Neha Sharma

Gallstone formation inside the gallbladder is a serious disease and causes health problems. To create preventative measures, it is crucial to examine the composition of gallstones and identify the minerals that are responsible for their pathogenesis. In the present work, we have employed laser-induced breakdown spectroscopy (LIBS)-based elemental studies in combination with partial least squares discriminative analyses (PLS-DA). Energy dispersive X-ray spectroscopy (EDX) was also applied to quantify the elements in the different parts (core and surface) of the gallstones. Fourier transform infrared (FT-IR) spectroscopy was used to classify the gallstone samples. Spectral studies using the LIBS and EDX techniques revealed the presence of trace and toxic elements in the center part of gallstones in higher amounts than that from surface parts. The EDX study also revealed that the elemental distribution is quite heterogenous, and their distribution is random. The EDX method revealed that the center (core) part of the stone samples contained the presence of numerous heavy and toxic elements such as arsenic (As), cadmium (Cd), mercury (Hg), lead (Pb), and nickel (Ni).

Gallstone disease is a severe health problem that affects millions of people globally. Gallstone production takes years in vivo, and it is difficult to investigate such processes from nucleation to consolidation (1,2). The mechanical irritation of the stones on the gallbladder tissues makes it one of the most painful disorders. Surprisingly, gallstone disease has become more common in children and adults over the last few decades. Adults are the subject of the great majority of reports (2). Gallstone disease is now treated solely by surgically removing the gallbladder. As a result, the disease has a significant impact on children. The primary job of the gallbladder is to concentrate bile by absorbing water and salt. It increases the concentration of impermeable solutes in hepatic bile by a factor of two.

The pathophysiology of gallstones is still unknown. Changes in hepatic bile composition, biliary glycoprotein, infection, age, genetics, sex, oestrogens, dietary variables, geographical distribution, and liver cirrhosis are all factors that may contribute to the formation of gallstones (2–5). Gallstones have a complex chemical composition. As a result, determining the major and trace elements present in the gallstones is critical. On the other hand, toxic elements are known to be exceedingly dangerous even at very low concentrations. Therefore, reliable elemental studies are needed to aid in the clarification and identification of effective treatment and management.

Gallstones of different compositions have a variety of color appearances. Gallstones are mostly comprised of organic components like cholesterol, bilirubin, bile acid, and fatty acid. According to their composition, there are three basic types of gallstones seen in patients: cholesterol type (cholesterol-rich); pigment type (bilirubin and calcium bilirubinate rich); and mixed type, which is the combination of cholesterol and bilirubin (6).

Many studies showed the presence of toxic elements, such as chromium, lead, nickel, cadmium, and arsenic in some of these stones, possibly as a result of people’s exposure to pollution, industrial waste, and smoking. Some research showed that toxic metals, such as chromium, lead, and cadmium, found in drinking water accumulate in the biliary system, eventually leading to gall

bladder stones. The growth in heavy metal toxicity has been linked to the gallbladder and other organ cancers (7). In some studies of gallstones, the authors used some techniques like X-ray fluorescence (XRF) and neutron activation analysis to show that the elements like calcium (Ca), sodium (Na), chlorine (Cl), potassium (K), magnesium (Mg), manganese (Mn), iron (Fe), phosphorus (P), copper (Cu), lead (Pb), titanium (Ti), zinc (Zn), aluminum (Al), nickel (Ni), and chro- mium (Cr) are the main constituents of gallstones (8).

Over the last few decades, a lot of research has been done to see how the accumulation and lack of particular elements in the human viscera cause disease. Many researchers are currently focused more on the elemental analysis of gallstones to detect the constituents and the presence of trace and toxic metal elements to understand their pathogenesis (9–11). For determining major and trace elements in gallstones, several analytical methods have been used, including atomic absorption spectrometry (AAS), particle-induced X-ray emission (PIXE), energy dispersive X-ray spectroscopy (EDX), wavelength dispersive X-ray fluorescence (WDXRF), inductively coupled plasma–optical emission spectrometry (ICP-OES), and laser-induced breakdown spectroscopy (LIBS).

LIBS is a well-known spectrochemical technique based on spectral analysis of radiation released by a micro-plasma created on the target surface by a laser pulse. The relative simplicity of the LIBS principle makes it incredibly appealing for a wide range of applications (1,12). The first analytical application of laser-plasma on surfaces, and therefore the origin of LIBS, occurred in 1962–1963. LIBS has been used in a wide range of applications and has a number of advantages over other well-known spectrochemical techniques. There is minimal or no sample preparation is required in LIBS.

Gazili and others (13) performed LIBS and photoacoustic spectroscopy (PAS) for the compositional analysis of gallstones. They classified the gallstones used in their study based on the organic and inorganic components presents in the samples and discriminate the gallstone samples using principal component analysis (PCA). For the first time, Singh and others (14) performed the variational analysis of gallstone samples using LIBS. They analyzed the variation in elemental composition from the center to the surface of the gallstones.

In numerous applications, including forensic evidence analysis and biomaterial identification, statistical approaches have been applied to analyze spectroscopic data. In some research publications, LIBS and chemometric approaches were used to test the feasibility of some biomaterial identification. Improved use of statistical approaches like PLS-DA is likely to play a big role in LIBS-based biological sample discrimination. PLS-DA is a type of multivariate analysis that identifies and maps interrelationships between data sets.

In the present work, we used LIBS to perform a comprehensive screening of the major and trace elements contained in the gallstone samples. The relative profiling of the com- ponents qualitatively present in the gallstone samples was done using the intensity ratio approach. For a better understanding of the characteristic variation of gallstone samples, the chemometric multivariate approach PLS-DA was applied to the LIBS data.

The gallstone samples were provided by the Department of Nephrology, Opal Hospital, in Varanasi, India, after surgical treatment of the patients. In the present study, five gallstones were used for the investigations. The stones were first washed thoroughly with doubly distilled water to get rid of blood or any other possible impurities and then dried at room temperature. The stones were observed microscopically and found of different colors (as can be seen from Figure 1a–f). During the visual examination, the surface and center portion of gallstones were also found to have a different appearance in color (Figure 1). The center parts were observed to be dark brown or black in coloration, whereas the colors of the surface parts varied, ranging from white to off white to yellow. This shows the different portions of major and minor chemical compositions. The center parts and surface parts of the stones were carefully removed. For convenience, the samples were coded G1, G2, G3, G4, G5, and G6. G5 and G6 represent the center and surface parts of a gallstone. The dried stone samples were crushed into fine powder using mortar and pestle and then analyzed using LIBS, EDX, and FT-IR spectroscopy.

FIGURE 1: Photographs of gallstone samples (G1–G6) used in the present study.

