Through-container, extremely low concentration detection of multiple chemical markers of counterfeit alcohol using a handheld SORS device

Shifted-excitation Raman difference spectroscopy and charge-shifting detection coupled with spatially offset Raman spectroscopy for heritage science

In situ measurements have great importance since in many scientific fields certain samples cannot be moved because of diverse reasons (excessive dimensions or weight, security, logistics etc.). In heritage science, this is a crucial requirement due to the high value of art objects, requiring non-invasive and in situ analyses. Therefore, it is important to have analytical methods capable of providing relevant information also outside laboratory environments. Such measurements face multiple challenges: for example, interference from ambient light or formation of artefacts due to undesired motions of the instruments. In Raman spectroscopy, a number of solutions have been demonstrated to mitigate these effects. For instance, Shifted Excitation Raman Difference Spectroscopy (SERDS) has proven efficient in removing the fluorescence of the sample and ambient light interference, and a charge-shifting detection approach was shown to be valuable in dealing with varying ambient light. In this study, we provide a comparison of conventional Raman spectroscopy, Shifted Excitation Raman Difference Spectroscopy (SERDS), charge-shifting detection technology and a combined SERDS and charge-shifting approach, in order to evaluate their effectiveness in mitigating fast evolving interfering backgrounds (e.g., varying ambient light). Further investigations were also carried out into the potential of coupling of these methods with Spatially Offset Raman Spectroscopy (SORS) to facilitate more effective non-invasive investigations of subsurface sample components (e.g. paint layers). The study was carried out using samples mimicking cultural heritage materials with different degrees of complexity and in the presence of fluorescence and ambient light interference. The results are, nevertheless, applicable more generally to other areas such as forensics or biomedical fields, where both dynamic and static interferences can hinder measurements.

Low-Cost Raman Spectroscopy Setup Combined with a Machine Learning Model

The diagnosis of kidney diseases presents significant challenges, including the reliance on variable and unstable biomarkers and the necessity for complex and expensive laboratory tests. Raman spectroscopy emerges as a promising technique for analyzing complex fluids, like urine, and detecting important disease biomarkers. However, its complexity, high cost and limited accessibility outside clinical contexts complicate its application. Moreover, the analysis of Raman spectra is a challenging and intensive task. In response to these challenges, in this study, we developed a portable, simplified and low-cost Raman system designed to acquire high-quality spectra of liquid complex samples. Using the "Starter Edition" methodology from the OpenRAMAN project, the system was optimized through laser temperature adjustments, by evaluating the laser emission spectrum under different temperatures with a spectrometer, and through adjustment of the acquisition parameters of the software used, by acquiring the ethanol spectra. The system validation was performed through the acquisition of Raman spectra from five urine samples, demonstrating its consistency and sensitivity to composition variations in urine samples. Additionally, a neural network was designed and trained using methanol and ethanol solutions. The model's hyperparameters were optimized to maximize its precision and accuracy, achieving 99.19% accuracy and 99.21% precision, with a training time of approximately 3 min, underlining the model's potential for classifying simple Raman spectra. While further system validation with more samples, a more in-depth analysis of the biomarkers present in urine and the integration with more sophisticated elements are necessary, this approach demonstrates the system characteristics of affordability and portability, making it a suitable solution for point-of-care applications and offering simplified accessibility for assessing the diseases risk outside clinical contexts.

In situ study of the interactions between metal surfaces and cationic surfactant corrosion inhibitors by surface-enhanced Raman spectroscopy coupled with visible spectroscopy

Cationic surfactants are widely used as corrosion inhibitors for industrial tubings and pipelines. They protect the surface of steel pipes through a film-forming mechanism, providing both anodic and cathodic inhibition. To improve the efficiency of the corrosion protection, it is essential to understand the interactions between the surfactants and metal surfaces. To achieve this, surface enhanced Raman spectroscopy (SERS) can serve as a powerful tool due to its surface sensitivity and potential to detect trace amounts of analytes in complex media. In this contribution, we have investigated the behaviour of in situ prepared AgNPs in the presence of benzalkonium chloride as a model corrosion inhibitor using SERS coupled to visible spectroscopy and combined with light scattering methods. By combining these experimental methods, we were able to correlate the aggregation of silver particles with the concentration of added surfactant in the resulting mixture. Using this insight, we also established a SERS method for the detection of benzalkonium chloride traces in water. For this, we utilised the quenching of the SERS response of methylene blue by competitive adsorption of methylene blue and the surfactant on SERS active AgNPs. We believe that our approach can serve a variety of applications to improve the industrial water treatment. For example, the modelling of the interaction of different surfactants with SERS can be used for process intensification, and ultimately, to move towards the digital twinning of corrosion processes for more efficient corrosion inhibition. Furthermore, the ability to adapt our sensing protocol for on-line corrosion inhibitor monitoring allows a fast response to process changes, hence, enabling resource-efficient, continuous process control.

