Fruit juice authentication by 1H NMR spectroscopy in combination with different chemometrics tools

Emerging trends and applications of metabolomics in food science and nutrition

The study of all chemical processes involving metabolites is known as metabolomics. It has been developed into an essential tool in several disciplines, such as the study of plant physiology, drug development, human diseases, and nutrition. The field of food science, diagnostic biomarker research, etiological analysis in the field of medical therapy, and raw material quality, processing, and safety have all benefited from the use of metabolomics recently. Food metabolomics includes the use of metabolomics in food production, processing, and human diets. As a result of changing consumer habits and the rising of food industries all over the world, there is a remarkable increase in interest in food quality and safety. It requires the employment of various technologies for the food supply chain, processing of food, and even plant breeding. This can be achieved by understanding the metabolome of food, including its biochemistry and composition. Additionally, Food metabolomics can be used to determine the similarities and differences across crop kinds, as an indicator for tracking the process of ripening to increase crops' shelf life and attractiveness, and identifying metabolites linked to pathways responsible for postharvest disorders. Moreover, nutritional metabolomics is used to investigate the connection between diet and human health through detection of certain biomarkers. This review assessed and compiled literature on food metabolomics research with an emphasis on metabolite extraction, detection, and data processing as well as its applications to the study of food nutrition, food-based illness, and phytochemical analysis. Several studies have been published on the applications of metabolomics in food but further research concerning the use of standard reproducible procedures must be done. The results published showed promising uses in the food industry in many areas such as food production, processing, and human diets. Finally, metabolome-wide association studies (MWASs) could also be a useful predictor to detect the connection between certain diseases and low molecular weight biomarkers.

Rapid Determination of Nutmeg Shell Content in Ground Nutmeg Using FT-NIR Spectroscopy and Machine Learning

Nutmeg is a popular spice often used in ground form, which makes it highly susceptible to food fraud. Therefore, the aim of the present study was to detect adulteration of ground nutmeg with nutmeg shell via Fourier transform near-infrared (FT-NIR) spectroscopy. For this purpose, 36 authentic nutmeg samples and 10 nutmeg shell samples were analyzed pure and in mixtures with up to 50% shell content. The spectra plot as well as a principal component analysis showed a clear separation trend as a function of shell content. A support vector machine regression used for shell content prediction achieved an R2 of 0.944 in the range of 0-10%. The limit of detection of the prediction model was estimated to be 1.5% nutmeg shell. Based on random sub-sampling, the likelihood was found to be 2% that a pure nutmeg sample is predicted with a nutmeg shell content of >1%. The results confirm the suitability of FT-NIR spectroscopy for rapid detection and quantitation of the shell content in ground nutmeg.

Contemporary Developments and Emerging Trends in the Application of Spectroscopy Techniques: A Particular Reference to Coconut (Cocos nucifera L.)

The number of food frauds in coconut-based products is increasing due to higher consumer demands for these products. Rising health consciousness, public awareness and increased concerns about food safety and quality have made authorities and various other certifying agencies focus more on the authentication of coconut products. As the conventional techniques for determining the quality attributes of coconut are destructive and time-consuming, non-destructive testing methods which are accurate, rapid, and easy to perform with no detrimental sampling methods are currently gaining importance. Spectroscopic methods such as nuclear magnetic resonance (NMR), infrared (IR)spectroscopy, mid-infrared (MIR)spectroscopy, near-infrared (NIR) spectroscopy, ultraviolet-visible (UV-VIS) spectroscopy, fluorescence spectroscopy, Fourier-transform infrared spectroscopy (FTIR), and Raman spectroscopy (RS) are gaining in importance for determining the oxidative stability of coconut oil, the adulteration of oils, and the detection of harmful additives, pathogens, and toxins in coconut products and are also employed in deducing the interactions in food constituents, and microbial contaminations. The objective of this review is to provide a comprehensive analysis on the various spectroscopic techniques along with different chemometric approaches for the successful authentication and quality determination of coconut products. The manuscript was prepared by analyzing and compiling the articles that were collected from various databases such as PubMed, Google Scholar, Scopus and ScienceDirect. The spectroscopic techniques in combination with chemometrics were shown to be successful in the authentication of coconut products. RS and NMR spectroscopy techniques proved their utility and accuracy in assessing the changes in coconut oil’s chemical and viscosity profile. FTIR spectroscopy was successfully utilized to analyze the oxidation levels and determine the authenticity of coconut oils. An FT-NIR-based analysis of various coconut samples confirmed the acceptable levels of accuracy in prediction. These non-destructive methods of spectroscopy offer a broad spectrum of applications in food processing industries to detect adulterants. Moreover, the combined chemometrics and spectroscopy detection method is a versatile and accurate measurement for adulterant identification.

