Rapid through-container detection of fake spirits and methanol quantification with handheld Raman spectroscopy

Comprehensive analysis of risk factors (methanol, acetaldehyde and higher alcohols) in alcoholic beverages and their reduction strategies: GC-MS analysis and modified activated carbon adsorption and characterization

This study endeavors to examine the levels of risk factors in alcoholic beverages and propose mitigation strategies. GC-MS analysis was utilized to assess risk factors in various distilled-spirits. The content of such risk factors in spirits ranked as follows: vodka ≈ gin < baijiu < whiskey < brandy, and all were adhering to the Chinese national standard. Additionally, a method was refined to alleviate these risks, employing various reagents for activated carbon modification and evaluating their adsorption efficiency for risk factors reduction. Oxalic acid-modified activated carbon exhibited promising adsorption rates for risk factors with acceptable flavor compounds loss, rendering it a prospective solution for health hazard reduction. Characterization via SEM and nitrogen-adsorption-desorption was conducted on the optimal material, complemented by sensory experiments to optimize its application. This study offers valuable insights into the content of risk factors in alcoholic beverages, aiding in improving quality and safety of alcoholic beverages.

A novel method for quantitative determination of multiple substances using Raman spectroscopy combined with CWT

The identification of mixed solutions is a challenging and important subject in chemical analysis. In this paper, we propose a novel workflow that enables rapid qualitative and quantitative detection of mixed solutions. We use a methanol-ethanol mixed solution as an example to demonstrate the superiority of this workflow. The workflow includes the following steps: (1) converting Raman spectra into Raman images through CWT; (2) using MobileNetV3 as the backbone network, improved multi-label and multi-channel synchronization enables simultaneous prediction of multiple mixture concentrations; and (3) using transfer learning and multi-stage training strategies for training to achieve accurate quantitative analysis. We compare six traditional machine learning algorithms and two deep learning models to evaluate the performance of our new method. The experimental results show that our model has achieved good prediction results when predicting the concentration of methanol and ethanol, and the coefficient of determination R2 is greater than 0.999. At different concentrations, both MAPE and RSD outperform other models, which demonstrates that our workflow has outstanding analytical capabilities. Importantly, we have solved the problem that current quantitative analysis algorithms for Raman spectroscopy are almost unable to accurately predict the concentration of multiple substances simultaneously. In conclusion, it is foreseeable that this non-destructive, automated, and highly accurate workflow can further advance Raman spectroscopy.

Optics miniaturization strategy for demanding Raman spectroscopy applications

Raman spectroscopy provides non-destructive, label-free quantitative studies of chemical compositions at the microscale as used on NASA's Perseverance rover on Mars. Such capabilities come at the cost of high requirements for instrumentation. Here we present a centimeter-scale miniaturization of a Raman spectrometer using cheap non-stabilized laser diodes, densely packed optics, and non-cooled small sensors. The performance is comparable with expensive bulky research-grade Raman systems. It has excellent sensitivity, low power consumption, perfect wavenumber, intensity calibration, and 7 cm-1 resolution within the 400-4000 cm-1 range using a built-in reference. High performance and versatility are demonstrated in use cases including quantification of methanol in beverages, in-vivo Raman measurements of human skin, fermentation monitoring, chemical Raman mapping at sub-micrometer resolution, quantitative SERS mapping of the anti-cancer drug methotrexate and in-vitro bacteria identification. We foresee that the miniaturization will allow realization of super-compact Raman spectrometers for integration in smartphones and medical devices, democratizing Raman technology.

CO2 utilization for methanol production: a review on the safety concerns and countermeasures

The catalytic conversion of carbon dioxide is one of the important ways to achieve the goal of carbon neutralization, which can be further divided into electrocatalysis, thermal catalysis, and photocatalysis. Although photocatalysis and electrocatalysis have the advantages of mild reaction conditions and low energy consumption, the thermal catalytic conversion of CO2 has larger processing capacity, better reduction effect, and more complete industrial foundation, which is a promising technology in the future. During the development of new technology from laboratory to industrial application, ensuring the safety of production process is essential. In this work, safety optimization design of equipment, safety performance of catalysts, accident types, and their countermeasures in the industrial applications of CO2 to methanol are reviewed and discussed in depth. Based on that, future research demands for industrial process safety of CO2 to methanol were proposed, which provide guidance for the large-scale application of CO2 thermal catalytic conversion technology.

