Panax notoginseng (P. notoginseng), a valuable herb, requires stringent quality control due to its diverse nutrients and bioactive compounds. This study utilized 1H NMR-based metabolomics and multivariate statistical methods to analyze P. notoginseng samples from Mengzi (MZ), Shizong (SZ), Luliang (LL), and Jianshui (JS) in Yunnan province. The main components of 52 medicinal materials were identified. OPLS-DA models revealed significant differences in constituents among regions, with chlorogenic acid, ferulic acid, and eleven other compounds as geographical markers for JS P. notoginseng, while sucrose is the marker for LL P. notoginseng. Inositol, maltotriose, and alpha-glucose for MZ P. notoginseng, aspartic acid, tryptophan, and nine other components for SZ P. notoginseng. Furthermore, PCA and PLS-DA results indicated substantial variation under two planting methods, with arabinose and gamma-aminobutyric acid serving as distinguishing markers for P. notoginseng from the forest understorey (PFU) and open field (POF). The PFU exhibited higher nutritional value due to increased saponin content. This study offers nutritional insights for consumption and aids in determining P. notoginseng geographical origin based on its nutritional profile.

Lycium barbarum L. (L. barbarum), revered for its nutritional and commercial value, exhibits variable nutritional contents depending on the consumption method. This study introduces an innovative approach, the Identification and Quantification of L. barbarum Components (IQ-LC) model, for rapid and accurate identification and quantification analysis of L. barbarum components by integrating NMR spectroscopy with a multi-label one-dimensional convolutional neural network. This model demonstrated exceptional performance in identifying 25 known-concentration mixtures, achieving an accuracy of 99.74 %, a true positive rate of 97.89 %, a true negative rate of 99.94 %, a root mean squared error (RMSE) of 0.15, and a coefficient of determination (R2) of 0.96. This method was then applied to analyze the nutritional content of L. barbarum across different consumption forms: fresh berries, puree, and tea. Fresh L. barbarum, though nutrient-rich, faces challenges related to transportation and storage. In contrast, L. barbarum tea exhibited the lowest nutrient levels. Therefore, L. barbarum puree is recommended as the most practical option for daily dietary supplementation. Finally, the IQ-LC model was employed to assess and compare the nutritional contents of L. barbarum puree from ten commercial brands available on the market, providing a fast and reliable method for both identification and quantification purposes of L. barbarum's nutritional components across various consumption methods. This study not only offers a novel tool for the market regulation of L. barbarum products but also contributes to the broader application of deep learning and NMR in the field of food science and nutritional analysis.

Early diagnosis and treatment are pivotal for enhancing the survival rates of pancreatic cancer patients, emphasizing the necessity for precise staging of pancreatic ductal adenocarcinoma (PDAC). This study presents a hybrid model that combines convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and traditional machine learning (ML) methods to predict PDAC staging based on metabolic characteristics. To address the data imbalance in PDAC datasets, the adaptive synthetic (ADASYN) sampling algorithm was utilized to augment minority class samples. The CNN-LSTM-ML hybrid model was developed and its performance was evaluated against traditional classification methods. The hybrid model achieved an optimal classification accuracy of 90.00 %, surpassing the performance of traditional methods. The confusion matrix indicated 100 % prediction accuracy for PDAC-I and PDAC-IV stages, and 66.67 % and 83.33 % for PDAC-II and PDAC-III stages, respectively. Validation across datasets with varying degrees of malnutrition confirmed the model's reliability. These results demonstrated the excellent predictive performance of the CNN-LSTM-ML hybrid model and its potential applicability to staging prediction in other clinical conditions, contributing to the advancement of precision and personalized medical interventions.

Micellar solubilization is considered as a key factor affecting the recovery efficiency in chemically enhanced oil recovery (cEOR). However, it is poorly understood how polymers in surfactant-polymer (SP) flooding influences the micellar solubilization. Guar gum (GG) and hydroxyethyl cellulose (HEC) are two important and widely used polysaccharide polymers. In this work, the effect of GG and HEC on the solubilization of a crude oil model compound methyl benzoate (MB) in anionic sodium dodecyl sulfate (SDS) micelles and the underlying mechanism were explored at the molecular scale by NMR spectroscopy. GG and HEC slightly increased the MB solubility in SDS micelles but did not change the solubilization capacity. No correlation peak was observed to demonstrate the attractive interaction between polymer and SDS. After examination, there is weak electrostatic repulsion between HEC/GG and SDS, which made the polymer independent from SDS micelles and was unable to participate in the formation of micelles to alter micellar structure and solubilization behavior. Although the solubility of MB was slightly increased, this increase should originate from weak molecular interactions between polymers, surfactants and hydrophobic molecules, including hydrogen bonding, van der Waals forces and hydrophobic interactions, etc. In summary, without strong electrostatic attraction between polymer and surfactant, the solubilization of polymers and the solubilization of micelles proceed independently, resulting in that the solubilization capacity of SDS micelles was not improved.

