Chemical Barcoding: A Nuclear-Magnetic-Resonance-Based Approach To Ensure the Quality and Safety of Natural Ingredients

Flower Species Ingredient Verification Using Orthogonal Molecular Methods

Flowers are gaining considerable interest among consumers as ingredients in food, beverages, cosmetics, and natural health products. The supply chain trades in multiple forms of botanicals, including fresh whole flowers, which are easier to identify than dried flowers or flowers processed as powdered or liquid extracts. There is a gap in the scientific methods available for the verification of flower species ingredients traded in the supply chains of multiple markets. The objective of this paper is to develop methods for flower species ingredient verification using two orthogonal methods. More specifically, the objectives of this study employed both (1) DNA-based molecular diagnostic methods and (2) NMR metabolite fingerprint methods in the identification of 23 common flower species ingredients. NMR data analysis reveals considerable information on the variation in metabolites present in different flower species, including color variants within species. This study provides a comprehensive comparison of two orthogonal methods for verifying flower species ingredient supply chains to ensure the highest quality products. By thoroughly analyzing the benefits and limitations of each approach, this research offers valuable insights to support quality assurance and improve consumer confidence.

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.

Analysis and Discrimination of Canadian Honey Using Quantitative NMR and Multivariate Statistical Methods

To address the growing concern of honey adulteration in Canada and globally, a quantitative NMR method was developed to analyze 424 honey samples collected across Canada as part of two surveys in 2018 and 2019 led by the Canadian Food Inspection Agency. Based on a robust and reproducible methodology, NMR data were recorded in triplicate on a 700 MHz NMR spectrometer equipped with a cryoprobe, and the data analysis led to the identification and quantification of 33 compounds characteristic of the chemical composition of honey. The high proportion of Canadian honey in the library provided a unique opportunity to apply multivariate statistical methods including PCA, PLS-DA, and SIMCA in order to differentiate Canadian samples from the rest of the world. Through satisfactory model validation, both PLS-DA as a discriminant modeling technique and SIMCA as a class modeling method proved to be reliable at differentiating Canadian honey from a diverse set of honeys with various countries of origins and floral types. The replacement method of optimization was successfully applied for variable selection, and trigonelline, proline, and ethanol at a lower extent were identified as potential chemical markers for the discrimination of Canadian and non-Canadian honeys.

Visualization of a Machine Learning Framework toward Highly Sensitive Qualitative Analysis by SERS

Surface-enhanced Raman spectroscopy (SERS), providing near-single-molecule-level fingerprint information, is a powerful tool for the trace analysis of a target in a complicated matrix and is especially facilitated by the development of modern machine learning algorithms. However, both the high demand of mass data and the low interpretability of the mysterious black-box operation significantly limit the well-trained model to real systems in practical applications. Aiming at these two issues, we constructed a novel machine learning algorithm-based framework (Vis-CAD), integrating visual random forest, characteristic amplifier, and data augmentation. The introduction of data augmentation significantly reduced the requirement of mass data, and the visualization of the random forest clearly presented the captured features, by which one was able to determine the reliability of the algorithm. Taking the trace analysis of individual polycyclic aromatic hydrocarbons in a mixture as an example, a trustworthy accuracy no less than 99% was realized under the optimized condition. The visualization of the algorithm framework distinctly demonstrated that the captured feature was well correlated to the characteristic Raman peaks of each individual. Furthermore, the sensitivity toward the trace individual could be improved by least 1 order of magnitude as compared to that with the naked eye. The proposed algorithm distinguished by the lesser demand of mass data and the visualization of the operation process offers a new way for the indestructible application of machine learning algorithms, which would bring push-to-the-limit sensitivity toward the qualitative and quantitative analysis of trace targets, not only in the field of SERS, but also in the much wider spectroscopy world. It is implemented in the Python programming language and is open-source at https://github.com/3331822w/Vis-CAD.

Kurtosis-based projection pursuit analysis to evaluate South American rapadura

Rapadura is an artisanal candy obtained from concentrated sugarcane juice. In this study, a differentiation between South American rapadura producers has been tried using a Kurtosis-based projection pursuit analysis (kPPA) concerning essential minerals, acrylamide, moisture contents, pH, and color. These parameters revealed significant inter- and intra-country differences. Based on the employed measurements, a multivariate exploration with kPPA extracted information from rapadura even though it is a very artisanal product and was effective in separating classes, especially Brazilian and Ecuadorian rapadura, where principal component analysis failed. Moreover, ellipse confidence regions showed significant differences between non-organic and organic rapadura from Colombia and Peru in granulated form. From a chemometric point of view, the application of kPPA can be used in cases when other metrics (as based on the variance) fail and can be useful in the exploratory analysis of complex multivariate chemical data.

