Raman spectroscopy in the analysis of food and pharmaceutical nanomaterials

The Role of Hydrogen Bonding in the Raman Spectral Signals of Caffeine in Aqueous Solution

The identification and quantification of caffeine is a common need in the food and pharmaceutical industries and lately also in the field of environmental science. For that purpose, Raman spectroscopy has been used as an analytical technique, but the interpretation of the spectra requires reliable and accurate computational protocols, especially as regards the Resonance Raman (RR) variant. Herein, caffeine solutions are sampled using Molecular Dynamics simulations. Upon quantification of the strength of the non-covalent intermolecular interactions such as hydrogen bonding between caffeine and water, UV-Vis, Raman, and RR spectra are computed. The results provide general insights into the hydrogen bonding role in mediating the Raman spectral signals of caffeine in aqueous solution. Also, by analyzing the dependence of RR enhancement on the absorption spectrum of caffeine, it is proposed that the sensitivity of the RR technique could be exploited at excitation wavelengths moderately far from 266 nm, yet achieving very low detection limits in the quantification caffeine content.

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

Nanomaterials based sensors for analysis of food safety

The freshnessof the food is a major issue because spoiled food lacks critical nutrients for growth and could be harmful to human health if consumed directly. Nanomaterials are captivating due to their unique properties like large surface area, high selectivity, small dimension, great biocompatibility and conductivity, real-time onsite analysis, etc. which give them an advantage over conventional evaluation techniques. Despite these advantages of nanomaterials used in food safety and their preservation, food products can still get affected by various environmental factors (like pH, temperature, etc.), making the use of time-temperature indicators more condescending. This review is a comprehensive study on food safety, its causes, the responsible analytes, their remedies by various nanomaterials, the development of various nanosensors, and the various challenges faced in maintaining food safety standards to reduce the risk of contaminants.

Rapid screening and multicomponent quantifications of active components of oral syrup over-the-counter medications by Raman and UV-visible spectroscopy and multivariate regression analysis

Over-the-counter medications (OTCMs) are frequently recommended as a first-line treatment for common ailments, diseases, and illnesses. Oral liquid dosage forms are advantageous for rapid absorption with no dissolution time and are easier for pediatric and geriatric consumers to swallow. The production of these medicines by pharmaceutical industry makes them readily available to the public. Although the US Food and Drug Administration (FDA) provides strict guidelines to drug manufacturers of these products; the risk of counterfeiting is a global issue. This can lead to several adverse effects and health issues. Here, we report a fast screening and quality assurance method using Raman and UV-visible spectroscopy combined with Principal Component Analysis (PCA) and Partial-Least-Square (PLS) regression of commonly used OTCM oral syrups. PLS regressions of UV-visible absorption spectra were used for multicomponent quantifications of the active component (acetaminophen, guaifenesin, dextromethorphan HBr, and phenylephrine HCl) concentrations of OTMCs in flavored (sugar or sugar-free) oral syrups. Raman and UV-visible spectral responses varied based on the type and concentration of the active component analyzed. PCA of the spectral data provided pattern recognition of the oral syrup OTCM. The developed PLS method demonstrated good linearity with an R2 > 0.9784 and high sensitivity with a low detection limit of 0.02 mg/mL for acetaminophen and guaifenesin. Moreover, the simultaneous quantification of concentrations of all active components by the described method yielded good accuracies ranging from 88 to 94%. This study provides an example of the benefits of the combined use of Raman and UV-vis spectral profiling, PCA, and PLS regression for the quality analysis of oral syrups OTCM providing multicomponent quantification of active components with no need for sample extraction. The reported method can be easily adapted and scaled for online detection analysis used in the drug manufacturing industry, both in-situ and field analysis, and for the quality control of syrups OTCM by regulatory agencies and quality control officers.

