Nuclear magnetic resonance spectroscopy for food quality evaluation

Nuclear magnetic resonance spectroscopy for food quality evaluation

11

Yongqi Tian, Qingyan He, Xu Chen, Shaoyun Wang College of Biological Science and Technology, Fuzhou University, Fuzhou, People’s Republic of China

11.1

Introduction

Nuclear magnetic resonance (NMR) is a physical phenomenon that uses the magnetic properties of certain nuclei to provide detailed structural, dynamic, and energy information of molecular compounds. Physicists and chemists have often used NMR as a specialized and precise research tool. The development of hardware and data processing has broadened the application of NMR in various industries. The most successful applications include structural and composition studies of food processing, and food analysis. In recent years, a series of related conferences has been dedicated to the “Application of Magnetic Resonance in Food Science.” There have been quite a few reviews on the work of NMR in food [1]. NMR spectroscopy has been successfully applied in food science [2,3], food analysis [4], authentication [5], and food quality control [6,7]. In addition, applications of low-field, solid-state NMR spectroscopy and magnetic resonance imaging (MRI) in food science have also been reported [8,9]. Meanwhile, the use of NMR in specific topics in food science and analysis, such as milk and dairy products [10], meat [11], fruits, vegetables [12], cereals [13], lipids [14], and edible oil [15], have also been published. The rapidly increasing use of NMR in food science is mainly due to two factors, advances in high-field magnets and probe design, which enhance the analytical capabilities of modern NMR spectrometers. Because of the success of liquid chromatography-NMR, food scientists can now access NMR spectrometers more easily. Although 1H nuclei are sensitive and the most exploited, other nuclei such as 13C and 31P have gained popularity lately because they can be used to solve specific problems in food science. Specifically, 31P NMR has a long history in food science. As early as 1985 there were papers on the application of 31P NMR in meat [16] and milk [17] from a food science perspective. At present, NMR has been considered to be a powerful tool in chemical, biological, and medical research. David [18] was the first to summarize its application in biochemistry, while Quin and Verkade [19] focused on chemical characterization and structural analysis. NMR work in food science has been covered as part of general NMR reviews for milk, meat, and lipids, while a review of NMR work related to olive oil analysis has been published [20]. Evaluation Technologies for Food Quality. https://doi.org/10.1016/B978-0-12-814217-2.00011-1 © 2019 Elsevier Inc. All rights reserved.

194

Evaluation Technologies for Food Quality

In this chapter, the theory and basic principles of NMR, experimental procedures, advantages, and limitations will be introduced and then focus will be given to the application of NMR in food science analysis (Fig. 11.1).

11.2

Theory and fundamentals [21]

11.2.1 Spin angular momentum and nuclear magnetism Most atomic nuclei have an inherent angular momentum called spin. Nuclear spin is a vector and is quantized. Its magnitude is pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi I ðI + 1Þh

(11.1)

where I is the spin quantum number of the nuclide in question and h is Planck’s constant divided by 2π. I could be zero, or a positive integer or half-integer (Table 11.1): 1 3 5 7 I ¼ 0, ,1, , 2, , 3, ,… 2 2 2 2

(11.2)

The projection of the angular momentum vector I onto an arbitrary axis (labeled z) is also quantized: Iz ¼ mh

(11.3)

where the magnetic quantum number, m, can have values between + I and  I in integral steps: m ¼ + I, + I  1,⋯,  I + 1,  I

(11.4)

pffiffiffiffiffiffiffiffi 3=2 h and a pffiffiffiffiffi 2 1 z component Iz ¼  2 h; for I ¼ 1 (e.g., H), the spin angular momentum is 2h, and Iz ¼ (x and γ) components of the angular momentum cannot be known once the magnitude and the z component of I have been specified. Closely related to nuclear spin is a magnetic moment μ: The spin of a nucleus with I ¼ 1/2 (e.g., 1H) has magnitude

μ ¼ γI

(11.5)

which is parallel or sometimes antiparallel to I, with a proportionality constant γ called the gyromagnetic ratio. As a result, both the magnitude and orientation of μ are quantized. In the absence of a magnetic field, all 2I + 1 states of a spin-I nucleus are degenerate, and the direction of the quantization axis is arbitrary. In an applied magnetic field B0 with strength B0, the spins are quantized along the field direction (the z-axis) and have an energy E ¼ μB0 ¼ μz B0

(11.6)

Nuclear magnetic resonance spectroscopy for food quality evaluation

Fig. 11.1 Review scheme of nuclear magnetic resonance for food quality evaluation. 195

196

Evaluation Technologies for Food Quality

Table 11.1 Nuclear spin quantum numbers of some popular nuclear magnetic resonance nuclides I

Nuclide

0  1

12

16

1

13

C H 2 H 11 B 17 O 10 B

2

1  2 5 3

2

3

O C 14 N 23 Na 27 Al

15

N

19

F

29

35

Cl

37

Cl

Si

31

P

Reprinted with permission from P.J. Hore, NMR Principles, 12 (1999) 1545–1553. Copyright Elsevier Publisher 2017.

where μB0 is the scalar product of the two vectors and μz is the projection of μ onto B0. Since μz ¼ γIz and Iz ¼ mh, it follows that E ¼ mhγB0

(11.7)

That is, the 2I + 1 states are split apart in energy, with a uniform gap Δ E ¼ hγB0 between adjacent levels (Fig. 11.2C and D). The NMR experiment includes the application of electromagnetic radiation of the correct frequency ν to “flip” spins from one energy level to another, under the selection rule Δ m ¼  1, i.e., hv ¼ ΔE ¼ hγB0

(11.8)

which may be rearranged to produce the resonance condition v¼

γB0 2π

Fig. 11.2 Space quantization and energy levels of spin 12 and spin-1 nuclei. (A) and (C) spin 12; (B) and (D) spin-1. The energy level splittings produced by an applied magnetic field depend on the value of the gyromagnetic ratio, γ (here taken as positive). Reprinted with permission from P.J. Hore, NMR Principles, 12 (1999) 1545–1553. Copyright Elsevier Publisher 2017.

(11.9) + 12 ប

m = – 12

m = + 12 DE = បg B0

– 12 ប

m = – 12

m = + 12

(A)

(C)

+ប

m = +1 m = –1 DE = បg B0

0

m=0

m=0 DE = បg B0

m = +1 –ប

(B)

m = –1

(D)

Nuclear magnetic resonance spectroscopy for food quality evaluation

197

Table 11.2 Gyromagnetic ratios, nuclear magnetic resonance frequencies (in a 9.4 T field), and natural isotopic abundances of selected nuclides

1

H H 13 C 14 N 15 N 17 O 19 F 29 Si 31 P 2

γ (107 T21 s21)

ν (MHz)

Natural abundance (%)

26.75 4.11 6.73 1.93 2.71 3.63 25.18 5.32 10.84

400.0 61.4 100.6 28.9 40.5 54.3 376.5 79.6 162.1

99.985 0.015 1.108 99.63 0.37 0.037 100.0 4.70 100.0

Reprinted with permission from P.J. Hore, NMR Principles, 12 (1999) 1545–1553. Copyright Elsevier Publisher 2017.

