Integrative two-dimensional correlation spectroscopy (i2DCOS) for the intuitive identification of adulterated herbal materials

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.

Rapid Identification of Medicinal Polygonatum Species and Predictive of Polysaccharides Using ATR-FTIR Spectroscopy Combined With Multivariate Analysis

INTRODUCTION

Medicinal Polygonatum species is a widely used traditional Chinese medicine with high nutritional value, known for its anti-fatigue properties, enhancement of immunity, delays aging, improves sleep, and other health benefits. However, the efficacy of different species varies, making the quality control of medicinal Polygonatum species increasingly important. Polysaccharides are important in medicinal Polygonatum species because of their potential functional properties, such as antioxidation, hypoglycemia, protection of intestinal health, and minimal toxicological effects on human health, as well as high polysaccharide levels.

OBJECTIVE

This study developed a qualitative medicinal Polygonatum species model and a polysaccharides predictive model based on attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) combined with a multivariate analysis approach.

MATERIALS AND METHODS

ATR-FTIR spectral information of 334 medicinal Polygonatum species samples was collected and the spectral information of different modes was analyzed. The ATR-FTIR spectral differences of three medicinal Polygonatum species were studied by multivariate analysis combined with four spectral preprocessing and three variable selection methods. For the prediction of polysaccharides in Polygonatum kingianum Collett & Hemsl. (PK), we initially determined the actual content of 110 PK polysaccharide samples using the anthrone-sulfuric acid method, then established partial least squares regression (PLSR) and kernel PLSR models in conjunction with attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy.

RESULTS

In the visualization analysis, the orthogonal partial least squares-discriminant analysis (OPLS-DA) model based on second-order derivative (SD) preprocessing was most suitable for medicinal Polygonatum species species binary classification, spectral differences between Polygonatum cyrtonema Hua (PC) and other species are evident; in the hard modeling, SD preprocessing improves the accuracy of non-deep learning models for the classification of three medicinal Polygonatum species. In contrast, residual neural network (ResNet) models were the best choice for species identification without preprocessing and variable selection. In addition, the partial least squares regression (PLSR) model and Kernel-PLSR model can quickly predict PK polysaccharides content, among them, the Kernel-PLSR model with SD pretreatment has the best prediction performance, residual prediction deviation (RPD) = 7.2870, Rp = 0.9905.

CONCLUSION

In this study, we employed ATR-FTIR spectroscopy and various treatments to discern different medicinal Polygonatum species. We also evaluated the effects of preprocessing methods and variable selection on the prediction of PK polysaccharides by PLSR and Kernel-PLSR models. Among them, the ResNet model can achieve 100% correct classification of medicinal Polygonatum species without complex spectral preprocessing. Furthermore, the Kernel-PLSR model based on SD-ATR-FTIR spectra had the best performance in polysaccharides prediction. In summary, by integrating ATR-FTIR spectroscopy with multivariate analysis, this research accomplished the classification of medicinal Polygonatum species and the prediction of polysaccharides. The methodology offers the benefits of speed, environmental sustainability, and precision, highlighting its significant potential for practical applications. In future research, on the one hand, it can be further investigated using a portable infrared spectrometer, and on the other hand, infrared spectroscopy can also be applied to the prediction of other chemical components of medicinal Polygonatum species.

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 (GAPDH, EGFR, and MMP9, etc.) and multiple pathways (non-small cell lung cancer, hepatitis C, etc.). The above HPLC, chemometrics, and network pharmacology results revealed that gentiopicroside, and swertiamarine was the best quality marker among origins. The accuracy of the ResNet model train, test, and external validation sets for synchronous spectral images were 100 %, which could be utilized as an effective tool for tracing G. rigescens's origins. The R2 of the calibration and validation sets of the PLSR model was higher than 0.70. This 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.

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.

