore we see that although the 

composition varies in equal increments 

(of 25%, as described in the 

experimental section in part V of 

this column series [5]), the spectral 

differences do not show the same 

equal spacing, but are visibly compressed 

at the higher absorbance 

levels. 

To achieve equal spacing of the 

spectra for equal changes in composition, 

the transmission spectra must 

be converted to absorbance spectra. 

A set of absorbance spectra paralleling 

the transmission spectra seen 

in Figures 1–3 is shown in Figures 

4–6. As we did with the transmission 

spectra, we plotted these absorption 

spectra both at the full wavelength 

range and in each of the wavelength 

ranges of interest. We also have limited 

the lower end of the wavelength 

range to 4500 cm -1 to prevent the 

“zero” transmission values from 

being converted to extremely high 

absorption values that would compress 

the bands at other wavelengths 

to the point of invisibility. 

We can compare the spacings between 

spectra in the various parts of 

Figures 4–6 with the spacings in the 

corresponding parts of Figures 1–3. 

Mostly we see that the spacings have 

tended to even out; this can be seen, 

for example, by comparing the band 

at 5700 cm -1 in Figure 1c with the 

corresponding absorbance band in 

Figure 4c. Nevertheless, although the 

bands in Figure 4c show less compression 

in the higher absorbance 

spectra than the bands in Figure 1c, 

there is still a noticeable amount. 

Surprisingly, an opposite effect 

sometimes occurs. For example, the 

band at roughly 5900 cm -1 in those 

same two figures is nearly evenly 

spaced in the transmission display, 

but shows increasing spacing for the 

higher-absorbance spectra. 

We will discuss this further in a 

future column. 

References 

(1) H. Mark and J. Workman, Spectroscopy 

25(5), 16–21 (2010). 


25(6), 20–25 (2010). 


25(10), 22–31 (2010). 


26(2), 26–33 (2011). 


26(5), 12–22 (2011). 

Howard Mark 

serves on the Editorial 

Advisory Board of 

Spectroscopy and runs 

a consulting service, 

Mark Electronics (Suffern, 

NY). He can be 

reached via e-mail: 

hlmark@prodigy.net 

Jerome Workman, 

Jr. serves on the Editorial 

Advisory Board of 

Spectroscopy and is the 

executive vice president 

of Engineering at 

Unity Scientific, LLC, 

(Brookfield, Connecticut). 

He is also an adjunct professor at 

U.S. National University (La Jolla, California), 

and Liberty University (Lynchburg, 

Virginia). His email address is 

JWorkman04@gsb.columbia.edu 

For more information on this topic, 

please visit our homepage at: 

www.spectroscopyonline.com

Differential ion 

Mobility SpectroMetry, 

a new Dimension in Selectivity for lc/MS/MS 

liVe WebcaSt: Thursday, June 3, 011 at 11:00AM EST 

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KEY LEARNING OBJECTIVES 

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For questions, contact Jamie Carpenter at jcarpenter@advanstar.com


Optimizing FT-IR Sampling 

for a Method to Determine 

the Chemical Composition of 

Microbial Materials 

The need for new energy sources has been a major concern for many years. The fear 

over global warming and CO 2 

emissions has drastically increased the worldwide awareness 

of this critical problem. The conversion of biomass into fuel is one of the most 

promising technologies to replace fossil fuels as a main energy source, particularly for 

transportation. Although food crops such as corn and soybeans have been successfully 

used to create bioethanol and biodiesel, the use of food crops and viable farmland is not 

really a sustainable long-term solution. A great deal of research is being funded to investigate 

alternative sources of biomass that can be economically converted into fuel. Algae 

and other aquatic species appear to be one of the most promising sources for the large 

quantities of biomass required for a successful biofuels program. The concept of producing 

biofuels from algae is not a recent idea and government funding for this research 

has been significant. A report from the National Renewable Energy Laboratory (Golden, 

Colorado) provides an excellent background in this area, describing research that started 

in 1978 and extended over 20 years (1). One reason for the interest in algae as a source 

of biodiesel is that CO 2 

and a source of nutrients are required for biomass production. 

In one scenario, large algae “farms” are positioned near power plants or incinerators to 

convert their waste CO 2 

into biomass. Waste-treatment facilities also could provide most 

of the required nutrients. 

Steve Lowry 

The rapid growth of many microalgae species is not 

only valuable for production, but greatly reduces the 

time required to optimize the strains for biofuels. A 

number of research groups are attempting to increase the 

amount of lipid produced by the algae under normal conditions 

through genetic improvements. Fourier-transform 

infrared (FT-IR) spectroscopy has been used extensively 

to characterize the chemical composition of biological 

systems including bacteria, single cells, and tissues (2–6). 

The infrared spectrum contains peaks that correspond to 

the presence of protein, carbohydrates, and lipids, as well 

as DNA and other biomolecules commonly found in cells. 

In a recent paper, Pistorius and colleagues (7) describe a 

method to determine the protein, carbohydrate, and lipid 

content in biomass from algae and other microbiological 

sources. Their analysis is based on obtaining a high quality 

FT-IR spectrum from a sample of microalgae and determining 

the amount of specific components in the sample

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Figure 1: Selecting the protein spectral region in OMNIC TQ Analyst software. 

Absorbance 

Absorbance 

0.12 

0.10 

0.08 

0.06 

0.04 

0.02 

0.12 

0.10 

0.08 

0.06 

0.04 

0.02 

*Algae powder by ATR 

*Algae deposited on foil ATR 

3500 3000 

based on specific features in the spectra. 

The authors emphasized that the 

sample preparation is a key step to 

2500 2000 1500 1000 

Wavenumbers ( cm-1 ) 

Figure 2: ATR spectra from powdered algae (top) and algae deposited on an aluminum foil 

surface (bottom). 

obtaining reproducible results. In this 

paper, several different infrared sampling 

techniques will be described that 

can be used to analyze microbiological 

samples. The goal of this work was to 

identify a cost-effective technique that 

might greatly increase the number of 

samples that can be analyzed with automated 

spectroscopy. 

Experimental 

Portions of dried samples of different 

algae specimens were directly 

analyzed by attenuated total reflectance 

(ATR) spectroscopy. Aqueous 

suspensions of the material were 

deposited on various substrates for 

both transmittance and reflectance 

studies. The actual chemical compositions 

of the algae samples available 

for this study were unknown. 

An analytical method modeled after 

the work of Pistorius and colleagues 

was created using Thermo Scientific 

OMNIC TQ Analyst software 

(Thermo Fisher Scientific, Waltham, 

Massachusetts). 

The key parameters from their 

work that were used in this analysis 

method are shown in Table I.



