Rapid Identification of Fupenzi (<i>Rubus chingii</i> Hu) and Its Adulteration by AuNP Visualization

Li, Yuan; Suo, Yixin; Wei, Liuna; Zhang, Yue; Wang, Youyou; Deng, Jiaxin; Chen, Hengye; Yang, Jian; Nan, Tiegui; Fu, Haiyan; Guo, Lanping

doi:https://doi.org/10.1155/2022/6278549

Journal of Food Quality

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Abstract Introduction Results and Discussion Conclusion Data Availability Disclosure Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

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Multiple Spectrum Analysis for Food Quality and Safety

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Research Article | Open Access

Volume 2022 | Article ID 6278549 | https://doi.org/10.1155/2022/6278549

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

Yuan Li,^1,2Yixin Suo,³Liuna Wei,³Yue Zhang,²Youyou Wang,²Jiaxin Deng,³Hengye Chen,³Jian Yang,²Tiegui Nan,²Haiyan Fu,³and Lanping Guo^1,2

Academic Editor: Nadica Maltar Strmečki

Received11 Nov 2021

Revised26 Feb 2022

Accepted23 May 2022

Published20 Jun 2022

Abstract

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.

1. 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.. Therefore, 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]. These 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. The 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. The 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. Thus, it is necessary to identify a detection method for Fupenzi adulteration.

There are a variety of identification methods and techniques for food and drugs. These 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. These 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 [20–22]. These 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 ultraviolet-visible (UV-Vis) spectrum has considerably improved the application of ultraviolet detection of various chemical substances [23–26]. 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]. The 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. The 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. The 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.

2. Experiment

2.1. Materials, Reagents, and Equipment

A total of 18 batches of Fupenzi were purchased from Zhejiang, Shanghai, Anhui, Guizhou, Guangdong, Guangxi, Chongqing, Fujian, Sichuan, and Yunan. Adulterated raspberries (Rubus corchorifolius L. f.) were purchased from a market in Wuhan, Hubei. Anhydrous methanol, anhydrous ethanol, and chloroauric acid were purchased from Yongda Chemical Reagent Co. Ltd (Tianjin, China). A microporous filtration membrane was purchased from Jiaxing Haining Kono Filtration Equipment Co. Ltd (Zhejiang, China). Sodium borohydride was purchased from Hongrui Chemical Co. Ltd (Shanghai, China). All the reagents used in the experiment are analytically pure, and the water used in the experiment is ultrapure water. A buckle-type small grinder was purchased from Guangzhou Xulang Machinery Equipment Co. Ltd (Guangdong, China). A total of 50 and 100 mesh screens were purchased from Xinxiang Jinhe Machinery Co. Ltd (Henan, China). The KQ-500DE CNC ultrasonic cleaner was purchased from Kunshan Ultrasonic Instrument Co. Ltd (Jiangsu, China). The TD4 small desktop centrifuge was purchased from Hetian Scientific Instrument Co. Ltd (Shanghai, China). Analytical ultrapure water equipment was purchased from Shanghai Moller Scientific Instrument Co. Ltd (Shanghai, China). The BSA224S analytical balance was purchased from Sedolis Instrument System Co. Ltd (Beijing, China). The UV-5800 (PC) ultraviolet-visible spectrophotometer was purchased from Yuanxi Instrument Co. Ltd (Shanghai, China). The high-resolution transmission electron microscopy TF20 Joel 2100 F microscope was purchased from Japan Electronics Corporation Company.

2.2. Preparation of AuNPs

The 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.

2.3. 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.

2.4. Condition Optimization

The influence of the pH, reaction time, and reaction temperature on the ultraviolet absorbance was investigated. There 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. The 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.

2.5. 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. Then, 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. Then, the RGB value was applied to obtain the color information of the different concentrations of the AuNP mixture solution. The flowchart of experimental steps is shown in Figure 1.

