A Review of the Latest Spectroscopic Research in Food and Beverage Analysis

Spectroscopic analytical techniques are essential to the economics and quality assessment of modern food and beverage products. Spectroscopy offers a powerful set of tools for detailed classification and quantification of essential parameters and contaminants in foods and beverages.

Techniques such as inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma optical emission spectroscopy (ICP-OES) are employed for trace elemental analysis, providing high sensitivity and precision. Raman spectroscopy, including its enhanced variants, such as surface-enhanced Raman spectroscopy (SERS), is utilized for molecular imaging, fingerprinting, and detecting low concentrations of analytes. Fourier-transform infrared spectroscopy (FT-IR) identifies chemical bonds and functional groups within molecules. Energy dispersive X-ray fluorescence (ED-XRF) and laser-induced breakdown spectroscopy (LIBS) are used to assess the presence of various micro- and macro-minerals in food products and raw materials. Ultraviolet-visible spectroscopy (UV-vis) measures absorbance and concentration of analytes, while fluorescence spectroscopy detects the emission of light by substances, often used for tracking molecular interactions, kinetics, and dynamics. Nuclear magnetic resonance (NMR) spectroscopy offers detailed information about molecular structure and conformational subtleties through the interaction of nuclear spin properties following the application of an external magnetic field.

This review highlights the most recent advances and applications of these spectroscopic techniques in food and beverage analysis. Specifically analytical methods are employed to evaluate the quality and authenticity of food products across different applications and sample types. ICP-MS is utilized to detect heavy metals in food packaging, while ICP-OES is used for elemental screening in solid food materials and investigating trace elements in coffee. Raman spectroscopy, with techniques such as SERS, spatially offset Raman spectroscopy (SORS), and surface-enhanced spatially offset Raman spectroscopy (SESORS), is applied for rapid assessment of food quality and safety. SERS is also used in conjunction with molecularly imprinted polymers (MIPs) for detecting trace toxic substances, and in microfluidic platforms for analyzing foodborne pathogens. Furthermore, Raman spectroscopy enables accurate measurement of ethanol and toxic alcohols in beverages and spirits.

Portable X-ray fluorescence (ppXRF) and ED-XRF, along with LIBS, are used for in situ and mineral composition analysis of many foods as well as non-conventional food plants. FT-IR spectroscopy assesses the physicochemical characteristics of different wheat species and identifies honey adulteration. Near-infrared (NIR) and visible (vis) spectroscopy are applied for food quality and safety through metabolomic fingerprinting and evaluating apple quality. UV-vis spectroscopy is used for detecting beverage components in beverage production rinse water, determining sensory attributes of wine, and classifying food additives. Fluorescence spectroscopy and fluorescent probes are employed for tracing rice origin, identifying adulteration, and analyzing adulteration in vegetable oils. Lastly, nuclear magnetic resonance (NMR) spectroscopy is utilized to assess quality and authenticity in milk and various spices.

The following article summarizes recent application developments for food and beverage analysis.

ICP-MS

Plastic food packaging is crucial for consumer convenience and maintaining food quality. However, heavy metals in the packaging can transfer to the food, necessitating detection. This study analyzed heavy metals such as Co, Ge, As, Cd, Sb, Pb, Al, and Zn in various plastic food packaging materials using inductively coupled plasma-mass spectrometry (ICP-MS). The method was validated for linearity, accuracy, precision, detection limits, and quantitation limits. Results showed detection limits ranging from 0.10 to 0.85 ng/mL and quantitation limits from 0.33 to 2.81 ng/mL for the metals. Recovery rates varied from 82.6% to 106%. The study also simulated the migration of these metals into foodstuffs, revealing that Zn, Al, and Pb were the most leached elements, with migration rates of 8.38%, 0.41%, and 0.19%, respectively (1).