Fast Identification of Food Thickeners by Nontargeted NMR-Spectroscopy

Food thickeners are carbohydrate additives that can only be determined by long-term, multistep analysis. Fast methods to directly determine thickeners in food matrixes are therefore welcome. In this study, a rapid procedure based on the direct ¹H NMR analysis of food samples dissolved in deuterated water was developed. Individual thickeners were assigned due to specific marker signals gleaned from two-dimensional NMR analyses. The combination of one-dimensional ¹H NMR and DOSY experiments enabled unequivocal assignments of thickeners even in complex matrixes. Using this approach, gum arabic, carrageenan, agar-agar, galactomannans, and pectin could be identified in pastille, glaze, and fruit spread. Because of low concentrations (<0.5%-1%, w/w), the same thickeners and others such as xanthan gum and alginate could not be determined directly by NMR in curry sauce, rice pudding, choco milk drink, and lemon peel flavor. Moreover, NMR analyses of the hydrolysate did not reveal the specific monomeric units of the thickeners under study, as shown for the hydrolysate of lemon peel flavor. Nevertheless, the NMR approach could provide welcome means in the future to directly determine intact thickeners in food.

A review of fruit juice authenticity assessments: Targeted and untargeted analyses

Fruit juices are becoming more and more popular in the whole world. However, the increasing fruit juice fraud cases are undermining the healthy development of fruit juice industry. Fruit juice authenticity represents an important food quality and safety parameter. Many techniques have been applied in fruit juices authenticity assessment. The purpose of this review is to provide a research overview of the targeted and untargeted analyses of fruit authentication, and a method selection guide for fruit juice authenticity assessment. Targeted markers, such as stable isotopes, phenolics, carbohydrates, organic acids, volatile components, DNAs, amino acids and proteins, as well as carotenoids, will be discussed. And untargeted techniques, including liquid/gas chromatography-mass spectrometer, nuclear magnetic resonance, infrared spectroscopy, inductively-coupled plasma-mass spectrometry/optical emission spectrometer, fluorescence spectra, electronic sensors and others, will be reviewed. The emerging untargeted for novel targeted marker analysis will be also summarized.

Potential of 1H NMR fingerprinting and a model system approach to study non-enzymatic browning in shelf-stable orange juice during storage

For the first time, a model system approach was combined with ¹H NMR fingerprinting in studying non-enzymatic browning (NEB) of pasteurized shelf-stable orange juice during storage. Various NEB precursors were used individually or in combinations to formulate simple or complex model systems, respectively, in citric acid buffer. Based on orange juice composition, ascorbic acid, sugars (sucrose, glucose and fructose) and amino acids (proline, arginine, asparagine, aspartic acid, serine and glutamic acid) were selected as the precursors for the model systems. After pasteurization and during subsequent accelerated storage (42 °C, 16 weeks) the model systems displayed a three-phase browning development. The initial browning phase was mainly the result of ascorbic acid degradation especially in the presence of amino acids and sugars. In the later phases, the contribution of reactions of sugars and amino acids to browning became apparent. The application of ¹H NMR fingerprinting on a simple model system containing ascorbic acid revealed that its degradation pathway to intermediates such as xylonic acid, acetic acid and erythrulose was responsible for the major changes during storage. When this model system was complexed by inclusion of sugars and amino acids, the hydrolysis of sucrose to glucose and fructose was identified as the main reaction leading to differences in the samples throughout storage. These three sugars dominated the NMR spectra of the samples, overshadowing several important compounds for NEB such as ascorbic acid and its degradation products. Other more advanced NMR experiments such as two-dimensional NMR analyses should be applied in future research to identify unknown compounds from NEB reactions.

