The global cost of corrosion imposes a significant burden on society, with safety and environmental consequences in addition to its direct impact. One challenge to its management is that the process of corrosion is complex, and common experimental techniques provide only limited information. The neutral salt spray test, a standardized test used for the evaluation of the corrosion resistance of steels and coatings, creates an aggressive environment not suitable for many analytical methods. Here, we report the first use of in situ Raman spectroscopy to detect the formation, growth, and evolution of corrosion products on metal surfaces, monitoring a mild steel plate in alternating salt fog and dry atmospheric conditions over a period of eight days to identify and track key corrosion products. Assisted by multivariate curve resolution alternating least squares (MCR-ALS) analysis and online weighing of the sample, we found the chemical conversion of iron hydroxides to oxides takes much longer than the physical drying of the corroded surface. Together, these results pave the way for real-time online observation of corrosion inside a salt fog chamber to obtain chemically relevant information with a single, compact apparatus.

Corrosion is the process of degradation of metals because of atmospheric phenomena or environmental conditions, incurring costs of approximately 3.4% of the global gross domestic product (1). To combat corrosion, many coatings and alloys have been developed to extend the life span of steel products. Research institutions and industries, including but not limited to the steel, automotive, oil and gas, and aerospace industries, have adopted different techniques to understand, predict, and ultimately control corrosion-related processes (2). The key to improving these techniques is a better understanding of the corrosion mechanisms involved.

Standardized tests, such as the neutral salt spray (NSS) test, are used to accelerate the corrosion process for research purposes (3). Samples are placed in a chamber with a fog of neutral 5% NaCl solution at 35 °C. Once the samples are corroded in the chamber, they are removed and can be analyzed with a variety of methods (4–6). Raman spectroscopy is a common analysis technique that can provide detailed chemical information with high specificity (7–9).

In contrast to such offline analyses, here we present for the first time a setup that collects in situ Raman spectra inside a working salt fog chamber, demonstrating the feasibility of online investigation of the corrosion process in mild steel samples (also known as low carbon steel).

In this study, we used a custom tabletop corrosion chamber constructed from a glass basin 45 x 25 x 30 cm in size (see Figure 1). A solvent-cleaned sample of 10 x 10 cm mild steel (DC04, voestalpine Stahl GmbH) was corroded for 8 days as follows: 48 h of salt fog, 48 h of drying, an additional 72 h of salt fog, and a further 24 h of drying. To generate salt fog, the basin was filled with several centimeters of a neutral 5.0 wt% sodium chloride solution (NaCl, Pharmasal, Salinen Austria AG). The salt fog was produced by a floating ultrasonic fog generator, and homogeneously dispersed with a stirrer. To monitor sample weight changes, the steel plate under study was suspended from a balance which was mounted on top of the chamber. The sample was placed at a 20° angle from vertical to facilitate run-off of the condensed salt water. During the salt fog experiments, the temperature of the sample and chamber was kept at 35±2 °C. These settings are similar to standardized tests such as EN ISO 9227 NSS (3) or ASTM B117 (10). The drying was performed in laboratory atmosphere conditions at 23±2 °C with 40–60% relative humidity.

The in situ Raman measurements were performed with a hermetically enclosed Raman probe pointed perpendicular to the sample surface and observed the same spot throughout the experiment. The working distance was kept to within ±0.5 mm of the optimal working distance of 11 mm to maintain good signal-to-noise (S/N) ratio, maintaining a 90° sample-laser angle for optimal signal intensity. The presence of salt fog in the chamber reduced the Raman signal intensity by approximately 50%.

The Raman system used for monitoring was comprised of a WP-785-R-SR-LMMFC-25 spectrometer (Wasatch Photonics) with an integrated diode laser (450 mW, 785 nm), connected to an RP-785 Raman probe (Wasatch Photonics) using a 600 μm core SMA-coupled detection fiber and a 550 μm core FC-coupled laser delivery fiber. The laser spot size on the sample was 900 μm, large enough to prevent local heating of the corrosion products, which could induce background emission in the Raman spectrum. Spectra were recorded at 6-min intervals with an integration time of 1 s, and 25 averages. The laser was turned off between acquisitions, and a new averaged dark spectrum was acquired prior to every measurement. The laser power was set to the full 450 mW during the salt fog periods, but reduced to 300 mW during dry conditions to avoid local sample heating.

Figure 2 shows examples of Raman spectra obtained for the corrosion process of the sample during the four phases of the experiment (salt fog I and II, drying phases I and II). Before the start of corrosion (0 h), the signal represents the broadband Raman signature of the encapsulation windows and focusing lens.

