Applications of nuclear magnetic resonance spectroscopy to the evaluation of complex food constituents

In vitro spectroscopic analysis of the chemical interaction between calcium hypochlorite and chlorhexidine

This study aimed to identify the chemical composition resulting from the chemical interaction between calcium hypochlorite [Ca(OCl)2] and chlorhexidine (CHX) using different 1H and 13C nuclear magnetic resonance spectrometry (NMR), correlated two-dimensional spectroscopy (2D COSY), heteronuclear single quantum coherence (HSQC), and Fourier Transform Infrared Spectroscopy (FTIR) experiments. The 5.25% Ca(OCl)2 was mixed with 2% CHX in a 1:1 ratio, obtaining an orange-brown precipitate that was filtered, washed in ultrapure water, dried and characterized by 1H and 13C NMR, 2D COSY, HSQC and FTIR. One-dimensional and two-dimensional NMR spectra demonstrated the chemical changes around the protons and carbon atoms of the initial CHX and after reacted with Ca(OCl)2. These techniques and FTIR show that the interaction generated two by-products from the degradation of CHX, none of which were parachloroaniline (PCA) despite both products obtained being substituted benzene compounds. Using NMR and FTIR data together with a previously proposed catalytic cleavage degradation mechanism for CHX, the products appear to be parachlorophenylurea (PCU) and parachlorophenylguanidyl-1,6-diguanidyl-hexane (PCGH). Therefore, this in vitro study demonstrated that the precipitate formed from the chemical interaction between Ca(OCl)₂ and CHX results in two by-products, PCU and PCGH, neither of which is PCA. Due to the absence of cytotoxicity studies on the products generated from the combination of Ca(OCl)₂ and CHX, an intermediate rinse with an inert solution is recommended to remove these compounds from the root canals.

A deep learning framework for multiplet splitting classification in 1H NMR

One-dimensional 1H Nuclear Magnetic Resonance (NMR) stands out as the quickest and simplest among various NMR experimental setups. Unfortunately, it suffers from lengthy annotation times and does not always have a clear and unique interpretation. From NMR discovery, efforts have been dedicated to introducing an automated approach to streamline the characterization of chemical compounds while ensuring consistency of the results across the scientific community. Nonetheless, this remains an ongoing challenge that has garnered renewed interest with the emergence of deep learning techniques. Here, we present MuSe Net, a novel supervised probabilistic deep learning framework that can emulate the tasks performed by an expert spectroscopist in annotating one-dimensional NMR spectra generated by small molecules. Considering only the spectrum, MuSe Net detects and classifies multiplets with up to four coupling constants for their splitting phenotype, providing a segmentation of the spectral range. We exploit uncertainty quantification to produce a confidence score to both assess classification reliability and to detect signals that do not fit into any other phenotype class. The results of the evaluation against 48 experimental 1H NMR spectra of small molecules annotated by experts demonstrate that MuSe Net can deal with anomalies and unclear signals while correctly classifying multiplets and detecting overlapping peaks.

NMR-based metabolomic approach to estimate chemical and sensorial profiles of olive oil

This study investigates the potential of 1H NMR spectroscopy for predicting key chemical and sensory attributes in olive oil. By integrating NMR data with traditional chemical analyses and sensory evaluation, we developed multivariate models to evaluate the predictive power of NMR spectra coupled with machine learning algorithms for 50 distinct olive oil quality parameters, including physicochemical properties, fatty acid composition, total polyphenols, tocopherols, and sensory attributes. We applied Random Forest regression models to correlate NMR spectra with these parameters, achieving promising results, particularly for predicting major fatty acids, total polyphenols, and tocopherols. We have also found the collected data to be highly effective in classifying olive cultivars and the years of harvest. Our findings highlight the potential of NMR spectroscopy as a rapid, non-destructive, and environmentally friendly tool for olive oil quality assessment. This study introduces a novel approach that combines machine learning with 1H NMR spectral analysis to correlate analytical data for predicting essential qualitative parameters in olive oil. By leveraging 1H NMR spectra as predictive proxies, this methodology offers a promising alternative to traditional assessment techniques, enabling rapid determination of several parameters related to chemical composition, sensory attributes, and geographical origin of olive oil samples.