Learning algorithms for identification of whisky using portable Raman spectroscopy

Reliable identification of high-value products such as whisky is vital due to rising issues of brand substitution and quality control in the industry. We have developed a novel framework that can perform whisky analysis directly from raw spectral data with no human intervention by integrating machine learning models with a portable Raman device. We demonstrate that machine learning models can achieve over 99% accuracy in brand or product identification across twenty-eight commercial samples. To demonstrate the flexibility of this approach, we utilized the same algorithms to quantify ethanol concentrations, as well as measuring methanol levels in spiked whisky samples. To demonstrate the potential use of these algorithms in a real-world environment we tested our algorithms on spectral measurements performed through the original whisky bottle. Through the bottle measurements are facilitated by a beam geometry hitherto not applied to whisky brand identification in conjunction with machine learning. Removing the need for decanting greatly enhances the practicality and commercial potential of this technique, enabling its use in detecting counterfeit or adulterated spirits and other high-value liquids. The techniques established in this paper aim to function as a rapid and non-destructive initial screening mechanism for detecting falsified and tampered spirits, complementing more comprehensive and stringent analytical methods.

Determination and Quantification of Acetaldehyde, Acetone, and Methanol in Hand Sanitizers Using Headspace GC/MS: Effect of Storage Time and Temperature

Accurate determination of the concentration of alcohols and their metabolites is important in forensics and in several life science areas. A new headspace gas chromatography-mass spectrometry method has been developed to quantify alcohols and their oxidative products using isotope-labeled internal standards. The limit of detection (LOD) of the analytes in the developed method was 0.211 µg/mL for methanol, 0.158 µg/mL for ethanol, 0.157 µg/mL for isopropanol, 0.010 µg/mL for n-propanol, 0.157 µg/mL for acetone, and 0.209 µg/mL for acetaldehyde. The precision and accuracy of the method were evaluated, and the relative standard deviation percentages were found to be less than 3%. This work demonstrates the application of this method, specifically in quantifying the concentration of oxidative products of alcohol and other minor alcohols found in hand sanitizers, which have become an essential household item since the COVID-19 pandemic. Apart from the major components, the minor alcohols found in hand sanitizers include methanol, isopropanol, and n-propanol. The concentration range of these minor alcohols found in ethanol-based hand sanitizer samples was as follows: methanol, 0.000921-0.0151 mg/mL; isopropanol, 0.454-13.8 mg/mL; and n-propanol, 0.00474-0.152 mg/mL. In ethanol-based hand sanitizers, a significant amount of acetaldehyde (0.00623-0.231 mg/mL) was observed as an oxidation product, while in the isopropanol-based hand sanitizer, acetone (0.697 mg/mL) was observed as an oxidation product. The concentration of acetaldehyde in ethanol-based hand sanitizers significantly increased with storage time and temperature, whereas no such increase in acetone concentration was observed in isopropanol-based hand sanitizers with storage time and temperature. In two of the selected hand sanitizers, the acetaldehyde levels increased by almost 200% within a week when stored at room temperature. Additionally, exposing the hand sanitizers to a temperature of 45 °C for 24 h resulted in a 100% increase in acetaldehyde concentration. On the contrary, the acetone level remained constant upon the change in storage time and temperature.

Geographical and varietal origin differentiation of alcoholic beverages through the association between FT-Raman spectroscopy and advanced data processing strategies

The present work aimed to test the efficiency of FT-Raman spectroscopy for fruit spirits discrimination by developing differentiation models based on two approaches, namely a supervised statistical method (Partial Least Squares Discriminant Analysis), and a Machine Learning technique (Support Vector Machines). For this purpose, a data set comprising 86 Romanian distillate samples was used, which aimed to be differentiated in terms of the raw material used for production (plum, apple, pear and grape) and county of origin (Cluj, Satu Mare and Salaj). Eight distinct preprocessing methods (autoscale, mean center, variance scaling, smoothing, 1st derivative, 2nd derivative, standard normal variate and Pareto) followed by a feature selection step were applied to identify the meaningful input data based on which the most efficient classification models can be constructed. Both types of models led to accuracy scores greater than 90% in differentiating the distillate samples in terms of geographical and botanical origin.