The emergence of the low-intensity bands at 297 and 378 cm

 (marked with solid diamonds) during the first salt fog phase marks the onset of corrosion with appearance of α-FeO(OH), also known as goethite (11–12). During the first drying phase, bands with more significant Raman emission at 289, 402, 489, and 604 cm

 (marked with solid triangles) were observed. Using library spectra (13) and literature spectra (14,15) with very similar band positions, we assign these bands to hematite (α-Fe

). Therefore, we interpret these spectral changes as the drying of the iron oxyhydroxides to iron oxides through the removal of chemically bound water.

Additional features were observed at 647 cm

 (marked with the open triangle and the star, respectively). Based on library spectra, we assign these bands to magnetite (Fe

) (17), respectively. We interpreted the emergence of the siderite band as an indication of a chemical reaction between the sample surface and CO

 from the laboratory atmosphere during the drying phase.

During the second salt fog phase, we observed bands at 306, 346, 376, and 525 cm

 (marked with the open diamonds). We assign these bands to lepidocrocite (γ-FeO[OH]) (18). An additional band at 649 cm

 was observed, and can again be assigned to magnetite (marked by an open triangle, as before). The persistence of magnetite throughout the salt fog phase can be explained by the stable nature of this iron oxide.

During the second drying phase, many of the aforementioned bands attributable to iron oxyhydrides and oxides reappeared (as marked), namely hematite, lepidocrocite, and magnetite, although it is hard to distinguish the α and γ oxyhydroxides bands around 376 cm

. Hence, the nature of the exact crystal structure present was uncertain.

In addition to qualitative assessment of the chemical changes, in situ Raman observations also allowed the extraction of dynamic information about the corrosion process. Even though corrosion occurs slowly, changes in the spectrum could be observed within a few hours, as shown in Figure 3. The bands at 298 and 376 cm

 (assigned to α-FeO(OH) above) emerged once the sample was exposed to the salt fog.

It was also possible to monitor and analyze a shift in the position of the dominant Raman band during drying phase I and II, as shown in Figure 4, revealing a shift in intensity from the band at 374 cm

. We interpret this spectral change as the transformation from FeO(OH) to α-Fe

The position of the main peak within this spectral window was determined through quadratic interpolation across the band maximum. Additionally, as shown in Figure 5, we compared the resulting shift of the maximum Raman peak position (in blue) with the recorded weight changes (in green). The sudden weight changes were caused by surface water evaporation in the drying phase and sample wetting in the salt fog phase, respectively, and take approximately 1–2 h to occur.

Comparing the sudden weight changes during drying phase I to the change in Raman band position, we find a much more gradual change in the spectrum, taking about 30 h. Based on earlier band assignments, this spectral change can be attributed to the chemical transformation from α-FeO(OH) to α-Fe

. For the second drying phase, Figure 4 illustrates a decreasing band at 376 cm

, leading to the appearance of an instantaneous transition of the maximum in Figure 5. We assign this change to the transformation from γ-FeO(OH) to α-Fe

, which may explain the difference between drying phases I and II.

Using in situ Raman spectroscopy, it was possible to identify the chemical transformations and also determine their timelines. Access to this information would be of significant benefit to both fundamental corrosion research and in the development of new coatings and alloys.

The complete set of in situ Raman spectra recorded during the week-long experiment contains a full representation of the chemical changes during this corrosion process. Multivariate statistical analysis tools can be used to transform the full set of spectra into a sum of spectra from individual chemical species, each with a contribution varying as a function of time. Therefore, this analysis provides a potential chemical interpretation of the process based on the recorded set of in situ Raman spectra.

One such analysis is multivariate curve resolution (MCR), often using alternating least squares (ALS) during the fitting process. The analysis is dependent on the chosen starting values; here, we use the orthogonal projection approach (OPA) to estimate the time changes of each species’ contribution. Five components were fitted, with the four main contributions discussed below. This number was determined through a combination of principal component analysis (PCA), chemical intuition, and trial and error. It should be noted that MCR-ALS has difficulties separating spectral contributions that are significantly correlated in time.

The MCR-ALS analysis resulted in spectra for four components, shown in Figure 6, which were assigned to chemical species based on the spectra referenced above. Three components were assigned as “FeO(OH),” “Fe

.” However, the fourth component was not clearly identifiable and is hence simply labeled as “other components.”

Based on these component spectra, the MCR-ALS analysis generated a visualization of the change in each component’s contribution to the experimental spectrum as a function of time, as shown in Figure 7. These plots represent the dynamics of the process, observed with in situ Raman spectroscopy, but interpreted from a chemical perspective. However, as MCR-ALS is a fitting technique, the results can only show one potential representation of the experimental spectral data set.

