Fourier Transform Mid-infrared Spectroscopy Research Articles

Rapid screening of tuna samples for food safety issues related to histamine content using fourier-transform mid-infrared (FT-MIR) and chemometrics

Biogenic amines (BAs) generally result from the decarboxylation reaction of free amino acids as a result of the activity of different microorganisms. A build-up of these compounds can result in food being spoilt. Therefore, the rapid and precise detection of BAs like histamine is an important task for food safety. This research aimed to explore the potential of Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy combined with chemometric methods to assess histamine in fresh tuna quantitatively. Based on the FT-MIR data, partial least squares regression models for the prediction of histamine were successfully constructed with R2 > 0.90. Machine learning algorithms (partial least squares-discrimination analysis, k-nearest neighbors, and support vector machine) were applied, and excellent discrimination results were achieved based on the limits specified in two different legislations (EU and FDA). The results support the use of a rapid, economic and reliable approach for the discrimination of samples that could pose a health risk to consumers.

Enhancing Bioactive Compound Classification through the Synergy of Fourier-Transform Infrared Spectroscopy and Advanced Machine Learning Methods.

Bacterial infections and resistance to antibiotic drugs represent the highest challenges to public health. The search for new and promising compounds with anti-bacterial activity is a very urgent matter. To promote the development of platforms enabling the discovery of compounds with anti-bacterial activity, Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy coupled with machine learning algorithms was used to predict the impact of compounds extracted from Cynara cardunculus against Escherichia coli. According to the plant tissues (seeds, dry and fresh leaves, and flowers) and the solvents used (ethanol, methanol, acetone, ethyl acetate, and water), compounds with different compositions concerning the phenol content and antioxidant and antimicrobial activities were obtained. A principal component analysis of the spectra allowed us to discriminate compounds that inhibited E. coli growth according to the conventional assay. The supervised classification models enabled the prediction of the compounds' impact on E. coli growth, showing the following values for accuracy: 94% for partial least squares-discriminant analysis; 89% for support vector machine; 72% for k-nearest neighbors; and 100% for a backpropagation network. According to the results, the integration of FT-MIR spectroscopy with machine learning presents a high potential to promote the discovery of new compounds with antibacterial activity, thereby streamlining the drug exploratory process.

Validating the feasibility of the method of ensemble learning combined with FT-MIR for the discrimination of wild Paris polyphylla var. Yunnanensis from different geographical sources

As a widely used medicinal plant with high economic value, Paris polyphylla var. yunnanensis (PPY), is mainly distributed in south-western China. Since different growing environments have a strong influence on the PPY quality, efficient means are needed to discriminate its origin source. The traditional machine learning model uses a single learner to classify and discriminate the samples, but it is difficult to apply its advantages to play for complex data samples. Ensemble learning can integrate the advantages of multiple learners to improve the model performance. In this experiment, Partial Least Squares Discriminant Analysis (PLS-DA), Random Subspace Method (RSM), Random Forest (RF), and Support Vector Machine (SVM) models were used to discriminate wild PPY from different origins, and then verify the feasibility of ensemble learning in discriminating them from different geographical sources. At the same time, the results of preprocessing the spectral data with multiplicative scattering correction (MSC), standard normal variate (SNV), and derivative operations (first, second and third derivatives) and their combination methods were compared. The results show that the combined preprocessing greatly improves the model performance. The RSM has the most stable performance with an accuracy of more than 85%. Indicating that the ensemble learning combined with Fourier transform mid-infrared spectroscopy (FT-MIR) has the feasibility of discriminating the wild PPY of different geographical origins.

Non-destructive discrimination of honey origin based on multispectral information fusion technology

Accurate discrimination of honey origin is of significant importance for safeguarding consumer rights and promoting the sustainable development of apiculture. Spectroscopic techniques, as rapid, efficient, and non-destructive detection methods, have been widely applied in honey research. However, studies often focus on a single analytical technique, limiting the acquisition of comprehensive chemical information and hindering improvements in discrimination accuracy. Data fusion, an information processing technique that enhances test results' accuracy by integrating data from multiple devices, holds vast potential for various applications. Therefore, this study employed data fusion technology to investigate 142 honey samples from five different origins by combining Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) and Raman spectroscopy. Discrimination models based on single-spectral and multispectral Probabilistic Neural Networks (PNN) and Support Vector Machines (SVM) were established using data-level and feature-level fusion strategies. Compared to single-spectral methods, multispectral data fusion significantly improved the geospatial traceability discrimination performance of the models. Particularly, the feature-level fusion strategy demonstrated significant discrimination effectiveness for honey origin. By optimizing the optimal SVM model with the Particle Swarm Optimization (PSO) algorithm, the final test accuracy reached 95.28 %, representing an 11.4 % increase over the best single-spectrum model's test accuracy. Therefore, the establishment of a PSO-SVM discrimination model using feature-level fusion technology enables rapid discrimination of honey origin, providing a reliable and stable method for non-destructive honey origin discrimination.

