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

School of Traditional Chinese Medicine, Guangdong Pharmaceutical University, Guangzhou 510006, China State Key Laboratory Breeding Base of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China .e Modernization Engineering Technology Research Center of Ethnic Minority Medicine of Hubei Province, School of Pharmaceutical Sciences, South-Central University for Nationalities, Wuhan 430074, China


Introduction
Food and drug safety has always been the focus of people's concern. At present, in the Chinese traditional medicine market, the phenomenon of adulteration of traditional Chinese medicine is not uncommon, especially some valuable medicinal materials such as saffron, Panax ginseng, and Panax quinquefolium L.. erefore, the identification of adulteration of Chinese medicinal materials is an important problem that perplexes consumers and relevant regulatory departments; at present, it is very necessary to develop a technical method for rapid detection of adulteration of Chinese medicinal materials.
Fupenzi (Rubus chingii Hu), a wild raspberry in China, is usually picked when it is not ripe. Fupenzi has different applications in the food and pharmaceutical industry because of its nutritional value and pharmacological activity. Fupenzi is rich in vitamins, amino acids, and mineral elements, which can be used to make fruit juice drinks and health products [1,2]. So far, more than 235 chemical constituents have been isolated and identified from R. chingii [2]. ese compounds include 15 triterpenoids, 15 diterpenoids, 18 flavonoids, 7 alkaloids, 95 volatile compounds, 5 coumarins, 9 steroids, 56 organic acids, and 15 other compounds [2], which have the pharmacological effects of reducing blood sugar and blood lipids [3], antitumor [4], antioxidation [5,6], anti-aging [6], and anti-inflammation properties [7], so it has been used in traditional Chinese medicine for a long time. Reducing costs and increasing profits are the main reasons for the frequent adulteration in the Chinese herbal medicine market, and Fupenzi is no exception. e adulteration way of Fupenzi in the market is mainly to use other plant materials with similar appearance but different or no effect as adulterants, in which raspberry (Rubus corchorifolius L. f.) is commonly used as Fupenzi adulteration. e adulteration of Fupenzi can not only harm the right of consumers and break the market orders but also cannot achieve the same therapeutic effect as Fupenzi. us, it is necessary to identify a detection method for Fupenzi adulteration.
ere are a variety of identification methods and techniques for food and drugs. ese techniques include analytical, physical, chemical, and most recent DNA-based molecular techniques [8]. In the aspects of qualitative and quantitative analysis and quality control of compounds, chromatographic techniques such as high performance liquid chromatography (HPLC), gas chromatography (GC), and chromatography-mass spectrometry technology are often used. Generally, those chromatography and mass spectrometry technologies are often used in metabonomics. For example, by detecting as many of these small molecules as possible, untargeted metabolomics approaches enable a holistic analysis in comparing complex samples [9], and it is an accurate method to identify the adulteration. In addition to chromatography and mass spectrometry technology, the spectroscopic technique is a common method to detect adulterants as well. Universal spectral techniques include near-infrared (NIR) and mid-infrared (MIR) spectroscopies, ultraviolet-visible (UV-vis) spectrum, and fluorescence spectrum. ese spectral techniques are often used to detect adulteration of edible oil [10], honey [11,12], and rare medicinal materials [13,14]. In addition, with the development of molecular biology techniques, many molecular biology techniques have been applied to identify the adulteration of food and drugs. For example, the adulteration identification of food and drugs such as plant oil [15] and Fritillariae cirrhosae bulbus [16] was realized by using DNA barcoding technology. Except for those methods, a stable isotope technique can also be used to identify adulterants [17,18]. To improve the sensitivity of identification, chemometrics methods are often used in combination with the aforementioned methods. Such as using near-infrared (NIR) spectroscopy and mid-infrared (MIR) spectroscopy combined with principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and partial least squares regression (PLS-R) to identify saffron and its adulteration [14]. Vieira et al. [19] used Fourier transform near-infrared (FT-NIR) and PLS-DA to establish a separate model to distinguish adulterated extravirgin olive oil samples.
Although these methods can be used to identify food quality sensitively, there are still some shortcomings. Chromatography, spectroscopy, and molecular biology techniques rely on large-scale instruments and time-consuming sample preparations, and operators should possess a high level of skill for those instruments. On the basis of these methods, chemometrics combined with nanomaterial methods came into being. In recent years, new methods based on nanomaterials combined with spectroscopy have been applied to fruit juice adulteration, drug adulteration, and food corruption [20][21][22].
ese new methods can distinguish adulterants effectively and have application prospects in the future. In recent years, by integrating metal nanoparticles into the detection process, an ultravioletvisible (UV-Vis) spectrum has considerably improved the application of ultraviolet detection of various chemical substances [23][24][25][26]. It has been applied in the detection of target analyses with high selectivity and high sensitivity, and the performance has been improved to a level comparable to that of precision instruments [27]. e surface plasmon resonance (SPR) effect significantly exists in nanonoble metal materials, and it occurs when the vibration frequency of the metal free conduction electron is equal to that of the incident light. SPR absorption of Au nanoparticles (AuNPs) is extremely sensitive to their size, shape, surrounding media, and interparticle distances [28]. With the change in the shape and size of AuNPs and the physical properties of the environment medium, the position of the plasmon resonance absorption peak is different, and the accumulation of AuNPs results in red shift of SPR, which changes the solution from red to blue or gray [28,29]. For example, Rohit et al. [30], based on the color change in AuNPs from red to blue, developed a colorimetric sensor for the determination of quinalphos in water and food samples. At present, a AuNP colorimetric sensor is widely used in the detection of antibiotics, pesticide residues, heavy metals, and so on [29,31,32].
Since gold nanoparticles have SPR characteristics and color changes can be seen intuitively by naked eyes, we choose AuNPs as a colorimetric sensor in this article. At present, there is no report on the application of AuNP colorimetric sensors in the adulteration identification of Fupenzi. In our research study, Fupenzi and its adulterants were quantitatively characterized according to the color change characteristics of AuNPs, and the detection results were compared and verified by UV-Vis spectroscopy. e results showed that the detection method was consistent with the results of UV-Vis spectroscopy, which proved that the method of Fupenzi adulteration rapid detection by AuNPs combined with the RGB value was feasible. e method could achieve simple, rapid, efficient, and sensitive detection of the Fupenzi adulteration effect. And it can be applied not only to the rapid identification of adulteration of other Chinese medicinal materials but also to a variety of fields, including food, biometric identification and drug quality control. e UV-5800 (PC) ultraviolet-visible spectrophotometer was purchased from Yuanxi Instrument Co. Ltd (Shanghai, China). e high-resolution transmission electron microscopy TF20 Joel 2100 F microscope was purchased from Japan Electronics Corporation Company.