The MCR-ALS results show even more clearly the slow transition from oxyhydroxides to oxides during the drying phases of the experiment, both in the disappearance of the “FeO(OH)” contribution, as well as in the appearance of the “Fe

“ contribution. The sudden jump in the various contributions during the first drying phase most likely stems from a small shift in the observed location, or some macroscopic change in the monitored spot on the sample surface.

This study shows how in situ Raman spectroscopy can facilitate the investigation of surface chemistry processes, such as the corrosion of mild steel, even in a harsh salt fog environment. Detailed chemical insights into the corrosion process that would otherwise be inaccessible can be obtained.

By performing a cyclical corrosion experiment consisting of alternating salt fog and drying periods for 1–3 d each, we find that initially α-FeO(OH) is formed after about 2 h of salt fog exposure. During the first drying phase, a slow change to α-Fe

 was accompanied by the formation of iron carbonate and magnetite; while the former rapidly converts to a mixture of γ-FeO(OH) and α-FeO(OH) during the second salt fog phase, some magnetite remains on the surface. The second drying period leads to a continuous conversion from iron oxyhydroxides to iron oxides. The results show that physi- cal drying of the sample occurred within 2 h (in contrast to the chemical conversion, which took about 24 h), with MCR-ALS analysis of the spectra revealing the detailed dynamics of these transformations.

The results confirm that a compact Raman spectroscopy setup can be used to monitor and understand complex corrosion processes taking place in a custom salt fog chamber, allowing new insights to be generated for academic and industrial corrosion investigations. The coupling of in situ Raman spectroscopy and a salt fog chamber represents a valuable tool for future investigations of corrosion mechanisms, and for evaluating the resistance of newly developed coatings and alloys.

The Comet Center CEST is funded within the framework of COMET—Competence Centers for Excellent Technologies by BMVIT, BMDW as well as the Province of Lower Austria and Upper Austria. The COMET program is run by FFG. This work originates from research in iMESA (FFG 865864, CEST-K1, 2019–2022) project. The authors gratefully thank voestalpine Stahl GmbH and Recendt GmbH for support, and Wasatch Photonics for providing the Raman spectroscopy system.

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(18) Raman spectrum RHX145, KnowItAll Informatics System 2021 (John Wiley & Sons, Inc., Hoboken, NJ, 2001–2021).

 are with the CEST Competence Center for Electrochemical Surface Technology GmbH in Linz, Austria. Dieter Bingemann and Cicely Rathmell are with Wasatch Photonics. 

 are with voestalpine Stahl GmbH in Linz, Austria. 

 are with the Vienna University of Technology in Vienna, Austria. 

 are with the Research Center for Non Destructive Testing GmbH in Linz, Austria. Direct correspondence to: 

Articles

In this study, in situ Raman spectroscopy was used to detect the formation, growth, and evolution of corrosion inside a salt fog chamber. These results pave the way for monitoring the real-time observation of corrosion on metal surfaces.

In situ Raman Spectroscopy Monitors the Corrosion of Mild Steel in a Salt Fog Chamber

A transdermal patch is a method of drug delivery through the skin into the bloodstream that is composed of layers that store and release the active pharmaceutical ingredient (API) over time. The ability to analyze these layers without damaging the patch allows for quality control and stability measurements. Raman spectroscopy has excellent chemical identification abilities, thanks to the fingerprint like spectrum it produces, that deliver rapid discrimination between components typically found in transdermal patches, such as polymers and APIs. Coupling this with a confocal microscope gives high spatial resolution on all three axes, with the pinhole providing otherwise unachievable axial resolution, permitting depth profile analysis. Using this technique, the patch can be analyzed from top to bottom without any risk of damage. This article highlights the use of confocal Raman microscopy to acquire a 3D Raman map of a transdermal patch for layer evaluation. The map revealed six layers which were composed of four polymers and the API layer at the center of the patch.

A transdermal patch is a method of drug delivery through the skin into the bloodstream (Figure 1). The patch contains a drug that absorbs into the skin; the patch releases a consistent and controlled amount of medication into the body over long periods time. This method of delivery is preferred in cases where the drug can have negative side effects on the gastrointestinal tract, for prolonged drug release, or as an option for people with a fear of needles looking to avoid medication delivered via injection.

One of the most common uses for transdermal patches is to help combat nicotine addiction. Nicotine patches were first developed in the 1980s as a method to help people quit smoking after several studies proved the absorption of nicotine into the body reduced cravings. Nicotine replacement therapy (NRT) releases small amounts of nicotine throughout the day, without the additional harmful components of cigarettes, such as tar. A recent review of data from the public and from clinical trials revealed that using NRT can increase the chance of successfully quitting smoking by up to 60% (1).