The Use of a Droplet Collar Accessory Attached to a Portable near Infrared Instrument to Identify Methanol Contamination in Whisky

The aim of this study was to evaluate the ability of a droplet collar accessory attached to a portable near-infrared (NIR) instrument to characterize the artificial contamination of methanol in commercial whisky samples. Unadulterated samples (n = 12) were purchased from local bottle shops where adulterated samples were created by adding methanol (99% pure methanol) at six levels (0.5%, 1%, 2%, 3%, 4% and 5% v/v) to the commercial whisky samples (controls). Samples were analyzed using a drop collar accessory attached to a MicroNIR Onsite instrument (900-1650 nm). Partial least squares (PLS) cross-validation statistics obtained for the prediction of all levels of methanol (from 0 to 5%) addition were considered adequate when the whole adulteration range was used, coefficient of determination in cross-validation (R2cv: 0.95) and standard error in cross of validation (SECV: 0.35% v/v). The cross-validation statistics were R2cv: 0.97, SECV: 0.28% v/v after the 0.5% and 1% v/v methanol addition was removed. These results showed the ability of using a new sample presentation attachment to a portable NIR instrument to analyze the adulteration of whisky with methanol. However, the low levels of methanol adulteration (0.5 and 1%) were not well predicted using the NIR method evaluated.

Recent advances in whiskey analysis for authentication, discrimination, and quality control

In order to safeguard authentic whiskey products from fraudulent or counterfeit practices, high throughput solutions that provide robust, rapid, and reliable solutions are required. The implementation of some analytical strategies is quite challenging or costly in routine analysis. Qualitative screening of whiskey products has been explored, but due to the nonspecificity of the chemical compounds, a more quantitative confirmatory technique is required to validate the result of the whiskey analysis. Hence, combining analytical and chemometric methods has been fundamental in whiskey sample differentiation and classification. A comprehensive update on the most relevant and current analytical techniques, including spectroscopic, chromatographic, and novel technologies employed within the last 5 years in whiskey analysis for authentication, discrimination, and quality control, are presented. Furthermore, the technical challenges in employing these analytical techniques, future trends, and perspectives are emphasized.

A novel dual-channel fluorescence sensor array based on the reaction of o-phenylenediamine/3,4-diaminotoluene and pyrocatechol for Baijiu discrimination

An effective method to discriminate Baijiu carries important applications for grade identification and quality control in the Baijiu industry. Herein, we report on a novel and straightforward dual-channel fluorescence sensor array for flavor compounds (FCs) and Chinese Baijiu discrimination. Unit 1 (U1) is the reaction between o-phenylenediamine (OPD) and pyrocatechol (ODHB), and unit 2 (U2) is the reaction between 3,4-diaminotoluene (3,4-DAT) and ODHB. The fluorescent products were changed via FCs in Baijiu relying on the influence of protonation of the amino group on OPD/3,4-DAT and chemical reactions. The array successfully achieves qualitative and quantitative identification of FCs with low detection limits and wide linear ranges. In addition, qualitative identification of 0.7 mmol/L FCs in Baijiu is achieved. Finally, this is applied to discriminate 32 Baijius varieties with different aromas, brands, and grades. The fluorescence sensor array is reliable and straightforward for FCs identification and Baijius discrimination, which is of great significance for authenticity identification in the Baijiu industry.

Rapid Iodine Value Estimation Using a Handheld Raman Spectrometer for On-Site, Reagent-Free Authentication of Edible Oils

Edible oil adulteration is a common and serious issue faced by human societies across the world. Iodine value (IV), the total unsaturation measure, is an authentication tool used by food safety officers and industries for edible oils. Current wet titrimetric methods (e.g., Wijs method) employed for IV estimation use dangerous chemicals and elaborate procedures for analysis. Alternate approaches for oil analysis require sophisticated and costly equipment such as gas chromatography (GC), liquid chromatography, high-performance liquid chromatography, mass spectrometry (MS), UV-Visible, and nuclear magnetic resonance spectroscopies. Mass screening of the samples from the market and industrial environment requires a greener, fast, and more robust technique and is an unmet need. Herein, we present a handheld Raman spectrometer-based methodology for fast IV estimation. We conducted a detailed Raman spectroscopic investigation of coconut oil, sunflower oil, and intentionally adulterated mixtures with a handheld device having a 785 nm excitation source. The obtained data were analyzed in conjunction with the GC-MS results and the conventional wet Wijs titrimetric estimated IVs. Based on these studies, a specific equation for IV estimation is derived from the intensity of identified Raman spectral bands. Further, an algorithm is designed to automate the signal processing and IV estimation, and a stand-alone graphical user interface is created in user-friendly LabVIEW software. The data acquisition and analysis require < 2 minutes, and the estimated statistical parameters such as the R ² value (0.9), root-mean-square error of calibration (1.3), and root-mean-square error of prediction (0.9) indicate that the demonstrated method has a high precision level. Also, the limit of detection and the limit of quantification for IV estimation through the current approach is ∼1 and ∼3 gI₂/100 g oil, respectively. The IVs of different oils, including hydrogenated vegetable oils, were evaluated, and the results show an excellent correlation between the estimated and reported ones.