A review of the emerging technologies and systems to mitigate food fraud in supply chains

Food fraud has serious consequences including reputational damage to businesses, health and safety risks and lack of consumer confidence. New technologies targeted at ensuring food authenticity has emerged and however, the penetration and diffusion of sophisticated analytical technologies are faced with challenges in the industry. This review is focused on investigating the emerging technologies and strategies for mitigating food fraud and exploring the key barriers to their application. The review discusses three key areas of focus for food fraud mitigation that include systematic approaches, analytical techniques and package-level anti-counterfeiting technologies. A notable gap exists in converting laboratory based sophisticated technologies and tools in high-paced, live industrial applications. New frontiers such as handheld laser-induced breakdown spectroscopy (LIBS) and smart-phone spectroscopy have emerged for rapid food authentication. Multifunctional devices with hyphenating sensing mechanisms together with deep learning strategies to compare food fingerprints can be a great leap forward in the industry. Combination of different technologies such as spectroscopy and separation techniques will also be superior where quantification of adulterants are preferred. With the advancement of automation these technologies will be able to be deployed as in-line scanning devices in industrial settings to detect food fraud across multiple points in food supply chains.

Theoretical and Practical Considerations of Spatially Offset Raman Spectroscopy (SORS) and Micro-SORS

Spatially offset Raman spectroscopy (SORS) is typically used to non-invasively investigate stratified samples that possess features on a millimeter scale, whereas micro-SORS usually deals with micrometer-thick layered samples. However, there are many instances where these boundaries are intertwined, sometimes indicating the possibility of using both techniques as well as circumstances that present mutual exclusion to their applicability. The aim of this study is to establish an application protocol that provides better insight into their suitability for deployment in various scenarios. The differences and similarities between the two approaches are investigated highlighting their strengths and limitations considering both theoretical and practical aspects. Diverse available parameters entail prospects and restrictions of both techniques and give rise to specific instrumental effects, namely, the overlap between the collection and excitation areas, the percentage of collected area for a given spatial offset, and the accuracy in the definition of the spatial offset (spread effect). These aspects are studied and exemplified on mockup samples relevant to the field of cultural heritage. The samples are characterized by high compositional complexity comprising features ranging from micrometer to millimeter scales. The conclusions reached are also relevant to other scientific areas such as biomedical, forensic, or energy harvest.

Application of Spatial Offset Raman Spectroscopy (SORS) and Machine Learning for Sugar Syrup Adulteration Detection in UK Honey

Honey authentication is a complex process which traditionally requires costly and time-consuming analytical techniques not readily available to the producers. This study aimed to develop non-invasive sensor methods coupled with a multivariate data analysis to detect the type and percentage of exogenous sugar adulteration in UK honeys. Through-container spatial offset Raman spectroscopy (SORS) was employed on 17 different types of natural honeys produced in the UK over a season. These samples were then spiked with rice and sugar beet syrups at the levels of 10%, 20%, 30%, and 50% w/w. The data acquired were used to construct prediction models for 14 types of honey with similar Raman fingerprints using different algorithms, namely PLS-DA, XGBoost, and Random Forest, with the aim to detect the level of adulteration per type of sugar syrup. The best-performing algorithm for classification was Random Forest, with only 1% of the pure honeys misclassified as adulterated and <3.5% of adulterated honey samples misclassified as pure. Random Forest was further employed to create a classification model which successfully classified samples according to the type of adulterant (rice or sugar beet) and the adulteration level. In addition, SORS spectra were collected from 27 samples of heather honey (24 Calluna vulgaris and 3 Erica cinerea) produced in the UK and corresponding subsamples spiked with high fructose sugar cane syrup, and an exploratory data analysis with PCA and a classification with Random Forest were performed, both showing clear separation between the pure and adulterated samples at medium (40%) and high (60%) adulteration levels and a 90% success at low adulteration levels (20%). The results of this study demonstrate the potential of SORS in combination with machine learning to be applied for the authentication of honey samples and the detection of exogenous sugars in the form of sugar syrups. A major advantage of the SORS technique is that it is a rapid, non-invasive method deployable in the field with potential application at all stages of the supply chain.

