Discrimination of Trichosanthis Fructus from Different Geographical Origins Using Near Infrared Spectroscopy Coupled with Chemometric Techniques

Differentiation of Polygonatum Cyrtonema Hua from Different Geographical Origins by Near-Infrared Spectroscopy with Chemometrics

BACKGROUND

The identification of the geographical origin of Polygonatum cyrtonema Hua is of particular importance because the quality and market value of Polygonatum cyrtonema Hua from different production areas are highly variable due to differences in the growing environment and climatic conditions.

OBJECTIVE

This study utilized near-infrared spectra (NIR) of Polygonatum cyrtonema Hua (n = 400) to develop qualitative models for effective differentiation of Polygonatum cyrtonema Hua from various regions.

METHODS

The models were produced under different conditions to distinguish the origins distinctly. Ten preprocessing methods have been used to preprocess the original spectra (OS) and to select the most optimal spectral preprocessing method. Principal component analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to determine appropriate models. For simplicity, the pretreated full spectrum was calculated by different wavelength selection methods, and the four most significant variables were selected as discriminant indicator variables.

RESULTS

The results show that Polygonatum cyrtonema Hua from different regions can be effectively distinguished using spectra from a series of samples analyzed by OPLS-DA. The accuracy of the OPLS-DA model is also satisfactory, with a good differentiation rate.

CONCLUSION

The study findings indicate the feasibility of using spectroscopy in combination with multivariate analysis to identify the geographical origins of Polygonatum cyrtonema Hua.

HIGHLIGHTS

The utilization of NIR spectroscopy combined with chemometrics exhibits high efficacy in discerning the provenance of herbal medicines and foods, thereby facilitating QA measures.

ESI(-)FT-ICR MS for the determination of best conditions for producing extract abundant in phenolic compounds from leaves of E. uniflora and FTIR-PCA as a sample screening method

E. uniflora leaves are a rich source of phenolic compounds with biological activities, including myricitrin. In this study, the chemical profile of nine extracts prepared with leaves collected in three regions (mountain, beach, and mangrove) and at three different times of the day (8 am, 1 pm, and 6 pm) was evaluated from spectra originating from ultra-high resolution mass spectrometry (Fourier transform ion cyclotron resonance, FT-ICR) coupled to electrospray ionisation (ESI). The best time of the day and location for collecting the leaves of E. uniflora used as raw materials for producing extracts and the best ethanol concentration for obtaining an extract more abundant in compounds of interest were verified. Several flavonoids and phenolic acids were detected in their deprotonated form in the regions from m/z 200 to 1200. Myricitrin ([C21H20O12-H]-, m/ztheo 463.08820), its chloride adduct ([C21H20O12+Cl]-, m/ztheo 499.06488), other myricitrin derivatives, and some tannins were the main compounds detected. Considering obtaining an extract rich in phenolic compounds, including myricitrin, the best place and time of the day to collect E. uniflora leaves is in the beach region at 1 pm. In contrast, the best ethanol concentration for extract production is 70 wt%. Therefore, extraction at 96 wt% ethanol is better for obtaining an extract more abundant in phenolic acids, although 70 wt% ethanol also extracted these compounds. FTIR-PCA models were used to check for possible similarities in the data according to collection time of the day and location. These models demonstrated an excellent solution for sample screening.

Natural complex substances: From molecules to the molecular complexes. Analytical and technological advances for their definition and differentiation from the corresponding synthetic substances