Analyzing multilayer polymers, such as transdermal patches, can provide manufacturers and researchers with important information on the quality of their product. Probing into the layers allows validation of the amount and distribution of the active pharmaceutical ingredient (API) within a patch, as well as identifying any contaminants. Achieving this without sample damage or alteration is critical for providing meaningful data on real-life products. Raman microscopy is an extremely sensitive technique for polymer identification, and coupling to a confocal microscope allows high spatial resolution in the 

-axis for investigating different layers.

Raman spectroscopy can be used for identification of polymers due to its fingerprint-like level of sensitivity. Each peak in a Raman spectrum can be thought of as the fingerprint of a specific molecular vibration from the sample, meaning polymer spectra can easily be distinguished. For example, the technique is sensitive enough to identify co-polymers and determine differences between high- and low-density polyethylene. Furthermore, its non-destructive nature allows analysis of samples to be analyzed without any damage or need for sample preparation (2).

Databases of known polymers can be developed, meaning unknown samples can be compared to these spectra for identification, thus adding to the speed and accuracy of polymer identification by Raman spectroscopy. For example, the database applies a best fit comparison of the measured spectra with those in its library, and carries out a series of optimized corrections to provide the best possible match against its database.

The skill set of Raman spectroscopy can be further enhanced by incorporating a confocal microscope where the Raman laser will be directed though a microscope containing a pinhole, enabling the analysis volume to be defined with simultaneously high lateral (

) resolution, allowing the analysis of discrete sample features and layers down to less than 1 μm in size (3).

When laser light is focused into a sample by a Raman microscope, a three-dimensional excitation volume is formed within the sample. This volume is known as the 

, and a representation of its shape is shown in Figure 2a. As the light converges towards the focal point, the diameter of the PSF decreases, reaching a diffraction-limited minimum diameter at the focal plane followed by an increase in diameter as the light diverges. The exact shape of the PSF is heavily dependent on the optical properties of the sample, such as opacity at the excitation wavelength, refractive index, and how it scatters the incident excitation. Raman scattering will occur throughout the entire excitation volume, and simply measuring the Raman spectrum from this excitation would, therefore, result in spectral contributions from the entire volume with limited axial (

The role of the confocal pinhole is to spatially filter the analysis volume so that the Raman scatter is detected from the focal plane only; its operation is illustrated in Figures 2b-e. Raman scatter that originates either above Figure 2c or below Figure 2d the focal plane is not brought to a focus in the confocal plane; rather, it is blocked by the confocal pinhole and not detected. In contrast, Raman scatter that originates from the focal plane is brought to a focus in the confocal plane and passes through the pinhole and is detected. This is the meaning of the word confocal; to share the same focal point, and Raman scatter from inside the excitation focus will also be in focus at the pinhole (4).

The addition of the pinhole allows for 3D analysis, which is particularly useful for multilayer samples, such as composite plastics. The polyethylene terephthalate (PET) and polyvinyl chloride (PVC) polymers have unique Raman spectra, and the Raman depth profile can identify the discrete layers within the PET-PVC-PET polymer stack. It can clearly be seen in Figure 3 that, as the diameter of the pinhole is decreased from 100 μm to 25 μm, it becomes increasingly effective at blocking the out-of-focus scatter (increased confocality), and the effective axial resolution improves. Using narrow pinhole diameters, axial resolutions of <1 μm can be achieved. The tradeoff with narrowing the pinhole diameter is that the throughput of the microscope is significantly lowered, and measurements will therefore take longer, due to decreased Raman scatter intensity at the detector. Due to this tradeoff, most Raman microscopes use a variable diameter confocal pinhole enabling the microscope mode to be adjusted from high confocality/low throughput to non-confocal/high throughput, depending on the application.

An Edinburgh Instruments RMS1000 Raman Microscope was used for the 3D measurement of a transdermal nicotine patch. The nicotine patch was a commercially available NRT purchased from a pharmacy. The microscope was equipped with a 100 x 0.9 numerical aperture (NA) objective; this also ensures high resolution. The choice of objective lens is important as the NA of the objective lens and the laser wavelength determine laser spot size on the sample and the spatial resolution of the Raman mapping.

The 3D Raman map of the patch was acquired with a 785 nm laser at 100% power with an exposure time of 0.5 s and the pinhole set to 200 μm. A slit width of 70 μm was used along with a 600 gr/mm grating and a back illuminated CCD. The total sample height was approximately 4 mm and Raman spectra were acquired every 10 μm in the 

-dimension. The library used for spectral identification was the KnowItAll Raman Database.

The transdermal patch analyzed contained nicotine as the active ingredient, surrounded by polymers. By carrying out a Raman 3D map, we can visualize the different material layers present in the patch, shown in Figure 4 as a 3D render.

