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.

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.

Network pharmacology and fingerprint for the integrated analysis of mechanism, identification and prediction in Panax notoginseng.

INTRODUCTION: Panax notoginseng (Burkill) F. H. Chen ex C. H. Chow, is a well-known herb with multitudinous efficacy. In this study, a series of overall analyses on the action mechanism, component content, origin identification, and content prediction of P. notoginseng are conducted. OBJECTIVES: The purpose was to analyse the mechanism of pharmacological efficacy, differences between contents and groups of P. notoginseng from different origins, and to identify the origin and predict the content. MATERIALS AND METHODS: The P. notoginseng samples from four different origins were used for analysis by the database, network pharmacology (Q-marker) and fingerprint analysis [high-performance liquid chromatography (HPLC), attenuated total reflectance Fourier-transform infrared (ATR-FTIR) and near-infrared (NIR)] combined with data fusion strategy (low- and feature-level). RESULTS: Four saponins were identified as Q-markers, and exerted pharmacological effects on signalling pathways through 24 core targets. The qualitative and quantitative analysis of HPLC showed that there were differences among groups and different origins. Therefore, considering the need to treat diseases, combined with network database and network pharmacology, the suitable producing areas were determined through the mechanism of action and the required saponin content. The low-level data fusion successfully identified the origin and predicted the content of P. notoginseng from different origins. The accuracy rate of each evaluation index of the partial least squares discriminant analysis (PLS-DA) model was 1, and the t-SNE (t-distributed stochastic neighbor embedding) visualisation results were good. The coefficient of determination (R2 ) of the partial least squares regression (PLSR) model ranged from 0.9235-0.9996, and the root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP) range is 0.301-1.519. CONCLUSION: This study was designed to provide a sufficient theoretical basis for the quality control of P. notoginseng.

Identification of geographical origin and different parts of Wolfiporia cocos from Yunnan in China using PLS-DA and ResNet based on FT-NIR.

INTRODUCTION: Wolfiporia cocos, as a kind of medicine food homologous fungus, is well-known and widely used in the world. Therefore, quality and safety have received worldwide attention, and there is a trend to identify the geographic origin of herbs with artificial intelligence technology. OBJECTIVE: This research aimed to identify the geographical traceability for different parts of W. cocos. METHODS: The exploratory analysis is executed by two multivariate statistical analysis methods. The two-dimensional correlation spectroscopy (2DCOS) images combined with residual convolutional neural network (ResNet) and partial least square discriminant analysis (PLS-DA) models were established to identify the different parts and regions of W. cocos. We compared and analysed 2DCOS images with different fingerprint bands including full band, 8900-6850 cm-1 , 6300-5150 cm-1 and 4450-4050 cm-1 of original spectra and the second-order derivative (SD) spectra preprocessed. RESULTS: From all results: the exploratory analysis results showed that t-distributed stochastic neighbour embedding was better than principal component analysis. The synchronous SD 2DCOS is more suitable for the identification and analysis of complex mixed systems for the small-band for Poria and Poriae cutis. Both models of PLS-DA and ResNet could successfully identify the geographical traceability of different parts based on different bands. The 10% external verification set of the ResNet model based on synchronous 2DCOS can be accurately identified. CONCLUSION: Therefore, the methods could be applied for the identification of geographical origins of this fungus, which may provide technical support for quality evaluation.

Anticoagulant activity analysis and origin identification of Panax notoginseng using HPLC and ATR-FTIR spectroscopy.