The NMR frequency of a nucleus is proportional to its γ and to the strength of the field; the 2I allowed transitions of a spin-I nucleus have identical frequencies (e.g., Fig. 11.2D). Typical magnetic fields used in modern NMR spectroscopy are in the range 4.7–22.3 T, giving proton (1H) resonance frequencies of 200–950 MHz, falling in the radiofrequency region of the electromagnetic spectrum. Table 11.2 gives the gyromagnetic ratios, resonance frequencies in a 9.4 T field, and natural isotopic abundances of some commonly studied NMR nuclei. The intensity of the observed NMR signal depends on the difference between the numbers of nuclei in the states involved in the transition. At thermal equilibrium the fractional difference in populations, of a spin 1/2 nucleus with positive γ, is given by the Boltzmann distribution: nα  nβ e△  e△ hγB0 hv ¼ ¼ △ △ ffi △ nα + n β e + e 2kT 2kT

(11.10)

where α and β denote the m ¼ + 12 and m ¼  12 levels, k is the Boltzmann constant, and T is the Kelvin temperature. The approximation made in Eq. (11.10) is that the NMR energy gap hγB0 is tiny compared to kT, which is the case in essentially all NMR experiments. For protons (1H) in a 9.4 T field, v ¼ 400 MHz so that △ ¼ 3.2  105, giving a population difference of about one part in 31,000.

11.2.2 Chemical shifts Although the resonant frequency of the nucleus in the magnetic field is primarily determined by γ, it also depends slightly on the immediate surroundings of the nucleus. Chemical shift is critical to the chemical applications of NMR because it allows one to distinguish nuclei in different environments, such that the 1H spectrum of EtOH (Fig. 11.3) shows that there are three types of protons (methyl, methylene, and hydroxyl).

198

Evaluation Technologies for Food Quality

OH

6

5

CH2

4

3 d (ppm)

CH3

2

1

0

Fig. 11.3 Schematic 1H NMR spectrum of liquid ethanol, C2H5OH. The three multiplets, at chemical shifts of 1.2, 3.6, and 5.1 ppm, arise from the CH3, CH2, and OH protons. The multiplet structure (quartet for the CH2, triplet for the CH3) arises from the spin–spin coupling of the two sets of protons. Splittings are not normally seen from the coupling of the OH and CH2 protons because the hydroxyl proton undergoes rapid intermolecular exchange, catalyzed by traces of acid or base. Reprinted with permission from P.J. Hore, NMR Principles, 12 (1999) 1545–1553. (Copyright Elsevier Publisher 2017).

Because the applied magnetic field B0 causes electrons in atoms and molecules to circulate around the nuclei, this results in the existence of chemical shifts. Somewhat like an electric current in a loop of wire, the swirling electrons generate a small local magnetic field that augments or opposes B0. Like an electric current in a loop of wire, the rotating electrons produce a small local magnetic field that augments or attenuates B0. This induced field Bind is proportional in strength to B0 and, in atoms, is antiparallel to it. The net field B experienced by the nucleus is thus slightly different from B0: B ¼ B0  Bind ¼ B0  σB0 ¼ B0 ð1  σ Þ

(11.11)

The proportionality constant σ is called the shielding or screening constant. The resonance condition, Eq. (11.9), thus becomes ν¼

γB γB0 ð1  σ Þ ¼ 2π 2π

(11.12)

The σ is determined by the electronic structure of the molecule near the nucleus; ν is thus a characteristic of the chemical environment. The relation between the energy levels of a pair of spin 12 nuclei A and X is E mA γB0 ð1  σ A Þ mX γB0 ð1  σ X Þ ¼  h 2π 2π ¼ mA vA  mX vX and the NMR spectrum is shown in Fig. 11.4.

(11.13)

Nuclear magnetic resonance spectroscopy for food quality evaluation mA

mX

–2

1

–2

1

+ 12 (nA+nX)

–2

1

+2

1

+ 12 (nA–nX)

+2

1

–2

1

– 12 (nA–nX)

1

+2

1

– 12 (nA+nX)

+2

A

E/h

199

Fig. 11.4 Energy levels and nuclear magnetic resonance spectrum of a pair of spin 12 nuclei, A and X. mA and mX are the magnetic quantum numbers, νA and νX are the two resonance frequencies, and E is the energy. The spin– spin coupling JAX is zero. Reprinted with permission from P.J. Hore, NMR Principles, 12 (1999) 1545–1553. Copyright Elsevier Publisher 2017.

X

The chemical shift is usually quantified by the parameter δ, which is defined by the resonance frequencies of the nucleus of interest and of a reference compound: 

v  vref δ ¼ 10  vref 6

 (11.14)

where δ is dimensionless and independent of B0; values are usually quoted in parts per million (ppm). (CH3)4Si (tetramethylsilane) is commonly used as a standard compound for 1H, and the 13C NMR spectrum is shown with δ decreasing from left to right; the δ of (CH3)4Si is 0. As a consequence, nuclei with higher resonance frequencies (i.e., those that are less shielded) appear to the left of the spectrum. Although the spectra are now usually recorded at a fixed field strength, the old terms “high field” (more shielded) and “low field” (less shielded) are still commonly used. Chemical shifts can be easily converted to frequency differences using Eq. (11.14). For example, the chemical shifts of the methyl and methylene signals of EtOH (Fig. 11.3) are 1.2 and 3.6 ppm, respectively, giving a difference in resonance frequencies in a 9.4 T field of (3.6 – 1.2)  10–6  400 MHz ¼ 960 Hz. The relative intensities of the signals in the NMR spectrum are proportional to the overall differences (Eq. 11.10), and therefore to the numbers of nuclei responsible for each signal. For example, the CH3, CH2, and OH resonances of EtOH (Fig. 11.3) have integrated areas with a ratio 3:2:1.

200

Evaluation Technologies for Food Quality

11.2.3 Spin–spin coupling Magnetic nuclei not only interact with the applied and induced magnetic fields, but also interact with each other. Molecules in liquids have fine structures called spin–spin coupling, scalar coupling, or J-coupling, as shown by the 1H spectrum of EtOH in Fig. 11.3. The effect of spin–spin coupling on a pair of nuclear spins A and X is to shift their energy levels by amounts determined by the two magnetic quantum numbers and by the parameter that quantifies the strength of the interaction, the spin–spin coupling constant, JAX. Thus Eq. (11.13) becomes E ¼ mA vA  mX vX + JAX mA mX h

(11.15)

For spin  12 nuclei, the energies are raised or lowered by 14 JAX according to whether the spins are parallel (mA mX ¼ + 14) or antiparallel (mA mX ¼  14). Eq. (11.15) leads to the modified resonance condition for spin A: v ¼ vA  JAX mX

(11.16)

i.e., the resonance frequency of A is shifted from its chemical shift position by an amount that depends on the orientation of the X spin to which it is coupled. Since X has in general 2I + 1 states, the A resonance is divided into 2I + 1 evenly spaced lines, with equal intensities (because the different orientations of X are almost identical). The effect of spin–spin coupling on the energy levels of two spin 12 nuclei is shown in Fig. 11.5. Each nucleus now has two NMR lines (a doublet). The origin of spin–spin coupling is not directly related to the two magnetic moments through space dipolar interaction; being purely anisotropic, this interaction is averaged to zero through the rapid end-over-end tumbling of molecules in liquids. Instead, the nuclei interact with the electrons in the chemical bonds that connect them. When the number of interventional bonds increases by more than three, the interaction usually decreases rapidly, so the presence of scalar coupling between the two cores usually indicates that they are close neighbors in the molecular framework. Eq. (11.16) can easily be extended to describe more than two nuclei: v ¼ vA 

X

JAi mi

(11.17)

i6¼A

where the sum runs over all spins to which A has an appreciable coupling. If A is coupled to N identical spin 12 nuclei (e.g., the three protons in a methyl group), it can be seen from Eq. (11.17) that its resonance is divided into N + 1 equally spaced lines. The relative intensity is given by the binomial coefficient   N N! ¼ ,i ¼ 0, 1,2, …, N i i!ðN  iÞ!

