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

Hybrid wavelength selection strategy combined with ATR-FTIR spectroscopy for preliminary exploration of vintage labeling traceability of sauce-flavor baijiu

The allure of substantial profits has perpetuated the illicit trade of counterfeit vintage labels for baijiu. While various approaches have been employed to intelligently ascertain the vintage of baijiu, many of them are both cost-intensive and time-consuming. This work pioneered the use of Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometric analysis, offering a non-destructive and economically viable method for discriminating sauce-flavor baijiu across different aging periods (1-, 2-, and 3-year). In this research, principal component analysis (PCA) was first conducted to explore clustering trends among distinct vintage groups. Subsequently, the effect of spectral pre-processing on modeling performance was explored. For wavelength selection, four wavelength selection methods (ReliefF, random forest variable importance (RFVI), variable importance in projection (VIP), and Venn) were first used to identify the subset of candidate features that potentially best mapped the vintage labels. Immediately following this, to explore the possibility of further improving the identification capabilities of the model as well as to reduce the redundant data that may still be present, sequential backward selection (SBS) was utilized for secondary feature reduction within the subset of candidates. The amalgamation of these two techniques is termed a "hybrid wavelength selection strategy." Additionally, the dimensionality reduction effects of PCA and kernel principal component analysis (KPCA) were compared to demonstrate the robustness of the proposed method. Finally, classification models such as partial least squares discriminant analysis (PLS-DA), random forest (RF), and grasshopper optimization algorithm-based support vector machine (GOA-SVM) were developed. The results show that the spectral data need not be pre-processed, and the proposed hybrid wavelength selection strategy can further improve the identification ability of the model. Among the many models developed, ReliefF-SBS-GOA-SVM emerged as the most proficient classification model, yielding accuracy, sensitivity, and specificity rates of 94.44%, 95.23%, and 94.44%, respectively. This method not only holds promise for the discrimination of baijiu class attributes such as brand, origin, flavor, and vintage but also exhibits potential applicability in other non-targeted identification studies involving spectroscopy methodologies.

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.

Visible and Near-Infrared Spectroscopy Combined With Bayes Classifier Based on Wavelength Model Optimization Applied to Wine Multibrand Identification

The identification of high-quality wine brands can avoid adulteration and fraud and protect the rights and interests of producers and consumers. Since the main components of wine are roughly the same, the characteristic components that can distinguish wine brands are usually trace amounts and not unique. The conventional quantitative detection method for brand identification is complicated and difficult. The naive Bayes (NB) classifier is an algorithm based on probability distribution, which is simple and particularly suitable for multiclass discriminant analysis. However, the absorbance probability between spectral wavelengths is not necessarily strongly independent, which limits the application of Bayes method in spectral pattern recognition. This research proposed a Bayes classifier algorithm based on wavelength optimization. First, a large-scale wavelength screening for equidistant combination (EC) was performed, and then wavelength step-by-step phase-out (WSP) was carried out to reduce the correlation between wavelengths and improve the accuracy of Bayes discrimination. The proposed EC-WSP-Bayes method was applied to the 5-category discriminant analysis of wine brand identification based on visible and near-infrared (Vis-NIR) spectroscopy. Among them, four types of wine brands were collected from regular sales channels as identification brands. The fifth type of samples was composed of 21 other commercial brand wines and home-brewed wines from various sources, as the interference brand. The optimal EC-WSP-Bayes model was selected, the corresponding wavelength combination was 404, 600, 992, 2,070, 2,266, and 2,462 nm located in the visible light, shortwave NIR, and combination frequency regions. In modeling and independent validation, the total recognition accuracy rate (RAR_Total) reached 98.1 and 97.6%, respectively. The technology is quick and easy, which is of great significance to regulate the alcohol market. The proposed model of less-wavelength and high-efficiency (N = 6) can provide a valuable reference for small special instruments. The proposed integrated chemometric method can reduce the correlation between wavelengths, improve the recognition accuracy, and improve the applicability of the Bayesian method.