INTRODUCTION: Panax notoginseng is one of the traditional precious and bulk-traded medicinal materials in China. Its anticoagulant activity is related to its saponin composition. However, the correlation between saponins and anticoagulant activities in P. notoginseng from different origins and identification of the origins have been rarely reported. OBJECTIVES: We aimed to analyze the correlation of components and activities of P. notoginseng from different origins and develop a rapid P. notoginseng origin identification method. MATERIALS AND METHODS: Pharmacological experiments, HPLC, and ATR-FTIR spectroscopy (variable selection) combined with chemometrics methods of P. notoginseng main roots from four different origins (359 individuals) in Yunnan Province were conducted. RESULTS: The pharmacological experiments and HPLC showed that the saponin content of P. notoginseng main roots was not significantly different. It was the highest in main roots from Wenshan Prefecture (9.86%). The coagulation time was prolonged to observe the strongest effect (4.99 s), and the anticoagulant activity was positively correlated with the contents of the three saponins. The content of ginsenoside Rg1 had the greatest influence on the anticoagulant effect. The results of spectroscopy combined with chemometrics show that the variable selection method could extract a small number of variables containing valid information and improve the performance of the model. The variable importance in projection has the best ability to identify the origins of P. notoginseng; the accuracy of the training set and the test set was 0.975 and 0.984, respectively. CONCLUSION: This method is a powerful analytical tool for the activity analysis and identification of Chinese medicinal materials from different origins.

Verified the rapid evaluation of the edible safety of wild porcini mushrooms, using deep learning and PLS-DA.

BACKGROUND: How to quickly identify poisonous mushrooms is a worldwide problem, because poisonous mushrooms and edible mushrooms have very similar appearances. Even some edible mushrooms must be processed further before they can be eaten. In addition, mushrooms from different geographical origins contain different levels of heavy metals. Eating frequent mushrooms with excessive heavy metal content can also cause food poisoning. This information is very important and needs to be informed to consumers in advance. Through the demand for the safety of porcini mushrooms in the Yunnan area we propose a hierarchical identification system based on Fourier-transform near-infrared (FT-NIR) spectroscopy to evaluate the edible safety of porcini species. RESULTS: We found that deep learning is the most effective means to identify the edible safety of porcini, and the recognition accuracy was 100%, by comparing two pattern recognition tools, deep learning and partial least square discriminant analysis (PLS-DA). Although the accuracy of the PLS-DA test set is 96.10%, the poisonous porcini is not allowed to be wrongly judged. In addition, the cadmium (Cd) content of Leccinum rugosiceps in the Midu area exceeded the standard. Deep learning can trace Le. rugosiceps geographic origin with an accuracy of 100%. CONCLUSION: The overall results show that deep learning methods based on FT-NIR can identify porcini that is at risk of being eaten. This has useful application prospects in food safety. © 2021 Society of Chemical Industry.

Effects of PI3K inhibition in AI-resistant breast cancer cell lines: autophagy, apoptosis, and cell cycle progression.

INTRODUCTION: Breast cancer is the leading cause of cancer death in women. The aromatase inhibitors (AIs), Anastrozole (Ana), Letrozole (Let), and Exemestane (Exe) are a first-line treatment option for estrogen receptor-positive (ER+) breast tumors, in postmenopausal women. Nevertheless, the development of acquired resistance to this therapy is a major drawback. The involvement of PI3K in resistance, through activation of the PI3K/AKT/mTOR survival pathway or through a cytoprotective autophagic process, is widely described. MATERIALS AND METHODS: The involvement of autophagy in response to Ana and Let treatments and the effects of the combination of BYL-719, a PI3K inhibitor, with AIs were explored in AI-resistant breast cancer cell lines (LTEDaro, AnaR, LetR, and ExeR). RESULTS: We demonstrate that Ana and Let treatments do not promote autophagy in resistant breast cancer cells, contrary to Exe. Moreover, the combinations of BYL-719 with AIs decrease cell viability by different mechanisms by nonsteroidal vs. steroidal AIs. The combination of BYL-719 with Ana or Let induced cell cycle arrest while the combination with Exe promoted cell cycle arrest and apoptosis. In addition, BYL-719 decreased AnaR, LetR, and ExeR cell viability in a dose- and time-dependent manner, being more effective in the ExeR cell line. This decrease was further exacerbated by ICI 182,780. CONCLUSION: These results corroborate the lack of cross-resistance between AIs verified in the clinic, excluding autophagy as a mechanism of resistance to Ana or Let and supporting the ongoing clinical trials combining BYL-719 with AIs.

Mercury in traditionally foraged species of fungi (macromycetes) from the karst area across Yunnan province in China.

