HR MAS 1H NMR and chemometrics as useful tool to assess the geographical origin of cocoa beans - Comparison with HR 1H NMR

Nuclear Magnetic Resonance Fingerprints and Mini DNA Markers for the Authentication of Cinnamon Species Ingredients Used in Food and Natural Health Products

Cinnamomum verum (syn C. zeylanicum) is considered 'true' cinnamon. However, it is reported that less expensive sources of cinnamon from C. cassia (syn C. aromaticum), C. loureiroi, and C. burmannii (toxic coumarin) may be used in the place of C. verum. We lack the quality assurance tools that are required to differentiate C. verum from other cinnamon species when verifying that the correct species is sourced from ingredient suppliers. The current research on cinnamon species authentication using DNA tools is limited to a few species and the use of high-quality DNA extracted from raw leaf materials. The cinnamon bark traded in the supply chain contains much less DNA and poorer-quality DNA than leaves. Our research advances DNA methods to authenticate cinnamon, as we utilized full-length chloroplast genomes via a genome skimming approach for C. burmannii and C. cassia to facilitate the design of optimal mini DNA markers. Furthermore, we developed and validated the use of NMR fingerprints for several commercial cinnamon species, including the quantification of 16 molecules. NMR fingerprints provided additional data that were useful for quality assessment in cinnamon extract powders and product consistency. Both the new mini DNA markers and NMR fingerprints were tested on commercial cinnamon products.

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.

Untargeted Metabolomics Analysis for Studying Differences in High-Quality Colombian Cocoa Beans

Colombia is a producer of fine cocoa, according to the International Cocoa Organization; however, most of its exports are in the ordinary cocoa category. To remedy this situation, several national organizations are working to create technological platforms for small producers to certify the quality of their beans. The objective of this study was to identify differential chemical markers in 36 cocoa bean samples from five Colombian departments and associate them with cocoa quality properties. For this purpose, a non-targeted metabolomics approach was performed using UHPLC-HRMS, along with sensory and physicochemical analyses. The 36 samples did not differ in sensory quality, polyphenol content, and theobromine/caffeine ratio. However, the multivariate statistical analysis allowed us to differentiate the samples into four clusters. In addition, a similar grouping of the samples was also observed in the physical analyses. The metabolites responsible for such clustering were investigated with univariate statistical analysis and presumptively identified by comparison of experimental mass spectra with those reported in databases. Alkaloids, flavonoids, terpenoids, peptides, quinolines, and sulfur compounds were identified as discriminants between sample groups. Here, it was presented the metabolic profiles as an important chemical feature for further studies in quality control and more specific characterization of fine cocoa.

Omics approaches to understand cocoa processing and chocolate flavor development: A review

The global market of chocolate has increased worldwide during the last decade and is expected to reach a value of USD 200 billion by 2028. Chocolate is obtained from different varieties of Theobroma cacao L, a plant domesticated more than 4000 years ago in the Amazon rainforest. However, chocolate production is a complex process requiring extensive post-harvesting, mainly involving cocoa bean fermentation, drying, and roasting. These steps have a critical impact on chocolate quality. Standardizing and better understanding cocoa processing is, therefore, a current challenge to boost the global production of high-quality cocoa worldwide. This knowledge can also help cocoa producers improve cocoa processing management and obtain a better chocolate. Several recent studies have been conducted to dissect cocoa processing via omics analysis. A vast amount of data has been produced regarding omics studies of cocoa processing performed worldwide. This review systematically analyzes the current data on cocoa omics using data mining techniques and discusses opportunities and gaps for cocoa processing standardization from this data. First, we observed a recurrent report in metagenomics studies of species of the fungi genus Candida and Pichia as well as bacteria from the genus Lactobacillus, Acetobacter, and Bacillus. Second, our analyzes of the available metabolomics data showed clear differences in the identified metabolites in cocoa and chocolate from different geographical origin, cocoa type, and processing stage. Finally, our analysis of peptidomics data revealed characteristic patterns in the gathered data including higher diversity and lower size distribution of peptides in fine-flavor cocoa. In addition, we discuss the current challenges in cocoa omics research. More research is still required to fill gaps in central matter in chocolate production as starter cultures for cocoa fermentation, flavor evolution of cocoa, and the role of peptides in the development of specific flavor notes. We also offer the most comprehensive collection of multi-omics data in cocoa processing gathered from different research articles.