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A 2024 study aimed to create an analytical method for determining the geographical origin of chicken breasts and drumsticks using inductively coupled plasma (ICP). Chemometrics was used to analyze sixty elements. Orthogonal partial least square discriminant analysis (OPLS-DA) identified twenty-three significant elements in chicken breasts and twenty-eight in drumsticks. The significance of these elements was confirmed using the area under the curve (AUC) value from the receiver operating characteristic (ROC). The method's validity was supported by a permutation test. A heatmap and canonical discriminant analysis (CDA) achieved 100% accuracy in classifying the geographic origin of the chicken meat, demonstrating the method's potential as a reliable food analysis tool (2).

ICP-OES

Laser ablation-inductively coupled plasma-optical emission spectrometry (LA-ICP-OES) shows promise for elemental screening of solid food materials, but it faces the challenge of lacking commercial solid standard reference materials for quantitative calibration. A 2024 study developed a reliable, internal standard-free LA-ICP-OES method for direct sampling and analysis of solid food materials using a carbon internally standardized relative sensitivity factor (CIS-RSF). This approach eliminates the need for calibration curves or matrix-matched external standards once CIS-RSF is determined. The method's long-term stability was ensured by evaluating the effect of instrumental operating parameters on CIS-RSF (3).

The method was applied to analyze various microelements, including sulfur (S), phosphorus (P), zinc (Zn), iron (Fe), manganese (Mg), magnesium (Mn), copper (Cu), calcium (Ca), strontium (Sr), barium (Ba), sodium (Na), and potassium (K). The limit of quantitation (LOQ) ranged from 0.06 μg/g (Sr) to 400 μg/g (S). The accuracy and reproducibility were assessed by analyzing three food National Institute of Standards and Technology (NIST) reference materials and six typical food samples. The results showed that the relative error for most target elements was under 20%, and the reproducibility (standard deviation, n = 3) was above 10%. This newly developed LA-ICP-OES screening method demonstrates significant potential for high-throughput multielement determination in various solid food materials (3).

A study published in 2024 investigated the presence of ten trace elements—arsenic (As), lead (Pb), chromium (Cr), zinc (Zn), iron (Fe), cobalt (Co), cadmium (Cd), nickel (Ni), manganese (Mn), and aluminum (Al)—in 36 coffee samples from Iran. These included three brands of both simple and instant coffee, as well as three brands of mixed coffee in four types: simple, creamy, chocolate, and sugar-free. The analysis employed ICP-OES (4).

The results revealed that the recovery rates for these trace elements ranged from 93.4% to 103.1%, with limits of quantification (LOQ) between 0.06 and 7.22 µg/kg and limits of detection (LOD) from 0.018 to 2.166 µg/kg. Among the samples, iron (Fe) exhibited the highest average concentration at 498.72 ± 23.07 μg/kg, while arsenic (As) had the lowest average at 3.01 ± 1.30 μg/kg. The maximum concentration of trace elements was found in Fe at 1353.61 µg/kg, whereas aluminum (Al), arsenic (As), cobalt (Co), chromium (Cr), nickel (Ni), lead (Pb), and zinc (Zn) were not detected in some samples (4). Mixed coffee samples generally contained higher levels of trace elements compared to simple coffee. Among coffee types, instant coffee had the highest trace element concentrations relative to simple coffee and mixed coffee. Within mixed coffee, creamy variants had elevated levels of trace elements, except for Ni and Cr, compared to other mixed coffee types. Overall, trace element concentrations in all samples were below the standard limits set by Iran and other countries, indicating no significant health risk to consumers (4).

Raman

Frequent food safety incidents have heightened global concerns, driving the development of rapid and sensitive analytical methods to ensure food quality and safety. Among these, Raman spectroscopy has emerged as a prominent technique due to its simplicity, speed, sensitivity, and nondestructive nature. A review published in 2023 explores recent applications of Raman-based techniques in food safety, covering various methods from traditional Raman spectroscopy to advanced techniques such as SERS, SORS, and SESORS. The review discussion includes the historical development, principles, design, and specific applications of these methods. Additionally, the review addresses future challenges and trends in the application of Raman spectroscopy for food safety (5).