To focus-match or not to focus-match inverse spatially offset Raman spectroscopy: a question of light penetration

The ability to identify the contents of a sealed container, without the need to extract a sample, is desirable in applications ranging from forensics to product quality control. One technique suited to this is inverse spatially offset Raman spectroscopy (ISORS) which illuminates a sample of interest with an annular beam of light and collects Raman scattering from the center of the ring, thereby retrieving the chemical signature of the contents while suppressing signal from the container. Here we explore in detail the relative benefits of a recently developed variant of ISORS, called focus-matched ISORS. In this variant, the Fourier relationship between the annular beam and a tightly focused Bessel beam is exploited to focus the excitation light inside the sample and to match the focal point of excitation and collection optics to increase the signal from the contents without compromising the suppression of the container signal. Using a flexible experimental setup which can realize both traditional and focus-matched ISORS, and Monte-Carlo simulations, we elucidate the relative advantages of the two techniques for a range of optical properties of sample and container.

Smartphone-based handheld Raman spectrometer and machine learning for essential oil quality evaluation

We present a method, utilising a smartphone-based miniaturized Raman spectrometer and machine learning for the fast identification and discrimination of adulterated essential oils (EOs). Firstly, the approach was evaluated for discrimination of pure EOs from those adulterated with solvent, namely benzyl alcohol. In the case of ylang-ylang EO, three different types of adulteration were examined, adulteration with solvent, cheaper vegetable oil and a lower price EO. Random Forest and partial least square discrimination analysis (PLS-DA) showed excellent performance in discriminating pure from adulterated EOs, whilst the same time identifying the type of adulteration. Also, utilising partial least squares regression analysis (PLS) all adulterants, namely benzyl alcohol, vegetable oil and lower price EO, were quantified based on spectra recorded using the smartphone Raman spectrometer, with relative error of prediction (REP) being between 2.41-7.59%.

Non-Invasive Monitoring of Ethanol and Methanol Levels in Grape-Derived Pisco Distillate by Vibrational Spectroscopy

Handheld Raman and portable FT-IR spectroscopy devices were evaluated for fast and non-invasive determination of methanol and ethanol levels in Peruvian Pisco. Commercial Peruvian Pisco (n = 171) samples were kindly provided by the UNALM Alliance for Research in Alcohol and its Derivatives (Lima, Peru) and supplemented by purchases at grocery and online stores. Pisco spectra were collected on handheld Raman spectrometers equipped with either a 1064 nm or a 785 nm excitation laser and a portable infrared unit operating in transmission mode. The alcohol levels were determined by GC-MS. Calibration models used partial least-squares regression (PLSR) to develop prediction algorithms. GC-MS data revealed that 10% of Pisco samples had ethanol levels lower than 38%, indicating possible water dilution. Methanol levels ranged from 10 to 130 mg/100 mL, well below the maximum levels allowed for fruit brandies. Handheld Raman equipped with a 1064 nm excitation laser gave the best results for determining ethanol (SEP = 1.2%; RPre = 0.95) and methanol (SEP = 1.8 mg/100 mL; RPre = 0.93). Randomly selected Pisco samples were spiked with methanol (75 to 2800 mg/100 mL), and their Raman spectra were collected through their genuine commercial bottles. The prediction models gave an excellent performance (SEP = 98 mg/100 mL; RPre = 0.97), allowing for the non-destructive and non-contact determination of methanol and ethanol concentrations without opening the bottles.