Advances in Starch-Based Nanocomposites for Functional Food Systems: Harnessing AI and Nuclear Magnetic Resonance Technologies for Tailored Stability and Bioactivity

Starch-based nanocomposites (SNCs) are at the forefront of innovations in food science, offering unparalleled opportunities for enhancing the stability, bioactivity, and overall functionality of food systems. This review delves into the potential of SNCs to address contemporary challenges in food formulation, focusing on the synergistic effects of their components. By integrating cutting-edge technologies, such as artificial intelligence (AI) and nuclear magnetic resonance (NMR), we explore new avenues for enhancing the precision, predictability, and functionality of SNCs. AI is applied to optimize the design of SNCs, leveraging predictive modeling to fine-tune material properties and streamline production processes. The role of NMR is also critically examined, with particular emphasis on its capacity to provide high-resolution structural insights, monitor stability over time, and elucidate molecular interactions within food matrices. Through detailed examples, the review highlights the impact of NMR in unraveling the complex behaviors of bioactive compounds encapsulated in SNCs. Additionally, we discuss the integration of functional assays and AI-driven analytics in assessing the bioactivity and sensory properties of these systems, providing a robust framework for the rational design of advanced food products. The synergy between AI, NMR, and SNCs opens new pathways for developing tailored, high-performance food formulations that address both health and consumer preferences.

Review of the Relationships Between Human Gut Microbiome, Diet, and Obesity

Obesity is a complex disease that increases the risk of other pathologies. Its prevention and long-term weight loss maintenance are problematic. Gut microbiome is considered a potential obesity modulator. The objective of the present study was to summarize recent findings regarding the relationships between obesity, gut microbiota, and diet (vegetable/animal proteins, high-fat diets, restriction of carbohydrates), with an emphasis on dietary fiber and resistant starch. The composition of the human gut microbiome and the methods of its quantification are described. Products of the gut microbiome metabolism, such as short-chain fatty acids and secondary bile acids, and their effects on the gut microbiota, intestinal barrier function and immune homeostasis are discussed in the context of obesity. The importance of dietary fiber and resistant starch is emphasized as far as effects of the host diet on the composition and function of the gut microbiome are concerned. The complex relationships between human gut microbiome and obesity are finally summarized.

Physicochemical, structural, digestive properties, and interaction mechanism of Tartary buckwheat starch-Rutin-calcium hydroxide complex

Tartary buckwheat is rich in starch and rutin, with Ca(OH)2 often used to improve the quality of buckwheat-extruded noodles. This study investigated the properties of a system combining tartary buckwheat starch (TBS), fixed Ca(OH)2 (0.6 %), and varying rutin addition (R, 2-10 %) to understand their interactions. Rutin addition in the presence of Ca(OH)2 significantly affected the structural and physicochemical properties of TBS in a dose-dependent manner. Microscopy showed that rutin particles precipitated on the gel walls of the TBS-R-Ca complexes at high rutin doses, more noticeably without Ca(OH)2. Rutin decreased gelatinization viscosity, whereas Ca(OH)2 alleviated this reduction in viscosity and gel structure. Ca(OH)2 and 2 % rutin significantly reduced the gelatinization and retrogradation enthalpies, relative crystallinity, short-range order degree, and TBS digestion rate. 1H NMR spectroscopy revealed interaction sites between rutin and TBS. Molecular dynamics results indicated that rutin and TBS can form effective hydrogen bonds and hydrophobic interactions, with Ca(OH)2 helping to maintain system stability.