(11.18)

Nuclear magnetic resonance spectroscopy for food quality evaluation mA

E/h

– 12

–2

1

+ 12 (nA+nX) + 14 JAX

–2

1

+2

1

+ 12 (nA–nX) – 14 JAX

+2

1

–2

1

– 12 (nA–nX) – 14 JAX

1

+2

1

– 12 (nA+nX) + 14 JAX

+2

A

mX

201

Fig. 11.5 Energy levels and nuclear magnetic resonance spectrum of a pair of spin 12 nuclei, A and X. mA and mX are the magnetic quantum numbers, νA and νX are the two resonance frequencies, JAX is the spin–spin coupling constant, and E is the energy. Reprinted with permission from P.J. Hore, NMR Principles, 12 (1999) 1545–1553. Copyright Elsevier Publisher 2017.

X

Fig. 11.6 Calculated nuclear magnetic resonance spectra of a pair of spin 12 nuclei for a range of δv ¼ vA  vX values between 16 JAX and zero. Reprinted with permission from P.J. Hore, NMR Principles, 12 (1999) 1545–1553. Copyright Elsevier Publisher 2017.

Thus the CH2 and CH3 resonances in ethanol (Fig. 11.3) are, respectively, a 1:3:3:1 quartet and a 1:2:1 triplet. The discussion of the structure of multiple states (i.e., doublet, triplet, quartet, etc.) arising from spin–spin coupling is effective in the weak coupling limit, i.e., when the difference in resonance frequencies of the coupled nuclei jvA  vX j is much larger than their interaction | JAX |. If this is not the case (strong coupling), the positions and intensities of the lines are modified, as shown in Fig. 11.6. The origin of these effects lies in the NMR transition probabilities. As the coupling becomes stronger, the outside of each double peak in Fig. 11.6 becomes weaker relative to the inside. Within the limit of zero chemical shift difference, the transitions leading to the two exterior lines

202

Evaluation Technologies for Food Quality

become completely forbidden, and the two interior lines coincide so that only a single line is observed. This is a general result: spin–spin interactions between protons in the same environment do not result in observable splittings.

11.2.4 Free induction decay So far it has been assumed that the unbalanced state generated by the radiofrequency pulse does not relax back to equilibrium. This is a reasonable approximation during the very short pulse. However, to describe the behavior of the spins during free precession after the pulse, relaxation must be included. Traditionally, this is done by allowing Mx and My to decay exponentially back to zero with a time constant T2, while Mz grows back to M0 with a time constant T1: dMx Mx ¼ + γ△BMγ  dt T2 dMy My ¼ + γB1 Mz  γ△BMx  dt T2

(11.19)

dMz ðM z  M 0 Þ ¼ γB1 Mγ  dt T1 where T1 and T2 are the spin–lattice and spin–spin relaxation times. These expressions are known as the Bloch equations.

11.2.5 Spin relaxation Relaxation processes allow nuclear spins to return to equilibrium after interference, for example, a radiofrequency pulse. The relaxation times T1 and T2 characterize the relaxation of the longitudinal and lateral components of the magnetization M, respectively, parallel and perpendicular to B0. Equivalently, T1 is the time constant for the restoration of equilibrium in the spin state population, while T2 is the time constant for the coherence dephasing between spin states. In the absence of any significant spatial heterogeneity of B0, or other spectral line broadening sources such as chemical exchange, the width of the NMR line (in hertz) is 1/πT2. Spin–lattice relaxation is caused by random fluctuations of local magnetic fields. A common source of such fields is the dipolar interaction between pairs of nuclei, which is regulated by tumbling molecules in a liquid. The component of these fields that oscillates at the resonance frequency can cause transitions between the spin states, thus transferring energy between the spin system and the “lattice” (i.e., everything else) and balancing the spin with their surroundings. In the simplest case, T1 depends  on the mean square strength of the local fields B2 loc , and the wave strength at the resonance frequency ω0

Nuclear magnetic resonance spectroscopy for food quality evaluation

 1 ¼ γ 2 B2loc J ðω0 Þ T1

203

(11.20)

where J ð ωÞ ¼

2τc 1 + ω2 τ2c

(11.21)

is the spectral density function and τc is the rotational correlation time (roughly the average time the molecule takes to rotate through 90 degrees). Spin–spin relaxation has two contributions: 1 1 2 2 1  ¼ γ Bloc J ðω0 Þ + γ 2 B2loc J ð0Þ T2 2 2

(11.22)

The first is closely related to spin–lattice relaxation, and is generated from the finite lifetime of the spin states by the uncertainty principle. The second term is due to the loss of coherence caused by very low-frequency local fields (therefore the J(0) factor), which increase or oppose B0 and thus cause an expansion of resonance frequencies, and therefore the phase shift of the transverse magnetization. Fig. 11.7 shows the dependence of T1 and T2 on τc.

Slow

10 T1 1

0.1 T2 0.01

Fast 10–12 Fast

10–10 tc(s)

10–8 Slow

Fig. 11.7 The dependence of T1 and T2 on the rotational correlation time τc, using γ 2 hB2loci ¼ 4.5  109 s2 and ω0/ 2π ¼ 400 MHz. The units for the vertical axis are seconds. Reprinted with permission from P.J. Hore, NMR Principles, 12 (1999) 1545–1553. Copyright Elsevier Publisher 2017.

204

Evaluation Technologies for Food Quality

Relaxation times contain information on both J(ω) (i.e., on molecular motion) and hB2loci (i.e., on molecular structure via, for example, the r–3 distance dependence of the dipolar interaction). A further relaxation phenomenon that provides important information on internuclear distances is the nuclear Overhauser effect.

11.3

NMR experiment procedures

11.3.1 Experiment 1: Determination of T2 relaxation time Relaxation measurements were carried out on a Niumag Desktop Pulsed NMR Analyzer (Shanghai Niumag Electronics Technology Co. Ltd.). The magnetic field intensity was 0.54 T and the protons of corresponding resonance frequency were 23.01 MHz. The NMR instrument was equipped with a 60 mm probe. Transverse relaxation (T2) was measured using the Carr–Purcell–Meiboom–Gill (CPMG) pulse sequence, with a τ value (time between the 90 and 180 degree pulses) of 75 μs. Data from 2000 echoes were obtained as eight-scanned repetitions. The repetition time between two consecutive scans was 2 s. All relaxation measurements were made at 25°C. Using Multi Exp Inv Analysis Software developed by Niumag, the T2 relaxation time was analyzed by the distribution index fitting analysis method. A continuous exponentials distribution of the CPMG experiment was defined by Eq. (11.23): Z∞

AðT Þeτi =T dT

gi ¼

(11.23)

0

where gi is the intensity of the decay at time τi and A(T) is the amplitude of the component with transverse relaxation time T. Eq. (11.23) was solved using Multi Exp Inv Analysis software by minimizing Eq. (11.24) 

Z gi 

m

x¼1

fx eτi =Tx dT

2 +λ

m X

fx 2

(11.24)

x¼1

P 2 In Eq. (11.24), λ is the weighting and λ m x¼1 fx is a linear combination of functions added to the equation to perform a zero-order regularization [22]. Using sampling pruning to reduce the data from 2000 to 200 points, this analysis yielded a plot of the relaxation amplitude versus relaxation time for an individual’s relaxation process. The time constant for each peak was calculated from the peak position, and the corresponding water contents were determined by cumulative integration. All calculations were measured using an internal program written in conjunction with MATLAB (Mathworks Inc., Natick, MA, USA) and Delphi (Borland, USA).