SERS is known for its rapid detection capabilities and high sensitivity, requiring minimal pretreatment. However, it can be hindered by interference from matrix components in samples. To address this issue, molecularly imprinted polymers (MIPs), which are designed to recognize specific targets, are integrated with SERS to form MIP–SERS sensors. These sensors enhance stability and sensitivity by effectively mitigating matrix interference. A 2023 review delves into the applications of MIP–SERS sensors for detecting trace toxic substances in food, discussing the mechanism and development of SERS technology and the principles and classifications of MIP–SERS. It introduces various types of MIP–SERS sensors, outlining their respective advantages and disadvantages. The review also highlights recent advancements in using MIP–SERS technology to detect mycotoxins, additives, prohibited dyes, pesticides, veterinary drug residues, and other hazardous substances in food. Lastly, it addresses the challenges faced by MIP–SERS technology and suggests potential future developments (6).

Ensuring food safety and public health hinges on the rapid and sensitive detection of foodborne pathogens. To address this critical need, ongoing research aims to enhance detection methods in terms of speed, sensitivity, portability, and cost-effectiveness. A review published in 2024 focuses on the promising use of Raman spectroscopy-based microfluidic platforms in foodborne pathogen analysis, which offer point-of-care (POC) diagnosis and multiplex detection capabilities. The persistent threat to food quality and public health from inadequate pathogen detection underscores the importance of this research (7).

This review explores various trapping strategies used in microfluidic platforms, such as optical, electrical, mechanical, and acoustic trapping, to capture microbial cells. It also examines key aspects of microbial detection in food products, highlighting recent advancements and challenges. These integrated techniques facilitate point-of-care assessments, making them attractive for inline and real-time detection of foodborne pathogens (7). However, the application of Raman spectroscopy-based microfluidic platforms in food products is still limited and requires further research. Challenges include addressing the complexity of the food matrix, reducing production costs, and ensuring real-time detection for diverse pathogens. This review aims to drive advancements in microbial detection, promoting enhanced food safety through state-of-the-art technologies (7).

The accurate measurement of ethanol and toxic alcohols like methanol and isopropanol in beverages and spirits is essential for health safety and identifying adulterated products. Traditional methods mainly involve chromatographic and spectroscopic techniques. Chromatographic methods, although precise, are labor-intensive and time-consuming, while spectroscopic methods are faster and require no special sample preparation. Typically, these methods are used offline or atline (8).

In a 2023 study, Raman spectroscopy was employed for quick, non-destructive alcohol measurements using a novel "through the container" approach, allowing for non-invasive analysis without unsealing bottles. Coupled with a portable Raman device, this method is suitable for inline measurements on production lines. The research optimized laser focus to maximize the signal from the measured samples while minimizing interference from the glass container (8).

Calibration curves for ethanol, methanol, and isopropanol were developed and validated, with detection limits found to be below legal thresholds. The study also examined the effects of liquor color and bottle attributes such as color, shape, and thickness. Twenty-eight alcoholic products were analyzed, and the measured concentrations were successfully compared with the nominal values indicated on the bottle labels (8).

X-ray (XRF)

Elemental analysis using portable XRF (ppXRF) spectrometers is advantageous due to its non-destructive nature, speed, environmental friendliness, and low cost, making it suitable for both solid and liquid samples. The application of ppXRF in food product analysis is particularly promising for detecting and fingerprinting elements due to its efficiency, convenience, and accuracy in handling low-persistence samples (meaning samples that do not maintain their stability or integrity over time) (9).

A review from 2023 focuses on evaluating existing studies, with an emphasis on analytical aspects such as calibration strategies, the operating modes of ppXRF devices, limits of detection (LOD) and quantification (LOQ), and linearity. The review aims to inspire food toxicologists by validating the potential of ppXRF in food elemental analysis (9).

The review suggests solutions to enhance the quality and reliability of future research using ppXRF for in situ analysis of various food samples. Additionally, it provides an overview of known ppXRF spectrometers and discusses issues related to analytical calibration strategies that can be effectively implemented in food analysis using ppXRF (9).

