Magnetic Resonance in Food Science: Challenges in a Changing World
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Magnetic Resonance in Food Science: Challenges in a Changing World
280Hardcover
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Product Details
| ISBN-13: | 9780854041176 |
|---|---|
| Publisher: | RSC |
| Publication date: | 04/03/2009 |
| Series: | Special Publications , #319 |
| Pages: | 280 |
| Product dimensions: | 6.30(w) x 9.30(h) x 0.80(d) |
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Magnetic Resonance in Food Science
Challenges in a Changing World
By María Guðjónsdóttir, Peter Belton, Graham Webb
The Royal Society of Chemistry
Copyright © 2009 The Royal Society of ChemistryAll rights reserved.
ISBN: 978-0-85404-117-6
CHAPTER 1
HIGH RESOLUTION NMR ANALYSIS OF COMPLEX MIXTURES
Adrian J. Charlton
Central Science Laboratory, Department for Environment, Food & Rural Affairs, Sand Hutton, York, Y041 1LZ, UK.
1 INTRODUCTION
Advances in analytical techniques and reduced computation times have facilitated broad ranging characterisation of complex chemical and biochemical systems. Resolution and sensitivity enhancements have ensured a key role for high-resolution liquid state nuclear magnetic resonance (NMR) spectroscopy for the determination of compositional variation in complex matrices including foods. A range of studies utilising NMR spectroscopy and advanced data exploration techniques have illustrated the applicability of these techniques to issues facing the food and agricultural sectors.
In the era of the discerning consumer, attitudes toward food choice have changed markedly from the need to provide basic nutrition to the desire to make informed choices relating to food intake. Consumer choices in relation to food intake are often made on the basis of the perceived health benefits that food borne components may impart, where scientific methods to substantiate these claims are often lacking. Whilst the organoleptic properties of food are doubtless a major factor for continued food consumption, it is also clear that initial choices are often made on the basis of promotional labelling. Information that is present on food labels acts as a comparative index by which initial consumer choices are made. For example, regional produce may be associated with a distinctive flavour. Similarly, production processes including organic, com-fed or free range are associated with superior quality produce or morality probing implications.
Informed consumer preferences are thus leading to a greater range of choices and a concurrent increase in the number of claims that are made by food producers, manufacturers and the broadcast media. These choices can only be considered as informed when public awareness of current scientific knowledge is prevalent. In the scientific communities an extensive phase of evidence gathering is being undertaken in relation to food composition. This is largely being driven by a desire to fully understand the composition of food and its implications with respect to consumer choice and public health. Large bodies of data are being collected and interpreted against specific claims relating to food authenticity, quality, safety and nutrition.
An insight is provided here into the current state-of-the-art for the compositional analysis of molecules in food utilising high-resolution NMR spectroscopy in conjunction with multivariate data analysis techniques. The recent Human Metabolome Project (http://www.metabolomics.ca) has identified 2,500 metabolites, 1,200 drugs, and 3,500 food components that can be found in the human body. Whilst numerical incidence is not a robust measure of significance, it is clear from this information that it would be impossible to completely understand human physiology without a detailed understanding of factors effecting food composition.
3 METHODS
2.1 Nuclear magnetic resonance (NMR) spectroscopy
The application of nuclear magnetic resonance spectroscopy within the food sector has, until recently, focussed primarily on the use of time domain (TD) techniques. These enable the quantitative measurement of bulk properties such as water and fat content in whole foods. The measurement relies on the intrinsic relaxation properties of the proton nucleus when a radio frequency pulse is applied to a sample placed in a magnetic field. The differential between the relaxation properties of major food components allows the proportion of these components to be estimated by reference to a calibration series. This form of NMR spectroscopy is routinely applied for quality and composition checks and is often undertaken in situ as the instrumentation is both inexpensive and robust.