Influence of ultrasound-assisted osmotic dehydration on texture, bioactive compounds and metabolites analysis of plum

The aim of this study was to investigate the effect of ultrasound-assisted osmotic dehydration on texture, retention of bioactive compounds, nutritional quality and metabolites of plum. The osmotic dehydration was performed using 50% glucose and sucrose in ultrasound (25 kHz) for 30 and 60 min. After osmotic treatment samples were dried at 55 °C using hot air oven. In this study, the texture of the sample was determined by 20% compression of texture profile analysis and solute diffusion coefficient was calculated by Fick's law of diffusion. Further, the volatile compound and nutritional quality were determined. Furthermore, treatment difference of osmo-dehydrated plum on metabolites was measured by ¹H NMR. The results showed that the ultrasound-assisted osmotic dehydration increased water loss and solid gain. It also increases the reduction of moisture content from the plum. Textural result of osmo-dehydrated plum in glucose increases the softness of plum and decreases hardness. Moreover, the increased antioxidant and phenolics were obtained in plum treated in 50% sucrose for 30 and 60 min and also at 30 min of glucose. Color of ultrasound-assisted osmotic dehydrated plum was also affected by treatment time and the osmotic solution used. Ultrasound-assisted osmotic dehydration reveals that water loss, solid gain, texture and bioactive compounds affected by treatment time and osmotic solution.

Combined Analysis of Primary Metabolites and Phenolic Compounds to Authenticate Commercial Monovarietal Peach Purees and Pear Juices

Here we authenticated single-varietal peach purees and pear juices on the basis of primary metabolite and phenolic compound analysis by Proton Nuclear Magnetic Resonance (¹H-NMR) and Ultra Performance Liquid Chromatography coupled to Photodiode Array and Tandem Mass Spectrometry (UPLC-PDA-MS/MS), respectively. After suitable preprocessing, the ¹H-NMR and chromatographic data were evaluated by principal component analysis (PCA). The PCA combining data from primary metabolites and phenolic compounds allowed the separation of the clusters in all cases, allowing discrimination of processed and unprocessed peach purees, both separately and pooled. The PCA of primary metabolites allowed the cluster separation of purees of distinct peach varieties but not between processed and non-processed purees. The PCA of phenolic compounds allowed better cluster separation than of primary metabolites. For pear juices, both PCA approaches allowed satisfactory discrimination of Alejandrina, Conference, and Blanquilla cultivars. These approaches may help to better control cultivar authenticity in fruit products. It could therefore contribute to the development of a process to achieve products characterized by a quality characteristic of a given cultivar.

Use of 1H NMR to Detect the Percentage of Pure Fruit Juices in Blends

The consumption of high-nutritional-value juice blends is increasing worldwide and, considering the large market volume, fraud and adulteration represent an ongoing problem. Therefore, advanced anti-fraud tools are needed. This study aims to verify the potential of ¹H NMR combined with partial least squares regression (PLS) to determine the relative percentage of pure fruit juices in commercial blends. Apple, orange, pineapple, and pomegranate juices were selected to set up an experimental plan and then mixed in different proportions according to a central composite design (CCD). NOESY (nuclear Overhauser enhancement spectroscopy) experiments that suppress the water signal were used. Considering the high complexity of the spectra, it was necessary to pretreat and then analyze by chemometric tools the large amount of information contained in the raw data. PLS analysis was performed using venetian-blind internal cross-validation, and the model was established using different chemometric indicators (RMSEC, RMSECV, RMSEP, R²_CAL, R²_CV, R²_PRED). PLS produced the best model, using five factors explaining 94.51 and 88.62% of the total variance in X and Y, respectively. The present work shows the feasibility and advantages of using ¹H NMR spectral data in combination with multivariate analysis to develop and optimize calibration models potentially useful for detecting fruit juice adulteration.

