a method for determining nutritional facts with raman ... · control, for compositional...
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Εργαστήριο Βιοϊατρικής Απεικόνισης και Εφαρμοσμένης Οπτικής
Τμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών – Πανεπιστήμιο Κύπρου
A Method For Determining Nutritional Facts with Raman Spectroscopy
Υποβάλλεται στο Πανεπιστήμιο Κύπρου ως μερική συμπλήρωση των απαιτήσεων για την απόκτηση
Πτυχίου Ηλεκτρολόγου Μηχανικού
Τμήμα Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών
Μάιος 2008
by Christos Moustakas
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A METHOD FOR DETERMINING NUTRITIONAL FACTS WITH RAMAN
SPECTROSCOPY
by
Christos Moustakas
Submitted to the University of Cyprus in partial fulfillment
of the requirements for the degree of Bachelor of Science in Electrical Engineering
Department of Electrical and Computer Engineering
May 2008
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A METHOD FOR DETERMINING OF NUTRITIONAL FACTS WITH RAMAN
SPECTROSCOPY
by
Christos Moustakas
Examination Committee: Constantinos Pitris Assistant Professor, Department of Electrical and Computer Engineering, Advisor Styliani Petroude Visiting Lecturer, Department of Electrical and Computer Engineering, Committee Member
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Abstract
The estimation of the nutritional parameters of food products is difficult and laborious
process. Current methods are even more unsuitable for day-to-day and home use.
Also, many companies spend considerable financial and budgetary resources to
frequently check the nutritional facts of their products. A new device, that would
automatically estimate the nutritional facts of any edible product, could prove very
useful in all of the above situations. Such a device could be in desktop or, even,
handled form. The purpose of this senior project was to find a new method for
automated estimation of the nutritional facts of edible or potable products which
would enable such a device.
To achieve that goal, we used the Raman Spectroscopy. Raman spectroscopy is a well
established method with a wide range of applications. It can be used for quality
control, for compositional identification, for detection of adulteration, for detection
of diseases and many other applications. In this senior project, we used Raman
Spectroscopy to examine solutions of food in water. Afterwards, we analyzed the
spectra that we obtained. We used principal component analysis and solutions to
linear differential equations for our estimates. When the analysis techniques were
optimized, we could estimate several nutritional facts (calories, fat, protein,
carbohydrates, sugars, fiber) with an error between 2.9 % and 6.4 %. The analysis was
performed either on the entire spectrum or on some ranges of intensities of the
spectra.
The results imply that Raman spectroscopy can be used for the estimation of the
nutritional facts of food products with an error less than what is required for labelling.
This method could be used for creating a new device, that can be used very easily to
inform the user about the nutritional facts of any food product. This device would be a
very useful tool for dieticians, hospitals, food companies, health care organizations,
restaurants and even home users, who want to be informed about the content of the
food that they consume.
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Περίληψη
Η εκτίμηση των θρεπτικών παραμέτρων των τροφίμων είναι δύσκολη και κοπιώδης
διαδικασία. Οι τωρινές μέθοδοι είναι ακόμη περισσότερο ακατάλληλες για
καθημερινή και οικιακή χρήση. Επίσης, πολλές βιομηχανίες ξοδεύουν αξιόλογους
οικονομικούς πόρους του προϋπολογισμού τους για να ελέγχουν συχνά τα θρεπτικά
δεδομένα των προϊόντων τους. Μια νέα συσκευή, η οποία θα υπολόγιζε αυτόματα τα
θρεπτικά δεδομένα οποιουδήποτε φαγώσιμου προϊόντος, θα μπορούσε να αποδειχτεί
πολύ χρήσιμη σε όλες τις παραπάνω περιπτώσεις. Μια τέτοια συσκευή θα μπορούσε
να είναι σε επιτραπέζια ή χειρός μορφή. Ο σκοπός αυτής της διπλωματικής εργασίας
ήταν να βρεθεί μια νέα μέθοδος για την αυτοματοποιημένη εκτίμηση των θρεπτικών
δεδομένων των φαγώσιμων ή πόσιμων προϊόντων, η οποία θα καθιστούσε εφικτή μια
τέτοια συσκευή.
Για την επίτευξη του στόχου μας, χρησιμοποιήσαμε τη φασματοσκοπία Ράμαν. Η
φασματοσκοπία Ράμαν είναι μια καλά καθιερωμένη μέθοδος με ένα ευρύ φάσμα
εφαρμογών. Μπορεί να χρησιμοποιηθεί για τον ποιοτικό έλεγχο, για το προσδιορισμό
των συστατικών, για την ανίχνευση της νόθευσης, για την ανίχνευση ασθενειών και
σε πολλές άλλες εφαρμογές. Σε αυτή τη διπλωματική εργασία, χρησιμοποιήσαμε τη
φασματοσκοπία Ράμαν για να εξετάσουμε διαλύματα τροφίμων σε νερό. Κατόπιν,
αναλύσαμε τα φάσματα, τα οποία πήραμε. Χρησιμοποιήσαμε ανάλυση κύριων
συνιστωσών (principal component analysis) και λύσεις γραμμικών διαφορικών
εξισώσεων για τις εκτιμήσεις μας. Όταν οι τεχνικές ανάλυσης βελτιστοποιήθηκαν,
μπορούσαμε να εκτιμήσουμε διάφορα θρεπτικά δεδομένα ( θερμίδες, λίπη, πρωτεΐνη,
υδατάνθρακες, σάκχαρα, φυτικές ίνες) με ένα λάθος μεταξύ 2,9% και 6,4 %.Η
ανάλυση εκτελέστηκε είτε σε ολόκληρο το φάσμα, είτε σε κάποια διαστήματα των
εντάσεων των φασμάτων.
Τα αποτελέσματα υποδηλώνουν ότι η φασματοσκοπία Ράμαν μπορεί να
χρησιμοποιηθεί για την εκτίμηση των θρεπτικών δεδομένων των τροφίμων με ένα
λάθος μικρότερο από αυτό που απαιτείται για την αναγραφή στις ετικέτες. Αυτή η
μέθοδος θα μπορούσε να χρησιμοποιηθεί για τη δημιουργία μιας νέας συσκευής, η
οποία θα μπορούσε να χρησιμοποιηθεί πολύ εύκολα για να ενημερώνει το χρήστη
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σχετικά με τα θρεπτικά δεδομένα οποιουδήποτε τροφίμου. Αυτή η συσκευή θα ήταν
ένα πολύ χρήσιμο εργαλείο για τους διαιτολόγους, τα νοσοκομεία, τις βιομηχανίες
τροφίμων, τις οργανώσεις υγειονομικής περίθαλψης, τα εστιατόρια, ακόμη και τους
οικιακούς χρήστες, οι οποίοι θέλουν να πληροφορηθούν σχετικά με το περιεχόμενο
του τροφίμου που καταναλώνουν.
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Acknowledgements
I would like to thank my advisor Prof. Pitris for helping me and encouraging me with
any problems I had to face. Also, I would like to thank all the people of the
Laboratory of Biomedical Imaging and Applied Optics for their patience during my
‘noisy’ experiments, continuously using the blender.
