Advancements in medical research: Exploring Fourier Transform Infrared (FTIR) spectroscopy for tissue, cell, and hair sample analysis

Abstract

Background

Fourier Transform Infrared (FTIR) spectroscopy has emerged as a powerful analytical tool in medical research, offering non‐invasive and precise examination of the molecular composition of biological samples. The primary objective of this review is to underscore the benefits of FTIR spectroscopy in medicinal research, emphasizing its ability to delineate molecular fingerprints and assist in the identification of biochemical structures and key peaks in biological samples.

Methods

This review comprehensively explores the diverse applications of FTIR spectroscopy in medical investigations, with a specific focus on its utility in analyzing tissue, cells, and hair samples. Various sources, including Google Scholar, PubMed, WorledCat and Scopus, were utilized to conduct this comprehensive literature review.

Results

Recent advancements showcase the versatility of FTIR spectroscopy in elucidating cellular and molecular processes, facilitating disease diagnostics, and enabling treatment monitoring. Notably, FTIR spectroscopy has found significant utility in clinical assessment, particularly in screening counterfeit medicines, owing to its user‐friendly operation and minimal sample preparation requirements. Furthermore, customs officials can leverage this technique for preliminary analysis of suspicious samples.

Conclusion

This review aims to bridge a gap in the literature and serve as a valuable resource for future research endeavors in FTIR spectroscopy within the medical domain. Additionally, it presents fundamental concepts of FTIR spectroscopy and spectral data interpretation, highlighting its utility as a tool for molecular analysis using Mid‐Infrared (MIR) radiation.

1. INTRODUCTION

The review commences by introducing FTIR spectroscopy as a technique that measures the interaction of molecules with infrared light, providing the principles of FTIR and unique fingerprint of their molecular composition.

1.1. FTIR spectroscopy

The study of how matter emits and absorbs light and other radiation is known as spectroscopy. The procedure entails dissecting electromagnetic radiation, or light, into its element wavelengths, or a spectrum. This is capable similarly to how a prism divides light into a rainbow of colors. ¹ The numerous studies demonstrating infrared (IR) spectroscopy's excellent specificity and sensitivity for disease identification and classification clearly illustrate its promise as a valuable clinical tool. ² , ³ , ⁴ , ⁵ , ⁶ , ⁷ The development of innovative, affordable, and quick diagnostic platforms for healthcare services is always being pursued in an effort to enhance patient outcomes and lessen the enormous financial burden on healthcare facilities. ⁸ , ⁹ An Infrared (IR) spectrum suggestion which bonds have absorbed IR radiation (wavelength in the range 2500 nm—25 000 nm), together with the absorbance efficiency (intensity).

Numerous absorption peaks are visible in FTIR spectroscopy, and these peaks are utilized to investigate the existence of specific functional groups, usually in molecules. It is a phenotypic approach that evaluates infrared light absorption by substances for instance lipopolysaccharides, lipids, carbohydrates, nucleic acids, and proteins. Since the absorption groups for various moieties are often acknowledged, the material can be recognized by relating the resulting spectrum to the well‐known values. To give a broad illustration, C‐C, C‐N, and C‐O absorb among 1300–800 cm⁻¹, C = O, N = O, C = N and C = C absorb at 1900–1500 cm⁻¹, C≡N and C≡C absorb from 2300–2000 cm⁻¹, and C‐H, O‐H and N‐H absorb at 3800–2700 cm⁻¹.

Structurally (and based on organic functional groups), a typical protein contains amide (carbonyl and amino), carboxylic acid (carbonyl and acyl), amino and sulfhydryl groups, which would give off IR‐resonance frequencies at 1590–1690 cm⁻¹ (amide carbonyl), 1700–1725 cm⁻¹ (carboxylate carbonyl), 2500–3300 cm⁻¹, 1380–1410 cm⁻¹, respectively. Depending on the class, lipids may contain moieties which give off an infrared resonance at 1025 cm⁻¹, 1230–1310 cm⁻¹, 1715 cm⁻¹, and 2843–2962 cm⁻¹, respectively. These moieties include ether, phosphate, carbonyl, and CH stretching vibration. For nucleic acid, the typical functional groups are ester (carbonyl and acyl), hydroxyl, and phosphate, and this would reflect as 1163–1210 cm⁻¹, 2300–3500 cm⁻¹, and 1230–1244 cm⁻¹, respectively, in an IR spectrum. Carbohydrates are typified by the functional groups aldehyde, ketone, and phosphate, and would correspond to 1720–1740 cm⁻¹, 1715–1750 cm⁻¹, and 1230–1244 cm⁻¹, respectively, on an IR spectrum. ¹⁰ , ¹¹