In more recent times, higher magnetic field strengths have enabled the measurement of specific resonance frequencies relating to individual components of food and it is the high-resolution (HR) form of NMR spectroscopy that has been adopted as one of the key techniques in a wide range of studies relating to food composition. HR NMR spectroscopy uses the same principles of pulse excitation that is used for TD NMR studies. However, it is the precession frequency of a given nucleus within a magnetic field that is the primary source of information. This frequency is recorded in the time domain as the free induction decay (FID) and the Fourier transformation is used to generate what is recognised as the frequency domain NMR spectrum. The spectrum is characterised by a number of resonance peaks which are plotted as spectral intensities and frequencies (chemical shifts) usually displayed in parts per million (ppm) of the NMR carrier frequency. Assuming the correct instrumental set-up there is a linear correlation between the number of nuclei that represent a resonance peak and its intensity. Therefore NMR measurements can largely be considered as quantitative. High-resolution NMR spectra of both liquid and solid food (or extracts thereof) can be recorded with minimal sample preparation. The ubiquity of the NMR response from the small organic molecules that are found in food make HR NMR spectroscopy an excellent method for compositional screening and molecular characterisation.
The sensitivity of the NMR measurement is a function of the time used for data acquisition with a high number of repetitions leading to a greater signal to noise ratio. This feature of NMR spectroscopy is one of the principle reasons for its increasing adoption for the determination of food composition, illustrating the high degree of analytical reproducibility that is offered.
The large amount of interpretable data that is obtainable by NMR spectroscopy when it is applied to complex mixtures is perhaps the main advantage that the technique offers over other spectroscopic techniques. NMR spectra can be routinely interpreted to determine the range of compounds that are present in complex mixtures particularly when two-dimensional (2D) NMR techniques are employed. Limitations of the technique are often quoted as being poor sensitivity and resolution. Both of these limitations are progressively being overcome by increasing the available magnetic field strengths (currently the maximum is 22.3 tesla [950MHz 1H]), and even greater strides towards high sensitivity measurements are being made by the development of polarisation techniques and the advent of cryogenically cooled probes.
2.2 Multivariate Analysis
Examination of the wide diversity of small molecules in complex mixtures requires multivariate methods that are able to reduce the high dimensionality of analytical datasets to fewer characteristic dimensions. Multivariate methods to extract information from large datasets are becoming increasingly widespread. One of the most common chemometric methods is principal components analysis (PCA). This method performs a coordinate transformation on multivariate data so that they are represented as a number of principal component scores on new coordinate axis (principal components). The initial principal components capture the most significant sources of variation and this decreases for each subsequent component. PCA is an unsupervised method in which no prior information about experimental groupings is used in the transformation. It therefore avoids the need for the extensive cross-validation required in supervised methods, but can be less efficient in finding differences between experimental groupings when there is a large degree of natural variation.
Soft independent modelling of class analogy (SIMCA) is a supervised multivariate statistical method. SIMCA is used to compute scores and residuals from or within a component, plane or hyperplane of a principal components analysis. Critical distances from (residuals) and within (scores) the plane of the model are then used to determine thresholds for class membership. SIMCA models allow predictions to be made, using test data, about the membership (or otherwise) of the modelled class. In judging class membership two types of outlier are defined: moderate and strong outliers. Strong outliers have a great leverage on a PCA and are thus detected by the PC scores. Threshold values for strong outliers can be set by the determination of a probability ellipsoid and this is often calculated using the Hotelling's T2 statistic, which is a multivariate generalisation of Student's t-test. Moderate outliers are detected by consideration of the model residuals. This is the distance from an observation to the plane of the SIMCA model and is also known as the residual error or the residual standard deviation (RSD).
Partial least squares (PLS) regression is a multivariate method that seeks to find linear combinations of the variables that result in the best separation between specific experimental groupings. In broad terms the method can be considered to be mathematically equivalent to PCA where vector rotation is performed until maximum categorical variance is captured as component planes. As a-priori knowledge of group membership is required when developing a PLS model it is a supervised method, and requires cross-validation to avoid overfitting the data. PLS is used in combination with an appropriate discriminant analysis technique to determine the probably of group membership. A PLS calculation also generates a measure of the contribution that each variable in the calculation makes to the discrimination of the tested groups. This is called the variable importance of projection (VIP) and is used to interpret the PLS calculation in the context of the non-transformed data.