Non-conventional food plants (PANC) are gaining significant attention in scientific research due to the growing interest in diets free from additives, preservatives, artificial colors, and requiring minimal industrial processing. This trend is also influenced by the increasing preference for plant-based diets. Traditional methods for analyzing the mineral composition of plants often involve intensive acid-based techniques and complex mineralization processes (10).

Recognizing the nutritional potential of PANC, a study published in 2024 aims to develop non-intrusive analytical methods for assessing their mineral content. The research explores the use of ED-XRF, and LIBS, and chemometric approaches to determine the macronutrient composition of PANC. By leveraging these techniques alongside chemometric methodologies, the study seeks to uncover the nutritional profiles of non-conventional food plants (10). The findings will enhance understanding of the macronutrient content in PANC and their potential applications in food science and nutrition, contributing valuable insights into the nutritional benefits of these plants (10).

FT-IR

Research published in 2023 analyzed the physicochemical characteristics of seventy wheat flours from three species: Einkorn (Triticum monococcum), Spelt (Triticum spelta), and common wheat (Triticum aestivum). Standard methods were used to examine various parameters of the wheat grains, including moisture, ash, protein, wet gluten, sedimentation index, pH, acidity, fat, starch, falling number, damaged starch, and Glutograph parameters for stretching and relaxation. Principal component analysis was employed using FT-IR spectra to assess the relationships between these characteristics and the wheat samples (11).

Significant differences were found between the species for nearly all physicochemical data, except for moisture and damaged starch. Ancient wheat species (Einkorn and Spelt) had higher levels of protein, wet gluten, fat, ash, and acidity, while common wheat was richer in starch and showed the highest Glutograph values for stretching and relaxation. A Glutograph is an instrument used to measure the rheological properties of dough, specifically its gluten strength and elasticity, by applying stretching and relaxation forces to a dough sample (11).

Additionally, FT-IR spectroscopy was utilized as a nondestructive method to analyze wheat flour composition. The FT-IR spectra were optimally pre-treated to enhance the prediction of various physicochemical parameters such as moisture, protein, starch, and gluten content, as well as falling number and undamaged starch content (UCDc). The most suitable prediction models were achieved using partial least squares regression (PLS-R) using first and second derivative pre-treatments of the spectra (11).

Honey is frequently adulterated in both national and international markets, making it crucial to develop reliable methods for detecting such adulteration. Research completed in 2023 explored the use of FT-IR spectroscopy coupled with multivariate analysis as an alternative analytical technique for identifying honey adulteration and ensuring authentication. Pure honey samples were mixed with common adulterants in ratios ranging from 0-50%, and honey samples from multiple markets were also tested. Pure unadulterated honey from Holeta Bee Research's bee farm served as the control sample (12).

FT-IR spectral data for honey and five adulterants were recorded in the 4000–400 cm−1 range. The combination of spectral measurements and multivariate analyses allowed for the classification and grouping of honey samples based on their functional groups. The spectral region between 1800–650 cm−1 was particularly effective for distinguishing different clusters. Cluster analysis (CA) successfully separated pure honey from adulterated samples. Principal component analysis (PCA) further enabled the visualization of differences between deliberately adulterated, commercially available, and authentic honey samples (12). The study concluded that FT-IR spectroscopy, combined with multivariate statistical analysis, is a promising fingerprinting technique for identifying pure and adulterated honey samples (12).

NIR

Recently, there has been a growing interest in ensuring food quality and safety through metabolomic fingerprinting using vibrational spectroscopy combined with multivariate chemometrics. Techniques like FT-IR, NIR, and Raman spectroscopy offer non-targeted, high-throughput, cost-effective, and rapid fingerprinting capabilities for metabolomic constituents in agricultural, natural, and food products (13).

A 2023 review examines the potential of FT-IR, NIR, and Raman spectroscopy, combined with multivariate analysis, for metabolic fingerprinting and profiling in foods. These analytical techniques have shown significant promise in detecting and quantifying essential constituents such as aromatic amino acids, peptides, aromatic acids, carotenoids, alcohols, terpenoids, and flavonoids in food matrices. Additionally, they have proven effective in identifying and characterizing various microorganism species, including fungi, bacterial strains, and yeasts, through both supervised and unsupervised pattern recognition techniques (13). In summary, the review highlights the advanced applications of FT-IR, NIR, and Raman spectroscopy, integrated with multivariate statistics, for food analysis and foodomics, emphasizing their role in metabolomic fingerprinting and profiling (13).