Nontargeted 1 H NMR fingerprinting and multivariate statistical analysis for traceability of Greek PDO Vostizza currants

In this study, non-targeted ¹ H NMR fingerprinting was used in combination with multivariate statistical analyses for the classification of Greek currants based on their geographical origins (Aeghion, Nemea, Kalamata, Zante, and Amaliada). As classification techniques, Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were carried out. To elucidate different components according to PDO (Protected Designation of Origin), products from Aeghion (Vostizza) were statistically compared with each one of the four other regions. PLS-DA plots ensure that currants from Kalamata, Nemea, Zante, and Amaliada are well classified with respect to the PDO currants, according to differences observed in metabolites. Results suggest that composition differences in carbohydrates, amino, and organic acids of currants are sufficient to discriminate them in correlation to their geographical origin. In conclusion, currants metabolites which mostly contribute to classification performance of such discriminant analysis model present a suitable alternative technique for currants traceability. The study results contribute information to the currants' metabolite fingerprinting by NMR spectroscopy and their geographical origin. PRACTICAL APPLICATION: This study presents an analytical approach for a high nutritional value Greek PDO product, Vostizza currant. A further research and implementation of this method in food industry, can be the key to food fraud incidents. Thus, application of this work opens up posibilities to "farm to table" mission.

Cocoa origin classifiability through LC-MS data: A statistical approach for large and long-term datasets

Classification of food samples based upon their countries of origin is an important task in food industry for quality assurance and development of fine flavor products. Liquid chromatography -mass spectrometry (LC-MS) provides a fast technique for obtaining in-depth information about chemical composition of foods. However, in a large dataset that is gathered over a period of few years, multiple, incoherent and hard to avoid sources of variations e.g., experimental conditions, transportation, batch and instrumental effects, etc. pose technical challenges that make the study of origin classification a difficult problem. Here, we use a large dataset gathered over a period of four years containing 297 LC-MS profiles of cocoa sourced from 10 countries to demonstrate these challenges by using two popular multivariate analysis methods: principal component analysis (PCA) and linear discriminant analysis (LDA). We show that PCA provides a limited separation in bean origin, while LDA suffers from a strong non-linear dependence on the set of compounds. Further, we show for LDA that a compound selection criterion based on Gaussian distribution of intensities across samples dramatically enhances origin clustering of samples thereby suggesting possibilities for studying marker compounds in such a disparate dataset through this approach. In essence, we show and develop a new approach that maximizes, avoiding overfitting, the utility of multivariate analysis in a highly complex dataset.

HR-MAS NMR Applications in Plant Metabolomics

Metabolomics is used to reduce the complexity of plants and to understand the underlying pathways of the plant phenotype. The metabolic profile of plants can be obtained by mass spectrometry or liquid-state NMR. The extraction of metabolites from the sample is necessary for both techniques to obtain the metabolic profile. This extraction step can be eliminated by making use of high-resolution magic angle spinning (HR-MAS) NMR. In this review, an HR-MAS NMR-based workflow is described in more detail, including used pulse sequences in metabolomics. The pre-processing steps of one-dimensional HR-MAS NMR spectra are presented, including spectral alignment, baseline correction, bucketing, normalisation and scaling procedures. We also highlight some of the models which can be used to perform multivariate analysis on the HR-MAS NMR spectra. Finally, applications of HR-MAS NMR in plant metabolomics are described and show that HR-MAS NMR is a powerful tool for plant metabolomics studies.