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Table of Contents
Chapter 1:Introduction...................................................................................................1
1.1 Introduction........................................................................................................1
1.2 The applications of optical instruments ...................................................................1
1.3 The nutrition facts labels and their accuracy ...........................................................4
1.4 Raman Spectroscopy................................................................................................9
1.4.1 Introduction.......................................................................................................9
1.4.2 Principles of Raman Spectroscopy .................................................................10
1.4.3 The Raman Spectrum of water: our solvent ...................................................12
1.4.4 The advantages of Raman Spectroscopy ........................................................14
1.4.5 Raman Experimental Instrumentation ............................................................15
1.5 The applications of Raman Spectroscopy in Food Science...................................20
1.6 Raman vs IR...........................................................................................................26
1.7 Report of the spectrums of several components of food .......................................31
1.8 Past projects about the determination of the energetic value or the nutritional
parameters of foods and drinks....................................................................................39
Chapter 2: Explanation of the process .......................................................................45
2.1 Introduction............................................................................................................45
2.2 Explanation of the experimental process ...............................................................45
2.3 Explanation of the algorithms................................................................................55
2.3.1 Introduction.....................................................................................................55
2.3.2 The algorithm for presentation of spectrums ..................................................55
2.3.3 The algorithm for reading and preparing the Data.........................................56
2.3.4 The algorithm for the processing of data -1st Method....................................57
2.3.5 The algorithm for the processing of data -2nd Method...................................58
Chapter 3: Results.......................................................................................................60
3.1 Introduction............................................................................................................60
3.2 Results of the 1st Method .......................................................................................60
3.3 Results of the 2nd Method .....................................................................................61
3.4 Comparison of the results ......................................................................................62
Chapter 4: Conclusions and future directions.............................................................64
4.1 Introduction............................................................................................................64
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4.2 Optimization of the process ...................................................................................64
4.3 Conclusion .............................................................................................................64
References....................................................................................................................66
Appendix A..................................................................................................................68
A-1: The algorithm for presentation of spectrums.......................................................68
A-2: The algorithm for reading and preparing the Data .............................................69
A-3: The algorithm for the processing of data -1st Method ........................................72
A-4: The algorithm for the processing of data -2nd Method......................................75
A-5: Functions .............................................................................................................79
A-5-1:Average Raman Data ....................................................................................79
A-5-2:Normalize Raman Data .................................................................................79
A-5-3:Project to Substrate........................................................................................80
A-5-4: Remove Cosmic Spikes................................................................................81
A-5-5: Filter Raman Data .......................................................................................82
A-5-6: Plot Raman Data...........................................................................................83
A-5-7:Raman............................................................................................................85
A-5-8: Read Raman Text .........................................................................................86
Appendix B ..................................................................................................................88
B-1:Results of the 1st-method ..................................................................................88
B-2: The results of the 2nd method...........................................................................92
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Chapter 1:Introduction
1.1 Introduction
Nowadays, the significance of determining the nutritional facts of food products is
increasing very fast. The reasons for this increased interest are various. First, it is
proven that the nutritional facts labels of several edible products are not as exact as
we were led to believe. Also, there are products that are not canned, so, it is not easy
for somebody to know the exact nutritional facts of these products. Moreover many
people, who have health problems or who want to have a healthier life, are interested
to know the nutritional parameters of what they eat. At a larger scale, many
companies would prefer to minimize the cost of the chemical analyses of their
products required for labelling. With this new method, they could save time a much
less costly method. All of the above reasons were our motivation for investigating the
use of Raman spectroscopy in nutritional fact identification.
1.2 The applications of optical instruments
The globalization of the food industry has made product safety and authentication top
priorities for government agencies, distributors, packagers, and growers alike.
Checking the expiration dates and ingredient lists on packaged foods has become
second nature for most consumers. Μost people want to be sure that the products they
are buying are fresh, sometimes organic and devoid of trans fats and too many
carbohydrates or calories. Some people want to know where certain items are
originated in order to take advantage of the expertise of a particular region. Also,
there are several recent instances of tainted food products being recalled due to illness
and even death following consumption of these products. These reasons have turned
food analysis and food safety into a big business.
There are a lot of traditional food monitoring methods, like high-performance liquid
chromatography, gas chromatography, graphite furnace atomic-absorption
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spectroscopy, and inductively coupled plasma optical-emission spectroscopy
.Although these methods are useful, there is a demand to detect more elements in less
time with better accuracy and precision. These can be achieved with the use of mass
spectrometry and near-infrared (NIR) spectroscopy as well. These systems are used in
laboratories and production settings to analyze fat, lactose, and protein levels in dairy
products, determine the freshness of eggs and egg products, and monitor bacteria
growth in meat products. Other applications include determining when a batch of beer
or barrel of wine is ready for consumption, anticipating the optimum harvest time for
fruit or vegetables, even overseeing fertilizer requirements for wheat crops grown for
cereal and bread .
So, the three main areas of concern that we want to know, using optical instruments in
food analysis, are the expected content, the origin of the product and if it is safe. The
point of origin can be found by measuring the natural metabolites. The other thing
that can be measured is contamination.. Also, optical instruments are used throughout
the “food chain” to determine, for example, how much lactose is in milk or cheese,
how much fat (and what kind) is in a chicken breast or turkey leg, and how much
sugar is in that “organic” peanut butter. This information is important for labelling
reasons, especially given that emulsifiers, stabilizers, carbohydrates, and thickeners
are commonly used to optimize the texture and flavour of food. The company
Aspectrics, for example, offers an encoded-photometric NIR (EP-NIR) spectrometer
that can analyze food for safety and labelling purposes. Particularly, the company’s
MultiComponent 2750 analyzer covers the spectral range of 1375 to 2750 nm, making
it ideal for analyzing edible oils, protein, fat, alcohols, and starch .Also, Aspectrics
recently demonstrated the ability of the MultiComponent 2750-coupled to an external
halogen NIR source and a 2 mm multimode fiber probe to quantify the percentage
volume of corn, canola, and olive oils in mixtures containing all three oils. Moreover,
there are the micrOTOF and micrOTOF-Q ESI-TOF mass spectrometers, that are
designed to enable users to automatically create their own application specific
accurate-mass libraries for pesticides in food or to determine country of origin
through the measurement and classification of metabolites. There are other products
which are geared toward the detection of bacteria in food products . When a bacteria
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gets on a piece of cheese or meat, the smaller proteins are indicative of what it is
feeding on, while the larger ones are indicative of its native proteins.
Another use of optical instruments is on production lines to improve the taste and
appearance of food by measuring the sugar content of onions, citrus, and other fruits
and vegetables. This data can help growers determine the ripeness, predict when the
fruit will be optimum for use, and do yield and crop analysis to pick the right fruit at
the right time-all of which increases revenues and yields significant savings by
optimizing both the product and the process.
Another possible use of optical instruments is during the wine producing process. By
looking at the quality of the grape while it is still on the vine, this technology can help
the grower pick the optimum products for each variety and thus batch the wine. Also,
it is possible to measure the ratios of different sugars produced in fermentation, which
relates to different flavor profiles. That’s significant for vintners in grading and
pricing their wines.
An additional element of interest in the food industry, particularly for prolonging the
shelf-life of perishables, is modified atmospheric packaging (MAP). In response to
this, Ocean Optics has developed the RedEye patch (a tiny sol-gel matrix sensor that
can be placed inside a package) and the FOXY fiber-optic probe to monitor oxygen
content of a food product on a production line, in a delivery truck, on a supermarket
shelf, or at a restaurant. The fact that people are able to directly and quantitatively
measure the oxygen concentration inside a food container after it has been sealed
enables a whole new set of applications for MAP. The sensors utilize doped
ruthenium compounds whose fluorescence is quenched by the presence of oxygen; the
probes are illuminated by 450 nm light from an LED, and the fluorescence in the 600
nm region provides information about the oxygen concentration. For example,
highoxygen-concentration MAP is ideal for food products such as fresh fruits and
vegetables or red meat, because the oxygen in the package will continue to support
the respiration of the produce or combine with the myoglobinin the meat to give a red
color. However, foods such as nuts or potatoes require significantly reduced oxygen
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levels. Visible spectroscopic techniques are used to indirectly and optically measure
the oxygen content of a product, even in a liquid environment such as wine.
The major problem for the use of optical instruments is the volume that they have.
There are efforts by Ocean Optics to take a modular approach to instrument design
that it believes will make spectrometers compact and portable enough for field- and
production-based food-analysis and food-safety applications.
1.3 The nutrition facts labels and their accuracy
The nutrition labels changed the way people buy food, especially for people on
special diets. It is not any more necessary to restrict food choices to those products
and brands that are known to be safe. With the use of labels, we can choose a
product, reading a label with its measured nutrients. But, in fact, the measuring is not
so accurate as we think.
When the Nutrition Facts label on a dry mix lists the number of grams of a nutritional
parameter per serving, that doesn’t include the ingredients we add at home. The
Nutrition Facts label always lists the nutrition of the product as purchased, even if it is
a dry powder. If the product is a mix, the value listed in grams is for the dry mix.
When the product requires preparation with additional ingredients, the next column
usually lists percentages of recommended nutrients as prepared according to the
package directions. But, usually, it is not identified if there is any possible variation
such as using skim vs. regular milk. Also, reported zero is not the same thing as "not
any." The basic rules for reporting the values call for rounding are mentioned below.
