Depth profiles of a laminar material have been measured on a confocal Raman microscope with a selection of microscope objectives. It is well known that the quality of the profiles will depend on the quality of the focus, which requires matching the objective’s engineered properties with the properties of the material, particularly the effects of the indices of refraction. Depth profile comparisons performed with a series of objectives will illustrate how better matching improves the spatial resolution and chemical speciation in the depth profile.

Since the introduction of the Raman microprobe in the mid-1970s, it has been said that the Raman microprobe is the ideal instrument for depth profiling transparent samples without cross-sectioning. Over the years, we have looked at different laminar materials with reasonable success. It was recognized, however, that the standard optics used on a Raman microscope are metallurgical, which means they are designed to image the surface of a flat opaque object. Anyone who has taken an optics course knows that when light penetrates a material, which by definition has an index of refraction greater than that of air (which is 1.0), the light will bend towards the normal of the interfacial boundary, and the degree of bending increases as the angle of incidence increases. This means that the optical parameters of the objective will not guarantee that all rays are focused at the same plane. Ironically, the degree of blurring will increase with the numerical aperture (NA) of the objective which is, in some ways, counterintuitive because as the NA increases, one expects the focus to become tighter. In thinking about this effect, it occurred to me that using an objective with a correction for an index greater than 1 might improve the situation. Such an objective could be an objective corrected to be used with a cover slip, or an immersion objective. This idea was explored in a short article where we showed the improvement in signal from a gaseous inclusion in a mineral when we compared the signal with a corrected objective vs. a metallurgical one (1). Subsequently, several laboratories have pursued this idea in more detail (2–4).

For a while, I have felt that it might be interesting to perform a depth profile on automobile windshield glass because it is made shatterproof by sandwiching a polymer layer between two layers of glass. The polymer that has the same index of refraction as the glass is selected for this application so one does not see multiple images in reflection or transmission. Well, I never got to an automobile junkyard to get a piece of this glass, but I began to consider the screen protectors used on cell phones as an alternative. Even though the thickness of these glass plates is of the order of a millimeter, they are quite flexible, suggesting that they might also be a sandwich structure similar to automobile windshield glass. So when I had to replace one of my screen protectors, I saved the old one for Raman analysis. I show in this column depth profiles taken with a 10x, 50x long working distance (LWD), 100x dry, 100x oil immersion, and 60x corrected for a cover slip. The numerical apertures for these objectives are 0.25, 0.50, 0.90, 1.4, and 0.9, respectively.

Because this column is meant to indicate the possibilities and limitations of depth profiling, I present the results of a low resolution cross-section measurement first. The sample is of the order of 0.5 mm thick, so it is not possible to probe the entire thickness with most objectives. When I started looking at the depth profiles with various objectives, I was confused because I did not know what was present. By measuring a cross-section, I was able to gain more knowledge to help me understand the depth profiles. Using a 5x objective, I was able to capture an image of the entire plate cross-section, and then map its composition without worrying about any loss of focus because such a low magnification objective is not subject to small changes in focus. I used the multiplicative curve resolution (MCR) algorithm to determine the factors and found that four factors accounted for the composition. The profile is shown in Figure 1. The top left part of Figure 1 shows the profile of the four factors shown in colors in the bottom right; the black and grey spectra represent reference spectra of polyethylene terephthalate (PET) black, and silicone (grey). Note that the silicone and PET spectra separate fairly well in the MCR factors.

Screen Shot 2021-05-27 at 9.32.16 AM.png

That brings the discussion back to the depth profiles recorded with the objectives mentioned earlier. Note that only the 10x and 50x LWD objective enable measurements through the entire sample; the other objectives have working distances of the order of 100–200 μm. We used either classical least squares (CLS) or MCR to generate the factors and depth profiles. The factors are always plotted with the reference spectra in black or grey at the bottom to evaluate how well the spectral species are being separated in the profiles. Figure 2 shows the depth profile recorded with the 10x objective. The factors have been defined by CLS. Note how the layer definition is severely compromised. There is a complete absence of a layer of PET below the silicone layer and no evidence for a second glass spectral species even though the non-normalized score for the glass shows a dip near 500 μm. This is not unexpected because of the low spatial resolution of this objective. Clearly one would not try to get good depth information from thin films using the 10x objective.

Figure 3 shows the depth profile with the 50x LWD objective. This objective has millimeters of working distance so it provides full access to the sample. Figure 3 shows results for 3 MCR factors, which include only one glass factor (adding a fourth factor to the calculation did not improve the result). Near the surface (

) the layer profiles do not look too bad; the silicone layer is of the order of 25 μm, but is apparently accompanied by glass, which is not correct. The second layer of the red silicone factor doesn’t make sense either because of the high glass content in that layer, so we know that this analysis cannot be accurate. This fact is also consistent with our knowledge of optics, which says that an objective with a high NA (in this case 0.5) that is not corrected for the index of the material will not provide good spatial selection. The dip in the scores at approximately 450 μm is presumably because of an air gap between the bottom of the sample, which is the glass side, and the glass microscope slide. Apparently, the glass in the screen protector is the same as that in the glass slide.