Determination of quality markers for quality control of Zanthoxylum nitidum using ultra-performance liquid chromatography coupled with near infrared spectroscopy

With the increasing demand for quality control in the traditional Chinese medicine industry, there is a need for the development of quality markers and a quick, non-destructive technique for the discrimination of related species. In our previous study, ultra-performance liquid chromatography (UPLC) was used for the simultaneous determination of five compounds, including three alkaloids (nitidine chloride, chelerythrine, and magnoflorine), one flavonoid (aurantiamarin), and one lignan (sesamin). In this study, the simultaneous quantification of the above-mentioned compounds could be used to discriminate the powders of roots from those of stems. To further test the reliability of the five compounds, seventy-two batches of wild and seventy-five batches of cultivated Zanthoxylum nitidum samples collected from Guangdong, Guangxi, and Fujian provinces in China were analyzed by UPLC and near-infrared spectroscopy (NIRS). In general, the quantitative results of UPLC were consistent with those of NIRS, and cultivated Z. nitidum has similar major bioactive compounds as the wild one, as supported by principal component analysis. Consequently, these five major bioactive compounds are suggested as potential quality markers. In addition, the NIRS method with discriminant analysis successfully differentiated Z. nitidum from three related species (Z. avicennae, Z. scandens and Toddalia asiatica) of the Rutaceae family. In summary, this study provides a method for the rapid identification of Z. nitidum and discrimination of root and stem powders, and suggests five compounds as quality markers for the evaluation of Z. nitidum.

Authentication of Herbal Medicines Based on Modern Analytical Technology Combined with Chemometrics Approach: A Review

Since ancient times, herbal medicines (HMs) have been widely popular with consumers as a "natural" drug for health care and disease treatment. With the emergence of problems, such as increasing demand for HMs and shortage of resources, it often occurs the phenomenon of shoddy exceed and mixing the false with the genuine in the market. There is an urgent need to evaluate the quality of HMs to ensure their important role in health care and disease treatment, and to reduce the possibility of threat to human health. Modern analytical technology is can be analyzed for analyzing chemical components of HMs or their preparations. Reflecting complex chemical components' characteristic curves in the analysis sample, and the comprehensive effect of active ingredients of HMs. In this review, modern analytical technology (chromatography, spectroscopy, mass spectrometry), chemometrics methods (unsupervised, supervised) and their advantages, disadvantages, and applicability were introduced and summarized. In addition, the authentication application of modern analytical technology combined with chemometrics methods in four aspects, including origin, processing methods, cultivation methods, and adulteration of HMs have also been discussed and illustrated by a few typical studies. This article offers a general workflow of analytical methods that have been applied for HMs authentication and explains that the accuracy of authentication in favor of the quality assurance of HMs. It was provided reference value for the development and application of modern HMs.

Microscopic investigations and pharmacognostic techniques for the standardization of the fruits of Rosa laxa Retz

Rosa laxa Retz., a shrub belonging to the family Rosaceae, is widely distributed in the northern foothills of the Tianshan Mountains in Xinjiang, China. The fruits of R. laxa (FRL) has antibacterial, hypolipidemic, and antioxidant effects. In this study, FRL was subjected to pharmacognostic identification of its source, morphology, microscopic characteristics, and physicochemical properties. The microscope showed that the cross-sectional features of FRL were obvious, and the FRL powder contained vessel, parenchyma cells, exocarp cells, pollen grains, and cluster crystals. Scanning electron microscopy (SEM) analysis results show that numerous villi and many small particles (particle size of 5-50 μm) were observed in the FRL powder, and there are many gullies on the surface of the particles. In addition, the secondary metabolites of FRL were characterized via ultraviolet-visible (UV-Vis) spectroscopy, Fourier transform infrared (FTIR) spectroscopy, and thin-layer chromatography (TLC). Results showed that FRL contains various secondary metabolites, including flavonoids, phenolic acids, glycosides, and tannins. Water as the extraction solvent had the highest extraction rate and the contented of total flavonoids was 2.88 mg/g, and the contented of total polyphenols was 54.89 mg/g. Moreover, TLC identification revealed that it contains catechin and tiliroside. These parameters of FRL, which are reported herein, are important to the development of the pharmacognostic standards, as well as in the identification and quality control of FRL.

