Rapid identification of organogelator types in oleogel using a near-infrared spectroscopy-based SIMCA model

Abstract

This research explores the potential of near-infrared (NIR) spectroscopy together with Soft Independent Modeling of Class Analogy (SIMCA), a chemometric approach, to promptly identify gelators in oleogels. Formulations of oleogels involved using diverse gelators such as beeswax, carnauba wax, shellac wax, sunflower wax, monoglycerides, and laurel oil as the oil component. Texture analysis, polarized light microscopy (PLM), and X-ray diffraction (XRD) were employed to assess the structural characteristics of the oleogels. These methods unveiled notable differences in the firmness, stickiness, crystalline structure, and crystallinity index based on the type of gelator utilized. Thermal characteristics were evaluated via thermogravimetric analysis (TGA), revealing weight loss ratios and thermal stability changes among the various formulations. The chemometric analysis of NIR spectra led to apparent discriminations among oleogels, demonstrating each gelator’s unique structural and thermal characteristics. The SIMCA analysis revealed high classification accuracy, indicating that the technique could be used for quality control and product authentication in the food and cosmetic industries. This study highlights the significance of NIR spectroscopy for analyzing and monitoring oleogel formulations, highlighting its non-destructive, fast, and environmentally friendly nature.

Graphical Abstract

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NIR spectroscopy together with chemometric methods were successfully classified oleogels according to the different oleogelators used in oleogel production.

Introduction

In recent years, the practice of organogel has become progressively prevalent for structuring liquid edible oils. Since the 1970s, the lubrication industry has gelled oil with 12-hydroxystearic acid to restrict oil mobility once they are applied to metallic surfaces. Organogels contain an organic liquid trapped in a three-dimensional network generated by gelling agents. When an edible oil switches into a liquid state, it is called an oleogel (Hughes et al., 2009; Yılmaz & Öğütcü, 2014).

Oleogels possess various benefits over conventional solid fat production procedures, such as hydrogenation, fractionation, and interesterification. Depending on the oils and gelators used, various oleogels with diverse colors, hardness levels, and spreadability can be produced. Critically, oleogels do not contain trans fats, do not modify the fatty acid composition of the oil, and have low saturated fat content. Among the numerous functions of oleogels in foods, limiting oil mobility, replacing saturated fats, stabilizing emulsions, and controlling the release of bioactive components can be listed. In addition to their use in foods, oleogels have customizable structure and texture, and therefore, they are known as excellent carriers for bioactive compounds, increasing their popularity in pharmacology and cosmetic products. There are further reviews in the literature focusing on the application and potential uses of oleogels (Hughes et al., 2009; Yılmaz & Öğütcü, 2014; Çokay et al., 2024).

Even though various oils and gel agents can be used in the formulation of oleogels, some factors related to the oils, including polarity, viscosity, degree of unsaturation, and presence of minor components, cause oleogel network structure alteration (Han et al., 2022; Scharfe et al., 2022). Additionally, the type and concentration of gelators included are also critical to the stability, texture, microstructure, thermal, and sensory properties of oleogels (Dassanayake et al., 2009; Hwang et al., 2012).

Easily accessible, affordable, non-toxic organogelators with low molecular weight are usually desired. Accordingly, plant and animal-based waxes, mono- and diglycerides, fatty acids, fatty alcohols, and beta-sitosterol+oryzanol mixtures are among the popular organogelators used (Dassanayake et al., 2009; Hughes et al., 2009). The properties of the organogelators directly impact the characteristics of oleogels, including their chemical composition, solubility, and crystal morphology (Hughes et al., 2009; Marangoni and Garti, 2011; Martins et al., 2016; Cerqueira et al., 2017; Martins et al., 2018; Scharfe et al., 2022; Goli et al., 2023). Hence, one must consider both the type and concentration of gelators during oleogel production. Some relevant studies have been published regarding the influence of different oils and gelators on the stability and properties of oleogels (Martins et al., 2016; Cerqueira et al., 2017; Han et al., 2022; Scharfe et al., 2022; Goli et al., 2023).

Rapid, reliable, and cost-effective solutions for identifying gelator types in oleogels and detecting potential adulteration of the gelators with their cheaper counterparts are required. Because of the complexity of counterfeit practices, food adulteration continues worldwide. Under food adulteration, Economically Motivated Adulteration (EMA) is a commercial abuse, targeted financial profit, introducing substantial concerns regarding health risks, product quality, ethical considerations, and religious beliefs (US, 2009; Robson et al., 2021). Infrared spectroscopy, which measures and interprets electromagnetic radiation absorbed or emitted during changes in the atomic, molecular, or ionic states, has emerged as a key technology for rapid analysis of foods and quality control. NIR spectroscopy, in particular, is more economical, faster, and practical than traditional methods, as it does not require chemicals, consumables, or a dedicated laboratory environment (Ayvaz et al., 2021).

