Rapid discrimination of adulteration in Radix Astragali combining diffuse reflectance mid-infrared Fourier transform spectroscopy with chemometrics

Distinction and quantification of Panax polysaccharide extracts via attenuated total reflectance-Fourier transform infrared spectroscopy with first-order derivative processing

Derivative spectroscopy is used to separate the small absorption peaks superimposed on the main absorption band, which is widely adopted in modern spectral analysis to increase both the valid spectral information and the identification accuracy. In this study, a method based on attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) with first-order derivative (FD) processing combined with chemometrics is proposed for rapid qualitative and quantitative analysis of Panax ginseng polysaccharides (PGP), Panax notoginseng polysaccharides (PNP), and Panax quinquefolius polysaccharides (PQP). First, ATR-FTIR with FD processing was used to establish the discriminant model combined with principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA). After that, two-dimensional ATR-FTIR based on single-characteristic temperature as external interference (2D-sATR-FTIR) was established using ATR-FTIR with FD processing. Then, ATR-FTIR with FD processing was combined with PLS to establish and optimize the quantitative regression model. Finally, the established discriminant model and 2D-sATR-FTIR successfully distinguished PGP, PNP and PQP, and the optimal PLS regression model had a good prediction ability for the Panax polysaccharide extracts content. This strategy provides an efficient, economical and nondestructive method for the distinction and quantification of PGP, PNP and PQP in a short detection time.

Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review

The quality of tropical fruits and vegetables and the expanding global interest in eating healthy foods have resulted in the continual development of reliable, quick, and cost-effective quality assurance methods. The present review discusses the advancement of non-destructive spectral measurements for evaluating the quality of major tropical fruits and vegetables. Fourier transform infrared (FTIR), Near-infrared (NIR), Raman spectroscopy, and hyperspectral imaging (HSI) were used to monitor the external and internal parameters of papaya, pineapple, avocado, mango, and banana. The ability of HSI to detect both spectral and spatial dimensions proved its efficiency in measuring external qualities such as grading 516 bananas, and defects in 10 mangoes and 10 avocados with 98.45%, 97.95%, and 99.9%, respectively. All of the techniques effectively assessed internal characteristics such as total soluble solids (TSS), soluble solid content (SSC), and moisture content (MC), with the exception of NIR, which was found to have limited penetration depth for fruits and vegetables with thick rinds or skins, including avocado, pineapple, and banana. The appropriate selection of NIR optical geometry and wavelength range can help to improve the prediction accuracy of these crops. The advancement of spectral measurements combined with machine learning and deep learning technologies have increased the efficiency of estimating the six maturity stages of papaya fruit, from the unripe to the overripe stages, with F1 scores of up to 0.90 by feature concatenation of data developed by HSI and visible light. The presented findings in the technological advancements of non-destructive spectral measurements offer promising quality assurance for tropical fruits and vegetables.

Detection of dextran, maltodextrin and soluble starch in the adulterated Lycium barbarum polysaccharides (LBPs) using Fourier-transform infrared spectroscopy (FTIR) and machine learning models

Due to the similar chemical structures and physicochemical properties, it is challenging to distinguish dextran, maltodextrin, and soluble starch from the polysaccharide products of plant origin, such as Lycium barbarum polysaccharides (LBPs). Using the first-order derivatives of Fourier-transformed infrared spectroscopy (FTIR, wave range 1800-400 cm^-1), this study proposed a two-step pipeline to identify dextran, maltodextrin, and soluble starch from adulterated LBPs samples qualitatively and quantitatively. We applied principal component analysis (PCA) to reduce the dimensionality of FTIR features. For the qualitative step, a set of machine learning models, including logistic regression, support vector machine (SVM), Naïve Bayes, and partial least squares (PLS), were used to classify the adulterants. For the quantitative step, linear regression, LASSO, random forest, and PLS were used to predict the concentration of LBPs adulterants. The results showed that logistic regression and SVM are suitable for classifying adulterants, and random forests is superior for predicting adulterant concentrations. This would be the first attempt to discriminate the adulterants from the polysaccharide's product of plant origin. The proposed two-step methods can be easily extended to other applications for the quantitative and qualitative detection of samples from adulterants with similar chemical structures.

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.