Now we look at results from the 100x metallurgical lens, the 100x oil immersion lens, and then the 60x dry lens with corrections (Both of these last two lenses are meant to be used with a cover slip.).

Figure 4 shows the depth profile from the 100x metallurgical objective that has no corrections. Note that the 200 μm working distance limits how many layers can be measured. The two factors were generated with MCR, and they separate the PET and silicone components fairly well as can be seen in the top of Figure 4. The depth profiles are shown both with normalization (left) and without (right) scores. The significant drop in intensities of the non-normalized plot on the right is an indication of the lack of optical correction of the lens, but the width of the silicone layer is about 25 μm, which is only a bit larger than what was seen in cross-section.

Figure 5 shows the results of the depth profile recorded with the oil immersion lens which has a 120 μm working distance. A 170-μm cover slip was placed above the sample, and oil was dropped both below and above the cover slip. The profile detects the presence of the immersion oil between the cover slip and the sample. It is curious that even with this lens, which is expected to produce good spatial resolution, the spectral components of PET appear in the other factors, especially that of silicone. But more puzzling is the apparent width of the silicone region, which we know is of the order of 20 μm. Because I do not know from where the 120 μm working distance is measured, I am assuming that the apparent large width of the silicone layer is because of the fact that the objective is in contact with the sample and the focal spot is not changing.

Figure 6 shows the results of the depth profile measurement recorded with the 60x objective corrected for a cover slip. A 170-μm thick cover slip was mounted, and the collar on the objective was set for 170 μm. This objective has a working distance of 200 μm. The factors were generated using CLS. This measurement was made after the measurement with the immersion oil, so the profile shows the presence of residual oil in a layer about 25 μm thick. The factor for PET is free of silicone, but the silicone factor has a rather larger contribution from PET. Note that the spectrum of PET is rather strong because of its aromatic character, and it does show up in the other factors. I remember hearing Neil Everall, one of the authors cited in the references, mention that when he was doing depth measurements, he was seeing evidence of a material even at depths where one would not predict to measure it. This story is not complete, so hopefully it can be sorted out because of the importance of making depth measurements by Raman spectroscopy.

One measure of how well an objective will be able to differentiate layers will be the slope of the signal intensity when the focus enters each layer. For the four objectives (50x LWD, 100x metallurgical, 100x oil immersion, and 60x corrected-for-a-cover-slip), the distance between approximately 10 and 90% intensity is respectively 19, 23, 9, and 11 μm. The 50x LWD does better than the 100x metallurgical because its NA is smaller (0.5 vs. 0.9) and therefore introduces fewer aberrations. Both of the corrected lenses do quite well, and significantly better than the uncorrected lenses.

We have measured the composition of a cell phone screen protector, first in cross-section, and then by depth profiling using five microscope objectives with varying optical properties. The profile taken with the 10x objective failed to separate the spectral features or to detect some of the layers. The 50x LWD objective was able to probe the entire sample, but the scores were mixed and thus the results unreliable. The 100x metallurgical, uncorrected objective produced reasonably pure factors and profiles for the first three layers, but the non-normalized scores plot shows the effect of the lack of correction in that the intensity is dropping as the focal plane is moving further into the sample. Like the 100x metallurgical objective, the 100x oil-immersion objective that was used here has a very short working distance, on the order of 120 μm so only the top layers can be probed. The ability to separate layers is judged good from the slopes at the interfaces at -25 and 0 μm. The larger slopes at the interface between the top PET and the silicone at approximately 50 μm could indicate a chemical or physical mixing there, but because the top PET layer is meant to be peeled off, this seems unlikely. In my judgement, the 60x objective corrected for use with a cover slip has given the best results. The PET film on top is measuring 30 μm and the silicone layer is measuring 20 μm, but the residual immersion oil layer from the previous measurement is about 13 μm wide. However, the lack of effective separation of the spectra is still troubling.

The take-home message from this exercise is that without the correct objective, the ability to measure a laminar sample will be limited. In fact, in researching the working distances for the objectives that I used, I found that there are objectives with longer working distances that are corrected for a cover slip, with or without oil immersion. So for any project that requires knowledge about thin layers, and cross-sectioning is not a possibility, acquisition of an appropriate objective with a high quality Raman microscope can provide the information required.

(1) F. Adar, C. Naudin, A. Whitley and R. Bodnar, 

Articles

Columns

We illustrate how matching the optical properties of the microscope objective with the sample properties improves the spatial resolution and chemical speciation in depth profile measurements using confocal Raman microscopy.

Effect of Sampling Optics on Raman Depth Profiling

Near-infrared (NIR) spectroscopy is a promising technique for identifying raw agricultural materials. However, it is rarely used because of its poor discriminant rate. In this study, we took tobacco leaves from five origins as experimental materials. An origin discriminant model by the discriminant partial least squares (DPLS) was established, and the correct discriminant rate of internal cross validation was 76.54%. Origins were divided into three groups. The discriminant model of the three groups was improved, and the correct discriminant rate of internal cross validation was 98.77% along with a 100% external validation rate. We analyzed the characteristics of the three groups’ average spectra under normal variable pretreatment and found they have different absorption characteristics in different regions, and that the classification is information-based. The results show that using information-based classifications can establish a better model, and that main chemical components and NIR spectra can determine whether the classification is information-based, and whether projection based on principal component and Fisher criterion (PPF), can be more effective.

