A Review of the Latest Research Applications Using FT-IR Spectroscopy

This review article explores recent advancements in Fourier transform infrared (FT-IR) spectroscopy, highlighting its advancing capabilities and applications across diverse scientific disciplines. With a focus on innovative spectroscopic techniques, such as attenuated total reflection/reflectance (ATR), and enhanced by chemometric methods like principal components analysis (PCA) and partial least squares (PLS), the review consolidates comprehensive insights into FT-IR’s versatility. Key applications include hydrogen bonding studies, environmental monitoring, food analysis, and clinical diagnostics. The article underscores FT-IR’s pivotal role in modern analytical research and its potential for further advancements.

Fourier transform infrared (FT-IR) spectroscopy has revolutionized analytical chemistry and continues to move forward with technological innovations and application expansions. FT-IR techniques encompass a variety of methods, including transmission, reflection, and attenuated total reflection/reflectance (ATR), each tailored to specific sample types and analytical goals. These methods enable the precise characterization of molecular vibrations in organic and inorganic compounds, facilitating applications in diverse fields such as pharmaceuticals, clinical analysis, environmental science, and food analysis.

The broad applicability of FT-IR is enhanced by advanced data processing techniques, notably chemometric methods like principal components analysis (PCA), partial least squares (PLS) modeling, and discriminant analysis (DA). These techniques extract meaningful information from complex spectral data, allowing for accurate classification and quantitative analysis. FT-IR’s ability to provide rapid, non-destructive analysis is particularly advantageous in fields requiring high-throughput screening or real-time monitoring.

This review synthesizes recent literature on FT-IR spectroscopy, emphasizing its role in fundamental studies such as hydrogen bonding research and practical applications including environmental pollutant detection and clinical diagnostics. Future directions include the development of portable FT-IR devices for field applications and further integration of advanced chemometric tools to enhance spectral analysis precision. By consolidating these advancements, the review aims to provide a comprehensive overview of FT-IR spectroscopy’s recent impact and potential in contemporary scientific and technological landscapes.

Advancements of FT-IR

A recent review paper, containing 155 references, explored the advancements in mid-infrared (mid-IR) spectroscopy and its applications across various scientific and technological fields. The authors highlighted significant innovations in spectroscopy, particularly the ATR technique, and chemometric data processing tools like PCA and PLS models. They aim to consolidate and present comprehensive information on the continuously evolving field of FT-IR spectroscopy. A notable aspect of this review is its emphasis on FT-IR’s versatility in addressing scientific issues, such as hydrogen bonding studies, and practical applications in areas such as medicine, environmental monitoring, and food analysis. The paper also provides a historical overview of hydrogen bonding theories, underscoring the role of FT-IR in hydrogen bond research, including unconventional spectral effects arising from proton-deuteron substitution in hydrogen bridges (1).

The review showcases the broad applicability of FT-IR spectroscopy, emphasizing its importance in modern scientific and analytical research. By comparing FT-IR with other spectroscopic tools like ultraviolet-visible (UV-vis) spectroscopy, the authors highlight its advantages, particularly in the preliminary assessment and prospective applications for researchers. The benefits of using FT-IR include ease of sample preparation, effectiveness in detecting biochemical changes at the cellular level, and precision in chemical compound identification. Future advancements in FT-IR gas analyzers could expand its applications, such as in greenhouse gas flux measurements, enhancing speed and integration into fields like micrometeorological studies. The review concludes by affirming the need for continued development in IR spectroscopy, driven by the focus on hydrogen bonds, which has propelled its rapid advancement over the past 50 years (1).

Pharmaceutical and Molecular Docking Analysis

The hybrid correlation method was applied to analyze the spectra of 2-Hydroxy-5-nitrobenzaldehyde (2H5NB) across FT-IR, FT-Raman, UV-vis, and nuclear magnetic resonance (NMR) analysis techniques. Using density functional theory (DFT) with B3LYP and the 6-311++G(d,p) basis set, the study focused on determining the optimum molecular shape, vibrational wavenumbers, IR intensities, and Raman spectra. MOLecular VIBrations (MOLVIB) software provided detailed interpretations of the vibrational spectra, revealing that intermolecular charge transfer results from bonding orbitals functioning as donors and acceptors in all phases of natural bond orbital (NBO) analysis, thereby stabilizing the molecules (2).