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Table 1: The basic rules for reporting the values call for rounding
Calories Carbohydrates-
Protein(grams)
Fat(grams)
1 rounded to nearest gram
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determined by chemical analysis. But the value reported on the "Total Carbohydrate"
line must still be calculated by difference, not by adding up the individual
components. This leads to problems, because it is possible to have a label where the
weight of any or all of the carbohydrate constituents reported somewhat exceeds the
weight listed for total carbohydrates and that can confuse consumers. That is due to
standard error. In fact, every measurement technique has a degree of imprecision .If
we add to it the problem that the error of the "total carbohydrates" reported may
include five different standard errors, we can understand the reason of this error.
However, an honest difference is small, probably no more than a gram. If the
difference is larger, they are likely "presubtracting" the fiber from the Total
Carbohydrates reported. Clearly that is not in accordance with the regulations.
Regarding the calculations of calories, calories may be calculated using any one of
several methods. The old-fashioned bomb calorimeter, one of the acceptable methods,
is a poor model for the human body. Ideally, calories represent physiological energy,
but the energy value remaining after digestive and metabolic losses are deducted from
the gross energy. For processed foods, manufacturers are permitted to calculate
calories from the average values of 4-4-9 kcal/g for protein, carbohydrate, and fat,
respectively. The only exception is that, optionally, they may subtract the insoluble
fiber from this calculation. In other words, even though alcohol is not reported as a
nutrient, its calories must be reported, as must be the 4 calories per gram imputed to
soluble fiber.
According to the regulations, nutrients are divided into two groups: the "good"
nutrients and the "bad" nutrients. "Good" nutrients consisted of: "vitamin, mineral,
protein, total carbohydrate, dietary fiber, other carbohydrate, polyunsaturated or
monounsaturated fat, or potassium" and must be present in at least 80% of the label
value in every unit tested. However, the amounts may exceed the label value by a
"reasonable" amount. Conversely, "bad" nutrients: calories, sugars, total fat, saturated
fat, cholesterol, or sodium must be present in no more than 120% of the label value
but may be less than the label value by a "reasonable" amount.
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The number of errors at labels is significant. When labels are wrong, the FDA has the
ability to require a recall. This is a drastic action and is rarely invoked unless the
problem is life-threatening. In the case of nutrition labels, manufacturers are
responsible for the accuracy of the nutrition label values on their products. The FDA
merely spot-checks accuracy. If errors are found, the FDA "works with" the company
to resolve the problem. Old labels are usually permitted to be used until supplies are
used up, even if they do not meet current standards or otherwise contain mistakes.
Manufacturers could be required to affix a sticker with corrected information.
Although this requirement, the manufactures do not implement this.
Sometimes there isn't any information at some foods .The reason is that food labels
are not always required, as long as no health claims are made. Because it is costly to
acquire and disseminate nutritional information, foods produced in limited quantities
and/or by small businesses, ready-to-eat foods, or foods packaged for immediate
consumption are exempt. In other words, foods sold in vending machines, snack bars,
bakeries, restaurants, etc. are not required to have nutritional information. Also, foods
that contain only insignificant amounts of nutrients considered important under the
law need not have labels.
Sometimes non-standard information is worse than nothing. Unfortunately, as long as
they don't have to follow the usual nutrition label regulations, manufacturers can
create their own rules for presenting nutrition information.
Another problem is the different treatment of fiber, depending of the country. For
example, in the EU dietary fiber is not reported with the total carbohydrates, as in
USA, but is treated as a separate nutrient. Scientifically, fiber is a carbohydrate, one
that is too complex for humans to digest. So it does belong under the total
carbohydrate line. However, nutritionally it does not behave like other carbohydrates
and dietary fiber plays such an important role in health that it also makes sense to treat
it separately.
Also, it has been observed that a number of companies omit certain ingredients from
their carbohydrate counts. They claim that these ingredients are not digested or that
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they are digested by a non-carbohydrate pathway. However, it should be noted that
unless and until the regulations are changed, these ingredients are supposed to be
counted as part of the Total Carbohydrates. Even insoluble fiber, a nutrient that
everyone agrees cannot be digested at all must be reported as part of the Total
Carbohydrates.
Another problem is that foods that contain only insignificant amounts of nutrients
considered important under the law need not have labels. These "insignificant
amounts" tend to be carbs which are insignificant only in the context of a high-
carbohydrate diet. The fact that a cup of coffee or tea contains a mere gram of
carbohydrate is insignificant to people who routinely consume more than 300g per
day, but a few cups of these supposedly no-calorie, presumably no-carb, beverages
can represent a big chunk of our carb allowance.
There are now millions of people buying lowcarb products, a fact that has not escaped
a number of manufacturers. In their rush to enter this market, some manufacturers
have not bothered to make real lowcarb products. Much of our modern food
manufacturing know how depends on using sugars and starches, so they try to get by
with modifying existing products. Some, realizing that accurate food labels would
belie their advertising, have chosen to ignore the label regulations even while using
the Nutrition Facts format. Some pre-subtract the fiber, some fail to report entire
classes of carbohydrates.
Lowcarb consumers are caught in a bind. We are offered information in a format that
implies that the government stands behind it, but the government has little interest in
monitoring it. This issue has become so serious that a group of lowcarbers went to the
trouble of paying for independent lab tests which, sadly, confirmed that none of the
products tested were as low in carbs as reported on their labels. It is also difficult to
get our concerns considered health claims. If a product is labelled as safe for ketosis,
and it turns out to be too high carb, it's not clear that the FDA would consider that a
health claim.
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In conclusion, we could say that the nutrition facts labels are not so accurate, as we
believe. There are a lot of inaccuracies in estimation of nutritional facts and there is
rounding to zero for several products that contain small amount of a nutritional
parameter. However, when we use more quantity, we have a countable value of this
parameter.
1.4 Raman Spectroscopy
1.4.1 Introduction
When a photon is incident on a molecule, it may be transmitted, absorbed, or
scattered. The various techniques that are based on these different light–tissue
interactions include:
• Absorption spectroscopy
• Reflectance spectroscopy
• Fluorescence spectroscopy
• Raman spectroscopy
Raman spectroscopy was named after the Indian scientist C.V. Raman, who together
with K. S. Krishnan first observed that some of the light scattered by material is
changed in frequency . The history of Raman spectroscopy thus dates back to 1928,
but it is relatively recently that it has begun to find applications in various fields of
science and technology. From 1928 to the end of 1960s, progress in Raman
spectroscopy was slow, and the technique was employed only in the limited fields of
physics and chemistry. It was in the early 1970s that Raman spectroscopy experienced
a renaissance. Gas lasers such as Ar and Kr ion lasers gradually became popular in the
early 1970s, and the development of the lasers awakened the "sleeping giant" of
Raman spectroscopy. The intensity of Raman scattering is only 10-10 until 10-12 that of
the incident light, it was not easy to observe Raman scattering light without a laser
light source. Application of Raman spectroscopy to biological materials also started in
the early 1970s. A number of research groups attempted Raman studies on proteins,
nucleic acids, lipids, and so on. Most of them aimed at investigating in vitro the
structure of biological molecules.
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In the last quarter-century Raman spectroscopy has made remarkable progress owing
to technological innovations in lasers, spectrometers, detectors, and computers.
Today, use of Raman spectroscopy is spread over wide areas of science and
technology. However, Raman spectroscopy is still a new, emerging technique in food
analysis, despite considerable promise in the identification, quantitative and
qualitative analysis, and structural investigation of food constituents, additives, and
contaminations in various matrices. Several factors have hindred the adoption of
Raman spectroscopy for use in food analysis. One is that most of foods and food
components emit strong fluorescence, which often obliterates weaker Raman signals.
Another is that the high photon flux of the laser beam can produce unwanted
photochemical effects on the samples. Yet another is that the sensitivity of Raman
spectroscopy is not always high. These three major problems have recently been
solved, largely thanks to the above-mentioned technological innovations.
1.4.2 Principles of Raman Spectroscopy
If a molecule is irradiated by monochromatic light of frequency vo, the scattering light
of frequency vo ± v as well as of vo is emitted (Fig. 1). The portion of scattering light
that undergoes a change in frequency is named Raman scattering, while that with the
same frequency as the incident light is known as Rayleigh scattering. Therefore,
Raman scattering is caused by inelastic collisions between the molecules and photons.
In other words, there is an exchange of energy between the molecule and photon. As a
result of the collision the electronic, vibrational, or rotational energy of the molecule
is changed by an amount of ΔE:
ΔE = h*v0-h(v0±v) = ± hv
where h is the Planck constant. If the molecule gains energy, Δ E is positive, giving
rise to Stokes Raman scattering (v0 - v), while if it loses energy, v E is negative,
providing anti-Stokes Raman scattering (v0 + v). The shift v is called the Raman shift.