Integrating mid-infrared spectroscopy, machine learning, and graphical bias correction for fatty acid prediction in water buffalo milk.

Buffalo milk, constituting 15% of global production, has higher fatty acids content than Holstein milk. Fourier-transform mid-infrared (FT-MIR) spectroscopy is widely used for dairy analysis, but its application to buffalo milk, with larger fat globules, remains understudied. The ultimate goal of this study is to develop machine learning models based on FT-MIR spectroscopy for predicting fatty acids in buffalo milk and to assess the accuracy of commercial milk analyzers. This research provides a convenient, fast, and environmentally friendly method for detecting the fatty acid composition in buffalo milk. We employed six machine learning algorithms to establish a detection model for 34 fatty acids in buffalo milk. The predictive models demonstrated robust capabilities for high-content fatty acids [C14:0, C15:0, C16:0, C17:0, C18:0, C18:1, saturated fatty acid (SFA), monounsaturated fatty acid (MUFA)], with errors within a 15% range. Traditional FT6000 detection methods exhibited limitations in measuring SFAs and polyunsaturated fatty acids (PUFA). Implementing a mean difference correction of 0.21 for MUFAs and applying regression equations (SFA × 1.0639 + 0.0705; PUFA × 0.5472 + 0.0047) significantly improved measurement accuracy. This study successfully developed a predictive model for fatty acids in Mediterranean buffalo milk based on FT-MIR spectroscopy. Additionally, a correction was applied to the existing measurement device, FT6000, enabling more accurate measurements of fatty acids in buffalo milk. The findings have practical implications for the food industry, offering a faster and more reliable approach to assess and monitor fatty acid composition in buffalo milk, potentially influencing product development and quality control processes. © 2024 Society of Chemical Industry.

The Prediction of Quality Parameters of Craft Beer with FT-MIR and Chemometrics.

Beer is one of the oldest and most known alcoholic beverages whose organoleptic characteristics are the attributes that the consumer seeks, which is why it is essential to ensure proper quality control of the final product. Fourier transform mid-infrared (FT-MIR) spectroscopy coupled with multivariate analysis can be an alternative to traditional methods to predict quality parameters in craft beer. This study aims to develop prediction models based on FT-MIR spectroscopy to simultaneously quantify quality parameters (color, specific gravity, alcohol volume, bitterness, turbidity, pH, and total acidity) in craft beer. Additionally, principal component analysis (PCA) was applied, and it was possible to classify craft beer samples according to their style. Partial least squares (PLS1) developed the best predictive model by obtaining higher R2c (0.9999) values and lower standard error of calibration (SEC: 0.01-0.11) and standard error of prediction (SEP: 0.01-0.14) values in comparison to the models developed with the other algorithms. Specific gravity could not be predicted due to the low variability in the values. Validation and prediction with external samples confirmed the predictive capacity of the developed model. By making a comparison to traditional techniques, FT-MIR coupled with multivariate analysis has a higher advantage, since it is rapid (approximately 6 min), efficient, cheap, and eco-friendly because it does not require the use of solvents or reagents, representing an alternative to simultaneously analyzing quality parameters in craft beer.

Rapid analysis of eucalyptus oil adulteration in Moroccan rosemary essential oil via GC-FID and mid-infrared spectroscopy

Essential Oil (EO) extracted from Rosemary is known for its therapeutic, antifungal, stimulant and antibacterial effects. This study aimed to detect and quantify the adulteration of Rosemary essential oil with different percentages of eucalyptus essential oil, using two analytical techniques: gas chromatography with Flame Ionization Detection (GC-FID) and Fourier Transform Mid-infrared spectroscopy (FT-MIR), combined with chemometric tools such as Principal Component Analysis (PCA), Partial Least Squares regression (PLS-R) and support vector regression (SVR). The use of PCA on the results obtained from GC-FID and FT-MIR indicates the possibility of categorizing the data into two distinct groups: adulterated essential oil and non-adulterated essential oil. However, it is noted that GC-FID can only detect adulteration starting from 40%, while spectroscopy is capable of detecting lower percentages of adulteration. The use of PLS-R and SVR calibration models for adulteration quantification demonstrates high performance capabilities for both techniques (GC-FID and FT-MIR), as indicated by high R2 correlation coefficients indicating good fit, with lower root mean square error (RMSE) values demonstrating predictive accuracy. The results suggest that FT-MIR spectroscopy is preferable to GC-FID for the quantification and discrimination of adulterated essential oils. FT-MIR spectroscopy is considered superior to GC-FID due to its non-destructiveness, speed and lack of sample preparation.