To the best of our knowledge, there is only one related study in the literature. In this study, Moraes et al. (2024) searched the classification of oleogels based on different oils used in oleogel productions using NIR and Raman spectroscopy combined with chemometric methods, using three types of oleogels, each containing 95% sunflower, soybean, or olive oil and 5% wax. Based on their results, the authors reported high classification accuracy. Correspondingly, we hypothesize that different gelators and their concentrations considerably affect the characteristic properties of oleogels, such as crystal structure, hardness, and thermal properties. We propose that NIR spectroscopy and chemometric analyses can quickly and reliably detect the types of gelators used in oleogel formulations, providing meaningful progress in the field.

Materials and methods

Materials

Cold-pressed laurel oil was bought from commercial manufacturers (Uludag-Agro Co. Ltd.) in Bursa, Türkiye. The beeswax, carnauba wax, shellac wax, and monoglyceride were acquired from Smart Kimya (Smart Chemicals Co. Ltd., Izmir, Türkiye) and sunflower wax was obtained from Kahlwax (Kahlwax Co., Hamburg, Germany). All chemicals used in this study were purchased from Sigma-Aldrich (St. Louis, MI, USA) and Merck (Darmstadt, Germany).

Because of its unique chemical composition, pleasing aroma, and compatibility with oleogelation, along with its essential oil characteristics, including volatility and bioactive properties, which provide potential health benefits and versatility in food, pharmaceutical, and cosmetic applications (Chahal et al., 2017), laurel oil was selected in this research.

Beeswax, carnauba wax, shellac wax, monoglyceride, and sunflower wax were picked as gel agents. Beeswax is an animal-based wax, encompassing around 71% esters, 15% hydrocarbons, 8% free fatty acids, and 6% other components. Beeswax, with its protective and moisturizing properties, is a low-priced gelator and benefited in skincare products, as a coating and carrier in pharmaceutical products, and as a natural preservative in food coatings and packaging. Carnauba is a plant-based wax and comprises primarily esters of fatty acids (80–85%), fatty alcohols (10–15%), acids (3–6%), and hydrocarbons (1–3%). Shellac wax is made up of long-chain esters of monovalent alcohols and acids. It contains more than 30% free wax alcohol with a chain length of C₂₈–C₃₂. Sunflower wax (SW) is a vegetable wax obtained during the winterization of sunflower oil and it consists of long chain saturated C₄₂-C₆₀ esters derived from alcohols and fatty acids. Sunflower wax is a high-value gelator due to its high melting point and stable structure, making it a natural and high-quality product widely used in cosmetics and pharmaceuticals for skincare products and formulations with moisturizing and protective properties. Monoglycerides, used as emulsifiers in food applications and as emulsion stabilizers in creams and lotions for cosmetics, are economically more affordable due to their low production cost and broad usage. Waxes (beeswax and carnauba wax) and mono- and diglycerides have been recognized as “Generally Recognized as Safe (GRAS)” by the Food and Drug Administration (FDA) (Yılmaz & Öğütcü, 2014; Calligaris et al., 2010; Öğütcü et al., 2017; de Freitas et al., 2019; FDA, 2024).

Oleogel preparation

For the preparation of oleogels, gel agents (beeswax, carnauba wax, shellac wax, sunflower wax, and monoglyceride) were used at concentrations of 7–14% (w/w) and heated to 85 °C to ensure complete melting. Laurel oil, comprising 86–93% (w/w), was then mixed with the gel agents under isothermal conditions. The mixture was stored at 25 °C overnight to stabilize the gel structure. Sealed containers were utilized during heating, cooling, and storage to minimize vaporization and preserve the integrity of the product. The prepared oleogels were subsequently stored at 25 °C and 40 °C for 60 days to analyze their structure and stability further. A total of 77 oleogels (14 with carnauba wax, 16 with beeswax, 16 with sunflower, 16 with monoglyceride, and 15 with shellac wax) were produced. Each oleogel class included samples prepared using the oil/gelator ratio of 93–7 and 86–14% (w/w).

