Determination of Antioxidant Content and Antioxidant Activity in Foods using Infrared Spectroscopy and Chemometrics: A Review

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1 This article was downloaded by: [Xiaonan Lu] On: 10 July 2012, At: 18:08 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: Registered office: Mortimer House, Mortimer Street, London W1T 3JH, UK Critical Reviews in Food Science and Nutrition Publication details, including instructions for authors and subscription information: Determination of Antioxidant Content and Antioxidant Activity in Foods using Infrared Spectroscopy and Chemometrics: A Review Xiaonan Lu a & Barbara A. Rasco a a School of Food Science, Washington State University, Pullman, WA, USA Accepted author version posted online: 27 Jul Version of record first published: 02 Jul 2012 To cite this article: Xiaonan Lu & Barbara A. Rasco (2012): Determination of Antioxidant Content and Antioxidant Activity in Foods using Infrared Spectroscopy and Chemometrics: A Review, Critical Reviews in Food Science and Nutrition, 52:10, To link to this article: PLEASE SCROLL DOWN FOR ARTICLE Full terms and conditions of use: This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae, and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand, or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material.

2 Critical Reviews in Food Science and Nutrition, 52: (2012) Copyright C Taylor and Francis Group, LLC ISSN: / online DOI: / Determination of Antioxidant Content and Antioxidant Activity in Foods using Infrared Spectroscopy and Chemometrics: A Review XIAONAN LU and BARBARA A. RASCO School of Food Science, Washington State University, Pullman, WA, USA Developing rapid analytical methods for bioactive components and predicting both the concentration and biological availability of nutraceutical components in foods is a topic of growing interest. Here, analysis of bioactive components and total antioxidant activity in food matrices using infrared spectroscopy coupled with chemometric predictive models is described. Infrared spectroscopy offers an alternative to wet chemistry, chromatographic determination of antioxidants, and in vitro biochemical assays for assessment of antioxidant activity. Spectroscopic methods provide a technique that can be used with biological tissues without extraction, which can often lead to degradation of the antioxidant components. Sample preparation time greatly decreases and analysis time is very short once a predictive model has been developed. Spectroscopic methods can have a high degree of precision when applied to analysis of nutraceutical compound concentration and antioxidant activity in foods. This article summarizes recent advances in vibrational spectroscopy and chemometrics and applications of these methods for antioxidant detection in foods. Keywords INTRODUCTION Antioxidants, IR spectroscopy, chemometric models, food, plant, bioactive The generation of reactive oxygen species is unavoidable during aerobic metabolic processes in human body cells and the oxidation products that are generated through these reactions can initiate cell damage which is associated with aging and cancer. Reactive oxygen species include singlet oxygen, hydroxyl radicals, hydrogen peroxide and hydrochlorous acid, and peroxynitrite (Fang et al., 2002). Reactive oxygen species can cause DNA strand breakage, induce modulation of gene expression, and oxidation of proteins and lipid (Lee et al., 2004). In the last decade, researchers have made substantial progress investigating the role of reactive oxygen species and their association within many types of age-related diseases, including stroke, cardiovascular disease, asthma, arthritis, retinal damage, chronic obstructive pulmonary disease, neurodegeneration, diabetes, and dermatitis (La Vecchia et al., 1998). The first line of defense in the body is a series of enzyme pathways involv- Address correspondence to Barbara A. Rasco, P.O. Box , School of Food Science, Washington State University, Pullman, WA , USA. rasco@wsu.edu ing catalase, glutathione peroxidase, and superoxide dismutase (Min and Boff, 2002). Recent literature supports the notion that antioxidant components in foods, supplied from the daily diet, can quench reactive oxygen species, aid in the functional performance of enzyme systems for self-defense mechanisms within cells and thereby reduce the risk of many human diseases (Choe and Min, 2009). Epidemiological studies have also reported the relevance of antioxidative functional foods, and nutritional supplements or nutraceutical compounds to maintain or improve health and reduce the risk or impact of chronic diseases (Kaur and Kapoor et al., 2001; Stanner et al., 2004). Numerous works over the past 20 years have studied the antioxidant capacity of various food components. Many analytical methods have been developed to determine concentrations of nutraceutical components as well as the antioxidant activity for these components within food matrices (Dillard and German, 2000). For nutraceutical compounds, high performance liquid chromatography (HPLC) is the primary method for separation, identification, and quantification (Hurst, 2002). Most recently, mass spectrometry has been incorporated as a detection method coupled with liquid chromatography to satisfy the requirement of many investigators for analytical methods with lower 853

3 854 X. LU AND B.A. RASCO detection limits ( parts per billion levels). For determination of antioxidant activity, in vitro chemical or cell culture assays are commonly utilized (Huang et al., 2005; Wolfe et al., 2008); animal bioassays are also becoming more common as a means to more accurately determining the effect of these compounds on organ function and growth (Sivalokanathan et al., 2006) as well as clinical trials. However, scientists are striving to find alternative simpler and potentially more rapid techniques that could complement or replace traditional assays particularly for screening food items for quality assurance applications. Infrared spectrometry is one technique that has garnered interest and has advantageous features of a rapid analysis time, high precision and simple sample preparation methods, involving use of few reagents or steps (Lin et al., 2004). These features make infrared spectrometry an excellent tool for screening foods for antioxidant activity and potentially for quantifying specific compounds within a complex matrix. Infrared spectroscopy is commonly used for routine analysis in food industry because of the ability to process large sample volumes in a short time period (Rubio- Diaz et al., 2010). This review provides an overview of the basic