Through UV-Vis spectrum, the limit of detection (LOD) was used to measure the low detection limit of the detection method. The LOD is expressed as the following function:where δ is the RSD of blank samples and S is the slope of the calibration curve.

2.7. Data Processing

The 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). The RGB values were extracted by Microsoft Office PowerPoint 2019 (Microsoft, USA).

3. Results and Discussion

3.1. 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)). Though 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)).

(a)

(b)

(c)

3.2. UV-Vis Spectra of Fupenzi and AuNP Mixtures

The UV-vis 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)). The absorbance of AuNPs and the concentration of the mixture showed a good linear relationship in the range of 1%–20%. The formula for the standard curve is y = −0.0034x + 0.6366, R² = 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. The LOD of the method that we proposed is 0.2%.

(a)

(b)

(c)

(d)

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)). This shows that the relationship between absorbance and adulterant concentrations can be transformed into the relationship between color and adulterant concentrations.

(a)

(b)

(c)

Figure 4

The relationship between concentration of different adulterants and particle size of AuNPs and solution color. (a) A–F represent the color of the AuNP mixture solution at the concentration of 0%, 1%, 2%, 5%, 10%, and 20% adulterants, respectively. (b) The linear fitting diagram of the concentration of different adulterants against the particle size of AuNP mixtures. (c) According to the relationship between the concentration of different adulterants and the color of the solution-produced color card. (R² = 0.9761).

3.3. FT-IR Spectral Analysis

The 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. The 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. The absorption peak at 1730 cm⁻¹ is attributed to the stretching vibration of C=O [34]. The bands of 1640 and 1470 cm⁻¹ are attributable to the C=O asymmetric and symmetric stretching vibration [35]. The strong absorption band of 1190 cm⁻¹ is due to the O-H out-of-plane bending vibration. The absorption bands of 960 and 920 cm⁻¹ were represented rhamnogalacturonan I and D-glucopyranosyl, respectively, [34, 36]. These 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).

3.4. 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. The 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, R² = 0.9761). The 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. These 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.

(a)

(b)

(c)

(d)

(e)

(f)

3.5. Identification of Fupenzi Adulteration

The 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 [37–39]. In order to achieve the purpose of rapid detection, we needed to eliminate instruments. The 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. Therefore, the RGB values could be used to bridge the color variation with the mixture concentration.

The 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. The 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). The concentrations of the mixture were (5.02 ± 0.32)% and (9.74 ± 0.52)% through calculation, respectively, and the recovery rate was good. Then, 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. The 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.

4. 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. The 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%. The correlation coefficient between the particle size of AuNPs and the concentration of adulterants was 0.9761, which indicated that the adulterants interacted with AuNPs. The 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. Nano-labeling 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

The 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

The 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.

Acknowledgments

This work was supported by the National Key R&D Program of China (Grant nos. 2020YFC1712700 and 2017YFC1700701), Scientific and Technological Innovation Project of the China Academy of Chinese Medical Sciences (Grant nos. CI2021A04005 and CI2021A01809), the Fundamental Research Funds for the Central Public Welfare Research Institutes (Grant no. ZZXT201906), and the Key Project at Central Government Level: the Ability Establishment of Sustainable Use for Valuable Chinese Medicine Resources (Grant no. 2060302).