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.

Discrimination/Classification of Edible Vegetable Oils from Raman Spatially Solved Fingerprints Obtained on Portable Instrumentation

Currently, the combination of fingerprinting methodology and environmentally friendly and economical analytical instrumentation is becoming increasingly relevant in the food sector. In this study, a highly versatile portable analyser based on Spatially Offset Raman Spectroscopy (SORS) obtained fingerprints of edible vegetable oils (sunflower and olive oils), and the capability of such fingerprints (obtained quickly, reliably and without any sample treatment) to discriminate/classify the analysed samples was evaluated. After data treatment, not only unsupervised pattern recognition techniques (as HCA and PCA), but also supervised pattern recognition techniques (such as SVM, kNN and SIMCA), showed that the main effect on discrimination/classification was associated with those regions of the Raman fingerprint related to free fatty acid content, especially oleic and linoleic acid. These facts allowed the discernment of the original raw material used in the oil's production. In all the models established, reliable qualimetric parameters were obtained.

Two-layer reconstruction of Raman spectra in diffusive media based on an analytical model in the time domain

We derive and validate an analytical model that describes the migration of Raman scattered photons in two-layer diffusive media, based on the diffusion equation in the time domain. The model is derived under a heuristic approximation that background optical properties are identical on the excitation and Raman emission wavelengths. Methods for the reconstruction of two-layer Raman spectra have been developed, tested in computer simulations and validated on tissue-mimicking phantom measurements data. Effects of different parameters were studied in simulations, showing that the thickness of the top layer and number of detected photon counts have the most significant impact on the reconstruction. The concept of quantitative, mathematically rigorous reconstruction using the proposed model was finally proven on experimental measurements, by successfully separating the spectra of silicone and calcium carbonate (calcite) layers, showing the potential for further development and eventual application in clinical diagnostics.

Growth of Plasmonic Nanoparticles for Aging Cask-Matured Whisky

The maturation of spirit in wooden casks is key to the production of whisky, a hugely popular and valuable product, with the transfer and reaction of molecules from the wooden cask with the alcoholic spirit imparting color and flavor. However, time in the cask adds significant cost to the final product, requiring expensive barrels and decades of careful storage. Thus, many producers are concerned with what "age" means in terms of the chemistry and flavor profiles of whisky. We demonstrate here a colorimetric test for spirit "agedness" based on the formation of gold nanoparticles (NPs) by whisky. Gold salts were reduced by barrel-aged spirit and produce colored gold NPs with distinct optical properties. Information from an extinction profile, such as peak position, growth rate, or profile shape, was analyzed, and our assay output was correlated with measurements of the whisky sample makeup, assays for key functional groups, and spiking experiments to explore the mechanism in more detail. We conclude that age is not just a number, that the chemical fingerprint of key flavor compounds is a useful marker for determining whisky "age", and that our simple reduction assay could assist in defining the aged character of a whisky and become a useful future tool on the warehouse floor.

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.

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.

Portable through Bottle SORS for the Authentication of Extra Virgin Olive Oil

The authenticity of olive oil has been a significant long-term challenge. Extra virgin olive oil (EVOO) is the most desirable of these products and commands a high price, thus unscrupulous individuals often alter its quality by adulteration with a lower grade oil. Most analytical methods employed for the detection of food adulteration require sample collection and transportation to a central laboratory for analysis. We explore the use of portable conventional Raman and spatially-offset Raman spectroscopy (SORS) technologies as non-destructive approaches to assess the adulteration status of EVOO quantitatively and for SORS directly through the original container, which means that after analysis the bottle is intact and the oil would still be fit for use. Three sample sets were generated, each with a different adulterant and varying levels of chemical similarity to EVOO. These included EVOO mixed with sunflower oil, pomace olive oil, or refined olive oil. Authentic EVOO samples were stretched/diluted from 0% to 100% with these adulterants and measured using two handheld Raman spectrometers (excitation at 785 or 1064 nm) and handheld SORS (830 nm). The PCA scores plots displayed clear trends which could be related to the level of adulteration for all three mixtures. Conventional Raman (at 785 or 1064 nm) and SORS (at 830 nm with a single spatial offset) conducted in sample vial mode resulted in prediction errors for the test set data ranging from 1.9–4.2% for sunflower oil, 6.5–10.7% for pomace olive oil and 8.0–12.8% for refined olive oil; with the limit of detection (LOD) typically being 3–12% of the adulterant. Container analysis using SORS produced very similar results: 1.4% for sunflower, 4.9% for pomace, and 10.1% for refined olive oil, with similar LODs ranging from 2–14%. It can be concluded that Raman spectroscopy, including through-container analysis using SORS, has significant potential as a rapid and accurate analytical method for the non-destructive detection of adulteration of extra virgin olive oil.