The resulting transmitted/reflected infrared light is then transformed mathematically via the Fourier transform, which converts raw IR data to an FTIR spectrum. This FTIR spectrum represents the overall composition of sample. ¹² By examining their “signature bands,” lipid, protein, and nucleic acid compositional information can be further extracted. For example, the location of the highest infrared absorptions in the amide I and amide II bands can be used to infer the secondary structure of the proteins. ¹³ , ¹⁴ Significant variations between samples can be identified in the “fingerprint” region (about 1800–800 cm⁻¹) with additional multivariate data analysis. Combining these methods enables the identification of aberrant cells or tissue as well as the analysis of molecular variations between stem progenitor cells and their more developed counterparts. ¹⁵ , ¹⁶ , ¹⁷

The purpose of this review is to provide an overview of FTIR spectroscopy's potential purposes in clinical laboratory medicine, including a discussion of the method's strengths, weaknesses, and prospects. Further, it provides a look at how various FTIR spectrum frequencies might be interpreted. It is hoped that laboratories focusing on FTIR spectroscopy of biological tissues, stem cell studies, and hair analysis would find this material to be a useful resource.

1.2. Measuring techniques

The technique has three modes; transmission, transflection, and attenuated total reflection (ATR), Figure 1, which are used according to the type of sample analyze. ¹⁸ Each method presents an advantage for certain examples while presenting difficulties for others. ¹⁸

FIGURE 1.

The 3 primary Fourier transform infrared spectral acquisition sampling methods. Attenuated complete reflection, transflection, and transmission.

The preoccupation of IR radiation after transmission throughout the sample, reflection off the substrate, and transmission back across the sample provides the basis for the Transflection‐mode. ¹⁹ Transflection is a widely used technique because it requires less substrate than the transmission window and produces a higher absorbance because it passes through the same sample twice. Transmission‐based methods function by transmitting IR radiation across the sample and substrate before the resultant radiation is identified. The frequencies of bands and their identities are closely related to physical measures in addition to being widely applicable and having a very simple application. ²⁰ ATR‐FTIR mechanism on the ideologies of whole interior reproduction. Once the angle of a radiation beam entering a crystal surpasses the critical angle of the crystal and the contacting medium, the requirements of refraction can no longer be satisfied, and the radiation beam will experience total internal reflection at the crystal surface. This crystal has a high refraction index and is composed of Ge or ZnSe. ²¹ The reflection of the beam at this angle is total. In the presence of an attenuating surface, such as a sample matrix, the beam from the internal reflecting element (IRE) loses energy as the sample selectively absorbs radiation by attenuation. ¹⁸ , ²² The reflected attenuated radiation is transmitted through IRE to a detector where it is collected by a detector, transformed by an interferogram, and presented as an IR spectrum. Compared to transmission and reflection assessments, the main advantages of ATR are its model stiffness independent measurements, its capacity to investigate extremely IR absorbing materials without requiring complicated sample preparation, and its enhanced resolution. ²³

1.3. Spectral data assessment

In order to get accurate results from FTIR analysis of biological materials, data pre‐processing is typically necessary. However, before spectra can be used for analysis, several pre‐treatment processes must be carried out. Absolute absorption, signal‐to‐noise ratio, and humidity should be checked as first steps in quality control for spectra. ²⁴ , ²⁵ Additional data pre‐treatment steps include baseline corrections, derivation to intensification of the amount of beneficial information via disbanding complicated and corresponding bands, smoothing to decrease background noise, and vector normalization to recompense for differentiations in absorption caused by changeable sample thickness or size. These steps are necessary to appropriate spectra that have dropped or varying baselines. ²⁴

2. SAMPLE VARIETIES IN FTIR SPECTROSCOPY: PROCEDURES FOR COMPREHENSIVE INSIGHTS

Even though ATR‐FTIR spectroscopy is nearly always useful and does not involve time‐consuming sample preparation or costly reagents, samples are essential to be properly prepared before ATR‐FTIR investigation to get the superlative possible spectra and high‐quality outcomes that can be repeated. Nevertheless of the kind of sample, spectroscopic investigation of biological material involves several similar challenges: Because water is a key part of biological samples and water absorbs substantially in the mid‐infrared territory, it is critical to remove all water from the samples before beginning capacities. Air‐dried or N2 flux‐dried samples must be totally dried before spectra capture. It can be difficult to tell if a sample is dry. Previewing the spectra during drying can help when employing ATR with the sample in the crystal. This makes it easy to see if the spectra still contain water or are fully dry. Figure 2 depicts a straightforward approach for the FTIR analysis of cells, biofluids, stem cells, hair, and tissues. Sample preparation and data acquisition for a variety of samples are discussed in detail.

FIGURE 2.

A potential roadmap in medical analysis based on the collection of IR spectral data and the interpretation of chemometric data. IR, Infrared.