A LIBS experimental setup RT100-EC instrument (Applied Spectra, Inc.) was used to record the LIBS spectra of the various gallstone samples. A laser source, sample stage, spectrometer with detector system, and plasma collection optics were the main components of the arrangement. The stone sample was put on a translational stage, and the plasma was created by focusing a Q-switched Nd:YAG laser operating at 1064 nm wavelength on the sample using a 15-cm focal length lens. The laser pulse energy, pulse repetition rate, and pulse duration were tuned at 17 mJ/pulse, 5 Hz, and 10 ns respectively to produce the best signal-to-background and signal-to-noise ratios. A five-channel charge-coupled device (CCD) spectrometer having approximately a 0.1 nm spectral resolution with 187–887 nm coverage was used to record the data. The size of the laser spot on the surface of the sample was 100 μm in diameter, which was measured by the LIBS instrument T100-EC from Applied Spectra, Inc. For each sample, 20 line scans were taken, and these 20 scans were then averaged to predict our LIBS results and to avoid the fluctuations because of shot-to-shot variation.

The chemical compositions of gallstones were recorded using a Hitachi SU 8010 cold field emission scanning electron microscope (SEM). EDX attachment provided the elemental composition of trace and toxic elements present in the different parts of the gallstone samples.

FT-IR spectroscopy has been used to characterize the gallstone samples. The surface parts of the stones were found rich in cholesterol, and the center parts were found rich in bilirubin using FT-IR spectroscopy. The contents of cholesterol and bilirubin were observed in varying amount in gallstones of different colors, and thus, the stones were categorized as mixed type gallstones.

Relative Concentrations of Elements in Gallstones (G1–G6) Using LIBS

LIBS spectra of gallstone samples (G1–G6) were recorded at optimized experimental conditions in the spectral region 187–887 nm. The spectra were recorded using twenty laser line scans. Identification of the atomic and ionic lines present in the LIBS spectra of gallstone samples were carried out by NIST database (15) and “Identification of Molecular Spectra” by Pearse and Gaydon (16). 

The detected elements were Al, Mg, Fe, silicon (Si), Mn, Ca, K, P, Na, Cu, Ti, strontium (Sr), and Zn.

The intensities of the spectral lines of the elements detected in the gallstone samples were significantly different in different gallstones. The intensity of the atomic emission lines of LIBS spectra was directly proportional to the concentrations of the elements present in the samples and therefore reflect the concentrations of the elements present in the gallstones (17). The relative LIBS concentration ratios were measured and indicated by the bar graph shown in Figures 2a–b. Because Ca is the major element detected in all the gallstones, the Ca line at 396.8 nm has been taken as the internal reference line for comparing the intensities of spectral lines of minor constituent elements in the stones (as can be seen from Figures 2a–b). We have chosen the resonance line of ionic Ca at 396.8 nm for taking the intensity ratio because it is free from the self-absorption effect. It can be seen that the concentrations of elements Fe, Ti, Mn, Al, Na, K, Mg, and Si are different in different stone samples. Figure 4 clearly shows that the concentration variations of these elements.

FIGURE 2: Relative LIBS concentration of (A) Fe, Ti, Mn, Al; and (B) Na, K, Mg, and Si in gallstones (G1–G6).

FIGURE 4: Score plot of PLS factor 1 compared to PLS factor 2.

Gallstones can easily be identified by the presence of molecular Swan bands of C

] present in their LIBS spectra. LIBS spectra of gallstones (Figure 3) show the presence of C

 Swan bands at 468.4, 469.7, and 471.5 nm. The occurrence of C

 swan bands is directly related to sputtering from the cholesterol and possible hydrocarbons present in the gallstones (18).

FIGURE 3: Typical LIBS spectra of center (G5-black spectra) and surface (G6-red spectra) parts of gallstone in different spectral range.

LIBS Analysis of Center Part (G5) and Surface Part (G6) of Gallstone Sample

We have analyzed the center part (G5) and surface part (G6) of a gallstone sample using LIBS. The typical LIBS spectra of center and surface parts are shown in Figure 3 in the wavelength region 200–225 nm, 278–286 nm, 320–345 nm, and 400–475 nm. It can be seen from Figure 3 that the concentrations of the mineral elements Ca, Zn, P, Fe, Mg, Si, Cu, and Ti were found higher in center part than that of the surface parts. To reduce the shot-to-shot fluctuations, each LIBS spectrum was obtained as the average of 50 laser shots and to ensure the reliability of the LIBS data.

The FT-IR results showed the presence of bilirubin in center parts of the stones than that of the surface parts, whereas the LIBS study revealed the presence of higher level of Cu in center part than that of the surface parts. This clearly shows that Cu in the form of copper bilirubinate plays an important role in the formation of the pigmented parts of the black and brown color (Figure 1, gallstone G5) inside the core region of gallstones. Our finds are well supported by the studies performed on gallstones using the technique UV-visible (UV-vis) and time-of-flight secondary ion mass spectrometry (ToF-SIMS) (19).

Partial Least-Square Discriminative Analysis (PLS-DA) Modeling of LIBS to Distinguish Gallstones 

According to our FT-IR study, we have observed that the amount of cholesterol and bilirubin is different in different gallstone samples and hence, their color is different. Our LIBS studies on gallstones revealed that the concentrations of elements are also different in these different gallstones. Also, in the center part, the elements were observed in higher concentration than that of surface parts. Therefore, the nature of gallstones was found to be heterogeneous. Using PLS-DA modeling of LIBS data, we attempted to classify them based on their elemental composition so that we could distinguish the gallstone based on their elemental composition.

We have performed PLS-DA statistical analysis on LIBS spectral data to model all the outcome variables simultaneously for gathering and interpreting the PLS factors. These PLS factors could be further used to obtain the relevant information about the varying characteristics of different sample groups because of their slightly different chemical compositions. The whole data has been represented by a data matrix 

 which includes wavelengths as variables for the total “

” number of LIBS spectra. It has been modeled for 

 = 10239 wavelengths ranging from 200–900 nm for 

 = 120 LIBS spectra corresponding to each sample groups G1, G2, G3, G4, G5, and G6. The class matrix, or category vector 

, comprised of different categories (G1, G2, G3, G4, G5, and G6) where 

 = 6 stands for the number of sample groups and 

 = 120 denoted the total number of spectral data values taken for each sample groups. The two data matrix 

 are modeled to get the new variables known as latent variables or PLS factors and are relevant to depict the maximum covariance of 

. These newly developed latent variables are found to be linear combination of old variables and are best suited to explain the maximum information of 

The interpretation of PLS-DA model gives two main outputs in the form of score and loading plots. Score plot reflects the clustering of sample groups in different quadrants whereas loading plot specifies the weight age of each variable on the basis of which sample groups cluster themselves in different quadrants of score plot of PLS-DA model. Each and every point or dot appeared in the clustering of sample groups in the score plot represents the variables of 

 data matrix that are best suited for the classification of the 

 matrix. The points or dots which are positioned closed to the sample groups are said to be positively correlated with that much sample group and the points, which positioned themselves near the origin line do not reflect any knowledge about the samples groups and cannot be used for the analysis purpose.