Near-infrared (NIR) light is an electromagnetic wave with a wavelength in the range of 780 to 2526 nm (1). The NIR spectrum absorbs the doubled and combined frequency of hydrogen-containing group vibrations and contains information on the composition of most organic compounds (1). NIR analytical technology is a fast and nondestructive technology for material analysis, and it can conduct two types of analysis: quantitative and qualitative (2). Using NIR qualitative discrimination, the source, type, storage time, grade, and identity of a sample can be determined. The use of NIR spectroscopy to qualitatively discriminate samples has been extensively studied; such studies have considered the varieties of strawberries (3), the origins of jujube (4), the origins of fritillaria (5), the classification of bamboo (6), and explored many other areas (7–11). In addition to NIR analysis technology, methods commonly used to distinguish the characteristics of agricultural products include electronic nose technology (12) and mineral element fingerprint analysis (13), among others.

Qualitative discrimination of agricultural raw materials not only help identify the authenticity, quality, and grade of the agricultural raw materials, but they also facilitate the classification and grading of the market as well as the breeding of varieties, among other processes. Qualitative discrimination of agricultural raw materials is also conducive to stable large-scale industrial production in terms of acquisition and processing. However, in actual production, not many types of at- tributes can be identified (generally only those that differ greatly); the recognition accuracy is not high (for some attribute types, the recognition accuracy is usually less than 90%) (14–17). Although using NIR technology has achieved the aforementioned goals, there are few examples of practical applications.

In this study, we took tobacco leaves from the Sichuan province of China as experimental materials and used discriminant partial least squares (DPLS) to establish a qualitative discriminant model first. However, the correct discriminant rate was poor. We speculated that the classification of origins by administrative divisions is insufficient, or that the classification does not have an information basis. Therefore, we analyzed the similarities between the five origins based on the main chemical components and NIR spectra. Doing this analysis helped establish the discriminant model of the three origin groups. The results showed that using information-based classifications can lead to a better model. This study also analyzed different absorption characteristics in different NIR wavenumber ranges and determined whether the classification is information-based by similarity analysis.

A multipurpose analyzer (MPA)-type Fourier transform NIR spectrometer (Bruker, Germany) was used. The working parameters were as follows: spectrum acquisition range, 12000–4000 cm

The quality characteristics and main chemical components differ greatly among the upper, middle, and lower parts of the same tobacco plant. To eliminate the influence of part differences on the determination of tobacco origins, the middle part of samples collected from different origins in 2014 to 2016 in Sichuan province, China, were selected. Information about the total experimental samples is provided in Table I. Tobacco routine chemical composition data, including the total sugars, reducing sugars, total nitrogen, nicotine, potassium, and chlorine, were obtained by the Sichuan Provincial Tobacco Quality Supervision and Test Station.

Discriminant Partial Least Squares (DPLS)

DPLS originated from partial least squares (PLS) analysis, which is a bilinear model based on an 

 matrix (response variable) (18). This algorithm maximizes the covariance with the 

Although PLS is regarded as a calibration method, it is also used for solving discrimination problems (20). Different from PLS analysis, 

 is a class variable in DPLS. A binary system was used to determine the class to which a sample belongs and a standard is set, where “1” denotes belonging to this class and “0” denotes not belonging to this class (21). When there are three classes or five classes, class variable Y is as shown in Table II. However, the predicted value is often close to 0 or 1 rather than exactly 0 or 1 (22). In general, it can be set to a threshold to discriminate between classes by comparing the relationship between the predicted values and the threshold value (23).

In this study, we use the Caunirs software (China Agricultural University) to establish the origin discriminant models. The correct discriminant rate (CDR) is discriminating one class to within the same class, and the incorrect discriminant rate (IDR) discriminates a class to other classes. The CDR and IDR are defined as follows:

Projection Based on Principal Component and Fisher Criterion (PPF)

The PPF is a projection method that combines principal component analysis with Fisher criterion (24,25). Spectral matrix 

 by a dimension reduction technique called 

 is projected though Fisher criterion. It searches for the projection directions on which the data points of different classes are separated as far as possible, while the data points of the same class are kept close to each other (19).

The PPF method maximizes the between-class distance and minimizes the within-class distance. Not only can the PPF method discriminate between classes, but it can also reflect the similarity between classes (26). The projection of PPF is a circle. In the PPF two-dimensional (2D) projection, the center of each circle represents the projection mean within-class, the within-class projection values reflect the degree of dispersion within the class, and the overlap of the circles reflects the similarity between the classes (19). The distance between the projection graphs can be visually evaluated to ascertain the degree of similarity among the origins; the diameter of the circle can evaluate consistency within an origin (19). This study mainly considers the interclass distances between different origins. In this study, we use the Caunirs software (China Agricultural University) to establish a projection analysis model.