knowledge and principles for both mid-infrared and near-infrared spectroscopy. Important factors for current analytical protocols of infrared spectroscopy such as the use of an attenuated total reflectance (ATR) cell for testing of biological materials such as food and food ingredients and for the data analysis currently utilized for Fourier transformation and development of chemometric based prediction models. Important factors associated with sample preparation and data collection will be introduced including handling biological samples and data preprocessing (i.e., binning and smoothing) as applied to raw spectra will be described. A presentation of important chemometric models such as partial least squares (PLS) regression models will be discussed in relevant detail including the theory, steps to establish the model, the factors affected model rigor, key parameters for model establishment (i.e., determination of the number of latent variables in a prediction model), and identification of analytes will be presented. Furthermore, what chemometric methods are and how these can be developed and validated to estimate the concentrations of nutraceuticals and antioxidant activity accurately are also introduced. The application of infrared spectrometry in this paper is divided into two parts for identification and prediction of the concentration of bioactive components in food and nutraceuticals, antioxidants, and phytochemicals in food components common in the daily diet will be presented with the current literatures on analysis by IR summarized. For antioxidant activity determination, several selective food matrices will be chosen including berries, tea, and red wine and provided as a basis for discussion of how IR methods can be used for other foods. Finally, suggestions for future research will be presented. GENERAL PRINCIPLES OF INFRA-RED SPECTROSCOPY The infrared region is defined as the region of the electromagnetic spectrum between the visible light region and microwave region and can be divided into three sub-regions based on different wavenumber areas, namely the far-, mid-, and near-infrared region (Workman, 1999). The mid-infrared region (from 4000 to 400 cm 1 ) provides more useful vibrational information about the functional groups of molecules and thus it is used frequently for research in biomedical science (Jackson et al., 1997; Naumann, 2001), measurement of components in biological tissues (Movasaghi et al., 2008), detection of atmosphere pollutants (Stoch et al., 2001), elucidation of protein and enzyme secondary structures (Raussens et al., 1997), medical diagnostics (Petrich, 2001), and analysis of food components (Griffiths and Pariente (1988); Proctor et al., 2004; Hashimoto and Kameoka, 2008; Lucas et al., 2008; Lin et al., 2009; Subramanian et al., 2009; Koca et al., 2010). In this spectral region, organic molecules vibrate or rotate at specific frequencies corresponding to discrete energy levels. To be infrared active, the molecules must have changes in the permanent dipole (Nicolaou and Goodacre, 2008). For example, the homonuclear diatomic molecules, such as hydrogen, nitrogen, and oxygen have zero dipole moment; thus, no IR absorption is observed. However, heteronuclear diatomic molecules, such as hydrogen chloride and carbon monoxide possess a permanent dipole moment, so they can absorb IR energy. However, the carbon dioxide is inactive in IR because the vibration produces no change in the dipole moment. Figure 1 illustrates how infrared active functional groups present in IR spectra can provide a great deal of analytical information about a food component; information that would otherwise involve complicated chromatographic analysis and could be difficult to obtain. Here an IR spectrum of a control soybean oil is compared to that of a photo-isomerized oil containing 10% total conjugated linoleic acid indicating changes in CH stretch, carboxylate moieties and relative amounts of transtrans and trans-cis isomers that can occur during isomerization (Fig. 1). Experiments could be conducted to monitor both the extent and rate of oxidation, and oxidation products could be quantified, presuming analytical standards are available. Most importantly, this figure illustrates how IR can be used to monitor loss of antioxidants in a food system, even when the exact components responsible for antioxidant activity have not been fully ascertained. Principles of Fourier Transform Infrared Spectroscopy The development of applications of infrared spectroscopy to food chemistry and appropriate data analysis require an understanding of the theory and principles involved with spectroscopy. In the infrared region, the IR spectrum is composed of innumerable infinite narrow bands of monochromatic light. In common instrument configurations beam splitters are used. When monochromatic light arrives at beam splitter (no light absorption), 50% light is reflected to a fixed mirror back to the beam splitter; the other half passes through a beam splitter, arrives at a moving mirror, then reflects back and recombines with the former beam (Fig. 2) (Chalmers and Griffiths, 2002). The different paths of these two beams are referred to as the optical path difference (OPD). A detector is used to record the

4 INFRARED SPECTROSOCOPY AND CHEMOMETRICS 855 Figure 1 ATR-FTIR spectra of the cm 1 region of (A) control soybean oil and (B) photo-isomerized oil containing 10% total conjugated linoleic acid (Cited from Kadamne et al., 2009). interference or superposition of these two beams, with the resulting spectrum referred to an interferogram. The interferogram (I) is a function of OPD (Smith, 1996): I(δ) = B(ν) cos(2πνδ) The interferogram equation presented here is for monochromatic light with the wave number of ν and provides the basis for Fourier transform spectroscopy. B(ν) stands for intensity of monochromatic light of the wave number of ν. I(δ) is the interferogram (cosine Fourier transformation of B(ν)). When a light source is continuous, or composed of numerous frequencies, the interferogram becomes more complicated. For a continuous light source, the signal intensity can be expressed as: I(δ) = + B(ν) cos(2πνδ)dν where I(δ) expresses the signal intensity at the point that OPD is δ. This signal (I(δ)) is the summation of all wavenumbers (δ) in the realm of - to +. Because the δ is changing continuously, a complete interferogram can be received. However, the equation above provides only the interferogram, and the data obtained in the interferogram cannot be interpreted directly. One more step is necessary, and a Fourier inverse transforma- Figure 2 A simple spectrometer layout (Cited from Thermo Nicolet Cooperation, 2001).