Preparation of AuNPs.
e synthesis of AuNPs was performed in the previous report and modified in [33]. 2 mL chloroauric acid (1%) was taken into a round bottom flask, 6.5 mL water was added and stirred at room temperature for 15 min, 1.5 mL 0.1 mol·L −1 cetyltrimethyl ammonium bromide (CTAB) was added, and 3 mL sodium borohydride (0.4 mol·L −1 ) was slowly added under the stirring state after 10 min and continued stirring for 3 h at room temperature. Finally, the color of the solution changed from light yellow to dark red.

Preparation of Pure and Adulterated Fupenzi.
Fupenzi and adulterants were crushed in a grinder, and the powder was passed through a 50 mesh sieve for reserve. A 0.1 g Fupenzi powder which was shifted was taken and put into a flask, and we added 20 mL boiling water into Fupenzi, soaked for 20 min, kept the temperature at 50°C and ultrasonic for 30 min, centrifuged for 10 min at 4000 r min −1 , took the supernatant, and filtered through a 0.22 μm microporous membrane to obtain Fupenzi and adulterant stock solutions. Similarly, the Fupenzi powder was mixed with different proportions of the adulterated powder, and the same extraction method was used to obtain the Fupenzi mixture of 1%, 2%, 5%, 10%, and 20% adulterated powders.