Analysis of produced oleogels

Determination of structural properties

The textural features of the oleogel samples were determined using a TA-XT Texture Analyser (TA-HD Plus, Stable Microsystems, Godalming, Surrey, UK) equipped with a spreadability rig (TA 425; TTC). The test specifications were as follows: a test speed of 3.0 mm/s, a post-test speed of 10 mm/s, and a distance of 23.00 mm. The results obtained from the texture analyzer were evaluated using the instrument software (Texture Exponent v.6.1.1.0, Stable Microsystems, Godalming, UK).

Polarized light microscopy (PLM) images of the oleogel samples were taken using a PLM (Nikon, Eclipse E200, Japan) equipped with a digital camera.

The x-ray diffraction patterns of the oleogel samples were determined using an XRD instrument (Empyrean PANalytical, Almelo, Netherlands). The angular scans from 2.0° to 50° 2-theta range were performed by 2°/min scan rate. The data were obtained using a Cu source x-ray tube (λ = 1.54056 Å, 45 kV, and 40 mA) and were evaluated using X’Pert Highscore Plus software (the Netherlands). The crystallite size (1) and index (2) were calculated using the Origin software according to equations that are given as follows;

where β is the crystallites’ average size, λ is the radiation’s wavelength, θ is the peak position, and K is a constant.

where Ac is the area of the crystallite peaks, and Aa is the area of the amorphous peaks.

Thermal measurements

The thermal properties of the oleogels were determined via thermogravimetric analysis (TGA 4000, Perkin-Elmer, USA). For TG/DTG measurements, the samples weighing 9–11 mg were placed into the TG pan and then heated from 30 to 600 °C at a rate of 10 °C/min, and the weight loss was calculated using the TGA software (Pyris Manager, Shelton, CT, USA).

Collection of near-infrared spectra

To collect the NIR spectra of the oleogels, the Nicolet iS50 Flex Gold Infrared Spectrometer (Thermo Fisher Scientific, Madison, WI, USA) equipped with FT-NIR diffuse reflectance was used. The NIR module of the Nicolet iS50 device, which includes a Ge-coated KBr beamsplitter and an Indium Gallium Arsenide (InGaAs) detector, was utilized for this purpose. During the measurements, a spectral resolution of 4 cm⁻¹ was used, and 64 spectra per measurement within the wavenumber range of 10,000–4000 cm⁻¹ were collected to enhance the signal-to-noise ratio.

The NIR spectra of oleogel samples were collected directly in transreflectance mode. NIR spectroscopy with transreflectance combines reflectance and transmission measurements, reliably capturing the scattered portion of light. In transmission mode, backscattered light is not measured, while in transreflectance, radiation scattered back before reaching the ceramic is also collected (Osborne et al., 1993). As temperature affects spectra, we allowed the samples to equilibrate at room temperature (25 ± 2 °C) for approximately 30 minutes before collecting spectra. A small sample of oleogel was placed at the bottom of a glass petri dish, and a metal reflector was placed on top. Subsequently, we collected spectra of all samples. A background spectrum of the environment was collected before each sample, and the absorption spectrum was obtained as the ratio of the sample’s spectrum to the background spectrum. NIR spectra for each sample were collected twice (changing the sample with each measurement) and averaged to obtain a single final spectrum for each sample. Between measurements, the glass petri dish was cleaned first with pure water and then with 70% ethanol/pure water (v/v). All spectra were saved in SPC format on a personal computer using Omnic 9 spectroscopic analysis software (Thermo Fisher Scientific, Madison, WI, USA).

Data analysis

Univariate analysis

The results for structural and thermal analysis of the oleogels were analyzed using ANOVA, and differences among the samples were evaluated using Tukey’s multiple tests with a significance level of p ≤ 0.05. All statistical analyses were performed using appropriate statistical software (Minitab 16v).

Multivariate analysis

The analysis of the collected spectra was performed using the multivariate analysis software Pirouette 4.5 (Infometrix, Inc., Bothell, WA, USA). The following chemometric analyses were conducted.

Principal component analysis

As an unsupervised chemometric technique, principal component analysis (PCA) was used to identify any spectra that may be affected by unknown factors or errors before developing NIR-based calibration models for the samples.