Nuclear magnetic resonance spectroscopy for food quality evaluation

205

11.3.2 Experiment 2: Magnetic resonance imaging The porosity of the crumb structure of the WWB sections was evaluated by observation with an MRI system (Mini MR-60, Shanghai Niumag Electronics Technology Co., Ltd., Shanghai, China). Image analysis was performed by the spin-echo 2D-FT method using a 0.1 ms echo time and a 0.5 s repetition time according to the testing parameters provided by the instrument manufacturer (Shanghai Niumag Electronics Technology Co. Ltd.). The images were reconstructed on a 192  192 matrix for 2D images, and three layers were scanned, each layer having a thickness of 4.9 mm. The porosity was calculated by using the image twice-threshold segmentation method of MATLAB (version R2010a) to offset the variation error caused by the signal-to-noise ratio of the scanned images. The gray value range of the image was 0–255. The contrast of the images was adjusted and selected from the gray value for detecting the edge of the bread sample; the number of pixels in the bread sample was specified as N1. The threshold value was adjusted and selected for testing the internal chamber of the bread again, and the pixels below the threshold were calculated and designated N2, representing the gas cells of the bread crumb. Therefore the pixels that were higher than the threshold represented the skeleton structure of the bread. The porosity can be calculated from Eq. (11.25) provided by the instrument manufacturer (Shanghai Niumag Electronics Technology Co. Ltd.): Vpore N2 Spixel h N2  100% ¼  100%  100% ¼ N2 Spixel h Vtotal N1

(11.25)

where N is the number of pixels, Spixel is the physical area of a single pixel, h is the thickness of a bread cross-section, Vpore is the total volume of the gas cells, and Vtotal is the total volume of the bread, including the gas cell volume and the volume of bread crumb.

11.4

Advantages and limitations of NMR

NMR spectroscopy is a research technique that provides the possibility of obtaining quantitative and structural information of any molecule characterized by atoms with an intrinsic magnetic moment and angular momentum with minimal sample preparation. The elements are mainly found in food, such as H, O, C, N, and P, having at least one detectable isotope, thus giving the NMR spectrum the title “universal detector.” Also, some NMR experiments do not require separation of multiple food components, requiring little effort for sample pretreatment, and preparation is required compared to conventional methods. The food samples contain lipid, semisolid, and solid. The resulting composite NMR spectra can be further processed with multivariate statistical analysis to obtain additional structural information of food systems. NMR is perhaps the only technique that is suitable for the study of food products at both molecular and microscopic scales. Its stability and inherent ease of quantification

206

Evaluation Technologies for Food Quality

have been exploited extensively to identify and quantify bioactive components in foods and dietary supplements. NMR signals offer the experimentalist a diverse array of measurable parameters such as intensity, frequency (normalized to chemical shift), line shape, line width, and relaxation times. These data have been used to determine structure, diffusion rate, viscosity, and association constants. With increasing computational power, reduced costs, and development of stronger magnetic fields, cryoprobes, solvent suppression techniques, and a large number of versatile 1D and 2D NMR pulse sequences have extended their application in the field of NMR in metabolomics and nutrigenomics because of the distinct advantage of reproducibility. The application of NMR has recently rapidly expanded in the field of food science and technology with the development of NMR instrumentation and improved programs to collect and analyze the data. A wide range of NMR food-related research has covered various fields of food science, including food microbiology, food chemistry, food engineering, and food packaging. However, prior to the potential to express ultrasensitive applications such as dynamic nuclear polarization, NMR spectroscopy is still considered a less sensitive technique than other spectrometric methods. In the exploration of the foodomics space, the second limitation of NMR spectroscopy has been traditionally considered to be a relatively reduced resonance window of proton spectra compared to 13C or 31P, so that many signals appear overlapped, especially when complex mixtures are analyzed.

11.5

Recent technology development of NMR

In conventional NMR instruments, the geometry of the magnet is known as closed geometry and the sample under investigation is placed in a uniform magnetic field. Although this facilitates high signal-to-noise, geometrically correct, spatially resolved MRI, it limits the range of detectable samples. Recently, this limitation has been solved by the development of portable or single-sided NMR [23–25]. The geometry of portable NMR sensors is referred to as an open geometry where the object is exposed to the stray field of the magnet. In this case, the magnet may be placed to one side of the object completely maintaining the integrity and dimension of the sample under study and also allowing the entire packaged product to be measured. Nowadays, single-sided NMR sensors can be divided into two categories. The first group operates in a strong magnetic field gradient [25], while the second one operates in a region under a more or less uniform magnetic field [23,24]. Both methods have their advantages and disadvantages. Unilateral NMR developed by Bl€umich et al. [26] is characterized by a high magnetic field strength operating in the proton frequency range of 13–18 MHz with a strong magnetic field gradient. This sensor requires very short (in the microsecond range) and very powerful radiofrequency pulses to achieve the desired frequency bandwidth. Another class of single-sided NMR instrument has been developed by Marble et al. [23,27]. Among these sensors, the single-sided magnet array is composed of three block magnets all magnetized along the same direction.

Nuclear magnetic resonance spectroscopy for food quality evaluation

207

The sensitive point is located approximately 1 cm above the surface of the magnet. The magnet spacings are optimized to create a locally uniform field in this region creating a relatively large magnetic resonance-sensitive volume above the surface radiofrequency coil. The NMR system has a resonant frequency of 4.68 MHz, and all pulse lengths are approximately 8 μs with 6 dB attenuation for the 90 degree pulse, and 180 degree pulses are not attenuated [28]. Manz et al. developed another portable NMR sensor with a novel one-sided entry magnet design called NMR-MOLE (mobile lateral explorer) [24]. This sensor is very effective in terms of sensitivity and penetration depth. The magnet array is based on a barrel magnet operating at 3.3 MHz and the center magnet is positioned to provide a uniform area from 4 to 16 mm away from the probe, with maximum sensitivity at a depth of 10 mm. Due to the lower diffusion attenuation in uniform field sensors, they are more suitable for studying liquid samples, for instance, aqueous solutions and biological tissue requiring unilateral or portable access.

11.6

Recent application progress of NMR

11.6.1 Potential application in food authentication Food certification is one of the main issues in food quality. NMR/MRI techniques applied for detecting authentication in different foods have been widely reported [29–32]. The potential use of NMR in food certification has been applied to several foods and beverages, such as milk and cheese [33], beef [34], truffles [35], vanillin [36], pistachios [37], and saffron extracts [38]. The scope of NMR in food certification will be expanded as the price of the instrument declines and can be more widely accessible allowing more regulatory agencies worldwide to choose the analytical testing method of using NMR samples, while allowing the sample to remain intact if required by laws or for any other reasons. Some representative foods cited in the following section include olive oil, fish, and beverages.

11.6.1.1 Application in virgin olive oil Virgin olive oils (VOOs) have been produced in countries around the Mediterranean Sea for thousands of years, with their quality being related to their geographical origin and processing methods. In terms of specific production areas and production methods, the European Union (EU) has very strict regulations on VOO labels. Because of the high market prices of VOOs, the fraudulent behavior of mislabeling the origin and the act of adulteration occur very often, although several analytical methods have been developed to detect VOO adulteration, based on their geographic origin. NMR fingerprinting has been proven to be a more effective VOO authentication method [39]. For authentication purposes, several variables have been studied, including 1H, 13C, and/or 31P NMR analyses, unsaponifiable fraction of VOOs, and phenolic compounds in the polar fraction of VOOs [40,41].