A Spectroscopic Method for Distinguishing Two Novel Sandwich-Type Tungsten Oxide Cluster Compounds

This study introduces two novel sandwich-type tungsten-oxygen cluster compounds synthesized by hydrothermal methods, H4(C6H12N2H2)3{Na(H2O)2[Mn2(H2O)(GeW9O34)]}2 (Compound 1) and H2(C6H12N2H2)3.5{Na3(H2O)4[Co2(H2O)(GeW9O34)]2}·17H2O (Compound 2). The two compounds comprise cluster anions [GeW9O34]10- coordinated with transition metal atoms, either Mn or Co, and are stabilized by organic ligands. These compounds are crystallized in the hexagonal crystal system and P63/m space group. The two compounds were characterized through various techniques. Fourier transform infrared (IR) spectroscopy showed absorption peaks of anionic backbone vibrations of the Keggin cluster at 500-1000 cm-1, IR spectral peaks of δ(N-H) and νas(C-N) of the ligand triethylenediamine at 1000-2000 cm-1, and IR spectral peaks of the ligand νas(N-H) and νas(O-H) of water at 3000-3500 cm-1. Despite similar one-dimensional (1D) IR spectra due to the same cluster anions and similar molecular structures, the two compounds exhibited distinct responses in two-dimensional correlation spectroscopy with IR under magnetic and thermal perturbations. Under magnetic perturbation, Compound 1 showed a strong response peak for νas(W-Ob-W), while Compound 2 exhibited a strong response peak for νas(W=Od), possibly linked to differing magnetic particles. Similarly, Compound 1 displayed a strong response peak under thermal perturbation for νas(W-Oc-W). In contrast, Compound 2 showed a strong response peak for νas(W=Od); these results may be attributed to the different hydrogen bonding connections between the two compounds, which affect the groups in distinct ways through vibration and transmit these vibrations to the W-O bonds. The research presented in this paper expands the theoretical and experimental data of 2D correlation IR spectroscopy.

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.

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.

Research on image recognition of three Fritillaria cirrhosa species based on deep learning

Based on the deep learning method, a network model that can quickly and accurately identify the species of Fritillaria cirrhosa species was constructed. The learning method based on deep residual convolutional neural network was used to input the unprocessed original image directly as input, and the features of the image were extracted through convolution and pooling operations. On this basis, the ResNet34 model was improved, and the additional fully connected layer was added in front of the Softmax classifier to improve the learning ability of the network model. Total of 3915 images of three kinds of Fritillaria cirrhosa were used as data sources for the experiments, among which 160 images of each type were randomly selected to form the validation set. The final training set recognition accuracy rate was 95.8%, the validation set accuracy rate reached 92.3%, and the test set accuracy rate was 88.7%. The image recognition method of Fritillaria cirrhosa based on deep learning proposed in this paper is effective and feasible, which can quickly and accurately identify the species of Fritillaria cirrhosa species, and provides a new idea for the intelligent recognition of Chinese medicinal materials.

A new effective method for identifying boletes species based on FT-MIR and three dimensional correlation spectroscopy projected image processing

This study proposed the necessity of identifying the species for boletes in combination with the medicinal value, nutritional value and the problems existing in the industrial development of boletes. Based on the preprocessing of Fourier transform mid-infrared spectroscopy (FT-MIR) by 1st, 2nd, SNV, 2nd + MSC and 2nd + SG, Multilayer Perceptron (MLP) and CatBoost models were established. To avoid complex preprocessing and feature extraction, we try deep learning modeling methods based on image processing. In this paper, the concept of three-dimensional correlation spectroscopy (3DCOS) projection image was proposed, and 9 datasets of synchronous, asynchronous and integrative images are generated by computer method. In addition, 18 deep learning models were established for 9 image datasets with different sizes. The results showed that the accuracy of the three types of synchronous spectral models reached 100%, while the accuracy of the asynchronous spectral and integrative spectral models of 3DCOS projection images were 96.97% and 97.98% in the case of big datasets, which overcame the defects of poor modeling effect of asynchronous spectral and integrative spectral in previous two-dimensional correlation spectroscopy (2DCOS) studies. In conclusion, the modeling results of 3DCOS projection images are perfect, and we can apply this method to other identification fields in the future.

