Application of FT-IR spectroscopy for the identification of three different parts of Camellia nitidissima and discrimination of its authenticated product
Application of FT-IR spectroscopy for the identification of three different parts of Camellia nitidissima and discrimination of its authenticated product
Camellia, also known as “cha-hua” in Chinese, is the largest genus of flowering plants in the Theaceae family. It consists of nearly 200 known species and is widely dispersed throughout China. They are ubiquitous in China and southeastern and eastern Asia, where over 80% of these species are present (Vijayan et al., 2009). Their flowers are usually red, purple, pink, or white. Additionally, they have less yellow blossoms, making them the most sought-after variation of this plant’s flower. Camellia with deep yellow blossoms is also referred to as “golden camellia” and was discovered in Guangxi, China, in 1965 by Hu, who named it Theopsis chrysantha (Hu, 1965). The taxon was then renamed “Camellia” and subsequently “Camellia chrysantha” (Tuyuma, 1975).
It is believed that golden camellia has a variety of pharmacologically active compounds, including polysaccharides, saponins, polyphenols, and flavonoids, which are frequently used by the Chinese to make both tea and traditional medicines (Do et al., 2019). According to a recent study, golden camellia contains more phenolic compounds than other species of the same genus, such as green tea (Camellia sinensis) (Song et al., 2011). In golden camellia, polyphenolic compounds such as quercetin, kaempferol derivatives, and apigenin have been identified, but only in trace amounts in normal tea leaves (Lin et al., 2013; Wang et al., 2017). These factors enhanced the therapeutic value and pharmacological potential of golden camellias.
Camellia nitidissima C.W. Chi is a rare and well-known ornamental camellia species that is listed in the Compendium of Materia Medica, Ben Cao Gang Mu (本草纲目). It was used to treat nephritis, hepatitis, jaundice, urinary tract infections, dysentery, hypertension, diarrhea, liver cirrhosis, sores, and irregular menstruation (He et al., 2017). Flowers and leaves of C. nitidissima have been commercially cultivated as a new source of tea leaves. Its seeds have been used to produce essential oils, and both flowers and leaves are extensively utilized by Chinese communities (Wang et al., 2018). In addition to offering functional benefits to the community via non-pharmacological and lifestyle adaptations, it has been discovered to exert a wide range of pharmacological effects. It can be an useful agent with anticancer, antioxidant, hypolipidemic, antidiabetic, antiallergic, antibacterial, and anxiolytic properties (He et al., 2017; An et al., 2020). Previous research study has shown that C.nitidissima has neuroprotective benefits through a synergistic interaction between antioxidant and neurotrophic signaling pathways (An et al., 2020). Despite the growing number of research studies describing various pharmacological activities of C. nitidissima, there are no data on its chemical fingerprinting or the chemical composition of its leaves, flowers, and seeds. The plant’s chemical composition may vary depending on the species, location, age, harvesting system, and drying method. Different parts of a similar plant may contain significantly different components, especially the major ones (Heinrich, 2015).
Several approaches, such as the widely employed chromatography techniques in laboratory research, can be utilized to examine chemical compositions and provide a comprehensive overview of medicinal plants (Yang et al., 2013; Kitanov et al., 2015). However, chromatography necessitates a difficult sample preparation method, and it is time-consuming to obtain a complete analysis (Peerapattana et al., 2015). Based on the current market value, 1 kg of C. nitidissima flower will cost around 2,000–3,000 Chinese yuan (RMB), whereas C. nitidissima leaf costs only 200–400 RMB. Due to the considerably larger market price of C. nitidissima flower, it is feasible for unscrupulous vendors to adulterate or mix the flower with the leaves in their beverage or product. Chromatography may make it difficult to discern between leaves and flowers with similar phyto-compound profiles.
Fourier-transform infrared spectroscopy (FT-IR) is another widely practiced method for obtaining chemical fingerprinting. In recent years, numerous studies have been conducted about the application of FT-IR in medicinal plants in various aspects (Liu et al., 2010). Compared to chromatography, it needs minimum sample preparation and can identify multiple components in a single study (Moros et al., 2010; Smith, 2011). FT-IR spectroscopy offers a broad scope for studying medicinal plants in conjunction with chemometric analysis. Chemometrics, a multidimensional statistical analysis methodology that retrieves information from data via the application of mathematics and statistics, will obtain valuable chemical information from the original spectral data via unsupervised (PCA) and supervised classification (PLS and OPLS) methods (Rohman et al., 2019). Our study categorized the different parts of C. nitidissima samples using FT-IR with principal component analysis (PCA), PCA-class, and OPLS-DA analysis methods. PCA-class and OPLS-DA were also used to discriminate the adulterated samples and TCM flowers from C. nitidissima.