Absorbance 

Absorbance 

2.0 

*Blank PTFE 

1.5 

1.0 

0.5 

0.8 Algae on PTFE membrane 

0.6 

0.4 

No Sample 

too Tough... 

Absorbance 

PE card blank 

1.5 

1.0 

0.5 

0.7 

*Algae on polythylene membrane 

Absorbance 

0.6 

0.5 

0.4 

0.3 

3500 3000 2500 2000 1500 1000 

Wavenumbers (cm -1 ) 

Figure 3: Transmittance spectra from algae samples deposited on 

polyethylene and PTFE microporous films. 

0.7 Algae on BaF 2 window 

0.6 

Absorbance 

0.5 

0.4 

0.3 

0.2 

Absorbance 

0.1 

0.10 Bacteria on silicon substrate 

0.08 

0.06 

0.04 

0.02 

0.00 

0.02 

0.04 

3500 3000 2500 2000 

Wavenumbers ( cm-1 ) 

1500 1000 

Analyze hard materials with 

no sample preparation 

Monitor structural changes 

at high temperatures 

Figure 4: An algae sample deposited on barium fluoride and a bacteria 

sample deposited on silicon. 

When the method was applied to ATR spectra, the 

actual scale factors reported in the Pistorius method 

were adjusted to account for the different relative intensities 

observed with the ATR spectra. Figure 1 shows 

the screen in TQ Analyst that sets up the spectral region 

used to determine the protein concentration. 

FT-IR spectra were obtained from several samples of 

algae using ATR spectroscopy, transmission spectroscopy, 

reflectance spectroscopy, and infrared microscopy. A 

Thermo Scientific Nicolet iS 10 FT-IR spectrometer configured 

with a Smart iTR diamond accessory (Thermo Fisher 

Scientific) was used to obtain spectra from dried algae. All 

spectra were acquired at 4-cm -1 resolution with a room 

temperature DTGS (deuterated triglycine sulfate) detector 

in less than 1 min. The pressure tower on the accessory 

applies a constant pressure on the sample, producing consistent 

spectra from the different samples. The transmission 

spectra were acquired using the same FT-IR spectrometer 

and the Smart OMNI-transmission accessory. The samples 

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with an automated well-plate reader 

was used with the OMNIC Array 

Automation software to analyze the 

samples deposited on the aluminum 

foil surface. The infrared microscope 

transmission data were obtained with 

a Nicolet 6700 FT-IR system and a 

Continuum infrared microscope configured 

with an automated X,Y stage. 

Figure 5: Diagram of the reflectance optics module used in the array autosampler. 

Figure 6: Screen showing the Array Automation results using the amount of lipid as the metric 

to color the wells. 

were deposited from aqueous solution 

onto barium fluoride or silicon windows, 

a polyethylene microporous 

membrane, or polytetrafluoroethylene 

(PTFE) membrane. For the reflectance 

study, the samples were deposited onto 

an aluminum foil surface marked with 

96 positions consistent with a standard 

well-plate format. A Nicolet 6700 spectrometer 

(Thermo Fisher Scientific) 

Results and Discussion 

ATR spectroscopy is an excellent technique 

to rapidly characterize discrete 

samples. The dried material is pressed 

against the 2-mm diameter ATR crystal 

and a spectrum is acquired. The 

depth of penetration into the sample 

in the ATR experiment is wavelength 

dependent and results in relatively 

lower intensity of the peaks in the 

higher wavenumber region. The peak 

intensity is also related to the total 

amount of sample in contact with the 

crystal. Thus, sample hardness and 

particle size can change the intensities 

of the peaks slightly, and spectra 

should be corrected before calculating 

the amount of each component. 

Although the samples were prepared 

for automated reflectance analysis, 

ATR spectra were also acquired from 

the algae deposited on the aluminum 

foil surface. These spectra were similar 

to the ones obtained by placing the 

dried algae directly on the ATR crystal. 

Figure 2 shows the ATR spectra 

from different algae samples that were 

acquired. 

One of the objectives of this research 

was to develop a better understanding 

of ways that the automated 

sampling techniques created 

for pharmaceutical high-throughput 

screening systems might be applied 

to the analysis of algae. In particular, 

can the automated weighing, 

dilution, and deposition systems designed 

around the 96-well SBS format 

improve productivity in FT-IR 

analysis of biological materials? A 

number of years ago, 3M (St. Paul, 

Minnesota) introduced a disposable 

IR sampling card based on a polymer 

microporous membrane. A publication 

by Mosoba and colleagues (8) reports 

using a polyethylene microporous 

film with this sampling concept



to obtain FT-IR spectra from bacteria samples. They created 

a multiple-sampling method by stretching the film 

across the holes in an autosampler wheel. A plate with 96 

holes in a well-plate format or a microscope slide with 16 

holes also could be developed for automated sampling. 

A major point of these analyses is to measure the 

amount of lipid in the algae samples using infrared spectroscopy. 

The analytical method described in the Pistorius 

publication uses the spectral features in the C-H 

stretching region to determine the lipid content. However, 

the spectral features from the polyethylene film are 

very similar to those from lipids, creating a large cross 

interference problem. The PTFE polymer shows no interference 

in the C-H stretch region and has no features 

in the amide or carbonyl regions, although the strong 

infrared peak for PTFE near 1200 cm -1 results in some 

spectral distortion near the region employed for the carbohydrate 

analysis. Figure 3 shows spectra from the two 

polymer films and from algae samples after subtraction 

of the spectra from the reference films. Although some 

spectral artifacts are observed from the polymer, the use 

of microporous polymer film as a sample substrate would 

result in a very low cost disposable sampling system. 

In a previous project, biological samples were deposited 

from a saline solution onto small 5-mm squares of 

barium fluoride (BaF 2 

) and dried before the acquisition 

of spectra in transmittance (2). Barium fluoride is an 

excellent IR window and is not soluble in water. However, 

it is quite expensive and probably would not be considered 

a disposable sample holder by most laboratories. 

An alternative approach that looks very promising is to 

deposit the samples on small squares of polished silicon 

that have been cut from a semiconductor wafer. A number 

of surplus double side polished 8-in. silicon wafers 

were purchased and were diced into 0.25-in. squares. 

Figure 4 shows the spectrum obtained by depositing an 

aqueous suspension onto a barium fluoride window and the 

spectrum from a bacteria sample deposited onto a silicon 

square after subtraction of a silicon reference spectrum. 