2.1. FTIR spectroscopy in tissue samples analysis

The initial section delves into the implementation of FTIR spectroscopy in tissue analysis. Studies have shown that FTIR can identify molecular changes associated with various pathologies, such as cancer and neurodegenerative diseases. The article highlights the capability of FTIR to discriminate between healthy and diseased tissues based on spectral differences, showcasing its potential for early disease detection.

2.1.1. Preparation of tissue samples

ATR‐FTIR shows promise as a clinical diagnostic tool due to its fast data acquisition and high accuracy in differentiating between normal and malignant tissues in cancer research. When working with limited sample size, the ability to reuse the sample after spectrum recording is a major benefit of ATR‐FTIR spectroscopy for tissue investigation. In addition to facilitating quick diagnosis and supplementing histology data, ATR‐FTIR enables in situ spectra to be obtained during surgery by directing IR radiation from the spectrometer to the ATR crystal via an optical cable. ²⁶ , ²⁷ In a more typical manner, Tissue analysis using ATR‐FTIR can be done on fixed, fresh, or frozen samples. Samples must be dewaxed using xylol or xylene before obtaining spectra from fixed tissue, commonly formalin‐fixed paraffin‐embedded (FFPE). After drying, these samples can then be loaded directly onto an ATR crystal, ²⁸ , ²⁹ or samples can be located in calcium fluoride slides, ³⁰ , ³¹ aluminum‐covered slides, ³² or low‐E thoughtful glass slides ³³ , ³⁴ prior to FTIR investigations.

Another approach that works better for clinical diagnosis is using fresh tissue. Samples can be combined in a neutral buffer, put on glass slides, and dried before FTIR measurements are performed when measures are made immediately after tissue collection. ³⁵ Otherwise, samples can be located directly on the ATR crystal and forced to contact the crystal with a clamp arm. If FTIR analysis cannot be done straightaway, samples can be frozen at −80°C ³⁶ or in liquid nitrogen ³⁷ , ³⁸ and then defrosted on ice or at room temperature before spectra are collected.

2.1.2. FTIR of biological samples for cancer and other disorders detection and monitoring

FTIR spectroscopy can distinguish between cells, tissue, and body fluids based on spectrum features that reflect their chemical conformation and construction. Additionally, it has the prospective to serve as a diagnostic instrument for the identification and differentiation of distinct diseases or disease progression conditions caused by bimolecular fluctuations. ³⁹ , ⁴⁰ , ⁴¹ , ⁴² , ⁴³ The FTIR approach for cell examination yields information about the components of the cells, including proteins, nucleic acids, lipids, and carbohydrates, ¹⁰ since it can distinguish many significant biochemical signatures incorporating protein phosphorylation (∼ 970 cm⁻¹), phosphate stretching vibrations (ν_sPO₂ ⁻; 1080 cm ⁻¹), amide I (∼ 1650 cm⁻¹), amide II (∼ 1550 cm⁻¹), protein (∼ 1425 cm⁻¹), amide III (∼ 1260 cm⁻¹), asymmetric phosphate stretching vibrations (ν_asPO₂ ⁻; 1225 cm⁻¹), and carbohydrates (∼ 1155 cm⁻¹). ¹¹

Proteins and peptides are released from the tumor microenvironment into the recently created microcirculation in cancer. As a result, timer markers can be detected in the circulatory system as either intact functional proteins or cleaved products, which can generate distinct patterns. ⁴⁴ Furthermore, a biopsy is the only way to establish a conclusive diagnosis for most malignancies. This is done to gather cells for a thorough study that will identify the precise cell lineage and provide suggestions for appropriate therapy. Since FTIR spectroscopy has the capacity to overcome the limits of delaying provision of diagnosis utilizing histopathological procedures such as light microscopic examination, and immunohistochemical investigation, it has developed a valuable tool in tissue cancer diagnosis in current years. This has opened the door for clinical applications such as a technique that uses sample scanning to identify cancerous cells during biopsies, enabling pathologists to more thoroughly describe cells that are cancerous but not diagnostic.

Using SHIMADZU 8000 series FTIR spectrophotometer diffuse reflectance, healthy and malignant blood samples were studied in 2013. Cancerous and healthy blood spectra were recorded at 4 cm⁻¹ in the 900−2000 cm⁻¹ range. The data demonstrate that malignant proteins, lipids, carbohydrates, and nucleic acids have 2 absorption bands at 1643–1550 cm⁻¹, recognized as amide I and amide II. Amide I arise from C = O stretching vibrations and amide II from C–N stretching and CNH bending. Healthy blood samples indicate this wavelength band clearly. ⁴⁵