The prepared PLS-DA model can be analyzed by looking upon both score and loading plot simultaneously in which one depicts the grouping of sample groups in the form of variables in different quadrants of score plot and other defined the weightage of each variable in the grouping of different samples groups in score plot. Figure 4 shows the score plot of PLS factors 1 and 2 which can clearly presents the distinction view of the gallstones (G1–G6) based on their relative elemental contents and grouped with the similar ones. By looking into score and loading plots of PLS factors 1 and 2 (Figures 4–5), it has been seen that emission intensities of elements such as K and Na are responsible for the clustered G4 sample group in the last quadrant of score plot (Figure 4). Therefore, the sample group G4 includes the increasing contents of elements K and Na and decreasing contents of other elements.

FIGURE 5: Loading plot of PLS factor 1 compared to PLS factor 2.

By looking into both score and loading plots of PLS factor 1 compared to PLS factor 3 (Figure not shown in this article), we concluded about the G1 sample group as it is clustered itself in the first quadrant of the same score plot where both PLS factor 1 and PLS factor 3 are observed positive ones. From the loading plots of the same PLS factor 1 and PLS factor 3 (Figure not shown here), it has been noticed that O and N are the only variables that satisfy the criteria of having both PLS factor 1 and PLS factor 3 as positive ones. This observation indicates that the increasing trends of emission intensities of variables O and N and decreasing contents of other elements are responsible for clustering the G1 sample group in the first quadrant of score plot of PLS-DA. On switching to the G3 sample group in the score plot of PLS factor 1 compared to PLS factor 3, we saw that the G3 sample group clustered itself in the second quadrant where PLS factor 1 is negative and PLS factor 3 is positive. From the loading plot of PLS factor 1 compared to PLS factor 3 (Figure not shown here), it was found that the wavelength of elements such as Ca, Al, Sr, and Mn are having PLS factor 1 as negative and PLS factor 3 as positive. This implies that the increasing content of elements, such as Ca, Al, Sr, and Mn, are responsible to cluster the G3 group in second quadrant of the score plot of PLS factor 1 and PLS factor 3. The same sample group G3 is also clustered in the last quadrant of the score plot of PLS factor 3 and PLS factor 4, where PLS factor 3 is positive and PLS factor 4 is negative. From loading plots of PLS factor 3 and PLS factor 4 (Figure not shown here), it has been found that the emission intensities of elements, such as Ca, Mn, Cn, H, Sr, and Al, are responsible for clustering the G3 sample group. Therefore, the content of elements, such as Ca, Mn, Sr, Al, copernicium (Cn), and hydrogen (H), were found to be more in the G3 sample group than other sample groups. The sample group G2 is found to be clustered in the last quadrant of the score plot of PLS factor 2 and PLS factor 3 (Figure not shown here), where PLS factor 2 is positive and PLS factor 3 is negative. From the loading plot of the same PLS factor 2 and PLS factor 3 (Figure not shown here), it was found that only the wavelengths of elements, such as the C2 band, satisfied the similar criteria, and their increasing trends in G2 make it clustered in the last quadrant of PLS factor 2 and PLS factor 3. In a similar way, the behavior of the center and surface parts of sample groups G5 and G6 have also been predicted by ana- lyzing the score and loading plots of PLS factor 1 and PLS factor 4 (Figures not shown here).

The behavior of sample groups can also be interpreted by looking over the spotted areas of points along their aligning directions. If we assume gridlines 

 = 0 as imaginary lines, then the alignment of the sample groups in different quadrants will give us idea about how close the behavior of sample groups are on the basis of their similar characteristics. The same alignment of the two sample groups has been seen in score plot of PLS factor 1 and PLS factor 3, in which the G3 sample group is clustered in the second quadrant and sample group G2 is clustered in the last quadrant. This alignment of sample groups indicates the idea about their inverse behavior (that is, the variables that are found to be more in the G3 group, such as CN, Mn, Ca, Mg, H, Sr, and Al, are found to be least in the G2 sample group).

The validity of the prepared PLS-DA model has been examined by the classification accuracy of the model. The whole LIBS spectra have been divided into two groups in which one is for training the model and other is for testing the model. This model has been calibrated by considering 20 × 6 = 120 spectral values and tested by randomly choosing 5 × 6 = 30 data values. The leave-one-out (LOO) method was adopted for the cross-validation of the prepared PLS-DA model. The prediction accuracy has been plotted as a function of number of PLS factors included in the model and shown in Figure 6. It has been noticed that PLS factors 1–10 are sufficient to discriminate all the six sample groups (G1–G6) with the explained variance of approximately 96% shown in Figure 7. Therefore, we can conclude that based on the PLS-DA modeling of LIBS data, the different colors of gallstones samples were successfully discriminated based on their relative elemental contents, which are responsible for stone formation and color appearance. This kind of discrimination is not possible by other elemental analysis techniques, which proves the utility of chemometrics coupled with LIBS techniques.

FIGURE 6: Predicted accuracy in the validation process as a function of number of PLS factors.

FIGURE 7: Explained class variance with the adoption of number of PLS factors.

EDX Analysis of Centre and Surface Parts of Gallstones

To verify our FT-IR and LIBS experimental data and also to quantify more numbers of heavy and toxic elements in gallstones, different parts (center as well as surface) were analyzed using the EDX technique. Different scans (scan 1 and scan 2) at two locations at the center and surface parts were carried out using EDX. The concentration (wt.%) of the heavy and toxic elements observed in center and surface parts are tabulated in Table I. The heavy and toxic elements detected were As, Cd, Pb, Hg, Sr, Ni, Cu, and Zn. From Table I, it can be seen that the concentrations of the elements at different locations (center as well as surface parts) were quite different, which indicates the heterogeneous distributions of the elements throughout the gallstones. For example, Sr has been observed in center region, which is absent at the surface parts during scan 2. Mercury (Hg) and nickel (Ni) were only observed in center region and absent at surface region. Overall, it has been found that the concentrations of the elements are higher in center part than that of surface parts, which is in good agreement with the LIBS elemental concentration of Ca, Mn, P, Cu, Zn, and Fe. Our findings are in good agreement with the results reported on gallstones using other techniques such as WDXRF and ToF-SIMS (10,19,21).

The main organic elemental composition of gallstones as observed by EDX was carbon (C), oxygen (O), and nitrogen (N), which shows the presence of cholesterol and bilirubin in gallstones and which have been detected using FT-IR spectroscopy. Hydrogen is not detected by EDX because hydrogen has no core electrons (20). The EDS analysis revealed that the gallstones contained 91.10% C (wt.%) and 4.49% O (wt.%). Nitrogen was present in the core region of gallstones and was absent from the surface parts. The presence of N indicates the presence of bilirubin in the core region and responsible for the black/brown color of core region. The ratio of carbon to oxygen (C:O) was 20:28, which coincides with pure cholesterol (C

O) composition after omitting the hydrogen (that is, C (83.1%)/O (4.14%) = 20.07.