Natural complex substances (NCSs) are a heterogeneous family of substances that are notably used as ingredients in several products classified as food supplements, medical devices, cosmetics and traditional medicines, according to the correspondent regulatory framework. The compositions of NCSs vary widely and hundreds to thousands of compounds can be present at the same time. A key concept is that NCSs are much more than the simple sum of the compounds that constitute them, in fact some emerging phenomena are the result of the supramolecular interaction of the constituents of the system. Therefore, close attention should be paid to produce and characterize these systems. Today many natural compounds are produced by chemical synthesis and are intentionally added to NCSs, or to formulated natural products, to enhance their properties, lowering their production costs. Market analysis shows a tendency of people to use products made with NCSs and, currently, products made with ingredients of natural origin only are not conveniently distinguishable from those containing compounds of synthetic origin. Furthermore, the uncertainty of the current European regulatory framework does not allow consumers to correctly differentiate and identify products containing only ingredients of natural origin. The high demand for specific and effective NCSs and their high-cost offer on the market, create the conditions to economically motivated sophistications, characterized by the addition of a cheap material to a more expensive one, just to increase profit. This type of practice can concern both the addition of less valuable natural materials and the addition of pure artificial compounds with the same structure as those naturally present. In this scenario, it becomes essential for producers of natural products to have advanced analytical techniques to evaluate the effective naturalness of NCSs. In fact, synthetically obtained compounds are not identical to their naturally occurring counterparts, due to the isotopic composition or chirality, as well as the presence of different trace metabolites (since pure substances in nature do not exist). For this reason, in this review, the main analytical tests that can be performed to differentiate natural compounds from their synthetic counterparts will be highlighted and the main analytical technologies will be described. At the same time, the main fingerprint techniques useful for characterizing the complexity of the NCSs, also allowing their identification and quali-quantitative evaluation, will be described. Furthermore, NCSs can be produced through different manufacturing processes, not all of which are on the same level of quality. In this review the most suitable technologies for green processes that operate according to physical extraction principles will be presented, as according to the authors they are the ones that come closest to creating more life-cycle compatible NCSs and that are well suited to the European green deal, a strategy with the aim of transforming the EU into a sustainable and resource-efficient society by 2050.

Deep Learning Combined with Hyperspectral Imaging Technology for Variety Discrimination of Fritillaria thunbergii

Traditional Chinese herbal medicine (TCHM) plays an essential role in the international pharmaceutical industry due to its rich resources and unique curative properties. The flowers, stems, and leaves of Fritillaria contain a wide range of phytochemical compounds, including flavonoids, essential oils, saponins, and alkaloids, which may be useful for medicinal purposes. Fritillaria thunbergii Miq. Bulbs are commonly used in traditional Chinese medicine as expectorants and antitussives. In this paper, a feasibility study is presented that examines the use of hyperspectral imaging integrated with convolutional neural networks (CNN) to distinguish twelve (12) Fritillaria varieties (n = 360). The performance of support vector machines (SVM) and partial least squares-discriminant analysis (PLS-DA) was compared with that of convolutional neural network (CNN). Principal component analysis (PCA) was used to assess the presence of cluster trends in the spectral data. To optimize the performance of the models, cross-validation was used. Among all the discriminant models, CNN was the most accurate with 98.88%, 88.89% in training and test sets, followed by PLS-DA and SVM with 92.59%, 81.94% and 99.65%, 79.17%, respectively. The results obtained in the present study revealed that application of HSI in conjunction with the deep learning technique can be used for classification of Fritillaria thunbergii varieties rapidly and non-destructively.

Methylnigakinone content determination and geographical origin discrimination for P. quassioides via fluorescence fingerprint and principal component analyses

Picrasma quassioides is used as a bittersweet stomach medicine. Because it is a natural product obtained from various geographical regions, the production area is important when P. quassioides is used as a crude drug. Herein, we developed a method to determine the content of methylnigakinone, one of the major active ingredients in P. quassioides, and a protocol for discriminating the geographical origin of this natural product using a fluorescence fingerprint analysis and principal component analysis (PCA). Because methylnigakinone is fluorescent (excitation wavelength: 352 nm, emission wavelength: 458 nm), the content of this molecule can be determined in the concentration range of 0.1-1 μg/mL. The quantification results of methylnigakinone obtained using the developed method were similar to those obtained from an HPLC analysis. Furthermore, the PCA of the fluorescence fingerprint of P. quassioides produced a score plot with the three different geographical origins (Kyushu island (Japan), Shikoku island (Japan), and China) plotted in the regions. Thus, it was possible to discriminate the geographical origin of the P. quassioides samples. The developed method is simple, quick, and has a minimal environmental impact. Therefore, the developed method will be useful for confirming the origin of P. quassioides.