Soft independent modeling of class analogy

This study identified the oleogel types based on NIR spectra of the produced oleogel samples using soft independent modeling of class analogy (SIMCA), a supervised chemometric classification technique. SIMCA is a multivariate analysis based on PCA that allows visual clustering of samples using a small number of principal components. Pre-processing steps such as normalization, smoothing, multiplicative scatter correction (MSC), standard normal variate (SNV), and 2^nd derivative were applied to the spectra before analysis. The SIMCA analysis examined features such as Cooman’s plot and discriminating power. Cooman’s plot in SIMCA determines the distance of categories from the boundaries of the classification model (within a 95% confidence interval). This allows for a graphical visualization of whether samples belong to different or similar classes. Discriminating power indicates which wavelengths’ absorptions are most effective in distinguishing between samples. The number of principal components used to build the model was determined by monitoring the variance explained by each additional factor. The inter-class distance (ICD) parameter will also indicate the similarities and differences between classes. The classes are considered distinct if the ICD exceeds 3 (Vogt & Knutsen, 1985).

Results and discussion

Structural properties

Textural features of food products are significant factors influencing consumer preferences, perceptions, and purchasing decisions. Texture is a crucial characteristic not only for table spreads but also for creams and lotions used in pharmacological and cosmetic applications. Specifically, spreadability impacts the determination of the dosage of creams or lotions and the amount of table spreads consumed. Spreadability is defined as a combination of firmness and stickiness. Products are considered spreadable when they exhibit moderate firmness and stickiness (Moskowitz, 1987; Djiobie Tchienou et al., 2018). The textural properties (firmness, work of shear, stickiness, and work of adhesion) of the LO oleogels prepared with beeswax, carnauba wax, shellac was, monoglyceride, and sunflower wax are presented in Figure 1. The oleogels prepared with sunflower wax exhibited the highest stickiness and firmness values at the 7% and 14% addition levels. Additionally, oleogels with 7% carnauba wax demonstrated the lowest firmness, while those with 14% monoglyceride and shellac wax exhibited the lowest firmness values. The work of shear values indicated that oleogels with 7% carnauba wax and monoglyceride possess similar spreadability, though their stability remains debatable. Conversely, LO oleogels prepared with 14% carnauba wax, shellac, and monoglyceride exhibited similar spreadability and were more stable than their 7% counterparts. According to Figure 1, the 7%-shellac wax and 14%-beeswax-based oleogels had moderate firmness and stickiness; hence, oleogels prepared with laurel oil and 7% shellac wax and 14% beeswax may offer better results in terms of product spreadability.

The PLM images of the oleogels prepared with LO and different gelators are presented in Figure 2. The beeswax and sunflower wax-based LO oleogels, identified as needle-like crystals, exhibited similar crystalline shapes. In contrast, the crystalline shapes of the shellac wax and sunflower wax-based LO oleogels were similar, while those of the monoglyceride-based oleogels were distinct from the others. As expected, oleogels containing 14% gelators formed a more robust crystalline network than those prepared with 7% gelators. This trend was observed across all oleogels, regardless of the type of gelator used. Similar findings regarding both textural properties and PLM images were reported by Öǧütcü & Yılmaz (2014) for carnauba wax and monoglyceride in olive oil and by Yılmaz & Öğütcü (2016) for beeswax and sunflower wax in pomegranate seed oil.

The X-ray diffraction (XRD) patterns, crystalline sizes (CS), and indices (CI) of the LO-gels are presented in Figure 3 and Table 1, respectively. Specific peaks around 3.70 and 4.10 Å were observed, indicating the β’-polymorphic form. Especially in spreadable products, β’-polymorphic crystals are the desired and preferred form due to providing a smooth and pleasant texture (Chrysam & Applewhite, 1985). Previous studies reported similar results in both MG and wax oleogels in different oils (Dassanayake et al., 2009; Öğütcü & Yılmaz, 2014; Yilmaz & Öğütcü, 2016; Çokay et al., 2024). The XRD patterns of MG-based oleogels differed from wax-based gels, as expected. Remarkably, the wax-based oleogels had XRD patterns that differed from each other. The differences among the MG and wax-based oleogels were observed in two peaks around 5.50° and 7.50°. The oleogels with 7% beeswax, carnauba wax, and monoglyceride exhibited amorphous, viscous, and unstable oleogel structures. It was determined that as the amorphous ratio increased, the peaks around 3.70 Å and 4.10 Å became barely noticeable (Figure 3). In other words, no peaks were observed at the minimum gelation concentrations of the gelators used.