208

Evaluation Technologies for Food Quality

11.6.1.2 Application in fish Rnm and Gamr [42] used a pulse 1H NMR technique to determine relaxation time (T2) from CPMG experiments on fillets of Pintado (Pseudoplatystoma corruscans) at –70 to 60°C and on freeze-dried fillets. This work aimed to determine water profiles with different mobilities in Pintado fish exposed to different environmental conditions of temperature, moisture, and water activity. The NMR technique proved that it was an alternative tool to better understand water behavior in complex biological systems. Results of the CPMG pulse sequence experiments are schematically shown in Fig. 11.8 and represent the entire water migration range measured at 35°C (T2 spectrum) in Pintado fish. There are three different sets of water protons with different relaxation times. Water molecules show different mobilities depending on the free energy of hydrogen bonds formed between water molecules and macromolecules of the food. The monolayer hydration formed on the macromolecule surface is referred to as “bound” water with very low mobility. At higher levels of T2, a new group begins to appear at the end of the spectrum, which represents the signal of the sample fat contents. The presence of this group was confirmed through the experimental data of T2 obtained in experiments with isolated fat from the Pintado samples (Fig. 11.9) [42].

Bulk water T22

T22 90,000 80,000

Amplitude

70,000

Free energy hydrogen bonds

60,000 50,000 40,000 30,000

Fat T23 T21

20,000 10,000 0 10–2

10–1

1

101

102

103

T2 (ms)

Fig. 11.8 Schematic representation of a spectrum of T2 of Pintado fish at 35°C. Reprinted with permission from P. Rnm, L. Gamr, Nuclear magnetic resonance and water activity in measuring the water mobility in Pintado (Pseudoplatystoma corruscans) fish, J. Food Eng. 58 (2003) 59–66. Copyright Elsevier Publisher 2003.

Nuclear magnetic resonance spectroscopy for food quality evaluation

209

T2 of fat (ms)

160

10°C

93.0

25°C

160.2

35°C

222.0

40°C

276.0 60 50 40 35 30 25 20 15 10

120 100 80 60 40 20 748196.6

362289.0

84944.2

175426.2

41131.4

9643.9

19916.5

4669.7

2261.2

530.2

1094.9

256.7

60.2

124.3

29.1

6.8

14.1

3.3

1.6

0.8

0.4

0.2

0.1

0.0

0.0

0.0

0

Temperature (°C)

Amplitude

140

T2 (ms)

Fig. 11.9 Spectrum of a fat T2 from samples of Pintado fish at various temperatures. Reprinted with permission from P. Rnm, L. Gamr, Nuclear magnetic resonance and water activity in measuring the water mobility in Pintado (Pseudoplatystoma corruscans) fish, J. Food Eng. 58 (2003) 59–66. Copyright Elsevier Publisher 2003.

11.6.1.3 Application in beverages The application of NMR for food certification also extends to beverages. 1H NMR spectroscopy showed potential in the discrimination of green tea based on the country of origin or with respect to quality [43]. 1H NMR was capable of simultaneously detecting catechins, amino, organic, phenolic, and fatty acids, as well as sugars from a single green tea extract. It was also used to detect catechins, caffeine, 5-galloyl quinic acid, and 2-O-(α-L-arabinopyranosyl)-myo-inositol, all of which are related to the quality of the tea. Another application field for NMR spectroscopy is to examine the source of the raw material used for making juices. 1H NMR has also been proved to accurately determine the origin or quality of juices [44]. NMR spectroscopy is also widely used in the certification of alcoholic beverages, because these beverages are available at higher prices on the market. Unfortunately, as with VOOs, this increases the risk of fraud by adulteration and intentional mislabeling. Zivania is a traditional Cypriot alcoholic beverage that has been subjected to 1H NMR spectroscopy to determine the authenticity of the country of origin [45]. The results obtained were slightly less accurate than traditional methods, but still considered acceptable. Beer is the third most popular drink in the world after water and tea and is very popular in many cultures [46]. Because some beers are expensive, this popular alcoholic beverage also suffers from adulteration practices, including falsely marking the place of origin. Initially, beer was characterized chemically by high-resolution

210

Evaluation Technologies for Food Quality

1

H NMR to observe many different chemicals between different beers and the potential of NMR [47]. Further experimentation explored the potential of NMR spectroscopy for the quality control of beer [48]. The same group explored the potential of NMR spectroscopy to observe the composition of beer and relate it to the brewing site and date of production, showing the potential to use principal component analysis/NMR to monitor and control the beer production process [49]. The quality control of beer was explored previously [50] and the results suggested that NMR could be used for quality control and authentication of beer.

11.6.2 Specific NMR application to representative foods 11.6.2.1 Wine and beer Since water, ethanol, and acetic acid constitute the main proton-containing components in degraded wine, the peak intensity measured in the 1H NMR spectrum should able to determine the extent of wine spoilage [51]. Several researchers studied spoilage properties of bottled wines by measuring the acetic acid content down to the level of complex sugars, phenols, and trace elements [51–53]. In some cases, dissolved cocaine is smuggled in bottled wine. Giulio et al. [54] solved this problem by detecting dissolved cocaine resonances in the unopened bottle. This was done with a standard clinical magnetic resonance scanner, which measured at levels of 5 mM (i.e., 1.5 g/L) within 1 min. This technique can check suspicious cargo because it allows nondestructive and fast content characterization. These studies emphasize the utilization of a full bottle NMR approach, being applicable to any type of wine [51,53]. This area of research extends to other alcoholic beverages as well. The synergetic combination of 1H NMR with Fourier transform infrared attenuated total reflectance can separate different beers based on alcoholic content [48]. This provided quick information regarding different types of beer fermentation, which is a key aspect of beer production. Rodrigues et al. [55] identified six useful organic acids: acetic, citric, lactic, malic, pyruvic, and succinic acids. Organic acids play an important role in beer, not only affecting flavor, color, and aroma, but they are also good indicators of fermentation performance. The partial least squares-NMR method for the quantification of organic acids in beer, providing important information on the product’s quality and history, was established.

11.6.2.2 Fruits and vegetables By quantifying certain NMR parameters (i.e., T1, T2, and diffusion coefficient to obtain information about several processes and material properties, such as ice crystallization and water mobility), the use of NMR methods to identify compositions and evaluate quality has also been popular in various fruits and vegetables [56,57]. Studies have applied NMR to leafy vegetables, and lettuce samples with a large number of water-soluble metabolites were distinguished along with key organic solvents [58]. The safety testing of genetically modified organisms is a high priority for