What's in this drink? Classification and adulterant detection in Irish Whiskey samples using near infrared spectroscopy combined with chemometrics

BACKGROUND

Near-infrared (NIR) spectroscopy coupled with principal component analysis (PCA) and partial least squares (PLS) regression was used to analyse a series of different Irish Whiskey samples in order to define their spectral profile and to assess the capability of the NIR method to identify samples based on their origin and storage (e.g. distiller, method of maturation). The ability of NIR spectroscopy to quantify the level of potential chemical adulterants was also investigated. Samples were spiked with 0.1%, 0.5%, 1.0%, 1.5% and 2.0% v/v of each adulterant (e.g. methanol, ethyl acetate, etc.) prior to NIR analysis.

RESULTS

The results of this study demonstrated the capability of NIR spectroscopy combined with PLS regression to classify the whiskey samples and to determine the level of adulteration. Moreover, the potential of NIR coupled with chemometric analysis as a rapid, portable, and non-destructive screening tool for quality control, traceability, and food/beverage adulteration for customs and other regulatory agencies, to mitigate beverage fraud was illustrated.

CONCLUSION

Given the non-specificity of the NIR technique, these positive preliminary results indicated that this method of analysis has the potential to be applied to identify the level of adulteration in distilled spirits. The rapid nature of the technique and lack of consumables or sample preparation required allows for a far more time and cost-effective analysis per sample. © 2021 Society of Chemical Industry.

Low-power swept-source Raman spectroscopy

'Molecular fingerprinting' with Raman spectroscopy can address important problems-from ensuring our food safety, detecting dangerous substances, to supporting disease diagnosis and management. However, the broad adoption of Raman spectroscopy demands low-cost, portable instruments that are sensitive and use lasers that are safe for human eye and skin. This is currently not possible with existing Raman spectroscopy approaches. Portability has been achieved with dispersive Raman spectrometers, however, fundamental entropic limits to light collection both limits sensitivity and demands high-power lasers and cooled expensive detectors. Here, we demonstrate a swept-source Raman spectrometer that improves light collection efficiency by up to 1000× compared to portable dispersive spectrometers. We demonstrate high detection sensitivity with only 1.5 mW average excitation power and an uncooled amplified silicon photodiode. The low optical power requirement allowed us to utilize miniature chip-scale MEMS-tunable lasers with close to eye-safe optical powers for excitation. We characterize the dynamic range and spectral characteristics of this Raman spectrometer in detail, and use it for fingerprinting of different molecular species consumed everyday including analgesic tablets, nutrients in vegetables, and contaminated alcohol. By moving the complexity of Raman spectroscopy from bulky spectrometers to chip-scale light sources, and by replacing expensive cooled detectors with low-cost uncooled alternatives, this swept-source Raman spectroscopy technique could make molecular fingerprinting more accessible.

Axially slanted laser illumination scheme for direct and accurate Raman spectroscopic determination of gemcitabine concentration in freeze-dried gemcitabine injection powder housed in a glass container

When Raman spectroscopy is employed for a direct in situ determination of ingredient concentration for a product stored in a glass container, minimization of the interfering glass background in the collected spectrum is demanding to secure a more accurate analysis. To meet this request, an axially slanted illumination (ASI) scheme slantingly irradiating laser on the headspace side of a glass container and positioning a detector beneath the container was demonstrated in this study. This ASI scheme was basically designed to increase the distance between the laser illumination spot and detector location to minimize the number of glass photons reaching the detector. The analytical utility of the scheme was evaluated for the determination of gemcitabine concentration (42.9-58.2 wt%) in the gemcitabine injection powder housed in a glass container. Using the ASI scheme, the spectral features of the gemcitabine powder became distinct with only a weak underlying glass background signal. For comparative purpose, when an axially perpendicular offset (APO) scheme perpendicularly irradiating laser on the side wall where the sample was filled was used, the magnitude of glass background was higher, and the most intense gemcitabine peak was largely buried in the glass peak. The accuracy for determination of gemcitabine concentration using the ASI scheme was superior with an error of 0.20 wt%, while 0.33 wt% with employing the APO scheme. Overall, this study demonstrates that the ASI scheme is a potentially versatile Raman spectroscopic tool for fast non-sampling analysis of other products stored in a glass container.