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Figure 1: Principle of Raman scattering.
Figure 2 depicts the energy level of a molecule. The energy of vibrational transitions
is roughly one-tenth of the energy of electronic transitions. Supposed that | m> and |
n> in Figure 2 are two quantum states of a given normal vibration in the ground
electronic state, infrared absorption arises from a direct transition from |m > to |n>.
Raman scattering involves two simultaneous transitions, from |m> (or | n>) to an
electronically excited state, |e> and from |e> to |n> (or |m>). The Raman scattering
arising from |m> and |n> corresponds to Stokes and anti-Stokes Raman scattering,
respectively. The Stokes process results from the transition initiated at a ground
vibrational energy level, where the population of molecules is much higher than in the
vibrationally excited energy levels at room temperature. Therefore, Stokes Raman
scattering is much stronger than anti-Stokes Raman scattering.
Figure 2: The vibrational and electronic energy levels of a molecule.
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Both infrared and Raman spectroscopy are concerned with the energy separation
between |m> and |n>, providing vibrational spectra in the electronic ground state.
However, their selection rules are quite different. When the transition from |m> to |n>
is accompanied by a change of a molecular dipole moment, the vibrational mode is
infrared active, and when it is accompanied by a change of molecular polarizability,
the mode is Raman active. In general, vibrational modes that give rise to strong
infrared absorption or Raman scattering are different from each other, so that the two
techniques are often complementary.
Figure 3: Schematic illustration of classical and quantum mechanical explanation of scattering.
1.4.3 The Raman Spectrum of water: our solvent
During our experiments, we used water as solvent. The reason of our choice was the
Raman spectrum of water.Figure 4 shows the Raman spectrum of water excited with
the 488.0 mn (the absolute wave-number of 20,492 cm-1) line of an Ar laser [5]. The
abscissa represents a Raman shift from the wavelength of 488.0 nm, while the
ordinate indicates Raman scattering intensity. The Raman spectrum of water can be
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13
characterized by an intense feature near 3400 cm-1 due to the OH stretching modes of
water and weak band at 1640 cm-1 assigned to the H-O-H bending mode.
Figure 4:A Raman spectrum of water excited with the 488.0 nm line of an
Ar laser.
On the absolute wave-number scale, the Raman shifts of 3400 and 1640 cm-1
correspond to 17,092 (20,492 - 3400 = 17,092) and 18,852 (20,492 - 1640 = 18,852)
cm-1, respectively, and this can be seen by referring to the bottom scale in Figure 4.
One can recognize from Figure 4 that water, which has a weak Raman spectrum
except for the intense feature near 3400 cm-1, is a very good solvent for Raman
spectroscopy.
In the analysis of Raman spectra, it is often very convenient to know the molecular
vibrations that give intense Raman bands. These are summarized as follows:
1. Chemical bonds including heavy atom(s) show strong Raman bands due to
stretching modes: for example, S-S, C-X (X=Cl, Br, or I) stretching modes.
2. Stretching modes of double and triple bonds give rise to strong Raman bands:
for example, C=C, C=S, C=N, C=N, stretching modes.
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14
3. In general, bands due to symmetric stretching modes are stronger than those
arising from antisymmetric stretching modes.
4. "Breathing" modes of ring compounds (e.g., ring breathing modes of benzene
and phenylalanine) show characteristic intense bands near 1000 cm-1.
5. Vibrations containing a whole molecule or a large part of a molecule provide
intense bands. Stretching modes of whole alkyl chains (named accordion
modes) are a good example.
1.4.4 The advantages of Raman Spectroscopy
We decided to use the Raman spectroscopy during our experiments, because the
advantages of this method in food analysis are a lot. Firstly, the Raman spectrum of a
molecule reflects the disposition of atomic nuclei and chemical bonds within the
molecule and the interaction between the molecule and its immediate environments.
Moreover, the Raman spectroscopy is a non-destructive analytical and structural
probe. In situ and in vivo analysis of agricultural products and foods may be possible.
Also, Raman spectra may be obtained for molecules in aqueous solutions, since water
shows only a weak Raman spectrum (Fig. 4) that interferes minimally with the
spectrum of the solute. Furthermore, the manoeuvrability of the laser beam permits
diversity of experimental conditions. The flexibility in experimental arrangements is
great. Only small amounts of sample are required. Besides these, the sample volume
needed, which is determined by the diameter of the focused laser beam, normally is
about 50 pm . Thus, the sample volume may be about 100 µL. Finally, Raman
spectroscopy can be applied to agricultural products and foods in solid fibrous,
aqueous solution, and liquid form. Raman spectroscopy also holds considerable
promise in the quantitative analysis of food components. As an analytical tool, it has
several strong points. For example, it can be used with no or minimal pretreatment;
indeed, a reagent for pretreatment and/or analysis may not be needed. Since Raman
bands are often independent and sharp, Raman spectroscopy is suitable for
multicomponent analysis. Chemometrics, which has been used extensively in the
near-infrared spectroscopic analysis of foods, may be powerful also in the quantitative
and qualitative analysis of foods by Raman spectroscopy. Moreover, fiberoptic
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15
techniques can be applied to Raman measurements, and thus on-line analysis based on
Raman spectroscopy may be possible.
1.4.5 Raman Experimental Instrumentation
A Raman experimental setup is conceptually simple. As shown in Figure 5, it consists
of a laser light source, light focusing optics, monochromator, and detector. Being a
narrow, unidirectional entity, a laser beam can be easily manipulated in a variety of
sample configurations. Raman spectra of foodstuffs and biological materials relevant
to foods may be obtained in situ or in a Raman cell. In the case of in situ
measurements, one can focus the laser beam directly onto the sample. It is even
possible to measure Raman spectra of samples in bottles or transparent bags.
Figure 5: Schematic of a Raman experiment
Modern Raman spectrometers may be classified roughly into three types:
multichannel Raman spectrometer systems with variable laser sources and a charge-
coupled device (CCD) detector, small compact Raman systems with fixed excitation
wavelength(s) and CCD detector, and Fourier transform (FT) Raman spectrometer
systems. Figure 6 illustrates an example of the first type . It consists of a diode laser,
a Raman intensity collection system, laser beam focusing optics, a sample cell,
Raman scattering collection optics, two notch filters, a receiver focus lens, a single
monochromator, and a two-dimensional CCD cooled by liquid nitrogen. If one uses a
diode laser, the system becomes inexpensive and compact. In general, however, the
output power of semiconductor laser is unstable. Thus one should use a Raman
intensity collection system in which both the reference beam from the diode laser and
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16
Raman scattering from a sample are allowed to enter the single monochromator at the
same time. This collection system permits the precise measurement of the intensities
of Raman bands in the 2500-0 cm-1 region.
Figure 7A depicts the second type of Raman system. This setup is commercially
available. In this system, the use of a holographic grating enables the analyst to
measure the whole spectral range (4000 cm-1) with the relatively small
instrumentation supplied. A probe head attached to this system is very useful for
Raman measurements of samples of various types (Fig. 7B). Applications to
microanalysis and on-line analysis are also straightforward.
Figure 6: Schematic diagram of a multichannel Raman system
In the case of FT-Raman spectroscopy, in general, the 1064 nm line of a Nd:YAG
laser is employed to excite a Raman spectrum, so that one seldom encounters the
interference from strong fluorescence and photodecomposition. Another advantage of
FT-Raman instruments is that Raman measurements become much easier because FT-
Raman spectroscopy does not require troublesome optical adjustment.
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17
Figure 7 (A): Schematic diagram of a small, compact Raman system
Figure 7 (B): Various arrangements for Raman measurements .
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18
Figure 8: Schematic of a typical near-infrared dispersive Raman spectrometer used for tissue diagnosis
today .
Figure 9 shows some representative techniques for measuring Raman spectra of solid
samples. For liquid samples, a standard cuvette cell of 1 cm path length is adequate,
provided, of course, the cell bottom is transparent (Fig.10a). It is recommended that
the cell be taped around the meniscus to reduce the amount of scattered light from the
interface that reaches the spectrometer. If only a small amount of liquid sample is
available, a capillary cell (Fig.10b ) may be employed. It often happens that samples
are damaged by the laser illumination. In such cases a rotating (Fig.10c ) or "stirred"
(Fig.10d ) Raman cell should be considered.
