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.

A Comprehensive Quality Evaluation for Gentiana rigescens Franch. by Fingerprinting Combined with Chemometrics and Network Pharmacology.

Gentiana rigescens Franch. (G. rigescens) is a unique traditional medicinal herb from southwestern China, and its clinical mechanism for the treatment of hepatitis and the quality differences between different origins are not clear. The research aims to analyze the mechanisms for the treatment of hepatitis and differences in inter-origin differences using analytical techniques, chemometrics, and network pharmacology. Through infrared spectroscopy, spectral images, and high-performance liquid chromatography (HPLC) analysis, it was found that there were differences in absorbance intensity and significant differences in compound content among the samples'origin. G. rigescens iridoids and flavonoids exert therapeutic effects on hepatitis through multiple targets and multiple pathways. The above HPLC, chemometrics, and network pharmacology results revealed that gentiopicroside, and swertiamarine was the best quality marker among origins. The ResNet model could be utilized as an effective tool for tracing G. rigescens's origins. The PLSR model had excellent predictive performance in determining the content of gentiopicroside and swertiamarine, and could quickly, accurately, and effectively predict these two compounds. The research investigates the differences in G. rigescens origins from multiple perspectives, establishes image recognition models and prediction models, and provides new methods and theoretical basis for quality control of G. rigescens.

Feasibility study on discrimination of Polygonatum kingianum origins by NIR and MIR spectra data.

Most existing studies have focused on identifying the origin of species with protected geographical indications while neglecting to determine the proximate geographical origin of different species. In this study, we investigated the feasibility of using near- and mid-infrared spectroscopy to identify the origin of 156 Polygonatum kingianum samples from six regions in Yunnan, China. In this work, spectral images of different modes reveal more information about the P. kingianum. Comparing the performance of traditional machine learning models according to single spectrum and data fusion, the middle-level data fusion-principal component model has the best performance, and its sensitivity, specificity, and accuracy are all 1, and the model has the least number of variables. The residual convolutional neural network (ResNet) model constructed in the 1050-850 cm-1 band confirms that fewer variables are beneficial in improving the accuracy of the model. In conclusion, this study verifies the feasibility of the proposed strategy and establishes a practical model to determine the source of P. kingianum.

Comprehensive quality evaluation of dried boletus slices based on fingerprinting and chemometrics.

Mushrooms not only serve as a source of a wide range of nutrients in the structure of the human diet, but they have also received a great deal of attention in the field of biopharmaceuticals because of their wide range of medicinal benefits. Rapid quality certification of boletus (porcini) mushrooms is particularly important as a health food and as a potential source of medicines before purchase and production. Infrared (IR) spectroscopy is commonly used for rapid qualitative and quantitative analyses of foods and herbs. The Ultra Performance Liquid Chromatography (UPLC) combined with systematic fingerprinting quantification was used to analyze the quality consistency of Boletus edulis (B. edulis) from different geographic sources, and a method based on Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy combined with chemometrics for origin traceability and rapid prediction of nucleoside quality marker content of B. edulis dried slices was developed with the aim of achieving rapid, lossless, high-throughput and green quality authentication of raw materials for pharmaceutical products.

Rapid and accurate identification of Gastrodia elata Blume species based on FTIR and NIR spectroscopy combined with chemometric methods.

Different varieties of Gastrodia elata Blume (G. elata Bl.) have different qualities and different contents of active ingredients, such as polysaccharide and gastrodin, and it is generally believed that the higher the active ingredients, the better the quality of G. elata Bl. and the stronger the medicinal effects. Therefore, effective identification of G. elata Bl. species is crucial and has important theoretical and practical significance. In this study, first unsupervised PCA and t-SNE are established for data visualisation, follow by traditional machine learning (PLS-DA, OPLS-DA and SVM) models and deep learning (ResNet) models were established based on the fourier transform infrared (FTIR) and near infrared (NIR) spectra data of three G. elata Bl. species. The results show that PLS-DA, OPLS-DA and SVM models require complex preprocessing of spectral data to build stable and reliable models. Compared with traditional machine learning models, ResNet models do not require complex spectral preprocessing, and the training and test sets of ResNet models built based on raw NIR and low-level data fusion (FTIR + NIR) spectra reach 100 % accuracy, the external validation set based on low-level data fusion reaches 100 % accuracy, and the external validation set based on NIR has only one sample classification error and no overfitting.