5 856 X. LU AND B.A. RASCO Figure 3 tion is used to decode the signal intensity at each individual wavenumber so as to make identification of individual spectral features possible (Griffiths, 1992). This transformation is presented here: B(ν) = + I(δ) cos(2πνδ) dδ Figure 3 illustrates the detailed process of Fourier transformation. There are many advantages of Fourier transformation including: the Fellgett advantage (or the ability to detect multiple wavenumber simultaneously), the Jacquinot advantage (higher optical throughput compared to other types of spectroscopy), and the Connes advantage (the ability to use an internal wavelength calibration). Burgula et al. (2007) and Subramanian and Rodriguez-Saona (2009) provided a detailed summary of these advantages applied in food science research. In general, the overall objective of FT-IR is to improve the signal-to-noise ratio (SNR) to increase sensitivity and provide a spectroscopic technique that is both accurate and reproducible (Mark and Griffiths, 2002; Shao et al., 2002). Coates (1999) reviewed the mathematical methods for elimination of broad background interferences from infrared spectra. Attenuated Total Reflectance (ATR) and its Role in IR Analysis FT-IR instrumental operation (Fourier transformation). Before 1993, the only infrared techniques appropriate for food science research were transmittance and reflectance spectroscopy, and these were commonly for applications in the near infra-red with dry samples. A classical sample preparation technique in the mid-ir required grinding a material to a fine powder and dispersing it into a potassium bromide (KBr) matrix and this was not usually appropriate for food materials with the exception of relatively pure, dry ingredients, and inevitably resulted in problems with reproducibility (Griffiths et al., 1986). The introduction of the Attenuated Total Reflectance (ATR) cell overcame many of the obstacles of analyzing food materials by infrared spectroscopy (Winder and Goodacre, 2004). ATR is arguably the most common detection system for widespread use for FT-IR (Milosevic, 2004). Use of this technique reduces the complexity of sample preparation and measurement of intact biological samples, minimizing tissue damage to the greatest extent possible. Sample extraction is not usually required, and for the most part, any food can be analyzed as long as enough water is removed to reduce interference of water with spectral features of lipid, protein, and carbohydrate. The ATR accessory with many FT-IR instruments is commonly made of zinc selenide crystal, but sometimes, germanium or silicon crystals may be used (Milosevic and Berets, 2002). The principle underlying the operation of an ATR is as follows. When a beam of light is launched with an incident angle of α 1 from the inside to the surface of the crystal, the incident angle (α 1 )issmaller than the refraction angle (γ 1 ) because the refractive index of the crystal is larger than that of air (which is arbitrarily designated as one). Along with the increase of α 1, γ 1 will also increase proportionately. When α 1 increases to the critical angle, α 2, the refraction angle is equal to 90 degrees, and at this point the refracted light will be transmitted through the surface

6 INFRARED SPECTROSOCOPY AND CHEMOMETRICS 857 Figure 4 γ 1 (1) Reflectance & Refraction (a1) γ 2 α 1 α 2 (2) Critical angle (a2) α 3 (3) Total reflectance (a3) Graphic representation of total reflectance. of the crystal. When the incident angle α 3 is greater than α 2,the radiation will be totally reflected (Fig. 4). Figure 5 shows light paths in an ATR crystal. The material to be measured is placed on top of the crystal cell. When the incident angle is greater than the critical angle, total reflectance will occur with no infrared light penetrating through the crystal surface and entering the test sample. However, if there is no infrared light passing through the surface of the crystal, how does the infrared light interact with the sample? When infrared light is reflected, it produces standing waves near the surface of the crystal called an evanescent wave (Herminghaus et al., 1994). When the sample touches with the outer surface of the crystal, the evanescent wave will protrude only a few microns (0.5 µ -5µ) beyond the crystal surface and penetrate into the sample at every reflectance point (Fig. 5). The absorbance can crystal cell Figure 5 wave. evanescent wave sample Horizontal ATR accessory with representation of an evanescent be collected by an attenuation of the energy of the evanescent wave. In regions of the infrared spectrum in which the sample absorbs energy, the evanescent wave will be attenuated. The vibration amplitude (intensity of evanescent wave) will decay exponentially from the surface of the crystal and finally disappear (Oberg and Fink, 1998). When the vibrational amplitude of the evanescent wave decays to 1/e of the original incident one, the distance is referred to as the penetration depth. The penetration depth depends upon the wavelength of the incident light, the refraction properties of the crystal and of the sample, and the incident angle between the impinging light and the surface of the crystal. One of the problems we face using ATR in the mid-ir region for foods analysis is the fact that there is one order of magnitude difference between the penetration depth into the sample of the radiation at higher frequencies (lower wavenumbers) than at lower frequencies (higher wavenumbers) over this spectral range. This results in the intensity of the absorbance peak at low frequencies being much higher than at the higher frequencies (Hebert et al., 2004). Therefore, a correction in ATR spectra is needed and can be conducted either before or after spectra collection (Coates, 1998) to compensate for this effect. In some cases, a liquid sample ( 20 µl) can be applied directly onto the surface of an ATR cell (Patz et al., 2004; Versari et al., 2010). However, extensive data processing work needs to be done comparing spectra from liquid samples with those of solid samples to ensure that measurement artifacts are not being incorporated into chemometric models. In such cases, an experimental determination needs to be made whether it is more efficient to increase sample preparation time to dehydrate samples or to develop a more involved computational model to compensate for water absorbance. Theory of Near Infrared (NIR) Spectrometry NIR spectroscopy utilizes the spectral range