Application of Portable and Benchtop Mid-Infrared Spectrometers in Profiling Composition and Quality of Edible Oils

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

By

Michael J Wenstrup

Graduate Program in Food Science and Nutrition

The Ohio State University

2013

Master's Examination Committee:

Dr. Luis Rodriguez-Saona, Advisor

Dr. John Litchfield

Dr. Michael Mangino

Copyrighted by

Michael J. Wenstrup

2013

Abstract

The application of infrared spectroscopy techniques to problems in the food and beverage industry has yielded many novel methods. With the increase in computational power, multivariate analysis has brought the unique properties of mid-infrared (MIR) systems to the forefront. The distinct, high intensity MIR absorption bands are well-suited to authentication, classification and quantification of components in complex matrices. This technique has been applied to a variety of food systems, with particular success in the analysis of edible oils. Methods for trans-fat determination, monitoring of oxidative indices, and discrimination of adulterated oils represent only a few of the developments in field of oils. The objective of this study was to utilize MIR spectroscopy in the analysis and characterization of products and processes related to edible oils. A temperature- controlled, ZnSe ATR sampling accessory was used with a benchtop FT-IR system to monitor oxidative changes in frying oils under the influence of a patented anti-oxidation device. In addition, reference methods for free fatty acids (FFA), anisidine value (AV), and color were used to monitor oil stability. FT-IR combined with chemometrics showed differences between control and treatment, with discrimination provided by regions associated with fatty acid hydrolysis and oxidation products. As compared to the control treatment, the anti-oxidation technology decreased the rate of FFA and aldehyde formation, as well as showing a marked effect on total color difference (ΔE). Overall, our results showed that a patented induction device slows the rate of lipid degradation,

ii resulting 7-20% reduction in formation rate of key quality parameters and significantly longer utilization of frying oil. Omega-3 dietary supplements have been linked with health benefits due to their contents of EPA and DHA. The concentration of these active components in fish is highly variable; species, catch location, environment, season and processing parameters are all factors in determining the ratio of fatty acids found in the oil. Our objective was to characterize the composition of commercial Omega-3 dietary supplements using mid-infrared spectroscopy, gas chromatography, and chemometrics.

Twenty-nine fish body oil (FBO), cod liver oil (CLO), and flaxseed supplements were purchased from retailers. Fatty acid composition of oils, determined by GC-FAME, encompassed a wide range of fatty acid profiles and delivery methods. Mid-infrared spectral data was collected on portable infrared systems and the spectra were used to classify supplements using SIMCA, a pattern recognition technique. In addition, PLSR was used to correlate the spectra with GC-FAME results. SIMCA analysis allowed for tight clustering of fish oil supplements into distinct classes, dependent upon the source and processing. Discriminating power showed a strong influence of the 1038 cm-1 band, which is typically associated with C-O stretching vibrations. PLSR generated multivariate models with high correlation (R ≥ 0.99) between the infrared spectrum and fatty acid composition, and SECV of 1.74 and 1.89 for EPA and DHA, respectively. Our results indicate that ATR FT-IR spectroscopy combined with pattern recognition analysis provides for rapid, robust screening and characterization of edible oils.

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Acknowledgments

Thank you to all of my co-workers and labmates for their guidance and for helping me keep my sanity. Thank you to my committee members, Drs. Litchfield and Mangino, and to my advisor, Dr. Rodriguez-Saona.

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Vita

June 2007 ...... …………………….Centerville High School

June 2011…………………………………..B.S. Biochemistry, The Ohio State University

January 2012 to present ...... Graduate Research Associate, Department

of Food Science and Technology, The Ohio

State University

Fields of Study

Major Field: Food Science and Nutrition

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Table of Contents

Abstract ...... ii

Acknowledgments...... iv

Vita ...... v

Table of Contents ...... vi

List of Tables ...... ix

List of Figures ...... x

Chapter 1: Literature Review ...... 1

1.1 Lipids in Food ...... 1

1.2 Chemical Reactions of Lipids in Food Systems ...... 2

1.2.1 Thermal Degradation ...... 2

1.2.2 Lipid Oxidation...... 3

1.2.3 Maintaining Oil Quality...... 6

1.3 Infrared Spectroscopy ...... 8

1.3.1 Near-Infrared Spectroscopy ...... 9

1.3.2 Mid-Infrared Spectroscopy ...... 10

1.4 Chemometrics and Multivariate Analysis ...... 15 vi

Chapter 2: Effect of a Novel Induction Food-Processing Device in Improving Frying Oil

Quality...... 18

2.1 Abstract ...... 19

2.2 Introduction ...... 19

2.3 Materials and Methods ...... 22

2.3.1 Frying Protocol ...... 22

2.3.2 Sample Collection...... 23

2.3.3 Fatty Acid Profile ...... 23

2.3.4 Oil Quality Metrics ...... 24

2.3.5 Fourier Transform Mid-Infrared Spectroscopy ...... 25

2.3.6 Multivariate Analysis ...... 25

2.3.7 Statistical Analysis ...... 25

2.4 Results and Discussion ...... 26

2.4.1 Fourier-Transform Infrared Spectroscopy ...... 27

2.4.2 Monitoring Oxidative Indicators ...... 33

2.5 Conclusions ...... 37

Chapter 3: Application of a Portable Infrared Spectrometer for Characterization of

Omega-3 Dietary Supplements ...... 39

3.1 Abstract ...... 40

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3.2 Introduction ...... 42

3.3 Materials and Methods ...... 45

3.3.1 Sample Preparation ...... 45

3.3.2 Fatty Acid Profile ...... 45

3.3.3 Fourier Transform Mid-Infrared Spectroscopy ...... 46

3.3.4 Multivariate Analysis ...... 47

3.4 Results and Discussion ...... 51

3.4.1 Characterization of Fish Oils ...... 51

3.4.2 Classification of Oil Supplements ...... 53

3.4.3 Partial Least Squares Regression Analysis ...... 57

3.5 Conclusions ...... 59

4 Reference List ...... 61

viii

List of Tables

Table 1. Fatty acid composition (%) of fresh corn and canola oils used for frying...... 26

Table 2. Formation rates of FFA, color (ΔE), and AV in relation to oil type and treatment

...... 34

Table 3. Label information from supplements selected for analysis...... 48

Table 4. Fatty acid composition for the oil samples, including estimated contents based on label information...... 52

Table 5. PLSR model statistical analysis for determining fatty acid composition in fish oils...... 59

ix

List of Figures

Figure 1. Triacylglycerol displaying three esterified fatty acids...... 1

Figure 2. Mechanism of lipid and thermal oxidation (Velasco et al., 2010)...... 5

Figure 3. MIR band assignments for typical oil spectra (Van de Voort 2002)...... 11

Figure 4. Diagram of FT-IR instrument with Michelson interferometer (Baeten 2002). . 13

Figure 5. Correlation of regression overlaid upon relative change of corn oil...... 30

Figure 6. SIMCA classification plots of canola (a) and corn (b) oils from day 5.

‘Treatment’ samples are marked by open points and ‘control’ samples with solid points.

...... 31

Figure 7. SIMCA discrimination plots of canola (a) and corn (b) oils, showing bands and regions responsible for separation of classes in Figure 6...... 32

Figure 8. Changes in Color, Free Fatty Acid, and Anisidine Value across the frying cycle.

...... 36

Figure 9. Spectral comparison of the 4 major supplement groups in this study...... 53

Figure 10. SIMCA classification plot from 900-1300 cm-1 region, showing classes (left).

Five major clusters and the EPA content trend (red arrows) are evident on the right...... 55

Figure 11. SIMCA discriminating power plot displaying regions responsible for separation of supplements in Figure 10...... 56

x

Figure 12. Spectral comparison of the 1700-1800 cm-1 regions of Fish groups A & B, showing the shift in the ester peak...... 57

Figure 13. PLS Regression models of EPA and DHA content. Reference EPA/DHA data from GC data in Table 4...... 58

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Chapter 1: Literature Review

1.1 Lipids in Food

In addition to carbohydrates, proteins, and water, lipids are one of the major components in food. The common definition of lipids: “a broad group of chemically diverse compounds that are soluble in organic solvents” can be further refined for studies of food systems (McClements & Decker, 2008). The primary components of lipids from edible plants and animals are fatty acids (FA); a nonpolar aliphatic chain attached to a carboxylic acid moiety. More than 99% of the FA found in plants and animals are esterified to glycerol (Figure 1), with trace amounts of free fatty acids, diacylglycerols, and monoacylglycerols (McClements & Decker, 2008).

Figure 1. Triacylglycerol displaying three esterified fatty acids.

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Fats and oils are not only calorically dense matrix component of foods, but have a large impact on the palatability of foods by modifying texture and acting as a flavor carrier.

Typically between 14 and 24 carbons in length, fatty acids found in nature are, with few exceptions, an even number of carbons structured in a straight chain. FAs containing all

C-C single bonds are classified as saturated, those containing C=C double bonds on their aliphatic chain are referred to as unsaturated. The number of double bonds and their stereochemistry, along with the chain length of the fatty acid, not only affect the physical properties and stability of FAs, but can be a determinant of nutrition (Simopoulos, 2002).

The degree of unsaturation of a fatty acid determines its susceptibility to oxidation and thermal degradation. As unsaturation in the fatty acid aliphatic chain increases, the rate of oxidation increases (Paul & Mittal, 1997). The positional arrangements of the FAs on the glycerol backbone can be random or follow a predominant pattern, depending on the source of the oil. As a result of lipase specificity during digestion, the stereospecific location of a FA can be an important determinant on absorption and nutrition (Schuchardt

& Hahn, 2013). During processing and storage, these characteristics influence the type of reactions lipids undergo, as well as the reaction kinetics. Lipids are relatively stable in bulk form, and the primary modes of degradation are results of heat, light, moisture, and oxygen exposure during production, processing, and storage.

1.2 Chemical Reactions of Lipids in Food Systems

1.2.1 Thermal Degradation

Thermal reactions of lipids occur without the participation of oxygen and result in products with lower polarity than oxidation (Velasco, Marmesat, & Dobarganes, 2010). 2

The lower solubility of oxygen at high frying temperatures promotes thermal reactions.