References

J.-Y. Sheng, S.-Q. Wang, K.-H. Liu et al., “Rubus chingii Hu: an overview of botany, traditional uses, phytochemistry, and pharmacology,” Chinese Journal of Natural Medicines, vol. 18, no. 6, pp. 401–416, 2020.
View at: Publisher Site | Google Scholar
G. Yu, Z. Luo, W. Wang, Y. Li, Y. Zhou, and Y. Shi, “Rubus chingii Hu: a review of the phytochemistry and pharmacology,” Frontiers in Pharmacology, vol. 10, p. 799, 2019.
View at: Publisher Site | Google Scholar
Y. Chen, Z. Chen, Q. Guo et al., “Identification of ellagitannins in the unripe fruit of Rubus chingii Hu and evaluation of its potential antidiabetic activity,” Journal of Agricultural and Food Chemistry, vol. 67, no. 25, pp. 7025–7039, 2019.
View at: Publisher Site | Google Scholar
K. Li, M. Zeng, Q. Li, and B. Zhou, “Identification of polyphenolic composition in the fruits of Rubus chingii Hu and its antioxidant and antiproliferative activity on human bladder cancer T24 cells,” Journal of Food Measurement and Characterization, vol. 13, no. 1, pp. 51–60, 2018.
View at: Publisher Site | Google Scholar
Y. He, S. Jin, Z. Ma et al., “The antioxidant compounds isolated from the fruits of Chinese wild raspberry Rubus chingii Hu,” Natural Product Research, vol. 34, no. 6, pp. 872–875, 2020.
View at: Publisher Site | Google Scholar
Y. Chen, L. Xu, Y. Wang et al., “Study on the active polyphenol constituents in differently colored Rubus Chingii Hu and the structure-activity relationship of the main ellagitannins and ellagic acid,” Lebensmittel-Wissenschaft & Technologie, vol. 121, Article ID 108967, 2020.
View at: Publisher Site | Google Scholar
T.-T. Zhang, M. Wang, L. Yang, J.-G. Jiang, J.-W. Zhao, and W. Zhu, “Flavonoid glycosides from Rubus chingii Hu fruits display anti-inflammatory activity through suppressing MAPKs activation in macrophages,” Journal of Functional Foods, vol. 18, pp. 235–243, 2015.
View at: Publisher Site | Google Scholar
S. Bansal, A. Singh, M. Mangal, A. K. Mangal, and S. Kumar, “Food adulteration: sources, health risks, and detection methods,” Critical Reviews in Food Science and Nutrition, vol. 57, no. 6, pp. 1174–1189, 2017.
View at: Publisher Site | Google Scholar
E. D. Wallace, D. A. Todd, J. M. Harnly, N. B. Cech, and J. J. Kellogg, “Identification of adulteration in botanical samples with untargeted metabolomics,” Analytical and Bioanalytical Chemistry, vol. 412, no. 18, pp. 4273–4286, 2020.
View at: Publisher Site | Google Scholar
S. Popa, M. S. Milea, S. Boran et al., “Rapid adulteration detection of cold pressed oils with their refined versions by UV-Vis spectroscopy,” Scientific Reports, vol. 10, no. 1, Article ID 16100, 2020.
View at: Publisher Site | Google Scholar
F. Huang, H. Song, L. Guo et al., “Detection of adulteration in Chinese honey using NIR and ATR-FTIR spectral data fusion,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 235, Article ID 118297, 2020.
View at: Publisher Site | Google Scholar
M. F. González, E. E. Bellido, L. G. Cueto, M. Palma, C. G. Barroso, and G. F. Barbero, “Rapid quantification of honey adulteration by visible-near infrared spectroscopy combined with chemometrics,” Talanta, vol. 188, pp. 288–292, 2018.
View at: Publisher Site | Google Scholar
J. Yang, C. Yin, X. Miao, X. Meng, Z. Liu, and L. Hu, “Rapid discrimination of adulteration in Radix Astragali combining diffuse reflectance mid-infrared Fourier transform spectroscopy with chemometrics,” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, vol. 248, Article ID 119251, 2021.
View at: Publisher Site | Google Scholar