This figure shows the similarities and differences between 

the algae and bacteria samples. A plastic holder was designed 

to hold the silicon and BaF 2 

squares. For compatibility 

with microscope slide handling stages, a 1 × 3 in. 

silicon sample holder was also designed, allowing use in a 

high throughput sampling system that matches two columns 

in the standard 96-well format. Another alternative 

sampling technique is to use a single piece of silicon with 

96 shallow wells created by a gasket. The silicon piece is 

mounted in a frame consistent with the SBS 96-well form 

factor. In a related study, samples of bacteria were deposited 

and dried on a silicon 96-spot plate (Pike Technologies, 

Madison, Wisconsin). Spectra were acquired from 

the samples deposited on the plate using a Nicolet 6700 

FT-IR spectrometer and a Continuum infrared microscope 

configured with a high-precision X,Y stage and a modified 

version of the OMNIC Array Automation software. Infrared 

microscopy is very valuable for obtaining high quality 

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Table I: Key parameters of the Pistorius method (7) used in this study 

Sample X Start X End Slope Intercept 

Protein 1477 1590 12.72 -0.27 

Lipid 2780 2984 78.96 -2.3 

Carbo 1133 1180 2.05 0.07 

spectra when the amount of sample is 

limited to less than a milligram. The 

sampling area in the ATR experiment 

and transmission is 2–5 mm, while the 

sampling area with an infrared microscope 

is routinely 100 µm and can be 

less than 10 µm. The Array Automation 

software would permit the analysis 

of micro-arrays including the SBS 

standard 1536-well plate format. 

A final, very promising, sampling 

technique is automated reflectance 

analysis. This involves depositing the 

algae samples onto a highly reflecting 

surface, such as aluminum foil, in 

an array format. In this example, the 

samples were deposited in a 96-well 

format and analyzed using the reflectance 

option with the well plate reader. 

The optical layout of the reflectance 

module that is positioned over the X,Y 

stage is shown in Figure 5. 

The 96-spot foil containing the 

samples was taped to a metal plate that 

mounted in the automated X,Y stage. A 

spectrum from a well with no sample 

was acquired as a background. Spectra 

were then automatically acquired 

from the original samples. Figure 6 

shows the screen after the software has 

acquired the data. The color of each 

well in the schematic in the upper left 

corresponds to the magnitude (result) 

of the chemometric analysis selected. 

This could be as simple as the intensity 

of the amide band from the protein, 

the similarity to a target spectrum, or 

the amount of lipid in the sample. In 

this case, the color corresponds to the 

amount of lipid in the sample based on 

this implementation of the Pistorius 

method (7). The wells colored red or 

orange correspond to samples containing 

more lipid material. 

Conclusions 

Many of the FT-IR techniques that 

have been reported for classifying 

and characterizing clinical samples 

can be applied to analyzing materials 

developed for use in biofuels. In 

this feasibility study spectra from 

algae samples were obtained using 

a number of different infrared sampling 

techniques, forming a basis for 

developing rapid screening methods 

to determine the lipid content of 

microbiological species intended for 

biofuels. These techniques would 

prove valuable during several steps 

in the development process including 

optimizing the algae strains through 

the actual production of biomass to 

ensure that the algae species are remaining 

true and are not contaminated 

by wild organisms. 

For a small number of samples, 

ATR spectroscopy provides an easy 

nondestructive way to quickly determine 

the chemical composition of 

dried samples. For applications with 

a large number of samples, the use 

of a “well plate” based on the microporous 

membrane would provide a 

low-cost way to rapidly analyze multiple 

samples. Multivariate statistical 

techniques could be employed to 

eliminate interference from the spectral 

features because of the polymer 

film, which might otherwise limit 

its general use. One interesting possibility 

of using a microporous film 

as the sample substrate is filtering 

samples directly onto the substrate 

using an automated 96-sample filtering 

system. 

The robust nature and relatively 

low cost of silicon make it an excellent 

choice as a substrate for a 96- 

well plate. Transmission sampling 

plates can be produced from a single 

piece of silicon (120 × 85 mm) 

with 96 sample positions or a plate 

that mounts 96 individual disposable 

windows. The results of the reflectance 

analysis using the Array 

Automation system are also very 

promising, and this appears to be an 

easy method for analyzing multiple 

samples. As with any analytical application, 

the best method depends 

on the details of the analysis. However, 

FT-IR spectroscopy has the 

flexibility and specificity to deliver 

a robust solution optimized to the 

specific problem. 

References 

(1) J. Sheehan, T. Dunahay, J. Benemann, 

and P. Roessler, “A Look Back at the 

U.S. Department of Energy’s Aquatic 

Species Program-Biodiesel from 

Algae”, NREL/TP-580–24190 (1998). 

(2) S.R. Lowry, Cellular and Molecular 

Biology 44, 169–177 (1998). 

(3) D. Naumann, D. Helm, and H. Labischinski, 

Nature 351, 81–82 (1991). 

(4) K. Stehfest, J. Toepel, and C. Wilhelm, 

Plant Physiology and Biochemistry 

43, 717–726 (2005). 

(5) A.P. Dean, M.C. Martin, and D.C. 

Sigee, Phycologia 46, 151–159 

(2005). 

(6) D.L. Wetzel, A.J. Eilert, L.N. Pietrzak, 

S.S. Miller, and J.A. Sweat, Cellular 

and Molecular Biology 44, 145–168 

(1998). 

(7) A.M. Pistorius, W.J. DeGrip, and T.A. 

Egorova-Zachernyuk, Biotechnology 

and Bioengineering 103, 123–129 

(2009). 

(8) M.M. Mossoba, F.M Khambaty, and 

F.S. Fry, Applied Spectroscopy 56, 

732–736 (2002). 

Steve Lowry is Application 

Scientist, Molecular Spectroscopy, with 

Thermo Fisher Scientific, Waltham, 

Massachusetts. ◾ 




2012 

Photonics 

West 

21–26 January 2012 

Call for Papers 

Submit your abstract 

by 11 July 2011 

Applications of optoelectronics, lasers, 

micro/nanophotonics, and biomedical optics 

Location 

The Moscone Center 

San Francisco, California, USA 

spie.org/pw 

Conference dates 

21–26 January 2012 

Exhibition dates 

BiOS: 21–22 January 

Photonics West: 

24–26 January 

Technologies 

- BiOS–Biomedical Optics 

- OPTO–Integrated Optoelectronics 

- LASE–Lasers and Applications 

- MOEMS-MEMS–Micro & Nanofabrication 

- Green Photonics



The pH Dependence of the 

SERS Spectra of Methyl Yellow 

in Silver Colloid 

Methyl yellow is an important azo dye that may be used as a pH indicator. In this study, the 

surface-enhanced Raman spectra of methyl yellow in silver colloids with various pH values were 

investigated. A relative intensity analysis indicated the intensities of the Raman bands changed 

primarily when the pH was lowered from 4 to 3. The enhancement of surface-enhanced Raman 

scattering (SERS) signals is determined mainly by surface plasmon resonance, charge-transfer 

resonance, and their combination. The varying intensities of SERS signals in solutions with different 

pH levels may result from the change in the contributions of their combined systems, such as 

the charge of methyl yellow molecules and the adsorption of molecules on the surface of silver. 