Condition Optimization.
e influence of the pH, reaction time, and reaction temperature on the ultraviolet absorbance was investigated. ere are three parallel tests for each condition optimization. When AuNPs were mixed with the adulterants, the pH value of the solution was changed to observe the ultraviolet absorbance peak intensity of the solution at different pH values. e gradient of the reaction time was set at 0, 5, 10, 20, 30, 45, and 60 min, respectively; and the temperature of the reaction was set at 5, 15, 25, 35, 45, 55, 65, 75, 85, and 95°C, respectively.

FT-IR Spectral Analysis.
In order to characterize the functional groups, the prepared AuNPs, the AuNPs-Fupenzi mixture, and the AuNPs-adulterant mixture were ground and pressed with the KBr powder and placed at wavenumbers of 4000 to 400 cm −1 for FT-IR analysis.
2.6. Detection of Fupenzi Adulteration. In order to quickly detect the raspberry adulteration, 1 mL of Fupenzi and the stock solution of the adulteration were added into the colorimetric dish to detect and record the absorbance. en, the absorbance of the Fupenzi mixture was detected. After that, 900 μL mixture and 100 μL AuNPs were added into the cuvette, the combined mixture was kept for 1 min, and its absorbance was detected. 900 μL ultrapure water was mixed with 100 μL AuNPs, which was left standing for 1 min as in the blank control group. Finally, transmission electron microscopy (TEM) was used to observe the diameter of the different concentrations of the AuNP mixture. en, the RGB value was applied to obtain the color information of the different concentrations of the AuNP mixture solution. e flowchart of experimental steps is shown in Figure 1.
rough UV-Vis spectrum, the limit of detection (LOD) was used to measure the low detection limit of the detection method. e LOD is expressed as the following function: where δ is the RSD of blank samples and S is the slope of the calibration curve.

Data
Processing. e UV-vis spectrum data, FT-IR data, and other statistical analyses were plotted by Origin 2018 (Massachusetts, USA). Data obtained by TEM were imported into nanomeasure particle size measurement software (Laboratory of Surface Chemistry and Catalysis, Department of Chemistry, Fudan University).
e RGB values were extracted by Microsoft Office PowerPoint 2019 (Microsoft, USA).

Condition Optimization.
According to the maximum absorption peak of the ultraviolet absorption spectrum, the pH value, reaction time, and temperature of the solution were optimized. It can be seen that when the pH value of the solution was 8-10, the absorbance was stable (Figure 2(a)). ough the reaction time was negatively correlated with the absorption intensity, the absorbance changed slowly and leveled off with the increase in time after 20 min (Figure 2(b)). However, when the reaction temperature rose gradually, the maximum absorption peak intensity did not change significantly before 65°C. However, when the reaction temperature was greater than 65°C, the absorbance showed an overall upward trend (Figure 2(c)).