The primary products of thermal degradation are isomerization products, containing cyclic or trans fatty acyl groups, and triacylglycerol dimers (Velasco, Marmesat, &

Dobarganes, 2010). Isomerization of cis C=C bonds to the trans form is a byproduct of incomplete hydrogenation of oils. Concerns over the health implications of trans fats have resulted in labeling requirements and in some places, acceptable limit levels in foods. Acyl chains from linoleic and linolenic acids are particularly susceptible to isomerization reactions at frying temperatures (Lambelet, Grandgirard, Gregoire,

Juaneda, Sebedio, & Bertoli, 2003; Wolff, 1992). The levels attained from isomerization during frying are very low, and, at levels of concern, are more likely to be supplied by the initial frying oils (Sebedio, Dobarganes, Marquez, Wester, Christie, Dobson, et al., 1996).

Cyclic thermal degradation products are thought to be toxic, but are found at very low levels in degraded oils (Velasco, Marmesat, & Dobarganes, 2010). Free fatty acids in oils are a result of triacylglycerol hydrolysis, driven by enzymatic or thermal factors.

Although the solubility of water in lipid systems is low, the introduction of fried foods with high water contents can promote this reaction (McClements & Decker, 2008). The oxidative stabilities of free fatty acids, monoacylglycerols, and diacylglycerols are lower than the intact triacylglycerol (Guillen & Cabo, 1997a). These hydrolyzed compounds directly affect oil quality by producing off-flavors, reducing the smoke point of the oil, and accelerating further hydrolysis reactions (Frega, Mozzon, & Lercker, 1999).

1.2.2 Lipid Oxidation

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Lipid oxidation refers to the interaction of fatty acids with oxygen. For processors and consumers, oxidation is the primary degradation reaction of concern in edible oils.

Thermal oxidation is a complex series of chemical reactions initiated by free radical species, promoted by high temperature and oxygen levels (Choe & Min, 2007). The free radical mechanism occurs in three phases: initiation, propagation, and termination (Figure

2). Alkyl radicals initially form with the loss of radical hydrogen on the fatty acid aliphatic chain. The carbon-hydrogen bond that is α to a double bond on an unsaturated fatty acid backbone has a lower bond energy as a result of localization of electrons on the double bond. In polyunsaturated fatty acids, where a carbon-hydrogen bond is α to two double bonds, the bond energy is even lower. This property is responsible for differences in oxidation rates of fatty acids with varying degrees of unsaturation. Radical formation in oil is a promoted by heat, light, metals, and reactive oxygen species (Choe & Min,

2007). Alkyl radicals from fatty acids can react with oxygen, forming unstable peroxy radicals, which degrade further, yielding alkoxy and hydroxyl radicals. These radicals propagate, robbing hydrogens from other fatty acyl chains and creating a new radical species. The large range of radicals that can be formed at different positions along the fatty acid aliphatic chain creates a complex mixture of radicals. The end of the oxidative process is known as the termination step; here, formation of non-radical compounds halts the reaction.

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Figure 2. Mechanism of lipid and thermal oxidation (Velasco et al., 2010).

Hydroperoxides are primary oxidation products, they are highly unstable and decompose to form a variety of volatile and non-volatile secondary oxidation products (Melton, Jafar,

Sykes, & Trigiano, 1994). Decomposition of alkoxy and hydroxyl radicals eventually yields non-radical compounds, which can undergo further reactions to form secondary oxidation products. Common examples of these secondary oxidation products include alcohols, acids, aldehydes, epoxides, and hydrocarbons (Frankel, 1987; Paul & Mittal,

1997). Although these compounds significantly contribute to flavor quality of frying oils 5 and foods, some may have toxic or carcinogenic properties (Marnett, 1999). Due to their small size and relatively non-polar nature, many secondary oxidation products are highly volatile. During frying, evaporation and reaction with other food components reduces the level of volatile compounds (Nawar & Witchwoot, 1985). Dimerization and polymerization of radicals generates large macromolecules, which may increase oil viscosity and color (Melton, Jafar, Sykes, & Trigiano, 1994). Studies have shown that triacylglycerol polymers are the most prevalent and complex group of degradation products formed during frying, constituting 12-15 wt% on oil (Velasco, Marmesat, &

Dobarganes, 2010). Highly polymerized oils are also more able to interact with the matrix of fried foods, resulting in higher oil absorption in the fried product (Paul &

Mittal, 1997). Unlike volatile oxidation products, polymerized compounds build up in the oil and may eventually produce a brown, resin-like residue on the sides of the fryer (Choe

& Min, 2007). Research into the chemistry of edible oils and fats has led to the development of techniques for controlling reactions that lead to undesirable products.

Although the nature of lipid oxidation makes it impossible to completely prevent lipid breakdown, slowing these processes has been the focus of much research.

1.2.3 Maintaining Oil Quality

As the oil used for frying or cooking becomes part of the food product, determining and maintaining oil quality is critical for achieving desirable sensory, nutritional, and stability properties (Lalas, 2010). Although oil quality is affected by both thermal and oxidative reactions, current methods to maintain oil quality focus on slowing oxidation or removal of degradation products. The use of antioxidants in lipid systems is widespread; they 6 function by quenching free radical molecules from the oil to prevent runaway oxidation

(Paul & Mittal, 1997). Antioxidant additives such as propyl galate, butylated hydroxyanisole (BHA), and butylated hydroxytoluene (BHT) are highly effective and inexpensive (Ghosh, Chatterjee, & Bhattacharjee, 2012). These synthetic antioxidants are less popular with consumers due to concerns over carcinogenicity and mutagenicity

(Farag, Badei, Hewedi, & Elbaroty, 1989). Natural antioxidants such as tocopherols and herbal (rosemary, sage, and oregano) extracts are label-friendly alternatives with moderate efficacy (Kalantzakis & Blekas, 2006; Paul & Mittal, 1997). The efficacy of antioxidants in frying systems is limited by the stability of these compounds at high temperatures and long-term efficacy (Gordon & Kourimská, 1995). Treatments for removal of free fatty acid are available, but the byproducts of alkaline treatments have been found to contribute significantly to further oil degradation (Paul & Mittal, 1997).

Complete removal of these components is difficult, and even trace levels of the soap byproducts can cause rapid oil oxidation (Gupta, 2010). Filtration of used oil provides an opportunity to remove metals and suspended solids, which may promote oxidation

(Jacobson, 1991). The polarity of degradation products from lipid hydrolysis and oxidation is higher than that of triacylglycerols. Continuous systems exist, but oils may have to be cooled before filtration and reheated before deposition back into the frying system, resulting in high energy usage (Gupta, 2010). Removing polar compounds through active filtration has been found to extend the frying life of oil (Bheemreddy,

Chinnan, Pannu, & Reynolds, 2002). Active filtration can be costly and must be done regularly to achieve the claimed results. In order to determine which of these method

7 works best for a particular application, the quality of the oil must be monitored. Although advanced instrumentation such as gas chromatography and high-performance liquid chromatography are in widespread use in the study of lipid chemistry, the cost and complexity of these techniques is beyond the grasp of many producers and processors.

This emphasis on maintaining oil quality has opened the door for more rapid and accessible analytical techniques.

1.3 Infrared Spectroscopy

Electromagnetic radiation interacts with matter in predictable and useful ways; these interactions are the basis of spectroscopy. Infrared (IR) spectroscopy is based on the absorption of infrared radiation by chemical bonds and bond structures. Methods for qualitative and quantitative analysis by means of IR spectroscopy have been widely used for moisture, lipid, protein, and carbohydrate determination in food products (Ismail,

Nicodemo, Sedman, VandeVoort, & Holzbauer, 1999). Since the middle of the 20th century, IR spectroscopy has been an important part of fundamental research on lipid systems (Chapman, 1965). The IR spectrum is a subset of the electromagnetic spectrum with wavelengths (λ) shorter than that of microwave radiation and longer than visible light (Wehling, 2003). The infrared spectra is divided into three regions: the far-IR (40-

400 cm-1), mid-IR (400-4,000 cm-1) and near-IR (4,000-14,000 cm-1) (Guillen & Cabo,

1997b). Absorption of infrared radiation by a molecule causes a shift in the dipole moment as a result of molecular vibrations. Of the many types of vibrational modes in organic molecules, stretching and bending motions are the primary vibrations of interest to IR spectroscopists. Vibrational energy is directly proportional to the strength of the 8 bond, and the unique connectivity and environment of each molecule gives it slightly different vibrational modes (Griffiths & de Haseth, 2006). The sensitivity of this spectral region to slight changes in structure and conditions makes it a powerful tool for analysis of components in a complex matrix. IR spectroscopy has been widely used for characterization (Arnold & Hartung, 1971; Guillen & Cabo, 1997a; Safar, Bertrand,

Robert, Devaux, & Genot, 1994), authentication (de la Mata, Dominguez-Vidal, Bosque-

Sendra, Ruiz-Medina, Cuadros-Rodriguez, & Ayora-Canada, 2012; Maurer, Hatta-

Sakoda, Pascual-Chagman, & Rodriguez-Saona, 2012; B. F. Ozen & Mauer, 2002), and classification (De Luca, Terouzi, Ioele, Kzaiber, Oussama, Oliverio, et al., 2011; Tapp,

Defernez, & Kemsley, 2003; Yang, Irudayaraj, & Paradkar, 2005) of edible oils. Near- and mid-infrared spectroscopies have both been the focus of a large amount of research into the determination of oil quality (Allendorf, Subramanian, & Rodriguez-Saona, 2011;

Du, Lai, Xiao, Shen, Wang, & Huang, 2012; Innawong, Mallikarjunan, Irudayaraj, &

Marcy, 2004; Ismail, Vandevoort, Emo, & Sedman, 1993; Vandevoort, Ismail, Sedman,

& Emo, 1994).

1.3.1 Near-Infrared Spectroscopy

Near-infrared (NIR) spectroscopy is a well-established branch of spectroscopy that correlates functional groups with overtone and combination bands in the spectrum from

4,000-14,000 cm-1. Although NIR bands are one or two orders of magnitude lower in intensity than corresponding bands in the MIR region, the NIR region has some distinct advantages over MIR (Rodriguez-Saona, Fry, McLaughlin, & Calvey, 2001). In comparison to the well-separated, distinctly identifiable bands of MIR, the overlapping 9 bands in the NIR region do not allow for determination of structure from spectra alone

(Ismail, Nicodemo, Sedman, VandeVoort, & Holzbauer, 1999). The wide, overlapping bands of NIR excel at determination of major components in complex matrices.