Artificial intelligence has also been applied to analytical data sets, including artificial neural networks, genetic algorithms and genetic programming (GP). Genetic algorithms and genetic programming are computational learning techniques based on Darwin's theory of evolution and are popular for solving optimization problems. A population of possible solutions to a problem is randomly created and this is considered the first generation. New generations are achieved through mutation and crossover (sexual reproduction) evolving optimal solutions. The fitness of each solution is evaluated using a fitness function, which for classification problems can be simply the number of correct classifications achieved. Where genetic algorithms create a string of numbers to represent solutions, genetic programming creates computer programs (symbolic expressions). These computer programs are often represented as trees and consist of mathematical operators and data variables (Figure 1). A program that provides an optimised solution can therefore be related to the data from which it was generated.
3 RESULTS AND DISCUSSION
3.1 Incident Detection
In 1997 the Laboratory Environmental Analysis Proficiency (LEAP) Emergency Scheme was initiated. The primary function of this scheme is to establish the reliability of water testing laboratories to determine the contents of a simulated potable water contamination sample. In the event of a potable water contamination incident, water testing laboratories may be called upon to rapidly identify contaminants that are present in the water with little information about the likely source of contamination. Samples are analysed for inorganic and organic chemical contamination.
Determination of organic contamination of the potable water supply is usually performed using techniques based on the application of high performance liquid chromatography (HPLC) or gas chromatography (GC). The choice of chromatographic method is determined by the suite of analytes that is to be detected and therefore these approaches are inherently targeted to detect specific compounds. Ideally, a non-targetted technique, such as NMR spectroscopy should be used, to minimise the probability that a contaminant will not be detected. NMR spectroscopy is commonly perceived to be a relatively insensitive technique, however developments in the field of NMR are constantly leading to improved sensitivity and include improvements such as increases in the maximum available magnetic field strength and cryoprobes. In the absence of high salt concentrations, the sensitivity obtained by using the cryoprobe is approximately 3 to 4 fold greater than that obtained by using the equivalent standard probe and this corresponds to a reduction of 9 to 16 fold in experimental time. Further improvements in the signal to noise ratio of NMR measurements can be made by effective instrument set-up. This includes the choice of pulse sequence, correct calibration of required pulses and effective choice of probehead. Recent advances in the sensitivity of NMR spectroscopy have facilitated the determination and characterisation of molecules present in solution at much lower concentrations than prior to these developments.
Pre-concentration of a sample using rotary evaporation has also been shown to improve detection limits for NMR measurement of non-volatile analytes in water. However, one of the principal advantages of NMR spectroscopy for the detection of unknown contaminants in potable water is that the analysis can be performed without significant sample preparation. Table 1 summarises the analysis of three consecutive LEAP organic contamination emergency samples using cryoprobe nuclear magnetic resonance (NMR)1 spectroscopy.
An efficient method for detecting malicious and accidental contamination of foods has been developed using a combined 1H nuclear magnetic resonance (NMR) and chemometrics approach. The method has been demonstrated using a commercially available carbonated soft drink, as being capable of identifying atypical products and to identify contaminant resonances. Soft independent modelling of class analogy (SIMCA) was used to compare 1H NMR profiles of genuine products (obtained from the manufacturer) against retail products spiked in the laboratory with impurities. The benefits of using feature selection for extracting contaminant NMR frequencies were also assessed. Using example impurities (paraquat, p -cresol and glyphosate) NMR spectra were analysed using multivariate methods resulting in detection limits of approximately 0.075 mM, 0.2 mM, and 0.06 mM for p-cresol, paraquat and glyphosate, respectively. These detection limits, assuming soft beverage consumption of 500 mL and an adult weight to be 70 kg, were shown to be approximately 100 fold lower than the minimum lethal dose for paraquat. The methodology is used to assess the composition of complex matrices for the presence of contaminating molecules without a priori knowledge of the nature of potential contaminants. The ability to detect if a sample does not fit into the expected profile without recourse to multiple targeted analyses is a valuable tool for incident detection and forensic applications.
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Excerpted from Magnetic Resonance in Food Science by María Guðjónsdóttir, Peter Belton, Graham Webb. Copyright © 2009 The Royal Society of Chemistry. Excerpted by permission of The Royal Society of Chemistry.
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