Cluster Analysis and Spectral Preprocessing

Based on the averages of the main chemical components from each origin, SPSS 19.0 software (IBM) was used to perform system cluster analysis. Cluster analysis technology can directly compare the properties of various entities and classify data with similar properties into one class (27). The spectrum is subjected to Savitzky–Golay smoothing preprocessing, first-derivative preprocessing, or standard normal variable (SNV) preprocessing (28). When establishing the DPLS qualitative discriminant models, we used the Savitzky–Golay smoothing preprocessing and first-derivative preprocessing to decrease spectral random deviation, systematic deviation and so on.

When analyzing the absorbance differences in the average spectra among different regions, we used SNV preprocessing to decrease spectral random deviation. SNV pretreatment does not change the original shape of spectrum, and it is convenient for observation of spectral features.

Result of the NIR Qualitative Model for Five Origins

DPLS was used, and the spectral data from 8000 cm

 were selected because of the higher absorbance in this range using the first-derivative and Savitzky–Golay smoothing with 15 points to reduce the spectrum noise (2). Then, based on internal cross-validation (26), the first six principal components were chosen. Table III shows the results of the model.

Table III shows that the total recognition accuracy of the tobacco origins discriminant model is only 76.54%, which does not meet the requirements for practical application. The correct discriminant rates in Panzhihua, Liangshan, Yibin, and Luzhou are low because there are many incorrectly classified samples between Panzhihua and Liangshan as well as between Yibin and Luzhou.

Regarding the reason behind the poor correct discriminant rate, we speculated that the classification of origins by administrative divisions is insufficient or unreasonable or the classification does not have an information basis. Therefore, we studied the similarities between the five origins to obtain a more reasonable or information-based classification based on their main chemical components and spectra to established a better discriminant model.

Similarity Analysis Between Five Origins Based on the Main Chemical Components

The average values of the main chemical components (total sugars, total nitrogen and nicotine) for the five origins were calculated, as shown in Table IV. Using systematic clustering, the contents of total sugars, total nitrogen, and nicotine from the five origins were analyzed. The cluster tree is shown in Figure 1.

Table IV shows the following results: Liangshan and Panzhihua tobacco samples have more total sugars, less total nitrogen, and less nicotine. Guangyuan tobacco samples have medium total sugars, more total nitrogen, and more nicotine. Luzhou and Yibin tobacco samples have less total sugars, more total nitrogen and more nicotine. As shown in Figure 1, according to the cluster analysis of the main chemical components, Liangshan and Panzhihua can be in one class, Yibin and Luzhou can be in one class, and Guangyuan can be in a separate class.

Similarity Analysis Between the Five Origins Based on the NIR Spectra

Liangshan, Panzhihua, Guangyuan, Luzhou, and Yibin were labeled with the symbols LSh, PZH, GY, LZ, and YB, respectively. PPF was used, and as in DPLS, the spectral data from 8000 cm

 were selected using first-derivative and Savitzky–Golay smoothing with 15 points. Then, based on the contribution of variance, the first five principal components were chosen. The projection map by PPF is shown in Figure 2.

As shown in Figure 2, the overlap between Liangshan and Panzhihua as well as between Yibin and Luzhou is nearly over half, and the similarity is high. If Liangshan and Panzhihua, Yibin and Luzhou, and Guangyuan can be put into three classifications, the distances between them are all relatively long and the similarity is low.

Result of the NIR Qualitative Model for Three Origin Groups

The results of the similarity analysis between the five origins based on the main chemical components or NIR spectra both show that the similarity between Liangshan and Panzhihua is high, and that the similarity between Luzhou and Yibin is also high. Based on the similarity analysis results, Liangshan and Panzhihua, Luzhou and Yibin, and Guangyuan can be classified as three origin groups, respectively, namely, PL, YL, and GY.

We established a discriminant model for the three origin groups using the same modeling method DPLS and pre-processing method as in the five-origin model. Based on internal cross-validation, the first seven principal components were chosen. Table V shows the qualitative discrimination results of internal cross-validation, and Table VI shows the external validation.

Table V and Table VI shows that, compared with the five origin discriminant model, the discriminant model of internal cross-validation shows a highly correct discriminant rate that increased from 76.54–98.77%, and the correct discriminant rate of external validation reaches 100%.

By comparing the results of the model for five origin groups and the model for three origin groups classified by the similarity of the main chemical components and spectral characteristics, it can be observed that using the information-based classification in distinguishing the origins of tobacco by NIR can establish a better and more reasonable qualitative discriminant model. This conclusion may be extended to other agricultural raw materials.

As shown in Figure 3, Figure 4, Table VII, and Table VIII, there are significant differences in the average spectra among Liangshan and Panzhihua, Luzhou and Yibin, and Guangyuan. In 4250–4350 cm

, the spectra mainly contain the absorption of C-H groups typically on cellulose, and the PL has a lower absorbance, while the YL and GY have higher absorbance. In 4700–4800 cm

, the spectra mainly contain the absorption of O-H groups typically on starch and other carbohydrates, and the PL has higher absorbance, while the YL and GY have lower absorbance.

, the spectra mainly contain the absorption of N-H groups typically on protein. The PL has a lower absorbance, the YL has a middle absorbance, and the GY has a higher absorbance.