Raman spectroscopy and its plasmon-enhanced counterparts: A toolbox to probe protein dynamics and aggregation

Protein unfolding and aggregation are often correlated with numerous diseases such as Alzheimer's, Parkinson's, Huntington's, and other debilitating neurological disorders. Such adverse events consist of a plethora of competing mechanisms, particularly interactions that control the stability and cooperativity of the process. However, it remains challenging to probe the molecular mechanism of protein dynamics such as aggregation, and monitor them in real-time under physiological conditions. Recently, Raman spectroscopy and its plasmon-enhanced counterparts, such as surface-enhanced Raman spectroscopy (SERS) and tip-enhanced Raman spectroscopy (TERS), have emerged as sensitive analytical tools that have the potential to perform molecular studies of functional groups and are showing significant promise in probing events related to protein aggregation. We summarize the fundamental working principles of Raman, SERS, and TERS as nondestructive, easy-to-perform, and fast tools for probing protein dynamics and aggregation. Finally, we highlight the utility of these techniques for the analysis of vibrational spectra of aggregation of proteins from various sources such as tissues, pathogens, food, biopharmaceuticals, and lastly, biological fouling to retrieve precise chemical information, which can be potentially translated to practical applications and point-of-care (PoC) devices. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Diagnostic Tools > Diagnostic Nanodevices Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Application of DNA-fueled molecular machines in food safety testing

Food is consumed by humans, which is indispensable to human life. Therefore, considerable attention of the whole society has been paid to food safety. Over the last few years, dramatic social development has brought new challenges to food safety, making developing new and quick methods for on-site food safety testing an important necessity. As a result, DNA-fueled molecular machines, characterized by high efficiency, accuracy, and sensitivity in testing, have come into the spotlight, based on which sensors can be constructed to detect toxic and harmful substances in food products. This study reviewed recent research on several DNA-fueled molecular machines, including DNA tweezers, DNA walkers, and DNA origami, for rapidly detecting toxic and harmful substances. Based on the above studies, the sensitivity and timeliness of several DNA molecular machines were summarized and compared, and the development prospect of DNA fuel molecular machines in the field of food safety detection was prospected.

Roles of polysaccharides-based nanomaterials in food preservation and extension of shelf-life of food products: A review

In food production sectors, food spoilage and contamination are major issues that threaten and negatively influence food standards and safety. Several physical, chemical, and biological methods are used to extend the shelf-life of food products, but they have their limitations. Henceforth, researchers and scientists resort to novel methods to resolve these existing issues. Nanomaterials-based extension of food shelf life has broad scope rendering a broad spectrum of activity including high antioxidant and antimicrobial activity. Numerous research investigations have been made to identify the possible roles of nanoparticles in food preservation. A wide range of nanomaterials via different approaches is ultimately applied for food preservation. Among them, chemically synthesized methods have several limitations, unlike biological synthesis. However, biological synthesis protocols are quite expensive and laborious. Predominant studies demonstrated that nanoparticles can protect fruits and vegetables by preventing microbial contamination. Though several nanomaterials designated for food preservation are available, detailed knowledge of the mechanism remains unclear. Hence, this review aims to highlight the various nanomaterials and their roles in increasing the shelf life of food products. Adding to the novel market trends, nano-packaging will open new frontiers and prospects for ensuring food safety and quality.

Future of Nanotechnology in Food Industry: Challenges in Processing, Packaging, and Food Safety

Over the course of the last several decades, nanotechnology has garnered a growing amount of attention as a potentially valuable technology that has significantly impacted the food industry. Nanotechnology helps in enhancing the properties of materials and structures that are used in various fields such as agriculture, food, pharmacy, and so on. Applications of nanotechnology in the food market have included the encapsulation and distribution of materials to specific locations, the improvement of flavor, the introduction of antibacterial nanoparticles into food, the betterment of prolonged storage, the detection of pollutants, enhanced storage facilities, locating, identifying, as well as consumer awareness. Labeling food goods with nano barcodes helps ensure their security and may also be used to track their distribution. This review article presents a discussion about current advances in nanotechnology along with its applications in the field of food-tech, food packaging, food security, enhancing life of food products, etc. A detailed description is provided about various synthesis routes of nanomaterials, that is, chemical, physical, and biological methods. Nanotechnology is a rapidly improving the field of food packaging and the future holds great opportunities for more enhancement via the development of new nanomaterials and nanosensors.