UV-Vis Spectra of Fupenzi and AuNP Mixtures.
e UVvis absorption spectra of the Fupenzi stock solution, adulterated stock solution, AuNPs, and mixtures of various concentrations are shown in Figure 3. It can be seen that the Fupenzi stock solution has a characteristic absorption peak at 540 nm, while the adulteration stock solution has almost no absorption peak (Figure 3(a)). When the Fupenzi stock solution was added with a small amount of adulterants, the absorption peak did not change and the shape of the peak did not change significantly with the increase in the proportion of adulterants (Figure 3(b)). However, the absorbance of the mixture decreased after the addition of AuNPs, and compared with that before the addition of AuNPs, it could be seen that the absorbance gradually decreased with the increase in the proportion of adulterants (Figure 3(c)). e absorbance of AuNPs and the concentration of the mixture showed a good linear relationship in the range of 1%-20%. e formula for the standard curve is y � −0.0034x + 0.6366, R 2 � 0.9962. By setting a blank control group (Figure 3(d)), it can be found that the intensity of the absorption peak of AuNPs changed significantly after the combination of the Fupenzi stock solution, which can be accurately distinguished by different concentrations from 1% to 20%. In addition, according to formula (1), the LOD of this method can be calculated. e LOD of the method that we proposed is 0.2%. In order to correlate the concentration with the color, the visualization experiment in the reagent bottle was carried out. In the course of the experiment, it was found that the color of the mixed solution of AuNPs and Fupenzi without adulterants was red. With the increasing proportion of adulterants, the color of solution gradually changed from red to gray, and the proportion of the adulterants was 1%, 2%, 5%, 10%, and 20%, respectively, (Figure 4(a)). is shows that the relationship between absorbance and adulterant concentrations can be transformed into the relationship between color and adulterant concentrations.

FT-IR Spectral Analysis.
e functional groups in Fupenzi and its adulterants were characterized by infrared  spectroscopy, the interaction of AuNPs with them was studied, and the results are shown in Figure 5. e strong and wide band near 3380 cm −1 is O-H stretching vibration, and the band at 2920 cm −1 is C-H stretching vibration. e absorption peak at 1730 cm −1 is attributed to the stretching vibration of C�O [34]. e bands of 1640 and 1470 cm −1 are attributable to the C�O asymmetric and symmetric stretching vibration [35]. e strong absorption band of 1190 cm −1 is due to the O-H out-of-plane bending vibration. e absorption bands of 960 and 920 cm −1 were represented rhamnogalacturonan I and D-glucopyranosyl, respectively, [34,36]. ese groups indicated that there were abundant phenols and carboxylic acid compounds in Fupenzi and its adulterants, such as ellagic acid and its glycoside derivatives [1]. When these phenolic acid compounds combined with the negatively charged CTAB-AuNPs, AuNPs were aggregated ( Figure 5).

TEM Images of AuNPs.
In order to verify the mechanism of color change, we used TEM to observe particle size change. All the particles in the TEM image were measured diameter through the Nano Measure particle size measurement software. As can be seen that the mixed solution of AuNPs and Fupenzi without adulterants was well dispersed with the mean particle size of 7.5 nm (Figure 6(a)). However, the AuNPs were significantly aggregated with the mean particle size of 12.3 nm when the concentration of adulterate is 20% (Figure 6(f )). With the increase of the concentration of adulterants, the aggregation degree of AuNPs increased and the particle size became larger, resulting in the color of gold nanoparticles from red to gray. e standard curve was drawn according to the relationship between the particle size of AuNPs and the concentration of adulterants (Figure 4(b)), which indicated that there was a certain linear relationship between the particle size of AuNPs and the concentration of adulterants (y � 0.2026x + 8.3405, R 2 � 0.9761). e possible reason for this phenomenon is the positive charge of CTAB modified AuNPs, and there were negatively charged acids and compounds with carboxyl and hydroxyl groups in raspberry. ese negatively charged compounds combined with CTAB with ammonium state to aggregate AuNPs and increase the particle size. At the same time, the color of solution became red to gray with the increase in the AuNP particle diameter.