NMR-based Lavado cocoa chemical characterization and comparison with fermented cocoa varieties: Insights on cocoa's anti-amyloidogenic activity

The metabolic profile of Lavado cocoa was characterized for the first time by NMR spectroscopy, then compared with the profiles of fermented and processed varieties, Natural and commercial cocoa. The significant difference in the contents of theobromine and flavanols prompted us to examine the cocoa varieties to seek correlations between these metabolite concentrations and the anti-amyloidogenic activity reported for cocoa in the literature. We combined NMR spectroscopy, preparative reversed-phase (RP) chromatography, atomic force microscopy, in vitro biochemical and cell assays, to investigate and compare the anti-amyloidogenic properties of extracts and fractions enriched in different metabolite classes. Lavado variety was the most active and the catechins and theobromine were the chemical components of cocoa hindering Aβ peptide on-pathway aggregation and toxicity in a human neuroblastoma SH-SY5Y cell line.

Pre-crystallization process in chocolate: Mechanism, importance and novel aspects

Pre-crystallization is an important step in the production of chocolate, which is defined as tempering of cocoa butter through primary and secondary nucleation. The goal of tempering is to obtain a sufficient amount of β_V polymorph of the right size. The pre-crystallization process has a great impact on the quality and production cost of final product. Development of chocolate technology requires the use of the most appropriate techniques and ingredients without negatively affecting the quality characteristics. Applications of novel technologies within the confectionery industry have allowed production of chocolate in sufficient quantities to meet the public needs. In order to provide and investigate the potential and usage of novel technologies, the present review focused on different pre-crystallization methods and factors affecting the processing conditions. Seeding and ultrasound-assisted pre-crystallization can be used as alternatives to conventional tempering process. However, in both methods, optimization of experimental conditions is required.

Volatile fingerprint of unroasted and roasted cocoa beans (Theobroma cacao L.) from different geographical origins

The aroma characterization of 58 unroasted cocoa beans from 22 different geographical origins was performed by head space solid phase micro-extraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS). Sampling is representative of the average world production (America, Africa, and Southeast Asia). Analysis of cocoa beans before and after roasting were performed to follow the aroma modification with the aim to achieve a cocoa volatile fingerprint and a discrimination model based on beans origin. A total of 57 volatiles was identified in unroasted cocoa beans, while 71 volatiles were identified in roasted cocoa beans. The compounds belong to several chemical groups including esters, alcohols, organic acids, aldehydes, ketones and pyrazines. Datasets were submitted to multivariate statistical analysis (Principal Component Analysis, PCA). Results allowed to discriminate unroasted cocoa beans based on their geographical origin: samples coming from African countries were separated from samples of American regions, whereas samples from Southeast Asia lie between the other two continents suggesting that Asian samples have intermediate characteristics between African and South American cocoa beans. PCA, applied on the corresponding roasted samples, showed that although the same roasting treatment has been applied to all the samples, the differences among the unroasted samples were also maintained in the aromatic profile after roasting. The discrimination model based on volatile fingerprint combined with chemometric tools, showed interesting potential for origin authentication of both unroasted and roasted cocoa beans.

Complementary use of 1H NMR and multi-element IRMS in association with chemometrics enables effective origin analysis of cocoa beans (Theobroma cacao L.)

Within the cocoa market (Theobroma cacao L.), quality and prices are often determined by geographical origin, making traceability indispensable. Therefore, to investigate possibilities of tracing by analytical methods, 48 carefully selected cocoa samples from 20 countries have been profiled using a combination of stable isotope-ratio mass spectrometry (IRMS) and proton nuclear magnetic resonance (¹H NMR). Chemometric analysis of combined data sets from both, stable isotope data (δ¹³C, δ¹⁵N, δ¹⁸O, δ²H, %C, %N, %O, %H) and ¹H NMR fingerprints, achieved good separation with increased classification rates compared to classification with data of the isolated methods. IRMS contributed primarily to discrimination between countries, while ¹H NMR significantly contributed to separation of varieties, but also the regions within individual countries. This study thus demonstrates that combination of two analytical methods is an effective tool to enhance both, accuracy and precision, in authenticity testing of cocoa.