Detection of the Adulteration of Motor Oil by Laser Induced Fluorescence Spectroscopy and Chemometric Techniques

Petroleum products are the target of fraudulent practices due to their high commercial value. The aim of this study is to provide a new analysis system to assess motor oil adulteration. For this purpose, Laser Induced Fluorescence (LIF) spectroscopy was exploited coupled with chemometric tools to detect motor oil adulteration by three types of cheap motor oils. Principal Component Analysis (PCA) was able to distinguish samples in three groups according to the type of adulterant. Besides, Partial Least Squares Regression (PLSR) was exploited to determine the percentage of adulteration. The best model was obtained with a regression coefficient of 0.96, Root Mean Square Error of Prediction (RMSEP) of 2.83, Standard Error of Prediction (SEP) of 2.83 and Bias of 0.40. The main results of this work provide new analysis system using the combination of LIF spectroscopy combined to PCA and PLS as an efficient and fast method for motor oil analysis.

Fourier Transform Infrared (FTIR) Spectroscopic Study of Biofilms Formed by the Rhizobacterium Azospirillum baldaniorum Sp245: Aspects of Methodology and Matrix Composition

Biofilms represent the main mode of existence of bacteria and play very significant roles in many industrial, medical and agricultural fields. Analysis of biofilms is a challenging task owing to their sophisticated composition, heterogeneity and variability. In this study, biofilms formed by the rhizobacterium Azospirillum baldaniorum (strain Sp245), isolated biofilm matrix and its macrocomponents have for the first time been studied in detail, using Fourier transform infrared (FTIR) spectroscopy, with a special emphasis on the methodology. The accompanying novel data of comparative chemical analyses of the biofilm matrix, its fractions and lipopolysaccharide isolated from the outer membrane of the cells of this strain, as well as their electrophoretic analyses (SDS-PAGE) have been found to be in good agreement with the FTIR spectroscopic results.

Hyperspectral imaging with machine learning for non-destructive classification of Astragalus membranaceus var. mongholicus, Astragalus membranaceus, and similar seeds

The roots of Astragalus membranaceus var. mongholicus (AMM) and A. membranaceus (AM) are widely used in traditional Chinese medicine. Although AMM has higher yields and accounts for a larger market share, its cultivation is fraught with challenges, including mixed germplasm resources and widespread adulteration of commercial seeds. Current methods for distinguishing Astragalus seeds from similar (SM) seeds are time-consuming, laborious, and destructive. To establish a non-destructive method, AMM, AM, and SM seeds were collected from various production areas. Machine vision and hyperspectral imaging (HSI) were used to collect morphological data and spectral data of each seed batch, which was used to establish discriminant models through various algorithms. Several preprocessing methods based on hyperspectral data were compared, including multiplicative scatter correction (MSC), standard normal variable (SNV), and first derivative (FD). Then selection methods for identifying informative features in the above data were compared, including successive projections algorithm (SPA), uninformative variable elimination (UVE), and competitive adaptive reweighted sampling (CARS). The results showed that support vector machine (SVM) modeling of machine vision data could distinguish Astragalus seeds from SM with >99% accuracy, but could not satisfactorily distinguish AMM seeds from AM. The FD-UVE-SVM model based on hyperspectral data reached 100.0% accuracy in the validation set. Another 90 seeds were tested, and the recognition accuracy was 100.0%, supporting the stability of the model. In summary, HSI data can be applied to discriminate among the seeds of AMM, AM, and SM non-destructively and with high accuracy, which can drive standardization in the Astragalus production industry.

Non-Targeted Analytical Technology in Herbal Medicines: Applications, Challenges, and Perspectives

Herbal medicines (HMs) have been utilized to prevent and treat human ailments for thousands of years. Especially, HMs have recently played a crucial role in the treatment of COVID-19 in China. However, HMs are susceptible to various factors during harvesting, processing, and marketing, affecting their clinical efficacy. Therefore, it is necessary to conclude a rapid and effective method to study HMs so that they can be used in the clinical setting with maximum medicinal value. Non-targeted analytical technology is a reliable analytical method for studying HMs because of its unique advantages in analyzing unknown components. Based on the extensive literature, the paper summarizes the benefits, limitations, and applicability of non-targeted analytical technology. Moreover, the article describes the application of non-targeted analytical technology in HMs from four aspects: structure analysis, authentication, real-time monitoring, and quality assessment. Finally, the review has prospected the development trend and challenges of non-targeted analytical technology. It can assist HMs industry researchers and engineers select non-targeted analytical technology to analyze HMs' quality and authenticity.