The 3D Raman map (Figures 4 and 5) reveals the patch contains six layers, and the spectrum of each layer can be extracted and analyzed for identification. The outer layers of the patch, layers 1 and 6, shown in red, can be identified as poly(ethylene terephthalate) (PET) using the spectral database. The 3D map is colored based on peaks specific to the material identified (for example, for the layers of PET the peak at 1293 cm

 was selected). Layer 2 can be identified as PET/polyisobutylene, labeled in pink, based on the 1233 cm

 peak, and layers 3 and 5, which surround the API, were determined to be polyethylene (PE), highlighted in blue from its peak at 1442 cm

. Finally, in the middle of the polymers is a thick layer of the API, nicotine, embedded in a polymer reservoir (ethylene). This is shown in green, and is attributed to peaks at 1045 cm

. Layer 1 is the release liner of the patch; this layer is responsible for protecting the medication and adhesive, and is removed before application. Layer 2 is the adhesive part of the nicotine patch, layers 3 and 5 are microporous PE which control the release of the API in layer 4, and layer 6 is the backing, which provides protection to the medication while the patch is being worn.

The data presented details how confocal Raman microscopy can be utilized to analyze multilayer polymer samples. In the case of this transdermal patch, the API (in this case, nicotine) can be visualized, as can the surrounding polymers responsible for protecting and ensuring the release of the API. Thanks to the fingerprint-like chemical identification offered by Raman spectroscopy, successful spectral evaluation was possible for each of the different polymers present. The confocal microscope allowed for the 

-axis to be investigated, resulting in layer thickness to also be assessed. The sample was rapidly analyzed and remained intact and unaltered throughout the experimental procedure. In this way, confocal Raman microscopy is a useful technique for pharmaceutical laboratories to quality control their products and ensuring they have been produced correctly without contaminants, as well as to help monitor the stability of adhesive layers.

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 is an applications scientist with Edinburgh Instruments in Livingston, in the United Kingdom. Direct correspondence to: 

As an alternative drug delivery route, transdermal patches control the amount of medication a patient receives. With confocal Raman microscopy it is possible to create a Raman 3D map and thus visualize and analyze the layers of a transdermal patch without damaging it, allowing for quality control and stability measurements.

Analysis of Transdermal Patch Layers via Confocal Raman Microscopy

Fats and oils are ubiquitous in natural and processed foods, providing necessary energy storage. Fat and oil content in foods also have important contributions to the shelf life, texture, compatibility with processing operations, and sensory profiles of food products. Understanding the molecular properties of fats and oils separately within a heterogeneous food matrix requires a multidisciplinary approach. Vibrational spectroscopy techniques are used throughout the food industry to gain product understanding, identify adulterated products, ensure quality, and control processes. In analyzing fats and oils in food, near-infrared (NIR) spectroscopy is an established analytical technique, and there are other growing applications of infrared (IR) and Raman spectroscopies. In particular, Raman spectroscopy is well suited to measure fats and oils because their C-H and C-C bonds are polarizable. In this article, we review the historical use of Raman spectroscopy in studying fats and oils in foods from Fourier transform (FT)–Raman spectroscopy to dispersive Raman spectroscopy. We also provide an overview of various Raman approaches to understand fat compositional heterogeneity in solid foods, identify polymorph or crystallinity, and measure fatty acid saturation. Examples in a variety of fat-containing foods demonstrate feasibility for Raman applications in the laboratory and process environments.

Lipids are a class of biological molecules that provide important energy storage, metabolism, and cell membrane structure functions. They encompass sterols, fatty acids, glycerides, phospholipids, and waxes. Saturated fatty acids are also called fats. Fats have higher melting points, are solid at room temperature, and are more stable. Unsaturated fatty acids are also called oils. Oils have lower melting points, are liquid at room temperature, and are less stable. Understanding lipids for biomedical, food and beverage, and now lipidomics applications, requires a variety of analytical tools and techniques, including chromatography, wet chemistry, molecular probes, mass spectrometry (MS), and vibrational spectroscopy (1–3). Raman spectroscopy and nonlinear Raman spectroscopy techniques are particularly well suited to measure lipids because of a high Raman cross section and high optical scattering. A book, titled 

 by Rich McCreery, starts with the theory and goes into calculating the cross sections of some simple molecules. C-H and C-C bonds are polarizable, and they comprise the majority of a lipid. As a result, it stands to reason that lipids would have a higher Raman cross section than other biological molecules, such as proteins or nucleic acids (4). This assumption was verified by calculating the cross-sections of cholesterol and proteins, where the cross section of the 1440 cm

 band is three times higher than that of proteins (5). Another reason that lipids are easily measured by Raman spectroscopy is because of optical scattering, particularly in biological tissues. The density of the scattering is considered in calculating the Raman intensity, but that calculation only considers Raman scatters. Optical scattering also occurs elastically, so that would affect light fluence and sampling volume. Fatty tissues have a higher optical scattering coefficient than muscle tissues (6,7).