The objective of this study is to better quantify the occurrence, intake, and potential risk from Hg in fungi traditionally foraged in SW China. The concentrations and intakes of Hg were measured from 42 species including a "hard" flesh type polypore fungi and a" soft" flesh type edible species that are used in traditional herbal medicine, collected during the period 2011-2017. Three profiles of forest topsoil from the Zhenyuan site in 2015 and Changning and Dulong sites in 2016 were also investigated. The concentrations of Hg in composite samples of polypore fungi were usually below 0.1 mg kg-1 dry weight (dw) but higher levels, 0.11 ± 0.01 and 0.24 ± 0.00 mg kg-1 dw, were noted in Ganoderma applanatum and Amauroderma niger respectively, both from the Nujiang site near the town of Lanping in NW Yunnan. Hg concentrations in Boletaceae species were usually well above 1.0 mg kg-1 dw and as high as 10 mg kg-1 dw. The quality of the mushrooms in this study in view of contamination with Hg showed a complex picture. The "worst case" estimations showed probable intake of Hg from 0.006 µg kg-1 body mass (bm) ("hard" type flesh) to 0.25 µg kg-1 bm ("soft" flesh) on a daily basis for capsulated products, from 17 to 83 µg kg-1 bm ("soft" flesh) in a meal ("hard" type flesh mushrooms are not cooked while used in traditional herbal medicine after processing), and from 0.042 to 1.7 and 120 to 580 µg kg-1 bm on a weekly basis, respectively. KEY POINTS: • Polypore species were slightly contaminated with Hg. • Hg maximal content in the polypore was < 0.25 mg kg-1 dry weight. • Many species from Boletaceae family in Yunnan showed elevated Hg. • Locals who often eat Boletus may take Hg at a dose above the daily reference dose.

SGK3 sustains ERα signaling and drives acquired aromatase inhibitor resistance through maintaining endoplasmic reticulum homeostasis.

Many estrogen receptor alpha (ERα)-positive breast cancers initially respond to aromatase inhibitors (AIs), but eventually acquire resistance. Here, we report that serum- and glucocorticoid-inducible kinase 3 (SGK3), a kinase transcriptionally regulated by ERα in breast cancer, sustains ERα signaling and drives acquired AI resistance. SGK3 is up-regulated and essential for endoplasmic reticulum (EnR) homeostasis through preserving sarcoplasmic/EnR calcium ATPase 2b (SERCA2b) function in AI-resistant cells. We have further found that EnR stress response down-regulates ERα expression through the protein kinase RNA-like EnR kinase (PERK) arm, and SGK3 retains ERα expression and signaling by preventing excessive EnR stress. Our study reveals regulation of ERα expression mediated by the EnR stress response and the feed-forward regulation between SGK3 and ERα in breast cancer. Given SGK3 inhibition reduces AI-resistant cell survival by eliciting excessive EnR stress and also depletes ERα expression/function, we propose SGK3 inhibition as a potential effective treatment of acquired AI-resistant breast cancer.

Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization.

Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000-4000 cm-1) and Fourier transform mid-infrared (MIR: 4000-600 cm-1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen's kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana.

Investigation of a Medical Plant for Hepatic Diseases with Secoiridoids Using HPLC and FT-IR Spectroscopy for a Case of Gentiana rigescens.

Secoiridoids could be used as a potential new drug for the treatment of hepatic disease. The content of secoiridoids of G. rigescens varied in different geographical origins and parts. In this study, a total of 783 samples collected from different parts of G. rigescens in Yunnan, Sichuan, and Guizhou Provinces. The content of secoiridoids including gentiopicroside, swertiamarin, and sweroside were determined by using HPLC and analyzed by one-way analysis of variance. Two selected variables including direct selected and variable importance in projection combined with partial least squares regression have been used to establish a method for the determination of secoiridoids using FT-IR spectroscopy. In addition, different pretreatments including multiplicative scatter correction (MSC), standard normal variate (SNV), first derivative and second derivative (SD), and orthogonal signal correction (OSC) were compared. The results indicated that the sample (root, stem, and leaf) with total secoiridoids, gentiopicroside, swertiamarin, and sweroside from west Yunnan had higher content than samples from the other regions. The sample from Baoshan had more total secoiridoids than other samples for the whole medicinal plant. The best performance using FT-IR for the total secoiridoid was with the direct selected variable method involving pretreatment of MSC+OSC+SD in the root and stem, while in leaf, of the best method involved using original data with MSC+OSC+SD. This method could be used to determine the bioactive compounds quickly for herbal medicines.