from 780 to 2500 nm (12,500 4,000 cm 1 ) and provides complex structural information related to the vibration behavior of combinations of bonds (Polesello and Giangiacomo, 1983). The NIR region of the electromagnetic spectrum involves the response of the molecule bonds O-H, C-H, and N-H. These bonds are subject to vibrational energy changes at NIR frequencies, and two vibration patterns exist for these bonds, a stretching and a bending vibration. The energy absorption of these molecular vibrations is reflected in an absorption spectrum (Bouveresse and Massart, 1996). In NIR spectroscopy, reflected or transmitted radiation is measured. The spectral characteristics of any particular material is affected by wavelength dependent scattering and absorption processes and is dependent upon the chemical composition of the product, as well as on its light scattering properties related to the microstructure, particularly for biological materials (Lin et al., 2008). The NIR region is divided into short-wave NIR (SW-NIR) and common NIR at the wavelength of 1300 nm. The SW-NIR

7 858 X. LU AND B.A. RASCO region is defined as the absorption band of high overtones, while NIR involves the first or second overtones (Huang et al., 2003). The absorption intensity will decrease at higher overtones; therefore, SW-NIR tends to be less sensitive and is commonly used in the transmittance mode, although reflectance is also common. SW-NIR has a longer path length making it a useful analytical method for bulk properties of materials such as moisture, fat, and protein in agricultural products (Lin et al., 2004) and for predicting the concentration of macronutrient components. NIR is less useful for analysis of micronutrients or for any component present at levels of approximately 0.5% or less. The sensitivity of NIR analyses is approximately 0.1% (Cen and He, 2007). However, the technique can be useful for predicting the stability of micronutrient components, for example, susceptibility of a material to lipid oxidation, oxidative browning, or enzymatic deterioration when these factors can be tied to concentrations of moisture or lipid. There are a number of recent publications summarizing current NIR techniques and applications to food quality and safety (Scotter, 1997; Jha and Matsuoka, 2000; Ellis and Goodacre, 2001; Cen and He, 2007; Nicolai et al., 2007; Jimare Benito et al., 2008; Sun, 2009), and pharmaceutical analyses and quality control (Aldrich and Smith, 1999; Reich, 2005; Roggo et al., 2007; Gowen et al., 2008). Because of the widespread use of NIR in industry, and with the analysis of bulk commodities which can be important sources of or carriers for nutraceutical components such as food oils, flour, and soy meal or protein, new applications will likely be developed for prediction of biologically active components and their stability. Conventional NIR is commonly used in the diffuse reflection mode. Hydrogen bonding features can be observed in this region of the spectra. A schematic of an NIR spectrometer is shown in Fig. 6, including light sources, beam splitter, system, reflector, sample chamber, diffuse reflection detector, and transmission Figure 6 Sketch of an NIR spectrometer. 1- light source, 2-beam splitter system, 3-reflector, 4-sample chamber/detector inlet valve, 5-diffuse reflection detector, 6-transmission detector, 7-control and data processing analyzed system, 8-printer (Cited from Cen and He, 2007). detector illustrating how the configuration of this spectrometer differs from one in the mid-ir range. SAMPLE PREPARATION AND SPECTRA COLLECTION For the FT-IR, sample preparation is relatively simple and no derivatization is necessary. The preparation steps may include extraction of specific bioactive compound from a matrix by filtration, mild dehydration of samples, and application of the analyte to a fiber or filter, such as an aluminum oxide membrane filter (Lin et al., 2005). These sample preparation steps improve the intensity of the signal, improves the signal-to-noise ratio (De Nardo et al., 2009), and decreases the incidence of overlapping peaks at the same wavenumber regions from compounds with chemically similar structures (i.e., different fatty acids or steroid hormones can have overlapping spectral features at around the wavenumber of 3000 cm 1 ). A mild dehydration step decreases the impact of spectral features of free water (Lu et al., 2010) exhibiting a large peak around 3300 cm 1 (O-H stretching of water) and around 1700 cm 1 (O-H anti-symmetric stretching of water). The water spectra can mask the most important peaks derived from other bioactive compounds in food matrices (Lu and Rasco, 2010). For the dehydrated sample, multiple spectra need to be obtained, and an average taken, with this value used for further PLS model establishment (described in a later section). The selection of the matrix for mounting the dehydrated sample for IR detection is a concern, with the most common being aluminum oxide membrane filter (Lin et al., 2007) which provides the least amount of interference, hydrophobic grid membrane filter (Mannig et al., 2008; Grasso et al., 2009), glass fiber filter disks (De Nardo et al., 2009), and cellulose ester membrane filter (Burgula et al., 2006). Glass slides can also be used when there is no need to concentrate the analytes in the sample by filtration prior to analysis (Lu et al., 2010). Generally for NIR measurements, no sample preparation is needed except to ensure that the particle size for dry samples is consistent from one sample to the next so that light scattering remains consistent (Cen and He, 2007). If samples need to be ground, care must be taken not to affect the moisture content during sample preparation and storage procedures (Huang et al., 2003). Because of the simplicity of sample preparation NIR is widely deployed in field studies and industry facilities without strict environmental control (Bouveresse and Massart, 1996). There are a number of rugged instruments available for field use with different detector configurations and sample chambers available for NIR spectrometers, for example, glass or quartz cuvettes of different size for liquid samples monitored in transmittance, and measurement cells for solids of different sizes and configurations for measurements in diffuse reflectance. Use of FT-IR in the field is possible, but remains a challenge since most instruments are not designed to operate in environments with vibration, dust, or high moisture levels.