Comparison of Raman and attenuated total reflectance (ATR) infrared spectroscopy for water quantification in natural deep eutectic solvent

Natural deep eutectic solvents (NADES) are ionic solutions, of great interest for extraction from biomass, biocatalysis, and nanoparticle synthesis. They are easily synthesised and eco-friendly, have low volatility and high dissolution power, and are biodegradable. However, water content in NADES is a critical parameter, affecting their optimal use and extraction efficiency. Vibrational spectroscopic techniques are rapid, label-free, non-destructive, non-invasive, and cost-effective analytical tools that can probe the molecular composition of samples. A direct comparison between a previous study using attenuated total reflectance infrared (ATR-IR) spectroscopy for water quantification in NADES and the same investigation performed with Raman spectroscopy is presently reported. Three NADES systems, namely betaine-glycerol (BG), choline chloride-glycerol (CCG), and glucose-glycerol (GG), containing a range of water concentrations between 0% (w/w) and 40% (w/w), have been analysed with Raman spectroscopy coupled to partial least squares regression multivariate analysis. The values of root mean square error of cross-validation (RMSECV) obtained from analysis performed on the pre-processed spectra over the full spectral range (150-3750 cm^-1) are respectively 0.2966% (w/w), 0.4703% (w/w), and 0.2351% (w/w) for BG, GG, and CCG. While the direct comparison to previous ATR-IR results shows essentially similar outcomes for BG, the RMSECV is 33.14% lower and 65.84% lower for CG and CCG. Furthermore, mean relative errors obtained with Raman spectroscopy, and calculated from a set of samples used as independent samples, were 1.452% (w/w), 1.175% (w/w), and 1.188% (w/w). Ultimately, Raman spectroscopy delivered performances for quantification of water in NADES with similar accuracy to ATR-IR. The present demonstration clearly highlights the potential of Raman spectroscopy to support the development of new analytical protocols in the field of green chemistry.

Performance evaluation of handheld Raman spectroscopy for cocaine detection in forensic case samples

Handheld Raman spectroscopy is an emerging technique for rapid on-site detection of drugs of abuse. Most devices are developed for on-scene operation with a user interface that only shows whether cocaine has been detected. Extensive validation studies are unavailable, and so are typically the insight in raw spectral data and the identification criteria. This work evaluates the performance of a commercial handheld Raman spectrometer for cocaine detection based on (i) its performance on 0-100 wt% binary cocaine mixtures, (ii) retrospective comparison of 3,168 case samples from 2015 to 2020 analyzed by both gas chromatography-mass spectrometry (GC-MS) and Raman, (iii) assessment of spectral selectivity, and (iv) comparison of the instrument's on-screen results with combined partial least square regression (PLS-R) and discriminant analysis (PLS-DA) models. The limit of detection was dependent on sample composition and varied between 10 wt% and 40 wt% cocaine. Because the average cocaine content in street samples is well above this limit, a 97.5% true positive rate was observed in case samples. No cocaine false positives were reported, although 12.5% of the negative samples were initially reported as inconclusive by the built-in software. The spectral assessment showed high selectivity for Raman peaks at 1,712 (cocaine base) and 1,716 cm-1 (cocaine HCl). Combined PLS-R and PLS-DA models using these features confirmed and further improved instrument performance. This study scientifically assessed the performance of a commercial Raman spectrometer, providing useful insight on its applicability for both presumptive detection and legally valid evidence of cocaine presence for law enforcement.

Methanol in Grape Derived, Fruit and Honey Spirits: A Critical Review on Source, Quality Control, and Legal Limits

Spirits are alcoholic beverages commonly consumed in European countries. Their raw materials are diverse and include fruits, cereals, honey, sugar cane, or grape pomace. The main aim of this work is to present and discuss the source, quality control, and legal limits of methanol in spirits produced using fruit and honey spirits. The impact of the raw material, alcoholic fermentation, and the distillation process and aging process on the characteristics and quality of the final distilled beverage are discussed. In addition, a critical view of the legal aspects related to the volatile composition of these distillates, the origin and presence of methanol, and the techniques used for quantification are also described. The methanol levels found in the different types of spirits are those expected based on the specific raw materials of each and, almost in all studies, respect the legal limits.