ATR‐FTIR spectroscopy can be utilized to diagnose cutaneous melanoma (skin cancer) and determine cancer cell metastatic potential, according to a 2018 study. IR spectroscopy can diagnose basal cell carcinoma, malignant melanoma, nevus, and metastatic potential by assessing hydration and molecular changes. ⁴⁶ The authors' spectra indicate normal and malignant samples' intensities and frequencies between 4000 and 400 cm⁻¹. O‐H and N‐H stretching vibrations match skin collagen and protein spectral regions between 4000 and 3000 cm⁻¹. ATR‐FTIR spectroscopy can distinguish cancer cells' metastatic potential based on their membrane permeability and hydration grade. Specifically, the assessment between smaller and more metastatic cells reveals that the plasma membrane hydration level distinguishes between the two cancer states. ⁴⁶

A 2020 study used reagent‐free ATR‐FTIR spectroscopy to assess protein concentration in extracellular vesicles (EV) samples without sample preparation. ⁴⁷ ATR‐FTIR spectroscopy measured red blood cell‐derived EVs (REVs) protein concentration after standardization with bovine serum albumin (BSA). The sample's protein amount was proportional to the desegregated region of amide I band determined from REVs' IR spectra. The prejudiced protein bands of amide A, I, and II were 3298, 1657, and 1546 cm⁻¹. In the described work, lipid component vibrations were additionally seen as antisymmetric, and symmetric methylene stretching of acyl chains in the range 2924 cm⁻¹ to 2850 cm⁻¹ and the C = O stretching of glycerol esters at 1738 cm. ¹ , ⁴⁷ This reagent‐free approach investigates EVs without sample preparation. Thus, protein quantification using IR spectroscopy can be utilized to routinely analyze extracellular vesicles. Figure 3 illustrates a biological FTIR spectrum along with typical molecular assignments.

FIGURE 3.

A typical biological sample's FTIR spectrum, with peak assignments ranging from 4000 to 800 cm⁻¹. The spectrum is a measurement of human skin made using Fourier transform infrared spectroscopy (ATR‐FTIR) attenuated total reflection. s = symmetric vibrations, as = asymmetric vibrations, V = stretching vibrations, and δ = bending vibrations. ATR‐FTIR, attenuated total reflection Fourier Transform Infrared; FTIR, Fourier Transform Infrared.

2.2. Cellular insights through FTIR spectroscopy

The second segment explores the utility of FTIR in cellular research. By analyzing the biochemical composition of cells, researchers have gained insights into cellular differentiation, apoptosis, and metabolic changes. The review emphasizes the role of FTIR in characterizing cellular components such as lipids, proteins, and nucleic acids, facilitating a deeper understanding of cellular dynamics.

2.2.1. Preparation of cells for analysis

Whether we are dealing with cells in interruption or adhering cells will determine how we prepare the samples. Trypsinization or a manual scraping procedure is necessary to separate adherent cells. In brief, the cells are separated from the developing surface, spun, and cleaned in saline solution ⁴⁸ , ⁴⁹ or Phosphate‐Buffered Saline (PBS). ⁵⁰ Then, the cell pellet is reconstituted in an appropriate solution, often NaCl ⁴⁸ , ⁴⁹ , ⁵¹ or PBS, ⁵⁰ , ⁵² but some instigators may resuspend cell pellet in growth media. ATR crystal size determines spectral acquisition cell count. Cell suspension must completely cover the crystal. Depending on cell size, 0.5–10 µL are used and 15 000−1 000 000 cells are used. To compare spectra and ensure all cell deferral is on the ATR crystal, all duplicates should utilize the same number of cells.

Using MirrIR low‐e microscope slides is an alternative method for ATR‐FTIR cell analysis. MirrIR slides is an alternative method for ATR‐FTIR cell analysis. These slides allow the direct growth of adherent cells on the surface of the slide. Before obtaining spectra, the slide can be withdrawn from the medium, rinsed with a saline solution, and dried. ⁴⁹ Alternatively, the cell suspension can be pipetted onto a MirrIR slide after trypsinization, fixed with paraformaldehyde, and then the spectra of the fixed cells can be obtained. ⁵³

2.2.2. FTIR spectroscopy of stem cells research

For the application of the FTIR technique to stem cell biomarker discovery, researchers have sought to delineate changes to specific wavenumbers, which correspond to signature bands of macromolecules. ⁵⁴ FTIR has been used to identify transformations in the macromolecular signature of stem cells and their consequent differentiation. Ami et al. monitored the spontaneous differentiation of marine Stem Cells (SCs) in their early progress. ¹³ linear discriminant analysis (LDA) and Principal Component Analysis (PCA) allowed the authors to separate stem cell bands into separate clusters equivalent to distinct delineation times and allowed the identification of meaningful spectral alterations throughout differentiation. The instigators demonstrated that the spectral changes during cell differentiation occurred in the protein amide I band 1600–1700 cm⁻¹ and the nucleic acid absorption region (850–1050 cm⁻¹). The changes in the amide I regions from 1600 to 1700 cm⁻¹ consisted mainly of 3 components: the antiparallel β‐sheets (1692 cm⁻¹), α‐helix (1657 cm⁻¹), and intramolecular β‐sheets (1639 cm⁻¹) of the protein structure. These modifications showed that mRNA transformation had taken place and that certain proteins were being produced that represented the emergence of a new phenotype.