A Novel Multi-Preprocessing Integration Method for the Qualitative and Quantitative Assessment of Wild Medicinal Plants: Gentiana rigescens as an Example

Until now, the over-exploitation of wild resources has increased growing concern over the quality of wild medicinal plants. This led to the necessity of developing a rapid method for the evaluation of wild medicinal plants. In this study, the content of total secoiridoids (gentiopicroside, swertiamarin, and sweroside) of Gentiana rigescens from 37 different regions in southwest China were analyzed by high performance liquid chromatography (HPLC). Furthermore, Fourier transform infrared (FT-IR) was adopted to trace the geographical origin (331 individuals) and predict the content of total secoiridoids (273 individuals). In the traditional FT-IR analysis, only one scatter correction technique could be selected from a series of preprocessing candidates to decrease the impact of the light correcting effect. Nevertheless, different scatter correction techniques may carry complementary information so that using the single scatter correction technique is sub-optimal. Hence, the emerging ensemble approach to preprocessing fusion, sequential preprocessing through orthogonalization (SPORT), was carried out to fuse the complementary information linked to different preprocessing methods. The results suggested that, compared with the best results obtained on the scatter correction modeling, SPORT increased the accuracy of the test set by 12.8% in qualitative analysis and decreased the RMSEP by 66.7% in quantitative analysis.

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.

High performance liquid chromatography fingerprint and headspace gas chromatography-mass spectrometry combined with chemometrics for the species authentication of Curcumae Rhizoma

Curcumae Rhizoma (Ezhu), a multi-origin Chinese medicine, originates from the dry rhizomes of C. kwangsiensis, C. phaeocaulis and C. wenyujin. The three species have great variation in chemical components and therapeutic effects. To improve safety and effectiveness in clinical use, a strategy integrating chromatographic analysis and chemometrics for the species authentication of Ezhu was proposed. Firstly, systematic analysis of chemical compositions in Ezhu was achieved using high performance liquid chromatography (HPLC) fingerprint and headspace gas chromatography-mass spectrometry (HS-GC-MS). HPLC fingerprints showed that seventeen peaks in common for C. kwangsiensis and eleven peaks in common for C. wenyujin both presented a good similarity (> 0.9, only several samples < 0.8). Eleven common peaks in C. phaeocaulis and the similarity values of most samples were higher than 0.700. Additionally, there were ten common peaks in all Ezhu samples and they had relatively poor similarity with the correlation coefficients ranging from 0.364 to 0.881. For HS-GC-MS, thirty-six volatile components were identified in the three species of Ezhu, mainly monoterpenes and sesquiterpenes. Subsequently, chemometrics including unsupervised principal component analysis (PCA), supervised linear discriminant analysis (LDA), K-nearest neighbors (KNN), back propagation neural network (BP-NN) and orthogonal partial least squares-discrimination analysis (OPLS-DA) was applied to extract useful information from chromatographic profiles. Based on HPLC fingerprint data, PCA could hardly differentiate Ezhu with the three species, and LDA, KNN and BP-NN models provided more than 85 % correct identification. With HS-GC-MS data, PCA could only distinguish C. wenyujin from the other two species, and LDA, KNN, BP-NN and OPLS-DA models achieved excellent classification with 100 % accuracy. Finally, five volatile components (eucalyptol, humulene, β-elemene, (+)-2-bornanone and linalool) with variable importance for the projection (VIP) values higher than 1 in the OPLS-DA model were selected as potential chemical markers for the species authentication of Ezhu. And the constructed OPLS-DA model using these markers obtained 100 % accuracy. Consequently, a rapid, precise and feasible strategy was established for the discrimination and quality control of Ezhu with different species.

Using near-infrared spectroscopy to discriminate closely related species: a case study of neotropical ferns

Identifying plant species requires considerable knowledge and can be difficult without complete specimens. Fourier-transform near-infrared spectroscopy (FT-NIR) is an effective technique for discriminating plant species, especially angiosperms. However, its efficacy has never been tested on ferns. Here we tested the accuracy of FT-NIR at discriminating species of the genus Microgramma. We obtained 16 spectral readings per individual from the adaxial and abaxial surfaces of 100 specimens belonging to 13 species. The analyses included all 1557 spectral variables. We tested different datasets (adaxial + abaxial, adaxial, and abaxial) to compare the correct identification of species through the construction of discriminant models (Linear discriminant analysis and partial least squares discriminant analysis) and cross-validation techniques (leave-one-out, K-fold). All analyses recovered an overall high percentage (> 90%) of correct predictions of specimen identifications for all datasets, regardless of the model or cross-validation used. On average, there was > 95% accuracy when using partial least squares discriminant analysis and both cross-validations. Our results show the high predictive power of FT-NIR at correctly discriminating fern species when using leaves of dried herbarium specimens. The technique is sensitive enough to reflect species delimitation problems and possible hybridization, and it has the potential of helping better delimit and identify fern species.