Differentiation of major components in food matrices by the type of molecule bound to hydrogen allows for quantitative determination of proteins (primarily N-H bonds), lipids

(C-H), and carbohydrates (O-H) (Li-Chan, Chalmers, & Griffiths, 2010). Unlike MIR, the higher-energy NIR radiation can be used to directly analyze solids with diffuse reflectance spectroscopy. In this technique, a small amount of incident radiation penetrates the solid surface of a sample and is reflected several times before exiting to the detector (Ismail, Nicodemo, Sedman, VandeVoort, & Holzbauer, 1999). More IR radiation is absorbed as a result of the multiple reflections, allowing for the analysis of solid, minimally prepared samples (Günzler & Heise, 2000). These properties have led to the development of NIR methods for a variety of dairy (Rodriguez-Otero & Hermida,

1996; Woodcock, Fagan, O'Donnell, & Downey, 2008) and lipid applications (Du, Lai,

Xiao, Shen, Wang, & Huang, 2012; Gonzaga & Pasquini, 2006). In one study, moisture, fat, protein, and lactose contents were predicted with NIR and regression analysis (Baer,

Frank, & Loewenstein, 1983). In many applications, the limitations of NIR in regards to sampling and spectral characteristics have promoted the development of more robust mid-infrared methods.

1.3.2 Mid-Infrared Spectroscopy

Mid-infrared (MIR) spectroscopy is very useful in the study of organic compounds because the absorption bands are related to the vibrational modes of specific functional

10 groups (Guillen & Cabo, 1997a). The positioning of the band (within a few wavenumbers) and its intensity are correlated with the energy of the bond and its concentration in the matrix (Figure 3). These characteristics, well-separated bands, make

MIR spectroscopy ideal for both qualitative and quantitative applications (Li-Chan,

Chalmers, & Griffiths, 2010).

Figure 3. MIR band assignments for typical oil spectra (Van de Voort 2002).

A major advance in the field of mid-infrared spectroscopy was the development of

Fourier transform infrared spectroscopy (FT-IR). Conventional dispersive IR systems separate the incident infrared radiation into its component wavelengths for analysis, to allow for discrimination at the detector. In contrast, FT-IR systems rely on an interferometer to encode intensity and frequency in a continuous, wideband infrared 11 signal that irradiates the sample before reaching the detector (Ismail, Nicodemo, Sedman,

VandeVoort, & Holzbauer, 1999). The most common form of interferometer in FT- systems is the Michelson interferometer, which is composed of a beamsplitter, a fixed mirror, and a moving mirror (Günzler & Heise, 2000). The movement of the non-fixed mirror is monitored with high precision with a laser, typically helium-neon, for calibration. The source radiation enters the beamsplitter, where its signal is divided between the fixed mirror and the moving mirror. The reflected signals recombine at the beamsplitter, resulting in a constructive/deconstructive interference pattern as a result of variation in path length of signal from the moving mirror (Ismail, Nicodemo, Sedman,

VandeVoort, & Holzbauer, 1999). A portion of this signal is reflected to the sample and selectively absorbed before arriving at the detector (Figure 4). The signal that arrives at the detector is an interferogram; a plot of intensity versus wavelength where the intensity of the signal is a function of the change in optical path length from the moving mirror.

The application of a Fast Fourier Transform (FFT) decomposes the signal into a typical

IR spectrum of absorbance versus frequency (Wehling, 2003).

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Figure 4. Diagram of FT-IR instrument with Michelson interferometer (Baeten 2002).

Fourier-transform systems are able to collect information from the entire IR spectrum in one scan, resulting in much faster data collection speeds. In addition, the increase in energy throughput over a monochromator produces a much higher signal-to-noise ratio than dispersive systems (Ismail, Nicodemo, Sedman, VandeVoort, & Holzbauer, 1999).

Also, FT-IR systems maintain internal laser wavelength calibration, as opposed to the external calibration required by dispersive systems, resulting in increased consistency between collected spectra and between instruments (Guillen & Cabo, 1997b).

Attenuated Total Reflectance (ATR) represents an important advance in FT-IR sampling technology, overcoming many of the sampling issues that have plagued MIR methods

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(Griffiths & de Haseth, 2006). Placed in the signal path between the beamsplitter and the detector, an ATR accessory consists of a high refraction-index crystal that is in direct contact with the sample. The IR beam enters the crystal and impinges on the interface of the crystal and sample. At the proper angle, the beam is completely reflected back into the crystal, giving rise to an evanescent wave that penetrates the sample and is absorbed

(Günzler & Heise, 2000). Although the wave penetrates minimally into the sample

(between 1 and 4 μm for MIR), multiple bounce ATR devices allow for many points of contact with the sample, effectively increasing the path length (Ismail, Nicodemo,

Sedman, VandeVoort, & Holzbauer, 1999). A wide array of high refractive-index materials can be used when constructing an ATR; ZnSe, Ge, and diamond crystals all provide unique properties for a variety of sample types. These ATR sampling accessories now include temperature and environmentally controlled units that allow for a great deal of control during data collection. Direct-heating of oils on ATR crystals has been used to monitor real-time oxidative changes in fats and oils (Ammari, Bouveresse, Eveleigh,

Boughanmi, & Rutledge, 2013; Dubois, vandeVoort, Sedman, Ismail, & Ramaswamy,

1996; Pinto, Locquet, Eveleigh, & Rutledge, 2010). In the case of high-value ingredients, such as olive oil, methods capable of predicting product category and variety, as well as discriminating olive oils adulterated with other edible oils have been demonstrated (de la

Mata, Dominguez-Vidal, Bosque-Sendra, Ruiz-Medina, Cuadros-Rodriguez, & Ayora-

Canada, 2012). In a direct comparison, a classification models for discrimination of edible oil varieties built with both near- and mid-infrared spectra showed a significantly higher classification accuracy with MIR (Yang, Irudayaraj, & Paradkar, 2005). Using the

14 entire MIR spectra, researchers have developed methods for classifying frying oils into acceptable, marginal, and unacceptable groups (Innawong, Mallikarjunan, Irudayaraj, &

Marcy, 2004). A study on vegetable oils showed the potential in portable MIR instruments for prediction of fatty acid composition, FFA, and PV values, with multiple models integrated into the same device for analysis (Allendorf, Subramanian, &

Rodriguez-Saona, 2011). The development of IR spectroscopic methods has been aided by increases in computing power, and specifically the application of chemometric tools that can extract information from large spectral data sets (Ismail, Nicodemo, Sedman,

VandeVoort, & Holzbauer, 1999). Of the cited studies, most have relied on one or more multivariate methods to aid in quantification or classification.

1.4 Chemometrics and Multivariate Analysis

Due to the complex matrices of most food products, deriving meaningful relationships from spectral data requires the use of multivariate analysis tools. Powerful multivariate techniques that include data reduction, compression, and classification methods are a significant feature of IR spectroscopy (Karoui & De Baerdemaeker, 2007). Principal

Component Analysis (PCA) attempts to reduce a large dataset to a smaller number of orthogonal variables, or Principal Components (PC), that retain the major variance of the original variables (Kemsley, 1996). Each sample is assigned a score on each of these principal components and graphical representation of these scores can be used to uncover relationships or clustering within a dataset (Karoui & De Baerdemaeker, 2007).

Graphical display is helpful in finding outliers or misclassified samples, revealing characteristics that can be hidden in the original data. Soft Independent Modeling of

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Class Analogy (SIMCA) is a supervised multivariate method, wherein a PCA is constructed for each group of samples (referred to as a “class”) and graphically displayed with regards to their relationships to the same set of PCs. SIMCA is particularly useful when analyzing datasets with many classes or when classes are related by more than two

PCs. In the study of lipids with infrared spectroscopy, PCA and SIMCA have been particularly useful for classification of oils by origin (Gurdeniz, Ozen, & Tokatli, 2008), variety (Sinelli, Casale, Di Egidio, Oliveri, Bassi, Tura, et al., 2010), and in determining oil authenticity (Downey, McIntyre, & Davies, 2002; Li, Zhu, Zhang, Li, Su, & Shan,

2012). It is possible to correlate spectral information with a set of reference variables with the help of Partial Least Squares Regression (PLSR) analysis. PLSR reduces the number of variables in a dataset by attempting to explain the maximum variance in both the spectra and reference data sets in the form of a linear equation (Karoui & De

Baerdemaeker, 2007). The selection of reference data and spectral regions plays an important role in the creation of PLSR quantification models. When constructing a PLSR model for determination of a particular chemical component of a matrix, the model must be calibrated for each particular matrix. For example; a model that predicts the free fatty acid (FFA) content in frying oil would not be expected to reasonably predict the FFA content of a French fry. The robustness of a model is determined by the variance included in the model and the number of samples (in both cases higher levels will yield a more robust model). PLSR has been used to produce methods for determination of composition

(Maurer, Hatta-Sakoda, Pascual-Chagman, & Rodriguez-Saona, 2012), adulteration levels (Downey, McIntyre, & Davies, 2002; Tay, Singh, Krishnan, & Gore, 2002) and

16 oxidative indices (Allendorf, Subramanian, & Rodriguez-Saona, 2011; Dubois, vandeVoort, Sedman, Ismail, & Ramaswamy, 1996; Guillen & Cabo, 2002; Inon,

Garrigues, Garrigues, Molina, & de la Guardia, 2003) in lipids, often with comparable or better reproducibility than traditional methods.

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Chapter 2: Effect of a Novel Induction Food-Processing Device in Improving Frying Oil

Quality

Michael J. Wenstrup, Marçal Plans, and Luis E. Rodriguez-Saona

Department of Food Science and Technology

The Ohio State University

110 Parker Food Science Building

2015 Fyffe Court

Columbus, OH 43210

18

2.1 Abstract

Producing quality, fried snacks involves monitoring the chemical processes that occur during frying to control oil degradation, which greatly impacts product nutrition, flavor and shelf life. Our objective was to evaluate the effects of a Novel Induction Food

Processing device on the quality indices of frying oil. A commercial bench top electric fryer (6 L) retrofitted with the induction technology was used and the induction device was turned off for the control experiment. Under standard frying conditions (185C), vegetable oil samples (corn and canola) were collected during a standardized frying cycle

(8hr/day for 5 consecutive days) to test oxidative stability. Fourier transform mid-infrared spectroscopy (FT-IR) was used to classify samples and standard reference methods used to monitor the oil stability included p-anisidine value, free fatty acid content (AOCS

Official Method Ca 5a-40), and CIELAB color. FT-IR combined with chemometrics showed differences between control and treatment, with discrimination provided by regions associated with fatty acid hydrolysis and oxidation products. As compared to the control treatment, the induction technology decreased the rate of free fatty acid and aldehyde formation. Tests also showed a marked effect on total color difference (ΔE) between the oil treated with the induction technology and the control, reducing color degradation. Overall, our results showed that a patented induction device slows the rate of lipid degradation, resulting 7-20% reduction in formation rate of key quality parameters and significantly longer utilization of frying oil.