Regarding changes in the nucleic acid region, cell differentiation according to their study was attributed to a reduction of 966 cm⁻¹ band and appearance of a new shoulder at 954 cm⁻¹ (CC stretching of DNA neckbone). The manifestation of the new element and the immediate presence of the deoxyribose ring at 899 cm⁻¹ indicated a vibrational mode of DNA.

Vazquez‐Zapien et al. defined SCs and distinguished ESCs. FTIR characterized developed mouse pluripotent stem cells into pancreatic cells Differentiated Pancreatic Cells (DPCs). ⁵⁵ FTIR spectra showed considerable spectrum shifts, supporting real‐time quantitative Polymerase Chain Reaction (PCR) (RT‐qPCR) and immunohistochemical differentiation. Protein and nucleic acid bands changed. DPCs expressed proteins in the amide group at 1500 to 1700 cm⁻¹, which indicated their total protein content compared to mPSCs. DPCs had higher glycogen and phosphate vibration bands (1030–1080 cm⁻¹) than mPSCs when nucleic acid was probed between 850 and 1100 cm⁻¹. ⁵⁵

Pijanka et al. in 2010, used both synchrotrons‐based FTIR and Raman micro spectroscopies to measure potential differences between human pluripotent (embryonic Human Smberyonic Stem Cells (hESCs)) and multipotent (adult mesenchymal) stem cells (hMSCs) and inspected the influence of oxygen concentration during cell culture on the spectral signatures of hESCs and hMSCs. The FTIR spectroscopy exposed that the significant alterations between the hESCs and hMSCs happened in the lipid region at 2920 cm⁻¹, 2850 cm⁻¹, and 1740 cm⁻¹ with hESCs exhibiting higher band intensities compared to hMSCs. The authors assigned the peaks at 2920 cm⁻¹ and 2850 cm⁻¹ to CH₂ stretching mode of methylene chain in membrane lipid and the band 1740 cm⁻¹ to the carbonyl C = O stretching mode of phospholipids. Loading plots of PCA assessment showed that other differences lay in the 700–800 cm⁻¹ regions of the FTIR spectra. The identified differences were an amplified intensity of the DNA band at 780 cm⁻¹ for hESCs while compared to hMSCs when cultured either in 2% or 21% oxygen. On the other hand, the tryptophan band at 760 cm⁻¹ was more intense in hMSCs. ⁵³

Two separate research teams used FTIR to demonstrate that embryonic stem cells (ESCs) experience alterations in protein and lipid arrangement and content during the cell differentiation process. ¹³ , ⁵⁶ The PCA of FTIR spectra discovered that the lipids bands (from CH₂ and CH₃ stretching vibrations at 2959, 2923, and 2852 cm^‐1) and the proteins in the amide I band at 1659 and 1637 cm^‐1 were associated with stem cell differentiations. This finding revealed that, during differentiation, glycerophospholipid expression increased, leading to a substantial rise in cell lipid content. Amide I profile alterations revealed an upregulation of helix‐rich proteins and a downregulation of sheet‐rich proteins as mESCs differentiated into ESNCs, which is consistent with alterations in cytoskeleton proteins. ⁵⁶

Summarily, based on the literature on FTIR and stem cell, the regions that were related to stem cells differentiation appeared to be at the proteins, lipid, nucleic acid, and CH stretching vibration. The proteins included amide III, II, I, B, and A bands were 1229–1301, 1480–1575, 1600−1700, 3100 and 3300 cm⁻¹, respectively. The lipid regions associated with spectral differences include phospholipids (1230–1244 cm⁻¹), fatty acid 1396 cm⁻¹, and glycogen 1021–1080 cm⁻¹. Other lipid changes were the CH stretching vibration including CH stretching 2843 cm⁻¹, CH₂ stretching 2920–2929 cm⁻¹, and CH₃ stretching at 2957–2962 cm⁻¹. The nucleic acid changes included deoxyribose ring at 859–899 cm⁻¹ and DNA backbone 954–959 cm⁻¹. A typical stem cells FTIR spectra illustration is exposed in Figure 4.

FIGURE 4.

A overall representative stem cell spectra in the 4000–400 cm⁻¹ region, showing the FTIR bands adapted from Aksoy et al. (2018). ⁵⁷ FTIR, Fourier Transform Infrared.