Effect of drying temperature on composition of edible mushrooms: Characterization and assessment via HS-GC-MS and IR spectral based volatile profiling and chemometrics.

Edible wild mushrooms are one of the popular ingredients due to their high quality and unique flavor and nutrients. To gain insight into the effect of drying temperature on its composition, 86 Boletus bainiugan were divided into 5 groups and dried at different temperatures. Headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) was used for the identification of volatile organic compounds (VOCs) of Boletus bainiugan. The 21 differential VOCs that distinguish different drying temperatures of Boletus bainiugan were identified. 65 °C retained more VOCs. There were differences in their types and content at different temperatures, proteins, polysaccharides, crude fibers, and fats. Fourier transform near-infrared (FT-NIR) spectroscopy, Fourier transform infrared (FTIR) spectroscopy, and two-dimensional correlation spectroscopy (2DCOS) images were successfully characterized for differences in the chemical composition of Boletus bainiugan. Partial least squares discriminant analysis (PLS-DA) verified the variability in the chemical composition of Boletus bainiugan with the coefficient of determination (R2) = 0.95 and predictive performance (Q2) = 0.75 with 92.31% accuracy. Next, infrared spectroscopy provides a fast and efficient assessment of the content of Boletus bainiugan nutrients (proteins, polysaccharides, crude fibers, and fats).

Application of ATR-FTIR and FT-NIR spectroscopy coupled with chemometrics for species identification and quality prediction of boletes.

The taste and aroma of edible mushrooms, which is a criterion of judgment for consumer purchases, are influenced by amino acids and their metabolites. Sixty-eight amino acids and their metabolites were identified using liquid chromatography mass spectrometry (LC-MS), and 16 critical marker components were screened. The chemical composition of different species of boletes was characterized by two-dimensional correlation spectroscopy (2DCOS) to determine the sequence of molecular vibrations or group changes. Identification of boletes species based on partial least squares discrimination (PLS-DA) combined with Fourier transform near-infrared spectroscopy (FT-NIR) and Fourier transform infrared spectroscopy (ATR-FTIR), residual convolutional neural network (ResNet) combined with three-dimensional correlation spectroscopy (3DCOS) was performed with 100% accuracy. Partial least squares regression (PLSR) analysis showed that FT-NIR and ATR-FTIR spectra were highly correlated with the amino acids and their metabolites detected by LC-MS. All models had achieved an R2p of 0.911 and an RPD >3.0. The results show that FT-NIR and ATR-FTIR spectroscopy in combination with chemometrics methods can be used for rapid species identification and estimation of amino acids and their metabolites content in boletes. This study provides new techniques and ideas for the authenticity of species information and the quality assessment of boletes.

Analysis of Chemical Changes during Maturation of Amomum tsao-ko Based on GC-MS, FT-NIR, and FT-MIR.

Amomum tsao-ko Crevost et Lemaire (A. tsao-ko) is widely grown for its high nutritional and economic value. However, the lack of a scientific harvesting and quality control system has resulted in an uneven product quality. The present study was based on A. tsao-ko from four maturity stages from the same growing area, and its chemical trends and quality were evaluated using a combination of agronomic trait analysis, spectroscopy, chromatography, chemometrics, and network pharmacology. The results showed that A. tsao-ko was phenotypically dominant in October. Spectroscopy showed that the absorbance intensity at different maturity stages showed a trend of October > September > August > July. Further chemical differences between A. tsao-ko at different stages of maturity were found by chromatography to originate mainly from alcohol, aromatic, acids, esters, hydrocarbons, ketone, heterocyclic, and aldehydes. The network pharmacology results showed that the active ingredient for the treatment of obesity was present in A. tsao-ko and had high levels in A. tsao-ko in September and October. The results of this study provide a new idea for the comprehensive evaluation of A. tsao-ko and a theoretical basis for the harvesting and resource utilization of A. tsao-ko.