Carbamoylase-based impedimetric electronic tongue for rapid detection of paralytic shellfish toxins

Phytotoxins produced by marine microalgae, such as paralytic shellfish toxins (PSTs), can accumulate in bivalve molluscs, representing a human health concern due to the life-threatening symptoms they cause. To avoid the commercialization of contaminated bivalves, monitoring programs were established in the EU. The purpose of this work is the implementation of a PST transforming enzyme—carbamoylase—in an impedimetric test for rapid simultaneous detection of several carbamate and N-sulfocarbamoyl PSTs. Carbamoylase hydrolyses carbamate and sulfocarbamoyl toxins, which may account for up to 90% of bivalve toxicity related to PSTs. Conformational changes of carbamoylase accompanying enzymatic reactions were probed by Fourier transform mid-infrared spectroscopy (FT-MIR) and electrochemical impedance spectroscopy (EIS). Furthermore, a combination of EIS with a metal electrode and a carbamoylase-based assay was employed to harness changes in the enzyme conformation and adsorption on the electrode surface during the enzymatic reaction as an analytical signal. After optimization of the working conditions, the developed impedimetric e-tongue could quantify N-sulfocarbamoyl toxins with a detection limit of 0.1 µM. The developed e-tongue allows the detection of these toxins at concentration levels observed in bivalves with PST toxicity close to the regulatory limit. The quantification of a sum of N-sulfocarbamoyl PSTs in naturally contaminated mussel extracts using the developed impedimetric e-tongue has been demonstrated.

Prediction of cholesterol content in gallstones by FTIR spectroscopy coupled with chemometric tools

Gallstones are stone-like deposits that most often develop inside the human gallbladder. In this study, the quantification of cholesterol gallstones was monitored using the Fourier transform mid-infrared (FT-MIR) spectroscopy technique and chemometrics tools. To detect the presence of outliers in the spectral data before developing a prediction model, principal component analysis (PCA) was utilized for qualitative analysis. The application of the partial least square (PLS) calibration models for the quantification of cholesterol demonstrated high-performance capabilities, as indicated by high values of correlation coefficients R2 greater than 0.999, and lower values of root mean square error (RMSE) less than 0.493. According to our results, FT-MIR spectroscopy combined with chemometrics approaches can be used successfully as a simple, quick, and non-destructive method for the quantification of cholesterol in biliary lithiasis.

Energy balance of dairy cows predicted by mid-infrared spectra data of milk using Bayesian approaches

Information on dry matter intake (DMI) and energy balance (EB) at the animal and herd level is important for management and breeding decisions. However, routine recording of these traits at commercial farms can be challenging and costly. Fourier-transform mid-infrared (FT-MIR) spectroscopy is a noninvasive technique applicable to a large cohort of animals that is routinely used to analyze milk components and is convenient for predicting complex phenotypes that are typically difficult and expensive to obtain on a large scale. We aimed to develop prediction models for EB and use the predicted phenotypes for genetic analysis. First, we assessed prediction equations using 4,485 phenotypic records from 167 Holstein cows from an experimental station. The phenotypes available were body weight (BW), milk yield (MY) and milk components, weekly-averaged DMI, and FT-MIR data from all milk samples available. We implemented mixed models with Bayesian approaches and assessed them through 50 randomized replicates of a 5-fold cross-validation. Second, we used the best prediction models to obtain predicted phenotypes of EB (EBp) and DMI (DMIp) on 5 commercial farms with 2,365 phenotypic records of MY, milk components and FT-MIR data, and BW from 1,441 Holstein cows. Third, we performed a GWAS and estimated heritability and genetic correlations for energy content in milk (EnM), BW, DMIp, and EBp using the genomic information available on the cows from commercial farms. The highest correlation between the predicted and observed phenotype (ry,y^) was obtained with DMI (0.88) and EB (0.86), while predicting BW was, as anticipated, more challenging (0.69). In our study, models that included FT-MIR information performed better than models without spectra information in the 3 traits analyzed, with increments in prediction correlation ranging from 5% to 10%. For the predicted phenotypes calculated by the prediction equations and data from the commercial farms the heritability ranged between 0.11 and 0.16 for EnM, DMIp and EBp, and 0.42 for BW. The genetic correlation between EnM and BW was -0.17, with DMIp was 0.40 and with EBp was -0.39. From the GWAS, we detected one significant QTL region for EnM, and 3 for BW, but none for DMIp and EBp. The results obtained in our study support previous evidence that FT-MIR information from milk samples contribute to improve the prediction equations for DMI, BW, and EB, and these predicted phenotypes may be used for herd management and contribute to the breeding strategy for improving cow performance.