PTR-QiToF-MS and HSI for the characterization of fermented cocoa beans from different origins

The wide range of geographical cocoa production areas and the increasing consumption trend towards single origin products induced the necessity to verify and certify cocoa beans origin for quality assurance purposes. In this study cocoa beans of various origins were examined by machine olfaction and machine vision techniques. Fifty-nine fermented and dried Forastero cocoa beans from 23 different geographical origins (Africa, Americas, Southeast Asia) were investigated using Proton Transfer Reaction-Quadrupole interface-Time of Flight-Mass Spectrometry and Hyperspectral Imaging to elucidate the geographical information in the beans. The volatile and spectral fingerprints showed the same tendency in clustering samples from Africa separate from those from the Americas. High variability was observed for the Southeast Asian samples, which is most likely related to differences in fermentation. Machine olfaction and machine vision characterization provided a similar degree of separation but are complementary rapid techniques, which may be further developed for use in practical settings.

NMR-based approach reveals seasonal metabolic changes in mate (Ilex paraguariensis A. St.-Hil.)

Ilex paraguariensis (mate) is a species native to South America and is widely consumed in countries such Argentina, Uruguay, Paraguay, and Brazil. Mate consumption is associated with several phytotherapeutic functions, in addition to its cultural and regional importance. However, the harvest period can affect the properties of the mate, due to variations in the constituent proportions, as a consequence of seasonal changes. In this work, we employed nuclear magnetic resonance and chemometrics to evaluate the chemical variations in leaf extracts of I. paraguariensis over the four seasons of the year. We found significant changes in the levels of glucose, myo-inositol, caffeine, theobromine, and fatty acids. These changes can be related to resource allocation for the flowering period, or to responses to environmental stresses, such as temperature.

NMR-based metabolic study of fruits of Physalis peruviana L. grown in eight different Peruvian ecosystems

The berry of Physalis peruviana L. (Solanaceae) represents an important socio-economical commodity for Latin America. The absence of a clear phenotype renders it difficult to trace its place of origin. In this study, Cape gooseberries from eight different regions within the Peruvian Andes were profiled for their metabolism implementing a NMR platform. Twenty-four compounds could be unequivocally identified and sixteen quantified. One-way ANOVA and post-hoc Tukey test revealed that all of the quantified metabolites changed significantly among regions: Bambamarca I showed the most accumulated significant differences. The coefficient of variation demonstrated high phenotypic plasticity for amino acids, while sugars displayed low phenotypic plasticity. Correlation analysis highlighted the closely coordinated behavior of the amino acid profile. Finally, PLS-DA revealed a clear separation among the regions based on their metabolic profiles, accentuating the discriminatory capacity of NMR in establishing significant phytochemical differences between producing regions of the fruit of P. peruviana L.

Trends in Chemometrics: Food Authentication, Microbiology, and Effects of Processing

In the last decade, the use of multivariate statistical techniques developed for analytical chemistry has been adopted widely in food science and technology. Usually, chemometrics is applied when there is a large and complex dataset, in terms of sample numbers, types, and responses. The results are used for authentication of geographical origin, farming systems, or even to trace adulteration of high value-added commodities. In this article, we provide an extensive practical and pragmatic overview on the use of the main chemometrics tools in food science studies, focusing on the effects of process variables on chemical composition and on the authentication of foods based on chemical markers. Pattern recognition methods, such as principal component analysis and cluster analysis, have been used to associate the level of bioactive components with in vitro functional properties, although supervised multivariate statistical methods have been used for authentication purposes. Overall, chemometrics is a useful aid when extensive, multiple, and complex real-life problems need to be addressed in a multifactorial and holistic context. Undoubtedly, chemometrics should be used by governmental bodies and industries that need to monitor the quality of foods, raw materials, and processes when high-dimensional data are available. We have focused on practical examples and listed the pros and cons of the most used chemometric tools to help the user choose the most appropriate statistical approach for analysis of complex and multivariate data.