Through-container quantitative analysis of hand sanitizers using spatially offset Raman spectroscopy

The COVID-19 pandemic created an increased demand for hygiene supplies such as hand sanitizers. In response, a large number of new domestic or imported hand sanitizer products entered the US market. Some of these products were later found to be out of specification. Here, to quickly assess the quality of the hand sanitizer products, a quantitative, through-container screening method was developed for rapid and non-destructive screening. Using spatially offset Raman spectroscopy (SORS) and support vector regression (SVR), active ingredients (e.g., type of alcohol) of 173 commercial and in-house products were identified and quantified regardless of the container material or opacity. Alcohol content in hand sanitizer formulations were predicted with high accuracy [Formula: see text] using SVR and [Formula: see text] of the substandard test samples were identified. In sum, a SORS-SVR method was developed and used for testing medical countermeasures used against COVID-19, demonstrating a potential for high-volume testing during public health threats.

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.

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.

Selected Instrumental Techniques Applied in Food and Feed: Quality, Safety and Adulteration Analysis

This review presents an overall glance at selected instrumental analytical techniques and methods used in food analysis, focusing on their primary food science research applications. The methods described represent approaches that have already been developed or are currently being implemented in our laboratories. Some techniques are widespread and well known and hence we will focus only in very specific examples, whilst the relatively less common techniques applied in food science are covered in a wider fashion. We made a particular emphasis on the works published on this topic in the last five years. When appropriate, we referred the reader to specialized reports highlighting each technique's principle and focused on said technologies' applications in the food analysis field. Each example forwarded will consider the advantages and limitations of the application. Certain study cases will typify that several of the techniques mentioned are used simultaneously to resolve an issue, support novel data, or gather further information from the food sample.

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.

How Fishy Is Your Fish? Authentication, Provenance and Traceability in Fish and Seafood by Means of Vibrational Spectroscopy

Food authenticity, traceability and provenance are emerging issues of major concern for consumers, industries and regulatory bodies worldwide. In addition, both food safety and security are an intrinsic component of food quality where the above issues are key in modern traceability and management systems. It has been reported that substitution of a high-quality species by less expensive ones might be a frequent practice in seafood products such as fish and shellfish. In this type of products, the source (e.g., origin) and identification of the species are complex. Although different countries have implemented strict regulations and labelling protocols, these issues still are of concern. This article briefly reviews some of the most recent applications of vibrational spectroscopy (near and mid infrared, Raman) combined with chemometrics to target some of these issues in the seafood and fish industries.

Determination of ethanol concentration in alcoholic beverages by direct analysis in real time mass spectrometry (DART-MS)

Determination of the ethanol concentration in beverages remains an occasional request to forensic laboratories. While traditionally determined using headspace gas chromatography (HS-GC), this process involves lengthy run times and the need for specialized headspace sampling equipment for the GC system. The work presented here highlights a potential alternative to HS-GC analysis, using direct analysis in real time mass spectrometry (DART-MS). By incorporating a simple T-junction to the orifice of the mass spectrometer, analysis of the headspace of a beverage can be completed, resulting in measurement of the ethanol concentration in a matter of seconds with greater than 99% accuracy. This work presents the development of a method for ethanol quantitation as well as results from the analysis of both ethanol reference material standards and in-house created ethanol-containing solutions to evaluate the precision and accuracy of the technique. Analysis of ethanolic beverages via DART-MS is shown to be a rapid and reliable alternative to traditional analysis by HS-GC.