Based on the information regarding the composition of organic groups and related substances absorbed by NIR light in different frequency ranges and the absorption characteristics of different origin groups of tobacco in different bands, it can be inferred that the PL tobacco samples may have less cellulose, more starch and other carbohydrates, and less protein; the YL tobacco samples may have more cellulose, less starch and other carbohydrates, and a middle amount of protein; and the GY tobacco samples may have less cellulose, less starch and other carbohydrates, and more protein.

The chemical composition characteristics of different origin groups are obtained by spectrum absorption characteristic analysis, and they are consistent with the characteristics of routine chemical composition analysis obtained by chemical measurement, such as carbohydrates. Average spectral information further verifies that the division into three origin groups is an information-based classification; it can also be used for determining whether the classification has an information basis. However, methods such as similarity analysis may be more effective because of the serious overlap of NIR spectra. At the same time, we can also use NIR spectroscopy to obtain content information of the major chemical groups in agricultural raw materials.

This study established a qualitative model for five origin groups, and the correct discriminant rate was poor. Based on the similarity of the main chemical components and near-infrared spectra of the different origin groups, a discriminant model for three origin groups was established, and the correct discriminant rates of internal cross-validation and external validation are both greatly improved.

The analysis of absorption characteristics in the spectra showed that they can be used for determining whether the classification has an information basis by analyzing the preprocessing average spectra. At the same time, we can also use near-infrared spectroscopy to obtain content information of the major chemical groups in agricultural raw materials.

Using the information-based classification can lead to a better model, and the classification algorithm is not most important. The chemical composition or spectral characteristics can be used to determine whether the classification has an information basis. Compared to chemical composition analysis, using NIR spectroscopy is more convenient, and spectrum similarity analysis methods, such as PPF, can be more effective because of the serious overlap of near-infrared spectra. The proposed method has possible applications for other agricultural products.

This research is supported by the China National Key Research and Development Program Subtopic (Project No.: 2016YFD0700304) and the Major Science and Technology Project in Sichuan Province, China (Project No.: SCYC201810).

Basic and Application of Near Infrared Spectroscopy

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(7) H. Li, F.R. van de Voort, A.A. Ismail, J. Sedman, R. Cox, C. Simard, and H. Buijs, 

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(29) Y.L. Yan, L.L. Zhao, D.H. Han, and S.M. Yang, 

Basics and Applications of Near Infrared Spectroscopy

 (China Light Industry Press, Beijing, China, 2005).

 are with the College of Information and Electrical Engineering at China Agricultural University in Beijing, China. 

 is with the Sichuan Tobacco Company of CNTC in Chengdu, China. 

 is with the Customs Technology Center of Chengdu in Chengdu, China. 

 is with the Panzhihua Tobacco Company of CNTC in Panzhihua, China. Direct correspondence to Junhui Li at 

Peer-Reviewed Research

Traditional qualitative analysis of agricultural materials using near-infrared spectroscopy can be improved using information-based classification methods, such as projection based on principal components and the Fisher criterion (PPF).

Using Information-Based Classifications to Distinguish Characteristics of Raw Agricultural Materials by Near-Infrared Spectroscopy

In pH 7.4, the UV-vis spectra showed that ytterbium (III) could react with the hematoxylin (HE) compound to form a binary complex, and that the complex also could interact with herring sperm DNA. Through analysis of the molar ratio, hematoxylin to ytterbium is 4:1, and the Yb(HE)

 complex to DNA is 10:1. Meanwhile, by means of the thermodynamic study, the enthalpy driving force of the reaction of the Yb(III)(HE)

 complex with DNA is obtained. In fluorescence spectroscopy, the nontoxic 4S Green Plus nucleic acid dye was used as a probe to prove the mode of the Yb(III)(HE)

 complex interaction with DNA. In addition, the interaction between the Yb(III)(HE)

 complex and DNA was further determined by using the Scatchard method, which indicated that there is electrostatic interaction between Yb(III)(HE)

 and DNA, including intercalation. Therefore, it is expected that this rare earth complex may have good anti-tumor properties.

Cancer has been regarded as the leading threat to human health, causing one in eight deaths around the world (1,2). Because changes in oncogenes and tumor suppressor genes are events at the level of nucleic acid molecules, nucleic acid detection is extremely important in the diagnosis and treatment of cancer (3). With the development of life sciences, researchers have discovered that changes in oncogenes and tumor suppressor genes are events that occur at the nucleic acid molecular level. Therefore, the study of the structure and function of DNA is of great importance in the diagnosis and treatment of some cancers (4–6). The molecular structure of DNA, as well as its characteristics, are closely related to environmental factors, such as temperature and pH (7). When the environment changes, the sequence of DNA base pairs and the interaction between hydrogen bonds and the interaction force between the phosphate groups will change accordingly, including a change in the interaction between the complex molecules and the DNA.