Nuclear magnetic resonance spectroscopy for food quality evaluation

211

regulatory authorities, and there is a need for techniques capable of detecting any unintended effect following a genetic modification [59]. With a deeper understanding of genetically modified (GM) foods, Sobolev et al. [58] used NMR to study GM lettuce. This 2010 study compared levels of water-soluble metabolites between GM lettuce and wild-type lettuce resulting in differences in both glucose and fructose contents. Piccioni et al. [59] explored differences between transgenic and conventional maize. Transgenic maize showed higher levels of certain compounds, including primarily ethanol, citric acid, glycine-betaine, and trehalose. Kerr et al. [60] found ice formation and freezing characteristics in various foods such as potatoes, carrots, peas, and chicken legs. Freezing is a very important tool in the food industry and can extend the shelf life of food. The ability to detect freezing times, patterns, and completion times is important to improve food quality. Using MRI, freezing behavior characteristics were monitored noninvasively in different foods, including ice formation associated with the loss of NMR signal intensity, and the time from ice formation to signal loss. NMR data regarding distribution during cooking correlated to texture attributes of potato have also successfully been demonstrated [61,62]. For example, in a study by Mortensen et al. [61], the content of dry matter (DM) was measured with a pulse NMR analyzer, and the water characteristics and water transition between two boxes (low DM and medium DM) of potatoes of the Sava variety during cooking were studied. DM content was of interest in this study because it is related to potato texture and plays a role in water mobility. These studies not only demonstrated the sensitivity of MRI to the changes in water structure and final texture of the potato during processing, but also provided a scientific basis for the development of NMR methodology for predicting the sensory texture properties of other fruits and vegetables. MRI texture analysis (TA) was used to study the effects of maturation and storage of sliced apples. Here TA refers to a series of techniques used for quantifying spatial variation of gray tones in magnetic resonance images. Certain TA parameters were calculated from magnetic resonance images of apple varieties during maturity and long-term storage. Different apple varieties are found to have different TA parameter dynamics during maturation and storage periods. These special TA parameters included skewness and kurtosis (based on histogram parameters) and absolute gradient variance (gradient-based parameters). In addition, different gray-level nonuniformities (parameters based on the run length matrix), and especially those derived from co-occurrence matrices, such as correlation, sum average, sum variance, and sum entropy (within 1-, 3-, and 5-pixel neighborhoods), were also found. These TA parameters were related to chemical and physical properties (firmness of fruits, bruising, soluble solids content, titratable acids) of three apple varieties (i.e., Topaz, Redspur, and Idared) [63]. MRI texture analysis is also suitable for studying other fruits, such as pears [64,65]. Researchers have been able to use 1H NMR to obtain information on the composition of a variety of fermented cocoa beans [66]. The study identified the amino acids, polyalcohols, organic acids, sugars, methylxanthines, catechins, and phenols in cocoa beans from different countries (Ecuador, Ghana, Grenada, and Trinidad) proving this approach to be a rapid method for country identification and quantification of beans.

212

Evaluation Technologies for Food Quality

Chen et al. [67] used low-field NMR to observe the water distribution and state of soybean. The distribution of water was uniform, and the distribution strength increased significantly with total water content. This proved to be a useful way to understand the role of water (in different states) in the extrusion cooking process, which is a popular manufacturing process for preparing various foods such as cereals and snacks.

11.6.2.3 Meat and fish Recent studies have quantified beef [68,69]. The aim of the study by Graham et al. [68] was to evaluate the ability of 1H NMR to characterize the changes in amino acids, nucleotides, and sugars during postmortem aging. It is worth noting that this method required minimal sample preparation to analyze beef samples and demonstrated that aging does affect the concentration of different metabolites. Their research showed an increase in proteolysis that ultimately affected the concentration of amino acids. To obtain information on the correlations between MRI, texture, and physicochemical parameters, three model systems—fibrinogen-thrombin gel (FTG), meat emulsion (ME), and meat emulsion supplemented with fibrinogen-thrombin (ME-FT)— were used in one study. MRI parameters (T2, T1, and apparent diffusion coefficients) showed that many macropores, large amounts of water, and higher water translational motion are characteristics of fibrinogen and thrombin (FTG and ME-FT) [70]. The main components of smoked salmon have also been identified using NMR spectroscopy [71]. This includes the determination of docosahexaenoic acid (DHA) and other polyunsaturated fatty acids (FAs) as well as carbohydrates, amino acids, dipeptides, and organic acids. Their research created new possibilities for identifying omega-3 FAs for fish and processed fish products. The advantage of these methods is that preparation (chemical pretreatment) and extraction that are essentially required by other methods are avoided. Similarly, Nestor et al. [72] aimed to avoid the extractions and determined the FA composition, eicosapentaenoic acid, and DHA in Arctic char. This study obtained direct information regarding the nutritional value of the fish with a simple analytical technique. Further applications of NMR analysis to fish sample processing methods were demonstrated with regard to salting [73]. In many cultures, salting fish has been a traditional preservation technique for centuries. These methods involved in the production of salting cod can affect the distribution of water in the muscle tissue of cod and protein denaturation. The investigation of water distribution shows that the process of salination and rehydration changes cells irreversibly. These analytical methods have proven to be fast and lossless techniques that can yield valuable information regarding food samples.

11.7

Conclusion and future research

In food science, obstacles to the development of NMR spectroscopy instruments are primarily due to high cost, the expertise involved, and safety issues associated with magnetic field maintenance. Because of lower costs and easier maintenance, food

Nuclear magnetic resonance spectroscopy for food quality evaluation

213

researchers can more easily obtain low-field NMR and MRI, but their applications are still limited. The application of NMR technology from research to industrial processes and quality control remains to be realized. To ensure proper data collection and analysis, more NMR-trained staff are needed for food application. Due to the complexity of food, food researchers also face challenges to establish standard operation procedures (SOPs) of NMR/MRI analysis for specific classified food products (i.e., wine, potato). Once SOPs are established, researchers can compare their NMR/MRI results for further improvements. On the other hand, NMR allows a variety of food-based applications, but still has limitations. Integrating with other analyses will provide a complete picture of the results.

References [1] P.S. Belton, I.J. Colquhoun, B.P. Hills, Applications of NMR to food science, Annu. Rep. NMR Spectr. 26 (1993) 1–53. [2] G.A. Webb (Ed.), Part 3: Applications in materials food, and marine sciences, Modern Magnetic Resonance, Springer, 2006. [3] A.M. Gil, Spectroscopy: nuclear magnetic resonance, in: B. Caballero (Ed.), Encyclopedia of Food Science and Nutrition, Elsevier, 2003, pp. 5447–5454. [4] G.L. Gall, I.J. Colquhoun, NMR spectroscopy in food authentication, in: Food Authenticity & Traceability, 53 2003, pp. 131–155. [5] I.J. Colquhoun, M. Lees, Nuclear magnetic resonance spectroscopy, in: P.R. Ashurst, M.D. Dennis (Eds.), Analytical Methods in Food Authentication, Blackie Academic & Professional, London, 1998, pp. 36–75. [6] R. Sacchi, L. Paolillo, NMR for food quality and traceability, in: L.M.L. Nollet, F. Toldra´ (Eds.), Advances in Food Diagnostics, Blackwell Science, 2007, pp. 101–118. [7] A. Spyros, P. Dais, 31P NMR spectroscopy in food analysis, Prog. Nucl. Mag. Res. Spectr. 54 (2009) 195–207. [8] M.J. Gidley, High-resolution solid-state NMR of food materials, Trends Food Sci. Technol. 3 (1992) 231–236. [9] C. Simoneau, M.J. Mccarthy, J.B. German, Magnetic resonance imaging and spectroscopy for food systems, Food Res. Int. 26 (1993) 387–398. [10] J. Belloque, M. Ramos, Application of NMR spectroscopy to milk and dairy products, Trends Food Sci. Technol. 10 (1999) 313–320. [11] W. Laurent, J.M. Bonny, J.P. Renou, Muscle characterisation by NMR imaging and spectroscopic techniques, Food Chem. 69 (2000) 419–426. [12] B.P. Hills, C.J. Clark, Quality assessment of horticultural products by NMR, Annu. Rep. NMR Spectr. (2003) 75–120. [13] B.P. Hills, A. Grant, P.S. Belton, NMR characterization of cereal and cereal based products, in: G. Kaletunc, K.J. Breslauer (Eds.), Characterization of Cereals and Flours: Properties, Analysis and Applications, Marcel Dekker, New York, 2003, pp. 409–436. [14] B.W.K. Diehl, High resolution NMR spectroscopy, Eur. J. Lipid Sci. Technol. 103 (2001) 830–834. [15] F.J. Hidalgo, R. Zamora, Edible oil analysis by high-resolution nuclear magnetic resonance spectroscopy: recent advances and future perspectives, Trends Food Sci. Technol. 14 (2003) 499–506. [16] H.J. Vogel, P. Lundberg, S. Fabiansson, H. Ruderus, E. Tornberg, Post-mortem energy metabolism in bovine muscles studied by non-invasive phosphorus-31 nuclear magnetic resonance, Meat Sci. 13 (1985) 1.