Figure 11 depicts a new type of Raman cell-cell holder system. The cell may be a
flow cell or a fixed cell. It is kept in a unique inner spherical integration type of cell
holder whose inside surface is roughly coating and overlaid with gold to collect
Raman scattering efficiently from a solid angle of 360°. A Raman signal obtained
with the use of this cell holder was found to be about 30 times stronger than signals
recorded without using such a holder .
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Figure 9:Some representative techniques for measuring Raman spectra of solid samples .
Figure 10:Some representative techniques for measuring Raman spectra of
liquid samples:(a) cuvette cell,(b) capillary cell,(c) rotating cell, and(d) "stirred" cell.
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Figure 11:Quartz flow cell and spherical integration type of cell holder whose inner surface is roughly
coated with gold.
1.5 The applications of Raman Spectroscopy in Food Science
Raman spectroscopy is a branch of vibrational spectroscopy in which a sample is
exposed to an intense light beam such as a laser, and the spectrum of Raman-active
vibrational modes induced in the sample molecules is obtained through analysis of the
inelastically scattered photons. The diversity of applications and high content of
molecular structure information provided, combined with recent advances in
instrumentation, have rekindled interest in this technique in many diverse disciplines,
including food science. Suitable analytes cover the entire range of food constituents,
including the macro-components (proteins, lipids, carbohydrates and water) as well as
minor components such as carotenoid pigments or synthetic dyes, and even
microorganisms or packaging materials in contact with foods. Raman spectroscopy
may be used as a tool for quality control, for compositional identification or for the
detection of adulteration, as well as for basic research in the elucidation of structural
or conformational changes that occur during processing of foods .In addition to its use
in the analysis of the food constituents, Raman spectroscopy could potentially be
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21
applied to many other areas of food science, including studies of trace components,
nucleic acids or their constituents, whole cells and tissues, microorganisms, and even
the packaging in which foods are contained.
.
The interactions of electromagnetic radiation with electrons and nuclei of molecules
give rise to a host of spectroscopic techniques that are based on absorption, emission
and scattering processes. Raman spectroscopy is a branch of vibrational spectroscopy
that is based on the shifts in the wavelength or frequency of an exciting incident beam
of radiation that result from inelastic scattering on interaction between the photons
and the sample molecules. Because both the intensity and frequency of induced
molecular vibrations are sensitive to the chemistry and environment around the
individual atoms, the Raman spectrum can be used as a monitor of molecular
chemistry.
In contrast to many other spectroscopic methods, Raman spectroscopy has a distinct
advantage in not requiring that samples be optically clear. It has been applied to study
molecules in aqueous solutions, nonaqueous liquids, fibres, films, powders,
precipitates, gels and crystals. However, because Raman scattering is inherently weak,
giving signal intensities in the order of 1 X 10-9 to 1 X lO-6 of those of elastic or
Rayleigh scattering, fairly high concentrations of the target analytes are required in
samples if they are to be measured by the classical, non-resonance Raman technique.
Thus, for example, the sample should usually contain protein or nucleic acid
concentrations in the range of 2-2Omg/ml or 0.03-0. 1 M,if expressed with respect to
the concentration of peptide or nucleotide groups. Although these concentrations may
seem high, they are in fact typical of food samples (e.g. protein concentrations are
approximately 3%, 10% and 15-20% in fluid milk, egg white, and fish or meat
systems, respectively). Raman spectroscopy is therefore suitable for direct in situ
analysis of the major constituents of food systems. Furthermore, despite the high
concentrations, only very small quantities are required, as little as 1 μl of solution or 1
mg of a solid biological sample in a static capillary tube. Also, much lower analyte
concentrations (e.g. 10 μM) may be studied using the technique of resonance Raman
spectroscopy, in which the exciting laser wavelength is adjusted to the absorption
range of particular chromophores within the sample.
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22
Table 2: Examples of applications of Raman spectroscopy in food systems
Concerning proteins, proteins and their components hold the distinction of being the
classical example of applying Raman spectroscopy to the analysis of biomolecules.
Information can be obtained on the microenvironment and chemistry of side chains as
well as on the conformation of the polypeptide backbone. Some of the Raman modes
that are useful in the interpretation of protein structure are listed in Table 2 .Included
are vibrational transitions assigned to various amino acid side chains, including the S-
S and S-H groups of cystine and cysteine, the aromatic rings of tryptophan, tyrosine
and phenylalanine, the C-H groups of aliphatic amino acids, the COO- and COOH
groups of aspartic and glutamic acids, and the imidazole ring of histidine. Bands
corresponding to the amide I, amide III and skeletal stretching modes can be used to
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23
characterize backbone conformation, giving information on the relative proportions of
different types of secondary structures in polypeptides or proteins. Disulfide and
sulfhydryl groups may be detected by the S-S stretching and S-H stretching bands in
the 500-55Ocm-1 and 2550-258Ocm-1 regions, respectively. Disulfide bonds under
conformational strain have dihedral angles that are significantly different from 85 + -
20°, and their Raman bands appear in the 450-5OOcm-1 region. Provided that the
disulfide bond is not under strain, the precise location of the Raman band
corresponding to the S-S stretching vibration depends on the internal rotation around
the C-S and C-C bonds, yielding additional information on the conformation around
the disulfide bond(s).
The amide (peptide) bond of proteins has several distinct vibrational modes, of which
the amide I and III bands are the most useful for the investigation of secondary
structure. Owing to the overlap of solvent water Raman bands with the amide I band
region in the spectra of aqueous protein samples, as well as the miscellaneous side-
chain vibrations that may contribute to bands in the amide III region, the
interpretation of protein secondary structure from Raman spectra should be based on
concurrent inspection of amide I and amide III regions. Additional information
obtained by monitoring either changes following deuteration or other skeletal
vibrational modes should also be considered. Fourier deconvolution and least squares
analysis of the amide I and III bands are the most frequently used techniques for
resolving and assigning band components to specific protein conformations. Based on
these techniques, proportions of the different types of secondary structures in proteins
have been investigated as a function of salt-induced conformational changes, thiol- or
heat-induced gelation, lyophilization, crystallization, freezing and frozen storage, and
interactions in different mixtures.
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Table 3: Raman modes useful in the interpretation of protein structure
Regarding the lipids, Raman spectroscopy can also be used for lipids’ estimation. In
the oils and fats industry, classical methods based on wet chemistry or gas-
chromatographic (GC) analysis are typically used to quantify cis and trans isomers
and the total degree of unsaturation, whereas X-ray studies using single-crystal,
powder diffraction and scattering, and differential scanning calorimetry have been
used to provide detailed information on the polymorphism or arrangement of
triacylglycerols and diacylglycerols. Especially with the advent of the Fourier-
transform vibrational techniques, both IR and Raman spectroscopy now have the
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25
potential to replace or at least complement the classical, time-consuming
methodologies, and thus could be used as rapid screening methods for quality control
purposes and also for basic research on the factors affecting polymorphic transitions
and stability. Using dispersive laser Raman spectroscopy, the intensities of Raman
bands near 1656 cm-1 and 167Ocm-1 were reported almost 25 years ago to be related
to the cis and trans isomer contents, respectively, of edible vegetable oils. Similarly,
the ratio of scattering intensity arising from the C=C stretching vibration to that from
the CH, scissoring mode was shown to be correlated with the iodine values of
reference triacylglycerols and unconjugated vegetable oils. For both analyses, a
precision of 1% was reported. Excellent correlations have been reported between the
iodine values and specific Raman band ratios for various oils, margarines and butters.
A quantitative program was established for the determination of the total degree of
unsaturation, by measuring the ratio of the area of the C=C stretching band from a
baseline of 1700-1601 cm-1, to the area of the C=O stretching band with a baseline of
1790-l713 cm-1 or alternatively, the ratio of the C=C band area to that of the CH,
scissoring band with a baseline of 1543-1382cm-1. Excellent linear correlations with
the total degree of unsaturation measured by GC analyses of the fatty acid methyl
ester derivatives were obtained. Ratios of cis and trans isomers can be quantified by
the relative intensities at 1657 cm-1 and 1667 cm-l, respectively, and the total cis
isomer content can be determined from the ratio of the intensity of a band at 1265
cm-1 to that of a band at 1303 cm-1, giving excellent correlation with the results from
GC analysis. Analysis of the C-H region of Raman spectra can provide information
directly in foodsystems. For example, most of the hydrocarbon chains at the surface
of fat globules in cow’s milk were suggested to be in a crystalline, close-packed form.