Because of their importance and strong Raman scattering, lipids have been studied by Raman spectroscopy since 1936. A 1936 paper by Jogarao is the first acknowledged paper on Raman spectroscopy analysis of food oils (8). Olive, gingelli, castor, coconut, groundnut oils, cocogem, and buffalo ghee were examined by Raman spectroscopy using an exposure of nearly 48 h to collect a spectrum. Bands at 1302, 1469, 1661, and 2873 cm

 were identified and assigned to C-H, C-O-C, and C=C groups. Other early work by van Den Hende examined various saturated fatty acids and identified cis- and trans-isomers in oleic acid (articles in French) (9,10). Additional early work studying oleic acid was described in papers written in 1945 and 1951, which focused on the chemistry of lipids and contained references to research papers written in 1943 and 1950 that were on Raman spectroscopy. However, those research papers could not be obtained by the authors. An experimental challenge that may have limited the number of Raman-based studies, in addition to the long exposure times, was the ability to purify samples (9). Thus, we can also draw on conclusions from relevant work that was performed in long chain hydrocarbons (11,12), small-chain carboxylic acids (13), and other model compounds (14).

The perception that oil or fat purification was a limitation of the Raman technique resulted in few papers being published in this application again until the 1970s. In the 1970s, studies using Raman spectroscopy to examine lipids received little attention relative to other biopolymers such as proteins, amino acids, and nucleic acids (15). However, the papers that were published in this decade demonstrated an increased sophistication of the Raman technique, including the use of Fourier-transform (FT)–Raman spectroscopy, and the scientific questions that Raman spectroscopy was trying to address. Some example applications include biophysics on mixed lipid-protein systems, measurement of chain length, cis- and trans-isomer composition, lipids in natural products, and classification of lipids (16–20).

Today, dispersive Raman, FT-Raman, enhanced Raman, and nonlinear Raman techniques are used to measure lipids. In particular, dispersive and non-linear techniques are growing in basic science and industrial applications. Readers are referred to excellent reviews by Li and others on coherent anti-Stokes Raman spectroscopy and by Prince and others on stimulated Raman scattering (21,22). This paper focuses on dispersive Raman spectroscopy of oils and fats in food and beverage applications.

A review of the literature was performed in Google Scholar using the terms 

 to describe the technology, and the search terms lipids, oils, fats, and fatty acids were used to describe the target molecule family. The references in several review papers were used to retrace the history of Raman spectroscopy to the earliest papers.

For experiments performed by the authors, such as the fats and oils identification and the salmon and chocolate studies, a Raman Rxn2 (Endress+Hauser) Raman analyzer operating at 785 nm or 1000 nm was used. Raman measurements were performed with a Rxn-10 collimated probe equipped with a non-contact optic, immersion optic, or a Rxn-20 large volumetric probe (Endress+Hauser). Measurements were performed at ambient laboratory conditions of commercially available samples with no additional sample preparation. Data were preprocessed using Grams/AI spectroscopy software (Thermo Scientific) and using routines written in-house.

Industrial Applications of Dispersive Raman

More recent applications have expanded on the historical uses to now include structure elucidation, the role of lipids in health and disease, and lipidomics (23). Within food and beverage applications, there has been an increased use of portable, benchtop, and process Raman spectroscopy in edible oil measurements. We highlight a few applications in oil and fat quality, and oils and fats in foods demonstrate the utility of Raman spectroscopy in the field. They establish scientific feasibility for additional tests on polymorphism, accelerated stability, or coproducts.

Figure 1 shows the Raman spectra of canola, peanut, and avocado oils between 600 and 1775 cm

 as representative oils. Visual inspection of the band position, width, and intensity provides an initial understanding of multiple functional groups. The main bands of fats and oils are noted in Figure 1, with the band assignments referenced in Table I. A parameter of interest is saturation. The position and width of the band at ~1444 cm

 shifts to a lower Raman shift and becomes more narrow with increasing saturation. Total unsaturation can be determined quantitatively by measuring the ratio of the C=C stretch (υC=C) centered at 1661 cm

. Calculating band intensity ratios is a straightforward approach to quantifying total unsaturation. Cis-isomer content can also be determined from the Raman spectrum. The band at 1272 cm

 is attributed to in-plane =C–H deformation in an unconjugated cis double bond. The intensity of this band ratioed to the in-phase methylene twisting vibration at 1306 cm

 provided a direct cis-isomer measurement, which is illustrated by comparing the Raman spectrum of vegetable oil with that of partially hydrogenated vegetable shortening. Hydrogenation results in a preferential decrease in the cis- or trans-isomer ratio.