Application of Raman spectroscopy and Machine Learning algorithms for fruit distillates discrimination

Through this pilot study, the association between Raman spectroscopy and Machine Learning algorithms were used for the first time with the purpose of distillates differentiation with respect to trademark, geographical and botanical origin. Two spectral Raman ranges (region I—200–600 cm⁻¹ and region II—1200–1400 cm⁻¹) appeared to have the higher discrimination potential for the investigated distillates. The proposed approach proved to be a very effective one for trademark fingerprint differentiation, a model accuracy of 95.5% being obtained (only one sample was misclassified). A comparable model accuracy (90.9%) was achieved for the geographical discrimination of the fruit spirits which can be considered as a very good one taking into account that this classification was made inside Transylvania region, among neighbouring areas. Because the trademark fingerprint is the prevailing one, the successfully distillate type differentiation, with respect to the fruit variety, was possible to be made only inside of each producing entity.

Preliminary Assessment of Parmigiano Reggiano Authenticity by Handheld Raman Spectroscopy

Raman spectroscopy, and handheld spectrometers in particular, are gaining increasing attention in food quality control as a fast, portable, non-destructive technique. Furthermore, this technology also allows for measuring the intact sample through the packaging and, with respect to near infrared spectroscopy, it is not affected by the water content of the samples. In this work, we evaluate the potential of the methodology to model, by multivariate data analysis, the authenticity of Parmigiano Reggiano cheese, which is one of the most well-known and appreciated hard cheeses worldwide, with protected denomination of origin (PDO). On the other hand, it is also highly subject to counterfeiting. In particular, it is critical to assess the authenticity of grated cheese, to which, under strictly specified conditions, the PDO is extended. To this aim, it would be highly valuable to develop an authenticity model based on a fast, non-destructive technique. In this work, we present preliminary results obtained by a handheld Raman spectrometer and class-modeling (Soft Independent Modeling of Class Analogy, SIMCA), which are extremely promising, showing sensitivity and specificity of 100% for the test set. Moreover, another salient issue, namely the percentage of rind in grated cheese, was addressed by developing a multivariate calibration model based on Raman spectra. It was possible to obtain a prediction error around 5%, with 18% being the maximum content allowed by the production protocol.

Through-bottle whisky sensing and classification using Raman spectroscopy in an axicon-based backscattering configuration

Non-intrusive detection systems have the potential to characterise materials through various transparent glass and plastic containers. Food and drink adulteration is increasingly problematic, representing a serious health risk as well as an economic issue. This is of particular concern for alcoholic spirits such as Scotch whisky which are often targeted for fraudulent activity. We have developed a Raman system with a novel geometry of excitation and collection, exploiting the beam propagation from an axicon lens, which results in an annular beam at the bottle surface before focusing within the sample. This facilitates the efficient acquisition of Raman signals from the alcoholic spirit contained inside the bottle, while avoiding the collection of auto-fluorescence signals generated by the bottle wall. Therefore, this technique provides a way of non-destructive and non-contact detection to precisely analyse the contents without the requirement to open the bottle.

A Brief History of Whiskey Adulteration and the Role of Spectroscopy Combined with Chemometrics in the Detection of Modern Whiskey Fraud

Food fraud and adulteration is a major concern in terms of economic and public health. Multivariate methods combined with spectroscopic techniques have shown promise as a novel analytical strategy for addressing issues related to food fraud that cannot be solved by the analysis of one variable, particularly in complex matrices such distilled beverages. This review describes and discusses different aspects of whisky production, and recent developments of laboratory, in field and high throughput analysis. In particular, recent applications detailing the use of vibrational spectroscopy techniques combined with data analytical methods used to not only distinguish between brand and origin of whisky but to also detect adulteration are presented.

Food authentication in real life: How to link nontargeted approaches with routine analytics?

In times of increasing globalization and the resulting complexity of trade flows, securing food quality is an increasing challenge. The development of analytical methods for checking the integrity and, thus, the safety of food is one of the central questions for actors from science, politics, and industry. Targeted methods, for the detection of a few selected analytes, still play the most important role in routine analysis. In the past 5 years, nontargeted methods that do not aim at individual analytes but on analyte profiles that are as comprehensive as possible have increasingly come into focus. Instead of investigating individual chemical structures, data patterns are collected, evaluated and, depending on the problem, fed into databases that can be used for further nontargeted approaches. Alternatively, individual markers can be extracted and transferred to targeted methods. Such an approach requires (i) the availability of authentic reference material, (ii) the corresponding high-resolution laboratory infrastructure, and (iii) extensive expertise in processing and storing very large amounts of data. Probably due to the requirements mentioned above, only a few methods have really established themselves in routine analysis. This review article focuses on the establishment of nontargeted methods in routine laboratories. Challenges are summarized and possible solutions are presented.