This interpretation was made by observing in the Raman spectra of cow’s milk a
characteristic dominance of the C-H stretching band at 289Ocm-1 that is typical of
crystalline packing. In contrast, the spectrum of the corresponding separated milk fat
showed an increased dominance of the band at 285Ocm-1, which is assigned to
symmetric vibrations of CH, groups in the liquid state.
In comparison with other biological macromolecules such as proteins and nucleic
acids, relatively few reports have been published on carbohydrates; nevertheless, the
recent developments in biotechnology and industrial applications have led to an
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26
upsurge in interest, coincident with the availability of the FT-Raman technique .FT-
Raman spectra show clear distinctions among closely related structures such as
crystalline D-glucose, D-galactose and D-fructose, or among α- and β-isomers. The
structural composition of di-, oligo- and polysaccharides can also be elucidated from
characteristic bands in the Raman spectrum. For example, the spectrum of sucrose
showed bands characteristic of the anomers of both monosaccharide components: α-
glucose at 847cm-1 and β-fructose at 868cm-1. Maltose showed bands characteristic
of α-glucose at 847cm-1 and β-glucose at 898 cm-1, whereas cellobiose showed a
band only at 885 cm-1 for the β-anomer. Detailed studies investigating conformation-
sensitive modes in the C-H stretching region and the effects of moisture on the
molecular structure of α- and β-anomers of D-glucose were conducted using FI-
Raman spectroscopy. Also, differences in the spectra of potato amylose, potato
amylopectin and waxy cornstarch powders as well as in a waxy cornstarch-water
system have been reported, particularly in the C-H stretching region of 2700-31OO
cm-1 and in the major skeletal mode at 48Ocm-1,dry powder samples of different
starch materials could be effectively distinguished by comparing their Raman spectra.
These spectral differences were suggested to reflect the relative degrees of
crystallinity in the samples .Furthermore, the study of the interactions of
carbohydrates with other components in food systems is another potential application.
In conclusion, Raman spectroscopy represents a tool that can be used for rapid quality
control as well as to provide a wealth of detailed in situ structural information about
food systems under conditions relevant to processing. However, the practical and
instrumental limitations of the various forms of Raman spectroscopy need to be
understood to obtain the desired information.
1.6 Raman vs IR
Raman and infrared (IR) spectroscopy are complementary techniques based on the
discrete vibrational transitions that occur in the ground electronic state of molecules,
which correspond to various stretching and bending deformation modes of individual
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27
chemical bonds. IR absorption and inelastic or Raman scattering are depicted in
Figure12.
A Raman spectrum is obtained by plotting the intensity of scattered light as a function
of the Raman shift, Δv in cm-1, and gives information based on stretching and
bending vibrational modes, similar to that provided by an IR spectrum, of the
absorption or transmission of energy as a function of the frequency. However,
although both IR absorption and Raman scattering involve transitions between
vibrational levels, their spectra are not identical, and thus IR and Raman spectroscopy
are complementary rather than alternative techniques. IR absorption requires a change
in the intrinsic dipole moment with molecular vibration, whereas Raman scattering
depends on changes in the polarizability of functional groups as the atoms vibrate.
Hence, polar groups such as C=O, N-H and O-H have strong IR stretching vibrations,
whereas nonpolar groups such as C=C, C-C and S-S have intense Raman bands.
Water is a polar molecule that possesses strong IR absorption. IR spectroscopy is
therefore most commonly applied to the analysis of dry or non-aqueous samples; to
analyze samples with a high water content, either very short path length sample cells
or an attenuated total accessory must be used, in conjunction with careful subtraction
of the water baseline spectrum. In contrast, water has weak Raman scattering
properties and produces less interference in Raman spectroscopy. As a consequence,
Raman spectroscopy is usually more suitable for the in viva or in situ study of
biological systems, including foods, which are primarily aqueous in nature.
In infrared (IR) spectroscopy, the absorption of incident electromagnetic radiation at
a particular frequency (vi) in the IR region is related to a specific vibrational
excitation energy (ΔE = hvi). In contrast, in Raman spectroscopy, the exciting or
incident light beam is at a frequency (v0) that may correspond to the visible, UV or
near-infrared region of the electromagnetic spectrum, The inelastic scattering of the
incident radiation, which results in a Raman shift (Δv = vi), is related to the energy of
a vibrational transition within a sample molecule. Stokes’ transitions are those in
which the molecule is excited by the radiation, whereas anti-Stokes’ transitions are
those in which the molecule is de-excited. Because the lowest vibrational level is the
most energetically favourable, there is a higher frequency of occurrence, and
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28
consequently a stronger signal intensity, for Raman scattering corresponding to
Stokes’ transitions. Most Raman spectroscopic studies therefore report data
corresponding to Stokes’ rather than anti-Stokes’ transitions. In the particular branch
of Raman spectroscopy known as resonance Raman spectroscopy, the incident photon
energy corresponds to an electronic absorption mode that allows a transition to an
excited electronic state, followed by inelastic scattering to produce Stokes’ and anti-
Stokes’ resonance Raman bands. In the resonance Raman technique, the UV region is
often used to study aromatic amino acid vibrational modes, whereas the visible region
is useful for monitoring pigments, metalloproteins or various prosthetic groups such
as haem or retinal groups.
Figure 12:The relationships between infrared absorption, Rayleigh scattering and Raman scattering
Newer methods of spectroscopy combine the FT-IR with Raman Spectroscopy . FT-
IR and Raman microspectroscopy may be combined with at least three different
mapping techniques:point, line and area. With point acquisition several spectra are
measured from different places in a sample selected after visual inspection through
the microscope, i.e. the spectra are not systematically related to each other spatially.
Line mapping defines a series of spectra to be obtained along one dimension (a line)
and can be used to investigate changes in a chemical component along a certain
direction, i.e. a profile. An area map uses two dimensions, providing a spectroscopic
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29
image that can be directly compared to the corresponding visual image, but with an
entire spectrum in each pixel instead of a simple colour.
Furthermore, NIR FT-Raman spectroscopy has the following advantages in food
analysis: (1) It is a non-destructive method and does not require any pre-treatment;
Raman spectra of various kinds of foods such as raw meat, living lobster, orange, rice,
cola in a glass, butter on bread, and boiled egg can be measured in situ. (2) Not only
qualitative and quantitative analysis of food components but also their structural
analysis can be carried out; for example, hydrogen bondings in food components may
be investigated. (3) The use of FT-Raman microspectroscopy makes possible
microanalysis of food components. (4) Selective analysis of trace components in
foodstuffs is possible by use of the resonance or pre-resonance Raman effect. (5)
Fiber techniques may be employed for remote measurements and on-line analysis of
food products.
Both Raman and infrared micro-spectroscopy may reveal useful information about
food samples. In infrared spectroscopy the sample is radiated with infrared light.
Different chemical bonds absorb at different infrared wavelengths depending on the
atoms connected, the surrounding molecules, and the type of vibration the absorbance
gives rise to (for example stretching or bending). In Raman spectroscopy, the sample
is radiated with monochromatic visible or near infrared light from a laser. This brings
the vibrational energy levels in the molecule to a short-lived, high - energy collision
state, which returns to a lower energy state by emission of a photon. Normally the
photon has a lower frequency than the laser light (Stokes Raman scattering),and the
difference in frequency (given in reciprocal centimetres) between the frequency of the
laser and that of the scattered photon is called the Raman shift. The Raman shift
corresponds to the frequency of the fundamental IR absorbance band of the bond.
Even though both methods probe molecular vibrations and structure, they do not
provide exactly the same information. While IR spectroscopy detects vibrations
during which the electrical dipole moment changes, Raman spectroscopy is based on
the detection of vibrations during which the electrical polarisability changes.
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30
Moreover, Raman spectroscopy has the potential of a better spatial resolution due to
the lower wavelengths used, and furthermore offers confocality, i.e. it is possible to
focus on different planes below the sample surface. It is, for example, possible to
focus beneath a quartz plate or through a food packaging material to obtain pure
spectra of food samples without exposing the food to the atmosphere. On the other
hand, the signal- to-noise ratio is much lower, and if the sample fluoresces,
measurements may even be impossible. For example, measurements on samples that
contain one or more of the three fluorescent amino acids (tyrosine, tryptophan and
phenylalanine) or chlorophyll may prove difficult or, in practice, impossible to study
by Raman spectroscopy. However, the problem may be overcome if a Raman
instrument is equipped with a near infrared laser instead of a laser in the visible range,
though the spatial resolution would be poorer. Another problem related to Raman
spectroscopy is heating of the sample . The heat generated by the laser may alter or
even destroy the sample during measurement. In some cases, the best setting of the
Raman instrument is therefore a compromise between destructive heating, which calls
for short sampling times, and a high signal to noise ratio, which calls for long
sampling times and/or repeated samplings.