Authentication, Adulteration, and Blending

An early application was in oil discrimination and identification of adulterated oils. Adulteration is the deliberate and undeclared substitution of low-quality, but indistinguishable using senses, ingredients to hide poor quality or increase the volume of high-value foods. Adulteration represents a significant food safety, labeling, and product quality concern. Olive oils are frequently adulterated because they are high-quality foods, and adulterated oils often are indistinguishable through physical or chemical constants such as iodine value, saponification, refractive index, or density. A 1994 review by Li-Chan summarized advances in chromatographic and spectroscopic techniques to measure chemical composition markers of adulteration including Raman spectroscopy (24). Since the time the review was published, studies have reported application success using FT-Raman spectroscopy, Raman microscopy, handheld Raman, and fiberoptic Raman for a variety of adulterants and data analysis approaches (25–29). A comprehensive three-year longitudinal study by Kwofie and others enabled an understanding of supplier variations and yearly variations for 15 varieties of oils (30). As hardware and chemometric techniques improved along with a better understanding about the field, we saw many sophisticated questions answered. Two recent papers that used Raman spectroscopy to authenticate geographical location, identify adulterants, and discriminate cultivars demonstrate how the hardware and chemometric techniques have significantly improved (31,32).

Univariate or multivariate analyses are useful for Raman-based oil adulteration measurements. Univariate analyses are straightforward and allow for chemical changes to be easily interpreted. Univariate markers of total unsaturation and cis isomer content have been proposed in the literature. Partial least squares (PLS) analysis was successful in identifying canola oil, rapeseed oil, and spent oil adulterants. The principles of measurement for adulteration can be applied toward blending because both involve the identification and quantification of a mixed product. Blending is the deliberate and declared substitution of different ingredients to improve labeling claims or sensory attributes. Some common examples of a blended product are butters blended with margarine or olive oil. Butter blended with margarine, starch, or plant-based oils is also common practice. An important aspect to blending is accurate and rapid quantification because errors can lead to product giveaway or reduce process efficiency. Similar to adulterated products, blended products generally do not change the taste of butter and detection relies upon analysis. Laboratory-based measurements can be expensive and require specially trained staff and consumables. A paper by Nedeljković and others demonstrates the Raman-based identification of margarine in butter–margarine mixtures (33). The motive behind the study was for adulteration purposes, but the study can also be viewed in the context of a product blending application.

In the study by Nedeljković and others, margarine was distinguished from butter using bands at 1268 and 1656 cm

 associated with the C=C double bond and at 973 cm

 associated with the choline group in the phospholipid. Raman-based calculations were similar to known values measured by weight. PLS regression further confirmed the very good overall performance of the model, with the coefficient plot showing large positive values at the locations of the peaks discussed above. For the calibration data, 

= 0.991 and RMSECV = 3.199. For the test data, 

 = 0.994 and RMSEP = 2.757. Principal component analysis (PCA) data showed a relationship between the second principal component and the margarine content. The loading plot confirms that the known chemical moieties of margarine (the C=C double bond and the choline group) vary together with margarine concentration because of the consistently negative peaks at 973, 1268, and 1656 cm

, the intensity of which closely follow the margarine content in the calibration samples. The PCA of model mixtures demonstrate that the three Raman peaks at 973, 1268, and 1656 cm

 can be attributed entirely to vegetable fat in margarine and those peaks can be used to quantify margarine content in butter. This important feature means that the fat does not have to be isolated from the rest of the sample as was necessary in a chromatographic method. The removal of the fat-extraction step is an improvement toward streamlining the analysis for the laboratory or process environment.

Different types of macronutrients are commonly found in foods, with fats, proteins, and carbohydrates being the main macronutrients. Measurement of these macronutrients in a heterogeneous matrix can be achieved using Raman spectroscopy. Milk powders is an example of a common food that contains fats, protein, and carbohydrates. Cheese is another dairy product that contains fats and proteins. Solid milk powders with various concentrations of fats, carbohydrates, and proteins as shown in Table II were obtained and analyzed as-received. All milk powder samples were analyzed in a laboratory using a Raman Rxn2 Hybrid analyzer (λ = 785 nm). The analyzer was equipped with a 6-mm Rxn-20 probe, which provided a 6-mm spot size. The Rxn-20 probe was positioned approximately 25 cm from the solid powder samples. Raman measurements of the milk powders were collected for 2 min directly from open containers or as loosely packed in a plastic container.