A Review of Gastrodia Elata Bl.: Extraction, Analysis and Application of Functional Food

Gastrodia elata Bl. still widely known as a medicinal plant due to its anti-inflammatory, neuroprotection, cardiovascular protection etc. Additionally, these medical applications cannot be separated from its antioxidant, anti-aging, regulating cell apoptosis ability, which make it have potential as a functional food as well as it has been eaten for more than 2,000 years in China. At present, although Gastrodia elata Bl. has appeared in a large number of studies, much of the research is based on drugs rather than foods. The review of Gastrodia elata Bl. from the perspective of food is one of the necessary steps to promote related development, by reviewing the literature on analytical methods of Gastrodia elata Bl. in recent years, critical components change in the extraction, analytical methods and improvement of food applications, all of aspects of it was summarized. Based on the report about physical and chemical changes in Gastrodia elata Bl. to discover the pathway of Gastrodia elata Bl. functional food development from current to the future.

Innovations in nondestructive assessment of baked products: Current trends and future prospects

Rising consumer awareness, coupled with advances in sensor technology, is propelling the food manufacturing industry to innovate and employ tools that ensure the production of safe, nutritious, and environmentally sustainable products. Amidst a plethora of nondestructive techniques available for evaluating the quality attributes of both raw and processed foods, the challenge lies in determining the most fitting solution for diverse products, given that each method possesses its unique strengths and limitations. This comprehensive review focuses on baked goods, wherein we delve into recently published literature on cutting-edge nondestructive methods to assess their feasibility for Industry 4.0 implementation. Emphasizing the need for quality control modalities that align with consumer expectations regarding sensory traits such as texture, flavor, appearance, and nutritional content, the review explores an array of advanced methodologies, including hyperspectral imaging, magnetic resonance imaging, terahertz, acoustics, ultrasound, X-ray systems, and infrared spectroscopy. By elucidating the principles, applications, and impacts of these techniques on the quality of baked goods, the review provides a thorough synthesis of the most current published studies and industry practices. It highlights how these methodologies enable defect detection, nutritional content prediction, texture evaluation, shelf-life forecasting, and real-time monitoring of baking processes. Additionally, the review addresses the inherent challenges these nondestructive techniques face, ranging from cost considerations to calibration, standardization, and the industry's overreliance on big data.

Aqueous preparation of arginyl-fructosyl-glucose (a maltose-arginine AC) and determination of Amadori compounds (ACs) in red ginseng by ultra-high performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS)

Amadori compounds (ACs) are key Maillard intermediates in various foods after thermal processing, and are also important non-saponin components in red ginseng. Currently, due to the difficulty in obtaining AC standards, the determination of multiple ACs is limited and far from optimal. In this study, an ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was developed and validated. A green synthetic method was developed for arginyl-fructosyl-glucose (AFG), the major AC in red ginseng with potential health benefits. The UPLC-MS/MS method was then applied in identification and quantification of ACs in red ginseng samples, which showed for the first time that 12 other ACs also exist in red ginseng in addition to AFG and arginyl-fructose (total 98.88 % of all ACs). Contents of AFG and arginyl-fructose in whole red ginseng were 36.23 and 10.80 mg/g dry weight, respectively. Raw ginseng can be steamed and then dried whole to obtain whole red ginseng, or sliced before drying to obtain sliced red ginseng. Slicing before drying was found to reduce ACs content. Results of the present study will help to reveal the biological functions of red ginseng and related products associated with ACs and promote the standardization of red ginseng manufacture.

Resistant starch and the gut microbiome: Exploring beneficial interactions and dietary impacts

The intricate relationship between resistant starch (RS) and the gut microbiome presents a dynamic frontier in nutrition science. This review synthesizes current understandings of how RS, an indigestible form of starch found naturally in certain foods and also enhanced through various modification methods, interacts with the gut microbiome. We particularly focus on how RS fermentation in the colon contributes to the production of beneficial volatile fatty acids (VFAs) such as butyrate, acetate, and propionate. These VFAs have been recognized for their vital roles in maintaining gut barrier integrity, modulating inflammation, and potentially influencing systemic health. Additionally, we discuss the dietary implications of consuming foods rich in RS, both in terms of gut health and broader metabolic outcomes. By consolidating these insights, we emphasize the significance of RS in the context of dietary strategies aimed at harnessing the gut microbiome's potential to impact human health.