Quantitative analysis method of Panax notoginseng based on thermal perturbation terahertz two-dimensional correlation spectroscopy

This paper proposes a Panax notoginseng (P. notoginseng) quantitative analysis based on terahertz time-domain spectroscopy and two-dimensional correlation spectroscopy (2DCOS). By imposing temperature perturbation combined with 2DCOS, the one-dimensional absorbance spectra were transformed into 2DCOS synchronous spectra, which reflected the differences in characteristic information between different P. notoginseng contents more clearly. Then, the feature information of P. notoginseng contents was extracted from the 2DCOS synchronous spectra by a competitive adaptive reweighted sampling (CARS) method and was used to build a quantitative model combined with a support vector regression machine (SVR), called 2DCOS-CARS-SVR. We obtained a more accurate analysis result than the commonly used principal component analysis (PCA)-partial least squares regression (PLSR) and PCA-SVR. The prediction set correlation coefficient and root mean square error reached 0.9915% and 0.8160%, respectively.

Identification of Gentian-Related Species Based on Two-Dimensional Correlation Spectroscopy (2D-COS) Combined with Residual Neural Network (ResNet)

Gentian is a traditional Chinese herb with heat-clearing, damp-drying, inflammation-alleviating and digestion-promoting effects, which is widely used in clinical practice. However, there are many species of gentian. According to the pharmacopoeia, Gentiana manshurica Kitag, Gentiana scabra Bge, Gentiana triflora Pall and Gentianarigescens Franch are included. Therefore, accurately identifying the species of gentian is important in clinical use. In recent years, with the advantages of low cost, convenience, fast analysis and high sensitivity, infrared spectroscopy (IR) has been extensively used in herbal identification. Unlike one-dimensional spectroscopy, a two-dimensional correlation spectrum (2D-COS) can improve the resolution of the spectrum and better highlight the details that are difficult to detect. In addition, the residual neural network (ResNet) is an important breakthrough in convolutional neural networks (CNNs) for significant advantages related to image recognition. Herein, we propose a new method for identifying gentian-related species using 2D-COS combined with ResNet. A total of 173 gentian samples from seven different species are collected in this study. In order to eliminate a large amount of redundant information and improve the efficiency of machine learning, the extracted feature band method was used to optimize the model. Four feature bands were selected from the infrared spectrum, namely 3500-3000 cm^-1, 3000-2750 cm^-1, 1750-1100 cm^-1 and 1100-400 cm^-1, respectively. The one-dimensional spectral data were converted into synchronous 2D-COS images, asynchronous 2D-COS images, and integrative 2D-COS images using Matlab (R2022a). The identification strategy for these three 2D-COS images was based on ResNet, which analyzes 2D-COS images based on single feature bands and full bands as well as fused feature bands. According to the results, (1) compared with the other two 2D-COS images, synchronous 2D-COS images are more suitable for the ResNet model, and (2) after extracting a single feature band 1750-1100 cm^-1 to optimize ResNet, the model has the best convergence performance, the accuracy of training, test and external validation is 1 and the loss value is only 0.155. In summary, 2D-COS combined with ResNet is an effective and accurate method to identify gentian-related species.

Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy

Herbs have been used as natural remedies for disease treatment, prevention, and health care. Some herbs with functional properties are also used as food or food additives for culinary purposes. The quality and safety inspection of herbs are influenced by various factors, which need to be assessed in each operation across the whole process of herb production. Traditional analysis methods are time-consuming and laborious, without quick response, which limits industry development and digital detection. Considering the efficiency and accuracy, faster, cheaper, and more environment-friendly techniques are highly needed to complement or replace the conventional chemical analysis methods. Infrared (IR) and Raman spectroscopy techniques have been applied to the quality control and safety inspection of herbs during the last several decades. In this paper, we generalize the current application using IR and Raman spectroscopy techniques across the whole process, from raw materials to patent herbal products. The challenges and remarks were proposed in the end, which serve as references for improving herb detection based on IR and Raman spectroscopy techniques. Meanwhile, make a path to driving intelligence and automation of herb products factories.