2.2 Introduction

19

Deep frying has become one of the most popular food preparation methods, with an estimated $83 billion market share in the United States (Choe and Min, 2007). Frying oils are subject to atmospheric oxygen, high heat, and moisture for extended periods of time, resulting in deterioration, and subsequent production of off-flavors and harmful compounds (Choe and Min, 2007; Paul and Mittal, 1997). The quality parameters of edible oils have health and economic impacts, and monitoring them has been the subject of a large body of research (Gonzaga and Pasquini, 2006; Innawong et al., 2004; Melton et al., 1994; Pinto et al., 2010). The fast-food industry has explored various methods intended to maintain quality and extend the useful life of frying oils. Frying products at elevated temperatures results in accelerated formation of thermal oxidation and polymerization products, and frying above 195 C can cause isomerization of polyunsaturated fatty acids (Aladedunye and Przybylski, 2009; Blumenthal, 1991).

Lowering frying temperatures can slow down oil degradation, but may result in products with less of the desirable texture and flavor characteristics associated with deep-fat frying. Hydrogenation of highly unsaturated oils results in a more stable oil with a higher melting point (Paul and Mittal, 1997). However, this also results in production of trans fatty acids, which have come under scrutiny for their associated health effects (Choe and

Min, 2007).

Antioxidants are administered to the frying oils to protect oils and fried products from degradative changes and enhance their shelf lives. Antioxidants such as butylated hydroxy anisole (BHA), butylated hydroxy toluene (BHT) and tertiary butylated hydroxy quinone (TBHQ) have been used to slow down lipid oxidation by quenching oxygen

20 radicals from the oil (Ghosh et al., 2012; Paul and Mittal, 1997). Citric acid, capable of chelating metals involved in lipid redox reactions, has been administered during processing to help protect oils from oxidation (Mahoney and Graf, 1986). Nevertheless, concerns over safety, consumer perception and efficacy at high temperatures have prompted research into novel methods for preserving oil quality (Gordon and Kourimska,

1995). An effective method to improve oil stability utilizes filtration and adsorbents to remove decomposition/oxidation products from used frying oil (Bheemreddy et al.,

2002). These methods can be expensive, must be performed daily for maximum benefits, and result in further waste that must be disposed of properly (Paul and Mittal, 1997).

A patented food processing technology (NuTruTech, LLC. Akron, OH) proposes to extend the life of frying oil by utilizing an induction device to reduce the damage caused during frying (Maupin & Johnson, 2011). The induction device produces a rippled direct current through a high-surface area conductor in the oil bath during frying oil use, idle, and storage. According to the patent, the device functions “by creating a reducing environment where electrons are supplied to the food during its preparation” reducing oxidative damage on the food. Although the exact mechanism has yet to be discovered, in fields such as chromatography and crude oil extraction and refining, electric currents have been used to modify chemical reaction rates, break emulsions, and partition polar compounds from complex matrices (Eow et al., 2001; Wang et al., 1998; Wittle et al.,

2011).

The objective of our study was to evaluate the effects of a Novel Induction Food

Processing device on the quality indices of two frying oils commonly used by the snack

21 industry (corn and canola). As the chemical processes affected by this technology have not been well studied, infrared spectroscopy in combination with chemometrics was chosen for its ability to provide information about the different functional groups present in complex matrices (Bosque-Sendra et al., 2012; Guillen and Cabo, 1997), providing a valuable tool for evaluating the complexity of chemical reactions during the oxidation of edible oils. Quality parameters were chosen that represent the main products of degradation and are of importance to industry. Percent free fatty acid (FFA) often serves as an indicator for frying oil replacement (O'Keefe and Pike, 2003) and anisidine value

(AV) is a general measure of aldehyde content; a source of off-flavor in fried products.

Oil color is an important metric for oil quality as the color of frying oil often becomes unacceptable long before off-flavor and odors deem the oil too degraded for use (Dubois et al., 1996).

2.3 Materials and Methods

Canola and corn oils were donated by a local company (Wyandot, Inc. Marion, OH) and frozen french fries (3/8”, straight cut, par-fried) were obtained from local grocery store

(Columbus, OH). All solvents and chemicals of analytical grade used in this study were purchased from Fisher Scientific (Waltham, MA), except for the p-anisidine reagent, purchased from Agros Organics (Geel, Belgium).

2.3.1 Frying Protocol

A commercial 6 L single-unit fryer (Admiral Craft Equipment Corp., Hicksville, NY) was used for frying. The patented induction technology applies a rippled DC current through the oil well, heating coils, and a 16-gauge stainless steel frying basket modified

22 from a perforated sterilization basket. Frying was conducted for 5 days, simulating commercial restaurant frying, with the fryer switched on for 8 hr per day. Oil (6L) was heated to 185 ± 5 °C. Twenty-five batches of French fries, each consisting of 200 g of

French fries recently removed from freezer storage, were fried at 2 hr intervals for 5 consecutive days. Control and treatment experiments were done separately, with control taking place the first week and treatment taking place the following week. During the treatment experiments, the induction technology was left on at all times, regardless of fryer status. Additional “top-up” oil was not added, as the oil loss was not significant enough to alter fryer function.

2.3.2 Sample Collection

Oil samples were collected after frying at 0 and 8 hr, the beginning and end of the daily frying cycle. A sample of fresh frying oil, before being subjected to heat, was collected at the beginning of day 1. The samples were collected in 60 mL glass jars, sealed with a cap and stored immediately, in the dark, at -4 C. Duplicate samples were collected at each time point.

2.3.3 Fatty Acid Profile

Determination of fatty acid content in fresh oils was achieved with a fatty acid methyl ester (FAME) procedure (Rodriguez-Saona et al., 1995). Esterification was achieved by adding methanolic-sulfuric acid (10 mL, 4% sulfuric acid) to the oil sample (0.05 mL) plus benzene (1 mL) in a glass test tube with a Teflon screw top cap. The mixture was heated to 80-90 C for 90 minutes. Methyl esters were extracted using a partition of hexane and distilled water. An aliquot of the hexane portion (1 mL) was collected in a 1.5

23 mL GC vial with crimp top and was evaporated under nitrogen. The dried samples were re-diluted using iso-octane (0.5 mL). Samples were analyzed, in duplicate, on an HP-

6890 GC equipped with a flame ionization detector (FID) and HP G1513A autosampler.

Separation of the components was done using an HP-FFAP 25 m x 0.32 mm x 0.5 um column (Agilent part number 19019F-112) using helium as the carrier gas. The injection volume was 1 μL with a split ratio of 20:1. The oven conditions were 110 C (1 min), to

220 C (5 C/min) hold for 15 minutes. The injector temperature was 220 C and the detector temperature was 250 C. The identification of fatty acids was carried out by comparing the retention times with a reference standard (NuChek Prep GC standard

15A).

2.3.4 Oil Quality Metrics

Percent free fatty acid was determined by titration using AOCS Official method Ca 5a-40

(AOCS, 1993). Oil color of frying oils was determined according to AOCS Official method Cc 13e-92, using a Labscan XE colorimeter (Hunter Lab, Inc., Reston, VA,

USA). Oil was decanted into 50 mL culture tubes (BD Biosciences, San Jose, CA) and data was collected in the transmittance mode, with a D65 luminant and 10 observer angle. Values for lightness (L*), and color-opponent dimension (a* and b*) were

2 2 2 1/2 recorded and color difference (ΔE) was calculated as ΔE*ab=[(ΔL*) +(Δa*) +(Δb*) ] .

Levels of secondary oxidation products were determined as anisidine value (AV), according to AOCS Official Method Cd 18-90 (AOCS, 1993). A 0.1 g sample size was used and data collection was completed on a Cary 50 Bio spectrophotometer (Agilent,

Santa Clara, CA).

24

2.3.5 Fourier Transform Mid-Infrared Spectroscopy

The collection of infrared spectral data was done on an ATR-MIR benchtop spectrometer

(Excalibur 3100, Agilent, Santa Clara, CA, USA), equipped with a KBr beam splitter and deuterated triglycine sulfate (DTGS) detector. Sealed sample containers of oil taken from the freezer were heated in an oven to 65 C for 1 hour prior to data collection and measured in triplicate. A standard 25 μL oil aliquot was deposited on a MIRacle™ triple- bounce, ZnSe ATR sampling accessory (Pike Technologies, Madison, WI). Spectra were collected over a range of 4,000-700 cm-1 at 4 cm-1 resolution. Spectra were collected in terms of absorbance and viewed using Resolutions Pro software (Agilent, Santa Clara,

CA, USA). Sixty-four scans were collected to increase the signal-to-noise ratio.

2.3.6 Multivariate Analysis

Soft Independent Modeling of Class Algorithm (SIMCA) was used to analyze samples from day 5 of the frying cycle. Classes were identified as ‘treatment’ and ‘control’, in order to determine the spectral differences between them. Identical SNV and 2nd derivative transforms were applied to all samples. Classes were considered significantly different if separated by an interclass distance >3 (Brereton, 1992). Chemometric analysis was completed using Pirouette 4.0 rev. 2 (Infometrix Inc., Bothell, WA).

2.3.7 Statistical Analysis

FFA, ∆E, and AV were analyzed by analysis of covariance (ANCOVA) to determine statistical significance between regressions at different levels over time. The main factors were oil type and treatment, with time as the covariate. The model was constructed using all possible interactions between the main factors and time. Factors with P-value > 0.05

25 were excluded from the model according to the likelihood-ratio test. Analysis of residuals, normality of residuals and R2-adjusted were used to determine the goodness of fit of the linear model. Bonferroni’s correction was used to avoid the multiple testing problems. Data were analyzed using Minitab version 16 statistical software (Minitab Inc.,

PA). Statistical significance was expressed at the P-value < 0.05 level unless otherwise indicated.