Characterization of petcoke and derived oxygen-functionalized products accompanied by XRD, FT-MIR and SS-NMR

This work applies Solid-State Nuclear Magnetic Resonance (SS-NMR) to characterize structural and motional aspects of petcoke and its derived humic-like products from alkaline wet oxidation. Heteronuclear dipolar coupling modulation schemes are used to characterize local mobility and structural heterogeneity of condensed aromatic layers of petcoke and its oxygen-functionalized products. The analytical agreements between heteronuclear H–C dipolar modulated and direct polarization SS-NMR schemes, C/O/H elemental analysis, X-ray diffraction and Fourier-Transform Mid-Infrared (FT-MIR) spectroscopy confirm that alkaline wet oxidation of petcoke provides structural heterogeneity and flexibility in direct correlation to an increase of oxygen content, an increase of O/C ratio, reduction of aromatic stack layers, reduction of crystallite size and increase of C–O, and O–H dipoles within hydrophobic aromatic ring frameworks submitted to multiple oxygen functionalization chemical processes. For the first time, a technique is proposed to distinguish between humic and fulvic acids analogs prepared from petcoke alkaline wet oxidation by means of 13C-CPMAS, 1H- and 13C-DPMAS experiments. Fulvic acid analogs with high non-paramagnetic ash content show 13C profiles only by direct polarization, not by equivalent heteronuclear dipolar coupling polarization-transfer.These analytical techniques propose orthogonality amongst characterizing alkaline wet oxidation methodologies applied to petcoke for valorization into humic-like substances.

Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) as a Method of Identifying Contaminants in Sugar Beet Production Process-Case Studies.

Food safety has received considerable attention in recent years. Methods for rapid identification of a variety contaminants in both the final product and the manufacturing process are constantly developing. This study used Fourier Transform Mid-Infrared Spectroscopy (FT-MIR) spectroscopy to identify various contaminants endangering white sugar production. It was demonstrated that inorganic compounds (calcium carbonate-CaCO3), plastic contaminants (polypropylene), and oily contaminants (compressor sealing and lubrication lubricant) can be identified with a high degree of precision. FT-MIR spectroscopy was proved to be a useful technique for detecting sugar contaminants rapidly and precisely even without the application of a sophisticated spectra analysis. Commercial databases of reference spectra usage significantly simplify and facilitate the application of this method.

The spatial differences of the Chinese sauce aroma liquor Daqu and identification by FT-MIR spectroscopy

Chinese sauce aroma liquor (CSL) is one of main types of Baijiu. As the fermentation starter and raw material, CSL Daqu (CSDQ) influences the flavor formation of CSL. Owing to the spatial difference during processing, fermented CSDQ has three types and the quality of a CSDQ is inconsistent. Traditional empirical method has limitations in classification precision and standardization. In this study, the subtype was defined by the layers and types of CSDQ. Differences of CSDQs were systematically evaluated by colorimetric, chemical, and Fourier transform mid-infrared spectroscopy (FT-MIR) analysis. Linear discriminant analysis (LDA), partial least squares-discriminant analysis (PLS-DA), and k-nearest neighbor method (KNN) algorithms were used to classify the subtypes of CSDQ based on the FT-MIR spectra. Furthermore, the models were optimized, and the wavenumbers with high discrimination were marked by chemical bonds. FT-MIR LDA model achieved 95.61% accuracy after proper preprocessing. An even sampling method was proposed to represent the overall properties of a CSDQ. These findings provided a better understanding of CSDQ and a theoretical basis for promoting industrial standardization.