Fish maw, derived from dried swim bladders of fish, is valued for its nutritional and medicinal properties, which has led to an increased market demand. However, price variability based on species and grades has results in unethical practices such as counterfeiting and mislabeling, highlighting the need for reliable quality authentication. To address this, an expert system using MATLAB software has been developed. This system employs nuclear magnetic resonance (NMR) technology and pattern recognition methods to identify fish maw species and classify their grade. The study analyzed ten species across three grades of fish maw, identifying 43 nutritional components from NMR spectra, including sugars, amino acids, fatty acids, organic acids, and vitamins. Univariate statistical analysis was integrated with multivariate statistical analyses, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares (OPLS-DA) using SIMCA software, to develop models for species and grades identification. A four-dimensional volcano map was constructed to highlight characteristic components of various fish maws, resulting in an NMR database for common fish maws. The expert system’s accuracy was validated with new samples, achieving 92.6% for species identification and 90.0% for grade classification. This study provides a valuable tool for the quality evaluation of fish maw and a scientific basis for market regulation.

Panax notoginseng (P. notoginseng) has excellent medicinal and food dual-use characteristics. However, P. notoginseng with a unique origin label has become the target of fraud because of people confusing or hiding its origin. In this study, an untargeted nuclear magnetic resonance (NMR)-based metabolomics approach was used to discriminate the geographical origins of P. notoginseng from four major producing areas in China. Fifty-two components, including various saccharides, amino acids, saponins, organic acids, and alcohols, were identified and quantified through the NMR spectrum, and the area-specific geographical identification components were further screened. P. notoginseng from Yunnan had strong hypoglycemic and cardiovascular protective effects due to its high acetic acid, dopamine, and serine content, while P. notoginseng from Sichuan was more beneficial for diseases of the nervous system because of its high content of fumarate. P. notoginseng from Guizhou and Tibet had high contents of malic acid, notoginsenoside R1, and amino acids. Our results can help to distinguish the geographical origin of P. notoginseng and are readily available for nutritional recommendations in human consumption.

Edible bird's nest (EBN) is a high-value health food with various nutrients and bioactive components. With increasing demand for EBN, they are often adulterated with cheaper ingredients or falsely labeled by the origin information, thus harming consumer interests. In this study, high- and low-field nuclear magnetic resonance (HF/LF-NMR) technology combined with multivariate statistical analysis was used to identify the geographical marker of EBN from different origins and authenticate the adulterated EBN with various adulterants at different adulteration rates. Authentic EBN samples from Malaysia were used to simulate adulteration using gelatin (GL), agar (AG) and starch (ST) at 10 %, 20 %, 40 %, 60 %, 80 %, and 100 % w/w, respectively. The results showed significant differences in composition among EBN from different origins, with isocaproate and citric acid serving as geographical markers for Malaysia and Vietnam, respectively. Leucine, glutamic acid, and N-acetylglycoprotein serving as geographical markers for Indonesia. In addition, PLS model further verified the accuracy of origin identification of EBN. The LF-NMR results of adulteration EBN showed a linear correlation between the transverse relaxation (T2, S2) and the adulterated ratio. The OPLS-DA based on T2 spectra could accurately identify authentic EBN from adulterated with GL, AG and ST at 40 %, 20 %, and 20 %, respectively. Fisher discrimination model was able to differentiate at 20 %, 20 %, and 40 %, respectively. These results show that the 1H NMR combined with multivariate statistical analysis method could be a potential tool for the detection of origin and adulteration of EBN.

The difference of nutrient composition between organic eggs and conventional eggs has always been a concern of people. In this study, 1H nuclear magnetic resonance (NMR) technique combined with multivariate statistical analyses was conducted to identify the metabolite different in egg yolk and egg white in order to reveal the nutritional components information between organic and conventional eggs. The results showed that the nutrient content and composition characteristics were different between organic and conventional eggs, among which the content of glucose, putrescine, amino acids and their derivatives were found higher in the organic eggs yolk, while phospholipids were demonstrated higher in conventional eggs yolk. Organic acid, alcohol, amine, choline and amino acids were higher in conventional eggs white, but glucose and lactate in organic egg were higher. Our study demonstrated that there are more nutritive components and higher nutritional value in organic eggs than conventional eggs, especially for the growth and development of infants and young children, and conventional eggs have more advantages in promoting lipid metabolism, preventing fatty liver, and reducing serum cholesterol. Eggs have important nutritional value to human body, and these two kinds of eggs can be selected according to the actual nutrient needs.