2.4 Results and Discussion

Two vegetable (canola and corn) oils, with markedly different fatty acid composition, were chosen to examine the effects of the technology in regards to different inherent stabilities. The fatty acid (FA) composition of canola and corn oils (Table 1) used for frying reflected the typical FA profiles (Ackman, 1990; Strocchi, 1982), with canola composed primarily of monounsaturated (65%) fatty acids and corn oil containing higher levels of polyunsaturates (57%). Nevertheless, canola showed higher levels of linolenic acid (7%) compared to corn (1%) oil.

Table 1. Fatty acid composition (%) of fresh corn and canola oils used for frying. Fatty Acid

Palmitic Stearic Oleic Linoleic Linolenic

Oil (16:0) (18:0) (18:1) (18:2) (18:3)

Corn 11.3 ± 0.4 1.8 ± 0.1 28.5 ± 0.2 56.3 ± 0.2 0.9 ± 0.1

Canola 4.2 ± 0.1 2.0 ± 0.1 64.9 ± 0.3 18.4 ± 0.1 7.4 ± 0.1

26

The rate of fatty acid breakdown is related to the number of double bonds in the carbon chain of the molecule and the rate of oxidation increases as the number of double bonds increases (Fatemi and Hammond, 1980) resulting in accelerated decomposition of hydroperoxides yielding secondary oxidation products (Yoshida, 1993). In addition, the glyceride structure can affect the relative rates of oxidation of acyl groups in a triglyceride, with sn-1 and 3 positions oxidizing faster than those in the sn-2 position

(Raghuvee and Hammond, 1967). Oils with low unsaturation are more stable at frying temperatures, but their high levels of saturated fatty acids reduce their usefulness in frying from a health point of view (Arroyo et al., 1995). The influence of these parameters on the effectiveness of the technology has yet to be determined and may play a role in optimizing its performance.

2.4.1 Fourier-Transform Infrared Spectroscopy

Mid-infrared has been used to monitor degradation reactions in edible oils, providing valuable information about the spectral changes during frying (Allendorf et al., 2011; Du et al., 2012; Guillen and Cabo, 1999, 2000; Ismail et al., 1993; Pinto et al., 2010;

Vandevoort et al., 1994). Spectral data across each day of the frying cycle was averaged and a standard normal variate (SNV) transformation was applied. The day 1 spectrum (no thermal treatment) was subtracted from the subsequent days of the frying cycle in order to show the relative change () in spectral bands (Figure 5). The relative change in bands of interest was consistent across the two oils, particularly in the 900 - 1000 and 1650 -

1800 cm-1 regions. Only corn oil is shown, as the change in canola oil occurred mostly in days 1 and 2, possibly a result of the natural antioxidants present in corn oil and

27 differences in fatty acid profile. This observation agrees with previous studies, which have shown that the nature of chemical reactions are same between oils, but the kinetics are different depending on the oil composition (Ammari, Bouveresse, Eveleigh,

Boughanmi, & Rutledge, 2013). In accordance with previous research, bands associated with C=O stretching of esters (1743 - 1749 cm-1) decreased, while the bands associated with C=O stretching of aldehydes and ketones (1728 cm-1) increased (Guillen and Cabo,

1999; Pinto et al., 2010). In addition, regions that have been found to represent C=C-H bending was found to increase during frying. The increasing bands at 970 and 987 cm-1 likely represent isomerization or oxidation products (Guillen & Cabo, 1999; Pinto,

Locquet, Eveleigh, & Rutledge, 2010; Vandevoort, Ismail, Sedman, & Emo, 1994).

As the oxidation processes are a function of time, a novel form of analysis was undertaken to examine correlation between the rates of control and treatment groups. The coefficient of regression was calculated between control and treatment groups, assuming a simple linear relation, as well as the 95% confidence interval for the coefficient at each wavenumber. When used in conjunction with the relative change data, the coefficient of regression (β’) reveals significant differences in spectral intensity changes between control and treatment. Beta-prime is a ternary coefficient that relates the regression parameters of control and treatment groups at each wavenumber, where β’=0 represents no significant difference in the rate of change between control and treatment during the frying cycle. In the case of a positive or negative β’, the coefficient is a function of the direction of relative change. For example, when the relative change of a region is positive

(>0), a positive β’ (β’>0) signifies a statistical reduction in intensity in treatment over

28 control, as shown between 950-1100 cm-1 (Figure 5). Conversely, when the relative change of a region is positive (>0), a negative β’ denotes a significant reduction in intensity in control over treatment. In summary, when β’ and  have the same sign, there is a significant effect of treatment over control. Regions with significant effects of treatment over control include 1650-1750 cm-1, which contains C=O stretching from free fatty acids, aldehydes and ketones. In addition, C-O stretching bands between 1100 and

1200 cm-1 demonstrated a slower rate of band disappearance in the treatment group than in the control. A region that develops consistently during frying, from 950-1100 cm-1, displayed a significant reduction in rates in the treatment compared to control. This region has many overlapping functional group bands, but is a consistent marker for oxidative and thermal degradation during frying (Vandevoort, Ismail, Sedman, & Emo,

1994).

29

0.3 1728

987.5 β’

0.2 970 Day 5 Day 4

1694 Day 3 0.1 Day 2

1147 1410 1640 0 1160 1137 -0.1

-0.2 1746

-0.3 Wavenumber (cm-1)

Figure 5. Correlation of regression overlaid upon relative change of corn oil.

Coupling the large amount of data gathered by infrared spectroscopy with multivariate techniques allowed for extraction of even more qualitative and quantitative information.

Supervised pattern recognition methods, such as Soft Independent Modeling of Class

Algorithms (SIMCA), reveal relationships between sets of samples with known classification (Bosque-Sendra et al., 2012) by using principal component analysis (PCA) to reduce the dimensionality of multivariate data sets. Differences were obvious between fresh and fried oils, even early in the frying cycle, but the most apparent separation between ‘control’ and ‘technology’ did not appear until day 3. In order to reduce the variance of frying time and highlight the variance between the ‘control’ and ‘technology’ groups, a SIMCA model using the region from 900-1900 cm-1 was developed only for 30 day 5 to show compositional differences between the ‘control’ and ‘treatment’ oils. As seen in Figure 6, samples clustered similarly between the different oils with interclass distances for canola oil and corn oil as 4.5 and 4.2 respectively.

Figure 6. SIMCA classification plots of canola (a) and corn (b) oils from day 5. ‘Treatment’ samples are marked by open points and ‘control’ samples with solid points.

The regions responsible for discrimination between canola and corn oil were not identical, but in both cases, the region extending from 1670-1815 cm-1 played an important role (Figure 7). In previous studies, the region from 1650-1750 cm-1 have been found to represent the carbonyl functional group (Vandevoort et al., 1994), and more specifically, aldehydes and ketones from 1670-1730 cm-1 (Ismail et al., 1993). The carbonyl functional group region includes the ester linkage in triglycerides, as well as the carboxylic acid moiety from free fatty acids. The most prominent source of discrimination in canola oil was the band around 1178 cm-1, which has also been found to correlate with carboxylic acid C=O stretching (Guillen and Cabo, 1997), and the band at 31

1568 cm-1 which has been related to carboxylate anion of free fatty acids (Ismail et al.,

1993). Less significant peaks in the region from 1000-1300 cm-1 have previously been found to represent C=O stretching bands from esters (Guillen and Cabo, 1997).

Figure 7. SIMCA discrimination plots of canola (a) and corn (b) oils, showing bands and regions responsible for separation of classes in Figure 6.

A very high level of discrimination in corn oil was provided by the band at 972 cm-1, which is associated with olephinic C=C-H stretching from aldehydes and ketones

(Vandevoort et al., 1994). Overall, the 1650-750 cm-1 region was responsible for the significant class separation. The infrared analysis also showed the different chemical

32 changes in the oils with regards to the frying process that could be attributed to their fatty acid composition and structure, which affects lipid oxidation.

2.4.2 Monitoring Oxidative Indicators

The formation of FFA during deep frying results from hydrolysis, as well as the cleavage and oxidation of double bonds (Paul and Mittal, 1997). The products of FFA hydrolysis accelerate further hydrolysis reactions and may have pro-oxidant effects on oils (Frega et al., 1999). During frying, FFA of both the ‘control’ and ‘treatment’ groups increased with frying time, resembling zero order kinetics (Figure 8). The rate of FFA formation in the

‘treatment’ group (0.009 %/hr) was significantly lower (p<0.05) than that of the ‘control’ group (0.010 %/hr) and the rate reduction was independent of oil type (Table 2).

33

Table 2. Formation rates of FFA, color (ΔE), and AV in relation to oil type and treatment

Oil Reduction in Slope (%) Parameter R2-adjust Group Slope Type (Treatment-Control)/Control *100 FFA 0.981 Canola Control 0.010a 16.78 Canola Treatment 0.009b

Corn Control 0.010a 16.78 Corn Treatment 0.009b

ΔE 0.976 Canola Control 0.582a 17.53 Canola Treatment 0.480c

Corn Control 0.511b 19.95 Corn Treatment 0.409d

AV 0.982 Canola Control 10.530a 7.03 Canola Treatment 9.789b

Corn Control 10.530a 7.03 Corn Treatment 9.789b

Different letter means different slope using Bonferroni’s multiple comparison correction

34

Color changes during frying result from oxidation, polymerization, and carbonization products, as well as browning reaction products (Paul and Mittal, 1997). Color difference

(ΔE) provided a measure of the distance between colors in CIELAB color space. During frying, color formation proceeded linearly with respect to time (zero order kinetics). In regards to both canola and corn oil, the rate of color formation (seen as increasing ΔE) was lower in the ‘treatment’ group than the ‘control’ group (Figure 8).

35

Canola Oil Corn Oil

120 125

115 Control Trt

110 115

105

Delta E 100 105

95

90 95 0 8 16 24 32 40 0 8 16 24 32 0.8 0.6

0.6 0.4

0.4

0.2

0.2 Free Fatty Acids Acids (%) Fatty Free 0.0 0.0 0 8 16 24 32 40 0 8 16 24 32 40 180 250 160 200 140 120 150 100 80 100 60

40 50 Anisidine Value Anisidine 20 0 0 0 4 8 12 16 0 4 8 12 16 Time (hours) Time (hours)

Figure 8. Changes in Color, Free Fatty Acid, and Anisidine Value across the frying cycle.

Corn oil showed a slightly larger decrease in color degradation rate, as well as lower ΔE values throughout the frying cycle (Table 2). The fatty acid profile of the corn oil, which contains a higher proportion of polyunsaturated fatty acids than canola oil, may make the oil more susceptible to polymerization and color formation (Choe and Min, 2007).