Zhen Long Zhang, Da Hu Chang, and Yu Jun Mo 

Surface-enhanced Raman scattering (SERS) is a highly 

sensitive analytical tool for investing the adsorption of 

molecules and characterizing the structure and orientation 

of the molecules on rough metal surfaces (1–5). It is well 

known that some combination of three resonances (surface 

plasmon resonance, molecular resonance, and charge-transfer 

resonance) is responsible for the SERS enhancement (6). Surface 

plasmon resonance is mostly a property of the metal, such as 

the nature of the conduction band and nanoscale surface features, 

whereas molecular resonance is a property of the molecule. 

Charge-transfer resonance results from interaction between the 

molecule and the rough metal surface (7,8). Some information 

about the molecules may be obtained by analyzing the SERS 

spectra. Raman shift and relative intensity changes in the SERS 

spectra provide information about the adsorption of molecules 

on the metal surface (9,10). 

Azo dyes have attracted much attention in recent years (11–13), 

because they represent the largest class of dyes used in industry 

and are widely applied in analytical chemistry as acid–base, 

redox, and metallochromic indicators (14). Methyl yellow is an 

important azo dye and may be used as a pH indicator. Methyl 

yellow presents as the azo form (Figure 1a) in the organic phase, 

and as the hydrazo form (MYH + , Figure 1b) in the acidified aqueous 

phase. That structure transformation has been studied by the 

UV–vis spectrum and resonance Raman spectra (15). In those 

studies, however, the Raman spectra were observed only in a 

higher wavenumber range, 1100–1650 cm -1 . Moreover, there have 

been few systemic investigations of the methyl yellow structure 

transformation with variations in pH. In this study, the methyl 

yellow structure transformation in solutions with varied pH values 

was investigated by SERS. The structure transformation and 

adsorption of the methyl yellow molecule on a silver surface were 

studied by analyzing the relative intensity change in the Raman 

bands in the SERS spectra. 


Methyl yellow was purchased from Aldrich Chemical Company 

(St. Louis, Missouri). All the chemicals used in the experiments 

were analytical grade. 

Silver colloids were prepared in an aqueous solution by the 

reduction of silver nitrate with sodium citrate following the 

method reported by Lee and Meisel (16). Transmission electron 

microscopy (TEM) examination of the silver particles revealed 

that the particles were homogeneous, with an average diameter 

of ~50 nm (17). 

The silver colloid was diluted with water in a ratio of 1:4 

(v/v). The silver colloid solution was activated by the addition 

of 0.01 M NaCl solution to the colloid solution in a ratio of 

1:5 (v/v). For SERS measurements, samples were prepared by 

incubating the methyl yellow solution (in ethanol) at a concentration 

of 5 × 10 -4 M with the NaCl-activated silver colloid at 

a ratio of 1:9, resulting in a final concentration of 5 × 10 -5 M. 

Diluted HCl was used to adjust the solution pH and pH paper 

was used to measure values.


Raman and SERS spectra were recorded 

with a Raman microscopic spectrometer 

(Renishaw RM-1000, New Mills, United 

Kingdom) composed of an optical microscope, 

a CCD video camera, and a spectrometer. 

A 632.8-nm HeNe laser was 

used as the excitation source. The SERS 

spectra of the probed molecules were measured 

with an accumulation time of 10 s 

using a 20× object lens. The laser power 

focused on the sample was ~5 mW, and the 

spectral resolution was set to 3 cm -1 . The 

analysis of the spectra for all pH values 

was performed by nonlinear curve fitting 

using Origin 7.0 software (OriginLab Corporation, 

Northampton, Massachusetts). 

Curve fitting was carried out considering 

the band as a Lorentzian curve. 

(a) 

(b) 

Results and Discussion 

Figure 2a shows the UV–vis absorption 

spectrum of the silver solution. The silver 

solution absorbs light at λ max 

= 432 nm. 

The absorption spectrum of methyl yellow 

solution (in ethanol) is shown in Figure 

2b. The absorption peak appears at λ max 

= 403 nm, which is near the absorption 

peak of the silver solution. Figure 2c shows 

the absorption spectrum of the combined 

system of silver solution and methyl yellow. 

The addition of methyl yellow to the 

silver solution resulted in a decreased intensity 

of the absorption peak, along with 

an appearance of a new wide band in the 

range of 600–800 nm resulting from the 

aggregated silver particles. It was found 

that a significant enhancement of the band 

intensities may be obtained when aggregates 

of two or more nanoparticles oscillate 

collectively and these collective oscillations 

of nanoparticles extend the range 

of useful localized surface plasmon effects 

throughout the visible and near-infrared 

region of the spectrum (6). The excitation 

wavelength of the laser, 632.8 nm, is far 

away from the absorption peak of methyl 

yellow, thus there are no surface-enhanced 

Raman resonance scattering components 

in the SERS spectra in this study. The contribution 

of molecular resonance to the 

SERS enhancement is small. 

Figure 3 shows the normal Raman 

spectrum of methyl yellow powder. Figures 

4 and 5 show the SERS spectra of 

methyl yellow in silver colloid solution 

with varied pH values. We have recorded 

SERS spectra of methyl yellow by changing 

the pH values in one-unit increments 

from 1 to 7. However, in Figures 4 and 5 

we have shown a few characteristic spectra. 

The Raman bands are labeled and the 

assignments of the Raman bands are listed 

in Table I (15,18–23). 

Compared with the normal Raman 

spectrum, the Raman signals of the molecules 

have been significantly enhanced 

in SERS. Some new bands appear in the 

SERS spectra, including bands at 892, 

997, 428, 474, 510, and 977 cm -1 (Table I). 

According to a unified approach to SERS 

(6), the presence of non-totally symmetric 

bands in the excitation profile indicates the 

presence of charge-transfer contributions 

to the enhancement. Although these molecules 

do not have enough symmetry for 

a definitive test of the strong changes in 

N 

N 

N 

H 

N 

Figure 1: Schematic structure of methyl yellow as (a) the azo-form in organic phase and as (b) the 

hydrazo-form in acidified aqueous phase. 

Absorbance (a.u) 

0.8 

0.6 

0.4 

0.2 

N 

N 

CH 3 

CH 3 

CH 3 

CH 3 

(b) 

(a) 

0.0 

300 400 500 600 700 800 

Wavelength (nm) 

Figure 2: UV–vis absorption spectra of (a) silver solution, (b) methyl yellow solution, and (c) the 

combined system of silver solution and methyl yellow. 

(c) 

spectral lines, the presence of the Raman 

lines mentioned above in the SERS spectra 

indicates strong charge-transfer effects between 

the molecules and the silver surface. 