A spectrum two Fourier-transform infrared (FT-IR) spectrometer (PerkinElmer, United States) characterized the samples. A programmable temperature controller (FTIR Solution, Malaysia) was utilized for thermal perturbation.
Samples and materials
A total of 121 Camellia nitidissima samples were provided by Prof. Chen Jingying (Fujian Academy of Agricultural Sciences), including 34 flowers, 80 leaves, and 7 seed samples. All samples were collected from Guangxi and Fujian (Zhangping, Yongfu, and Shanghang provinces) between 2016 and 2017. In addition, 22 Camellia nitidissima adulterated samples from the market and 10 TCM reference flower samples from the National Institutes for Food and Drug Control, China, were included for OPLS-DA and PCA-class discrimination and classification studies, respectively. Potassium bromide (KBr) was purchased from Merck (Germany) and used as the background for FT-IR analysis (Merck, Germany).
Procedure for FT-IR spectral acquisition
Before the experiment, KBr was dried overnight at 120°C to remove all traces of moisture. Each sample was pulverized and mixed with crystalline KBr. Subsequently, the mixtures were ground and compressed into tablets. The spectra were recorded from 4000 to 400 cm−1 (mid-infrared region) at a resolution of 4 cm−1 with a 1 cm−1 interval. Each spectrum was calculated from 16 co-added scans to minimize the signal-to-noise ratio (SNR) and improve the spectral quality. The raw FT-IR spectrum was processed using Spectrum 10.5.3 (PerkinElmer, United States). The FT-IR spectrum was accepted when the achieved transmission was 60% or higher (the lowest peak located within 30–10% of transmission). Alternately, the test was repeated with KBr or sample addition until at least 60% transmission was achieved. The pellets were inserted into a sample holder equipped with a programmed heating jacket (FTIR Solution, Malaysia) to acquire the 2D correlation infrared spectrum. The sample holder was then heated, and the spectra were collected at different temperatures ranging from 20°C to 120°C at a 10°C interval.
Data processing for second-derivative and two-dimensional correlation spectrum
After obtaining the FT-IR spectra, they were converted to a second-derivative spectrum with 13 data points for slope calculation. Moreover, the FT-IR spectrum also undergoes baseline correction and smoothing before performing arithmetic correction. The 2D-IR correlation spectra were calculated and obtained via treating the spectra with 2D-IR correlation analysis software developed by Tsinghua University (Beijing, China).
In the preliminary phase, unsupervised pattern recognition techniques known as principal component analysis (PCA) were utilized to examine variations in the FT-IR spectral characteristics of various parts of C. nitidissima, adulterated, and TCM reference flower samples. After PCA analysis, PCA-class and orthogonal partial least square discriminant analysis (OPLS-DA) were adapted for the discrimination study. The samples were randomly divided into calibration and validation sets. When conducting PCA-class and OPLS-DA model, 60% of the spectra from different parts were considered a calibration set, and the remaining spectra (40% of C. nitidissima, adulterated, and TCM reference flower samples) were considered a validation set. The specificity, accuracy, and sensitivity of PCA-class and OPLS-DA for differentiating the validation set were determined. For the OPLS-DA study, R2Y(presentation of the variation of the calibration set-Y, explained by the model), Q2Y (presentation of the variation of the calibration set-Y, predicted by the model according to cross-validation), root mean-squared error of estimation (RMSEE), root mean-squared error of cross-validation (RMSECV), and root mean-squared error of prediction (RMSEP) were calculated. The cross-validation method with seven cancellation groups was conducted to verify the robustness of the model. The permutation test was conducted as internal validation, with 100 permutations predetermined. In addition, three parameters, namely, sensitivity, specificity, and accuracy, were computed to evaluate the performance of the calibration model. All OPLS-DA, PCA, and PCA-class models were established using SIMCA 14.1 (Umetrics, Sweden).