8 INFRARED SPECTROSOCOPY AND CHEMOMETRICS 859 Table 1 spectra Frequency (cm 1 ) Frequency of band assignments for biochemical features FT-IR Assignment 3600 O-H stretch of hydroxyl groups 3150 N-H stretch 2960 C-H asymmetric stretch of -CH C-H asymmetric stretch of >CH C-H symmetric stretch of -CH C-H symmetric stretch of >CH >C = O stretch of esters 1650 Amide I of α-helix 1540 Amide II of β-sheet 1470 C-H deformation of >CH C = O symmetric stretch of COO Amide III of proteins 1240 P = O asymmetric stretch of >PO C-O, C-C stretch, C-O-H, C-O-C deformation 1080 P = O symmetric stretch of >PO 2 References: Naumann, 2001; Maquelin et al., 2002; Lu and Rasco, 2010; Movasaghi et al., 2008 BAND ASSIGNMENTS There are numerous publications summarizing band assignments in the mid- and near-infrared region. Mid-infrared band assignment is more important due to extensive available information of molecular structure. Movasaghi et al. (2008) reviewed numerous studies providing a database on the most important mid-infrared characteristic peak frequencies for natural tissues analysis in the mid-ir region. Other recent publications provide compilations on important frequencies for food analysis (Naumann, 2001; Maquelin et al., 2002; Burgula et al., 2007; Lu and Rasco, 2010). Table 1 provides a short list of band assignment for the most important IR absorbance features for biological sample matrices. It is critical to remember that IR absorbance bands can only reflect information of molecular functional groups and do not provide an identification of a specific chemical compound. Therefore, it is important to be knowledgeable of the contents of the infrared library and how this information can assist an analyst to determine sample composition. The first priority is to determine and then justify band assignments in the targeted compounds, and secondly to understand how matrix composition could affect the position of absorbance bands and the subsequent interpretation of IR absorbance spectra so that spectral data are correctly interpreted. DATA PREPROCESSING Data preprocessing algorithms are useful tools to enhance the spectral differences between samples and the use of one or more of these algorithms is often a prerequisite for multivariate data analysis routines (Javis and Goodacre, 2005). It is important to understand what each of these preprocessing techniques do, and not to overuse them to compensate for collection of poor quality spectra. The processes of automatic baseline correction and normalizing the raw spectra make comparison of spectra features much easier. However, these techniques must be used properly and conducting automated baseline correction routines can do more harm than good for quantitative analysis if there is no understanding on the part of the analyst why these corrections would be appropriate (Lu and Rasco, 2010). An automatic baseline correction is commonly first performed on the raw spectra to flatten baselines (Al-Qadiri et al., 2008). Secondly, a normalization may be performed to compensate for differences in sample thickness (Al-Holy et al., 2006; Al-Qadiri et al., 2006). These two steps for preprocessing the FT-IR raw spectra are important, because during the acquisition of ATR spectra, sample thickness can change to some extent (Al-Qadiri et al., 2006). For instance, a thicker material can exhibit higher absorption than a thinner one, resulting in greater peak heights and peak areas, and sometimes an accompanying peak shift to a higher or lower wavenumber, for example, because of the different salt concentrations in a food matrix (Huang et al., 2001). Furthermore, measurement errors during spectral collection may cause the baseline to tilt. Without correcting the baseline, comparing spectra and conducting quantitative analysis would not be possible. Other data preprocessing methods, such as binning, smoothing followed by second derivative transformation, magnify minor differences among IR spectra (Goodacre, 2003). Binning reduces the number of data points in a spectrum by n points into one point and eliminates the optical imbalance problem associated with many array based spectrophotometers (Al-Qadiri et al., 2008). Smoothing eliminates high frequency instrumental noise by averaging adjacent data points (Al-Holy et al., 2006). Second derivative transformation separates overlapping absorption bands, eliminates baseline offsets, increases the apparent spectral resolution, and provides an estimate of the number of overlapping bands within a spectral region (Al-Qadiri et al., 2008). PARTIAL LEAST SQUARES MULTIPLE LINEAR REGRESSION MODELS Partial Least Squares (PLS) model is a multivariate regression method commonly used to establish a relationship between reference values for attributes such as cell numbers or concentration of a particular analyte, and predicted values for that attribute in a test sample based upon its infrared spectral features (Geladi and Kowalski, 1986; Al-Qadiri et al., 2008). It is an efficient statistical prediction technique, especially suitable to small sample data with many correlated variables (Alsberg et al., 1998). To establish a PLS model, the first step is to choose the optimal number of latent variables (Huang et al., 2001). Loading plots in the PLS model can be developed to justify a selection of a small number of orthogonal factors (known as latent variables) for construction of a PLS mode. These loadings correlate to the principal components within a defined wavenumber region that account for the greatest difference between samples in a data

9 860 X. LU AND B.A. RASCO set. Using too many latent variables decreases the precision of the model due to data over-fitting. Too low a number of latent variables will reduce the utility of the model since not all of the relevant data has been incorporated into for its construction (Henningsson et al., 2001). During the review of the many publications in this area, we found major problems in a number of published works with overfitting data, specifically with selection of a number of latent variables too great to justify chemically, casting doubt on the validity of the chemometric models presented. Latent variables can be assigned based upon weighting of spectral changes at specific wavelengths that contribute to the chemometric model, but without a credible assignment of a chemical feature to a latent variable, a model is weak. Models with latent variable values higher than 10 are suspect unless a good understanding of the composition of the matrix and analytes within it exists and