Figure 1 shows the molecular structure of hematoxylin. Hematoxylin (HE) is a natural dye extracted from the dried branches of the tree, 

, and is mixed with ether. HE is one of the most bioactive flavonoids in existence and possesses strong coordinating qualities (8). It is well known that flavonoids have a variety of biological effects in numerous mammalian cell systems such as antiviral, anti-allergic, anti-platelet, anti-inflammatory, and anti-tumor activities, and possibly even protective effects against chronic diseases (9,10). Because of the existence of a planar ring system, many polycyclic aromatic hydrocarbons and their derivatives can intercalate into DNA, thereby disrupting the normal function of cellular DNA, and some even exhibit anticancer activity. Studies in recent years have shown that HE has a significant inhibitory effect on gastric cancer (11). Therefore, the study of the binding between the HE complex and DNA is very useful for exploring the pharmacology of this complex and provides guidance for the design of new and more effective anticancer drugs.

Rare earth metal elements have been called the vitamins of industry and have become a key resource for development in various countries. In recent years, rare earth elements have attracted more research attention because of their important pharmacological effects, especially the role of rare earth complexes in anticancer activity (12). Rare earth metals combine with small molecules to form complexes, which help reduce the toxicity of the earth metals and allows for further research on anticancer drugs (13).

In general, the majority of current assays used to study the binding of DNA to other complexes are mainly dependent on fluorescence spectroscopy (14), ultraviolet (UV) spectrophotometry (15), electrophoresis (16), liquid chromatography–mass spectrometry (LC–MS) (17), and electrochemical methods (18). Because the methods of ultraviolet–visible (UV-vis) spectroscopy and fluorescence spectroscopy are sensitive, fast, and simple, they are considered to be effective methods (19). In this work, the interaction between the Yb(III)(HE)

 complex and DNA was investigated systematically by UV-vis absorption, fluorescence spectroscopy, electrochemical methods, and viscosity methods, which provided an experimental basis for studying the thermodynamics relationship and the action mode between the Yb-complex and DNA. The study of these reaction mechanisms will provide some theoretical guidance for the development of efficient, safe, and stable anticancer drugs.

O, and acridine orange (AO) were obtained from the Sigma-Aldrich Corporation. HE was purchased from Regal Biology Technology Co, Ltd. 4S Green Plus nucleic acid was obtained from Sangon Biotechnology Co. Ltd. All other chemicals were of analytical grade and used without further purification. An ultra-pure Milli-Q water system was used as the water source.

The absorption spectra were recorded using a U-3900H spectrophotometer (Hitachi Co. Ltd.)., and the fluorescence spectra were recorded by employing a F-7000 fluorescence photometer (Hitachi Co. Ltd.). Cyclic voltammetric measurements were conducted using a CHI 760E electrochemical system (CHI Co. Ltd.). Viscosity measurements were performed using a Cone and plate viscometer DVII +Pro (Brookfield Co. Ltd.). The measurement of the pH values was carried out with a FE20K pH-meter (Mettler-Toledo Co. Ltd.). In fluorescence, both excitation and emission bandwidths were set at 10 nm, λ

In a series of 10 mL colorimetric tubes, a certain molar concentration of HE, Yb(III), DNA, HE-Yb(III), HE-DNA, Yb(III)-DNA, and Yb(III)-HE-DNA solution was added, respectively. The volume of the seven samples was set to 10 mL with PBS buffer solution (pH = 7.40), and solutions were adjusted, mixed, and allowed to stand for 10 min to measure the UV absorption spectrum of each sample.

The ratio of YB(III) to HE and the ratio of YB-HE complex to hsDNA were determined using spectrophotometric titration. The concentration of the fixed substrate also gradually increases the concentration of the ligand substance. Each injection volume is 10 μL, the volume effect is very small and can be ignored.

To a series of 10 mL colorimetric tubes, certain molar volume of 4S Green Plus or HE-Yb(III) solution was added, and solutions were diluted to the 10 mL mark with pH 7.40 PBS buffer solution. Then, 4S Green Plus (2.5 × 10

 mol/L, 3.5 mL) solution in a 1 cm pathlength cuvette was titrated by adding successively 10 μL of hsDNA, and the volume effect was so small that it could be ignored. After each addition, the fluorescence was recorded. The same process as above was carried out to the 4S Green Plus and DNA sample, which was added with 10 μL of HE-Yb (III) solution in succession. The excitation wavelength of 4S Green Plus is 490 nm, the scanning range is 450–700 nm, the voltage is 400 V, and the slit width was set to 10 nm.

The required working electrode is pretreated. The working electrode is polished with Al

 powder, and then it washed with anhydrous ethanol and ultrapure water for 2–3 times, respectively. Preparing a certain concentration of HE-Yb(III) solution with a difference concentration of DNA, the cyclic voltammetry curve is obtained by an electrochemical workstation, the scanning range is -0.5–0.5 V, and the scanning speed is 0.05 V/s.

Viscosity measurements were performed using a viscometer, which was immersed in a thermostated water bath. Different amounts of Yb(III)(HE)

 complex were added into the viscometer while keeping the concentration of DNA constant. The flow times of the samples were repeatedly measured three times with an accuracy of ± 0.20 s by a digital stopwatch. The value of relative viscosity (ƞ/ƞ

 are the viscosity of DNA in the presence and absence of the complex, respectively.