36

Anisidine Value (AV) measures secondary oxidation products, primarily 2-alkenals and

2,4-dienals (O'Keefe and Pike, 2003). Aldehydes are of interest during deep frying due to their unappealing odor and flavor attributes (Dubois et al., 1996). For both ‘control’ and

‘treatment’ groups, AV increased until hour 16 of frying, when the values remained relatively stable until the end of the frying cycle. The secondary oxidation products measured by AV are volatile and reactive, and may be lost to evaporation, decomposition, or further reaction with matrix or food components (Nawar and

Witchwoot, 1985). Only the first 16 hrs of the frying cycle showed a significant decrease in rate (~7%) in the treatment group compared to the control group, and this effect was independent of oil type (Figure 8).

2.5 Conclusions

Spectral changes during frying were found to be consistent with previous studies, confirming the impact on regions associated with C=O and C=C-H functional groups. In the SIMCA model, the significant interclass distances and consistency in discriminating bands between oil types points to a significant difference between ‘control’ and

‘treatment’ groups on day 5 of the frying cycle. The discrimination bands correspond with products of lipid hydrolysis and oxidation, such as free fatty acids, aldehydes, and ketones. Stability indices indicate the technology significantly slows the development of hydrolytic and oxidative products, as indicated by both FFA and AV. In addition, the development of color was retarded, extending the usable life of the frying oil. FFA and

ΔE, important markers for industry, were subject to improvements in rates of between 15 and 20 percent. In situations where the frying usage of oil extends far into the oxidation

37 cycle, this technology can preserve quality characteristics while allowing for longer utilization of oil. Although further investigation is required into the mechanism preventing oxidation of edible oils, other examples in literature raise questions about the effects of electrophoresis, electromigration and/or disruption of water/oil emulsions in similar systems. The patented, induction device produces no waste, requires little upkeep and is cost- and energy-efficient. It may be retrofit onto existing devices or built into new pieces of equipment.

38

Chapter 3: Application of a Portable Infrared Spectrometer for Characterization of

Omega-3 Dietary Supplements

Michael J. Wenstrup, Marçal Plans, and Luis E. Rodriguez-Saona

Department of Food Science and Technology

The Ohio State University

110 Parker Food Science Building

2015 Fyffe Court

Columbus, OH 43210

39

3.1 Abstract

Omega-3 dietary supplements have been linked with health benefits including decreased cardiovascular disease mortality, anti-inflammatory effects, reduced blood pressure, and antiatherogenic activity. The sources of these effects, polyunsaturated fatty acids (PUFA) such as eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are almost exclusively found in seafood products and are essential fatty acids for humans. The concentration of EPA and DHA in fish is highly variable; species, catch location, environment, season and processing parameters are all factors in determining the ratio of fatty acids found in the oil. Our objective was to characterize the composition of commercial Omega-3 dietary supplements using mid-infrared spectroscopy, gas chromatography, and chemometrics. Supplements were purchased from retailers that included a large amount of source, processing, and compositional variability. Fatty acid composition of oils was determined by fatty acid methyl ester gas chromatography. The supplements encompassed a wide range of fatty acid profiles and delivery methods. Mid- infrared spectral data was collected on portable infrared systems and the spectra were used to classify supplements using SIMCA, a pattern recognition technique. In addition,

PLSR was used to correlate the spectra with GC-FAME results. SIMCA analysis using the 900-1300 cm-1 region allowed for tight clustering of fish oil supplements into distinct classes, depending on the source and processing, with significant (>3) interclass distances. Discriminating power showed a strong influence of the 1038 cm-1 band, which is typically associated with C=C and C-O stretching vibrations. PLSR generated multivariate models with high correlation (R ≥ 0.99) between the infrared spectrum and

40 fatty acid composition, and SECV of 1.74 and 1.89 for EPA and DHA, respectively. Our results indicate that ATR FT-IR spectroscopy combined with pattern recognition analysis provides for robust screening and determination of fatty acid composition of fish oil supplements.

41

3.2 Introduction

Omega-3 fatty acids are long-chain n-3 fatty acids that are essential to the human diet and have been found to provide a wide array of health benefits, including anti-inflammatory, cardioprotective, and anti-arthritic effects (Sidhu, 2003). The compounds primarily responsible for these benefits, polyunsaturated fatty acids (PUFA) such as eicosapentaenoic acid (EPA), 20:5, and docosahexaenoic acid (DHA), 22:6, are almost exclusively found in seafood products (de Leiris, de Lorgeril, & Boucher, 2009). Some research has shown that western diets are deficient in omega-3 fatty acids, leading to increased consumer awareness of dietary supplements that contain these compounds

(Simopoulos, 2002). The concentration of EPA and DHA in fish depends on the species, with salmon, herring, mackerel, tuna, and sardines having the highest concentrations

(Moffat & McGill, 1993). The composition of fish oils is highly variable, with fish type, catch location, season, and processing all playing a role in determining the ratios of the fatty acids found in the oils (Ackman, Ratnayake, & Olsson, 1988). As each PUFA has a different role in health promotion and disease prevention, it is important to understand the variability in composition within fish oil products, and whether they are supplied as triglycerides, esters, or fatty acids. The structure of these essential fatty acids plays a role in determining their health promoting properties and efficacy. Most fish oil supplements on the market are heavily processed and may be provided as ethyl esters, triacylglycerol’s, or less commonly in phospholipid form. Research has yielded mixed results on the bioavailability and bioactivity of different delivery forms. But at least two studies have demonstrated a small, yet significant, increase in bioavailability of

42 triglycerides versus ethyl esters (Schuchardt & Hahn, 2013). In addition, the effect of triglyceride substitution pattern on the bioavailability and metabolic fates of omega-3 fatty acids is not yet completely understood (Dyerberg, Madsen, Moller, Aardestrup, &

Schmidt, 2010). The high cost of fish oils and their myriad uses makes them prime candidates for adulteration and mislabeling. The Dietary Supplement Health and

Education Act of 1994, which outlines regulations for dietary supplements, does not provide the same level of oversight for supplements that the FDA provides for food and drug manufacturers. The delivery form, whether ethyl ester, triglyceride, or other, is not required to be stated on packaging. Many supplements are also processed and packaged outside the country of sale, making it difficult to consistently track quality and safety.

Fish oils are susceptible to contamination with toxic and carcinogenic compounds, either from the fish itself or during processing (Sidhu, 2003). Improper or incomplete purification can lead to high levels of PCBs, dioxins, pesticides and other toxins (Maria,

Arianna, & Giuseppe, 2004; Mozaffarian & Rimm, 2006). Modern, rapid techniques to determine composition, authenticity, and quality of fish oils are not yet readily available, but development of Fourier transform infrared (FT-IR) and attenuated total reflectance

(ATR) spectrometry equipment has opened the door to novel methods. FT-IR systems provide a more robust alternative to older dispersive systems, and ATR systems can accommodate a wide variety of sample types, from gases, to liquids, to powders.

Although the food and supplement industries have utilized near-infrared (NIR) spectroscopy for decades, the applications of mid-infrared (MIR) spectroscopy have only recently gained ground. ATR FT-IR is a non-destructive and rapid technique that allows

43 for analysis of all the chemical bonds in a system. When combined with multivariate statistical analysis and modern computational power (collectively known as chemometrics), the large amount of information provided by MIR can be used to replace traditional techniques (Bosque-Sendra, Cuadros-Rodriguez, Ruiz-Samblas, & de la Mata,

2012). In oil analysis, these traditional techniques are both time consuming and expensive. Gas chromatography (GC) and high-performance liquid chromatography

(HPLC) are used in the analysis of oils, but the extensive sample preparation, long run- times, and destructive nature of the analysis has piqued interest in spectroscopic alternatives. New methods, such as 1H and 13C NMR, have been used to classify fish oils by source, and provide a large amount of information regarding the connectivity of fatty acids (Aursand, Standal, & Axelson, 2007; Guillen, Carton, Goicoechea, & Uriarte,

2008). The high equipment costs and the difficulty nature of quantitative methods have prevented widespread adoption of these techniques. Several studies have demonstrated the potential for classification and detection of adulteration in edible oils, including fish oils, with infrared (Bellorini, Strathmann, Baeten, Fumiere, Berben, Tirendi, et al., 2005;

Banu F. Ozen, Weiss, & Mauer, 2003). Other studies have looked at determining fish oil quality (Zhang & Lee, 1997) and composition in different supplement forms of fish oils

(Vongsvivut, Heraud, Zhang, Kralovec, McNaughton, & Barrow, 2012). While several studies have undertaken analyses of retail fish oil supplements (Hamilton, Brooks,

Holmes, Cunningham, & Russell, 2010; Ritter, Budge, & Jovica, 2013; Tatarczyk, Engl,

Ciardi, Laimer, Kaser, Salzmann, et al., 2007), more study is needed on the applications of IR spectroscopy to these products. The objective of this research was to evaluate

44 portable ATR FT-IR spectroscopy for rapid determination of fatty acid composition and classification of fish oil supplements.

3.3 Materials and Methods

Seventeen unique fish oil (FO) supplements from thirteen manufacturers were purchased at retail. In addition, four cod liver oil (CLO) supplements and two flaxseed oil supplements were selected (Table 3). Duplicates of five FO and one flaxseed oil were purchased at a later date, ensuring separate lots from the original supplements. All fatty acid standards were purchased from Nu-Chek Prep Inc. (Elysian, MN). All solvents and chemicals of analytical grade used in this study were purchased from Fisher Scientific

(Waltham, MA).

3.3.1 Sample Preparation

Oils supplied in gel capsule form were cleanly perforated and the oil transferred to sealed, 1.5 mL flip-cap tubes for cold storage (4 C). For each supplement, five capsules were selected from the bottle at random for spectral collection, and three of these were further analyzed for fatty acid composition. Bulk oils were agitated and an aliquot was removed and stored in the same manner as encapsulated oils.