When the pH levels are varied from 4 to 

7, no new Raman bands appear in the SERS 

spectra. At pH 2 and pH 3, however, new 

Raman bands appear at 892 cm -1 (Figure 

4a), 979 cm -1 (Figure 4b), 1222 cm -1 (Figure 

5a), and 1492 cm -1 (Figure 5a). Although the 

assignments of these bands are not clear, the 

presence of the bands could be attributed 

to the vibrational modes of hydrazo form 

(MYH + ). MYH + is dominant when the pH 

of the solution is less than 3.3, the pK a 

of 

methyl yellow. The characteristic Raman 

bands of MYH + , 1619 and 1174 cm -1 , were 

observed (Figure 5a) and are attributed to 

υ(C=C) and υ(N-N), respectively (18).



(a) 

1.6 

(d) 

6 

921 

Intensity (a.u) 

1.2 

0.8 

0.4 

426 

515 

614 

732 

828 


4 

2 

275 

427 

472 

512 

542 

585 

623 

728 

768 

824 

1000 

(b) 


0.0 

80 

60 

40 

20 

1000 

1138 

400 

1195 

1311 

1365 

600 800 

Raman shift (cm -1 ) 

0 

1000 1200 1400 1600 1800 

The relative intensity changes of all the SERS Raman bands 

with pH variation were analyzed. Figure 6 shows the relative intensity 

variations of three representative SERS bands at 1140, 1408, 

and 824 cm -1 with pH variations from 2 to 7. The intensities of the 

SERS Raman bands change slightly when the pH values change 

from 4 to 7 and from 2 to 3, respectively, whereas there is a large 

change in the intensities when the pH value is lowered from 4 to 3. 

The azo form predominates when the pH is greater than 3.3, 

the pK a 

of methyl yellow, and the hydrazo form predominates 

when the pH is lower than 3.3. This can be seen clearly by comparing 

the SERS from pH 2 to pH 7. A band is present at 276 cm -1 

at pH 4 (Figure 4c) and 275 cm -1 at pH 7 (Figure 4d), whereas this 

band disappears at pH 2 (Figure 4a) and pH 3 (Figure 4b). It is 

reported that Ag-N vibration appears at very close frequencies in 

this range (21–23). It is reasonable to think that the methyl yellow 

molecule in azo form at high pH levels is adsorbed on the silver 

surface through the nonbonding electron of the N atom. On the 

other hand, at low pH levels, the dominant structure of methyl 

yellow is the hydrazo form, with a positively charged N atom, and 

there are more chlorinated sites on the silver surface as a result 

of the high Cl - concentration. It is reasonable to suppose that the 

1408 

1418 

1442 1462 

1584 

1582 


Figure 3: Normal Raman spectrum of methyl yellow powder in the 

ranges of (a) 200–950 cm -1 and (b) 950–1800 cm -1 . 

(c) 


(b) 


(a) 


0 

6 

4 

2 

0 

1 

0 

1 

276 

322 

511 542 

582 

623 

727 

768 

424 

471 

824 

921 

978 1000 

428 

429 

474 

473 

510 

512 

528 

543 

583 

626 

625 

726 

726 

824 

892 

923 

977 999 

0 

200 400 600 800 1000 

MYH + molecule is adsorbed on the silver surface through the 

bridge of N + -Cl - -Ag + . 

The Raman spectra provide vibrational information that is 

specific for the chemical bonds in molecules. The enhancement 

of SERS signals is determined by the surface plasmon resonance, 

molecular resonance, charge-transfer resonance, and their combi- 

824 


Figure 4: SERS spectra of methyl yellow (5 × 10 -5 M) in 200–1100 cm -1 

range at pH (a) 2, (b) 3, (c) 4, and (d) 7. 

921 

1000


Table I: Observed normal Raman and SERS bands of DAB in varied environments and their tentative assignments 

NRS 

(cm -1 ) 

SERS (cm -1 ) 

pH 2 pH 3 pH 4 pH 7 

1592 1619 1619 1619 1619 

1584 1596 1597 1598 1598 

1492 

1462 1463 1462 1463 1463 

1442 1442 1441 1441 1441 

Tentative 

Assignment 

υ(C=C) 

(MYH + ) 

υ(C-C) 

υ(C Ph 

-N Me 

) 

δ(C-C) 

υ(C-C) 

δ(C-N) 

δ(C-C) 

δ(C-H) 

υ(C-C) 

δ(C-H) Me 

δ(C-N) 

References 

1418 υ(N=N) 

τ(C-N) Me 

15,18 

1408 1408 1406 1407 1407 

1365 1370 1368 1368 1368 υ(C-C) 20 

1311 1310 1310 1311 1310 

1222 

1195 1191 1193 1195 1195 

1174 

1138 1140 1140 1140 1140 

1000 999 1000 1000 1000 

977 978 

923 921 921 921 

892 

821 824 824 824 824 

768 768 

732 726 726 727 728 

υ(C-C) 

δ(C-N) 

δ(C-H) 

υ(C-C) 

δ(C-H) 

υ(N-N) 

(MYH + ) 

υ(C-N) 

υ(C-C) 

δ(C-C) 

δ(C-C) 

υ(C-C) 

υ(C-C) 

δ(C-C) 

δ(N=N) 

τ(C-C) 

γ(C-H) 

τ(C-N) Me 

τ(C-C) 

γ(C-H) 

18,19 

20 

20 

20 

20 

20 

18,19 

20 

20 

20 

20 

20 

δ(C-C) 

υ(C-C) 

δ(C-N) 

υ(C-N) Me 

20 

614 626 625 623 623 δ(C-C) 20 

583 582 585 

528 543 542 542 

515 510 512 511 512 

τ(C-C) 

γ(C-H) 

τ(C-C) 

γ(C-H) 

474 473 471 472 τ(C-C) 20 

426 428 429 424 427 τ(C-C) 20 

322 

Note: υ, stretch; δ, in-plane bend; γ, out-of-plane bend; τ, torsion 

276 275 υ(Ag-N) 21–23 

20 

20



(d) 


60 

40 

20 

0 

(c) 

60 


(b) 


(a) 


40 

20 

0 

12 

8 

4 

0 

12 

8 

4 

0 

1140 

1140 

1140 

1140 

1195 

1195 

1193 

1174 

1191 

1222 

1310 

1311 

1310 

1310 

1368 

1368 

1368 

1370 

1407 

1406 

1408 

1441 

1463 

1407 

1441 

nation (6). The UV–vis absorption spectra 

shown in Figure 2 indicate that the contribution 

of molecular resonance to the SERS 

is small. The surface plasmon resonance is 

excited by laser radiation and may result 

from the aggregations of silver nanoscale 

particles. Some new lines appear in the 

SERS spectra, indicating strong chargetransfer 

effects between the molecules and 

the silver surface. Therefore, the enhancement 

of SERS signals is determined mainly 

by surface plasmon resonance, chargetransfer 

resonance, and their combination. 