The characterization of gallbladder stones using the fast and reliable techniques EDX and LIBS are advantageous compared to the traditional chemical analytical methods. The presence of cholesterol was confirmed using FT-IR spectroscopy as the major chemical constituent in gallstone. In addition, bilirubin, calcium carbonate, and calcium phosphate were also noticed in gallstones. Elemental analyses of gallstones were carried out using LIBS. The chemometric method PLS-DA coupled with LIBS data were successfully used to discriminate the gallstones under present study. EDX was used to analyze the heterogeneous distribution of heavy and toxic elements throughout the gallstones and data were well correlated with the LIBS data. High contents of heavy and toxic elements in the core region suggested the nucleation and growth of gallbladder stones. In conclusion, it is recommended that foods having high amount of cholesterol, low-fiber diets, and contaminated water intake having heavy and toxic elements should be avoided to minimize, which prevents the formation of gallstones.

Author V.K. Singh is much thankful to UGC-DAE CSR Kolkata Centre for partial financial support. Authors V. Bali, Y. Khajuria, N. Sharma, and V. K. Singh are also grateful to SMVD University, India, for providing the necessary facility and support.

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 are with Shri Mata Vaishno Devi University, in the Jammu and Kashmir region of India. 

 are with Mokpo National University, in Muan-gun, South Korea. 

 is with Opal Hospital in Uttar Pradesh, India. 

 is also with University of Lucknow, in Uttar Pradesh, India. 

 is also with Cluster University of Jammu, in the Jammu and Kashmir region of India. Direct correspondence to: 

Articles

Understanding gallstone formation requires examining their elemental composition. Here, EDS and LIBS were used with PLS-DA to quantify elements found in human gallstones.

EDX and LIBS Studies on Gallstones Using Partial Least Squares Discriminant Analysis (PLS-DA)

X-ray fluorescence (XRF) is a useful technique in agricultural science to determine the elemental constituents of plant materials. It allows for quick analysis, high spatial resolution, measurement of multiple elements within a single scan, and requires minimal sample preparation while being nondestructive. In this study, we used wavelength dispersive XRF (WDXRF) to study agricultural diseases. We performed a spectroscopic investigation in agricultural samples to measure the elemental changes caused by infestation. A broad range of major, trace, and heavy elements were detected and quantified in healthy and infested crop samples, and we observed significant changes in the elemental concentrations. We also used laser-induced breakdown spectroscopy (LIBS) in combination with XRF spectrometry. Further improvements will provide exciting perspectives for the application of XRF in the area of plant and agricultural science.

It has been observed that approximately 14 essential elements are needed for the proper growth and development of plants, along with water, hydrogen, oxygen, and carbon dioxide. These nutrient elements are further categorized as macronutrients and micronutrients. Macronutrients, such as calcium, magnesium, nitrogen, potassium, phosphorus, and sulfur, are needed by plants in large amounts (>1000 mg per kg dry weight), whereas micronutrients, including boron, chlorine, copper, iron, manganese, molybdenum, nickel, and zinc, are required by plants in small amounts (<100 mg per kg dry weight) (2). Even though plants require only small amounts of micronutrients, these nutrients are equally important for the proper growth and development of plants.

Trace element detection is important in plant science. The concentration of trace mineral elements in plants, as well as in the soil, can change, and this variability can lead to deficiencies or excess accumulation of these mineral metals (3). Knowledge of the concentrations of trace elements in plants is important for diagnosis of various agricultural crop diseases. Crop disease development depends on three main factors: a susceptible host plant; favorable environmental conditions; and the pathogen involved, sometimes displayed as a “disease triangle” (Figure 1). Environmental conditions, in particular, play a major role in the occurrence and the growth and development of plant diseases; many diseases will not emerge if environmental conditions, such as temperature, relative humidity, soil moisture, soil pH, rain, and water content, are not favorable.

FIGURE 1: Illustration of the plant disease triangle.

Atomic spectroscopy methods are the first choice for assessing the mineral composition of agricultural crop samples. For most atomic spectroscopic techniques, samples preparation is required, such as sample dissolution using a mixture of strong acids (generally nitric and sulfuric) and oxidants (commonly hydrogen peroxide). In many cases, it is easy to dissolve the samples in the solution. Because of the presence of silicon in plant samples, sample preparation usually requires a mechanisms that ensure complete decomposition, to ensure efficient dissolution of metallic elements into solution. Hydrofluoric acid addition, along with evaporation for achieving dryness, is needed to complete the proper dissolution of the analyte; otherwise, the presence of silicon may result in poor recoveries of some elements due to the binding of analytes with the insoluble residues (4).

Proposed methods found in the literature, from large numbers of acid mixtures used in wet washing procedures, reveal a lack of consensus for achieving the complete dissolution of plant specimens. The choice of decomposition method for a given plant sample material must be evaluated carefully, considering each of the matrices and analytes under study. In reviewing these challenges of sample digestion, analytical methods that do not require matrix destruction can be advantageous for such studies.

Nearly all X-ray fluorescence (XRF) techniques have desirable features for the analysis of plant samples. These features include the capability to carry out direct analysis of solid samples, multielemental analysis, the capability to carry out the qualitative analysis along with quantitative determination, a broad dynamic range, high throughput, and low maintenance and analysis cost. The major drawback of XRF is its limited sensitivity for elements like cadmium and lead. The poor precision and accuracy of XRF compared to other atomic absorption spectroscopic techniques is also a limitation. However, some recent improvements, such as the advancement of digital processing-based spectrometers, along with better detector designs in XRF instrumentation, have increased instrumental sensitivity and improved both the precision and productivity of XRF. These improvements have increased interest in adopting XRF spectroscopy in environmental science for analyzing various plant materials. The use of different quantification strategies employing distinct configurations of XRF instrumentation for the analysis of various vegetation matrices has been discussed.

Identification of Nematode Species in Okra and Papaya

We have collected healthy and nematode-infested okra root samples, which are shown in Figures 2a and 2b, from field sites of Uttar Pradesh, India. We have also collected nematode-infested and healthy papaya root samples, as depicted in Figure 2c. After collection, the samples were washed, and after drying the samples, they were milled into a fine powder for spectroscopic studies.

FIGURE 2: (a–d) Photographs of nematode-infected okra and papaya roots, and rice grains infected with false smut disease.

Cobb’s sieving and decanting methods were used to draw out the body parts of male, female, and juvenile nematodes to identify the species of nematodes in the diseased okra and papaya plant root samples. We adopted the procedure for species identification described by Goodey (1963) (5), and the species was identified on the basis of the characteristics observed in the perineal regions of the female nematodes by employing the method provided by Eisenback and others (1985) (6). Our examinations revealed that the species of root-knot nematode pathogens in the diseased okra and papaya roots was Meloidogyne incognita. Details regarding the sample preparation and identification of nematode species have been described by Sharma and others (7,8).

Collection and Isolation of Rice Samples (Healthy and Infected with False Smut)

A survey was conducted in rice fields suspected of false smut infestation in Uttar Pradesh, India, during the Kharib season of 2018–2020. Healthy rice grains and rice grains infested with false smut disease were collected (Figure 2d). The rice grain samples were carefully separated along with smutty balls from rice spikelets and milled into a fine powder form. The upper surface of smutty balls was sterilized by dipping them in a solution of 1% sodium hypochlorite for approximately 1 min and then cleaning with 70% ethanol solution for about 1 min. More details regarding the isolation of false smut disease pathogens from rice grains has been described by Sharma and others (9).