3D Printing Technology: Role in Safeguarding Food Security

The rising threats to food security include several factors, such as population growth, low agricultural investment, and poor distribution systems. Consequently, food insecurity results from a confluence of issues, including diseases, processing limitations, and distribution deficiencies. Food insecurity usually occurs in vulnerable areas where certain technologies and traditional food safety testing are not a viable solution for foodborne disease detection. In this regard, 3D printing technologies and 3D printed sensors open the platform to produce portable, accurate, and low-cost sensors that address the gaps and challenges in food security. In this paper, we discuss the perspective role of 3D printed sensors in food security in terms of food safety and food quality monitoring to provide reliable access to nutritious, affordable food. In each section, we highlight the advantages of 3D printing technology in terms of cost-effectiveness, accuracy, accessibility, and reproducibility compared to traditional manufacturing methodologies. Recent developments in robotic technologies for mechanization, such as food handling with soft grippers, are also discussed. Lastly, we delve into the applications of advanced 3D printing technologies in agricultural monitoring, particularly the future of plant wearables, environmental sensing, and overall plant health monitoring.

Study by means of 1H nuclear magnetic resonance of the oxidation process in high oleic sunflower oil and palm oil during deep-frying of fish cakes

This study aimed to compare the frying performance of palm oil (PO) and high oleic sunflower oil (HOSO) during frying aquatic products. The quality change and frying performance of HOSO and PO during frying of fish cakes were investigated. The oxidation and hydrolysis products of both oils were explored by the nuclear magnetic resonance technique. The results showed that the color deepening rate of PO was higher than that of HOSO. After 18 h of frying, the total polar compound content of PO and HOSO reached 25.67% and 27.50%, respectively. HOSO had lower degree of oxidation than PO after 24 h of continuous frying. The polyunsaturated fatty acid content in HOSO and PO significantly decreased. The oleic acid content in HOSO remained above 80% during the frying process. The major aldehydes in both oils were (E, E)-2,4-alkadienals and n-alkanals and glycerol diesters (DAGs) were abundant in PO. Furthermore, the addition of fish cakes had slight effect on the quality of the frying oil. Therefore, HOSO is an appropriate candidate for frying owing to its excellent frying stability and nutritional value.

Skin healthcare protection with antioxidant and anti-melanogenesis activity of polysaccharide purification from Bletilla striata

In this study, we investigated the structural characterization and biological activities of Bletilla striata polysaccharides (BSPs) for their role as antioxidants and anti-melanogenesis agents in skin healthcare protection. Three neutral polysaccharides (BSP-1, BSP-2, and BSP-3) with molecular weights of 269.121 kDa, 57.389 kDa, and 28.153 kDa were extracted and purified. Their structural characteristics were analyzed by ion chromatography, GC-MS, and 1D/2D NMR. The results showed that BSP-1, which constitutes the major part of BSPs, was composed of α-D-Glcp, β-D-Glcp, β-D-Manp, and 2-O-acetyl-β-D-Manp, with the branched-chain accompanied by β-D-Galp and α-D-Glcp. BSP-1, BSP-2, and BSP-3 can enhance the total antioxidant capacity of skin fibroblasts with non-toxicity. Meanwhile, BSP-1, BSP-2, and BSP-3 could significantly inhibit the proliferative activity of melanoma cells. Among them, BSP-1 and BSP-2 showed more significance in anti-melanogenesis, tyrosinase inhibition activity, and cell migration inhibition. BSPs have effective antioxidant capacity and anti-melanogenesis effects, which should be further emphasized and developed as skin protection components.