214

Evaluation Technologies for Food Quality

[17] B. PS, L. RL, R. CP, The 31P nuclear magnetic resonance spectrum of cows’ milk, J. Dairy Res. 52 (1985) 47–54. [18] G. David, Phosphorus-31 NMR: Principles and Applications, Academic Press, London, 1984. [19] L.D. Quin, J.G. Verkade, Phosphorus-31 NMR spectral properties in compound characterization and structural analysis, Z. Phys. Chem. 191 (1995) 282–283. [20] P. Dais, A. Spyros, 31P NMR spectroscopy in the quality control and authentication of extra-virgin olive oil: a review of recent progress, Magn. Reson. Chem. 45 (2007) 367. [21] P.J. Hore, NMR principles, Encycl. Spectrosc. Spectrom. 12 (1999) 1545–1553. [22] S.A. Teukolsky, W.T. Vetterling, B.P. Flannery, Modeling of data, in: Modeling of data, in: Numerical Recipes in C: The Art of Scientific Computing, vol. 2, second ed., Cambridge University Press, New York, 1992, pp. 656–706. [23] A.E. Marble, I.V. Mastikhin, B.G. Colpitts, B.J. Balcom, A compact permanent magnet array with a remote homogeneous field, J. Magn. Reson. 186 (2007) 100–104. [24] B. Manz, A. Coy, R. Dykstra, C.D. Eccles, M.W. Hunter, B.J. Parkinson, et al., A mobile one-sided NMR sensor with a homogeneous magnetic field: the NMR-MOLE, J. Magn. Reson. 183 (2006) 25–31. [25] Z. Xu, R.H. Morris, M. Bencsik, M.I. Newton, Detection of virgin olive oil adulteration using low field unilateral NMR, Sensors 14 (2014) 2028. [26] B. Bl€umich, S. Anferova, S. Sharma, A.L. Segre, C. Federici, Degradation of historical paper: nondestructive analysis by the NMR-MOUSE, J. Magn. Reson. 161 (2003) 204–209. [27] A.E. Marble, I.V. Mastikhin, R.P. Macgregor, M. Akl, G. Laplante, B.G. Colpitts, et al., Distortion-free single point imaging of multi-layered composite sandwich panel structures, J. Magn. Reson. 168 (2004) 164. [28] E. Veliyulin, I.V. Mastikhin, A.E. Marble, B.J. Balcom, Rapid determination of the fat content in packaged dairy products by unilateral NMR, J. Sci. Food Agric. 88 (2010) 2563–2567. [29] D. Bertelli, M. Lolli, G. Papotti, L. Bortolotti, G. Serra, M. Plessi, Detection of honey adulteration by sugar syrups using one-dimensional and two-dimensional high-resolution nuclear magnetic resonance, J. Agric. Food Chem. 58 (2010) 8495. [30] M. Cuny, E. Vigneau, G.G. Le, I. Colquhoun, M. Lees, D.N. Rutledge, Fruit juice authentication by 1H NMR spectroscopy in combination with different chemometrics tools, Anal. Bioanal. Chem. 390 (2008) 419. [31] D.I. Ellis, V.L. Brewster, W.B. Dunn, J.W. Allwood, A.P. Golovanov, R. Goodacre, Fingerprinting food: current technologies for the detection of food adulteration and contamination, Chem. Soc. Rev. 41 (2012) 5706–5727. [32] S. Masoum, C. Malabat, M. Jalali-Heravi, C. Guillou, S. Rezzi, D.N. Rutledge, Application of support vector machines to 1H NMR data of fish oils: methodology for the confirmation of wild and farmed salmon and their origins, J. Radiat. Res. 387 (2007) 1499–1510. [33] M.A. Brescia, M. Monfreda, A. Buccolieri, C. Carrino, Characterisation of the geographical origin of buffalo milk and mozzarella cheese by means of analytical and spectroscopic determinations, Food Chem. 89 (2005) 139–147. [34] L. Shintu, S. Caldarelli, B.M. Franke, Pre-selection of potential molecular markers for the geographic origin of dried beef by HR-MAS NMR spectroscopy, Meat Sci. 76 (2007) 700–707.

Nuclear magnetic resonance spectroscopy for food quality evaluation

215

[35] L. Mannin a, A.P.S. Michela Cristinzio, A. Pietro Ragni, A. Segre, High-field nuclear magnetic resonance (NMR) study of truffles (Tuber aestivum vittadini), J. Agric. Food Chem. 52 (2004) 7988–7996. [36] E.J. Tenailleau, P. Lancelin, R.J. Robins, S. Akoka, Authentication of the origin of vanillin using quantitative natural abundance 13C NMR, J. Agric. Food Chem. 52 (2004) 7782. [37] K. Zur, A. Heier, K.W. Blaas, C. Fauhl-Hassek, Authenticity control of pistachios based on 1H- and 13C-NMR spectroscopy and multivariate statistics, Eur. Food Res. Technol. 227 (2008) 969–977. [38] A. Yilmaz, N.T. Nyberg, P. Mølgaard, J. Asili, J.W. Jaroszewski, 1H NMR metabolic fingerprinting of saffron extracts, Metabolomics 6 (2010) 511–517. [39] R.M. Alonsosalces, J.M. Morenorojas, M.V. Holland, F. Reniero, C. Guillou, K. Heberger, Virgin olive oil authentication by multivariate analyses of 1H NMR fingerprints and δ13C and δ2H data, J. Agric. Food Chem. 58 (2010) 5586–5596. [40] R.M. Alonso-Salces, K. Heberger, M.V. Holland, J.M. Moreno-Rojas, C. Mariani, G. Bellan, et al., Multivariate analysis of NMR fingerprint of the unsaponifiable fraction of virgin olive oils for authentication purposes, Food Chem. 118 (2010) 956–965. [41] S. Christophoridou, P. Dais, A. Lihong Tseng, M. Spraul, Separation and identification of phenolic compounds in olive oil by coupling high-performance liquid chromatography with postcolumn solid-phase extraction to nuclear magnetic resonance spectroscopy (LC-SPE-NMR), J. Agric. Food Chem. 53 (2005) 4667. [42] P. Rnm, L. Gamr, Nuclear magnetic resonance and water activity in measuring the water mobility in Pintado (Pseudoplatystoma corruscans) fish, J. Food Eng. 58 (2003) 59–66. [43] G.L. Gall, I.J.C. And, M. Defernez, Metabolite profiling using 1H NMR spectroscopy for quality assessment of green tea, Camellia sinensis (L.), J. Agric. Food Chem. 52 (2004) 692–700. [44] P. Rinke, S. Moitrier, E. Humpfer, S. Keller, M. M€ ortter, M. Godejohann, et al., An 1H-NMR-technique for high throughput screening in quality and authenticity control of fruit juice and fruit juice raw materials—SGF-profiling, Fruit Process 1 (2007) 10–18. [45] P. Petrakis, I. Touris, M. Liouni, M. Zervou, I. Kyrikou, R. Kokkinofta, et al., Authenticity of the traditional cypriot spirit “zivania” on the basis of 1h NMR spectroscopy diagnostic parameters and statistical analysis, J. Agric. Food Chem. 53 (2005) 5293. [46] M. Nelson, The Barbarian’s Beverage: A History of Beer in Ancient Europe, Routledge, London, New York, 2005. [47] I. Duarte, A. Barros, P.S.B. Renton Righelato, M. Spraul, A. Eberhard Humpfer, et al., High-resolution nuclear magnetic resonance spectroscopy and multivariate analysis for the characterization of beer, J. Agric. Food Chem. 50 (2002) 2475–2481. [48] I.F. Duarte, A. Barros, C. Almeida, M. Spraul, A.M. Gil, Multivariate analysis of NMR and FTIR data as a potential tool for the quality control of beer, J. Agric. Food Chem. 52 (2004) 1031–1038. [49] C. Almeida, I.F. Duarte, A. Barros, J. Rodrigues, M. Spraul, A.M. Gil, Composition of beer by 1H NMR spectroscopy: effects of brewing site and date of production, J. Agric. Food Chem. 54 (2006) 700. [50] D.W. Lachenmeier, W. Frank, E. Humpfer, H. Schafer, S. Keller, M. Mortter, et al., Quality control of beer using high-resolution nuclear magnetic resonance spectroscopy and multivariate analysis, Eur. Food Res. Technol. 220 (2005) 215–221. [51] A.J. Weekley, P. Bruins, M. Sisto, M.P. Augustine, Using NMR to study full intact wine bottles, J. Magn. Reson. 161 (2003) 91–98.