The change in SERS intensities with 

variations in the pH may result from the 

change in the contributions of the various 

resonances. At high pH levels (pH 4–7), the 

azo form of methyl yellow molecules predominates, 

because these molecules adsorb 

onto the silver surface through Ag-N bonding. 

At low pH levels (pH 2–3), the hydrazo 

form predominates, because these molecules 

are adsorbed on silver surface through 

the bridge of N + -Cl - -Ag + . The adsorption 

1463 

1441 

1462 

1442 

1463 

1492 

1598 

1619 

1598 

1619 

1597 

1619 

1596 

1619 

1200 1400 1600 1800 


Figure 5: SERS spectra of methyl yellow (5 × 

10 -5 M) in 1100–1800 cm -1 range at pH (a) 2, 

(b) 3, (c) 4, and (d) 7. 


change of the molecules may make the molecular 

orientation change, thus causing the 

intensities to change. This is consistent with 

the surface selection rules based on surface 

plasmon resonance (6). The interaction of 

molecules with a silver surface may change 

primarily as a result of the charge of the 

methyl yellow molecule (MYH + ). 

Conclusion 

The SERS spectra of methyl yellow in 

varied pH solutions were studied. The enhancement 

of SERS signals is determined 

mainly by surface plasmon resonance, 

charge-transfer resonance, and their 

combination. A relative intensity analysis 

of the Raman bands indicated that the 

intensities change primarily when the pH 

changes from 4 to 3. This may be a result 

of a change in the contributions of their 

combined system, such as the charge of 

methyl yellow molecules and adsorption 

of molecules on the silver surface. 

References 

(1) M. Fleischman, P.J. Hendra, and A.J. McQuillian, 

Chem. Phys. Lett. 26, 163–166 (1974). 

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G. Deinum, I. Itzkan, R.R. Dasari, and 

M.S. Feld, Phys. Rev. E 57, R6281–R6284 

(1998). 

(3) S. Nie, and S.R. Emory, Science 275, 1102–1106 

(1997). 

(a) (b) (c) 

1800 

1200 

600 

0 

(4) A. Sackmann and A. Materny, J. Raman Spectrosc. 

37, 305–310 (2006). 

(5) P.W. Barber, R.K. Chang, and H. Massoudi, Phys. 

Rev. Lett. 50, 997–1000 (1983). 

(6) J.R. Lombardi and R.L. Birke, J. Phys. Chem. C 

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Phys. 84, 4174–4180 (1986). 

(8) J.F. Arenas, I. Lopez Tacon, M.S. Wooley, J. Cotero, 

and J.I. Marcos, J. Raman Spectrosc. 29, 

673–678 (1998). 

300 

200 

100 

0 

0 

2 3 4 5 6 7 2 3 4 5 6 7 2 3 4 5 6 7 

pH 

Figure 6: Relative intensity variation of methyl yellow SERS bands at (a) 1140, (b) 1408, and (c) 

824 cm -1 with pH variation from 2 to 7. 

(9) A.K. Ojha, A. Singha, S. Dasgupta, R.K. Singh, 

pH 

and A. Roy, Chem. Phys. Lett. 431, 121–126 

(2006). 

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76, 636–645 (2008). 

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and H. Cho, J. Mol. Model. 14, 293–302 (2008). 

(13) H. Destaillats, A.G. Turjanski, D.A. Estrin, and 

M.R. Hoffmann, J. Phys. Org. Chem. 15, 287– 

292 (2002). 

50 

40 

30 

20 

10 

(14) H. Zollinger, Color Chemistry, Syntheses, Properties, 

and Applications of Organic Dyes and Pigments 

(VCH, New York, 1987), pp.1436–1451. 

(15) F.B. James and B.Y. Ernest, J. Phys. Chem. 85, 

1005–1014 (1981). 

(16) P.C. Lee and D. Meisel, J. Phys. Chem. 86, 3391– 

3395 (1982). 

(17) Z.L. Zhang, Y.F. Yin, J.W. Jiang, and Y.J. Mo, J. Mol. 

Stru. 920, 297–300 (2009). 

(18) M. Michl, B. Vlckova, and P. Mojzes, J. Mol. Stru. 

482, 217–223 (1999). 

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Uno, Bull. Chem. Soc. Jpn. 47, 78–82 (1974). 

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2734–2745 (2000). 

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Zhen Long Zhang and Yu Jun Mo 

are with the School of Physics and Electronics 

at Henan University, in Kaifeng, China. 

Da Hu Chang is with the Department 

of Mathematics and Science at the Luoyang 

Institute of Science and Technology, in 

Luoyang, China. ◾ 

pH 




ADVERTISEMENT 

Atomic Spectroscopy 

Determination of Hg in Environmental Samples Using a Direct 

Mercury Analyzer 

Johan Nortje, Milestone Inc. 

As emphasis is continually being placed on the monitoring 

of mercury emissions, both private and public 

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analysis, an alternative to these methods, has been 

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Determine mercury content at various concentrations in environmental 

samples. In addition to the environmental samples, NIST 

2709 San Joaquin Soil, a matrix-matched standard reference material 

(SRM) is periodically analyzed to ensure accuracy. 

Principle of Operation 

A direct mercury analyzer, as referenced in EPA Method 7473, incorporates 

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Absorbance is measured at 253.7 nm as a function of 

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Figure 1: Principles of operation of direct mercury analyzer 

(DMA-80). 

with the certified value. Discrepancy on the soil samples can be 

attributed to sample homogeneity, and possibly analytical errors, 

during the sample preparation step for CVAA analysis. 

Table I: Unknown Environmental Samples on DMA-80 vs. 

Contract Laboratory (Using CVAA)* 

Sample DMA-80 Conc. (µg/kg) CVAA Conc. (µg.kg) 

200 ppt standard 0.21 

1 ppb standard 0.91 

5 ppb standard 4.72 

Soil 5.09 5 

Soil 0.54 0.63 

LCS (5 ppb) 5.54 n/a 

Soil 398.01 520 

Soil 603.39 1000 

Soil 3282.22 2000 

Soil 7684.28 6590 

*Samples provided by an independent contract laboratory. 

Results 

See Table I. 

Discussion and Conclusion 

The DMA-80 successfully processed the environmental and powdered 

standard reference material and is an acceptable alternative 

to traditional techniques like CVAA and ICP-MS. The SRM concentrations 

obtained on the DMA-80 were in excellent agreement 

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25 Controls Drive, Shelton, CT 06484 

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expanded temperature control 

options that can be configured 

for heating to temperatures as 

high as 300 °C. According to the 

company, the accessory can be 

used for high-temperature kinetic 

or material degradation studies. 