3.3.2 Fatty Acid Profile

Determination of fatty acid content in fresh oils was achieved with a fatty acid methyl ester (FAME) procedure (Rodriguez-Saona et al., 1995). Esterification was achieved by adding methanolic-sulfuric acid (10 mL, 4% sulfuric acid) to the oil sample (~0.05 g, recorded) plus benzene (1 mL) in a glass test tube with a Teflon screw top cap. The mixture was heated to 80-90 C for 120 minutes. Methyl esters were extracted using a

45 partition of hexane and distilled water. An aliquot of the hexane portion (1 mL) was collected in a 1.5 mL GC vial with crimp top and was evaporated under nitrogen. The dried samples were re-diluted using iso-octane (0.5 mL). Samples were analyzed, in duplicate, on an HP-6890 GC equipped with a flame ionization detector (FID) and HP

G1513A autosampler. Separation of the components was done using an HP-88 60 m x

0.25 mm x 0.2 um column (Agilent part number 112-8867) using helium as the carrier gas. The injection volume was 0.2 μL in the splitless mode. The oven conditions were

125 C to 145 C (8 C/min) hold for 26 minutes, 145 C to 220 C (2 C/min) hold for 1 minute. The injector temperature was 125 C and the detector temperature was 250 C.

The identification of fatty acids was carried out by comparing the retention times with

EPA and DHA reference standards (NuChek Prep U-99-A and U-84-A). An internal standard (Heptadecanoic acid, NuChek Prep N-17-A) was added at 0.005g per methylation and the ratio of omega-3 peak area to internal standard (IS) peak area was used for quantification via standard curve.

3.3.3 Fourier Transform Mid-Infrared Spectroscopy

The collection of infrared spectral data was done on an FT-IR portable spectrometer

(Cary 630, Agilent, Santa Clara, CA, USA), equipped with a temperature controlled, 5- bounce ZnSe crystal ATR. Sealed sample containers of oil taken from the refrigerator were allowed to equilibrate to room temperature prior to data collection and measured in duplicate. A standard 80 μL oil aliquot was deposited on the ATR, which was heated to

65 C, and spectra were collected over a range of 4,000-700 cm-1 at 4 cm-1 resolution.

46

Spectra were collected in terms of absorbance and viewed using MicroLab software

(Agilent, Santa Clara, CA, USA).

3.3.4 Multivariate Analysis

Soft Independent Modeling of Class Algorithm (SIMCA) was used to analyze supplements from all sources. The collective spectra from each supplement bottle were given unique class identities. Identical SNV and 2nd derivative (Savitzky-Golay second order polynomial filter with a 35-point window) transforms were applied to all samples

(Savitzky & Golay, 1964). Classes were considered significantly different if separated by an interclass distance >3 (Brereton, 1992). Partial Least Squares Regression (PLSR) analysis was used to correlate GC-FAME fatty acid composition information with the spectra. PLSR models were validated using a cross-validation, leave-one-out approach, and the goodness of fit was evaluated by means of standard error of calibration (SEC), standard error of cross-validation (SECV), coefficient of correlation (Rcv) and relative percent difference (RPD), which is the ratio between the standard deviation of the reference data and SECV. Chemometric analysis was completed using Pirouette 4.0 rev.

2 (Infometrix Inc., Bothell, WA).

47

Table 3. Label information from supplements selected for analysis.

Oil Sample Name Code Supplement Facts Other Ingredients Location Source Vitamin E (as d-alpha tocopherol 1 IU) Gelatin (softgel), Nature's Way pollock, Fish Oil 500 mg, Omega-3 fatty acids: Glycerin, Purified water, Aqueous coating (Modified Fisol 50% whiting, Alaska K 250mg EPA 150 mg DHA 100 mg Cellulose, Fractionated coconut oil (non-hydrogenated), cod EPA/DHA EXP Sodium Alginate, Stearic acid, Soybean oil MusclePharm Fish Fish oil: 70% omega-3 fatty acids 1000mg, Gelatin, Water, Glycerol, Methacrylic acid (copolymer MF - - Oil EPA 400mg, DHA 300mg dispersion), Triacetin, Natural citrus flavor

NOW FOODS Natural fish oil concentrate: 2000 mg Omega sardines, Softgel Capsule (gelatin, glycerin,water) and Vitamin E Molecularly 3 fatty acids, 680 mg, EPA 360 mg, DHA anchovies, Peru NF (as d-alpha tocopherol) Vitamin E from soy Distilled Omega-3 240 mg, Other omega 3 fatty acids 80mg mackerel

48 Optimum Gelatin, glycerin, Food Glaze, Ethylcellulose, Enteric sardines, Fish oil: 1000mg Total omega 3 fatty acids, Nutrition Enteric coating (Sodium Alginate, Steric Acid), Mixed anchovies, - ON 300mg EPA/DHA Coated Fish Oil Tocopherols, Vanillin mackerel DEVA Organic Alpha-linolenic Acid 535 mg, Linoleic Acid Vegan Capsule (Cellulose), Silica, Mixed Tocopherols, Vegan Flaxseed - - V 120mg, Oleic Acid 120mg Citric acid Oil Flaxseed oil with Lignans 1300mg Omega-3 Member's Mark (Alpha Linolenic Acid) 845mg Omega-6 Gelatin (Non Bovine), Glycerin, Purified Water, MM - - Flaxseed Oil (linoleic Acid) 117mg Omega-9 (Oleic Acid) Caramel Color 117mg Welby Health anchovies, Fish Oil: 1200 mg, Total Omega-3 fatty Gelatin, Glycerin, Mixed Tocopherols, and Purified Fish Oil mackerel, - W acids: 360 mg Water Concentrate sardines (Continued)

48

Table 3. Continued.

Sample Name Code Supplement Facts Other Ingredients Oil Source Location Gelatin, Water, Glycerin, Tocopherol MAY ALSO CONTAIN: Methacrylic Acid Copolymer, Triethyl Nature Made Fish Fish oil Concentrate: 2400 mg, Total anchovies, Citrate, Polysorbate 80, Propylene Glycol, Oil 1200 mg 360 Omega-3 Fatty acids: 720mg, EPA 360 mg, sardines, Peru NM Ethylcellulose, Ammonium Hydroxide, Medium DHA 240 mg, Other Omega-3 120 mg. mackerel OMEGA-3 Chain Triglycerides, Oleic Acid, Sodium Alginate, Stearic Acid, Talc. Carlson Super Omega-3 Gems - Total Omega-3 Fatty acids: 600 mg, EPA CS Softgel Shell: Beef gelatin, glycerin, water - - Fish Oil 300 mg, DHA 200 mg Concentrate Carlson Elite Omega-3 Gems 49 Omega-3 Fatty acids: 800 mg, EPA 400 Natural Lemon Flavor. Softgel Shell: Beef gelatin, Fish Oil CE - - mg, DHA 300 mg, Other Omega-3s 100 mg glycerin, water Professional Strength Vitacost Norwegian Cod Liver Oil 2200 mg, DHA Norwegian Norwegian Cod Gelatin, glycerin, and purified water - VC 250 mg, EPA 170 mg Cod Liver Liver Oil

Champion Nutrition Marine Lipid Concentrate 2000 mg, Total sardines, Wellness Nutrition CN Omega-3 FAs 600 mg, EPA 360 mg, DHA Orange Oil (10 mg) mackerel, - Omega-3 Fish Oil 240 mg, anchovy Source Naturals Fish Oil Concentrate: 1250 mg, EPA 450 Arctic Pure Ultra Gelatin, Glycerin, Purified Water, and natural mg, DHA 270 mg, Other Omega-3 FAs 120 - - S tocopherols Potency Omega-3 mg Fish Oil anchovy, Natrol Extreme Fish Oil 2400 mg, EPA 646 mg, DHA 430 Gelatin, Glycerin, Water, Mixed Natural Tocopherols, cod, N - Omega mg Natural Lemon Oil mackerel, sardine (Continued) 49

Sample Name Code Supplement Facts Other Ingredients Oil Source Location MRM Total Omega-3 Fatty acids: 175 mg, EPA 50 FreshOMEGA mg, DHA 60 mg, DPA 20 mg, Other omeGo, Fish Gelatin, Purified Water, Vegetable Glycerin, Norwegian Pure Extra Virgin Omega-3s 45 mg, Total ω-5 0.5 mg, Total ω- Midsund, F Rosemary Extract, Astaxanthin (6 mcg) salmon Norwegian 6 120 mg, Total ω-7 70 mg, Total ω-9 300 Norway Salmon Oil mg Twinlab Total Omega-3 Fatty Acids 970 mg, EPA Norwegian Norwegian Cod - - T 554 mg, DHA 369 mg cod Liver Oil Nordic Naturals Total Omega-3 Fatty Acids 1725 mg, EPA Purified deep sea fish oils (from anchovies and anchovies, Omega-3 Purified 825 mg, DHA 550 mg, Other Omega-3s 350 sardines), natural lemon flavor, d-alpha tocopherol, - U sardines Fish Oil mg rosemary extract Spectrum Chemicals Cod CLO N/A - cod liver -

50 Liver Oil U.S.P. Natural Fish oil concentrate: 1000 mg, sardines, NOW FOODS NU Omega-3 Fatty Acids 750 mg, EPA 500 mg, Natural Vitamin E (as d-alpha tocopherol) anchovies, Peru Ultra Omega-3 DHA 250 mg mackerel NOW FOODS sardines, Natural Fish oil concentrate: 1000 mg, EPA Omega-3 Mini Natural Vitamin E (as d-alpha tocopherol) anchovies, Peru MI 360 mg, DHA 240 mg Gels mackerel Fish Oil, Gelatin, Natural Flavors, Vegetable Solgar Minnows Fish Oil Concentrate 1120, Total Omega-3 Glycerin, Modified Starch, Soy Lecithin, Stevia anchovy, Omega-3 Mini Fatty Acids (as ethyl esters) 700 mg, EPA Canada BU Extract, Mixed Tocopherols, Rosemary Extract, sardine, tuna 364 mg, DHA 280 mg Bursts Ascorbyl Palmitate Barlean's Fresh Cod liver oil, natural citrus flavoring, rosemary extract, ascorbyl palmitate, ascorbic acid, soy lecithin, Catch Cod Liver Total Fat 2.0 g, EPA 170 mg, DHA 240 mg cod liver - BA sunflower lecithin, d-alpha tocopherol, and vitamin D Oil (as cholecalciferol)

50

3.4 Results and Discussion

3.4.1 Characterization of Fish Oils

Analysis of fatty acid composition by gas chromatography revealed a wide range of EPA

(6-60%) and DHA (3-40%) concentrations (Table 2). These values are comparable to the label claims, with most samples having more EPA/DHA than claimed. Studies on whether commercially available fish oil products meet their label claims have produced mixed results (Fantoni, Cuccio, & Barrera-Arellano, 1996; Fierens & Corthout, 2007;

Kolanowski, 2010; Opperman, Marais de, & Spinnler Benade, 2011; Ritter, Budge, &

Jovica, 2013; Tatarczyk, et al., 2007). For the fish body oils, samples clustered in two ranges; samples containing 65-93% total EPA/DHA and samples containing 31-38% total

EPA/DHA. Natural, non-concentrated fish oils contains approximately 18% EPA and

12% DHA, although the levels are highly dependent on factors previously noted

(Schuchardt & Hahn, 2013). Oils with higher concentrations will have undergone a range of processing, most commonly trans-esterification and molecular distillation. One fish oil sample, “F”, had the lowest total EPA/DHA of any marine oil in this study. This sample purports to be minimally processed and not molecularly distilled, which may explain the distinction from the rest of the oils. Products from different lots were subject to variation in actual EPA/DHA content. Highly concentrated samples tended to have more lot-to-lot variation than less concentrated oils.