The study found that 2H5NB exhibited high gastrointestinal absorption but no penetration of the brain-blood barrier or inhibition of cytochrome P450 enzymes, contrary to the expected absorption, distribution, metabolism, excretion, and toxicity (ADMET) characteristics. Molecular docking results showed that 2H5NB had the highest negative mean binding affinity of -5.717 kcal/mol, indicating a stronger interaction compared to ephedrine (EDE) and caffeine (CFN). Additionally, 2H5NB formed more significant hydrogen bonds with amino acid residues of selected receptor proteins, suggesting its potential as an analeptic agent (2).

A study employed a satellite laboratory toolkit comprising a handheld Raman spectrometer, a portable direct analysis in real-time mass spectrometer (DART-MS), and a portable FT-IR spectrometer to screen 926 pharmaceutical, unknown, and dietary supplement products at an international mail facility. Over 68 working days, the toolkit successfully identified more than 650 active pharmaceutical ingredients (APIs), including over 200 unique ones, using multiple devices for each product (3).

The performance of each device and the toolkit overall was assessed by comparing their results to those from full-service laboratories, which conducted confirmatory analyses on a subset of products. This subset included seven negative items (found not to contain APIs by the toolkit) and 124 positive items (found to contain APIs by the toolkit). Results showed no false positives among the negative items. In the positive items, there were only four false negatives and five false positives detected by the toolkit (3).

Specifically, 119 out of 124 positive items were correctly identified by the toolkit as containing APIs using at least two devices, and these identifications were confirmed by the full-service laboratories. Moreover, 90.2% of the APIs detected by the confirmatory analysis were also detected by at least two toolkit devices (3).

Based on these findings and the absence of false positives detected by more than one device, researchers concluded that when the toolkit identifies and subsequently confirms an API using two or more devices, the results are highly reliable and comparable to those obtained by full-service laboratories. This validates the toolkit’s effectiveness in screening and identifying declared and undeclared APIs in various types of products (3).

Clinical and Biomedical Analysis

FT-IR spectroscopy has shown great potential for the rapid diagnosis of various pathologies, including Covid-19. However, its adoption by clinicians remains low because of a lack of awareness and training, creating a gap between researchers and medical practitioners. Integrating FT-IR into clinical practice requires educating clinicians about its benefits and applications, particularly for non-invasive screening and diagnostic tests using biofluids like blood, saliva, and urine (4).

Blood analysis through FT-IR can benefit fields such as hematology, infectiology, oncology, and endocrinology, despite the invasive collection method. Urine analysis offers insights into kidney health with simple collection, whereas saliva can be used to diagnose oral and digestive diseases, including early-stage oral cancer. The involvement of industries in developing user-friendly, portable FT-IR instruments and the establishment of legal frameworks for its use are crucial. Decentralized funding hinders technology training and engagement, delaying clinical validation. Collaborative efforts to validate and test similar technologies globally can enhance the integration of FT-IR in medical practice, benefiting clinicians, researchers, and the industry (4).

Fibromyalgia syndrome (FM), which causes chronic widespread pain, presents significant diagnostic challenges. This study aimed to develop a rapid, vibrational biomarker-based method for diagnosing FM and related rheumatologic disorders, including systemic lupus erythematosus (SLE), osteoarthritis (OA), and rheumatoid arthritis (RA), using portable FT-IR techniques. Bloodspot samples from patients with FM (n=122) and other rheumatologic disorders (SLE n=17, RA n=43, OA n=10) were collected and prepared through four methods. Spectral data were obtained using a portable FT-IR spectrometer. Pattern recognition analysis, using orthogonal partial least squares discriminant analysis (OPLS-DA), successfully classified the spectra into corresponding disorders with high sensitivity and specificity (R_cv > 0.93), identifying peptide backbones and aromatic amino acids as potential biomarkers (5).

This study demonstrated the feasibility of using portable FT-IR combined with chemometrics for accurate, high-throughput diagnostics of FM in clinical settings. The OPLS-DA algorithms showed excellent sensitivity and specificity with no misclassification. Unique IR spectral signatures, dominated by amide bands and aromatic ring structures, were identified as biomarkers. These findings suggest that portable FT-IR could enable real-time, in-clinic diagnostics of FM, potentially leading to significant advances in treatment and cost savings (5).

A recent research paper focused on investigating protein dynamics through FT-IR spectroscopy using amide hydrogen/deuterium (H/D) exchange in biological systems. It outlined steps for preparing protein samples, collecting FT-IR spectra, and analyzing them to study changes in protein dynamics. Applications include examining the impact of protein mutations, interactions with metal ions, or ligands on H/D exchange rates (6).