One-year variation in quantity and properties of microplastics in mussels (Mytilus galloprovincialis) and cockles (Cerastoderma edule) from Aveiro lagoon

As filter feeders, marine bivalves inhabiting estuarine and coastal areas are directly exposed to microplastics (MPs) in water. To assess whether MPs number, and their shape, size, colour, and polymer type present in mussels (Mytilus galloprovincialis) and cockles (Cerastoderma edule) varied over one year, bivalves were collected over the year of 2019 in the lower part of the coastal Aveiro lagoon, Portugal. After extraction from the bivalve's whole-body soft tissues, a subset of the visually inspected particles was randomly separated for identification using the Fourier-transform mid-infrared (FT-MIR) spectroscopy. A fraction of the inspected particles, 26–32% of particles >100 μm, and 59–100% of smaller ones were confirmed as MPs. Concentrations varied within the intervals of 0.77–4.3 items g−1 in mussels and 0.83–5.1 items g−1 in cockles, with the lowest values observed in January. In winter, the accumulation of large-sized fibers was composed of a mixture of plastic types, which contrasted against the most abundant MPs in summer consisting mainly of polyethylene of diverse size classes and shapes. Temperature decrease registered in winter might have triggered a lower filtration rate, resulting in lower MPs concentrations in the whole-soft body tissues of organisms. Different properties of MPs found in bivalves between January–February and August–September appear to reflect changes in the characteristics of MPs available in the Aveiro lagoon.

Molecular characterization of green lentil flours using synchrotron X-rays and Fourier transform mid-infrared techniques

This study examines the molecular changes in roller-milled green lentil flours as a consequence of mill settings and blending of fractions. Traditional laboratory instruments such as small-angle X-ray scattering and powder X-ray diffraction have limited resolution, which makes it challenging to explore the structure-function interactions of flours. Herein, super high-resolution synchrotron X-rays, Fourier transform mid-infrared spectroscopy (FTIR), and laser diffraction data conclude that flour blends differ from streams in particle size, crystallinity, and starch/protein quality. A principal component analysis model was also developed to classify samples based on streams and blends. Classification of samples was done using carbohydrate (1200–900 cm−1) and protein (1700–1500 cm−1) regions. The state-of-the-art techniques used in this study unravel a deeper understanding of the macromolecular changes that take place in flours during milling. This seminal information will be key to the food industry in utilizing pulse flours for developing and creating nutritionally dense foods.

Spectrochemical analysis of seasonal and sexual variation of antioxidants in Corema album (L.) D. Don leaf extracts

Bioactive phytoconstituents have been increasingly investigated for their potential human health benefits. Corema album (L.) D. Don, an Ericaceae, reportedly has antioxidant, antimicrobial and anticancer properties. Aiming at enhancing its nutraceutical potential, we performed a spectrochemical analysis of hydroethanolic extracts from C. album leaves. We report on changes in the antioxidant activity of the extracts, as well as in the accumulation of key phytoconstituents (namely phenolic compounds), in female and male samples, throughout three harvesting seasons (February, July, and October). For each extract, the antioxidant activity was assessed by different spectrophotometric methods. Simultaneously, attenuated total reflectance Fourier transform mid-infrared spectroscopy (FTIR-ATR), and high-performance liquid chromatography – electrospray ionisation – quadrupole time-of-flight mass spectrometry (HPLC-ESI-Q-TOF-MS), were used to identify and monitor variations in the composition of phenolic compounds in the extracts. The main compounds identified were epicatechin, laricitrin-O-hexoside isomers, and myricetin-O-hexoside isomers. Significant differences were found in the composition and relative abundance of the compounds of interest, according to sex and season. Overall, a trend was observed whereby phenolic content and antioxidant activities were higher in males and increased between the earlier and the latest harvests. Based on these results, we may conclude that late summer or early autumn harvests are preferable when aiming at the highest yearly content of bioactive compounds. Additionally, it should be considered that extracts from male individuals typically display higher antioxidant activities. Ultimately, our understanding of C. album in the context of nutraceutical applications is benefited from the quantitative and qualitative portrait provided here, thus promoting its relevance as a source of bioactive compounds.