Structural characterisation and discrimination of the aerial parts of Paris polyphylla var. yunnanensis and Paris polyphylla var. chinensis by UHPLC-QTOF-MS coupled with multivariate data analysis.

INTRODUCTION: As sources of Rhizoma Paridis are facing shortages, utilising the aerial parts of Paris polyphylla has emerged as a promising additional source. However, the components in the aerial parts still need to be explored, and it is difficult to distinguish the aerial parts of P. polyphylla Smith var. yunnanensis (PPY) and P. polyphylla var. chinensis (PPC), two varieties of P. polyphylla. OBJECTIVE: This study aimed to establish a comprehensive platform to characterise steroid saponins from the aerial parts of PPY and PPC and to discriminate these two varieties. METHODOLOGY: A dereplication approach and ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) analysis were used for the characterisation of steroidal saponins in the aerial parts of PPY and PPC. Multivariate statistical analysis was performed to differentiate these two varieties and screen discriminant variables. In addition, a genetic algorithm-optimised for support vector machines (GA-SVM) model was developed to predict P. polyphylla samples. The distribution of steroidal saponins in PPY and PPC was visualised by a heatmap. RESULTS: A total of 102 compounds were characterised from the aerial parts of PPY and PPC by dereplication. A clear separation of PPY and PPC was achieved, and 35 saponins were screened as marker compounds. The established GA-SVM model showed excellent prediction performance with a prediction accuracy of 100%. CONCLUSIONS: Many steroid saponins that have been reported in Rhizoma Paridis also exist in the aerial parts of P. polyphylla. Samples from the aerial parts of PPY and PPC could be discriminated using our platform.

Assessing Geographical Origin of Gentiana Rigescens Using Untargeted Chromatographic Fingerprint, Data Fusion and Chemometrics.

Gentiana rigescens Franchet, which is famous for its bitter properties, is a traditional drug of chronic hepatitis and important raw materials for the pharmaceutical industry in China. In the study, high-performance liquid chromatography (HPLC), coupled with diode array detector (DAD) and chemometrics, were used to investigate the chemical geographical variation of G. rigescens and to classify medicinal materials, according to their grown latitudes. The chromatographic fingerprints of 280 individuals and 840 samples from rhizomes, stems, and leaves of four different latitude areas were recorded and analyzed for tracing the geographical origin of medicinal materials. At first, HPLC fingerprints of underground and aerial parts were generated while using reversed-phase liquid chromatography. After the preliminary data exploration, two supervised pattern recognition techniques, random forest (RF) and orthogonal partial least-squares discriminant analysis (OPLS-DA), were applied to the three HPLC fingerprint data sets of rhizomes, stems, and leaves, respectively. Furthermore, fingerprint data sets of aerial and underground parts were separately processed and joined while using two data fusion strategies ("low-level" and "mid-level"). The results showed that classification models that are based OPLS-DA were more efficient than RF models. The classification models using low-level data fusion method built showed considerably good recognition and prediction abilities (the accuracy is higher than 99% and sensibility, specificity, Matthews correlation coefficient, and efficiency range from 0.95 to 1.00). Low-level data fusion strategy combined with OPLS-DA could provide the best discrimination result. In summary, this study explored the latitude variation of phytochemical of G. rigescens and developed a reliable and accurate identification method for G. rigescens that were grown at different latitudes based on untargeted HPLC fingerprint, data fusion, and chemometrics. The study results are meaningful for authentication and the quality control of Chinese medicinal materials.

Geographical Authentication of Macrohyporia cocos by a Data Fusion Method Combining Ultra-Fast Liquid Chromatography and Fourier Transform Infrared Spectroscopy.