Quantitative analysis of near infrared spectroscopic data based on dual-band transformation and competitive adaptive reweighted sampling

Near infrared (NIR) spectroscopy has the characteristics of rapid processing, nondestructive analysis and on-line detection. This technique has been widely used in the fields of quantitative determination and substance content analysis. However, for complex NIR spectral data, most traditional machine learning models cannot carry out effective quantitative analyses (manifested as underfitting; that is, the training effect of the model is not good). Small amounts of available data limit the performance of deep learning-based infrared spectroscopy methods, while the traditional threshold-based feature selection methods require more prior knowledge. To address the above problems, this paper proposes a competitive adaptive reweighted sampling method based on dual band transformation (DWT-CARS). DWT-CARS includes four types in total: CARS based on integrated two-dimensional correlation spectrum (i2DCOS-CARS), CARS based on difference coefficient (DI-CARS), CARS based on ratio coefficient (RI-CARS) and CARS based on normalized difference coefficient (NDI-CARS). We conducted comparative experiments on three datasets; compared to traditional machine learning methods, our method achieved good results, demonstrating that this method has considerable prospects for the quantitative analysis of near-infrared spectroscopic data. To further improve the performance and stability of this method, we combined the idea of integrated modeling and constructed a partial least squares model based on Monte Carlo sampling for the samples obtained by CARS (DWT-CARS-MC-PLS). Through comparative experiments, we verified that the integrated model could further enhance the accuracy and stability of the results.

Application of spectral image processing with different dimensions combined with large-screen visualization in the identification of boletes species

Boletes are favored by consumers because of their unique flavor, rich nutrition and delicious taste. However, the different nutritional values of each species lead to obvious price differences, so shoddy products appear on the market, which affects food safety. The aim of this study was to find a rapid and effective method for boletes species identification. In this paper, 1,707 samples of eight boletes species were selected as the research objects. The original Mid-Infrared (MIR) spectroscopy data were adopted for support vector machine (SVM) modeling. The 11,949 spectral images belong to seven data sets such as two-dimensional correlation spectroscopy (2DCOS) and three-dimensional correlation spectroscopy (3DCOS) were used to carry out Alexnet and Residual network (Resnet) modeling, thus we established 15 models for the identification of boletes species. The results show that the SVM method needs to process complex feature data, the time cost is more than 11 times of other models, and the accuracy is not high enough, so it is not recommended to be used in data processing with large sample size. From the perspective of datasets, synchronous 2DCOS and synchronous 3DCOS have the best modeling results, while one-dimensional (1D) MIR Spectrum dataset has the worst modeling results. After comprehensive analysis, the modeling effect of Resnet on the synchronous 2DCOS dataset is the best. Moreover, we use large-screen visualization technology to visually display the sample information of this research and obtain their distribution rules in terms of species and geographical location. This research shows that deep learning combined with 2DCOS and 3DCOS spectral images can effectively and accurately identify boletes species, which provides a reference for the identification of other fields, such as food and Chinese herbal medicine.

Continuing progress in the field of two-dimensional correlation spectroscopy (2D-COS), part II. Recent noteworthy developments

This comprehensive survey review compiles noteworthy developments and new concepts of two-dimensional correlation spectroscopy (2D-COS) for the last two years. It covers review articles, books, proceedings, and numerous research papers published on 2D-COS, as well as patent and publication trends. 2D-COS continues to evolve and grow with new significant developments and versatile applications in diverse scientific fields. The healthy, vigorous, and diverse progress of 2D-COS studies in many fields strongly confirms that it is well accepted as a powerful analytical technique to provide an in-depth understanding of systems of interest.