However, the method has limitations. Factors like the quality of the FT-IR spectra in water, experimental temperature, and conditions during lyophilization of proteins or buffers can affect the accuracy of results, rendering the approach semi-quantitative. Moreover, the method protocol is most effective for monitoring protein dynamics over minutes to hours and may not adequately capture dynamics occurring at a shorter timescale (6). Specifically, the method protocol uses FT-IR with transmission windows to analyze proteins in aqueous solutions globally. It is best suited for proteins that are insoluble at high concentrations, those in buffers with cosolvents that affect IR absorbance, or proteins in buffers with high salt concentrations (>200 mM). Overall, while this protocol provides a structured framework for investigating protein dynamics using FT-IR and H/D exchange, researchers should consider its limitations when applying it to specific experimental conditions (6).

Research in 2023 focused on studying lipid components within human cells, crucial for numerous cellular processes like cell adhesion, membrane formation, and response to DNA damage. FT-IR spectroscopy was employed to detect and characterize various phospholipids and sphingolipids in biological samples (7).

Commercial lipid samples of phosphatidylethanolamine (PE), phosphatidylcholine (PC), phosphatidylinositol (PI), phosphatidylserine (PS), ceramide (Cer), ceramide 1-phosphate (C1P), sphingosine 1-phosphate (S1P), and sphingomyelin (SM) were analyzed using FT-IR with an attenuated total reflectance/reflection (ATR) accessory for data acquisition. This method enabled the identification of distinctive infrared spectra associated with different functional groups in lipid hydrocarbon chains and polar head groups (7).

By analyzing these lipid spectra, researchers gained insights into the IR characteristics of individual lipid components. This foundational study lays the groundwork for future FT-IR investigations into lipid extracts from human cells affected by diseases or exposed to various environmental factors. Ultimately, this approach enhances our understanding of how lipid composition and structure influence cellular functions and responses in health and disease contexts (7).

Environmental Analysis

Microplastics (MPs) are emerging pollutants requiring precise identification and classification for effective monitoring and management. Unlike most studies relying on small datasets and library searches, this study developed and compared four machine learning-based classifiers using two large-scale blended plastic datasets. A 1D convolutional neural network (CNN), decision tree (DT), and random forest (RF) were trained with raw FT-IR spectral data, while a 2D CNN used spectral images. The 1D CNN achieved the highest overall accuracy, 96.43% on a small data set and 97.44% on a large data set, excelling particularly in predicting environmental samples. The RF model was noted for its robustness with limited spectral data. Despite the effectiveness of RF and 2D CNNs with fewer data, 1D CNNs proved most efficient with ample spectral data. Consequently, an open-source MP spectroscopic analysis tool was developed to enable quick and accurate MP sample analysis (8).

In 2023, a study focused on the escalating environmental impact of accumulating garbage in water bodies, particularly the formation of microplastics through plastic degradation. Conducted on Mahitam Island, Indonesia, the research aimed to assess the quantity, shapes, and types of microplastic polymers present in both water and sediment samples. Three sampling stations (Station I, II, and III) were strategically chosen based on plastic sources, each with distinct characteristics (9).

Sampling involved laboratory-based analysis of seawater and sediment samples. Seawater underwent treatment with a solution of 70% ethanol, 30% H₂O₂, and NaCl, while sediment samples were treated with FeSO₄ (0.05 M), NaCl, and H₂O₂. Microscopic examination was employed to analyze microplastic particle count and shapes, including fibers, films, fragments, and pellets. Polymer types were identified using FT-IR (9).

Results revealed that films were the most prevalent microplastic type in water samples from Station I and II, and in sediment samples across these stations. Polymer analysis identified polyvinyl chloride (PVC), polyethylene (PE), polypropylene (PP), and polystyrene (PS) as the predominant types, originating mainly from plastic waste associated with tourist activities and sea currents around Mahitam Island (9). Overall, this research underscored the significant presence and diverse sources of microplastics in the island’s marine environment, highlighting the urgent need for mitigation strategies to address plastic pollution impacts (9).