A new effective method for identifying boletes species based on FT-MIR and three dimensional correlation spectroscopy projected image processing

This study proposed the necessity of identifying the species for boletes in combination with the medicinal value, nutritional value and the problems existing in the industrial development of boletes. Based on the preprocessing of Fourier transform mid-infrared spectroscopy (FT-MIR) by 1st, 2nd, SNV, 2nd + MSC and 2nd + SG, Multilayer Perceptron (MLP) and CatBoost models were established. To avoid complex preprocessing and feature extraction, we try deep learning modeling methods based on image processing. In this paper, the concept of three-dimensional correlation spectroscopy (3DCOS) projection image was proposed, and 9 datasets of synchronous, asynchronous and integrative images are generated by computer method. In addition, 18 deep learning models were established for 9 image datasets with different sizes. The results showed that the accuracy of the three types of synchronous spectral models reached 100%, while the accuracy of the asynchronous spectral and integrative spectral models of 3DCOS projection images were 96.97% and 97.98% in the case of big datasets, which overcame the defects of poor modeling effect of asynchronous spectral and integrative spectral in previous two-dimensional correlation spectroscopy (2DCOS) studies. In conclusion, the modeling results of 3DCOS projection images are perfect, and we can apply this method to other identification fields in the future.

Machine learning and deep learning based on the small FT-MIR dataset for fine-grained sampling site recognition of boletus tomentipes

This study proposed the necessity of identifying the sampling sites for Boletus tomentipes (B.tomentipes) in combination with cadmium content and environmental factors. Based on fourier transform mid-infrared spectroscopy (FT-MIR) preprocessing by 1st, 2nd, MSC, SNV and SG, five machine learning (ML) algorithms (NB, DT, KNN, RF, SVM) and three Gradient Boosting Machine (GBM) algorithms (XGBoost, LightGBM, CatBoost) were built. To avoid complex preprocessing, we construct BoletusResnet model, propose the concepts of 3DCOS, 3DCOS projected images, index images in addition to 2DCOS, and combine them with deep learning (DL) for classification for the first time. It shows that GBM has higher accuracy than ML and DL has better accuracy than GBM. The four DL models presented in this paper achieve fine-grained sampling sites recognition based on small samples with 100 % accuracy, and a computer application system was developed on them. Therefore, spectral image processing combined with DL is a rapid and efficient classification method which can be widely used in food identification.

Establishment and Validation of Fourier Transform Infrared Spectroscopy (FT–MIR) Methodology for the Detection of Linoleic Acid in Buffalo Milk

Buffalo milk is a dairy product that is considered to have a higher nutritional value compared to cow's milk. Linoleic acid (LA) is an essential fatty acid that is important for human health. This study aimed to investigate and validate the use of Fourier transform mid-infrared spectroscopy (FT-MIR) for the quantification of the linoleic acid in buffalo milk. Three machine learning models were used to predict linoleic acid content, and random forest was employed to select the most important subset of spectra for improved model performance. The validity of the FT-MIR methods was evaluated in accordance with ICH Q2 (R1) guidelines using the accuracy profile method, and the precision, the accuracy, and the limit of quantification were determined. The results showed that Fourier transform infrared spectroscopy is a suitable technique for the analysis of linoleic acid, with a lower limit of quantification of 0.15 mg/mL milk. Our results showed that FT-MIR spectroscopy is a viable method for LA concentration analysis.

Toward the Non-Targeted Detection of Adulterated Virgin Olive Oil with Edible Oils via FTIR Spectroscopy & Chemometrics: Research Methodology Trends, Gaps and Future Perspectives.

Fourier-Transform mid-infrared (FTIR) spectroscopy offers a strong candidate screening tool for rapid, non-destructive and early detection of unauthorized virgin olive oil blends with other edible oils. Potential applications to the official anti-fraud control are supported by dozens of research articles with a "proof-of-concept" study approach through different chemometric workflows for comprehensive spectral analysis. It may also assist non-targeted authenticity testing, an emerging goal for modern food fraud inspection systems. Hence, FTIR-based methods need to be standardized and validated to be accepted by the olive industry and official regulators. Thus far, several literature reviews evaluated the competence of FTIR standalone or compared with other vibrational techniques only in view of the chemometric methodology, regardless of the inherent characteristics of the product spectra or the application scope. Regarding authenticity testing, every step of the methodology workflow, and not only the post-acquisition steps, need thorough validation. In this context, the present review investigates the progress in the research methodology on FTIR-based detection of virgin olive oil adulteration over a period of more than 25 years with the aim to capture the trends, identify gaps or misuses in the existing literature and highlight intriguing topics for future studies. An extensive search in Scopus, Web of Science and Google Scholar, combined with bibliometric analysis, helped to extract qualitative and quantitative information from publication sources. Our findings verified that intercomparison of literature results is often impossible; sampling design, FTIR spectral acquisition and performance evaluation are critical methodological issues that need more specific guidance and criteria for application to product authenticity testing.