The accessory reportedly operates with the company’s software 

and comprises reflective optics and a monolithic diamond for high 

throughput across the full mid-IR and far-IR spectral ranges. Pike 

Technologies, Madison, WI; www.piketech.com 

ICP-MS system 

The aurora M90 ICP-MS system from 

Bruker Daltonics reportedly combines 

the company’s CRI II collision reaction 

interface for interference elimination with 

the Nitrox 500 accessory to achieve lower 

limits of detection on elements such as As 

and Se. The system has an ion mirror and 

curved-fringe rods designed to provide 

high sensitivity (1 million counts/s for 1 

µg/L), low background noise, and low 

detection limits. According to the company, 

the system can be used for environmental, 

food, and clinical applications. 

Bruker Daltonics, Billerica, MA; www.bdal.com 

Raman analyzer 

The EZRaman-1-9 portable Raman 

analyzer from Enwave Optronics 

is designed to minimize fluorescence 

and maximize capability 

of Raman analysis in difficult-tomeasure 

samples. According to the 

company, the analyzer’s excitation 

wavelength is above 900 nm. 

Enwave Optronics, Inc., 

Irvine, CA; www.enwaveopt.com 

Mercury analyzer 

The Model RA-3000F Gold+ mercury 

analyzer from Nippon Instruments 

Corporation is designed for EPA 

Methods 1631E and 245.7. According 

to the company, the instrument simplifies 

low to sub-ppt mercury analysis 

and reduces reagent consumption 

and wastes by as much as 80%. 

Nippon Instruments North 

America, College Station, TX; www.hg-nic.us 

Raman microscope 

Renishaw’s inVia Raman microscope 

can be used for nondestructive 

testing of sperm DNA for assessing 

the healthiness of sperm cells. The 

instrument can be customized to 

integrate optical tweezing, which 

enables researchers to immobilize 

sperm cells with a tightly focused 

laser beam. The resulting Raman spectra contain information about 

the vibrations of molecules within the sperm cells and can be used 

to assess the state of its DNA. Renishaw, Hoffman Estates, IL; 

www.renishaw.com



DNA quality control standard 

Starna’s DNA quality control 

standard is provided in a 1.5- 

mL liquid vial that offers the 

DNACON 260/280 reference 

material in format suitable for 

use in drop technology systems. 

The vial reportedly is produced 

in an ISO 17025 and ISO Guide 

34 accredited environment and 

provides a NIST-traceable quality 

control standard. According to the company, the concentration is 

matched for use with ultralow volume and short-pathlength measurement 

systems. Applications include DNA purity evaluations in 

clinical and bioscience laboratories. Starna Cells, Inc., 

Atascadero, CA; www.starna.com 

Topographic Raman imaging 

WITec’s True Surface Microscopy 

imaging mode is 

designed to allow large-area 

topographic coordinates from 

the profilometer measurement 

to be precisely correlated with 

the large-area confocal Raman 

imaging data. This option 

reportedly enables samples 

that would normally require 

extensive preparation to obtain 

a certain surface flatness to be automatically characterized as they 

are. According to the company, it allows scan ranges as large as 50 

mm × 100 mm with a spatial resolution of 100 nm vertically and 

10 µm laterally. WITec GmbH, Ulm, Germany; www.witec.de 

Silicon drift detectors 

Amptek’s Super silicon drift 

detectors are designed for XRF 

applications with OEM handheld 

instruments and benchtop 

analyzers. According to the 

company, the detectors have 

125-eV FWHM resolution, an 

11.2-µs peaking time, and a 

P/B of 8200 with an area of 25 

mm 2 and a silicon thickness of 500 µm. The detectors reportedly 

are contained inside the same TO-8 package and do not require liquid 

nitrogen. Amptek Inc., Bedford, MA; www.amptek.com 

Immersion probes 

Hellma’s immersion probes 

are designed for use in fully 

automated tablet dissolution 

UV–vis analysis. According 

to the company, the probes 

have custom-built conical 

measuring heads and modified 

fiber-optic cables with 

five different optical path 

lengths available. Hellma 

USA, Plainview, NY; 

www.hellmausa.com 

ICP liquid flow monitor 

The TruFlo liquid flow monitor 

from Glass Expansion is designed 

to measure and digitally display 

sample flow rates for an ICP-OES 

or ICP-MS nebulizer. According 

to the company, the monitor can 

report when deviation from specified 

flow rates occurs or when 

problems such as worn pump tubing, a clogged nebulizer, or an 

improperly adjusted peristaltic pump occur. The system reportedly 

can provide an electronic record of sample flow rates for regulatory 

compliance. Glass Expansion, Pocasset, MA; www.geicp.com 

EDXRF spectrometer 

The EDX-LE spectrometer from Shimadzu 

Scientific Instruments is an 

energy dispersive X-ray fluorescence 

spectrometer designed to screen 

elements regulated by RoHS/ELV 

directives. According to the company, 

the instrument incorporates 

automated analysis functions and a 

detector that does not require liquid 

nitrogen. Users reportedly can customize set-up functions according 

to management methods, set threshold values for each material 

or element, and change screening judgment according to the input 

method used for threshold values. Shimadzu Scientific 

Instruments, Columbia, MD; www.ssi.shimadzu.com 

X-ray source 

Moxtek’s 50-kV monoblock X-ray 

source is designed for applications 

including handheld, portable, 

and benchtop instrumentation. 


the source is a battery-operated, 

lowpower device that uses passive 

air cooling and delivers a 

high intensity flux of approximately 

2 × 10 11 photons/s/steradian/0.10 µA. The source reportedly 

weighs ~350 g and provides a variable energy output up to 50 

kV, a maximum beam current of 0.20 mA, and a total power of up 

to 4 W. Moxtek, Orem, UT; www.moxtek.com 

Raman system for inverted microscopy 

The XploRA INV compact analytical 

Ramam chemical imaging microscope 

from HORIBA Scientific reportedly combines 

the automatization features and 

small footprint of a standard confocal 

Raman microscope with the capabilities 

of an inverted microscope for biological 

applications such as cell research, cancer 

detection, pharmaceutical verification of 

intercellular activities, inclusion of microreactors, and incorporation 

of AFM units for tip-enhanced Raman spectroscopy. According to 

the company, the microscope’s open structure permits the use of 

options and add-ons for inverted microscopes, such as micromanipulators, 

“optical tweezers,” and specific enclosures for cell applications. 