Food Analysis

Physicochemical characteristics of 70 wheat flours from different species, including Einkorn (Triticum monococcum), Spelt (Triticum spelta), and common wheat (Triticum aestivum), were analyzed using various standard methods. The analysis covered moisture, ash, protein, wet gluten, sedimentation index, pH, acidity, fat, starch, falling number, damaged starch, and Glutograph parameters for stretching and relaxation (10). PCA was used to examine the relationships between these characteristics and the wheat samples. Significant differences were found between species for almost all characteristics except moisture and damaged starch, with ancient wheat species exhibiting higher levels of protein, wet gluten, fat, ash, and acidity, while common wheat was richer in starch and had the highest Glutograph values for stretching and relaxation (10).

FT-IR spectroscopy was also employed as a nondestructive method to analyze wheat flour composition. Chemical values of the grain were determined based on FT-IR spectral peaks. To enhance the prediction of physicochemical parameters such as moisture, protein, starch, and gluten content, as well as falling number and mycotoxin contamination (UCDc); partial least squares regression (PLS-R) was used with pretreated spectra. The first and second derivative pre-treatments of the spectra provided the most accurate models for predicting these parameters (10).

Italian carrot varieties from the Fucino upland (Abruzzo) and Ispica (Sicily), both Protected Geographical Indication specialties, along with a common variety from the Apulia region, were analyzed using ATR FT-IR and headspace solid-phase microextraction followed by gas chromatography–mass spectrometry (HS-SPME/GC–MS). A total of 180 IR spectra from both the internal and external sides of the carrot roots, and 80 volatile profiles from the three varieties, were processed using partial least square discriminant analysis (PLS-DA) to determine their geographical origins. The PLS-DA model based on 120 internal spectra accurately predicted 59 out of 60 test spectra, while the model using external spectra achieved perfect classification. Additionally, PLS-DA classification using 32 volatile components identified by HS-SPME/GC–MS resulted in an 82.5% accuracy rate, with minor misclassifications from Ispica and Apulia samples. Variance Importance in Projection (VIP) analysis identified the key volatile components and vibrational modes crucial for distinguishing the carrot varieties in the PLS-DA models (11).

The biochemical metabolism during cheese ripening significantly influences the production of amino acids, organic acids, and fatty acids. This study from 2023 aimed to evaluate the unique IR spectra of the soluble fractions in different solvents (water-based, methanol, and ethanol) of Turkish white cheese for rapid monitoring of cheese composition during ripening (12). Turkish white cheese was produced on a pilot plant scale using a mesophilic culture (Lactococcus lactis subsp. lactis, Lactococcus lactis subsp. cremoris) and ripened for 100 days, with samples collected every 20 days. The soluble cheese fractions were extracted using three solvents: water, methanol, and ethanol. Reference methods, including GC for amino acids and fatty acids, and liquid chromatography (LC) for organic acids, were employed to obtain baseline data (12).

FT-IR spectra were correlated with chromatographic data using pattern recognition analysis to develop predictive models for regression and classification. All models demonstrated a good fit (R_pre ≥ 0.91) for predicting target compounds during cheese ripening. Ethanol extracts provided the best predictions for individual free fatty acids (0.99 ≥ R_pre ≥ 0.93, 1.95 ≥ SEP ≥ 0.38), while water-based extracts were superior for predicting organic acids (0.98 ≥ R_pre ≥ 0.97, 10.51 ≥ SEP ≥ 0.57) and total free amino acids (R_pre = 0.99, SEP = 0.0037). Methanol extracts yielded the best soft independent modeling of class analogy (SIMCA) classification results, effectively distinguishing different stages of cheese ripening. Using a simple methanolic extraction and collecting spectra with a portable FT-IR device proved to be a fast, simple, and cost-effective technique to monitor the ripening process and predict the levels of key compounds involved in the biochemical metabolism of Turkish white cheese (12).

There is a growing need for a reliable method to quickly and accurately assess the quality of baijiu in the beverage industry. A 2023 study aimed to develop a strategy for rapid quantitative analysis of the primary flavor components in Nongxiangxing baijiu. Nongxiangxing baijiu, often referred to simply as Nongxiangxing, is a type of traditional Chinese liquor known as baijiu, which translates to “white alcohol.” It is produced using a distillation process that typically involves sorghum, although other grains such as wheat or barley may also be used. Using FT-IR combined with quantitative chemometric modeling, the study effectively quantified seven of the 10 major flavor components, including ethyl butyrate (R² = 0.9942), ethyl lactate (R² = 0.9438), n-butanol (R² = 0.9048), isobutanol (R² = 0.9696), acetic acid (R² = 0.9600), butyric acid (R² = 0.8448), and caproic acid (R² = 0.9971)(13). These results suggest that this method could be a potential approach for the rapid quality assessment of Nongxiangxing baijiu, providing a theoretical foundation for future research in this area (13).