Spectroscopic methods offer non-destructive analytical tools that allow for both qualitative and quantitative characterization of various samples. Apples, one of the most widely consumed crops globally, face quality maintenance challenges due to climate issues and environmental impacts. A 2023 review extensively examines the use of spectroscopy in the NIR and visible (vis) regions for evaluating apple quality parameters and optimizing production and supply processes (14).

Spectroscopy in these regions can assess both external and internal apple characteristics, such as color, size, shape, surface defects, soluble solids content (SSC), total titratable acidity (TA), firmness, starch pattern index (SPI), total dry matter concentration (DM), and nutritional value. The review highlights techniques and approaches used in vis-NIR studies for purposes such as determining authenticity, origin, identification, adulteration, and quality control (14).

Optical sensors and associated methods are essential for practical industry needs, including efficient sorting and grading based on sweetness and other quality parameters, thereby facilitating quality control throughout the production and supply chain. This recent review also discusses current trends in the development of handheld and portable instruments in the vis-NIR and NIR spectral regions for apple quality control (14).

The adoption of these technologies can significantly enhance apple crop quality, maintain industry competitiveness, and meet consumer demands. This review article focuses on literature from the past five years (2018–2023), except for seminal works that have significantly influenced the field or representative studies showcasing progress in specific areas (14).

UV-vis

Water management in beverage production involves balancing food safety, product quality, and environmental impact. Efficient water use is exemplified by optimizing rinsing between flavor changes. This study explored the ultraviolet–visible (UV-vis) absorbance characteristics of 20 different beverages, both undiluted and diluted (from 2× to 5000×), to simulate rinsing conditions. UV-vis absorbance proved to be a reliable and sensitive method for detecting beverage components in rinse water, outperforming conductivity in most cases, particularly at dilutions of 1000× or higher. The sensitivity of UV-vis absorbance also enabled the detection of odor and taste thresholds for various beverages. Additionally, a machine learning model was developed to predict UV-vis absorbance based on ingredient composition and dilution levels. These findings indicate that in-line UV-vis absorbance sensors could be effective in monitoring and controlling rinsing and other processes during beverage production, thereby enhancing water use efficiency (15).

Phenolic compounds are crucial in determining the sensory attributes of wine, such as color and astringency, making accurate measurement essential for winemakers. UV-vis spectroscopy offers a cost-effective and straightforward method for quantifying these compounds. Although prior research has investigated UV-vis spectroscopy for various red wines, Pinot noir, which has lower average phenolic levels compared to varieties like Cabernet Sauvignon and Merlot, has not been extensively studied. This research analyzed 155 Pinot noir wines from New Zealand, using UV-vis spectra in conjunction with high performance liquid chromatography (HPLC) to reference phenolic content. Partial least squares (PLS) regression models were developed from these UV-vis spectra and HPLC data. The models demonstrated high accuracy in predicting phenolic compounds with high concentrations and compound groups, while predictions for compounds with lower concentrations were generally less precise (16).

Food additives are commonly used in various products to enhance flavor, extend shelf life, or achieve desired textures. This study introduces an automatic classification system for five different food additives based on their ultraviolet (UV) absorbance properties. Solutions of varying concentrations were prepared by dissolving specific masses of each additive in distilled water. The study analyzed both single-additive and mixed-additive solutions. The additives showed absorbance peaks between 190 nm and 360 nm, with each additive exhibiting unique absorbance peaks at distinct wavelengths—such as 226 nm for acesulfame potassium and 254 nm for potassium sorbate. These unique spectral signatures were used for classification. Deep learning techniques were employed for classification, with samples labeled numerically and divided into training, validation, and testing datasets. The convolutional neural network (CNN) model, featuring three convolutional layers, achieved the best results, with a mean testing accuracy of 92.38% ± 1.48% and a mean validation accuracy of 93.43% ± 2.01% for the 404 analyzed spectra (17).