HORIBA Scientific, Edison, NJ; www.horiba.com/scientific


Calendar of Events 

August 2011 

11–15 EUROanalysis 2011 

Belgrad, Serbia 

www.euroanalysis2011.rs/ 

1–3 The Conference on Small 

Molecule Science (CoSMoS) 2011 

Chapel Hill, NC 

www.cosmoscience.org 

1–5 60th Annual Conference on Applications 

of X-Ray Analysis (Denver 

X-Ray Converence) 

Colorado Springs, CO 

www.dxcicdd.com 

7–11 Microscopy & Microanalysis 2011 

Meeting 

Nashville, TN 

www.microscopy.org/mandm/2011 

14–18 ISOS XVI — Sixteenth International 

Symposium on Silicon 

Chemistry 

Hamilton, Canada 

www.isos-xvi.org 

28 August–1 September 242nd ACS 

National Meeting & Exposition 

Denver, CO 

help@acs.org, portal.acs.org 

September 2011 

5–8 RAA2011 — 6th International 

Congress on the Application of 

Raman Spectroscopy in Art and 

Archaeology 

Parma, Italy 

www.fis.unipr.it/raa2011/ 

11–14 MAF 12 — 12th Conference on 

Methods and Applications of 

Fluorescence 

Strasbourg, France 

maf12.unistra.fr/ 

13–16 8th Symposium on the 

Practical Application of Mass 

Spectrometry in the Biotechnology 

Industry 

Raleigh, NC 

casss.org/displayconvention. 

cfm?conventionnbr=9404 

26–28 1st International Symposium 

on Biophysical Characterization of 

Protein Therapeutics 

Rockville, MD 



October 2011 

3–7 19th International Solvent Extraction 

Conference (ISEC 2011) 

Santiago, Chile 

www.isec2011.com 

8–12 LABTECH2011 — The 2nd International 

Laboratory Technology 

Conference & Exhibition 

Manama, Bahrain 

www.lab-tech.info 

9–13 13th Symposium on the Practical 

Applications for the Analysis 

of Proteins, Nucleotides and Small 

Molecules 

Amelia Island, FL 



14 New England Conference on Process 

Analytical Technology 

Bedford, MA 

http://necpat.com 

30 October–2 November 

17th Microoptics Conference (MOC’11) 

Sendai, Japan 

Microoptics Group, ogura_at_comemoc. 

com, www.comemoc.com/topics_e.html 

November 2011 

14–16 Eastern Analytical Symposium 

Somerset, NJ 

www.eas.org 

December 2011 

11–15 International Symposium 

on Surface Science — Focusing on 

Nano-, Bio-, and Green Technologies 

(ISSS-6) 

Tokyo, Japan 

Prof. Susumu Fukatsu, isss6_at_sssj.org, 

www.sssj.org/isss6/ 

January 2012 

8–14 2012 Winter Conference on 

Plasma Spectrochemistry 

Tucson, AZ 

icpinformation.org 

21–26 SPIE Photonics West 

San Francisco, CA 

spie.org/x2584.xml 

March 2012 

11–16 Pittcon 2012 — Pittsburgh Conference 

on Analytical Chemistry and 

Applied Spectroscopy 

Orlando, FL 

www.pittcon.org 

25–29 243rd ACS National Meeting & 

Exposition 

San Diego, CA 

portal.acs.org/portal/acs/corg/content?_nfpb=true&_pageLabel=PP_ 

ARTICLEMAIN&node_id=9&use_sec=false


Short Courses 

August 2011 

11–15 Basics in Mass Spectrometry 

and Applications in the 

1 X-ray Metrology / Centennial Pharmaceutical Industry 


(at EUROanalysis 16) 


Belgrade, Serbia 

www.euroanalysis2011.rs/short-courses 

2 Fundamentals of Digital Signal 

Processing and X-ray Detectors/ 

Gold Camp 



7–11 Advanced Tools in Environmental 

Biogeochemistry — Opportunities 

and Limitations 

Tuebingen, Germany 

www.asms.org/ 

27–28 Introduction to Modern Mass 

Spectrometry 

Denver, CO 

www.proed.acs.org/courses/search_ 

results.cfm?categoryID=1&sbc=Y 

27-28 Infrared Spectral Interpretation: 

A Systematic Approach 

Denver, CO 



27-28 Structure Determination of 

Small Molecule Organic Compounds 

Using One and Two Dimensional 

NMR Spectroscopy 

Denver, CO 



September 2011 

11–15 Applied Spectroscopy: 

Structure Elucidation of Organic 

Compounds (at EUROanalysis 16) 

Belgrade, Serbia 

www.euroanalysis2011.rs/short-courses 

21–22 Practical Applications of 

Mass Spectrometry for Small 

Molecules 

Boston, MA 

Michael Balogh, instructor 

http://www.proed.acs.org/courses/ 

search_results.cfm?categoryID=1&sbc=Y 

October 2011 

31 Advanced Impedance Spectroscopy 

for Fuel Cells (Electrochemical 

Society sponsored short course at 

the Fuel Cell Seminar) 

Orlando, FL 

www.che.ufl.edu/orazem. 

Short_Courses.htm 

November 2011 

14 High-Throughput Drug Analysis 

by LC–MS 

Somerset, NJ 

www.eas.org 

14 Hands-on FTIR, NIR and Data 

Analysis — What Is the Right Tool to 

Solve Your Problem 

Somerset, NJ 

www.eas.org 

14 Introduction to Metabolomics 

Somerset, NJ 

www.eas.org 

14 Introduction to Near-Infrared 

Spectroscopy: Applications in the 

Pharmaceutical and Biotech 

Industries 

Somerset, NJ 

www.eas.org 

14 Interpretation of Mass Spectra 

with Practical Solutions to 

Problems 

Somerset, NJ 

www.eas.org 

14 Practical Introduction to Raman 

Spectroscopy 

Somerset, NJ 

www.eas.org 

14 Impurity and Degradant Identification: 

Strategies for Structure 

Elucidation via Chromatography, 

MS, and NMR 

Somerset, NJ 

www.eas.org 

14–15 LC–MS: Theory, Instruments, 

and Applications 

Somerset, NJ 

www.eas.org 

14 Interpretation of Mass Spectra 

with Practical Solutions to 

Problems 

Somerset, NJ 

www.eas.org 

14–15 Interpretation of Mass 

Spectra with Practical Solutions 

to Problems 

Somerset, NJ 

www.eas.org 

14–15 Infrared (IR) Spectral Interpretation 

I & II 

Somerset, NJ 

www.eas.org


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PerkinElmer 7, 15, 17 

Pike Technologies 33, 35, 50 

Amptek, Inc 31 

Hellma Cells, Inc. 21 

Shimadzu Scientific Instruments 

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Applied Rigaku Technologies 28 

Microscopy and Microanalysis 9 

SPIE 

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Avantes BV 50 

Milestone, Inc. CV Tip, 43 

Starna Cells, Inc. 8 

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Experimental - Spectroscopy

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