2.3. Diagnostic applications with hair sample analysis

The subsequent section focuses on the innovative use of FTIR spectroscopy in hair sample analysis. Hair, often overlooked as a diagnostic matrix, can offer valuable information about an individual's health history, exposure to toxins, and nutritional status. The review showcases how FTIR can identify markers related to conditions like malnutrition, drug abuse, and environmental exposures, expanding the diagnostic repertoire.

2.3.1. Preparation of hair samples for analysis

The standard procedure for treating hair samples involved a 1‐min soak in acetone followed by a 1‐min soak in distilled water. Hair specimens used in these methods are typically dirtier and therefore washed more frequently before being subjected to laminar air flow to eliminate any remaining moisture. ⁵⁸

2.3.2. FTIR spectroscopy in disease detection using hair samples

Human hair is comprised of fibrous essential proteins known as keratin, and its composition contains a wealth of data on the individual. ⁵⁹ It has been demonstrated that hair can detect infections, ⁶⁰ drugs of abuse, chemical treatments and weathering, ⁶¹ and even starvation. ⁵⁹ Using infrared spectroscopy, all the characteristics can be identified. A typical FTIR hair spectrum is shown in Figure 5.

FIGURE 5.

A representation of FTIR spectrum of hair fibers. FTIR, Fourier Transform Infrared.

At crime scenes, FTIR was utilized to distinguish between animal and human hair. ATR‐FTIR and chemometrics accurately distinguished animal and human hairs. Thus, this method was recommended as a rapid, non‐destructive confirmation test in addition to microscopic testing. ⁶²

The FTIR‐ATR spectroscopic method could be used in a clinical laboratory as a powerful way to find hypothyroid. The FTIR‐ATR spectroscopic method shows qualitative information about the biomolecules that are present, and the internal ratio parameter method is used to describe the hair sample in a quantitative way. People with hypothyroid had higher peak height ratios of the lipids, proteins, and glycogen bands 1450/1080, 1180/1118, and 930/510 in their hair. Fourth derivative spectroscopy showed more than 20 bands, which made it easier to see the differences and similarities between a single hair fiber from a healthy person and one from a person with hypothyroidism. This study shows that Attenuated Total Reflectance spectroscopy has the potential to not only find hypothyroid but also to show how hair can be used as a biosensor to find hypothyroid by showing how molecules and signals work. ⁵⁸

FTIR was used to evaluate human scalp hair with “normal” and “high” plasma glucose levels. FTIR on hair samples showed chemical deposition changes with blood sugar levels. ⁶³ There were two absorptions seen at 747 and 1105 cm⁻¹ in this region, in comparison to the “typical” spectrum. Additionally, there was an enhanced absorption band at 1226 cm⁻¹ and a transmission band at 1145 cm⁻¹. Changes in the molecular composition and structure of molecules with C‐O‐C functional groups were revealed by differences at 1170 cm⁻¹ (anti‐symmetric C‐O‐C stretch vibration). Variations in the symmetric p = O stretch vibration of PO₂ groups in nucleic acids and phospholipids were detected at a frequency of 1080 cm⁻¹. A lower absorption peak at 1244 cm⁻¹ most likely resulted from shifts in antisymmetric p = O stretching vibration. There is less of an absorption peak at 1244 cm⁻¹ resulting from shifts in antisymmetric p = O stretching vibration, most likely. The alterations to the phenyl ring substance bands were caused by the 745 cm⁻¹ absorption peaks. ⁶³

A preliminary 2022 investigation reveals dissimilarities among a control sample and a group of individuals with mood syndromes through hair analysis utilizing ATR‐FTIR spectroscopy ⁶⁴ . In the event of alterations in hair construction, no alterations were detected in the peaks formed from the proteins responsible for hair growth. Most of the hair is composed of proteins, although it also contains melanin, lipids, and minerals. Certain variations in the peaks formed from CH₂/CH₃ were noticed, including a large drop in the strength of the bands around 2800−3100 cm⁻¹ and lesser variations at approximately 1460 cm⁻¹. This was probably related to a reduction in the quantity of lipids in the treatment group or to modest essential modifications in proteins (amino acid side chains). The study accomplished that the control group might be separated immediately from the group of patients with mood syndromes based on the average spectra. Nonetheless, the standard deviation of the spectra from the control group was considerable, indicating that this group was extremely diverse. ⁶⁴ The expanded explanation for the cancer, stem cells, and hair FTIR bands pooled from the literature is presented in Table 1.

TABLE 1.

The most important wavenumber's assignments of human stem cells, cancer, and hair analysis.