Conversely, as the gelator concentration increased, the peak intensity also increased. This situation directly relates to the minimum gel formation concentrations and gelator types. Similar results have been reported for long and medium-chain triacylglycerol and beeswax organogels (Martins et al., 2016). Literature data indicate that the degree of saturation/unsaturation of the oils used is at least as influential as the types and concentrations of gelators. Specifically, the relative crystallinity increased with the oils’ unsaturation degree (Han et al., 2022). However, the results of the present study demonstrate that different gelator types and addition levels of the same gelator in the same oils lead to varying structural and textural features. Literature data reported that SW-based rice bran oil oleogels had XRD peaks around 0.415 nm and 0.373 nm, characteristic of an orthorhombic sub-cell structure (β’-morphology). In contrast, berry wax-based oleogels exhibited XRD peaks around 0.415 nm, indicating hexagonal symmetry (α-morphology). The same study reported that heterogeneous crystal habits and polymorphism were responsible for the varying gel strength and hardness of oleogels (Doan et al., 2017).

Additionally, XRD measurements were performed on fresh samples (Figure 3A and B) and those stored at 40 °C for 60 days (Figure 3C and D). Differences were observed not only in the XRD patterns of the fresh and stored oleogels but also in the CS and CI of the oleogels. The crystal size of fresh beeswax and monoglyceride increased with gelator concentration, while that of carnauba wax and sunflower wax decreased; shellac wax-based gels showed no remarkable change. Crystal sizes of the oleogels changed at the end of the storage period, except for oleogels with 7% beeswax and 14% sunflower wax. The most noticeable change was in monoglyceride gels at 7 and 14% concentrations.

Conversely, the CI of the 7%-carnauba wax, shellac wax, and monoglyceride-based gels decreased, while that of the sunflower wax-based gels increased; beeswax-based gels showed no remarkable change at the end of storage. Additionally, the CI of the 14%- beeswax, carnauba, and sunflower wax-based gels increased with storage while monoglyceride-based gel decreased; shellac-based gels showed no remarkable change at the end of storage (Table 1). These findings indicate that storage conditions such as temperature and duration are as influential on crystal morphology as the quality of the oil used and the type and concentration of gelators.

Thermal properties

The thermogravimetric analysis (TGA) results of the oleogels are presented in Table 1 and Figure 4. The weight loss ratio (WLR) of the oleogels varied depending on the concentration and the gelator types. The oleogels with 7% carnauba wax exhibited the highest WLR (14.32%/min) compared to other oleogels and the control samples (laurel oil [LO]). Conversely, the oleogels with 14% monoglyceride demonstrated the lowest WLR among the oleogels and the control samples. Oleogels with a 7% gelator concentration generally had higher WLR values than those prepared with a 14% gelator concentration, except for the sunflower wax-based gels, which showed no significant changes. These findings suggest that increasing the gelator concentration not only enhances the stability of the gels but also affects the volatile adsorption capacities of the samples. This is particularly important when oleogels are prepared with volatile or essential oils for cosmetic, pharmaceutical, and nutraceutical applications.

Near-infrared spectra-based calibration model development

Figure 5 displays the NIR spectra of the control sample (laurel oil) and various oleogels. The figure illustrates explicitly the average spectra of oleogels produced with different gelators. Figure 5A shows the full NIR spectral range (10,000–4000 cm⁻¹), with major vibrational assignments referenced from the study by Rodriguez-Saona et al. (2017). The spectra in Figure 5A have been normalized, MSC-corrected, and smoothed using a 25-window process. While the control sample and the oleogels exhibit a similar spectral pattern, slight shifts are noted. These subtle variations are more pronounced in the partial NIR region between 6645 and 4000 cm⁻¹, which is also utilized in the subsequent development of the SIMCA model.

According to Rodriguez-Saona et al. (2017) and Moraes et al. (2024), the vibrations at 8265 cm⁻¹ are attributed to the second overtone of the C-H stretch, associated with hydrocarbons and aliphatics. The vibrations in the 7206–7100 cm⁻¹ range result from the combination of C-H stretch and C-H bending, indicative of hydrocarbons and aromatics. The vibrations observed at 5779 and 5671 cm⁻¹ arise from the first overtone of the asymmetric C-H stretch. The vibration at 4661 cm⁻¹ is linked to C-H vibrations in isolated C=C bonds, while the vibrations at 4331 and 4258 cm⁻¹ are due to combination bands involving C-H and C-O stretching.