Study of Root-Knot Nematode–Infected Okra Plant Roots

In the present study, we employed wavelength dispersive X-ray fluorescence (WDXRF) spectroscopy to examine in healthy and nematode-diseased okra root samples. We detected many elements, such as aluminum, calcium, copper, chromium, chlorine, iron, manganese, magnesium, nickel, phosphorus, potassium, rubidium, sodium, silicon, strontium, sulfur, titanium, zinc, and zirconium. We noticed significant differences in the elemental composition of healthy and diseased samples. Figure 3 shows WDXRF spectra of healthy and nematode-infested okra roots in the energy range of 5–10 keV, which shows the relative abundance of the elements chromium, manganese, iron, nickel, zinc, and copper.

FIGURE 3: WDXRF spectra of healthy and nematode-infested okra roots in the energy range of 5–10 keV, which shows the relative abundance of the elements chromium, manganese, iron, nickel, zinc, and copper. Data were taken from reference (7).

Several mineral elements—particularly calcium, chlorine, magnesium, potassium, sodium, and sulfur—were found to have higher concentrations in nematode-infested okra plant samples compared to healthy root samples. Other elements, including copper, phosphorus, manganese, silicon, and zinc, were found to be in lower concentrations in nematode-infected root samples as compared to healthy okra root samples. Some toxic elements, such as aluminum and chromium, were observed in lower concentrations in the infested root samples. No significant differences in the concentration of elements like bromine, rubidium, nickel, and zirconium were observed. Some traces of silver were found only in nematode-infected okra root samples, whereas traces of palladium were found only in nematode-infested root samples.

Our experimental findings were supported by the literature and other published reports. The observation of lower concentrations of iron, phosphorus, and zinc in nematode-infected okra root samples aligns with results reported by Farahat and others (10). Lower concentrations of iron, phosphorus, and zinc also have been observed in the diseased tissues of nematode-infected citrus, eggplant, jasmine, cowpea, and papaya plants (10). Sharma and others have also witnessed the decreasing concentration of phosphorus in nematode-infested okra root samples as compared to root samples from healthy okra plants (11). We also observed a higher concentration of sodium and a lower concentration of phosphorus in the nematode-infested okra root samples, which also aligns with results reported by Sharma and others (11).

Some nonessential elements, such as silver and palladium, were detected only in infected root samples, which indicates that their presence may be associated with the formation and development of nematodic pathogens on okra roots. Elements like strontium and titanium were also observed in increased concentrations in nematode-infested okra root samples. Heavy elements such as aluminum, chromium, and titanium were also observed in higher concentrations in nematode-diseased root samples of okra plants, and appears to be related to the formation of galls on the surface of roots with the invasion of pathogens. The higher concentration of aluminum can be considered a threat to agricultural crops because aluminum obstructs root growth and disturbs nutritional balance, encouraging the induction of oxidative stress. This stress leads to peroxidation reactions in the cytoplasmic membrane and ultimately leads to cell death (12). The physiological and biochemical functions of chromium in plants is not particularly clear (13). It has been observed that excessive levels of chromium can encourage several detrimental biochemical, morphological, and physiological effects on plant health (13,14). Nevertheless, the overall toxicity of chromium in plants is well documented (13). All these elemental changes are capable of posing serious and toxic effects in plants such as hindering growth mechanisms, disturbing photosynthesis, translocating mineral nutrients, disrupting the water intake capacity, and ultimately declining the overall quality of the okra plant.

Study of Root-Knot Nematode–Infected Papaya Plant Roots

We have also studied root-knot nematode-infected papaya plant root samples employing WDXRF to obtain the elemental variations between healthy and diseased papaya root samples. Using WDXRF, numerous elements, such as aluminum, chlorine, copper, chromium, calcium, magnesium, manganese, iron, potassium, sodium, phosphorus, nickel, silicon, rubidium, sulfur, and zinc were detected in root samples of healthy and diseased papaya plants (8). Figures 4a and 4b show the overlapping WDXRF spectra of healthy and diseased papaya root samples within different energy ranges, indicating the presence of elements such as aluminum, calcium, and potassium. The WDXRF spectra of papaya root samples showed that concentration of mineral elements changed with nematode infestation. It has been documented that the induction of antioxidants, creation of mechanical obstructions, and triggering of defense systems in plant bodies are all associated with good accessibility of mineral nutrients in plants (15). It was observed that the concentrations of one major nutrient, calcium, increased from 2.4 to 3.14% from healthy to diseased root samples, whereas another major element, magnesium, was found to increase from 0.39 in healthy root samples to 0.46% in diseased samples.

FIGURE 4: (a,b) WDXRF spectra of healthy (black) and nematode (green) infected papaya root samples. Data were taken from reference (8).

The concentration of silicon was found to be elevated from 0.23 to 0.53% from uninfected to nematode-infected papaya root samples. Sharma and others also observed the same elevated concentration patterns of calcium, magnesium, and silicon while performing elemental and molecular studies of nematode-diseased okra root samples (7). It has been observed that the elements calcium, magnesium, and silicon are the major nutrients that are beneficial in maintaining the outer coating of cell membranes and thereby helping plant cells defend against pathogen invasions. The increasing concentrations of calcium, magnesium, and silicon seen in our findings can be correlated with the induction and activation of defense systems in response to the attack of pathogens. In addition to these elements, we also found increasing concentrations of iron, aluminum, and titanium in papaya plants with the arrival of nematodic infection. The particular role of aluminum and chromium in the physiological and biochemical behavior of plants has not been determined yet.

Macroelements, such as potassium and phosphorus, are essential and play various roles, like activating enzymatic systems, inducing protein synthesis, managing photosynthesis processes, moving stomata, maintaining balance between cations and anions, and transferring energy (8). It has been observed from WDXRF analysis of healthy and diseased papaya root samples that the concentrations of these two macroelements decreased with the arrival of nematodes and fungal infections on papaya plants. It is well reported that potassium and phosphorus encourage the thickening of cell walls and thereby induce barriers that hinder the further movement of the attacking pathogens inside the plant body (8).

Decreasing concentrations of some other elements, such as copper, manganese, sodium, and zinc, were observed in the papaya root samples with the arrival of a nematode attack. Similar results were also reported by Sharma and others. Traces of the elements zirconium and molybdenum were found in the diseased papaya root samples only, also similar to observations made by Sharma and others (7).

The relative concentrations of the elements calcium, magnesium, silicon, potassium, sodium, and iron, are shown in Figure 5. Changes in the concentrations of several of these elements can lead to serious consequences for the health and viability of papaya plants.

FIGURE 5: Relative concentrations of elements in papaya root samples and comparison with LIBS data. Data were taken from reference (8). (a) WDXRF data, and the (b) LIBS data.