Fluorescence

Fluorescent probes, typically used for detecting specific metal ions due to their high selectivity, can show varied fluorescence spectra when interacting with multiple metal ions if they are weakly selective. A 2023 study explored using a weakly selective fluorescence probe combined with chemometric methods for tracing rice origin and identifying adulteration. The study involved collecting excitation-emission matrix (EEM) spectra from rice extracts of various geographical origins and adulterated samples using the probe. Given the complex 3D nature of EEM data, multi-dimensional principal component analysis (M-PCA) and unfold partial least squares discriminant analysis (U-PLS-DA) were employed to analyze and classify the data (18). M-PCA results indicated a clustering trend in rice samples from different origins, although they could not be completely distinguished. U-PLS-DA analysis achieved 100% accuracy in classifying training sets and 98% in predicted sets for rice origin. For adulterated rice, U-PLS-DA analysis yielded 99% accuracy for training sets and 95% for predicted sets. These findings demonstrate that a weakly selective probe, when coupled with pattern recognition techniques, can effectively trace rice origins and identify adulteration, extending the use of fluorescent probes to food quality control in cases where the food itself does not fluoresce (18).

The integrity of vegetable oil is a significant concern in the global food industry, especially in regions where economic factors can affect authenticity. In 2023, a study utilized three-dimensional (3D) fluorescence spectroscopy to analyze both the qualitative and quantitative aspects of adulteration in various vegetable oils, highlighting its benefits in terms of speed and cost. Nine types of adulterated vegetable oils were examined using five machine learning techniques: K-nearest neighbor (KNN), random forest (RF), support vector machine (SVM), Partial least squares (PLS), and convolutional neural network (CNN) (19). For qualitative analysis, the coefficient of determination for prediction (Rp2) values ranged from 0.89 to 0.99, and root mean square error of prediction (RMSEP) values were between 0.01 and 0.05. A 5% adulteration detection threshold was set for soybean oil mixed with palm oil and sesame oil mixed with palm oil, achieving accuracy (ACC) above 90%. For the other seven types of adulterated oils, a 10% threshold was effective, also resulting in over 90% accuracy. At a 15% threshold, the accuracy rate exceeded 95%. These results demonstrate that 3D fluorescence spectroscopy is a powerful method for detecting adulterated oils, offering significant improvements in food safety and quality control (19).

NMR

Spices and herbs are frequently adulterated due to their widespread use in food processing. They not only enhance flavor but also provide numerous health benefits through their bioactive compounds, which have antimicrobial, anti-inflammatory, and carminative properties. However, consuming adulterated spices can be harmful, as adulterants are often unhealthy. Therefore, reliable analytical methods are essential for ensuring spice quality and authenticity. Spectroscopic techniques, valued for their speed, minimal sample preparation, and detailed structural insights, are increasingly utilized for this purpose. A review published in 2023 highlights the use of NMR spectroscopy combined with chemometric analysis for assessing the quality and detecting adulteration in spices (20).

NMR spectroscopy is becoming an increasingly valuable method for analyzing bovine milk due to its non-destructive nature, minimal sample preparation, and comprehensive untargeted analysis capabilities. These advantages position NMR as a strong complement to mass spectrometry in milk metabolomic research. This review provides a detailed overview of how NMR is applied to assess milk quality and authenticity. It covers the experimental setups and data processing methods necessary for obtaining accurate and reproducible results. A 2023 review article discusses key studies employing various NMR techniques in milk analysis, addressing applications such as identifying milk animal species, determining feeding practices, and evaluating nutritional quality and authenticity. Overall, the review underscores the versatility and importance of NMR spectroscopy in milk and dairy metabolomics, highlighting its role in ensuring the quality and authenticity of bovine milk (21).

Spectroscopic analysis plays a crucial role in food and beverage production, quality assessment, and safety, supporting the capability of modern civilization to sustain and expand the global food supply.

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