Table 4:Raman spectroscopy vs. Infrared (IR) spectroscopy
The above characteristics of the Raman spectroscopy were very significant for our
decision to use the Raman Spectroscopy.
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1.7 Report of the spectrums of several components of food
FT-IR and Raman microspectroscopy were applied to potatoes, bitter almonds, bread,
barley kernels and shrimp shells to obtain information about microstructure and
chemical composition .In general, the conclusions are performed in the following
table 5.
Table 5:Infrared and Raman characteristic group frequencies
About the in situ starch and pectin analysis in the potato cell, it was found that pectin
was characterised by signals near 858 cm-1 (α-anomer carbohydrate and indicative of
a very low degree of esterification),at 1455 cm-1 (ester O–CH3 stretch) and at 1745
cm-1 (ester carbonyl C=O stretch). The signal near 1000 cm-1 indicates that aromatic
compounds interfere with the pectin spectrum. The starch spectrum in Figure 13 is of
remarkable quality, equal to or even better than a typical Raman spectrum obtained
from isolated and purified potato starch. This indicates that the high crystallinity of
the intact granule is well reflected in the spectrum.
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Figure 13: Raman spectra of pectin and starch obtained directly in the potato cell using a LabRam
Infinity instrument equipped with a green laser (532 nm, Nd:YAG), a Peltier-cooled CCD detector and
a long working distance 100_X Leica objective. Laser power at the sample was approximately 40mW.
Concerning the distribution of amygdalin in bitter almonds, Raman
microspectroscopy was chosen, because nitrile groups give rise to relatively low
intensity bands in IR spectroscopy (very small changes in dipole moment), whereas
they are strongly Raman active (large change in polaisability during stretching
vibrations). The nitrile vibration band is highly specific, as the nitrile group is rarely
found in natural compounds. The band is found near 2240 cm-1 in the Raman
spectrum (Figure 14) and this area is almost free from interference from other
chemical components (Table 5).
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33
Figure 14:Raman measurements of amygdalin in bitter almond cotyledons using the same equipment as
in Fig. 1. The plots show for two different almonds the area of the nitrile peak at approximately 2242
cm-1 for 81 positions along a line from the epidermis to the centre of the almond.
Regarding the composition of blisters found on the surface of bread, it was found that
the bands in the ranges 1663–1630cm-1 and 1595–1528 cm-1 could differentiate
between spectra of blisters and of breadcrumb from comparable positions just below
the crust. The two wavelength ranges are characteristic of the amide I band
(overlapped with the HOH bending vibration from water) and the amide II band from
gluten (Table ). The spectra of the inside ‘‘walls’’ of the blisters showed higher
intensity at the ‘‘starch’’ band and lower intensity at the ‘‘gluten’’ band compared to
breadcrumb. This strongly indicates that the ratio of starch to gluten was higher in the
breadcrumb just around the blisters than in the ordinary breadcrumb.
Also, concerning the microstructure of high-lysine barley, it was found that specific
wave numbers indicate the contents of different nutritional facts, i.e. 1543 cm-1
(Amide II) indicates protein,1148 cm-1 (coupled C–C and C–O vibrations) indicates
carbohydrate, and 1738 cm-1 (C=O stretch) indicates the content of lipid.
Another application in food analysis took part, giving us many information about the
spectra of some foods . The 1064-nm excited Fourier transform (FT) Raman spectra
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34
have been measured in situ for various foods in order to investigate the potential of
near-infrared (NIR) FT-Raman spectroscopy in food analysis. Foods consisting
largely of lipids such as oils, tallow, and butter show bands near 1658 and 1443 cm -1
due to C=C stretching modes of cis unsaturated fatty acid parts and CH2 scissoring
modes of saturated fatty acid parts, respectively. It has been found that there is a
linear correlation for various kinds of lipid-containing foods between the iodine value
(number) and the intensity ratio of two bands at 1658 and 1443 cm -1 indicating that
the ratio can be used as a practical indicator for estimating the unsaturation level of a
wide range of lipid-containing foods. A comparison of the Raman spectra of raw and
boiled egg white shows that the amide I band shifts from 1666 to 1677 cm-1 and the
intensity of the amide III band at 1275 cm-1 decreases upon boiling. These
observations indicate that most α-helix structure changes into unordered structure in
the proteins constituting egg white upon boiling .The following figures show the
spectra:
Figure 15: NIR FT-Raman spectra of fresh spinach leaf (a), peel of mandarin orange (b), fresh-leaf
Japanese tea (c), and hen's yolk (d).
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35
Figure 16: NIR FT-Raman spectra of the leaf of black tea (a) and quail's yolk (b).
Figure 17: NIR FT-Raman spectra of the outer (a) and center (b) parts of the section of a carrot.
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36
Figure 18: NIR FT-Raman spectra of sunflower (a), corn (b), sesame (c), rapeseed (d), and olive (e)
oils.
Figure19 : NIR FT-Raman spectra of peanut (a), beef tallow (b), and butter (c).
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37
Figure 20: FT-Raman spectra of hen's raw (a) and boiled (b) egg white
It was also measured in situ the Raman spectra of carbohydrate-containing foodstuffs.
Figure 21 displays the FT-Raman spectra of cornstarch, semolina, tapioca, and rice .
Cornstarch is the only pure compound among the four, and thus gives a typical
Raman spectrum for carbohydrate. The other three compounds contain small amounts
of proteins. Weak features around 1650 cm-1 observed in the spectra of semolina and
rice may be due to the proteins.
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38
Figure 21:FT-Raman spectra of (a) cornstarch, (b) semolina, (c) tapioca, and (d) rice.
Figure 15 shows the 1064-nm excited Raman spectra of spinach leaf (a), peel of
mandarin orange (b), fresh-leaf Japanese green tea (regular grade) (c), and hen's egg
yolk (d). The four spectra show common bands at 1529, 1161, and 1007 cm-1
assignable to carotenoids, which are their trace, but very important, components.
Leaves of Japanese tea contain 20-35 % proteins. A band at 1446 cm-1 in Fig. l5c
may be assignable to CH2 bending modes of the proteins included in the tea. In
Figure 16 is shown the NIR FT-Raman spectrum of the leaf of black tea. Bands at
1524 and 1124 cm-1 and that at 1440 cm-1 in Figure16 are due to carotenoids and
proteins included in the black tea, respectively.
Figure 15d is very similar to the spectra of lipids except for the bands due to
carotenoids, although hen's yolk contains not only lipids but also considerable
amounts of protein.
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1.8 Past projects about the determination of the energetic value or
the nutritional parameters of foods and drinks
Previous work with Raman spectroscopy in food identification focused on analysis of
particular, specific, contents of food products of one type. Three characteristic
publications about this subject are reviewed here. These publications refer to
evaluation of nutritional parameters of infant formulas and powdered milk, yogurt
samples, and determination of the energetic value of fruit and milk-based beverages.
Below, is given a summary of these publications, referring to what they achieved, the
method they used and the mean square error that their results had.