A method of two-dimensional correlation spectroscopy combined with residual neural network for comparison and differentiation of medicinal plants raw materials superior to traditional machine learning: a case study on Eucommia ulmoides leaves

BACKGROUND

Eucommia ulmoides leaf (EUL), as a medicine and food homology plant, is a high-quality industrial raw material with great development potential for a valuable economic crop. There are many factors affecting the quality of EULs, such as different drying methods and regions. Therefore, quality and safety have received worldwide attention, and there is a trend to identify medicinal plants with artificial intelligence technology. In this study, we attempted to evaluate the comparison and differentiation for different drying methods and geographical traceability of EULs. As a superior strategy, the two-dimensional correlation spectroscopy (2DCOS) was used to directly combined with residual neural network (ResNet) based on Fourier transform near-infrared spectroscopy.

RESULTS

(1) Each category samples from different regions could be clustered together better than different drying methods through exploratory analysis and hierarchical clustering analysis; (2) A total of 3204 2DCOS images were obtained, synchronous 2DCOS was more suitable for the identification and analysis of EULs compared with asynchronous 2DCOS and integrated 2DCOS; (3) The superior ResNet model about synchronous 2DCOS used to identify different drying method and regions of EULs than the partial least squares discriminant model that the accuracy of train set, test set, and external verification was 100%; (4) The Xinjiang samples was significant differences than others with correlation analysis of 19 climate data and different regions.

CONCLUSIONS

This study verifies the superiority of the ResNet model to identify through this example, which provides a practical reference for related research on other medicinal plants or fungus.

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.

A fast multi-source information fusion strategy based on deep learning for species identification of boletes

Wild mushroom market is an important economic source of Yunnan province in China, and its wild mushroom resources are also valuable wealth in the world. This work will put forward a method of species identification and optimize the method in order to maintain the market order and protect the economic benefits of wild mushrooms. Here we establish deep learning (DL) models based on the two-dimensional correlation spectroscopy (2DCOS) images of near-infrared spectroscopy from boletes, and optimize the identification effect of the model. The results show that synchronous 2DCOS is the best method to establish DL model, and when the learning rate was 0.01, the epochs were 40, using stipes and caps data, the identification effect would be further improved. This method retains the complete information of the samples and can provide a fast and noninvasive method for identifying boletes species for market regulators.

Determination of Peak Purity in HPLC by Coupling Coulometric Array Detection and Two-Dimensional Correlation Analysis

This work aims to evaluate the purity of chromatographic peaks by a two-dimensional correlation (2D-corr) analysis. Such an analysis leads to two contour plots: synchronous and asynchronous. The synchronous contour plot provides information on the number of peaks present in the chromatogram. The asynchronous contour plot reveals the presence of overlapping species on each peak. The utility of 2D-corr analysis was demonstrated by the chromatographic analysis of Capsicum chili extracts obtained by HPLC coupled with a coulometric array of sixteen detectors. Thanks to 16 electrochemical sensors, each poised at increasing potentials, the resulting 2D-corr analysis revealed the presence of at least three species on the peak located at a retention time of 0.93 min. Mass spectrometry (MS) analysis was used to analyze the coeluting species, which were identified as: quinic acid (3.593 min), ascorbic acid (3.943 min), and phenylalanine (4.229 min). Overall, this work supports the use of 2D-corr analysis to reveal the presence of overlapping compounds and, thus, verify the signal purity of chromatographic peaks.

A novel machine learning scheme for classification of medicinal herbs based on 2D-FTIR fingerprints