The UV-vis absorption spectra of Yb(III), HE, DNA, and its mixture were measured in the wavelength range of 350 to 800 nm at room temperature. Figure 2 exhibited that the samples in the absence of HE can only cause negligible response in the specified wavelength range (curve 

), and when only HE is present in the sample (curve 

), a significant absorption peak can be observed. Upon adding DNA to the sample containing HE (curve 

), a significant signal reduction occurs because of the combination of DNA and HE. However, adding Yb(III) to the HE sample markedly increases the absorption intensity of HE at 558 nm (curve 

 has a 3 nm blue shift compared to curve 

, which indicated the formation of a complex between Yb(III) and HE. After mixing Yb(III)-HE complex and DNA, the UV absorption intensity increases as shown in curve 

, while the weak UV absorption of the DNA alone is shown in curve 

, which fully indicates that the Yb(III)-HE complex reacts with DNA.

Determination of the Binding Ratio of HE and Yb(III)

Absorption spectra were obtained by titrating the HE solution with the Yb(III) solution. The results presented in Figure 3a indicate that the peak absorption intensity at 558 nm of the solution gradually increases with the successively microscale addition of rare earth Yb(III) ions, which indicates that HE reacts with Yb(III) to form a new complex, verified because the absorbance of only the Yb(III) ions solution was nonexistent at about 558 nm.

To obtain the stoichiometry of the compound, we used the mole ratio method. As shown in Figure 3b, it could be concluded that the molar ratio of HE to Yb(III) is 4:1. According to the Beer-Lambert law: 

Absorption Spectra of Yb(III)(HE) and hsDNA 

UV-vis spectroscopy is an effective and convenient method to study the interaction between small molecules and nucleic acid. The interaction of Yb(III)(HE)4 complex with hsDNA has been studied with UV spectroscopy to investigate the possible binding mode and calculate the binding constants. Each time 10 μL of 2 × 10

 complex solution, the UV-vis spectrum of the solution was recorded. As shown in Figure 4a, the absorption intensity of Yb(III)(HE)

-DNA solution gradually increases with an increase of the amount of DNA. This result indicates that Yb(III)(HE)

The intensity of the absorption peak was used to determine the binding ratio at 558 nm. As seen from Figure 4b, the mole ratio of Yb(III)(HE)

 complex to DNA is 10:1. According to the Beer-Lambert law: 

 = 1 cm), The molar absorption coefficient ε of Yb(III)(HE)

Thermodynamics Study by the Double-Reciprocal Method

To investigate the thermodynamic result of the interaction between the Yb(III)(HE)

 complex and DNA, ultraviolet-visible spectroscopy was generally used. The UV absorption spectrum of the Yb(III)(HE)

 complex was determined when the hsDNA solution was successively added at the temperatures of 298.15 K and 310.15 K, respectively. The following double-reciprocal equation was employed (20):

 complex in the absence and presence of DNA, respectively. K is the binding constant between Yb(III)(HE)

 is the concentration of DNA. Thus, the double reciprocal plot of 1/(

 was obtained (Figure 5), then K was obtained from the ratio of the intercept to the slope. The values of 

 at two temperatures (298.15 K, 310.15 K) were demonstrated: 

If temperature changes slightly, the enthalpy change (Δr

) could be considered to be a constant. Then, the value of Δr

) can be determined from the van’t Hoff equation:

 represent the binding constant and gas constant, respectively. By calculation, Δr

 = -134.70 J/mol·K. The free energy change (Δr

) was estimated from the following relationship:

 < 0. Therefore, the interaction between Yb(III)(HE)

 and DNA could occur spontaneously. Because Δr

 < 0—it was speculated that the reaction was an enthalpy-driven reaction.

Fluorescent Probes to Explore Interaction of Yb(III)(HE)

Nowadays, fluorescent probe technology can be used for studying the interaction between nucleic acids and complexes. After the interaction of small molecules and DNA, the fluorescence spectrum will show energy transfer. From this phenomenon, a lot of useful information can be obtained (21). Among these, 4S Green Plus nucleic acid dye is a nontoxic and noncarcinogenic substance. The interaction between 4S Green Plus and DNA is embedded in the DNA sequence. The dye plays an important role in studying the interaction mode between functional molecules and DNA. When the nucleic acid dye is mixed with hsDNA, the fluorescence spectrum can be recorded. Figure 6a shows that the fluorescence intensity at 520 nm also increased after the hsDNA solution was added to 4S Green Plus, which indicated that the nucleic acid probes had inserted into the hsDNA.