Early pregnancy diagnosis of rabbits: A non-invasive approach using Vis-NIR spatially resolved spectroscopy

Pregnancy diagnosis is essential for rabbit's reproductive management. The early identification of non-pregnant rabbits allows for earlier re-insemination, increases the service rate, and reduces the laboring interval in commercial operations. The objective of this study was to establish the feasibility of using a Vis-NIR spatially resolved spectroscopy for diagnosing pregnancy in female rabbits. A total of 141 female rabbits, including 67 pregnant female rabbits (PRs) and 74 non-pregnant female rabbits (NPRs), were measured spectrally between 350 and 1000 nm with different source-detector distances (SDD). Different preprocessing methods were used to transform and enhance the spectral signal. A partial least squares-discriminant analysis (PLS-DA) classification model of the original and preprocessed spectra was established. The highest accuracy of the calibration set and prediction set was 91.75% and 86.05%, respectively. Competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were used to select characteristic wavelengths from the variables of VIP > 1 (Variable importance in projection),and four classification models were established based on selected wavelengths, including PLS-DA, support vector machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes. SPA-SVM was the optimal classification model, the sensitivity, specificity, and accuracy of the validation set and prediction set were 93.18%, 94.44%, 93.88%, 86.96%, 90.00%, 90.69% respectively. The results showed that Vis-NIR spatially resolved spectroscopy combined with classification models could discriminate the PRs and NPRs.

Geographic Origin Discrimination of Millet Using Vis-NIR Spectroscopy Combined with Machine Learning Techniques

Millet is a primary food for people living in the dry and semi-dry regions and is dispersed within most parts of Europe, Africa, and Asian countries. As part of the European Union (EU) efforts to establish food originality, there is a global need to create Protected Geographical Indication (PGI) and Protected Designation of Origin (PDO) of crops and agricultural products to ensure the integrity of the food supply. In the present work, Visible and Near-Infrared Spectroscopy (Vis-NIR) combined with machine learning techniques was used to discriminate 16 millet varieties (n = 480) originating from various regions of China. Five different machine learning algorithms, namely, K-nearest neighbor (K-NN), Linear discriminant analysis (LDA), Logistic regression (LR), Random Forest (RF), and Support vector machine (SVM), were used to train the NIR spectra of these millet samples and to assess their discrimination performance. Visible cluster trends were obtained from the Principal Component Analysis (PCA) of the spectral data. Cross-validation was used to optimize the performance of the models. Overall, the F-Score values were as follows: SVM with 99.5%, accompanied by RF with 99.5%, LDA with 99.5%, K-NN with 99.1%, and LR with 98.8%. Both the linear and non-linear algorithms yielded positive results, but the non-linear models appear slightly better. The study revealed that applying Vis-NIR spectroscopy assisted by machine learning technique can be an essential tool for tracing the origins of millet, contributing to a safe authentication method in a quick, relatively cheap, and non-destructive way.

Rapid detection of cellulose and hemicellulose contents of corn stover based on near-infrared spectroscopy combined with chemometrics