Spectral data fusion in nondestructive detection of food products: Strategies, recent applications, and future perspectives

In recent years, the food industry has shown a growing interest in the development of rapid and nondestructive analytical methods. However, the utilization of a solitary nondestructive detection technique offers only a constrained extent of physical or chemical insights regarding the sample under examination. To overcome this limitation, the amalgamation of spectroscopy with data fusion strategies has emerged as a promising approach. This comprehensive review delves into the fundamental principles and merits of low-level, mid-level, and high-level data fusion strategies within the domain of food analysis. Various data fusion techniques encompassing spectra-to-spectra, spectra-to-machine vision, spectra-to-electronic nose, and spectra-to-nuclear magnetic resonance are summarized. Moreover, this review also provides an overview of the latest applications of spectral data fusion techniques (SDFTs) for classification, adulteration, quality evaluation, and contaminant detection within the purview of food safety analysis. It also addresses current challenges and future prospects associated with SDFTs in real-world applications. Despite the extant technical intricacy, the ongoing evolution of online data fusion platforms and the emergence of smartphone-based multi-sensor fusion detection technology augur well for the pragmatic realization of SDFTs, endowing them with formidable capabilities for both qualitative and quantitative analysis in the realm of food analysis.

Deep-Learning-Based Mixture Identification for Nuclear Magnetic Resonance Spectroscopy Applied to Plant Flavors

Nuclear magnetic resonance (NMR) is a crucial technique for analyzing mixtures consisting of small molecules, providing non-destructive, fast, reproducible, and unbiased benefits. However, it is challenging to perform mixture identification because of the offset of chemical shifts and peak overlaps that often exist in mixtures such as plant flavors. Here, we propose a deep-learning-based mixture identification method (DeepMID) that can be used to identify plant flavors (mixtures) in a formulated flavor (mixture consisting of several plant flavors) without the need to know the specific components in the plant flavors. A pseudo-Siamese convolutional neural network (pSCNN) and a spatial pyramid pooling (SPP) layer were used to solve the problems due to their high accuracy and robustness. The DeepMID model is trained, validated, and tested on an augmented data set containing 50,000 pairs of formulated and plant flavors. We demonstrate that DeepMID can achieve excellent prediction results in the augmented test set: ACC = 99.58%, TPR = 99.48%, FPR = 0.32%; and two experimentally obtained data sets: one shows ACC = 97.60%, TPR = 92.81%, FPR = 0.78% and the other shows ACC = 92.31%, TPR = 80.00%, FPR = 0.00%. In conclusion, DeepMID is a reliable method for identifying plant flavors in formulated flavors based on NMR spectroscopy, which can assist researchers in accelerating the design of flavor formulations.

Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects

The quality and safety of edible crops are key links inseparable from human health and nutrition. In the era of rapid development of artificial intelligence, using it to mine multi-source information on edible crops provides new opportunities for industrial development and market supervision of edible crops. This review comprehensively summarized the applications of multi-source data combined with machine learning in the quality evaluation of edible crops. Multi-source data can provide more comprehensive and rich information from a single data source, as it can integrate different data information. Supervised and unsupervised machine learning is applied to data analysis to achieve different requirements for the quality evaluation of edible crops. Emphasized the advantages and disadvantages of techniques and analysis methods, the problems that need to be overcome, and promising development directions were proposed. To monitor the market in real-time, the quality evaluation methods of edible crops must be innovated.