A proof-of-concept medicinal herbs identification scheme using machine learning classifiers is proposed in the form of an automated computational package. The scheme makes use of two-dimensional correlation Fourier Transformed Infrared (FTIR) fingerprinting maps derived from the FTIR of raw herb spectra as digital input. The prototype package admits a collection of 11 machine learning classifiers to form a voting pool. A common set of oversampled dataset containing 5 different herbal classes is used to train the pool of classifiers on a one-verses-others manner. The collections of trained models, dubbed the voting classifiers, are deployed in a collective manner to cast their votes to support or against a given inference fingerprint whether it belongs to a particular class. By collecting the votes casted by all voting classifiers, a logically designed scoring system will select out the most probable guess of the identity of the inference fingerprint. The same scoring system is also capable of discriminating an inference fingerprint that does not belong to any of the classes the voting classifiers are trained for as the 'others' type. The proposed classification scheme is stress-tested to evaluate its performance and expected consistency. Our experimental runs show that, by and large, a satisfactory performance of the classification scheme of up to 90 % accuracy is achieved, providing a proof-of-concept viability that the proposed scheme is a feasible, practical, and convenient tool for herbal classification. The scheme is implemented in the form of a packaged Python code, dubbed the "Collective Voting" (CV) package, which is easily scalable, maintained and used in practice.

The Storage Period Discrimination of Bolete Mushrooms Based on Deep Learning Methods Combined With Two-Dimensional Correlation Spectroscopy and Integrative Two-Dimensional Correlation Spectroscopy

Boletes are favored by consumers because of their delicious taste and high nutritional value. However, as the storage period increases, their fruiting bodies will grow microorganisms and produce substances harmful to the human body. Therefore, we need to identify the storage period of boletes to ensure their quality. In this article, two-dimensional correlation spectroscopy (2DCOS) images are directly used for deep learning modeling, and the complex spectral data analysis process is transformed into a simple digital image processing problem. We collected 2,018 samples of boletes. After laboratory cleaning, drying, grinding, and tablet compression, their Fourier transform mid-infrared (FT-MIR) spectroscopy data were obtained. Then, we acquired 18,162 spectral images belonging to nine datasets which are synchronous 2DCOS, asynchronous 2DCOS, and integrative 2DCOS (i2DCOS) spectra of 1,750–400, 1,450–1,000, and 1,150–1,000 cm^–1 bands. For these data sets, we established nine deep residual convolutional neural network (ResNet) models to identify the storage period of boletes. The result shows that the accuracy with the train set, test set, and external validation set of the synchronous 2DCOS model on the 1,750–400-cm^–1 band is 100%, and the loss value is close to zero, so this model is the best. The synchronous 2DCOS model on the 1,150–1,000-cm^–1 band comes next, and these two models have high accuracy and generalization ability which can be used to identify the storage period of boletes. The results have certain practical application value and provide a scientific basis for the quality control and market management of bolete mushrooms. In conclusion, our method is novel and extends the application of deep learning in the food field. At the same time, it can be applied to other fields such as agriculture and herbal medicine.

Study on the identification and evaluation of growth years for Paris polyphylla var. yunnanensis using deep learning combined with 2DCOS

Paris polyphylla var. yunnanensis, as perennial plants, its quality is closely related to growth period. Different harvest years determine the dry matter accumulation of its medicinal parts and the dynamic accumulation of active ingredients, as well as its economic value and medicinal value. Therefore, it is necessary to establish a systematic evaluation method for the identification and evaluation of P. polyphylla var. yunnanensis with different growth years. Deep learning has a powerful ability in recognition. This study extends it to the identification analysis of medicinal plants from the perspective of spectrum. For the first time, two-dimensional correlation spectroscopy (2DCOS) based on the attenuated total reflection Fourier transformed infrared spectroscopy (ATR-FTIR) combined with residual neural network (Resnet) was used to identify growth years. 525 samples were collected, 4725 2DCOS images were drawn, and the dry matter accumulation in rhizomes of different growth years and different sampling sites were briefly analyzed. The results show that the eight-year-old P. polyphylla var. yunnanensis in Dali has higher economic value and medicinal value. The synchronous 2DCOS models based on ATR-FTIR can realize the identification of growth years with accuracy of 100%. Synchronous 2DCOS are more suitable for the identification of medicinal plants with complex systems. 2DCOS images with different colors and second derivative processing cannot optimize the modeling results. In summary, the method we established is innovative and feasible. It not only solved the identification of growth years, expanded the application field of deep learning, but could also be extended to further research on other medicinal plants.