Detection of adulterants in grape nectars by attenuated total reflectance Fourier-transform mid-infrared spectroscopy and multivariate classification strategies

There is no any doubt about the importance of food fraud control, as it has implications in food safety and in consumer health. Focusing on fruit beverages, some types of adulterations have been detected more frequently, such as substitution with less expensive fruits. A methodology based on attenuated total reflectance Fourier-transform mid-infrared spectroscopy (ATR-FTIR) and multivariate classification was applied to detect whether grape nectars were adulterated by substitution with apple juice or cashew juice. A total of 126 samples were obtained and analyzed. Two strategies were proposed: one-class and multiclass approaches. Soft independent modeling of class analogy (SIMCA), partial least squares discriminant analysis (PLS-DA) and partial least squares density modeling (PLS-DM) were used to build the models. Among them, PLS-DA presented the best performance with a sensitivity and specificity of nearly 100%. The multiclass strategy was preferred if the adulterants to be studied are known because it provides additional information.

Recent developments in the food quality detected by non-invasive nuclear magnetic resonance technology

Nuclear magnetic resonance (NMR) is a rapid, accurate and non-invasive technology and widely used to detect the quality of food, particularly to fruits and vegetables, meat and aquatic products. This review is a survey of recent developments in experimental results for the quality of food on various NMR technologies in processing and storage over the past decade. Following a discussion of the quality discrimination and classification of food, analysis of food compositions and detection of physical, chemical, structural and microbiological properties of food are outlined. Owing to high cost, low detection limit and sensitivity, the professional knowledge involved and the safety issues related to the maintenance of the magnetic field, so far the practical applications are limited to detect small range of food. In order to promote applications for a broader range of foods further research and development efforts are needed to overcome the limitations of NMR in the detection process. The needs and opportunities for future research and developments are outlined.

Determination of main fruits in adulterated nectars by ATR-FTIR spectroscopy combined with multivariate calibration and variable selection methods

Grape, orange, peach and passion fruit nectars were formulated and adulterated by dilution with syrup, apple and cashew juices at 10 levels for each adulterant. Attenuated total reflectance Fourier transform mid infrared (ATR-FTIR) spectra were obtained. Partial least squares (PLS) multivariate calibration models allied to different variable selection methods, such as interval partial least squares (iPLS), ordered predictors selection (OPS) and genetic algorithm (GA), were used to quantify the main fruits. PLS improved by iPLS-OPS variable selection showed the highest predictive capacity to quantify the main fruit contents. The selected variables in the final models varied from 72 to 100; the root mean square errors of prediction were estimated from 0.5 to 2.6%; the correlation coefficients of prediction ranged from 0.948 to 0.990; and, the mean relative errors of prediction varied from 3.0 to 6.7%. All of the developed models were validated.

Prediction of Antimicrobial and Antioxidant Activities of Mexican Propolis by 1H-NMR Spectroscopy and Chemometrics Data Analysis

A feasibility study to predict antimicrobial and antioxidant activity properties of propolis extracts using 700-MHz ¹H-NMR spectra and multivariate regression data analysis is presented. The study was conducted with thirty-five propolis samples to develop a rapid and reliable method for the evaluation of their quality. The extracts have been evaluated by measuring phenolic and flavonoid contents; the antioxidant activity; and the antimicrobial activity. The obtained spectral data were submitted to multivariate calibration (partial least squares (PLS) and orthogonal partial least squares (OPLS)) to correlate the relative intensity and position of NMR resonance peaks with the metabolites contents and biological activities. The developed PLS and OPLS model were successfully applied to the determination of the target properties for proof of the concept. The OPLS observed vs. predicted properties plots indicate the absence of systematic errors with determination coefficients between the ranges 0.7207 to 0.9990. Up to 86.1% of explication of variation in the spectral data and 99.9% in the measured properties were attained with 88.6% of prediction capabilities in the best case (S. mutans activity) according to the cross-validation procedure. The figures of merit of the developed PLS and OPLS methods were evaluated and compared as well.