can credibly provide an explanation for the basis for the model. Too many latent variables overfit the data, so no matter how bad the collected spectra are, increasing the number of latent variable value to some extent (i.e., latent variable = 15) will always provide a good linear regression model. Specifically, if the number of latent variables are higher than 10 for biological experiments (i.e., biology, pharmacy, and food science), the PLS model is likely not reliable; a reasonable number of latent variables (vector numbers) is around to 5 to 8 (Boulesteix and Strimmer, 2006; Yang and Ren, 2008). The following factors are important if a robust calibration models is to be established: 1) having a sufficient number of spectra, 2) having spectra for samples that have analyte concentrations evenly distributed over the entire range of interest, and 3) a spectral library for verifying peak assignments are important for developing a chemometric model that will be useful for predicting concentration of an analyte from spectral features of a complex biological material, for example, the antioxidant content or total pathogen counts in a food matrix (Stahle and Wold, (1988). For a PLS correlation, reference data is needed from which correlations between reference values and the predicted values can be determined based upon spectral features (Fig. 7). The standard error of prediction (SEP) is the most commonly used parameter to calculate the predictive performance of a PLS calibration model. PLS MODEL STANDARD PROCEDURES The optimal number of PLS latent variables to use in the PLS models is obtained by the cross-validation method with the objective of selecting latent variables to obtain the smallest root mean square error of prediction values (Zhang et al., 2004; Lin et al., 2008; Liu et al., 2010). Two cross-validation methods are common: (1) leave-one-out and (2) a training model in which 75% of the total samples are selected for calibration and the remaining 25% for cross-validation. With a limited sample number, (1) is almost always the best option; otherwise (2) provides a better method for cross validation, but is more time consuming and requires a greater sample number. Statistics as- Figure 7 Cross-validated (leave-one-out) PLS plots for lycopene content in tomato juice using the direct method (A) and the lipid extraction method (B) (Cited from De Nardo et al., 2009). sociated with PLS models include a predicted residual sum of squares and a correlation coefficient (R) values. The objective is to optimize the model and improve the correlation. Following cross-validation, the model is used to predict the concentration of an analyte in a previously untested sample. The samples selected for the calibration and the validation set have to be independent; and preferably they should consist of samples from different batches of material, taken at different times (Boulesteix and Strimmer, 2006). There are three steps to establish a robust PLS model, namely calibration, cross-validation, and prediction. The R value of the cross-validation model is always lower than for the calibration model and the standard error of cross-validation (SECV) which is higher than standard error of calibration (SEC). For prediction, it is important to evaluate two parameters if the model is to provide a good result standard deviation and the coefficient of variability (Martens, 2001). There are advantages and disadvantages for PLS models. A major advantage is, after a regression model has been established and validated, analyte concentrations for a new sample can be predicted quickly, usually within 5 min, including spectral collection and data conversion. This provides for a very convenient and highly efficient analytical method in many situations, such

10 INFRARED SPECTROSOCOPY AND CHEMOMETRICS 861 as the ones faced in quality control in the food industry, where numerous samples need to be processed quickly and there is neither the time nor resources to run the conventional reference method. However, there are disadvantages to spectral analyses as well. First, infrared spectrometry is only sensitive to a certain components and over a limited range of concentrations. Mid-IR can quantify analytes in the low part per thousand range and NIR only to 0.1%. Secondly, the PLS model must be built based upon samples that contain a range of analyte concentrations. These samples are often difficult to obtain, and as the analyte concentration changes, spectral features of the matrix may also change in a manner that is not necessarily easy to predict and which may be difficult to compensate for in the predictive model. A simple example is moisture in a dried material. As the moisture content decreases, a loss in volatile constituents may occur, lipid oxidation and browning reaction products may increase in concentration, all changing the background spectra for the matrix. Although we have much experience with moisture models and can compensate for these effects from decades of experience and millions of spectra on grains, oilseed, and meat, these changes would be much more difficult to account for in models involving changes in the total content and relative distribution of antioxidant components in plant tissue as a result of storage or processing. Researchers will be able to improve the sophistication of their models as a greater understanding of how the spectral features of the matrices change during processing is obtained, but this is an important consideration, and one that researchers should not loose site of. Extrapolation beyond the concentration range for which there are reliable reference values is also a risk and one that we have observed in our examination of published work. Third, the predictive model needs to be checked routinely to account for optical shifts with spectral calibrations updated frequently to keep the model accurate. This is time consuming and laborious, and requires a continuous source of biological material with a wide range of analyte concentration; however, it is critical if chemometric models are to remain reliable. Finally and most important, during the establishment of the PLS model, operational conditions, all the parameter settings (i.e., preprocessing steps), and the measurement temperature are required to be standardized and kept constant since these factors will affect the reliability of the spectral data and the resulting rigor of the analytical model. Recently, some publications have focused on a complimentary topic after the model established. It is called transfer of