A fast and effective way for authentication of Dendrobium species: 2DCOS combined with ResNet based on feature bands extracted by spectrum standard deviation

Dendrobium Sw., as a traditional herb and function food with over 1500 years of history, shows a significant effect in improving immunity and fatigue resistance. However, due of course the large number of species and the quality fluctuating in different species, a fast and effective discrimination method is in need. Recently, spectroscopic techniques combined with chemometrics have become an effective method for low-cost and fast analysis in food and herb. Nevertheless, chemometrics method which based on one-dimensional spectral dataset still encounter the difficulty that can not effectively extract useful information from the spectra. Different from one-dimensional spectra, the two-dimensional correlation spectroscopy (2DCOS) can reveal more detail information of the spectral dataset. Moreover, the appearance of convolutional neural network makes the application of deep learning in image recognition faster and more accurate. In this study, a novel method 2DCOS combined with residual convolutional neural network (ResNet) was used to discriminate the 20 species of Dendrobium. Five feature bands were selected based on spectrum standard deviation (SDD) method in NIR and MIR spectra. Moreover, the models based on full band, total five feature bands, and their fusion-bands had been compared. The results showed that two feature bands 1800-450 cm^-1 and 2400-1900 cm^-1 displayed 100% accuracy in both training set and test set. And also, the accurate discrimination of 10% external validation showed that these models have good generalization ability. In conclusion, 2DCOS combined with ResNet could be an effective and accurate method for classify different Dendrobium species.

Superiority Verification of Deep Learning in the Identification of Medicinal Plants: Taking Paris polyphylla var. yunnanensis as an Example

Medicinal plants have a variety of values and are an important source of new drugs and their lead compounds. They have played an important role in the treatment of cancer, AIDS, COVID-19 and other major and unconquered diseases. However, there are problems such as uneven quality and adulteration. Therefore, it is of great significance to find comprehensive, efficient and modern technology for its identification and evaluation to ensure quality and efficacy. In this study, deep learning, which is superior to conventional identification techniques, was extended to the identification of the part and region of the medicinal plant Paris polyphylla var. yunnanensis from the perspective of spectroscopy. Two pattern recognition models, partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM), were established, and the overall discrimination performance of the three types of models was compared. In addition, we also compared the effects of different sample sizes on the discriminant performance of the models for the first time to explore whether the three models had sample size dependence. The results showed that the deep learning model had absolute superiority in the identification of medicinal plant. It was almost unaffected by factors such as data type and sample size. The overall identification ability was significantly better than the PLS-DA and SVM models. This study verified the superiority of the deep learning from examples, and provided a practical reference for related research on other medicinal plants.

Deep learning for species identification of bolete mushrooms with two-dimensional correlation spectral (2DCOS) images

Bolete is well-known and widely consumed mushroom in the world. However, its medicinal properties and nutritional are completely different from one species to another. Therefore, the consumers need a fast and effective detection method to discriminate their species. A new method using directly digital images of two-dimensional correlation spectroscopy (2DCOS) for the species discrimination with deep learning is proposed in this paper. In our study, a total of 2054 fruiting bodies of 21 wild-grown bolete species were collected in 52 regions from 2011 to 2014. Firstly, we intercepted 1750-400 cm^-1 fingerprint regions of each species from their mid-infrared (MIR) spectra, and converted them to 2DCOS spectra with matlab2017b. At the same time, we developed a specific method for the calculation of the 2DCOS spectra. Secondly, we established a deep residual convolutional neural network (Resnet) with 1848 (90%) 2DCOS spectral images. Therein, the discrimination of the bolete species using directly 2DCOS spectral images instead of data matric from the spectra was first to be reported. The results displayed that the respective identification accuracy of these samples was 100% in the training set and 99.76% in the test set. Then, 203 samples were accurately discriminated in 206 (10%) samples of external validation set. Thirdly, we employed t-SNE method to visualize and evaluate the spectral dataset. The result indicated that most samples can be clustered according to different species. Finally, a smartphone applications (APP) was developed based on the established 2DCOS spectral images strategy, which can make the discrimination of bolete mushrooms more easily in practice. In conclusion, deep learning method by using directly 2DCOS spectral image was considered to be an innovative and feasible way for the species discrimination of bolete mushrooms. Moreover, this method may be generalized to other edible mushrooms, food, herb and agricultural products in the further research.