Rapid Identification of Fupenzi (Rubus chingii Hu) and Its Adulteration by AuNP Visualization

Fupenzi (Rubus chingii Hu) is a dried and immature fruit in East China, which has effects of nourishing kidneys, solidifying essence, and otherwise. Because Fupenzi was often adulterated and replaced with inferior things, this paper had researched Fupenzi and its adulterant raspberry. A new type of visible sensor was constructed by using Au nanoparticles (AuNPs), which was modified by the surface-active agent and combined with the ultraviolet-visible (UV-vis) spectrum technology. It was found that the change in particle size after the interaction of AuNPs and adulterants will lead to color change. In this paper, the RGB (red, green, and blue) values of the solution were extracted to correlate the color with the concentration of adulterants, and the relationship between the absorption peak intensity and the concentration of adulterants was established. The results showed that the intensity of an absorption peak is related to adulteration concentration, and the color of the solution changed from red to gray as the particle size changed. The visual sensor constructed based on the above principle is a fast and precise method to detect adulteration with different concentrations, which has a potential application value in real-time and rapid detection of Fupenzi’s quality.

Surface-Enhanced Raman Spectroscopy Analysis of Astragalus Saponins and Identification of Metabolites After Oral Administration in Rats by Ultrahigh-Performance Liquid Chromatography/Quadrupole Time-of-Flight Mass Spectrometry Analysis

Astragalus mongholicus Bunge (Fabaceae) is an ancient Chinese herbal medicine, and Astragalus saponins are the main active components, which have a wide range of biological activities, such as immunomodulation, antioxidation, and neuroprotection. In this study, silver nanoparticles obtained by sodium borohydride reduction were used as the enhanced substrate to detect astragaloside I (1), astragaloside II (2), astragaloside III (3), astragaloside IV (4), isoastragaloside I (5), and isoastragaloside II (6) in the phloem, xylem, and cork by surface-enhanced Raman spectroscopy (SERS). In the SERS spectrum of Astragalus slices, the characteristic peaks were observed at 562, 671, 732, 801, 836, 950, 1,026, 1,391, and 1,584 cm⁻¹, among which 950 cm⁻¹ and 1,391 cm⁻¹ were strong SERS signals. Subsequently, the metabolites of the six kinds of Astragalus saponins were identified by UPLC/ESI/Q-TOF-MS. Totally, 80, 89, and 90 metabolites were identified in rat plasma, urine, and feces, respectively. The metabolism of saponins mainly involves dehydration, deacetylation, dihydroxylation, dexylose reaction, deglycosylation, methylation, deacetylation, and glycol dehydration. Ten metabolites (1-M2, 1-M11, 2-M3, 2-M12, 3-M14, 4-M9, 5-M2, 5-M17, 6-M3, and 6-M12) were identified by comparison with reference standards. Interestingly, Astragalus saponins 1, 2, 5, and 6 were deacetylated to form astragaloside IV (4), which has been reported to have good pharmacological neuroprotective, liver protective, anticancer, and antidiabetic effects. Six kinds of active Astragalus saponins from different parts of Astragalus mongholicus were identified by SERS spectroscopy. Six kinds of active Astragalus saponins from different parts of Astragalus mongholicus were identified by SERS spectrum, and the metabolites were characterized by UPLC/ESI/Q-TOF-MS, which not only provided a new method for the identification of traditional Chinese medicine but also provided a theoretical basis for the study of the pharmacodynamic substance basis of Astragalus mongholicus saponins.

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.

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.

Fourier Transform Infrared (FTIR) Spectroscopic Analyses of Microbiological Samples and Biogenic Selenium Nanoparticles of Microbial Origin: Sample Preparation Effects

To demonstrate the importance of sample preparation used in Fourier transform infrared (FTIR) spectroscopy of microbiological materials, bacterial biomass samples with and without grinding and after different drying periods (1.5-23 h at 45 °C), as well as biogenic selenium nanoparticles (SeNPs; without washing and after one to three washing steps) were comparatively studied by transmission FTIR spectroscopy. For preparing bacterial biomass samples, Azospirillum brasilense Sp7 and A. baldaniorum Sp245 (earlier known as A. brasilense Sp245) were used. The SeNPs were obtained using A. brasilense Sp7 incubated with selenite. Grinding of the biomass samples was shown to result in slight downshifting of the bands related to cellular poly-3-hydroxybutyrate (PHB) present in the samples in small amounts (under ~10%), reflecting its partial crystallisation. Drying for 23 h was shown to give more reproducible FTIR spectra of bacterial samples. SeNPs were shown to contain capping layers of proteins, polysaccharides and lipids. The as-prepared SeNPs contained significant amounts of carboxylated components in their bioorganic capping, which appeared to be weakly bound and were largely removed after washing. Spectroscopic characteristics and changes induced by various sample preparation steps are discussed with regard to optimising sample treatment procedures for FTIR spectroscopic analyses of microbiological specimens.