We have also validated our experimental findings using laser-induced breakdown spectroscopy (LIBS), as shown in Figure 5. For a LIBS method that is free from self-absorption, it has been observed that the intensity of the elemental characteristic line is more closely related to the concentrations of the sample analytes (8). To confirm that there is no self-absorption in the LIBS method, we have verified all the conditions, such as the existence of stoichiometric ablation, local thermal equilibrium, and optical thickness of the plasma. Furthermore, we have calculated the peak area intensity ratios of certain elements like calcium (393.36 nm), potassium (766.48 nm), magnesium (279.55 nm), sodium (588.99 nm), iron (238.40 nm), and silicon (288.15 nm) from the LIBS results of papaya plant samples. These calculated values were further taken as the concentrations of these elements and directly compared with the concentration values of the WDXRF spectra of papaya plants. Given that LIBS and WDXRF follow different criteria, to compare the results of the techniques, we focused only on the trends we observed for each element in the papaya plant samples, which are clearly indicated in Figure 5.

Figures 5a and 5b show bar graphs of the results obtained using LIBS and WDXRF and reveals the patterns of the concentrations of major elements such as calcium, sodium, iron, magnesium, potassium, and silicon in the diseased and healthy papaya root samples. Because the same trends of the element concentrations were observed by both techniques for the analysis of papaya plant samples, we believe that both techniques can be considered proficient methods for assessing the composition of mineral elements in healthy and diseased plant samples, both qualitatively and quantitatively.

Study of Rice Crop Infected by False Smut Disease

WDXRF spectroscopy was also employed for the spectroscopic investigation of rice grains from rice crops infected with false smut disease. The WDXRF spectra of healthy and diseased rice grains were recorded within different energy ranges. Changes in the elemental composition of the rice grains associated with black and yellow smutty fungal spores on the upper and lower surface of the rice spikelets, were investigated. For the sake of simplicity, we have encoded our samples R1, R2, and R3 on the basis of severity of false smut disease, where R1 stands for uninfected rice grain samples, R2 stands for rice grain samples with low levels of infection, and R3 for highly infested rice grain samples.

Using WDXRF spectroscopy, we have quantified the presence of numerous essential and non-essential elements in rice samples, including calcium, aluminum, copper, iron, chlorine, magnesium, manganese, nickel, phosphorus, chromium, potassium, rubidium, silicon, sulfur, and zinc. Figures 6a and 6b show the overlapping WDXRF spectra for the different rice grain samples, indicating the relative presence of phosphorus, silicon, sulfur, chromium, manganese, iron, nickel, copper, and zinc in the various rice grain samples (R1, R2, and R3). We also verified our results using LIBS. The detailed results of similar studies have been discussed by Sharma and others (9).

FIGURE 6: (a,b) WDXRF spectra for elements in healthy rice grains and in rice grains infected with false smut. Data were taken from reference (9).

The trends of relative concentrations of mineral nutrients observed using LIBS and WDXRF were similar. Using LIBS and WDXRF, we have observed the pattern for copper, silicon, and calcium as R1 > R2 > R3, whereas for chromium, iron, and magnesium, the trends was R2 > R1 > R3. Some mineral elements, such as potassium and manganese, followed patterns of R3 > R2 > R1 and R2 > R3 > R1. From these studies, it was concluded that the two techniques are complementary to each other and can be used collectively for investigating elemental changes that may take place in the composition of the plant samples with the association of various plant diseases.

As mentioned previously, it is documented in the literature that the induction of some mechanical obstructions, production of anti-oxidant compounds, and triggering of defense systems in the plant body all are correlated with good accessibility of the important mineral nutrients in the plant body (9). In our study, we have observed that some major elements, such as potassium, phosphorus, and sulfur, were present in higher concentrations in the diseased rice grain samples, and it is feasible to relate these increases with the production, induction, and activation of various defense systems that hinder the further movement of the pathogens into the plants body. Sieverding and others have also reported increased concentrations of potassium and phosphorus in corn crop plants following inoculation with 

 (16). The concentration values of the nutrient element sulfur increased from 0.14 in uninfected rice grains to 0.2% in diseased rice grain samples.

We also noticed significant increases in the concentrations of chlorine and manganese in diseased rice grain samples. The increased level of chlorine can be related with the initiation and activation of defense responses against fungal spores (15). Identical increasing trends were also seen and reported by other researchers (7,17). In one study, where an elevated concentration of manganese was observed in the barley plant samples, the author claimed that such elevation is because of the stimulation of manganese content with the association of microbes present in the rhizosphere of soil (18). The same patterns corresponding to elements such as chlorine, manganese, potassium, phosphorus, nickel, and sulfur has been seen and reported by Singh and others in studies of nematode-infested wheat grains by WDXRF spectroscopy (17).

In our studies, we also observed lower concentrations of aluminum, calcium, copper, iron, magnesium, silicon, and zinc with the infestation of black and yellow smutty fungal spores in rice. The decreased level of magnesium was also seen and reported by Bateman and others while doing investigations of beans from plants infected with the fungus 

 (19). Because major elements such as calcium and magnesium are related with the maintenance of outer cell walls, the decreasing levels of these elements may be associated with the weakening and diminishing of cell walls, leading to escape of sugars and amino acids from plant tissues and thereby attracting the attention of several fungal species. Imbalances in the contents of the macroelements such as calcium, potassium, and magnesium, were also reported with the occurrence of green stem and foliar retention (GSFR) disease on the plant samples (9).

Rice plants are well-known for their capacity to accumulate high levels of silicon from the soil (20). In our findings, we noticed a decrease in the content of silicon with the association of smutty balls in the rice crop plant. The content of silicon falls from 1.27% in uninfected rice grains to 0.45% in diseased rice grain samples with the occurrence of fungal disease. The role of silicon is similar to that of calcium and magnesium: It is known for maintaining the strength of the outer cell walls, so a decreased level of silicon indicates the diminishing of the outer layer of cell walls (21).

We also saw lower concentrations of aluminum, copper, iron, and zinc in diseased rice grain samples with the arrival of fungal diseases on rice crop plants. The particular role of aluminum and chromium elements in the physiological and biological behavior of plants has not been determined yet. In our findings, we have observed that the level of aluminum decreases from 0.03 to 0.01% with the occurrence of fungal disease in rice crop plants.

The element chromium is known for its toxicity and causes various detrimental effects on the physiological behavior of a plant system, such as disturbing the uptake capacity of mineral nutrients and water intake, inducing oxidative stresses, interfering the photosynthesis mechanism, and causing plant cell death (9). These toxic effects of chromium begin with the association of 0.0005% of chromium in the agronomic plants (9). The concentration of chromium in our results rose from 0.0011 in uninfected rice grains to 0.0046% in rice grain samples infected with fungal disease. Similar results for chromium were observed by Singh and others (17) in spectroscopic investigations of nematode- and fungal-infested wheat seeds and by Sharma and others (7) in nematode- and fungal-infested okra root samples.

It is well-documented in the literature that the pathogens started developing strategies to minimize its defense systems and make them totally depriving of basic nutrients after intruding the host body whereas the host body in response makes some changes in its internal composition to restrict further movement of the pathogens into the plant body (9). Thus, the decreasing and increasing contents of some nutrient elements with the occurrence of smutty fungal spores on the surface of rice spikelets due to the arrival of false smut disease can be considered the consequences and responses of the host body with the attack of pathogens.