Translating 'big data': better understanding of host-pathogen interactions to control bacterial foodborne pathogens in poultry

Recent technological advances has led to the generation, storage, and sharing of colossal sets of information ('big data'), and the expansion of 'omics' in science. To date, genomics/metagenomics, transcriptomics, proteomics, and metabolomics are arguably the most ground breaking approaches in food and public safety. Here we review some of the recent studies of foodborne pathogens (Campylobacter spp., Salmonella spp., and Escherichia coli) in poultry using big data. Genomic/metagenomic approaches have reveal the importance of the gut microbiota in health and disease. They have also been used to identify, monitor, and understand the epidemiology of antibiotic-resistance mechanisms and provide concrete evidence about the role of poultry in human infections. Transcriptomics studies have increased our understanding of the pathophysiology and immunopathology of foodborne pathogens in poultry and have led to the identification of host-resistance mechanisms. Proteomic/metabolomic approaches have aided in identifying biomarkers and the rapid detection of low levels of foodborne pathogens. Overall, 'omics' approaches complement each other and may provide, at least in part, a solution to our current food-safety issues by facilitating the development of new rapid diagnostics, therapeutic drugs, and vaccines to control foodborne pathogens in poultry. However, at this time most 'omics' approaches still remain underutilized due to their high cost and the high level of technical skills required.

Quantitation of active pharmaceutical ingredient through the packaging using Raman handheld spectrophotometers: A comparison study

Handheld Raman spectroscopy is actually booming. Recent devices improvements aim at addressing the usual Raman spectroscopy issues: fluorescence with shifted-excitation Raman difference spectroscopy (SERDS), poor sensitivity with surface enhanced Raman scattering (SERS) and information only about the sample surface with spatially offset Raman spectroscopy (SORS). While qualitative performances of handheld devices are generally well established, the quantitative analysis of pharmaceutical samples remains challenging. The aim of this study was to compare the quantitative performances of three commercially available handheld Raman spectroscopy devices. Two of them (TruScan and IDRaman mini) are equipped with a 785 nm laser wavelength and operate in a conventional backscattering mode. The IDRaman has the Orbital Raster Scanning (ORS) option to increase the analyzed surface. The third device (Resolve) operates with an 830 nm laser wavelength both in backscattering and in SORS modes. The comparative study was carried out on ibuprofen-mannitol-microcrystalline cellulose ternary mixtures. The concentration of ibuprofen ranged from 24 to 52% (w/w) while the proportions of the two excipients were varied to avoid cross-correlation as much as possible. Analyses were performed either directly through a glass vial or with the glass vial in an opaque polypropylene flask, using a validated FT-NIR spectroscopy method as a reference method. Chemometric analyses were carried out with the Partial Least Squares Regression (PLS-R) algorithm. The quantitative models were validated using the total error approach and the ICH Q2 (R1) guidelines with ± 15% as acceptance limits.

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

The spirits drinks industry is of significant global economic importance and a major employer worldwide, and the ability to ensure product authenticity and maintain consumer confidence in these high-value products is absolutely essential. Spirit drinks counterfeiting is a worldwide problem, with counterfeiting and adulteration of spirit drinks taking many forms, such as substitution, stretching with lower-grade products, or creation of counterfeits with industrial, surrogate, or locally produced alcohols. Methanol for example, which has been used as a substitute alcohol for ethanol, has a high toxicity in humans. The counterfeiting of spirit drinks is consequently one of the few leading reported types of food fraud which can be directly and unequivocally linked to food safety and health concerns. Here, for the first time, we use handheld Raman spectroscopy with excitation in the near IR (1064 nm) for the through-container differentiation of multiple spirit drinks, detection of multiple chemical markers of counterfeit alcohol, and for the quantification of methanol. We established the limits of detection (LOD) of methanol in the analysed samples from four different spirit types (between 0.23-0.39%), which were considerably lower than a quoted maximum tolerable concentration (MTC) of 2% (v/v) methanol for humans in a 40% alcohol by volume (ABV) spirit drink, and even lower than the general EU limit for naturally occurring methanol in fruit spirits of 0.5% v/v (10 g methanol per L ethanol). We believe that Raman spectroscopy has considerable practicable potential for the rapid in situ through-container detection of counterfeit spirits drinks, as well as for the analysis and protection of other beverages and liquid samples.