Figure 2 shows the representative Raman spectra from four different milk powders. Bands on the spectra arising from lipids (or fats), proteins, and carbohydrates were identified using known literature values (15,34). Importantly, bands from the individual components were well-resolved and enable univariate models for quantification. Although there is spectral overlap of the constituents, the noted areas in Figure 2 can be used to monitor changes in macronutrients. In the example of the 1620–1800 cm

 region, a more narrow shape of the carbonyl stretch envelope at ~1650 cm

 and the emergence of relatively narrow bands at ~1740 and 1301 cm

 indicates a preponderance of lipids in powders 3 and 4. More intense and broader bands in the 1040–1120 cm

 region is indicative of a higher carbohydrate amount in powders 3 and 4. By contrast, a broader carbonyl stretch envelope at ~1650 cm

 and a narrow ring breathing band at ~1005 cm

 indicates an observable presence of proteins in powders 1 and 2.

Fish and meats are other common foods where there are different types of macronutrients, which are mostly proteins and fats. Raman spectra were collected from store-bought samples of Atlantic salmon, wild coho, and smoked salmon. Two experiments were performed. In the first experiment, laboratory tests were performed using a benchtop Raman analyzer (Endress+Hauser) operating at 1000 nm equipped with a small-area (measured area ~100 μm

) contact probe. The probe was manually placed on the surface of the fish and signal collected for 10–60 s. In the second experiment, a large volumetric noncontact probe (measured volume ~3–6 mm

) was used to assess compatibility with processing plant conditions. Spectra were presented without any preprocessing. The initial tests performed on the store-bought salmon indicated a feasibility of 1000 nm excitation for laboratory measurements. The small-area probe was able to collect the signal from each of these zones without significant interference from adjacent zones. Figure 3 shows the representative Raman spectra from Atlantic salmon, where there were visually apparent fat-rich and muscle-rich zones. The Raman spectra were also visually different. The top spectrum contained features from mostly lipids in fat, and the bottom spectrum contained the protein features. The approach of using a small-area contact probe was applicable when more precise zonal measurements were required for quality control purposes. Notably, there were no bands observed from carotenoid pigments in spectra from Atlantic salmon. By contrast, the spectra of wild coho salmon contained a signal from carotenoid pigments in addition to lipid and protein bands (data not shown).

Mock process measurements were performed on Atlantic salmon. The large volumetric probe provides a noncontact measurement over a large volume, and the resulting spectra had contributions from the fatty and muscle portions of the tissue. Measurement times were optimized to be compatible with conveyor belt speeds. The Raman spectra collected at 1, 3, and 5 s were suitable for input into a univariate or multivariate model with good signal-to-noise (S/N) and spectral resolution. Similar to the measurements collected in dairy solids, the width of the amide I envelope at ~1660 cm

 and the presence of the ring breathing band at ~1000 cm

The primary constituents of chocolate are cocoa butter and sugar, and measurement of dark or milk chocolate is a challenge for Raman spectroscopy performed in the visible or shortwave NIR because cocoa solids strongly fluoresce (35). To mitigate fluorescence, FT-Raman and surface-enhanced Raman spectroscopy (SERS) was used to study chocolate, cocoa butter, and fermented cocoa beans (36–39). Recent studies reporting dispersive unenhanced Raman with 1064 nm excitation piqued our curiosity to use a benchtop 1000 nm Raman system for chocolate measurements (40). A 1000 nm Raman Rxn2 analyzer (laser intensity = 100 mW), equipped with an Rxn-10 probe and non-contact optic that resulted in a spot size of 150 μm, was used to collect Raman spectra from commercially available white-, milk-, and dark-chocolate samples (41). Raman spectra were compared with literature studies in cocoa butter polymorphs, lipid extractions of chocolate, and chocolate samples (35,36,42). Figure 4 shows an example dark-chocolate spectrum collected with 1000 nm excitation. Qualitative and quantitative analysis of several functional groups of cocoa butter and sucrose was performed directly from the chocolate Raman spectrum without further sample preparation. Our initial results showed that Raman spectra collected at 1000 nm demonstrated sufficient background reduction to enable observation of bands throughout the fingerprint region for cacao nibs and white-, milk-, and dark-chocolate samples.

Raman spectroscopy is well suited for measuring oils and fats because it provides a highly specific, multicomponent measurement in a process or laboratory environment. The specificity of Raman spectroscopy enables a simple visual inspection or spectral library comparison for qualitative insight or quantification using univariate or multivariate models. Examples in the historical literature and recent laboratory-to-process studies show that there is still much potential in the technique for basic science to process control applications. We are enthusiastic about emerging nonlinear techniques, new applications in darkly colored foods, and their increased use in manufacturing.

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Several types of Raman spectroscopy, including Fourier transform (FT)–Raman and dispersive Raman, are well suited to examine and understand the fat compositional heterogeneity in solid foods, identify polymorph or crystallinity, and measure fatty acid saturation.

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