Figure 6A presents Cooman’s plot of the SIMCA model, developed based on NIR spectra of the oleogels samples produced. The plot in Figure 6A demonstrates a clear separation of oleogel samples prepared with five different gelators. Furthermore, distinct clusters are observed within each oleogel class, corresponding to the samples prepared with 7% and 14% gelator concentrations. As shown in the confusion matrix of the SIMCA model in Table 2, all samples from the five different oleogel classes were accurately classified, resulting in zero misclassifications and no match. SIMCA discriminating power plot indicating spectral signatures that allow discrimination among the oleogels is given in Figure 6B. Based on Figure 6B, several notable vibrations contributing to the separation of levels were observed at 4690, 4044, 4423, 6240, and 4336 cm⁻¹. The vibration at 4690 cm⁻¹ likely results from a combination of C-H stretching and C=O stretching in lipids (Moraes et al., 2024). The 4044 cm⁻¹ vibration may be associated with C-H stretching overtones from hydrocarbons in the oils or waxes used in the oleogel. The 4423 cm⁻¹ vibration is probably due to the CH₃/CH₂ stretching and deformation combination bands of hydrocarbons and aliphatics. The vibration at 6240 cm⁻¹ could originate from amine or amide groups, possibly due to additives in the formulation from oils or gelators. Lastly, the 4336 cm⁻¹ vibration is likely a result of combination bands involving C-H and C-O stretching (Rodriguez-Saona et al., 2017).

As visualized in Figure 6A, oleogel samples made with monoglyceride and shellac wax are placed in boxes opposite each other, indicating that these oleogels differ the most based on the SIMCA model. The other three classes of oleogels—those containing beeswax, carnauba wax, and sunflower wax—are clustered in the top correct box, indicating that they differ from the monoglyceride and shellac wax classes. However, within this group, the oleogels with beeswax, carnauba wax, and sunflower wax still form separate clusters, highlighting their differences.

Table 3 presents the Inter-Class Distance (ICD) values among the classes in the developed SIMCA model. As previously noted, the ICD parameter indicates the degree of similarity or difference between classes, with values greater than three generally indicating distinct classes (Vogt & Knutsen, 1985). As shown in Table 3, all ICD values between the classes exceeded 3, ranging from 3.75 (between oleogels with carnauba and shellac waxes) to 26.4 (between oleogels with monoglyceride and beeswax). Notable distances were also observed between oleogels with monoglycerides and carnauba wax (ICD of 23.6), monoglyceride and sunflower wax (ICD of 23.5), and monoglyceride and shellac wax (ICD of 17.1). Oleogel samples formulated with monoglyceride as the gelator exhibited significant differences from all other classes, with ICD values of 23.6 (carnauba wax), 26.4 (beeswax), 23.5 (sunflower wax), and 17.1 (shellac wax).

Our results align with the findings of Moraes et al. (2024), who utilized portable NIR spectrometers to classify oleogels formulated with different oils with high accuracy using DD-SIMCA and PLS-DA models. While Moraes et al. (2024) focused on oleogels containing 95% oil (sunflower, soybean, or olive) and 5% beeswax, our study expanded the scope by including a broader range of gelators. Both studies have revealed that NIR spectroscopy, along with chemometrics, is beneficial in oleogel analysis and can be efficiently implemented across the analysis of oleogels with various formulations to be used in food and cosmetic industries for quality assurance and the detection of possible adulterations.

Conclusion

This study demonstrated the influence of the use of various gelators in oleogel production on their structural and thermal properties. Although the number of samples per class was limited and no cross-validation was performed, the NIR spectroscopy and SIMCA analysis implemented in this study showed that rapid and non-destructive identification of oleogelators in oleogels could be possible. Chemometric analysis of the NIR spectra yielded a clear separation amongst the various oleogel samples, featuring the potential of this technique for quality control and product authentication in the food and cosmetic industries.

NIR spectroscopy offers quite a few gains, including non-destructive assessment, prompt analysis, and eliminating the need for chemicals, making it a reasonable and environmentally friendly alternative for industrial applications. Considering the outcome of this study, NIR spectroscopy, coupled with chemometrics, is a strong candidate for oleogel screening. By implementing a broader range of oleogel formulations, other structured lipid systems, and other rapid spectroscopic techniques, further research can be conducted in the near future.

Data availability

The data that support the findings of this study are available on reasonable request from the corresponding author.

Author contributions

Hüseyin Ayvaz (Conceptualization, Methodology, Data curation, Writing—original draft, Formal analysis), Elif Albayrak (Investigation, Formal analysis), and Mustafa Öğütcü (Resources, Validation, Project administration, Supervision, Writing—review & editing)

Conflicts of interest

The authours declare that they have no conflict of interest.

Funding

None declared.

References