Applications of Solution NMR Spectroscopy in Quality Assessment and Authentication of Bovine Milk

Nuclear magnetic resonance (NMR) spectroscopy is emerging as a promising technique for the analysis of bovine milk, primarily due to its non-destructive nature, minimal sample preparation requirements, and comprehensive approach to untargeted milk analysis. These inherent strengths of NMR make it a formidable complementary tool to mass spectrometry-based techniques in milk metabolomic studies. This review aims to provide a comprehensive overview of the applications of NMR techniques in the quality assessment and authentication of bovine milk. It will focus on the experimental setup and data processing techniques that contribute to achieving accurate and highly reproducible results. The review will also highlight key studies that have utilized commonly used NMR methodologies in milk analysis, covering a wide range of application fields. These applications include determining milk animal species and feeding regimes, as well as assessing milk nutritional quality and authenticity. By providing an overview of the diverse applications of NMR in milk analysis, this review aims to demonstrate the versatility and significance of NMR spectroscopy as an invaluable tool for milk and dairy metabolomics research and hence, for assessing the quality and authenticity of bovine milk.

Phytobioactive compounds as therapeutic agents for human diseases: A review

Phytobioactive compounds are plant secondary metabolites and bioactive compounds abundantly present in medicinal plants and have remarkable therapeutic potential. Oxidative stress and antibiotic resistance are major causes of present-day ailments such as diabetes, atherosclerosis, cardiovascular disorders, cancer, and inflammation. The data for this review were collected from Google Scholar, PubMed, Directory of Open Access Journals (DOAJ), and Science Direct by using keywords: "Medicinal plants, Phytobioactive compounds, Polyphenols, Alkaloids, Carotenoids etc." Several studies have reported the pharmacological and therapeutic potential of the phytobioactives. Polyphenols, alkaloids, terpenes, and polysaccharides isolated from medicinal plants showed remarkable antioxidant, anticancer, cytotoxic, anti-inflammatory, cardioprotective, hepatoprotective, immunomodulatory, neuroprotective, and antidiabetic activities. This literature review was planned to provide comprehensive insight into the biopharmacological and therapeutic potential of phytobioactive compounds. The techniques used for the extraction and isolation of phytobioactive compounds, and bioassays required for their biological activities such as antioxidant, antimicrobial, anti-inflammatory, and cytotoxic activities, have been discussed. Characterization techniques for the structural elucidation of phytobioactive compounds such as HPLC, TLC, FTIR, GC-MS/MS, and NMR have also been discussed. This review concludes that phytobioactive compounds may be used as potential alternative to synthetic compounds as therapeutic agents for the treatment of various diseases.

Activity and potential mechanisms of action of persimmon tannins according to their structures: A review

One distinguishing feature of the persimmon, that differentiates it from other fruits, is its high proanthocyanidins content, known as persimmon tannin (PT). Despite the poor absorption of PT in the small intestine, results from animal studies demonstrate that PT has many health benefits. Our goal in this review is to summarize the literature that elucidates the relationship between PT structure and activity. In addition, we also summarize the potential mechanisms underlying the health benefits that result from PT consumption; this includes the hypolipidemic, hypoglycemic, antioxidant, anti-inflammatory, antiradiation, antibacterial and antiviral, detoxification effects on snake venom, and the absorption of heavy metals and dyes. Studies show that PT is a structurally distinct proanthocyanidins that exhibits a high degree of polymerization. It is galloylation-rich and possesses unique A-type interflavan linkages in addition to the more common B-type interflavan bonds. Thus, PT is converted into oligomeric proanthocyanidins by depolymerization strategies, including the nucleophilic substitution reaction, acid hydrolysis, and hydrogenolysis. In addition, multiple health benefits exerted by PT mainly involve the inactivation of lipogenic and intracellular inflammatory signaling pathways, activation of the fatty acid oxidation signaling pathway, regulation of gut microbiota, and highly absorptive properties.

Nanomaterials for hyperpolarized nuclear magnetic resonance and magnetic resonance imaging

Nanomaterials play an important role in the development and application of hyperpolarized materials for magnetic resonance imaging (MRI). In this context they can not only act as hyperpolarized materials which are directly imaged but also play a role as carriers for hyperpolarized gases and catalysts for para-hydrogen induced polarization (PHIP) to generate hyperpolarized substrates for metabolic imaging. Those three application possibilities are discussed, focusing on carbon-based materials for the directly imaged particles. An overview over recent developments in all three fields is given, including the early developments in each field as well as important steps towards applications in MRI, such as making the initially developed methods more biocompatible and first imaging experiments with spatial resolution in either phantoms or in vivo studies. Focusing on the important features nanomaterials need to display to be applicable in the MRI context, a wide range of different approaches to that extent is covered, giving the reader a general idea of different possibilities as well as recent developments in those different fields of hyperpolarized magnetic resonance. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Diagnostic Tools > In Vivo Nanodiagnostics and Imaging.