Wavenumber	Definition	Biochemical molecule	References
3294–3328–3330	Amide A; mostly N‐H stretching of proteins thru involvement from intermolecular H bindings, and O–H stretching mode of polysaccharides.	mainly proteins	57 , 65 , 66
3129	Amide B, arise from N–H stretching vibrations of proteins.	Proteins	66
3060–3065	Amide B; C–N, N–H stretching of proteins	Proteins	65 , 66
3011–3015	Olefinic, C–H stretching mode of the HC = CH groups	unsaturated lipids	65 , 66
2957–2962	Mostly CH3 antisymmetric stretching	mainly lipids	57 , 67
2920–2929	Mainly CH2 antisymmetric stretching	mainly lipids	57 , 65 , 66 , 67 , 68
2907	CH2 and CH3 stretching of phospholipids, cholesterol, and creatine	mainly lipids	65
2871–2875,2873	CH3 antisymmetric stretching	protein side chains, lipids, thru some contribution from carbohydrates and nucleic acids.	57 , 65 , 66
2850–2858	CH2 symmetric stretching	mostly lipids, with slight contribution from proteins, carbohydrates, nucleic acids.	57 , 65 , 66 , 67 , 69
2843	C–H stretching	essentially lipids	65 , 70
1720–1732 1740–1745	C = O stretching Saturated ester	cholesterol esters, phospholipids, fatty acid ester functional groups in lipids	10 , 12 , 57 , 65 , 66
1710−1716	Antisymmetric stretching C = O	RNA and purine base	66
1705−1690	C = O antisymmetric stretching vibrations:	RNA, DNA	66 , 68
1685	β‐turn protein secondary structure	Protein	13
1653, 1654	Amide I; 80% C = O stretching, 10% N–H bending, and 10% C–N stretching proteins.	protein α‐helix	65 , 66 , 67 , 68
1648	Amide I (C = O) (C–N) (N–R2)	Protein	70
1630−1640, 1684–1656	Amide I: C = O (80%) and C–N (10%) stretching, N–H (10%) bending vibrations:	proteins β‐structure	10 , 57 , 65 , 66 , 67
1610, 1578	C4‐C5 and C = N stretching in imidazole ring of DNA, RNA		66
1554	Overall protein absorbance		57 , 67
1540–1550 1452, 1388	Amide II: N–H (60%) bending and C–N (40%) stretching vibrations	protein α‐helix	10 , 66 , 68
1530–1535	Amide II: N–H (60%) bending and C–N (40%) stretching vibrations:	protein β‐structure	66 , 70
1452, 1453,1455–1467	CH2 bending vibrations:	lipids and proteins	57
1370−1400–1452, 1455	CH2 and CH3 deformation vibrations due to lipid contribution	lipids, proteins	10 , 66 , 67 , 71
1396	COO– symmetric stretching vibrations of amino acid side chains	fatty acids	65 , 67
1376–1378	Methyl or CH3, CH2 wagging	lipids/proteins	10 , 70 , 72
1330−1200 1284, 1280	Amide III	Proteins	10 , 14 , 66 , 68 , 71 , 72
1310 1304	Peptide side‐chain vibrations Deformation N‐H cytosine		57 70
1230−1244	PO2‐ antisymmetric stretching's	Completely hydrogen bonded: mainly nucleic acids with little contribution from phospholipids	57 , 65 , 66
1147, 1152–1156 1124	CO–O–C antisymmetric stretching vibrations of glycogen and nucleic acid ribose νC‐O Carbohydrates		10 , 57 , 65 , 70 , 73
1090–1084	PO2‐ symmetric stretching vibrations	: RNA, DNA	10 , 65 , 66
1060, 1050	C–O stretching vibrations	deoxyribose/ribose DNA, RNA	66 , 70
1045–1050	CO stretching vibrations of carbohydrates, glycogen; deoxyribose/ribose of nucleic acids		57
1021–1041	C–O stretching, coupled with C–O bending of the C–OH groups of carbohydrates oligosaccharides, polysaccharides	Mainly from glycogen	13 , 57 , 65
996, 995, 994	C–O stretch from RNA ribose chain and other carbohydrates		13 , 66 , 67 , 71
975.1, 976−875	C–N+–C stretching nucleic acids (DNA, RNA), ribose phosphate main chain vibrations of RNA, phosphate monoesters		13 , 65
958.7–954.9, 962.6	νCC of the DNA backbone		13 , 69
950‐	C–C vibrations from nucleic acids		67
957	CH3 deformation (lipid, protein)		66
936	C–C residue α‐helix
916.3, 925–929	Sugar vibrations in the backbone of DNA‐Z form	Ribose ring	13 , 57
921	C–C stretch proline
895.1–899.9	Deoxyribose ring		13
898	C–C stretch residue
889.3	DNA band or C‐C, C‐O deoxyribose	Fatty acid, saccharide	13 , 70
870	C‐DNA
855	Vibrations in N‐type sugars in nucleic acid backbone		57
853	Ring breathing Tyr‐C–C stretch proline		66
767–786	C–C and C–N stretch PO3 2− stretching		66
746–727

3. ANALYSIS USING CHEMOMETRIC DATA: MULTIVARIATE DATA ANALYSIS AND INTERPRETATION

To navigate the complexity of FTIR spectra, the review discusses the integration of multivariate analysis approaches. PCA and Cluster Analysis are highlighted as strategies to discern patterns and groupings within data sets, enabling meaningful interpretation and classification of samples.