Although DNA has a natural fluorescence, the intensity is so weak that it is limited to study the interaction between drug molecules and DNA. We usually use fluorescent probes to explore its mechanism. There are mature nucleic acid probes that were used to carry out the research, such as 

. These probes are nonspecific probes that intercalate deoxyribonucleic acid molecules. If the Yb(III)(HE)

 complex has the same binding mode with DNA as well as 4S Green Plus-DNA, then there is a competitive mode between 4S Green Plus and the Yb(III)(HE)

 complex with DNA, and as a result, the intensity of the fluorescence spectrum would be reduced. The decrease of fluorescence intensity in Figure 6b indicated that the Yb(III)(HE)

 complex could squeeze off 4S Green Plus form DNA. This result indicates that the binding mode between the Yb(III) (HE)

Studying the binding mode between Yb(III)(HE)

 complex and hsDNA is crucial for understanding the mechanism of action of drug molecules. Thus, an acridine orange (AO) probe was used to study the interaction of Yb(III)(HE)

 complex and DNA according to the Scatchard equation (22). The Scatchard equation expresses the binding of AO–DNA in the presence of a Yb(III)(HE)

 refers to the molecular amount of bound AO to total nucleotide concentration, 

 is the number of binding sites of DNA, and 

 is the intrinsic binding constant of AO–DNA.

There are different concentrations of the Yb(III)(HE)

 = 0, 0.2, 0.4, 0.6, respectively). When a drug binds to DNA by intercalation mode, the 

 value in the Scatchard plot would change but the 

 value would not. When a drug binds to DNA by non-intercalation mode, the 

 value would not change, when a drug binds to DNA by both intercalation and non-intercalation mode, both 

Figure 7a and 7b display Scatchard plot charts that show the absence of sodium chloride (NaCl) and the presence of NaCl. According to the Scatchard equation, the values of 

 were listed in Table I. The strong electrolyte sodium chloride (

 mol/L) was used in Scatchard studies to test if there was electrostatic binding between Yb(III)(HE)

 complex and DNA (24). As seen in Figures 7a and 7b, the values of both 

 value. It suggests that the interaction sites of Yb(III)(HE)

 and AO are not identical, when a binding mode mixed between DNA and the complex. As shown in Table I, the values of 

 in the present of NaCl are basically lower than those in the absence of NaCl, it suggests that there are also electrostatic binding mode between the Yb(III)(HE)

Electrochemical method can also provide good information in studying the interaction between DNA and other molecules. The internal base pairs of denatured DNA can undergo significant redox reactions and enhance electrical signal (25). When cyclic voltammetry was used to study the mode of action of pharmaceutical molecule and DNA, it was considered that the positive shift of the peak potential was the intercalation binding, while the negative shift of the peak potential was the electrostatic combination (26). Scanning Yb(III)(HE)

 complex solution in the presence of different concentrations of DNA with a working electrode (gold electrode) at a scan rate of 0.05 V/s in the range of −0.5V to + 0.5 V. Figure 8 shows the whole peak moved up and the current increased. This experiment result proved that the Yb(III)(HE)

 complex binded to DNA by an intercalation form.

Optical photophysical probe is necessary but not a sufficient condition for the judgment of binding mode, whereas hydrodynamic measurements is sensitive to the length change (27,28), it is regarded as the most critical tests of a binding model in solution. Thus, to further clarify the interaction between Yb(III)(HE)

 complex and DNA, viscosity measurements were performed. A classical intercalation model is known to cause a significant increase in the viscosity of the DNA solution because of the length of the DNA helix being increased. In contrast, a partial or non-classical intercalation mode could bend the DNA helix and reduce its effective length. Hence, non-intercalation binding causes no obvious increase in the viscosity of a DNA solution (29). As illustrated in Figure 9, the relative viscosity of DNA steadily decreases with the addition of Yb(III)(HE)

 complex. The reason is related to Yb(III)(HE)

 being partially inserted into the DNA base pairs and the DNA secondary structure was changed, which decreased its effective length. Such behavior further suggested that an intercalation binding should be the interaction mode of the Yb(III)(HE)

 complex and hsDNA has been studied by spectral analysis, cyclic voltammetry, and the viscosity method. The findings demonstrated that the binding mode between Yb(III)(HE)

 complex and DNA are intercalation binding and electrostatic binding. And the complexation ratio of HE and Yb(III) was determined to be 4:1 by the molar ratio method. The complexation ratio between the Yb(III)(HE)

 complex and DNA was 10:1. The main driving force between Yb(III)(HE)

 complex and DNA is enthalpy driven. These data provided important biophysical information related to the structure–activity relationship, which helps understand the nature of the complex binding to DNA and provides references for the development of new anticancer drugs.

This work was supported by National Natural Science Foundation of China (NSFC) (Grant No: 21565015 and 21663014), the Foundation of Jiangxi Educational Committee (GJJ150363), the State Key Laboratory of Chemical Biosensing & Chemometrics (Z2015022), and Key Laboratory of Functional Small organic molecule (KLFS-KF-201916).

(1) F. Bray, J. Ferlay, I. Soerjomataram, R.L. Siegel, L.A. Torre, and A. Jemal, 

(4) J. Czapla-Masztafiak, J.J. Nogueira, E. Lipiec, W.M. Kwiatek, B.R. Wood, G.B. Deacon, Y. Kayser, D.L.A. Fernandes, M.V Pavliuk, J. Szlachetko, L. González, and J. Sá, 

Evaluation of the UV-vis spectra of the reaction product of ytterbium (III) with hematoxylin (HE) indicates the formation of a rare earth complex that further reacts with marine mammal DNA, indicating the potential that this complex may have anti-tumor properties.

June 2021

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