Perimenopausal and Menopausal Mammary Glands In A 4-Vinylcyclohexene Diepoxide Mouse Model.

As both perimenopausal and menopausal periods are recognized critical windows of susceptibility for breast carcinogenesis, development of a physiologically relevant model has been warranted. The traditional ovariectomy model causes instant removal of the entire hormonal repertoire produced by the ovary, which does not accurately approximate human natural menopause with gradual transition. Here, we characterized the mammary glands of 4-vinylcyclohexene diepoxide (VCD)-treated animals at different time points, revealing that the model can provide the mammary glands with both perimenopausal and menopausal states. The perimenopausal gland showed moderate regression in ductal structure with no responsiveness to external hormones, while the menopausal gland showed severe regression with hypersensitivity to hormones. Leveraging the findings on the VCD model, effects of a major endocrine disruptor (polybrominated diphenyl ethers, PBDEs) on the mammary gland were examined during and after menopausal transition, with the two exposure modes; low-dose, chronic (environmental) and high-dose, subacute (experimental). All conditions of PBDE exposure did not augment or compromise the macroscopic ductal reorganization resulting from menopausal transition and/or hormonal treatments. Single-cell RNA sequencing revealed that the experimental PBDE exposure during the post-menopausal period caused specific transcriptomic changes in the non-epithelial compartment such as Errfi1 upregulation in fibroblasts. The environmental PBDE exposure resulted in similar transcriptomic changes to a lesser extent. In summary, the VCD mouse model provides both perimenopausal and menopausal windows of susceptibility for the breast cancer research community. PBDEs, including all tested models, may affect the post-menopausal gland including impacts on the non-epithelial compartments.

Identification of geographical origins of Gastrodia elata Blume based on multisource data fusion.

INTRODUCTION: Identifying the geographical origin of Gastrodia elata Blume contributes to the scientific and rational utilization of medicinal materials. In this study, infrared spectroscopy was combined with machine learning algorithms to distinguish the origin of G. elata BI. OBJECTIVE: Realization of rapid and accurate identification of the origin of G. elata BI. MATERIALS AND METHODS: Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectra and Fourier transform near-infrared (FT-NIR) spectra were collected for 306 samples of G. elata BI. SAMPLES: Firstly, a support vector machine (SVM) model was established based on the single-spectrum and the full-spectrum fusion data. To investigate whether feature-level fusion strategy can enhance the model's performance, the sequential and orthogonalized partial least squares discriminant analysis (SO-PLS-DA) model was established to extract and combine two types of spectral features. Next, six algorithms were employed to extract feature variables, SVM model was established based on the feature-level fusion data. To avoid complicated preprocessing and feature extraction processes, a residual convolutional neural network (ResNet) model was established after converting the raw spectral data into spectral images. RESULTS: The accuracy of the feature-level fusion model is better as compared to the single-spectrum model and the fusion model with full-spectrum, and SO-PLS-DA is simpler than feature-level fusion based on the SVM model. The ResNet model performs well in classification but requires more data to enhance its generalization capability and training effectiveness. CONCLUSION: Sequential and orthogonalized data fusion approaches and ResNet models are powerful solutions for identifying the geographic origin of G. elata BI.

The genus Litsea: A comprehensive review of traditional uses, phytochemistry, pharmacological activities and other studies.