Macrohyporia cocos is a medicinal and edible fungi, which is consumed widely. The epidermis and inner part of its sclerotium are used separately. M. cocos quality is influenced by geographical origins, so an effective and accurate geographical authentication method is required. Liquid chromatograms at 242 nm and 210 nm (LC242 and LC210) and Fourier transform infrared (FTIR) spectra of two parts were applied to authenticate the geographical origin of cultivated M. cocos combined with low and mid-level data fusion strategies, and partial least squares discriminant analysis. Data pretreatment involved correlation optimized warping and second derivative. The results showed that the potential of the chromatographic fingerprint was greater than that of five triterpene acids contents. LC242-FTIR low-level fusion took full advantage of information synergy and showed good performance. Further, the predictive ability of the FTIR low-level fusion model of two parts was satisfactory. The performance of the low-level fusion strategy preceded those of the single technique and mid-level fusion strategy. The inner parts were more suitable for origin identification than the epidermis. This study proved the feasibility of the data fusion of chromatograms and spectra, and the data fusion of different parts for the accurate authentication of geographical origin. This method is meaningful for the quality control of food and the protection of geographical indication products.

Data Fusion of Fourier Transform Mid-Infrared (MIR) and Near-Infrared (NIR) Spectroscopies to Identify Geographical Origin of Wild Paris polyphylla var. yunnanensis.

Origin traceability is important for controlling the effect of Chinese medicinal materials and Chinese patent medicines. Paris polyphylla var. yunnanensis is widely distributed and well-known all over the world. In our study, two spectroscopic techniques (Fourier transform mid-infrared (FT-MIR) and near-infrared (NIR)) were applied for the geographical origin traceability of 196 wild P. yunnanensis samples combined with low-, mid-, and high-level data fusion strategies. Partial least squares discriminant analysis (PLS-DA) and random forest (RF) were used to establish classification models. Feature variables extraction (principal component analysis-PCA) and important variables selection models (recursive feature elimination and Boruta) were applied for geographical origin traceability, while the classification ability of models with the former model is better than with the latter. FT-MIR spectra are considered to contribute more than NIR spectra. Besides, the result of high-level data fusion based on principal components (PCs) feature variables extraction is satisfactory with an accuracy of 100%. Hence, data fusion of FT-MIR and NIR signals can effectively identify the geographical origin of wild P. yunnanensis.

Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR) Combined with Chemometrics Methods for the Classification of Lingzhi Species.

Due to the existence of Lingzhi adulteration, there is a growing demand for species classification of medicinal mushrooms by various techniques. The objective of this study was to explore a rapid and reliable way to distinguish between different Lingzhi species and compare the influence of data pretreatment methods on the recognition results. To this end, 120 fresh fruiting bodies of Lingzhi were collected, and all of them were analyzed by attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR). Random forest (RF), support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) classification models were established for raw and pretreated second derivative (SD) spectral matrices to authenticate different Lingzhi species. The results of multivariate statistical analysis indicated that the SD preprocessing method displayed a higher classification ability, which may be attributed to the analysis of powder samples that requires removal of overlapping peaks and baseline shifts. Compared with RF, the results of the SVM and PLS-DA methods were more satisfying, and their accuracies for the test set were both 100%. Among SVM and PLS-DA, the training set and test set accuracy of PLS-DA were both 100%. In conclusion, ATR-FTIR spectroscopy data pretreated by SD combined with PLS-DA is a simple, rapid, non-destructive and relatively inexpensive method to discriminate between mushroom species and provide a good reference to quality assessment.

[Research progress in chemical constituents in plants of Polygonatum and their pharmacological effects].

Polygonatum plants are perennial plants of Liliaceae. There are about 60 species reported at home and abroad,32 species in China,mainly distributed in the north temperate zone. Their main chemical components are steroidal saponins,polysaccharides,flavonoids,alkaloids,etc. They have anti-aging,anti-tumor,immunomodulation,antibacterial,antiviral,hypoglycemic and blood lipid effects. With the development of health industry,Polygonati Rhizome used as medicine and food has attracted great attention in recent years,and has become a research hotspot. However,the material basis of its efficacy is unclear and the product quality is uneven,which seriously limited the rapid upgrading of the industry. This review summarizes Polygonatum plants system classification,the chemical composition and pharmacological activity to provide theoretical basis for the development and utilization of Polygonatum plants.

FT-MIR and NIR spectral data fusion: a synergetic strategy for the geographical traceability of Panax notoginseng.