Results and discussion
Differentiation by FT-IR spectra
Tri-step infrared spectroscopy is an analytical method explicitly applied for analyzing complex systems. It comprises conventional FT-IR, second-derivative infrared spectroscopy (SD-IR), and two-dimensional correlation infrared spectroscopy (2D-IR) (Liu et al., 2018). FT-IR spectroscopy was utilized to acquire the infrared spectrum corresponding to the chemical fingerprint of samples. It generates an infrared spectrum by detecting and quantifying the vibrational bond between functional groups, thereby disclosing the entire chemical characteristics of its analytes. Figure 1 shows the conventional FT-IR spectra of flowers, seeds, and leaves of C. nitidissima. It can be noticed that the spectra of flowers, seeds, and leaves of C. nitidissima have some variances in shape and intensity, particularly after 1800 cm−1. The assignments of the peaks and their possible compounds are presented in Table 1.
According to Figure 1’s infrared spectra, the flowers, seeds, and leaves of C. nitidissima exhibited absorption peaks within the range of 3500–3300 cm−1, with an absorption peak at 2925 cm−1 attributed to the detection of stretching vibration of O-H groups and asymmetric C-H stretching. A symmetric C-H stretching absorption peak of about 2854 cm−1 was also found in the spectra of the seeds and leaves. By examining the spectra, it can be detected that seed samples have a greater absorption peak at 2927 cm−1, which postulates that seeds possess a greater amount of asymmetric C-H bonds than flower and leaf samples.
As seen in Figure 1, the seed of C. nitidissima contains a large amount of lipid, but no lipid is detected in the flower and leaf. The absorption peaks at 1460 and 1376 cm−1 displayed in the seed spectrum could be assigned as the bending mode of the C-H group (Fang et al., 2012; Hofko et al., 2018). The peaks at 1743 cm−1 and 1417 cm−1, respectively, represent the ester carbonyl C=O group stretching and C-H bending of the unsaturated fatty acid chain. These are classic spectra with a high lipid content (Sun S. et al., 2010). The portrayal of absorption at 1648 and 1548 cm−1 in the seed spectra corresponds to the N-H bending and symmetric stretching of N-H groups, respectively (Sun et al., 2011). This suggests that in addition to lipids, seeds contain amide I and amide II proteins. The absorption peaks at 1624 cm−1, 1317 cm−1, 781 cm−1, 661 cm−1, and 518 cm−1 were observed in the spectrum of the leaf samples. These prominent peaks are attributed to calcium oxalate. The peak at 1317 cm−1 might be due to the asymmetric stretching of C=O groups within the oxalate ion (Sun et al., 2011). The peaks at 1317 cm−1, 781 cm−1, 663 cm−1, and 518 cm−1 contribute to a strong peak at 1624 cm−1, which could also be attributed to asymmetric stretching of C=O groups.
The absorption peaks within the range 1200–950 cm−1 are the attributes of various C-O stretches in saccharides and glycosides. C. nitidissima seeds had the highest saccharide concentration of the three parts. The absorption peaks at 1159 cm−1, 1080 cm−1, 1019 cm−1, and 999 cm−1 were characteristic absorption peaks found in the infrared spectrum of starch, together with the peaks below 950 cm−1, showed at 930 cm−, 852 cm−1, 766 cm−1, 576 cm−1, and 530 cm−1 (Sun S. Q. et al., 2010; Li and Ren, 2011). The occurrence of the highest peaks between 1300–950 cm−1 allows us to confidently infer that saccharide, followed by lipid, was the predominant chemical constituent of the seed. Likewise, the major component in the flowers of C. nitidissima was saccharides. The highest absorption peaks were found at 1059 cm−1, 1103 cm−1, and 1145 cm−1. By observing the spectrum of leaves, the absorption peaks appeared at 1317 cm−1, 1157 cm−1, 1098 cm−1, and 1051 cm−-1, indicating the presence of cellulose (Li and Ren, 2011). Future research should be attempted to confirm the presence of these components.
This study revealed that different portions of the same plant, C. nitidissima, contain distinct major constituents. The operational technique for identifying and differentiating the elements of a plant—the flowers, seeds, and leaves—produced a blueprint for future studies. This research demonstrated the dependability and rapidness of multi-step infrared and chemometric analysis to identify sample chemical components. Chemical fingerprinting, including peak shape, position, and intensities, gave unique markers for detecting chemical compositions, thus relating to the separation of C. nitidissima plant segments. This is crucial in the study of herbs because it allows for the rapid and thorough evaluation of samples and offers the data needed to be zero in the components of the herbs that have a specific pharmacological effect on the human body. Although this study established a clear differentiation between the chemical composition of C. nitidissima flowers, leaves, and seeds, additional research is needed to determine C. nitidissima’s whole chemical composition.
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