Automatic classification of signal regions in 1H Nuclear Magnetic Resonance spectra

The identification and characterization of signal regions in Nuclear Magnetic Resonance (NMR) spectra is a challenging but crucial phase in the analysis and determination of complex chemical compounds. Here, we present a novel supervised deep learning approach to perform automatic detection and classification of multiplets in 1H NMR spectra. Our deep neural network was trained on a large number of synthetic spectra, with complete control over the features represented in the samples. We show that our model can detect signal regions effectively and minimize classification errors between different types of resonance patterns. We demonstrate that the network generalizes remarkably well on real experimental 1H NMR spectra.

1H NMR to analyse the lipid profile in the glyceride fraction of different categories of Iberian dry-cured hams

The extraordinary organoleptic qualities of Iberian ham derive from the singular producing pig breed and from the traditional rearing conditions, both of which define its lipid content and composition. In this work ¹H NMR spectroscopy is used for the first time to analyse the lipid profile of Iberian hams as determinant of quality. Quantification of fatty acids is readily obtained from the spectra, with the monounsaturated fatty acids standing out, especially in the higher quality hams. Unprecedently, triacylglyceride hydrolysis products formed during the curing process can also be directly detected and quantified. Furthermore, chemometric analysis of the NMR data allows to classify Iberian hams according to the pig's crossbreed and feeding regime. Principal component analysis shows fatty acid unsaturation and triacylglyceride hydrolysis as discriminating variables. ¹H NMR spectroscopy has thus revealed as a convenient and powerful tool for the lipid analysis and classification of Iberian hams and for detection of fraud.

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures

Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same compound in different mixtures and peak overlapping among molecules. Here, we present a pseudo-Siamese convolutional neural network method (pSCNN) to identify compounds in mixtures for NMR spectroscopy. A data augmentation method was implemented for the superposition of several NMR spectra sampled from a spectral database with random noises. The augmented dataset was split and used to train, validate and test the pSCNN model. Two experimental NMR datasets (flavor mixtures and additional flavor mixture) were acquired to benchmark its performance in real applications. The results show that the proposed method can achieve good performances in the augmented test set (ACC = 99.80%, TPR = 99.70% and FPR = 0.10%), the flavor mixtures dataset (ACC = 97.62%, TPR = 96.44% and FPR = 2.29%) and the additional flavor mixture dataset (ACC = 91.67%, TPR = 100.00% and FPR = 10.53%). We have demonstrated that the translational invariance of convolutional neural networks can solve the chemical shift variation problem in NMR spectra. In summary, pSCNN is an off-the-shelf method to identify compounds in mixtures for NMR spectroscopy because of its accuracy in compound identification and robustness to chemical shift variation.

Metabolomics application for the design of an optimal diet

Precision nutrition is an emerging branch of nutrition science that aims to use modern omics technologies (genomics, proteomics, and metabolomics) to assess an individual's response to specific foods or dietary patterns and thereby determine the most effective diet or lifestyle interventions to prevent or treat specific diseases. Metabolomics is vital to nearly every aspect of precision nutrition. It can be targeted or untargeted, and it has many applications. Indeed, it can be used to comprehensively characterize the thousands of chemicals in foods, identify food by-products in human biofluids or tissues, characterize nutrient deficiencies or excesses, monitor biochemical responses to dietary interventions, track long- or short-term dietary habits, and guide the development of nutritional therapies. Indeed, metabolomics can be coupled with genomics and proteomics to study and advance the field of precision nutrition. Integrating omics with epidemiological and clinical data will begin to define the beneficial effects of human food metabolites. In this review, we present the metabolome and its relationship to precision nutrition. Moreover, we describe the different techniques used in metabolomics and present how metabolomics has been applied to advance the field of precision nutrition by providing notable examples and cases.