Study on the source of magnetic properties of three novel boratopolyoxovanadates via two-dimensional infrared correlation spectroscopy

Three novel polyoxovanadoborates namely [V₁₂B₁₈O₄₆(OH)₁₄(H₂O)_0.75]·20.5H₂O 1, Na₂[V₁₂B₁₈O₄₈(OH)₁₂(H₂O)_0.5]·26.5H₂O 2 and Cd_0.5{[Na(H₂O)₂]2[Na(H₂O)]₂[Na(H₂O)₃]2V₁₂B₁₈O₅₃(OH)₇(H₂O)_0.5}·11H₂O 3 have been hydrothermally synthesized and structurally characterized. The boratopolyoxovanadate cage [V₁₂B₁₈O₆₀] backbones in 1-3 are constructed by the combination of two hexameric oxovanadate units [V₆O₉] and one puckered [B₁₈O₄₂] ring via sharing O atoms. All three compounds were obtained under alkaline conditions, and the cluster anions were all [V₁₂B₁₈O₆₀]. But the cations were different, it is inferred that the protonation of the three compound cluster ions is different, respectively [V₁₂B₁₈O₄₆(OH)₁₄(H₂O)_0.75] in 1, [V₁₂B₁₈O₄₈(OH)₁₂(H₂O)_0.5]^2- in 2 and [V₁₂B₁₈O₅₃(OH)₇(H₂O)_0.5]^7- in 3. The V oxidation states ratio of V^IV to V^V were 2:1 confirmed by valence bond calculation and XPS. We studied the magnetic properties of three compounds by two methods: The variable temperature magnetic susceptibility and the 2D IR COS under magnetic perturbation. From the 2D IR COS under magnetic perturbation map, it is showed that all three: (1) the presence of V^IV. (2) Certain quasi-aromaticity from B₃O₃ six-membered ring. (3) The difference of protonation and the charge of the cluster anions. This work enriches the theory of two-dimensional correlation spectroscopy and also provides a new approach to the study of magnetic materials.

Rapid and Integrated Quality Assessment of Organic-Inorganic Composite Herbs by FTIR Spectroscopy-Global Chemical Fingerprints Identification and Multiple Marker Components Quantification of Indigo Naturalis (Qing Dai)

This research aimed to develop an FTIR-based method for rapid and low-cost integrated quality assessment of organic-inorganic composite herbs, which are kinds of herbs composed of both organic and inorganic active ingredients or matrix components. A two-step quality assessment route was designed and verified using the example of Indigo Naturalis (Qing Dai). First, the FTIR spectra were used as global chemical fingerprints to identify the true and fake samples. Next, the contents of the organic and inorganic marker components were estimated by FTIR quantification models to assess the quality of the true samples. Using the above approaches, all the 56 true samples and five fake samples of Indigo Naturalis could be identified correctly by the correlation threshold of the FTIR chemical fingerprints. Furthermore, the FTIR calibration models provided an accurate estimation of the contents of marker components with respect to HPLC and inductively coupled plasma optical emission spectrometry (ICP-OES). The coefficients of determination (R²) for the independent validation of indigo, indirubin, and calcium were 0.977, 0.983, and 0.971, respectively. Meanwhile, the mean relative errors (MRE) for the independent validation of indigo, indirubin, and calcium were 2.2%, 2.4%, and 1.8%, respectively. In conclusion, this research shows the potential of FTIR spectroscopy for the rapid and integrated quality assessment of organic-inorganic composite herbs in both chemical fingerprints identification and marker components quantification.