In conclusion, we present a summary of the plant species, disease, and elements detected in healthy and diseased plant samples in Table I. Elements whose concentrations increased and decreased because of the disease are listed in Table I. Table I indicates the observable changes in the concentrations of elements, related to the diseases that occurred in the plant samples.

In the present work, we explored potential applications of WDXRF for analyzing important agricultural crops, which provided valuable information regarding their elemental composition. This information is then used to characterize their behavior after infestation by plant diseases such as nematodes and false smut disease.

However, elemental imaging techniques, such as scanning electron microscopy (SEM) with energy dispersive X-ray analysis (EDX), field emission (FE)-SEM, and micro-XRF, would provide additional information regarding the role of heavy and toxic elements in these plant diseases. These methods would enable a useful visualization and mapping of the distribution of organic matter as well as the changes in the mineral elements on a micrometer scale. Also, more exhaustive work on the multivariate analysis of WDXRF data will provide more information related to these kinds of diseases (nematodes, false smut and other bacterial and fungal diseases) in agricultural crops.

Ms. Neha Sharma (Senior Research Fellow, SRF) is thankful to DST, Government of India for INSPIRE Fellowship (IF160893). Author Vivek K. Singh is thankful to Shri Mata Vaishno Devi University, India, for all kinds of support to carry out this research.

Encyclopaedia of Life Support Systems (EOLSS)

(2) L. Trevizan, D. Santos, R. Samad, N. Vieira, C. Nomura, L. Nunes, L. Rufini, and F. Krug, 

(3) G. Dalcorso, A.Manara, S. Piasentin, and A. Furini, 

(4) E. Margui, M. Hidalgo, and I. Queralt, 

(5) J.B. Godey, Laboratory Methods for Work with Soil and Plant Nematodes (Technical Bulletin Ministry of Agriculture, London, United Kingdom, 1963). 

(7) N. Sharma, Y. Khajuria, J. Sharma, D.K. Tripathi, D.K. Chauhan, V.K. Singh, V. Kumar, and V.K. Singh, 

(8) N. Sharma, Y. Khajuria, V.K. Singh, S. Kumar, Y. Lee, P.K. Rai, and V.K. Singh, 

(9) N. Sharma, Kamni, V.K. Singh, S. Kumar, Y. Lee, P.K. Rai, and V.K. Singh, 

(10) A.A. Farahat, A. Al-Sayed, and N.A. Mahfoud, 

(12) H. Brhane, E. Fikru, T. Haileselassie, and K. Tesfaye, 

(13) L. Reale, F. Ferranti, S. Mantilacci, M. Corboli, S. Aversa, F. Landucci, C. Baldisserotto, L. Ferroni, S. Pancaldi, and R. Venanzoni, 

(16) E. Sieverding, G.A. Silva, R. Berndt, and F. Oehl, 

(17) V.K. Singh, A. Devi, S. Pathania, V. Kumar, D.K. Tripathi, S. Sharma, D.K. Chauhan, et al, 

(20) E. Takahashi, J.F. Ma, and Y. Miyake, 

(21) D. Bhaduri, R. Rakshit, and K. Chakraborty, 

 are with the School of Physics at Shri Mata Vaishno Devi University, in Katra, India. 

 is with the Department of Plant Pathology in the College of Agriculture at Banda University of Agriculture and Technology, in Banda, Uttar Pradesh, India. Direct correspondence to: 

In these studies, wavelength dispersive X-ray fluorescence (WDXRF) was used to examine differences in the elemental composition of agricultural samples, comparing healthy and diseased samples of okra, papaya, and rice. Both the mineral nutrient profiles (macro and micronutrients) and toxic metals were examined, revealing common patterns.

Application of Wavelength Dispersive X-ray Fluorescence to Agricultural Disease Research

n this study, we analyzed a tooth sample of a renal patient using wavelength-dispersive X-ray fluorescence (WDXRF) spectrometry and Fourier transform infrared (FT-IR) spectrometry to quantify major and trace elements, and heavy and toxic elements in particular. The elements detected and quantified in the tooth sample were calcium (Ca), phosphorus (P), carbon (C), oxygen (O), magnesium (Mg), sodium (Na), chlorine (Cl), sulfur (S), potassium (K), silicon (Si), iron (Fe), aluminum (Al), zinc (Zn), strontium (Sr), chromium (Cr), nickel (Ni), copper (Cu), and zirconium (Zr). FT-IR spectrometry has been used to extract relevant information about the molecular contents of the tooth sample. We have compared our results with those obtained in the laboratory for the healthy subjects, and also with the values reported in the literature as obtained using XRF spectrometry only. Elements found in the tooth sample were correlated with the chronic kidney disease (CKD) of the patients. An outline of the XRF apparatus and applications of XRF as a tool for analyzing to dental specimens are also briefly highlighted.

Vivek K. Singh, Shikha Sharma, Mahima Sharma, Neha Sharma, Jitendra Sharma, and Pradeep K. Rai

	
	Calcified tissues, such as bones and teeth, contain biological signatures from their living phase. The habitat and nutritional and environmental conditions of people can be determined by monitoring the trace elements present in these samples (1). This is not unexpected, as teeth have both an organic and inorganic phase. Teeth and bones consist of an inorganic calcium phosphate mineral approximated by hydroxylapatite and matrix proteins with a small amount of proteolglycans and lipids (2). In contrast, the inorganic phase is composed of well-packed nanocrystals of calcium apatite, together with trace elements (3). The trace elements in the tooth can vary, depending on the source of food and water, as well as from soil and dermal absorption (1–4). Accurate determination of trace elements in calcified tissues still remains a challenge for biomedical researchers. X-ray fluorescence (XRF) spectrometry offers a reliable approach to provide rapid multi-elemental analysis of biological samples in µg/g (ppm) range, and it is a non-destructive technique that allows samples to be preserved for further experimentation (5). Instrumentation, such as wavelength-dispersive XRF (WDXRF), can now be effectively used to accurately measure trace elements in the human tooth. Such information not only provides a useful assessment of the diet, but reflects specific environmental and disease conditions as well.

	Recent biochemical studies provide insight into the factors controlling the formation and growth of bioapatite crystals, and how alterations in the mineralization process can lead to diseases such as osteoporosis. The relationship between chronic kidney disease (CKD), specifically hypercalciuric nephrolithiasis, osteoporosis, and bone mineral density (BMD), is well established. A large number of epidemiologic studies linked to evaluating factors associated with BMD in men have amply demonstrated that history of renal disease is indeed associated with lower BMD (6). The kidneys play an important role in maintaining healthy bone mass and structure by balancing calcium (Ca) and phosphorus (P) levels in the blood. Mineral and bone disorders in CKD occur when damaged kidneys and abnormal hormone levels cause Ca and P levels in a patient’s blood to be out of balance. CKD causes mineral and bone disorders, and thus kidneys stop activating calcitriol. The low levels of calcitriol in the body in turn create an imbalance of Ca in the blood by not removing the P in the blood proportionately, so the P level rises in the blood. The extra P pulls Ca out of the bones, causing them to weaken. Consequently, bone losses from the jaw occur in the CKD patients. Such weaker bones can cause teeth to become loose, and eventually fall out.