Preparation, investigation and storage application of thymol-chitooligosaccharide complex with enhanced antioxidant and antibacterial properties

BACKGROUND

Thymol (Thy) is a natural bioactive agent which possesses various properties and has been widely used in medicine and food industries. However, its poor bioavailability can limit its application.

RESULTS

In this study, Thy was interacted with chitooligosaccharide (COS) as Thy-COS complex via an ionic crosslinking method using sodium tripolyphosphate as a crosslinker. The characteristics and thermal stability of Thy-COS were evaluated by ultraviolet-visible (UV-vis), Fourier-transform infrared (FTIR) spectroscopy, proton nuclear magnetic resonance (¹ H-NMR) and thermogravimetric analysis, and its antioxidant and antibacterial properties were also evaluated. The highest loading capacity of Thy (52.3%) in Thy-COS formed at mass ratio of 1:5. Results indicated the Thy-COS complex was formed mainly by hydrophobic interactions and hydrogen bonds. Upon complexation, the thermal stability, antioxidant and antibacterial activity of Thy were significantly improved. Thy-COS complex was made into a coated film for Nanguo pears and greatly improved its storage quality. Thy-COS delayed the weight loss and softening of Nanguo pears and kept more vitamin-C content (2.12 mg (100 g)^-1 ).

CONCLUSION

In conclusion, Thy-COS was successfully prepared and improved antioxidant and antibacterial properties of Thy, which has great potential in the food industry. © 2021 Society of Chemical Industry.

Determination of Drugs in Clinical Trials: Current Status and Outlook

All pharmaceutical drugs, vaccines, cosmetic products, and many medical breakthroughs must first be approved through clinical research and trials before advancing to standard practice or entering the marketplace. Clinical trials are sets of tests that are required to determine the safety and efficacy of pharmaceutical compounds, drugs, and treatments. There is one pre-phase and four main clinical phase requirements that every drug must pass to obtain final approval. Analytical techniques play a unique role in clinical trials for measuring the concentrations of pharmaceutical compounds in biological matrices and monitoring the conditions of patients (or volunteers) during various clinical phases. This review focuses on recent analytical methods that are employed to determine the concentrations of drugs and medications in biological matrices, including whole blood, plasma, urine, and breast milk. Four primary analytical techniques (extraction, spectroscopy, chromatography, and electrochemical) are discussed, and their advantages and limitations are assessed. Subsequent to a survey of evidence and results, it is clear that microelectromechanical system (MEMS) based electrochemical sensor and biosensor technologies exhibit several notable advantages over other analytical methods, and their future prospects are discussed.

NMR technique and methodology in botanical health product analysis and quality control

Botanicals have played an important role in maintaining human health and well-being throughout history. During the past few decades in particular, the use of botanical health products has gained more popularity. Whereas, quality, safety and efficacy concerns have continuously been critical issues due to the intrinsic chemical complexity of botanicals. Chemical analytical technologies play an imperative role in addressing these issues. Nuclear magnetic resonance (NMR) spectroscopy has proven to be a powerful and useful tool for the investigation of botanical health products. In this review, NMR techniques and methodologies that have been successfully applied to the research and development of botanical health products in all stages, from plants to products, are discussed and summarized. Furthermore, applications of NMR together with other analytical techniques in a variety of domains of botanical health products investigation, such as plant species differentiation, adulteration detection, and bio-activity evaluation, are discussed and illustrated with typical examples. This article provides an overview of the potential uses of NMR techniques and methodologies in an attempt to further promote their recognition and utilization in the field of botanical health products analysis and quality control.