Three data fusion strategies (low-llevel, mid-llevel, and high-llevel) combined with a multivariate classification algorithm (random forest, RF) were applied to authenticate the geographical origins of Panax notoginseng collected from five regions of Yunnan province in China. In low-level fusion, the original data from two spectra (Fourier transform mid-IR spectrum and near-IR spectrum) were directly concatenated into a new matrix, which then was applied for the classification. Mid-level fusion was the strategy that inputted variables extracted from the spectral data into an RF classification model. The extracted variables were processed by iterate variable selection of the RF model and principal component analysis. The use of high-level fusion combined the decision making of each spectroscopic technique and resulted in an ensemble decision. The results showed that the mid-level and high-level data fusion take advantage of the information synergy from two spectroscopic techniques and had better classification performance than that of independent decision making. High-level data fusion is the most effective strategy since the classification results are better than those of the other fusion strategies: accuracy rates ranged between 93% and 96% for the low-level data fusion, between 95% and 98% for the mid-level data fusion, and between 98% and 100% for the high-level data fusion. In conclusion, the high-level data fusion strategy for Fourier transform mid-IR and near-IR spectra can be used as a reliable tool for correct geographical identification of P. notoginseng. Graphical abstract The analytical steps of Fourier transform mid-IR and near-IR spectral data fusion for the geographical traceability of Panax notoginseng.

Feature Fusion of ICP-AES, UV-Vis and FT-MIR for Origin Traceability of Boletus edulis Mushrooms in Combination with Chemometrics.

Origin traceability is an important step to control the nutritional and pharmacological quality of food products. Boletus edulis mushroom is a well-known food resource in the world. Its nutritional and medicinal properties are drastically varied depending on geographical origins. In this study, three sensor systems (inductively coupled plasma atomic emission spectrophotometer (ICP-AES), ultraviolet-visible (UV-Vis) and Fourier transform mid-infrared spectroscopy (FT-MIR)) were applied for the origin traceability of 192 mushroom samples (caps and stipes) in combination with chemometrics. The difference between cap and stipe was clearly illustrated based on a single sensor technique, respectively. Feature variables from three instruments were used for origin traceability. Two supervised classification methods, partial least square discriminant analysis (FLS-DA) and grid search support vector machine (GS-SVM), were applied to develop mathematical models. Two steps (internal cross-validation and external prediction for unknown samples) were used to evaluate the performance of a classification model. The result is satisfactory with high accuracies ranging from 90.625% to 100%. These models also have an excellent generalization ability with the optimal parameters. Based on the combination of three sensory systems, our study provides a multi-sensory and comprehensive origin traceability of B. edulis mushrooms.

Comparison and Identification for Rhizomes and Leaves of Paris yunnanensis Based on Fourier Transform Mid-Infrared Spectroscopy Combined with Chemometrics.

Paris polyphylla, as a traditional herb with long history, has been widely used to treat diseases in multiple nationalities of China. Nevertheless, the quality of P. yunnanensis fluctuates among from different geographical origins, so that a fast and accurate classification method was necessary for establishment. In our study, the geographical origin identification of 462 P. yunnanensis rhizome and leaf samples from Kunming, Yuxi, Chuxiong, Dali, Lijiang, and Honghe were analyzed by Fourier transform mid infrared (FT-MIR) spectra, combined with partial least squares discriminant analysis (PLS-DA), random forest (RF), and hierarchical cluster analysis (HCA) methods. The obvious cluster tendency of rhizomes and leaves FT-MIR spectra was displayed by principal component analysis (PCA). The distribution of the variable importance for the projection (VIP) was more uniform than the important variables obtained by RF, while PLS-DA models obtained higher classification abilities. Hence, a PLS-DA model was more suitably used to classify the different geographical origins of P. yunnanensis than the RF model. Additionally, the clustering results of different geographical origins obtained by HCA dendrograms also proved the chemical information difference between rhizomes and leaves. The identification performances of PLS-DA and the RF models of leaves FT-MIR matrixes were better than those of rhizomes datasets. In addition, the model classification abilities of combination datasets were higher than the individual matrixes of rhizomes and leaves spectra. Our study provides a reference to the rational utilization of resources, as well as a fast and accurate identification research for P. yunnanensis samples.