51

Table 4. Fatty acid composition for the oil samples, including estimated contents based on label information.

GC Determination Label

Sample EPA DHA EPA DHA SD SD Code (g/ 100g oil) (g/ 100g oil) (g/ 100g oil) (g/ 100g oil) K1 41.8 3.5 25.2 3.3 30 20 K2 58.6 3.1 35.2 2.8 MF1 43 0.7 34.7 1.7 40 30 MF2 48.7 2.1 37.9 3 S 47.6 0.7 34.7 0 36 21.6

FishA N 43.5 1.1 28.3 0.4 26.9 17.9 CE 42.1 4.8 30.9 3.7 32 24 CS 41.1 3.4 26.8 2.7 30 20 NF 23.9 1.2 13.5 1.5 18 12

NU 59.3 2.2 31.6 1.9 50 25

MI 38.8 0.3 26.5 0.4 36 24

BU 41.7 0.2 31.4 0.9 32.5 25

W1 24.3 3.6 7.4 1.9 30* W2 26.2 1.4 7.5 0.5 ON1 22 1.8 10.4 1.5 30* ON2 21.2 0.4 11.3 0.7 NM1 22.9 1.6 14.5 4.1 FishB 15 10 NM2 21.7 0 10.3 0.2 CN 21.3 0.2 10.6 0.2 18 12 U 21.7 1.5 11.2 1.2 16.5 11 Raw F 6.7 0.1 3.8 0.2 6 5 T 14.5 1 10.1 1 12.3 8.2 VC 13.1 0.3 11.7 0.8 7.7 11.4

CLO CLO 13.5 0.2 14.6 0.4 - BA 11.5 0.3 11.9 0.3 8.5 12 *Value reflects total Omega-3 contents.

Spectral comparison of four samples, chosen to represent low concentration fish oil, high concentration fish oil, CLO, and flaxseed oil, revealed strong similarities between spectra

(Figure 9). Differences are evident in the position of the 3006-3012 cm-1 =C-H band and 52 the 1720-1746 cm-1 C=O carbonyl band (Guillen & Cabo, 1997a; Guillen, Carton,

Goicoechea, & Uriarte, 2008). The fingerprint region (800-1500 cm-1) showed a large amount of variation, especially in the High EPA/DHA Fish sample.

Figure 9. Spectral comparison of the 4 major supplement groups in this study.

3.4.2 Classification of Oil Supplements

SIMCA classification showed good separation between oil supplements, and revealed the association of these classes into 5 major clusters (Fish A, Fish B, CLO, Flax, and Raw,

Figure 10). Further examination revealed that these clusters were highly correlated with oil source, and in some cases processing characteristics. The fish body oils associated in one of two clusters, separated down PC1. The Fish A group contained the highly concentrated fish oils; those with EPA and DHA contents higher than 30% and 25%, respectively. The one sample associated with this group, but clustered farthest away (NF, 53 in pink), has a lower level of DHA (~14%) than the rest of the Fish A group. The Fish B group is clustered much more tightly than the Fish A group, and contains fish oils with

EPA and DHA contents of 20-27% and 7-15%, respectively. These levels are consistent with EPA and DHA concentrations found in fish body oils (Schuchardt & Hahn, 2013).

Flaxseed samples clustered the farthest away from the fish groups, and showed high amounts of variability between brands. The presence of lignans, which may or may not be removed during processing, may be responsible for the distance between clusters in this case (Bayrak, Kiralan, Ipek, Arslan, Cosge, & Khawar, 2010). CLO clustered between the Fish B group and the Raw sample, which clustered more closely to the

Flaxseed group than to either fish oil group. The slightly higher ratio of DHA to EPA found in CLO versus fish body oils may play a role in differentiating these groups

(Moffat & McGill, 1993). The five CLO samples have similar EPA and DHA contents, as reflected in their tight SIMCA clustering (Figure 10). The nature of their processing and the fact that CLO concentrates are not readily available may explain the low variability within this group (Ritter, Budge, & Jovica, 2013). The interclass distances

(ICD) between samples from different major clusters (Fish A, B, CLO, Raw, and Flax) were all significant (>3, from 80-910), whereas ICDs between samples within classes ranged from 2 to 20 for all classes except Fish A and Flax. Fish A and flax both displayed

ICDs of between 5 and 120, reflecting the high amount of variance between samples within these classes.

54

Figure 10. SIMCA classification plot from 900-1300 cm-1 region, showing classes (left). Five major clusters and the EPA content trend (red arrows) are evident on the right.

The region responsible for separation in the SIMCA model, 900-1300 cm-1, is associated with C=C and C-O stretching (Guillen & Cabo, 1997a). The band primarily responsible for discrimination, 1038 cm-1, has been found to correlate with the C-O stretching in esters (Figure 11). The axis responsible for separation of the Fish A group from the rest of the samples is PC1, which also the principal influence when determining the discriminating power. To obtain the high levels of EPA/DHA found in this group, many fish oil supplements undergo esterification, fractionation and concentration of omega-3 fatty alkyl esters. As a result, this separation may be due to the presence of these ethyl esters in the supplements, either intentionally as a cheap way to increase levels of

EPA/DHA, or as a result of incomplete re-esterification after molecular distillation

(Ritter, Budge, & Jovica, 2013). Only one supplement in this study claims to contain

55 ethyl esters (BU, in group Fish A); with most others claiming “Fish Oil” or “Fish Oil

Concentrate”.

Figure 11. SIMCA discriminating power plot displaying regions responsible for separation of supplements in Figure 10.

As shown in Figure 12, examination of raw spectra reveals a distinct carbonyl band shift

(from 1745 cm-1 to 1737 cm-1) in the Fish A group. The band at 1745 cm-1 is associated with the triglyceride ester, whereas the shifted band is seen with ethyl esters (Guillen &

Cabo, 1997a). This may reflect the delivery of these supplements as primarily fatty acid ethyl esters instead of triglycerides. Alkyl esters of omega-3 fatty acids are more prone to oxidation than their triglyceride counterparts, and some evidence suggests that the bioavailability of alkyl esters is lower than that of intact triglycerides (Schuchardt &

Hahn, 2013). The lack of distinction between products containing triglycerides and fatty

56 acid alkyl esters reveals a weakness of the Dietary Supplements Health and Education

Act. Consumers that wish to choose one form of supplement over the other have very little reliable information on which to base their decision.

Figure 12. Spectral comparison of the 1700-1800 cm-1 regions of Fish groups A & B, showing the shift in the ester peak.

3.4.3 Partial Least Squares Regression Analysis

PLSR models for prediction of EPA and DHA contents were constructed using the majority of the usable IR spectra for oils; 3200-2800 and 1800-800 cm-1 (Guillen &

Cabo, 1997b; Guillen, Carton, Goicoechea, & Uriarte, 2008). The excluded regions were major sources of background noise, associated primarily with wideband absorption of

H2O and CO2 functional groups. Good correlation was found between the spectra and both DHA and EPA (Figure 13).

57

Figure 13. PLS Regression models of EPA and DHA content. Reference EPA/DHA data from GC data in Table 4.

With Rcv of ≥0.99 and SECV of 1.74% and 1.89%, for EPA and DHA respectively,

PLSR fatty acid determination has a very high goodness of fit (Table 5) showing that this method is capable of quantifying EPA & DHA across a broad compositional range.

Relative Percent Difference (RPD) is defined as the ratio of the standard deviation (SD) of the reference information to the standard error of cross validation (SECV), which is used as an indicator of predictive ability of the models. Results can be broken down into the following ranges: 0 to 2.3 indicates very poor model or predictions and using this model is not recommended; RPD value between 2.4 and 3 shows that models can only distinguish between high and low values; a value between 3.1 and 4.9 can be good only for screening applications. RPD values between 5.0 and 6.4 is acceptable for quality control applications; RPD value between 6.5 and 8 is considered very good and at RPD values above 8.0, the prediction is classified as excellent for any application (Williams &

Norris, 1987). As shown in Table 5, the EPA model is classified as excellent and DHA

58 model is considered very good, with enough predictive ability to use for process control applications.

Table 5. PLSR model statistical analysis for determining fatty acid composition in fish oils.

Factors SECVa R b RPDc cv EPA 4 1.74 0.99 8.3 DHA 5 1.89 0.99 6.6 aStandard Error of Cross Validation bCorrelation of Cross Validation Model cRelative Percent Difference of the models versus the reference data

3.5 Conclusions

This study demonstrated a fast, simple, and reliable method for characterization of omega-3 supplements and for quantification of the two primary omega-3 fatty acids present, to support and validate labeling claims. The samples analyzed covered a wide range of EPA/DHA concentrations (3-60%) and sources (fish body oils, cod liver oil, and flaxseed oil). Classification of samples was associated with DHA/EPA content, oil source, and factors associated with processing (alkyl ester or triglyceride). A discriminating factor was the carbonyl band, and regions associated with C-O and C=C bonds. The difference in the ester carbonyl peak found in comparison of Fish A & B groups is consistent with that found in fatty acid methyl and ethyl esters versus triglyceride esters. A model was developed for rapidly determining EPA & DHA with

SECVs of 1.74 and 1.89, respectively. In the range of EPA and DHA contents expected

59 for fish oils, ATR FT-MIR and chemometrics perform similarly to traditional methods, with excellent RPD values (>6.6). The portable method not only provides precise fatty acid composition information, but can be used to determine the state of these fatty acids; whether they be methyl/ethyl esters, free fatty acids, or triglycerides. We have shown the feasibility of using a portable unit that can provide in-field opportunities for testing in industry, replacing or complementing traditional techniques.

60

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