The first publication is referring to the determination of the energetic value of fruit
and milk-based beverages .The estimation of important nutritional parameters, such as
carbohydrates content and energetic value (calories) in commercially available fruit
juice and flavour milk shakes has been made by attenuated total reflectance-Fourier
transform infrared spectroscopy (ATR-FTIR) using a partial-least-square (PLS)
calibration approach. An heterogeneous population of 65 samples obtained from the
Spanish market, covering fruit juices, flavour milk shakes and milk-added fruit juices
was used. Firstly ,the spectral range and the size of the calibration set for building the
PLS model was evaluated. It was considered a calibration set comprised of 27
samples, selected via hierarchical cluster analysis, and a validation data set of 38
samples. The samples were covering an important range of available types of fruit
juices, flavour milk shakes(cocoa, vanilla, etc.) and mixtures of fruit juices and milk
products. Thirty eight samples were juices from different fruit mixtures (19 of them
also contained milk) and the others 27 samples were composed by: 10 juices obtained
from a single fruit, 13 flavour milk shakes and 3 milks (whole, semi-skimmed and
skimmed) The concentration reference data of energetic value (EV) and total
carbohydrates (CH) values of the samples were provided by the producers. Also, the
absolute mean difference (dx–y) and standard deviation of mean differences (Sx–y) of
the total carbohydrate content were 0.06 and 0.66 g/100 mL, respectively. The
reproducibility of this determination established as the mean standard deviation of
each triplicate analysis was 0.05 g/100 mL. In the case of energetic value, the dx–y
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and Sx–y were 2.8 and 18 kJ/100 mL, respectively. The reproducibility of this
determination corresponded to a standard deviation of 2.4 kJ/100 mL, for three
replicate analyses. Finally, the root-mean-square error of prediction (RMSEP) was
18.4 kJ/100mL and 0.72 g/100mL for energetic value and total carbohydrates,
respectively. The energetic value was found in two spectral ranges. These ranges are
the 1300–1500 cm−1 and the 2760–2923 cm−1 regions. Also, it was found that the
total carbohydrates has optimum spectral range:1020–1175 cm−1.The table 6,
below, shows a brief description of each sample.
Table 6 : General description of samples employed in this study
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Table 7: Prediction capabilities of PLS-ATR-FTIR for energetic value determination of fruit and milk-
based beverages
Another related publication was the evaluation of the application of attenuated total
reflectance–Fourier transform infrared spectroscopy (ATR–FTIR) and partial least
squares (PLS) to the determination of several nutritional parameters, such as the
energetic value and the carbohydrate, protein and calcium contents, in commercially
available yogurt samples. To this end, a highly heterogeneous population of 48
samples covering a wide range of yogurts obtained from the Spanish market was used.
This population of samples was covering a wide range of the available types of
yogurt: plain, added-sugar or non-fat, low fat or high fat content, flavored yogurts,
yogurt mousse, etc. The reference concentration data for the nutritional parameters in
the samples were provided by the manufacturers. After correcting the spectra,
hierarchical cluster analysis was performed in order to select a representative
calibration set and the corresponding validation sample set. Different PLS models and
several spectral windows were tested in order to evaluate their prediction capabilities
for the validation set. For all nutritional parameters, with the exception of fat content,
the procedure developed here provided good precision. The spectral ranges used to
predict the energetic values of yogurt samples were the 1,076–1,230 cm−1 and the
1,083– 2,850 cm−1 regions. The mean standard deviation of each replicate and the
standard error of prediction, for the energetic value, were 4 and 40 kJ/100 g,
respectively. The spectral regions of the total carbohydrates were between 1,369 and
1,601 cm−1 and from 1,045 to 1,161 cm−1 .Also, the dx−y and the Sx−y values were
0.3 and 0.7 g/100 g, respectively and the mean standard deviation of each replicate
and the standard error of prediction for this parameter were 0.09 and 0.3 g/100 g,
respectively. The proteins’ prediction were determined by the mean standard
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deviation of each triplicate and from the standard error of prediction, which were 0.03
and 0.08 g/100 g, respectively. Moreover, after evaluating different spectral regions,
the region between 1,461 and 1,636 cm−1 found to be suitable for Ca .The
determination of Ca was made with 0.7 mg/100 g the mean standard deviation of each
triplicate and the standard error of prediction was 2 mg/100 g. However, it was not
possible to obtain accurate estimates for the fat in the yogurt samples.
Table 8 : Mean values for the nutritional parameters considered in this study for both single model and
extended model calibration and validation sets, established after dendographic classification of all
yogurt samples
The last publication was about evaluation of nutritional parameters in infant formulas
and powdered milk by Raman spectroscopy. Particularly, it has been made a critical
evaluation of the application of near infrared Fourier transform-Raman spectroscopy
for the simultaneous determination of the most important nutritional parameters such
as energetic value, carbohydrate, protein and fat contents of infant formula and
powdered milk samples based on the use of partial least squares (PLS) regression
analysis. A heterogeneous population of 23 samples, covering a wide range of infant
food formula and powdered milk, were obtained from the Spanish market. Also, the
Raman spectra, obtained by excitation with a Nd:YAG laser at 1064 nm, showed no
disturbing fluorescence effects; therefore sample spectra could be recorded without
any previous preparation step. After correcting the spectra, hierarchical cluster
analysis was performed in order to select a representative calibration set and the
corresponding validation sample set. Different PLS models and several spectral
windows were tested in order to evaluate their prediction capabilities for the
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validation set. It was used a calibration set comprised of three replicate spectra of 15
samples and a validation data set of eight samples.
Table 9 : Composition of IFF and powdered milk samples employed in this study
Concerning the energetic value, the best spectral range used to predict the energetic
values of IFF and powdered milk samples was the 1048–1404 cm−1 region.
Furthermore, the best dx−y and the Sx−y values were 15 and 59 kJ 100 g−1,
respectively and the mean standard deviation of each replicate for this parameter was
12 kJ 100 g−1.Moreover, it was calculated that the mean prediction error on the
samples takes a value of 2%.About the total carbohydrates, the spectral region
between 1288 and 987 cm−1 was used and the % mean relative error was
4%.Regarding the proteins, two joint spectral regions (1702–1594 and 1266–1108
cm−1) were used for FT-Raman determination of proteins in powdered milk. The
dx−y and the Sx−y values were 0.5 and 1.3 g 100 g−1, respectively. The mean
standard deviation of each replicate was 0.6 and the mean prediction error on the
samples assayed was 7%. Concerning the fat, the scattering intensities at Raman
shifts values of 2856, 1748, and 1437 cm−1 show high correlations with fatty acid
content, hence, three spectral regions, between 2899–2699, 1766–1729 and 1448–
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1417 cm−1, were considered for evaluating the analytical performance of the PLS-
Raman methodology for fat determination. The dx−y and the Sx−y values were 0.8
and 1.7 g 100 g−1, respectively. The mean standard deviation of each replicate was
0.3. Also, the results of 8% mean relative prediction error achieved when determining
fat using this method.
The above publications were very useful during the implementation of this senior
project .The techniques used and their conclusions gave us useful information for the
experimental part and the processing data methods .
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Chapter 2: Explanation of the process
2.1 Introduction
Using spectroscopy Raman, we accomplished to realize the automation of the process
of evaluation the nutritional facts of any food product. The categories of nutritional
facts, that we can predict are shown below:
1. Energy (Calories/100grams of product)
2. Fat (grams/100grams of product)
3. Carbohydrates (grams/100grams of product)
4. Sugars (grams/100grams of product)
5. Dietary Fiber (grams/100grams of product)
6. Protein (grams/100grams of product)
The procedure that we followed is composed of the experimental process and the
programming process. At this chapter, we explain, analytically, these two processes.
2.2 Explanation of the experimental process
For performing the experiments, we chose the samples, firstly, under some criteria.
We chose canned products (samples) with different comprehensiveness of nutritional
facts, of which the labels at the can were used as the true value of the nutritional
parameter. The main condition for choosing a product is that it has all the following
information :
1. Energy (Calories)
2. Fat
3. Carbohydrates
4. Sugars
5. Dietary Fiber
6. Protein
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Moreover, we chose samples that they had big value of one nutritional parameter of
the above categories for the purpose of training the algorithm. Also, we made known
combinations of the previous samples, for controlling of our results. Particularly, we
took a % percentage of some foods (in grams) and we made a mixture, of which we
calculated the true values of the nutritional parameters, depending on the %percentage
of the samples that it contains. Also, we chose some products that contain quantities
of all the above categories. The exact samples, that we used, are shown in Table 10
below. The highlighted products are the products with big value of one nutritional
parameter .
Table 10:The samples that we examined
Products (per 100 gr or 100ml)
High in Total Fat 1 ambrosia Sunflower mayonnaise
2 Olive-oil S.E.K.E.P Ltd Cyprus
3 Helianthemum-oil Orfanidis
4 original peperoni
High in Total Carbohydrates 5 Cococrispers
6 Cereals Special Shape
7 Pasta di semola di grano duro (shell)-Dry pasta
8 Rice Uncle Ben's (not boiled product)
High in Dietary Fiber 9 Garden Peas Crosse & Blackwell
10 Organic Rice Cakes/lightly salted
11 Oatbran Mornflakes
High in Sugars 12 Mars 5
13 Twix 5x2
14 Morfat Frou Zele - 100gr of ready product
15 Snack Pack Chocolate (Amount per serving 100gr)
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High In Protein 16