Application of nuclear magnetic resonance spectroscopy in food adulteration determination: the example of Sudan dye I in paprika powder

Carcinogenic Sudan I has been added illegally into spices for an apparent freshness. ¹H solution and solid-state (SS) nuclear magnetic resonance (NMR) spectroscopies were applied and compared for determination of Sudan I in paprika powders (PPs). For solution NMR, PPs spiked with Sudan I were extracted with acetonitrile, centrifuged, rotor-evaporated, and re-dissolved in DMSO-d6 for spectral collection. For SSNMR, Sudan I contaminated PPs were mixed with DMSO-d6 solution and used for spectral collection. Linear regression models constructed for quantitative analyses resulted in the average accuracies for unknown samples as 98% and 105%, respectively. Limits of detection for the solution NMR and SSNMR spectrometers were 6.7 and 128.6 mg kg^-1, while the limits of quantification were 22.5 and 313.7 mg kg^-1. The overall analysis time required by both methods was similar (35 and 32 min). Both NMR techniques are feasible for rapid and accurate determination of Sudan I adulteration in PPs.

Differentiation of Organically and Conventionally Grown Tomatoes by Chemometric Analysis of Combined Data from Proton Nuclear Magnetic Resonance and Mid-infrared Spectroscopy and Stable Isotope Analysis

Because the basic suitability of proton nuclear magnetic resonance spectroscopy ((1)H NMR) to differentiate organic versus conventional tomatoes was recently proven, the approach to optimize (1)H NMR classification models (comprising overall 205 authentic tomato samples) by including additional data of isotope ratio mass spectrometry (IRMS, δ(13)C, δ(15)N, and δ(18)O) and mid-infrared (MIR) spectroscopy was assessed. Both individual and combined analytical methods ((1)H NMR + MIR, (1)H NMR + IRMS, MIR + IRMS, and (1)H NMR + MIR + IRMS) were examined using principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), linear discriminant analysis (LDA), and common components and specific weight analysis (ComDim). With regard to classification abilities, fused data of (1)H NMR + MIR + IRMS yielded better validation results (ranging between 95.0 and 100.0%) than individual methods ((1)H NMR, 91.3-100%; MIR, 75.6-91.7%), suggesting that the combined examination of analytical profiles enhances authentication of organically produced tomatoes.

Independent components analysis to increase efficiency of discriminant analysis methods (FDA and LDA): Application to NMR fingerprinting of wine

Discriminant analysis (DA) methods, such as linear discriminant analysis (LDA) or factorial discriminant analysis (FDA), are well-known chemometric approaches for solving classification problems in chemistry. In most applications, principle components analysis (PCA) is used as the first step to generate orthogonal eigenvectors and the corresponding sample scores are utilized to generate discriminant features for the discrimination. Independent components analysis (ICA) based on the minimization of mutual information can be used as an alternative to PCA as a preprocessing tool for LDA and FDA classification. To illustrate the performance of this ICA/DA methodology, four representative nuclear magnetic resonance (NMR) data sets of wine samples were used. The classification was performed regarding grape variety, year of vintage and geographical origin. The average increase for ICA/DA in comparison with PCA/DA in the percentage of correct classification varied between 6±1% and 8±2%. The maximum increase in classification efficiency of 11±2% was observed for discrimination of the year of vintage (ICA/FDA) and geographical origin (ICA/LDA). The procedure to determine the number of extracted features (PCs, ICs) for the optimum DA models was discussed. The use of independent components (ICs) instead of principle components (PCs) resulted in improved classification performance of DA methods. The ICA/LDA method is preferable to ICA/FDA for recognition tasks based on NMR spectroscopic measurements.

Metabolic fingerprinting based on high-resolution tandem mass spectrometry: a reliable tool for wine authentication?

Ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (MS) and an alternative technology represented by direct analysis in real time coupled with quadrupole time-of-flight MS were investigated for metabolic fingerprinting of 343 red and white wine samples. Direct injection of pure wine and an extraction procedure optimized for isolation of polyphenols were used to compare different analytical and data handling strategies. After data processing and data pretreatment, principal component analysis was initially used to explore the data structure. Initially, the unsupervised models revealed a notable clustering according to the grape varieties, and therefore supervised orthogonal partial least squares discriminant analysis models were created and validated for separation of red and white wines according to the grape variety. The validated orthogonal partial least squares discriminant analysis models based on data (ions) recorded in positive ionization mode were able to classify correctly 95% of samples. In parallel, authentication parameters, such as origin and vintage, were evaluated, and they are discussed. A tentative identification of markers was performed using accurate mass measurement of MS and MS/MS spectra, different software packages and different online libraries. In this way, different flavonol glucosides and polyphenols were identified as wine markers according to the grape varieties.