Researchers sought to streamline the authentication of the geographical origin of virgin olive oils (VOO), traditionally reliant on complex and time-consuming methods. They aimed to leverage FT-IR as a rapid and straightforward alternative. Collecting samples from six regions in Morocco across 2020 and 2021, they researchers conducted detailed physico-chemical analyses (14).

FT-IR was employed to generate distinctive spectral “fingerprints” of the oils, initially processed to enhance data quality. Applying PCA alongside linear discriminant analysis (LDA) and partial least-squares discriminant analysis (PLS-DA), researchers developed robust classification models. These models successfully identified six distinct clusters based on geographical origin (14).

Results demonstrated high accuracy in predicting origin, with classification rates ranging from 84.09% to 100% using PCA-LDA and PLS-DA. Conclusively, the FT-IR-based approach proved efficient, economical, and eliminated the need for prior oil separation, offering significant time and resource savings. This method promises expedited and reliable geographical origin authentication for olive oils, enhancing practicality in routine analysis (14).

Another study focused on detecting and quantifying allergenic nutshell adulterants (peanut, pecan, and walnut shells) in cumin powder, a valuable spice prone to contamination (15). Researchers compared a portable near-infrared spectrometer (NIRS) with a FT-IR benchtop spectrometer for this purpose (15).

PCA effectively distinguished between pure cumin and adulterated samples by highlighting peaks associated with essential oils in cumin and cellulosic compounds in nutshells. Data-driven SIMCA (DD-SIMCA) was utilized to authenticate pure cumin, achieving a sensitivity of 82.4% and a specificity of 85.4% with portable NIRS, and a sensitivity of 94.1% and a specificity of 91.7% with FT-IR (15).

For quantification, PLSR models were employed, yielding residual prediction deviation (RPD) values exceeding 3.5 for portable NIRS and 8.9 for FT-IR benchtop, indicating reliable predictive capability (15).

Although FT-IR spectroscopy demonstrated superior accuracy over NIRS, the latter was recommended as a practical screening tool because of its portability, ease of use, and cost-effectiveness throughout the supply chain. It was proposed that portable NIRS could complement laboratory analyses using benchtop FT-IR, ensuring comprehensive detection and quantification of nutshell adulterants in cumin powder (15).

One study addressed the critical issue of cinnamon authentication, crucial in preventing adulteration in food and pharmaceutical industries where Cinnamomum verum (C. verum) is often substituted with lower-cost species like Cinnamomum cassia (C. cassia), black pepper, or clove. The research employed diffuse reflectance IR Fourier transform spectroscopy (DRIFTS) combined with advanced chemometric techniques to accurately detect and classify adulterated cinnamon samples (16).

C. verum was specifically targeted as the analyte, while C. cassia, black pepper, and clove constituted the adulterant group. Two main objectives were pursued: first, classifying samples into different levels of adulteration (low, medium, high); second, quantifying the concentration of C. verum in adulterated samples (16).

PLS-DA and support vector machine-discrimination analysis (SVM-DA) were employed for classification, demonstrating high accuracy with SVM-DA slightly outperforming PLS-DA (accuracy of 0.972 vs. 0.960). For regression analysis to predict C. verum concentration, the robust principal component analysis-alternating conditional expectation (rPCA-ACE) model achieved a calibration R² of 0.838, indicating strong predictive ability (16).

Overall, the results highlight DRIFTS coupled with robust chemometric analysis as a reliable method for detecting and classifying adulterated cinnamon samples. This approach offers a rapid, non-destructive means to ensure the authenticity and quality of cinnamon products in the market, addressing significant concerns in the spice industry (16).

References

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Jerome Workman, Jr. serves on the Editorial Advisory Board of Spectroscopy and is the Executive Editor for LCGC and Spectroscopy. He is the co-host of the Analytically Speaking podcast and has published multiple reference text volumes, including the three-volume Academic Press Handbook of Organic Compounds, the five-volume The Concise Handbook of Analytical Spectroscopy, the 2nd edition of Practical Guide and Spectral Atlas for Interpretive Near-Infrared Spectroscopy, the 2nd edition of Chemometrics in Spectroscopy, and the 4th edition of The Handbook of Near-Infrared Analysis.●