The feasibility of near-infrared spectroscopy (NIRS) combined with chemometrics for the rapid detection of the cellulose and hemicellulose contents in corn stover is discussed. Competitive adaptive reweighted sampling (CARS) and genetic simulated annealing algorithm (GSA) were combined (CARS-GSA) to select the characteristic wavelengths of cellulose and hemicellulose and to reduce the dimensionality and multicollinearity of the NIRS data. The whole spectra contained 1845 wavelength variables. After CARS-GSA optimization, the number of characteristic wavelengths of cellulose (hemicellulose) was reduced to 152 (260), accounting for 8.24% (14.09%) of all wavelengths. The coefficients of determination of the regression models for predicting the cellulose and hemicellulose contents were 0.968 and 0.996, the root mean square errors of prediction (RMSEPs) were 0.683 and 0.648, and the residual predictive deviations (RPDs) were 5.213 and 16.499, respectively. The RMSEP of the cellulose and hemicellulose regression models was 0.152 and 0.190 lower for CARS-GSA than for the full-spectrum, and the RPD was increased by 0.949 and 3.47, respectively. The results showed that the CARS-GSA model substantially reduced the number of characteristic wavelengths and significantly improved the predictive ability of the regression model.

Nondestructive qualitative and quantitative analysis of Yaobitong capsule using near-infrared spectroscopy in tandem with chemometrics

The purpose of the study is to present a nondestructive qualitative and quantitative approach of hard-shell capsule using near-infrared (NIR) spectroscopy combined with chemometrics. The Yaobitong capsule (YBTC) was used for demonstration of the proposed approach and the NIR spectra were collected using a handheld fiber probe (FP) without the damage of capsule shell. By comparing the differences and similarities of the NIR spectra of capsule shells, contents and intact capsules, a preliminary conclusion can be drawn that the NIR spectra contained the information of the contents. Characteristic variables were selected by competitive adaptive weighted resampling (CARS) method, and least squares support vector machine (LSSVM) method based on particle swarm optimization (PSO) algorithm was applied to the construction of quantitative models. The relative standard error of prediction (RSEP) values of five saponins including notoginsenoside R1, ginsenoside Rg1, Re, Rb1, and Rd were 3.240%, 5.468%, 5.303%, 5.043%, and 3.745%, respectively. In addition, for qualitative model, three different types of adulterated capsules were designed. The model established by data driven version of soft independent modeling of class analogy (DD-SIMCA) demonstrated a satisfactory result that all adulterated capsules were identified accurately after an appropriate number of principal components (PCs) were chosen. The results indicated that although the NIR spectra collection was affected by capsule shell, sufficient content information can be obtained for quantitative and qualitative analysis after combining with chemometrics. It further proved that acquired NIR spectra do contain the effective component information of the capsule. This study provided a reference for the rapid nondestructive quality analysis of traditional Chinese medicine (TCM) capsule without damaging capsule shell.

Gender discrimination of Populus tomentosa barks by HPLC fingerprint combined with multivariate statistics

A high-performance liquid chromatography (HPLC) fingerprint method with multivariate statistical analyses was applied to discriminate the male and female barks of Populus tomentosa for the first time. The samples of 11 male and 13 female barks of mature P. tomentosa were collected in Beijing. The chemical fingerprint of methanol extract was established by HPLC method with diode array detector (DAD). The principal component analysis (PCA), hierarchical clustering analysis (HCA), and supervised orthogonal partial least squares discriminant analysis (OPLS-DA) were applied to discriminate male and female barks based on the area of common peaks identified in HPLC fingerprints. A clear grouping trend (R 2 X, 0.83; Q 2, 0.595) among the male and female samples was exhibited by PCA score plot. Two groups were clearly divided into male and female samples by HCA. Both male and female samples were well discriminated with OPLS-DA (R 2 X, 0.775; Q 2, 0.795). Seven potential chemical markers were screened by variable importance in projection (VIP values >1.0) of OPLS-DA model and four of them were identified as micranthoside, siebolside B, sakuranin, and isosakuranin. The HPLC fingerprint combined with multivariate statistical analyses could be used to discriminate the gender of barks of P. tomentosa and revealed the differences in chemical components, which enriched the basic studies on dioecious plant.