Independent component analysis (ICA) algorithms for improved spectral deconvolution of overlapped signals in 1H NMR analysis: application to foods and related products

The major challenge facing NMR spectroscopic mixture analysis is the overlapping of signals and the arising impossibility to easily recover the structures for identification of the individual components and to integrate separated signals for quantification. In this paper, various independent component analysis (ICA) algorithms [mutual information least dependent component analysis (MILCA); stochastic non-negative ICA (SNICA); joint approximate diagonalization of eigenmatrices (JADE); and robust, accurate, direct ICA algorithm (RADICAL)] as well as deconvolution methods [simple-to-use-interactive self-modeling mixture analysis (SIMPLISMA) and multivariate curve resolution-alternating least squares (MCR-ALS)] are applied for simultaneous (1)H NMR spectroscopic determination of organic substances in complex mixtures. Among others, we studied constituents of the following matrices: honey, soft drinks, and liquids used in electronic cigarettes. Good quality spectral resolution of up to eight-component mixtures was achieved (correlation coefficients between resolved and experimental spectra were not less than 0.90). In general, the relative errors in the recovered concentrations were below 12%. SIMPLISMA and MILCA algorithms were found to be preferable for NMR spectra deconvolution and showed similar performance. The proposed method was used for analysis of authentic samples. The resolved ICA concentrations match well with the results of reference gas chromatography-mass spectrometry as well as the MCR-ALS algorithm used for comparison. ICA deconvolution considerably improves the application range of direct NMR spectroscopy for analysis of complex mixtures.

The role of metabonomics as a tool for augmenting nutritional information in epidemiological studies

Most chronic diseases have been demonstrated to have a link to nutrition. Within food and nutritional research there is a major driver to understand the relationship between diet and disease in order to improve health of individuals. However, the lack of accurate dietary intake assessment in free-living populations, makes accurate estimation of how diet is associated with disease risk difficulty. Thus, there is a pressing need to find solutions to the inaccuracy of dietary reporting. Metabolic profiling of urine or plasma can provide an unbiased approach to characterizing dietary intake and various high-throughput analytical platforms have been used in order to implement targeted and nontargeted assays in nutritional clinical trials and nutritional epidemiology studies. This review describes first the challenges presented in interpreting the relationship between diet and health within individual and epidemiological frameworks. Second, we aim to explore how metabonomics can benefit different types of nutritional studies and discuss the critical importance of selecting appropriate analytical techniques in these studies. Third, we propose a strategy capable of providing accurate assessment of food intake within an epidemiological framework in order establish accurate associations between diet and health.

Assessment of a 1H high-resolution magic angle spinning NMR spectroscopy procedure for free sugars quantification in intact plant tissue

In most plants, sucrose is the primary product of photosynthesis, the transport form of assimilated carbon, and also one of the main factors determining sweetness in fresh fruits. Traditional methods for sugar quantification (mainly sucrose, glucose and fructose) require obtaining crude plant extracts, which sometimes involve substantial sample manipulation, making the process time-consuming and increasing the risk of sample degradation. Here, we describe and validate a fast method to determine sugar content in intact plant tissue by using high-resolution magic angle spinning nuclear magnetic resonance spectroscopy (HR-MAS NMR). The HR-MAS NMR method was used for quantifying sucrose, glucose and fructose in mesocarp tissues from melon fruits (Cucumis melo var. reticulatus and Cucumis melo var. cantalupensis). The resulting sugar content varied among individual melons, ranging from 1.4 to 7.3 g of sucrose, 0.4-2.5 g of glucose; and 0.73-2.83 g of fructose (values per 100 g fw). These values were in agreement with those described in the literature for melon fruit tissue, and no significant differences were found when comparing them with those obtained using the traditional, enzymatic procedure, on melon tissue extracts. The HR-MAS NMR method offers a fast (usually <30 min) and sensitive method for sugar quantification in intact plant tissues, it requires a small amount of tissue (typically 50 mg fw) and avoids the interferences and risks associated with obtaining plant extracts. Furthermore, this method might also allow the quantification of additional metabolites detectable in the plant tissue NMR spectrum.