	Therefore, in this paper, we present results from our investigation of major (Ca, P, magnesium [Mg], sodium [Na], chlorine [Cl], sulfur [S], potassium [K], silicon [Si], iron [Fe], aluminum [Al], zinc [Zn], strontium [Sr}, chromium [Cr], nickel [Ni], copper [Cu], and zirconium [Zr]), trace elements, and toxic metals using WDXRF spectrometry in a tooth sample obtained from a CKD patient. We have compared our results with literature reported values (using XRF spectrometry only) and laboratory values for healthy subjects. Fourier transform infrared (FT-IR) spectrometry has also been used to investigate the phosphate, carbonate, and amide contents of teeth samples. This paper presents a very crucial application of WDXRF spectrometry to quantify heavy and toxic elements of calcified tissue materials like tooth samples from renal patients with CKD.

	The tooth sample from the CKD patient was kindly provided by the Department of Nephrology at Opal Hospital in Varanasi, India. In the present work, the studied tooth sample was one that had become loose, and had eventually fallen out of the jaw of a CKD patient. The received tooth sample was dried and stored in a sealed pot after washing in deionized water to remove all traces of possible contaminants. The as-received tooth sample was examined visually, to check for caries. The tooth sample was not affected by caries, and therefore the present study was not influenced by caries formation. The tooth was then powdered using a mortar and pestle, and the powdered tooth sample was used subsequently for experimental measurements using FT-IR and WDXRF spectrometry.

	The ground tooth sample was used to record the FT-IR spectrum in a transmittance mode setting for the spectral region of 4000-400 cm

 on an IRTracer-100 Fourier transform infrared spectrophotometer (Shimadzu), using a single-reflection attenuated total reflection (ATR) accessory. In this spectrophotometer, the FT-IR spectrum is recorded in a very simple manner, as only few milligram (mg) of sample (solid or liquid ) are required to place on the surface of the ATR prism. However, solid samples must be held on the surface of the prism using the provided pressure clamp. The spectra can be recorded either in absorption mode or in transmission mode.

	XRF analysis provides elemental mapping and imaging information using emitted characteristic X-rays from the sample. Figure 1 shows the mechanism of characteristic X-ray generation. With the bombardment of high-energy electrons or high-energy X-rays, a bound electron is struck out of the target atom. After the electron is ejected, an outer shell electron falls into the vacant inner shell, resulting in the emission of characteristic X-rays with an energy equal to the difference of energy between the outer and inner shell energy levels. These X-rays generated through the bombardment of high energy X-ray irradiation are termed

. Each element with inherently different energy levels (for the set of electrons it possesses) emits X-rays of energies that uniquely identify this characteristic element. Therefore, such characteristic X-rays can easily be exploited to perform elemental analysis of a sample. Further details of the theory and applications of XRF spectrometry can be found in reference sources (7–11).

	High performance WDXRF spectrometry is now being used for the determination of traces in plant materials, biological samples, and geological samples. To fulfill today’s requirements for quality data, modern instruments must provide high elemental sensitivities to achieve low detection limits and optimal spectral resolution to minimize line overlays. In this work, we have used a WDXRF spectrometer S8 Tiger (Bruker) for the quantitative measurements of major, trace, and heavy elements in the sample. It provided the optimum quantitative results in terms of detection limits, resolution, and reliability for each element with the best excitation and highest intensity over the entire elemental range.

	In this WDXRF spectrometer, an end-window X-ray tube with 

Rh anode is adjusted to a voltage of 60 kV and a current of 170 mA, correlating to a maximum power supply of 4kW to generate the primary beam. To ensure more accurate and reproducible results, the tooth sample was analyzed three times. For the detection of lighter elements, a flow proportional counter is used, and, subsequently, a scintillation counter detector is used to detect heavier elements. The acquisition time taken to capture a single spectrum of the sample was 20−25 min. The analyzer crystal LiF(220) ensures that in combination with the 0.23° collimator, the best separation is achieved for adjacent elements. The mask size was 23 mm, and the spectrum was recorded in vacuum. Technical details of the spectrometer used in the present study can be found elsewhere (12). In the present work, while analyzing data, the maximum relative standard deviation (RSD) obtained was 5%. To measure the concentration of trace and heavy elements, the S8 Tiger WDXRF spectrometer uses a standardless semi-quantitative Spectraplus software (Quant Express) package. The technical details of this software can be found elsewhere (12). The unique feature of this software is that it corrects all the matrix effects based on theoretically fundamental physical parameters, such as absorption coefficients and secondary fluorescence enhancement, automatically. In this software, the elemental concentration has been determined using Sherman’s equation (13):

 is the measured intensity of the corresponding line; 

 are the concentrations of the other elements 

 are the coefficients of the calibration regression line, respectively, namely offset and slope. The advantage of this software is that it does not need any standard reference materials to check its reliability and reproducibility with respect to the given samples.

	Because of the limited amount of the tooth sample, we have used the sandwich method to create a pellet. The powdered tooth samples are pressed onto approximately two spatulas full of boric acid using a constant pressure of 15 tons for 15 s on the stainless steel die head using a hydraulic press to prepare a smooth and uniform surface. This method is generally used in cases of limited sample size, and, in such cases, the use of a non backscattering boric acid matrix is recommended (7–11).

Analysis of Tooth Sample Using FT-IR Spectrometry

	Unless otherwise noted, the literature citations for band assignments are given in references (14,15). The FT-IR spectra of the tooth sample are shown in Figures 2 and 3, and the functional groups found in the sample are indicated in their respective wide scan FT-IR spectra (Figures 2 and 3). A typical -OH stretching band is clearly visible at 3300 cm

. The characteristics peaks of phosphates appear at 579, 590, 608, and 614 cm

 asymmetric bending mode of ), 1023, 1043, 1052, and 1069 cm

 asymmetric stretching mode of ), 431, and 446 cm

 symmetric stretching mode of ). Phosphates in the sample are identified by the characteristic absorption band at 1035 cm

 asymmetric stretching of . Other phosphate bands (relatively broader) appeared around at 985 and 925 cm

 stretching in , is assigned to type B carbonate
	( ions substituting  sites), located at 870 cm

 mode presented two bands, one at 1412 cm

In this study, WDXRF and FT-IR are used to analyze a tooth sample of a renal patient, and to compare the results to healthy patients. The quantities of multiple elements are reported using the XRF technique, and FT-IR spectroscopy is used to extract relevant information about the molecular contents of the sample with the important absorption bands identified.

Neha Sharma

Articles

EDX and LIBS Studies on Gallstones Using Partial Least Squares Discriminant Analysis (PLS-DA)

Application of Wavelength Dispersive X-ray Fluorescence to Agricultural Disease Research

Application of WDXRF and FT-IR for Human Tooth Analysis