multivariate models (Feudale et al., 2002). Multivariate calibration models play a key role in the analytical measurement. The reliability of measurement from both FT-IR and NIR methods depends upon the calibration model used and selection can vary with instrument type and instrument performance. The application will be promising if the same spectral data set could be used in different environments or instruments (Shenk et al., 1985). The calibration model built by NIRS can be used to analyze a large amount of samples on-line. Thus, sharing model libraries and making it possible to transfer multivariate calibration models and revalidate those on different instruments can help to improve the utility of IR technology. More details about transfer of multivariate models has been recently reviewed (Cen and He, 2007). APPLICATIONS OF IR MEASUREMENTS TO DETERMINE CONCENTRATION AND PREDICT THE BIOLOGICAL ACTIVITY OF FOOD COMPONENTS In general, these applications can be separated into the following divisions: (1) qualitative and quantitative analysis of a specific bioactive compound (2) rapid determination and prediction of a category of bioactive compounds (i.e., polyphenols) and (3) rapid determination and prediction of antioxidant activity. NIR is mainly used for (2) and (3) and MIR is extensively employed for (1) and (3). MIR, especially FT-IR, has been used to identify specific compounds and for qualitative and quantitative determinations of antioxidants in foods. However, no matter whether NIR or MIR are used for determination of bioactive compounds or nutraceuticals, an appropriate reference method needs to be available and reference assays conducted at the same time as spectral measurements if quantitation is anticipated. Infrared spectrometry is an indirect method for quantification and reference methods such as HPLC are often conducted to provide quantitative information about bioactive components. Then, a PLS model could be established and prediction of concentrations of either a specific compound or total antioxidant capacity could be achieved depending upon how the model is constructed. A number of the important bioactive nutraceutical compounds and antioxidant activity will be discussed in the following sections, focusing on comparisons of parameters and techniques for analysis of each component (Table 2). It is important to know that while all foods are functional in that they provide nutrients, foods that contain nutraceuticals allegedly contain health promoting components in addition to nutrients. In the current review, only the small molecule nutraceuticals will be discussed. The larger molecule nutraceutical compounds such as dietary fiber and bioactive peptides are not covered. Before we go into details of each antioxidant compound in various foods, we will use two examples to explain how infrared spectroscopy can be coupled with chemometrics in a step by step analysis. Because the general steps for analysis of nutraceutical components in foods by this technique are similar, a representative example will help readers unfamiliar with these methods to understand the principle and then be able to use this information to design and perform specific experiments. Example 1: Use of FT-IR with PLS to Determine and Predict the Content of Bioactive Constituents in Foods Figure 8 shows the flowchart of steps of this technique. For either calibration or cross validation PLS model, the reference value collection and the spectral feature collection need to be

11 Table 2 Summary of representative nutraceutical compounds in foods from comparative studies performed using infrared spectrometry and partial least squares regression models Reference Preparation Method Compounds Food Matrix Extraction Quantification Infrared Spectra Collection Spectral Features PLS Latent Variables Wavenumber Region PLS R Value Standard Error of Calibration Standard Error of Prediction Reference Tannin Red wine Solid phase extraction Lycopene and β-carotene Tomato juice Hexane to isolate carotenoids Catechin Green tea Methanol extraction Protein precipitation assay (BSA); phyloroglucinolysis FTIR: Phenolic extract was cast onto ZeSn crystal and dried; 32 scan; 8 cm 1 resolution HPLC FTIR: Juice solid was placed onto an AP-15 glass fiber filter disk and samples were pressed onto the ATR-IR diamond crystal using a high-pressure clamp, 128 scans, 8 cm 1 Reverse phase HPLC Vitamin C NA NA Pure compounds diluted to different conc. resolution FT-NIR spectrophotometer with integrating sphere; 32 scan; spectral range: 10, cm 1 ; dry sample powder was put on the cup of NIR NIR: 3.5 g powder sample was placed in a 50-mL aluminum weighing dish for measurement; 30 scans; nm wavelength FT-NIR: 16 cm 1 resolution; scan number: 256; spectral range: cm cm 1, ethereal C-O stretching vibration from pyran-derived ring structure of flavonoid-based tannin 957 cm 1 for lycopene and 968 cm 1 for β-carotene cm 1 contain many info. about catechin Pure compound spectrum is available and all the functional groups are identified 4 10 (based on various PLS model) 6 12 (based on selected sample extraction methods) EGC: 13 EC: 10 EGCG: 12 ECG: 14 FTIR: 11 NIR: 5 FT-NIR: 3 Several regions (based on various PLS model) 1500 to 800 cm 1 (fingerprint region) cm 1 > 0.96 Small (except EGCG) Various wavenumber regions > 0.95 Small Small Fernandez et al., 2007 > 0.95 Small Small Nardo et al., 2009 Small (except EGCG) Chen et al., 2009 > % % Yang and Irudayaraj, 2002 FTIR-ATR: ZnSe ATR sampling technique; 256 scans with 16 cm 1 resolution 862

12 α-linolenic acid and linoleic acid Total phenols, total flavonoids and total anthocyanins Cyanidinquercetin complex Edible oil Boil with hexane Blueberry EtOH/HCl/ H2O (70:1:29, v/v) GC-FID NIR: resolution: 4cm 1 ; spectroscopy region: nm; sample was dropped into the fixed liquid cell with a 1.0 mm light path holder. Total phenols: Folin- Ciocalteau method Total flavonoids: 280 nm UV-Vis Total anthocyanins: malvidin 3-glucoside at 520 nm NA NA Pure compounds diluted to different conc. FT-NIR: samples poured into glass vials and set into sample holder for spectral acquisition; 64 scans and 8cm 1 resolution FT-IR: homogenized samples positioned on ATR plate; 16 scans and 4 cm 1 resolution MIR: Pure substance mixed with KBr; cm 1 ; resolution of 2 cm 1 α-tocopherol Vegetable oils Ethanol HPLC NIR: cm 1 ; 8cm 1 resolution; 50 scans Procyanidins Cocoa Cocoa liquor preparation HPLC NIR: 400 to 2500 nm; 20 scans; liquid sample presented to NIR instrument in an open cell Resveratrol NA NA NA MIR: KBr pellet; cm 1 ; 512 scans and 1 cm 1 resolution 1726 and 1765 nm assigned to 2 C-H stretching vibration of CH2 FT-NIR: cm 1 (C-H first overtone), cm 1 (O-H first overtone), cm 1 (C-H first overtone) Cyanidin: intensive bands around 3370 and 3150 cm 1 ; Quercetin: intensive bands around 3400 and 3270 cm 1 Hydrogen bonds in copigment complexes structures were revealed 4703 cm 1 : aromatic CH stretching and ring C = C stretching; 5142 cm 1 : combination OH stretching and OH bending α-linolenic acid: 15 linoleic acid: 15 Total phenols: 6 (FT- NIR and FTIR) Total flavonoids: 4 (FT-NIR) and 3 (FTIR) Total anthocyanins: 4 (FT-NIR and FTIR) Selective wavelet regions Selective regions for each bioactive compound for FT-NIR and FTIR > Wu et al., Sinelli et al., 2008 NA NA NA NA NA Markovic JMD et al., 2005 No reported cm 1 > % 0.2% Szlyk et al., 2005 NA nm > Whitacre et al., 2003 NA NA NA NA NA NA Billes et al., 2007 (Continued on next page) 863