Synthetic DNA applications in information technology

Synthetic DNA is a growing alternative to electronic-based technologies in fields such as data storage, product tagging, or signal processing. Its value lies in its characteristic attributes, namely Watson-Crick base pairing, array synthesis, sequencing, toehold displacement and polymerase chain reaction (PCR) capabilities. In this review, we provide an overview of the most prevalent applications of synthetic DNA that could shape the future of information technology. We emphasize the reasons why the biomolecule can be a valuable alternative for conventional electronic-based media, and give insights on where the DNA-analog technology stands with respect to its electronic counterparts.

Combinatorial projection pursuit analysis for exploring multivariate chemical data

Kurtosis-based projection pursuit analysis (kPPA) has demonstrated the ability to visualize multivariate data in a way that complements other exploratory data analysis tools, such as principal components analysis (PCA). It is especially useful for partitioning binary data sets (2^k classes) with a balanced design. Since kPPA is not a variance-based method, it can often provide unsupervised class separation where other methods fail. However, when multiple classifications are possible (e.g. by gender, age, disease state, etc.), the projection provided by kPPA (corresponding to the global minimum kurtosis) will not necessarily be the one of greatest interest to the researcher. Fortunately, the optimization algorithm for kPPA allows for interrogation of projections obtained from numerous local minima. This strategy provides the basis of a new method described here, referred to as combinatorial projection pursuit analysis (CombPPA) because it presents alternative combinations of class separation. The method is truly exploratory in that it allows the landscape of interesting projections to be more fully probed. The approach uses Procrustes rotation to map local minima among the kPPA solutions, whereupon the researcher can visualize different projections. To demonstrate the new method, the clustering of grape juice samples using visible spectroscopy is presented as a model problem. This problem is well-suited to this type of study because there are eight classes of samples symmetrically partitioned into two classes by type (organic/non-organic) or four classes by brand. Results presented show the different combinations of projections that can be obtained, including the desired partitions. In addition, this work describes new enhancements to the kPPA algorithm that improve the orthogonality of solutions obtained.

Quantitative NMR Methodology for the Authentication of Roasted Coffee and Prediction of Blends

In response to the need from the food industry for new analytical solutions, a fit-for-purpose quantitative ¹H NMR methodology was developed to authenticate pure coffee (100% arabica or robusta) as well as predict the percentage of robusta in blends through the study of 292 roasted coffee samples in triplicate. Methanol was chosen as the extraction solvent, which led to the quantitation of 12 coffee constituents: caffeine, trigonelline, 3- and 5-caffeoylquinic acid, lipids, cafestol, nicotinic acid, N-methylpyridinium, formic acid, acetic acid, kahweol, and 16-O-methylcafestol. To overcome the chemical complexity of the methanolic extract, quantitative analysis was performed using a combination of traditional integration and spectral deconvolution methods. As a result, the proposed methodology provides a systematic methodology and a linear regression model to support the classification of known and unknown roasted coffees and their blends.

Genome skimming and NMR chemical fingerprinting provide quality assurance biotechnology to validate Sarsaparilla identity and purity

Sarsaparilla is a popular natural health product (NHP) that has been reported to be one of the most adulterated botanicals in the marketplace. Several plausible explanations are documented including economically motivated product substitution, unintentional errors due to ambiguous trade name associated with several different taxa, and wild harvesting of incorrect non-commercial plants. Unfortunately, this includes the case of an adulterant species Decalepis hamiltonii, a Red listed medicinal plant species by the International Union for Conservation of Nature (IUCN) and declared as a species with high conservation concern by the National Biodiversity Authority of India (NBA). This study provides validated genomic (genome skimming & DNA probes) and metabolomic (NMR chemical fingerprints) biotechnology solutions to prevent adulteration on both raw materials and finished products. This is also the first use of Oxford Nanopore on herbal products enabling the use of genome skimming as a tool for quality assurance within the supply chain of botanical ingredients. The validation of both genomics and metabolomics approach provided quality assurance perspective for both product identity and purity. This research enables manufactures and retailers to verify their supply chain is authentic and that consumers can enjoy safe, healthy products.