PGI chicory (Cichorium intybus L.) traceability by means of HRMAS-NMR spectroscopy: a preliminary study

BACKGROUND

Analytical traceability of PGI and PDO foods (Protected Geographical Indication and Protected Denomination Origin respectively) is one of the most challenging tasks of current applied research.

RESULTS

Here we proposed a metabolomic approach based on the combination of (1)H high-resolution magic angle spinning-nuclear magnetic resonance (HRMAS-NMR) spectroscopy with multivariate analysis, i.e. PLS-DA, as a reliable tool for the traceability of Italian PGI chicories (Cichorium intybus L.), i.e. Radicchio Rosso di Treviso and Radicchio Variegato di Castelfranco, also known as red and red-spotted, respectively. The metabolic profile was gained by means of HRMAS-NMR, and multivariate data analysis allowed us to build statistical models capable of providing clear discrimination among the two varieties and classification according to the geographical origin.

CONCLUSION

Based on Variable Importance in Projection values, the molecular markers for classifying the different types of red chicories analysed were found accounting for both the cultivar and the place of origin.

Metabolomic analysis of citrus infection by 'Candidatus Liberibacter' reveals insight into pathogenicity

Huanglongbing (HLB), considered the most serious citrus disease in the world, is associated with the nonculturable bacterium 'Candidatus Liberibacter asiaticus' (Las). Infection of citrus by this pathogen leads to reduced plant vigor and productivity, ultimately resulting in death of the infected tree. It can take up to two years following initial infection before outward symptoms become apparent, making detection difficult. The existing knowledge gap in our understanding of Las and its pathogenesis leading to HLB has stymied development of treatments and methods to mitigate the pathogen's influence. To evaluate the influence of Las on fruit quality in both symptomatic and asymptomatic fruit, and gain further insight into the pathogenesis of the disease, a 1H NMR metabolomics investigation, complemented with physicochemical and analyte-specific analyses, was undertaken. Comparison of the juice obtained from oranges gathered from Las+ (symptomatic and asymptomatic) and Las- (healthy) trees revealed significant differences in the concentrations of sugars, amino and organic acids, limonin glucoside, and limonin. This study demonstrates differing metabolic profiles in the juice of oranges from Las+ and Las- and proposes how Las may be able to evade citrus defense responses.

Traceability of Italian garlic (Allium sativum L.) by means of HRMAS-NMR spectroscopy and multivariate data analysis

(1)H High Resolution Magic Angle Spinning-Nuclear Magnetic Resonance (HRMAS-NMR) spectroscopy was used to analyse garlic (Allium sativum L.) belonging to red and white varieties and collected in different Italian regions, in order to address the traceability issue. 1D and 2D NMR spectra, performed directly on untreated small pieces of garlic, so without any sample manipulation, allowed the assignment of several compounds: organic acids, sugars, fatty acids, amino acids and the nutritionally important fructo-oligosaccharides and allyl-organosulphur compounds. Application of Partial Least Squares projections to latent structures-Discrimination Analysis provided an excellent model for the discrimination of both the variety and, most important, the place origin, allowing the identification of the metabolites contributing to such classifications. The presence of organosulphurs, allicin and some allyl-organosulphurs found by HRMAS-NMR, was confirmed also by SPME-GC-MS; 11 molecules were identified, containing from one up to three sulphur atoms and with and without allyl moieties.

Tissue preparation using Arabidopsis

The ability to track changes in the levels of many metabolites in plants has great utility in a number of biological contexts. A metabolomics experiment usually requires the comparison of different varieties in either a functional genomics context or in response to perturbation by an external treatment. Such treatments can result in subtle changes in the final chemical signature of the plant tissue, and therefore, any unwanted variance produced in the generation of that tissue must be minimised. Procedures for plant growth, harvesting, preparation of extracts, and the subsequent collection of data have been optimised to minimise experimental variation within the dataset. This chapter describes in detail how to generate reproducible Arabidopsis tissue suitable for a typical plant metabolomics experiment. Issues concerned with tissue sampling, harvesting, and storage are also discussed.