ETHNOPHARMACOLOGICAL RELEVANCE: The genus L. has high medicinal value and has traditional been used to treat a variety of gastrointestinal disorders, as well as diabetes, edema, colds, arthritis, asthma, and traumatic injuries. AIM OF THE REVIEW: This work addresses the missing information by conducting a comprehensive analysis of the traditional uses, chemical components, and pharmacological applications of the more reported species of the genus L. The origin of the genus, its toxicology, and the use of classical therapies in modern medicine were also discussed. It provides references for historical evidence, resource development, and medical research on the genus. METHOD: ology: Data about the genus L. were gathered via Web of Science, PubMed, Science Direct, Google Scholar, Connected Papers, China National Knowledge Infrastructure (CNKI), electronic ancient books and local chronicles. The WFO Plant List (wfoplantlist.org) and Flora of China (www.iplant.cn) confirmed L.'s Latin name, and the species information. The program ChemBioDraw Ultra 14.0 was used to create the molecular structures of the compounds that were displayed in the text. RESULT: Currently, at least 740 constituents have been isolated and identified from L. These include 9 groups of chemicals, such as flavonoids, alkaloids, and terpenoids. They have been shown to have over 20 biological properties in vivo and in vitro, such as antibacterial, anti-inflammatory, and anti-oxidant effects. CONCLUSION: Based on pharmacological investigations, chemical components, and traditional folk applications, L. is considered a medicinal plant having a variety of pharmacological actions. However, although the pharmacological activity of the L. genus has been preliminary demonstrated, most have only been assessed using simple in vitro cell lines or animal disease models. In order to fully elucidate the pharmacological activity and mechanisms of L., future studies should be conducted in a more comprehensive clinical manner.

Development of machine learning models using multi-source data for geographical traceability and content prediction of Eucommia ulmoides leaves.

Rapid and scientific quality evaluation is a hot topic in the research of food and medicinal plants. With the increasing popularity of derivative products from Eucommia ulmoides leaves, quality and safety have attracted public attention. The present study utilized multi-source data and traditional machine learning to conduct geographical traceability and content prediction research on Eucommia ulmoides leaves. Explored the impact of different preprocessing methods and low-level data fusion strategy on the performance of classification and regression models. The classification analysis results indicated that the partial least squares discriminant analysis (PLS-DA) established by low-level fusion of two infrared spectroscopy techniques based on first derivative (FD) preprocessing was most suitable for geographical traceability of Eucommia ulmoides leaves, with an accuracy rate of up to 100 %. Through regression analysis, it was found that the preprocessing methods and data blocks applicable to the four chemical components were inconsistent. The optimal partial least squares regression (PLSR) model based on aucubin (AU), geniposidic acid (GPA), and chlorogenic acid (CA) had a residual predictive deviation (RPD) value higher than 2.0, achieving satisfactory predictive performance. However, the PLSR model based on quercetin (QU) had poor performance (RPD = 1.541) and needed further improvement. Overall, the present study proposed a strategy that can effectively evaluate the quality of Eucommia ulmoides leaves, while also providing new ideas for the quality evaluation of food and medicinal plants.

Optimization of the selection of suitable harvesting periods for medicinal plants: taking Dendrobium officinale as an example.

BACKGROUND: Dendrobium officinale is a medicinal plant with high commercial value. The Dendrobium officinale market in Yunnan is affected by the standardization of medicinal material quality control and the increase in market demand, mainly due to the inappropriate harvest time, which puts it under increasing resource pressure. In this study, considering the high polysaccharide content of Dendrobium leaves and its contribution to today's medical industry, (Fourier Transform Infrared Spectrometer) FTIR combined with chemometrics was used to combine the yields of both stem and leaf parts of Dendrobium officinale to identify the different harvesting periods and to predict the dry matter content for the selection of the optimal harvesting period. RESULTS: The Three-dimensional correlation spectroscopy (3DCOS) images of Dendrobium stems to build a (Split-Attention Networks) ResNet model can identify different harvesting periods 100%, which is 90% faster than (Support Vector Machine) SVM, and provides a scientific basis for modeling a large number of samples. The (Partial Least Squares Regression) PLSR model based on MSC preprocessing can predict the dry matter content of Dendrobium stems with Factor = 7, RMSE = 0.47, R2 = 0.99, RPD = 8.79; the PLSR model based on SG preprocessing can predict the dry matter content of Dendrobium leaves with Factor = 9, RMSE = 0.2, R2 = 0.99, RPD = 9.55. CONCLUSIONS: These results show that the ResNet model possesses a fast and accurate recognition ability, and at the same time can provide a scientific basis for the processing of a large number of sample data; the PLSR model with MSC and SG preprocessing can predict the dry matter content of Dendrobium stems and leaves, respectively; The suitable harvesting period for D. officinale is from November to April of the following year, with the best harvesting period being December. During this period, it is necessary to ensure sufficient water supply between 7:00 and 10:00 every day and to provide a certain degree of light blocking between 14:00 and 17:00.