Identification of Fupenzi
Adulteration. e color of AuNPs changed with the particle size, and it can be speculated that the adulteration of raspberry can be judged by the color of AuNPs. Based on the relationship between the solution color and adulterant concentration, we made the colorimetric card of the Fupenzi mixture solution of AuNPs with different concentrations of adulterants (Figure 4(c)). We introduced the concept of the RGB color mode to characterize the color of solution. RGB (red, green, and blue) is applied in many areas, such as electronics, medicine, and food [37][38][39]. In order to achieve the purpose of rapid detection, we needed to eliminate instruments. e pipette function was added on the basis of colorimetry to achieve more portable quantitative analysis, in which the qualitative color changes were converted into the quantitative RGB values. erefore, the RGB values could be used to bridge the color variation with the mixture concentration. e average RGB values (any 10 points) of the known concentration of adulteration with AuNP Fupenzi mixtures were measured to make a colorimetric card by using the pipette function using Microsoft Office PowerPoint 2019 software. Secondly, after obtaining the average RGB values (any 10 points) of the unknown sample photos in the same way, similar RGB values were searched and located in the color comparison card. Finally, quantitative information could be obtained by converting RGB values into mixture concentrations.
In order to verify the accuracy of the colorimetric method, two AuNP samples (5% concentration and 10% concentration of the mixture) were selected for testing. e two samples were treated under the same conditions, and the average RGB values were (156, 109, and 118) and (157, 145, and 149). Similar RGB values were found in the colorimetric card (155, 109, and 119) and (158, 146, and 151). e concentrations of the mixture were (5.02 ± 0.32)% and (9.74 ± 0.52)% through calculation, respectively, and the recovery rate was good. en, mixtures containing 5% and 10% adulterants were analyzed by UV-vis. 100 μL AuNPs and 900 μL 5% mixture solution were added into the colorimetric dish to detect the absorbance. It can be found that  the mixture concentrations of the two samples quantitatively obtained by the UV-vis spectrophotometer are (5.06 ± 0.07)% and (9.94 ± 0.18)%, respectively. e results are shown in Table 1. By comparison, it can be found that the quantitative results of colorimetry and UV-vis spectroscopy are similar, but the colorimetric method does not rely on large instruments and can achieve real-time on-site identification, and the operation is more convenient and rapid.

Conclusion
In this paper, a new type of a visual sensor was constructed by using surfactant-modified AuNPs as sensing materials, combined with UV-vis spectroscopy, which was successfully applied to the identification of Fupenzi adulteration. e changes in the solution color and absorbance caused by AuNPs can reflect the degree of Fupenzi adulteration. After adding AuNPs, the absorbance was linearly correlated with the concentration of adulterants, and the range of detection is 0%-20%. e correlation coefficient between the particle size of AuNPs and the concentration of adulterants was 0.9761, which indicated that the adulterants interacted with AuNPs. e relationship between the mixture concentration and AuNP color was constructed according to RGB values. Compared with UV-vis spectroscopy, the accuracy of the quantitative results is almost the same, and the accuracy and effectiveness of this method have been confirmed. Nanolabeling technology shows a broad application prospect in the on-site rapid detection of adulteration of traditional Chinese medicine. However, because of the complexity of the components of traditional Chinese medicine, there are still some challenges in the detection of traditional Chinese medicine. For example, the content of some characteristic chemical components in traditional Chinese medicine will change with the extension in storage time and resulting in false positive results, which will have certain interference in the identification of traditional Chinese medicine. To solve the problem of interference component diversity, the next step of this paper is to establish a reliable detection standard by studying the differences in Fupenzi components from different producing areas, which lays a foundation for adulteration analysis of traditional Chinese medicine products on the spot and ensuring the quality and safety of traditional Chinese medicine.

Data Availability
e data used to support the findings of this study are included in the article.

Disclosure
Yuan Li and Yixin Suo should be considered joint first author.

Conflicts of Interest
e authors declare they have no conflicts of interest regarding the publication of this article.

Authors' Contributions
Yuan Li was involved in conceptualization and was responsible for writing, reviewing and editing the manuscript. Yixin Suo was involved in data curation and investigation. Liuna Wei was involved in data curation and investigation and wrote the manuscript. Yue Zhang, Youyou Wang, and Jiaxin Deng were responsible for investigation. Hengye Chen was responsible for Methodology. Jian Yang was responsible for resources and supervision. Tiegui Nan was responsible for supervision. Haiyan Fu was involved in conceptualization, funding acquisition and supervision. Lanping Guo was responsible for resources, supervision, conceptualization, and funding acquisition.