Intentional addition of cheaper oils into olive oil (OL) for economic motivation has been becoming particularly attractive due to the favorable flavor and healthy characteristics of OL, but it is very challenging to identify such adulteration because of the compositional similarity between the oils. In this study, low-field nuclear magnetic resonance (LF-NMR) in combination with multivariate statistical analysis was used to identify the adulterated olive oil with different rations of soybean oil (SO) or corn oil (CO). Significant differences in multi-component relaxation time (T-21 and T-22) and peak area proportions (S-21 and S-22) were detected between pure and adulterated OL. As the adulteration ratio increased, S-21 and S-22 changed linearly, while T-21 and T-22 only changed slightly. The detection by gas chromatography suggested that T-21 and T-22 values might be influenced by triacylglycerol components, and the changes of S-21 and S-22 were attributed to the varied mono-/polyunsaturated fatty acids. In the relaxation time-based pattern recognition models, the authentic OL could be correctly identified from the adulterated ones with at least 20% of SO or CO by principal component analysis (PCA) or partial least squares discriminant analysis (PLS-DA). The multi-blended oil could be 100% classified by orthogonal partial least squares discriminant analysis (OPLS-DA) and 98.8% classified by principal component analysis followed by linear discriminant analysis (PCA-LDA) when the adulteration ratio was above 30%, demonstrating a promising technique of LF-NMR combined with pattern recognition in rapid screening of the edible oils.

Camellia oil (CA), mainly produced in southern China, has always been called Oriental olive oil (OL) due to its similar physicochemical properties to OL. The high nutritional value and high selling price of CA make mixing it with other low-quality oils prevalent, in order to make huge profits. In this paper, the transverse relaxation time (T2) distribution of different brands of CA and OL, and the variation in transverse relaxation parameters when adulterated with corn oil (CO), were assessed via low field nuclear magnetic resonance (LF-NMR) imagery. The nutritional compositions of CA and OL and their quality indices were obtained via high field NMR (HF-NMR) spectroscopy. The results show that the fatty acid evaluation indices values, including for squalene, oleic acid, linolenic acid and iodine, were higher in CA than in OL, indicating the nutritional value of CA. The adulterated CA with a content of CO more than 20% can be correctly identified by principal component analysis or partial least squares discriminant analysis, and the blended oils could be successfully classified by orthogonal partial least squares discriminant analysis, with an accuracy of 100% when the adulteration ratio was above 30%. These results indicate the practicability of LF-NMR in the rapid screening of food authenticity.

Background As the preferred drug for single chemotherapeutic application in pancreatic cancer, gemcitabine often demonstrated low sensitivity and strong chemotherapy resistance in patients. Therefore, the search for other drugs with high efficiency and low side effects has become of high importance. The aim of this study was to assess the therapeutic effects of cucurmosin on pancreatic cancer as an alternative of gemcitabine and explore its underlying biochemical mechanism. Methods The subcutaneous xenograft mice with pancreatic cancer were treated by high- and low-dose cucurmosin and gemcitabine, respectively. A comparative metabolomic analysis was performed on the serum samples from the different groups by 1H nuclear magnetic resonance (NMR) techniques and then subjected to univariate and multivariate statistical analysis. Results Cucurmosin demonstrated a dose-dependent inhibition to the pancreatic tumors. High-dose cucurmosin provided similar chemotherapeutic efficacy with gemcitabine by positively regulating pyruvate metabolism, glycolysis or gluconeogenesis, and cysteine and methionine metabolism. Inactivating GFR signaling pathway and further inducing apoptosis of tumor cells are the important mechanism of anti-tumor function of cucurmosin. Conclusions Cucurmosin is a promising chemotherapeutic drug for pancreatic cancer. However, the dose selection and surface modification should be optimized according to the stage of pancreatic cancer, and an expanded study in both laboratory and clinical regimes needs to be performed.

Taurine is an indispensable amino acid for many fish species and taurine supplementation is needed when plant-based diets are used as the primary protein source for these species. However, there is limited information available to understand the physiological or metabolic effects of taurine on fish. In this study, 1H nuclear magnetic resonance (NMR)-based metabolomic analysis was conducted to identify the metabolic profile change in the fish intestine with the aim to assess the effect of dietary taurine supplementation on the physiological and metabolomic variation of fish, and reveal the possible mechanism of taurine’s metabolic effect. Grouper (Epinephelus coioides) were divided into four groups and fed diets containing 0.0%, 0.5%, 1.0%, and 1.5% taurine supplementation for 84 days. After extraction using aqueous and organic solvents, 25 significant taurine-induced metabolic changes were identified. These metabolic changes in grouper intestine were characterized by differences in carbohydrate, amino acid, lipid and nucleotide. The results reflected both the physiological state and growth of the fish, and indicated that taurine supplementation significantly affects the metabolome of fish, improves energy utilization and amino acid uptake, promotes protein, lipid and purine synthesis, and accelerates fish growth.