A rapid method for identification of Lanxangia tsaoko origin and fruit shape: FT-NIR combined with chemometrics and image recognition.

Lanxangia tsaoko's accurate classifications of different origins and fruit shapes are significant for research in L. tsaoko difference between origin and species as well as for variety breeding, cultivation, and market management. In this work, Fourier transform-near infrared (FT-NIR) spectroscopy was transformed into two-dimensional and three-dimensional correlation spectroscopies to further investigate the spectral characteristics of L. tsaoko. Before building the classification model, the raw FT-NIR spectra were preprocessed using multiplicative scatter correction and second derivative, whereas principal component analysis, successive projections algorithm, and competitive adaptive reweighted sampling were used for spectral feature variable extraction. Then combined with partial least squares-discriminant analysis (PLS-DA), support vector machine (SVM), decision tree, and residual network (ResNet) models for origin and fruit shape discriminated in L. tsaoko. The PLS-DA and SVM models can achieve 100% classification in origin classification, but what is difficult to avoid is the complex process of model optimization. The ResNet image recognition model classifies the origin and shape of L. tsaoko with 100% accuracy, and without the need for complex preprocessing and feature extraction, the model facilitates the realization of fast, accurate, and efficient identification.

ResD-Net: A model for rapid prediction of antioxidant activity in gentian root using FT-IR spectroscopy.

Gentian, an herb resource known for its antioxidant properties, has garnered significant attention. However, existing methods are time-consuming and destructive for assessing the antioxidant activity in gentian root samples. In this study, we propose a method for swiftly predicting the antioxidant activity of gentian root using FT-IR spectroscopy combined with chemometrics. We employed machine learning and deep learning models to establish the relationship between FT-IR spectra and DPPH free radical scavenging activity. The results of model fitting reveal that the deep learning model outperforms the machine learning model. The model's performance was enhanced by incorporating the Double-Net and residual connection strategy. The enhanced model, named ResD-Net, excels in feature extraction and also avoids gradient vanishing. The ResD-Net model achieves an R2 of 0.933, an RMSE of 0.02, and an RPD of 3.856. These results support the accuracy and applicability of this method for rapidly predicting antioxidant activity in gentian root samples.

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.

Volatile compounds and aroma characteristics of mushrooms: a review.

Mushrooms are popular due to their rich medicinal and nutritional value. Of the many characteristics of mushrooms, aroma has received extensive attention and research as a key determinant of consumer preference. This paper reviews the production, role and contribution of common volatile compounds (VCs) in wild and cultivated mushrooms, and explores the methods used to characterize them and the factors influencing aroma. To date, more than 347 common VCs have been identified in mushrooms, such as aldehydes, ketones, alcohols and sulfur-containing compounds. Extraction and identification of VCs is a critical step and combining multiple analytical methods is an effective strategy in mushroom aroma studies. In addition, the VCs and the aroma of mushrooms are affected by a variety of factors such as genetics, growing conditions, and processing methods. However, the mechanism of influence is unknown. Further studies on the production mechanisms of VCs, their contribution to aroma, and the factors influencing their formation need to be determined in order to fully elucidate aroma and flavor of mushrooms.