After data pre‐processing, choosing the proper chemometric method to analyze qualitative or quantitative data is essential. Several statistical methods are used in chemometric data analysis to extract useful information from chemical data. Through the application of qualitative analysis techniques grounded in pattern recognition methodology, samples can be classified based on their infrared spectrum. Techniques of classification can be classified into supervised and unsupervised categories. Using unsupervised methodologies such as PCA or hierarchical cluster analysis (HCA), both of which do not require any prior sample information. They are helpful in arranging spectra and providing an initial perspective of the dataset's complexity, heterogeneity, and similarity. As an extra benefit, they can be employed to check for outliers, ensure that the measurement is reproducible, and get a vantage point of the general separation of groups. ²⁵ For the identification of samples, supervised approaches are preferred since they use the category involvement of samples to express classes and construct a model for identification. ⁷⁴ A grouping model is advanced using a training set of data annotated with labels, and its efficacy is measured by comparing the actual labels assigned to validation samples with the labels predicted by the model. ⁷⁴ Supervised techniques include LDA, partial least squares discriminant analysis (PLS‐DA), orthogonal projections to Latent Squares Discriminant Analysis (OPLS‐DA), K‐nearest neighbors (KNN), and canonical variate analysis (CVA). ⁷⁵ Infrared spectroscopy is typically employed for qualitative purposes. In addition, it lends perfectly to quantitative analysis, which can help clarify the degree to which two samples varied from one another. ⁷⁴

4. CHALLENGES AND FUTURE DIRECTIONS

The article acknowledges challenges such as spectral variability, data processing complexities, and the need for standardized protocols. It also envisions the future of FTIR spectroscopy in medical research, including its potential integration with imaging modalities and machine learning algorithms for enhanced diagnostic accuracy.

Notwithstanding the growing number of research in this field, FTIR spectroscopy has some boundaries that must be impressed before it can be regularly used in clinical settings: first, it cannot identify specific molecules, which can be a problem in biochemical laboratories where colorimetric analysis is used to measure many compounds. Libraries containing orientation spectra of varied concentrations could solve this issue. FTIR spectroscopy's molecular designations are based on literature tables. Peak assignment is difficult and slow since it requires searching for studies with similar samples and critical thinking to determine if tiny wavenumber discrepancies between studies indicate different chemicals or differences in approach and equipment. FTIR spectroscopy's major benefit is screening without compound identification. However, more research is publishing tables of assignments, making it possible to categorize some biomolecules in complicated samples. It would be interesting to build a database where researchers may enter their data to be selected and used by the scientific society, like UniProt for protein sequences.

5. CONCLUSION

The review concludes by underlining the significance of FTIR spectroscopy in medical research. Its ability to provide detailed molecular information from tissues, cells, and hair samples has paved the way for innovative diagnostic and therapeutic strategies, promising a transformative impact on healthcare and personalized medicine.

In this overview, FTIR is one of several spectroscopic methods discussed that have the capacity to separate normal from abnormal materials. Combining FTIR with other methods, like ATR or micro‐FTIR, has been done extensively to enhance and streamline the spectral output of FTIR spectroscopy. The ATR‐FTIR technique shows promise as a tool for measuring the differences between normal and sick tissues, but it also has applications for the study of cells, hair, and tissues more generally.

Infrared Fourier transform infrared is the measurement technique of the future that has enormous promise and effective answers to a significant number of diagnostic complications that are currently confronted by medical practitioners. For example, FTIR may be useful in early cancer detection by differentiating between normal and malignant samples, allowing for sample diagnosis and treatment prior to patient symptoms. This is because the current gold standard techniques have limitations that prevent them from making this distinction. Because of all these benefits, the researchers have no choice but to look deeper into FTIR technology to transform it from a recognized technique into a viable one that can be used in the biological field. Similar circumstances exist in other domains, such as chemical and environmental engineering, which are already in the process of hiring FTIR for several applications.

Al‐Kelani M, Buthelezi N. Advancements in medical research: Exploring Fourier Transform Infrared (FTIR) spectroscopy for tissue, cell, and hair sample analysis. Skin Res Technol. 2024;30:e13733. 10.1111/srt.13733

DATA AVAILABILITY STATEMENT

The data that support the findings will be available in Madeha Alkelani at https://www.researchgate.net/profile/Madeha‐Alkelani following an embargo from the date of publication to allow for commercialization of research findings.