216

Evaluation Technologies for Food Quality

[52] E. Lo´pez-Rituerto, S. Cabredo, M. Lo´pez, A. Avenoza, J.H. Busto, J.M. Peregrina, A thorough study on the use of quantitative 1H NMR in Rioja red wine fermentation processes, J. Agric. Food Chem. 57 (2009) 2112–2118. [53] D.N. Sobieski, G. Mulvihill, J.S. Broz, M.P. Augustine, Towards rapid throughput NMR studies of full wine bottles, Solid State Nucl. Mag. 29 (2006) 191–198. [54] G. Giulio, P. Chiara, L. Antoine, M. Reto, M. Patrice, A. Marc, et al., Non-invasive detection of cocaine dissolved in wine bottles by (1) H magnetic resonance spectroscopy, Drug Test. Anal. 3 (2011) 544. [55] J.E.A. Rodrigues, G.L. Erny, A.S. Barros, V.I. Esteves, T. Branda˜o, A.A. Ferreira, et al., Quantification of organic acids in beer by nuclear magnetic resonance (NMR)-based methods, Anal. Chim. Acta 674 (2010) 166–175. [56] G.H. Brusewitz, M.L. Stone, Wheat moisture by NMR, Am. Soc. Agric. Eng. Microfiche Collect. 30 (1987) 858–862. [57] P. Chen, M.J. Mccarthy, R. Kauten, NMR for internal quality evaluation of fruits and vegetables, Anal. Chim. Acta 32 (1989) 1747–1753. [58] A.P. Sobolev, E. Brosio, R. Gianferri, A.L. Segre, Metabolic profile of lettuce leaves by high-field NMR spectra, Magn. Reson. Chem. 43 (2005) 625. [59] F. Piccioni, D. Capitani, L. Zolla, L. Mannina, NMR metabolic profiling of transgenic maize with the Cry1A(b) gene, J. Agric. Food Chem. 57 (2009) 6041–6049. [60] W.L. Kerr, R.J. Kauten, M.J. Mccarthy, D.S. Reid, Monitoring the formation of ice during food freezing by magnetic resonance imaging, LWT Food Sci. Technol. 31 (1998) 215–220. [61] M. Mortensen, A.K. Thybo, H.C. Bertram, H.J.A. And, S.B. Engelsen, Cooking effects on water distribution in potatoes using nuclear magnetic resonance relaxation, J. Agric. Food Chem. 53 (2005) 5976–5981. [62] A.K. Thybo, P.M. Szczypinski, A.H. Karlsson, S. Dønstrup, H.S. Stødkilde-Jørgensen, H.J. Andersen, Prediction of sensory texture quality attributes of cooked potatoes by NMR-imaging (MRI) of raw potatoes in combination with different image analysis methods, J. Food Eng. 61 (2004) 91–100. [63] J. Letal, D. Jira´k, L. Sˇuderlova´, M. Ha´jek, MRI “texture” analysis of MR images of apples during ripening and storage, LWT Food Sci. Technol. 36 (2003) 719–727. [64] J. Lammertyn, T. Dresselaers, H.P. Van, P. Jancso´k, M. Wevers, B.M. Nicolaı¨, MRI and X-ray CT study of spatial distribution of core breakdown in “conference” pears, Magn. Reson. Imaging 21 (2003) 805–815. [65] R. Zhou, Y. Li, Texture analysis of MR image for predicting the firmness of Huanghua pears (Pyrus pyrifolia Nakai, cv. Huanghua) during storage using an artificial neural network, Magn. Reson. Imaging 25 (2007) 727. [66] A. Caligiani, D. Acquotti, M. Cirlini, G. Palla, 1H NMR study of fermented cocoa (Theobroma cacao L.) beans, J. Agric. Food Chem. 58 (2010) 12105–12111. [67] F.L. Chen, Y.M. Wei, B. Zhang, Characterization of water state and distribution in textured soybean protein using DSC and NMR, J. Food Eng. 100 (2010) 522–526. [68] S.F. Graham, T. Kennedy, O. Chevallier, A. Gordon, L. Farmer, C. Elliott, et al., The application of NMR to study changes in polar metabolite concentrations in beef longissimus dorsi stored for different periods post mortem, Metabolomics 6 (2010) 395–404. [69] J. Youngae, L. Jueun, J. Kwon, L. KwangSik, R. DoHyun, H. GeumSook, Discrimination of the geographical origin of beef by 1H NMR-based metabolomics, J. Agric. Food Chem. 58 (2010) 10458–10466. [70] A.M. Herrero, M.I. Cambero, J.A. Ordo´n˜ez, D. Castejo´n, M.D.R.D. Avila, L.D.L. Hoz, Magnetic resonance imaging, rheological properties, and physicochemical characteristics

Nuclear magnetic resonance spectroscopy for food quality evaluation

217

of meat systems with fibrinogen and thrombin, J. Agric. Food Chem. 55 (2007) 9357–9364. [71] D. Castejo´n, P. Villa, M.M. Calvo, G. Santa-Marı´a, M. Herraiz, A. Herrera, 1H-HRMAS NMR study of smoked Atlantic salmon (Salmo salar), Magn. Reson. Chem. 48 (2010) 693. [72] G. Nestor, J. Bankefors, C. Schlechtriem, E. Br€ann€as, J. Pickova, C. Sandstr€ om, Highresolution 1H magic angle spinning NMR spectroscopy of intact Arctic char (Salvelinus Alpinus) muscle. Quantitative analysis of n-3 fatty acids, EPA and DHA, J. Agric. Food Chem. 58 (2010) 10799–10803. [73] M. Gudjonsdottir, V.N. Gunnlaugsson, G.A. Finnbogadottir, K. Sveinsdottir, H. Magnusson, S. Arason, et al., Process control of lightly salted wild and farmed Atlantic cod (Gadus morhua) by brine injection, brining, and freezing—a low field NMR study, J. Food Sci. 75 (2010) E527.