A. Amirvaresi, N. Nikounezhad, M. Amirahmadi, B. Daraei, and H. Parastar, “Comparison of near-infrared (NIR) and mid-infrared (MIR) spectroscopy based on chemometrics for saffron authentication and adulteration detection,” Food Chemistry, vol. 344, Article ID 128647, 2021.
View at: Publisher Site | Google Scholar
A. O. Uncu and A. T. Uncu, “A barcode-DNA analysis method for the identification of plant oil adulteration in milk and dairy products,” Food Chemistry, vol. 326, Article ID 126986, 2020.
View at: Publisher Site | Google Scholar
Q. Chen, X. Wu, and D. Zhang, “Comparison of the abilities of universal, super, and specific DNA barcodes to discriminate among the original species of fritillariae cirrhosae bulbus and its adulterants,” PLoS One, vol. 15, no. 2, Article ID e0229181, 2020.
View at: Publisher Site | Google Scholar
K. El Hawari, M. Al Iskandarani, F. Jaber et al., “Evaluation of honey authenticity in Lebanon by analysis of carbon stable isotope ratio using elemental analyzer and liquid chromatography coupled to isotope ratio mass spectrometry,” Journal of Mass Spectrometry, vol. 56, no. 6, Article ID e4730, 2021.
View at: Publisher Site | Google Scholar
M. Perini, L. Strojnik, M. Paolini, and F. Camin, “Gas chromatography combustion isotope ratio mass spectrometry for improving the detection of authenticity of grape must,” Journal of Agricultural and Food Chemistry, vol. 68, no. 11, pp. 3322–3329, 2020.
View at: Publisher Site | Google Scholar
L. S. Vieira, C. Assis, M. E. L. R. de Queiroz, A. A. Neves, and A. F. de Oliveira, “Building robust models for identification of adulteration in olive oil using FT-NIR, PLS-DA and variable selection,” Food Chemistry, vol. 345, Article ID 128866, 2021.
View at: Publisher Site | Google Scholar
L. Xu, L. Wei, Q. Shi, C. Cai, H.-Y. Fu, and Y.-B. She, “Non-targeted detection of multiple frauds in orange juice using double water-soluble fluorescence quantum dots and chemometrics,” Food Analytical Methods, vol. 12, no. 11, pp. 2614–2622, 2019.
View at: Publisher Site | Google Scholar
L. Xu, Q. Shi, D. Lu et al., “Simultaneous detection of multiple frauds in kiwifruit juice by fusion of traditional and double-quantum-dots enhanced fluorescent spectroscopic techniques and chemometrics,” Microchemical Journal, vol. 157, Article ID 105105, 2020.
View at: Publisher Site | Google Scholar
Z. Mohammadi and S. M. Jafari, “Detection of food spoilage and adulteration by novel nanomaterial-based sensors,” Advances in Colloid and Interface Science, vol. 286, Article ID 102297, 2020.
View at: Publisher Site | Google Scholar
C. Karami, A. Alizadeh, M. A. Taher, Z. Hamidi, and B. Bahrami, “UV-visible spectroscopy detection of iron (III) ion on modified gold nanoparticles with a hydroxamic acid,” Journal of Applied Spectroscopy, vol. 83, no. 4, pp. 687–693, 2016.
View at: Publisher Site | Google Scholar
S. Pandey and K. K. Nanda, “Au nanocomposite based chemiresistive ammonia sensor for health monitoring,” ACS Sensors, vol. 1, no. 1, pp. 55–62, 2015.
View at: Publisher Site | Google Scholar
S. Pandey, G. K. Goswami, and K. K. Nanda, “Green synthesis of biopolymer-silver nanoparticle nanocomposite: an optical sensor for ammonia detection,” International Journal of Biological Macromolecules, vol. 51, no. 4, pp. 583–589, 2012.
View at: Publisher Site | Google Scholar
M. Hassanisaadi, G. H. S. Bonjar, A. Rahdar, S. Pandey, A. Hosseinipour, and R. Abdolshahi, “Environmentally safe biosynthesis of gold nanoparticles using plant water extracts,” Nanomaterials, vol. 11, no. 8, p. 2033, 2021.
View at: Publisher Site | Google Scholar