13 Table 2 Summary of representative nutraceutical compounds in foods from comparative studies performed using infrared spectrometry and partial least squares regression models (Continued) Reference Preparation Method Compounds Food Matrix Extraction Quantification Infrared Spectra Collection Spectral Features PLS Latent Variables Wavenumber Region PLS R Value Standard Error of Calibration Standard Error of Prediction Reference Hydroxycinnamic acids Grass Methanol Reverse phase HPLC FTIR: dried and powdered samples of methanol extracted; cm 1 ;32 scans, 4 cm 1 resolution Lutein Chinese Kale Acetone HPLC NIR: 0.5 g freeze dried powder was loaded in a small ring cup; nm; 2 nm intervals Rutin Tartary buckwheat Ethanol: water (90%, v/v) Carotenoids Apricot Methanol/ hexane 1:1 HPLC NIR: 15 g sample was packed into a small cup; cm 1 ; 64 scans HPLC-DAD FTNIR: nm; 32 scans; 2 nm resolution FTMIR: flesh powder put on the crystal cell; cm 1 ;32 scans; 4 cm 1 resolution 1585, 1074, 1162, 897, 945, 1356, 1128, and 1232 cm , 2336, 1770 and 2066 nm cm Allison et al., 2009 NA nm Chen et al., 2009 NA and cm 1 NA FTNIR: 5 11 FTMIR: 5 11 FTNIR: nm FTMIR: cm Yang and Ren, 2008 > 0.9 for calibration but < 0.1 for cross validation (PLS model failed) Reasonable Very high Ruiz et al, 2008 Note: small: less than

14 INFRARED SPECTROSOCOPY AND CHEMOMETRICS 865 Raw sample Table 3 A comparison of time needed to quantify lycopene in tomatoes using HPLC and FT-IR HPLC FT-IR Extraction of analyte Reference methods (i.e. HPLC) Reference values PLS calibration PLS cross validation PLS prediction Infrared spectrometry NIR MIR Spectral collection Data preprocessing New sample Figure 8 Flow chart for construction of a quantitative PLS regression model based on IR spectra features and chromatographic reference values. performed at the same time. After the calibration model is established and tested, new samples in the future could be only directly determined by FT-IR. We chose lycopene in tomato as the targeted compound for this example. For determination of reference value, carotenoids are extracted from tomato and then separated by HPLC on a C18 column using gradient elution. UV or mass detection can be employed (Pedro et al., 2005). Identification of lycopene is conducted by comparing the retention times of a lycopene external standard with presumptive lycopene in the test sample. The pure lycopene is used to establish calibration curves for quantitation based on peak area or height. For FT-IR analysis, either tomato puree or a simple lipid extract is directly applied to the ATR crystal detector of an infrared spectrometer for spectra collection (De Nardo et al., 2009). Each spectrum could be collected within 32 seconds, which can save up substantial time compared with HPLC gradient elution ( 1 hr). Infrared spectra are converted using appropriate preprocessing algorithms and chemometric models, most likely PLS. Spectra of pure lycopene should be determined as a means of determining band assignments, and a lycopene standard should also be added to the sample matrix to determine both an estimate of the lower limit of detection as well as the presence of possible Sample 2 3 hr for extraction and preparation sample preparation Data collection 1 hr per sample Data analysis 2 hr standard curve established and content calculation 20 min for sample preparation 1 min per sample (1) 6 8 hrs for PLS regression model established (2) 5 min for new sample prediction after model established interfering compounds and potential peak shifts for the analyte as affected by components within the matrix. For model establishment, a calibration model is created first, followed with cross validation according to the selection of the best latent variable numbers. If the prediction results are satisfactory for testing samples, the calibrated model could be used to predict the concentration of lycopene for other tomato samples in future. Standard deviation and coefficient of variance are two major parameters used to evaluate if the prediction results are satisfactory compared to results from the traditional HPLC reference method. A detailed comparison of time requiring of each step for each method is summarized in Table 3. Example 2: Use of NIR with PLS to Determine and Predict the Content of Bioactive Components in Foods In this example, the total phenolic content and total flavonoids of fresh blueberry were determined. The steps for this analysis are similar to those for lycopene analysis by FT-IR as described above with some modifications. For determination of the reference value, the total phenolic content was determined using the Folin-Ciocalteu method and the results are expressed as milligrams of catechin per gram of fresh weight; the total flavonoids are evaluated using a spectrophotometric assay with catechin as a standard to establish a standard curve (Iriti et al., 2005). The results are reported as milligrams of catechin per gram of fresh weight. For the NIR part of this analysis, homogenized blueberry samples are poured directly into glass vials and set into a sample holder and spectra acquired over a range of 12,000 3,600 cm 1 in the transmittance mode appropriate for NIR analysis of liquid samples (Sinelli et al., 2008). The PLS model established part is the same as the one described for FT-IR above. An Assessment of Bioactive Plant Compounds In this section, we focus on the analysis of major bioactive plant compounds in various vegetables and fruits which have nutraceutical properties. The purpose is to provide basic information about the bioactive function of important compounds