Authenticity and geographic origin of global honeys determined using carbon isotope ratios and trace elements

Honey is the world's third most adulterated food. The addition of cane sugar or corn syrup and the mislabelling of geographic origin are common fraudulent practices in honey markets. This study examined 100 honey samples from Australia (mainland and Tasmania) along with 18 other countries covering Africa, Asia, Europe, North America and Oceania. Carbon isotopic analyses of honey and protein showed that 27% of commercial honey samples tested were of questionable authenticity. The remaining 69 authentic samples were subject to trace element analysis for geographic determination. One-way ANOVA analysis showed a statistical difference (p < 0.05) in trace element concentrations of honey from Australian regions and different continents. Principal component analysis (PCA) and canonical discriminant analysis (CDA) coupled with C5.0 classification modelling of honey carbon isotopes and trace element concentrations showed distinct clusters according to their geographic origin. The C5.0 model revealed trace elements Sr, P, Mn and K can be used to differentiate honey according to its geographic origin. The findings show the common and prevalent issues of honey authenticity and the mislabelling of its geographic origin can be identified using a combination of stable carbon isotopes and trace element concentrations.

Multiplex imaging of live breast cancer tumour models through tissue using handheld surface enhanced spatially offset resonance Raman spectroscopy (SESORRS)

Through utilizing the depth penetration capabilities of SESORS, multiplexed imaging and classification of three singleplex nanotags and a triplex of nanotags within breast cancer tumour models is reported for the first time through depths of 10 mm using a handheld SORS instrument.

Rapid quantification of the adulteration of fresh coconut water by dilution and sugars using Raman spectroscopy and chemometrics

Here, for the first time, we developed Raman spectroscopy in combination with chemometrics for the quantification of adulteration of fresh coconut water by dilution, and its masking with sugars. Coconut water was extracted from young Costa Rican coconuts and heat treated to emulate pasteurization. Samples were then adulterated by dilution with water and single sugars, mixtures of sugars, and high-fructose corn syrup (HFCS). A total of 155 samples were analysed with Raman spectroscopy at 785 nm excitation and 620 spectra analysed with chemometrics. Results showed successful quantification of dilution and adulteration with single sugars between 1.9 and 2.6%, masking of dilution with mixtures of sugars at 9.8%, and masking of dilution with HFCS at 7.1%. It can be concluded that Raman spectroscopy has significant potential as a rapid accurate analytical method for the detection of adulteration in this product, with the ability to discern small abnormalities in sugar ratios within coconut water.

Through tissue imaging of a live breast cancer tumour model using handheld surface enhanced spatially offset resonance Raman spectroscopy (SESORRS)

Detection of a live 3D tumour model through 15 mm of tissue using SESORRS.

In order to improve patient survival and reduce the amount of unnecessary and traumatic biopsies, non-invasive detection of cancerous tumours is of imperative and urgent need. Multicellular tumour spheroids (MTS) can be used as an ex vivo cancer tumour model, to model in vivo nanoparticle (NP) uptake by the enhanced permeability and retention (EPR) effect. Surface enhanced spatially offset Raman spectroscopy (SESORS) combines both surface enhanced Raman spectroscopy (SERS) and spatially offset Raman spectroscopy (SORS) to yield enhanced Raman signals at much greater sub-surface levels. By utilizing a reporter that has an electronic transition in resonance with the laser frequency, surface enhanced resonance Raman scattering (SERRS) yields even greater enhancement in Raman signal. Using a handheld SORS spectrometer with back scattering optics, we demonstrate the detection of live breast cancer 3D MTS containing SERRS active NPs through 15 mm of porcine tissue. False color 2D heat intensity maps were used to determine tumour model location. In addition, we demonstrate the tracking of SERRS-active NPs through porcine tissue to depths of up to 25 mm. This unprecedented performance is due to the use of red-shifted chalcogenpyrylium-based Raman reporters to demonstrate the novel technique of surface enhanced spatially offset resonance Raman spectroscopy (SESORRS) for the first time. Our results demonstrate a significant step forward in the ability to detect vibrational fingerprints from a tumour model at depth through tissue. Such an approach offers significant promise for the translation of NPs into clinical applications for non-invasive disease diagnostics based on this new chemical principle of measurement.

Surface enhanced resonance Raman spectroscopy (SERRS) for probing through plastic and tissue barriers using a handheld spectrometer

Through tissue imaging of a live breast cancer tumour model using handheld surface enhanced resonance Raman spectroscopy (SERRS).

Towards establishing a minimal nanoparticle concentration for applications involving surface enhanced spatially offset resonance Raman spectroscopy (SESORRS) in vivo

Detection of SERRS nanotags at picomolar concentrations through 5 mm of tissue using SESORS.