L. B. Roa, J. Hill, S. Notman et al., “Analogues with fluorescent leaving groups for screening and selection of enzymes that efficiently hydrolyze organophosphorus nerve agents,” Journal of Medicinal Chemistry, vol. 49, no. 1, pp. 246–255, 2006.
View at: Publisher Site | Google Scholar
E. Deymehkar, M. A. Taher, C. Karami, and A. Arman, “Synthesis of SPR nanosensor using gold nanoparticles and its application to copper (II) determination,” Silicon, vol. 10, no. 4, pp. 1329–1336, 2017.
View at: Publisher Site | Google Scholar
C. Karami, S. Y. Mehr, E. Deymehkar, and M. A. Taher, “Naked eye detection of Cr³⁺ and Co²⁺ ions by gold nanoparticle modified with azomethine,” Plasmonics, vol. 13, no. 2, pp. 537–544, 2017.
View at: Publisher Site | Google Scholar
J. V. Rohit, H. Basu, R. K. Singhal, and S. K. Kailasa, “Development of p-nitroaniline dithiocarbamate capped gold nanoparticles-based microvolume UV-vis spectrometric method for facile and selective detection of quinalphos insecticide in environmental samples,” Sensors and Actuators B: Chemical, vol. 237, pp. 826–835, 2016.
View at: Publisher Site | Google Scholar
J. Sun, J. Ge, W. Liu, Z. Fan, H. Zhang, and P. Wang, “Highly sensitive and selective colorimetric visualization of streptomycin in raw milk using Au nanoparticles supramolecular assembly,” Chemical Communications, vol. 47, no. 35, p. 9888, 2011.
View at: Publisher Site | Google Scholar
R. Zhang, N. Li, J. Sun, and F. Gao, “Colorimetric and phosphorimetric dual-signaling strategy mediated by inner filter effect for highly sensitive assay of organophosphorus pesticides,” Journal of Agricultural and Food Chemistry, vol. 63, no. 40, pp. 8947–8954, 2015.
View at: Publisher Site | Google Scholar
Y. Xia, X. Wu, J. Zhao et al., “Three dimensional plasmonic assemblies of AuNPs with an overall size of sub-200 nm for chemo-photothermal synergistic therapy of breast cancer,” Nanoscale, vol. 8, no. 44, Article ID 18692, 2016.
View at: Publisher Site | Google Scholar
Y. Chen, Y. Wang, L. Xu et al., “Ultrasound-assisted modified pectin from unripe fruit pomace of raspberry (Rubus chingii Hu): structural characterization and antioxidant activities,” Lebensmittel-Wissenschaft & Technologie, vol. 134, Article ID 110007, 2020.
View at: Publisher Site | Google Scholar
H. Ke, T. Bao, and W. Chen, “Polysaccharide from Rubus chingii Hu affords protection against palmitic acid-induced lipotoxicity in human hepatocytes,” International Journal of Biological Macromolecules, vol. 133, pp. 1063–1071, 2019.
View at: Publisher Site | Google Scholar
Y. Chen, L. Xu, Y. Wang, Z. Chen, M. Zhang, and H. Chen, “Characterization and functional properties of a pectin/tara gum based edible film with ellagitannins from the unripe fruits of Rubus chingii Hu,” Food Chemistry, vol. 325, Article ID 126964, 2020.
View at: Publisher Site | Google Scholar
T.-T. Wang, C. k. Lio, H. Huang et al., “A feasible image-based colorimetric assay using a smartphone RGB camera for point-of-care monitoring of diabetes,” Talanta, vol. 206, Article ID 120211, 2020.
View at: Publisher Site | Google Scholar
J. Yang and Y. Xu, “Prediction of fruit quality based on the RGB values of time-temperature indicator,” Journal of Food Science, vol. 86, no. 3, pp. 932–941, 2021.
View at: Publisher Site | Google Scholar
S. Hong, D.-W. Zheng, Q.-L. Zhang et al., “An RGB-emitting molecular cocktail for the detection of bacterial fingerprints,” Chemical Science, vol. 11, no. 17, pp. 4403–4409, 2020.
View at: Publisher Site | Google Scholar

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Copyright © 2022 Yuan Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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