Rapid Geographical Origin Identification and Quality Assessment of Angelicae Sinensis Radix by FT-NIR Spectroscopy

Angelicae Sinensis Radix is a widely used traditional Chinese medicine and spice in China. The purpose of this study was to develop a methodology for geographical classification of Angelicae Sinensis Radix and determine the contents of ferulic acid and Z-ligustilide in the samples using near-infrared spectroscopy. A qualitative model was established to identify the geographical origin of Angelicae Sinensis Radix using Fourier transform near-infrared (FT-NIR) spectroscopy. Support vector machine (SVM) algorithms were used for the establishment of a qualitative model. The optimum SVM model had a recognition rate of 100% for the calibration set and 83.72% for the prediction set. In addition, a quantitative model was established to predict the content of ferulic acid and Z-ligustilide using FT-NIR. Partial least squares regression (PLSR) algorithms were used for the establishment of a quantitative model. Synergy interval-PLS (Si-PLS) was used to screen the characteristic spectral interval to obtain the best PLSR model. The coefficient of determination for calibration (R2C) for the best PLSR models established with the optimal spectral preprocessing method and selected important spectral regions for the quantitative determination of ferulic acid and Z-ligustilide was 0.9659 and 0.9611, respectively, while the coefficient of determination for prediction (R2P) was 0.9118 and 0.9206, respectively. The values of the ratio of prediction to deviation (RPD) of the two final optimized PLSR models were greater than 2. The results suggested that NIR spectroscopy combined with SVM and PLSR algorithms could be exploited in the discrimination of Angelicae Sinensis Radix from different geographical locations for quality assurance and monitoring. This study might serve as a reference for quality evaluation of agricultural, pharmaceutical, and food products.

Novel Similarity Methods Evaluation and Feasible Application for Pharmaceutical Raw Material Identification with Near-Infrared Spectroscopy

Raw material identification (RMID) is necessary and important to fulfill the quality and safety requirements in the pharmaceutical industry. Near-infrared (NIR) spectroscopy is a rapid, nondestructive, and commonly used analytical technique that could offer great advantages for RMID. In this study, two brand new similarity methods S1 and S2, which could reflect the similarity from the perspective of the inner product of the two vectors and the closeness with the cosine of the vectorial angle or correlation coefficient, were proposed. The ability of u and v factors to distinguish the difference between small peaks was investigated with the spectra of NIR. The results showed that the distinguishing ability of u is greater than v, and the distinguishing ability of S2 is greater than S1. Adjusting exponents u and v in these methods, which are variable and configurable parameters greater than 0 and less than infinity, could identify small peaks in different situations. Meanwhile, S1 and S2 could rapidly identify raw materials, suggesting that the on-site and in situ pharmaceutical RMID for large-volume applications can be highly achievable. The methods provided in this study are accurate and easier to use than traditional chemometric methods, which are important for the pharmaceutical RMID or other analysis.

NIR spectroscopy of natural medicines supported by novel instrumentation and methods for data analysis and interpretation

Near-infrared (NIR) spectroscopy is a powerful tool for qualitative and quantitative phytoanalysis. It is a rapid and high-throughput analytical method, with on-site capability, high chemical specificity, and no/minimal sample preparation. NIR spectroscopy is a powerful non-invasive and low-cost alternative with significant practical advantages compared to the conventional methods of analysis. These advantages are particularly exposed in the field of phytoanalysis. In contrast to synthetic medicines, natural products feature chemical diversity that can vary depending on the medicinal plant cultivation conditions, geographical origin or harvest time. The content of bioactive compounds and their derivatives, and thus, the quality parameters of the natural medicine need to be controlled with respect to a number of conditions. NIR spectroscopy has been proved to be particularly competitive in such difficult scenarios. In recent years, remarkable advances in the field of spectroscopic instrumentation and methods of analysis have appeared. Noteworthy was the appearance and dynamic continuing development of miniaturized, on-site capable NIR spectrometers. This was accompanied by application of new tools increasing the potential and reliability of NIR spectroscopy in phytoanalytical applications. The present review discussed the major principles of this technique and critically assesses its future application potential in phytoanalytical strategies. Major attention is given to the current development trends based on the most recent literature published in the field.