An integrated chemical characterization based on FT-NIR, and GC-MS for the comparative metabolite profiling of 3 species of the genus Amomum.

BACKGROUND: The fruits and seeds of genus Amomum are well-known as medicinal plants and edible spices, and are used in countries such as China, India and Vietnam to treat malaria, gastrointestinal disorders and indigestion. The morphological differences between different species are relatively small, and technical characterization and identification techniques are needed. RESULTS: Fourier transform near infrared spectroscopy (FT-NIR) and gas chromatography-mass spectrometry (GC-MS), combined with principal component analysis and two-dimensional correlation analysis were used to characterize the chemical differences of Amomum tsao-ko, Amomum koenigii, and Amomum paratsaoko. The targets and pathways for the treatment of diabetes mellitus in three species were predicted using network pharmacology and screened for the corresponding pharmacodynamic components as potential quality markers. The results of "component-target-pathway" network showed that (+)-Nerolidol, 2-Nonanol, α-Terpineol, α-Pinene, 2-Nonanone had high degree values and may be the main active components. Partial least squares-discriminant analysis (PLS-DA) was further used to select for differential metabolites and was identified as a potential quality marker, 11 in total. PLS-DA and residual network (ResNet) classification models were developed for the identification of 3 species of the genus Amomum, ResNet model is more suitable for the identification study of large volume samples. SIGNIFICANCE: This study characterizes the differences between the three species in a visual way and also provides a reliable technique for their identification, while demonstrating the ability of FT-NIR spectroscopy for fast, easy and accurate species identification. The results of this study lay the foundation for quality evaluation studies of genus Amomum and provide new ideas for the development of new drugs for the treatment of diabetes mellitus.

Comparison of Metabolites and Species Classification of Thirteen Zingiberaceae Spices Based on GC-MS and Multi-Spectral Fusion Technology.

Due to a similar plant morphology in the majority of Zingiberaceae spices, substitution and adulteration frequently take place during the sales process. Therefore, it is important to analyze the metabolites and species classification of different Zingiberaceae spices. This study preliminarily explored the differences in the metabolites in thirteen Zingiberaceae spices through untargeted gas chromatography-mass spectrometry (GC-MS) and combined spectroscopy, establishing models for classifying different Zingiberaceae spices. On one hand, a total of 81 metabolites were successfully identified by GC-MS. Thirty-seven differential metabolites were screened using variable important in projection (VIP ≥ 1). However, the orthogonal partial least squares discriminant analysis (OPLS-DA) model established using GC-MS data only explained about 30% of the variation. On the other hand, the partial least squares discriminant analysis (PLS-DA) models with three spectral data fusion strategies were compared, and their classification accuracy reached 100%. Among them, the mid-level data fusion model based on latent variables had the best performance. This study provides a powerful tool for distinguishing different Zingiberaceae spices and assists in reducing the occurrence of substitution and adulteration phenomena.

Building deep learning and traditional chemometric models based on Fourier transform mid-infrared spectroscopy: Identification of wild and cultivated Gastrodia elata.

To identify wild and cultivated Gastrodia elata quickly and accurately, this study is the first to apply three-dimensional correlation spectroscopy (3DCOS) images combined with deep learning models to the identification of G. elata. The spectral data used for model building do not require any preprocessing, and the spectral data are converted into three-dimensional spectral images for model building. For large sample studies, the time cost is minimized. In addition, a partial least squares discriminant analysis (PLS-DA) model and a support vector machine (SVM) model are built for comparison with the deep learning model. The overall effect of the deep learning model is significantly better than that of the traditional chemometric models. The results show that the model achieves 100% accuracy in the training set, test set, and external validation set of the model built after 46 iterations without preprocessing the original spectral data. The sensitivity, specificity, and the effectiveness of the model are all 1. The results concluded that the deep learning model is more effective than the traditional chemometric model and has greater potential for application in the identification of wild and cultivated G. elata.