Imaging-assisted Raman and photoluminescence spectroscopy for diamond jewelry identification and evaluation

Jewelry identification and evaluation are limited owing to interference from the surrounding metal mount and adjacent gemstones. To maintain transparency in the jewelry market, this study proposes imaging-assisted Raman and photoluminescence spectroscopy for jewelry measurement. The system can automatically measure multiple gemstones on a jewelry piece sequentially, using the image as a reference for alignment. The experimental prototype demonstrates the capability of noninvasive measurement for separating natural diamonds from their laboratory-grown counterparts and diamond simulants. Furthermore, the image can be used for gemstone color evaluation and weight estimation.

Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy

Herbs have been used as natural remedies for disease treatment, prevention, and health care. Some herbs with functional properties are also used as food or food additives for culinary purposes. The quality and safety inspection of herbs are influenced by various factors, which need to be assessed in each operation across the whole process of herb production. Traditional analysis methods are time-consuming and laborious, without quick response, which limits industry development and digital detection. Considering the efficiency and accuracy, faster, cheaper, and more environment-friendly techniques are highly needed to complement or replace the conventional chemical analysis methods. Infrared (IR) and Raman spectroscopy techniques have been applied to the quality control and safety inspection of herbs during the last several decades. In this paper, we generalize the current application using IR and Raman spectroscopy techniques across the whole process, from raw materials to patent herbal products. The challenges and remarks were proposed in the end, which serve as references for improving herb detection based on IR and Raman spectroscopy techniques. Meanwhile, make a path to driving intelligence and automation of herb products factories.

Nanoparticle applications in food - a review

The use of nanotechnology in the food industry raises uncertainty in many respects. For years, achievements of nanotechnology have been applied mainly in biomedicine and computer science, but recently it has also been used in the food industry. Due to the extremely small (nano) scale, the properties and behavior of nanomaterials may differ from their macroscopic counterparts. They can be used as biosensors to detect reagents or microorganisms, monitor bacterial growth conditions, increase food durability e.g. when placed in food packaging, reducing the amount of certain ingredients without changing the consistency of the product (research on fat substitutes is underway), improve the taste of food, make some nutrients get better absorbed by the body, etc. There are companies on the market that are already introducing nanoparticles into the economy to improve their functionality, e.g. baby feeding bottles. This review focuses on the use of nanoparticles in the food industry, both organic (chitosan, cellulose, proteins) and inorganic (silver, iron, zinc oxide, titanium oxide, etc.). The use of nanomaterials in food production requires compliance with all legal requirements regarding the safety and quantity of nano-processed food products described in this review. In the future, new methods of testing nanoparticles should be developed that would ensure the effectiveness of compounds subjected to, for example, nano-encapsulation, i.e. whether the encapsulation process had a positive impact on the specific properties of these compounds. Nanotechnology has revolutionized our approach towards food engineering (from production to processing), food storage and the creation of new materials and products, and the search for new product applications.

Current and Emerging Techniques for Diagnosis and MRD Detection in AML: A Comprehensive Narrative Review

Acute myeloid leukemia (AML) comprises a group of hematologic neoplasms characterized by abnormal differentiation and proliferation of myeloid progenitor cells. AML is associated with poor outcome due to the lack of efficient therapies and early diagnostic tools. The current gold standard diagnostic tools are based on bone marrow biopsy. These biopsies, apart from being very invasive, painful, and costly, have low sensitivity. Despite the progress uncovering the molecular pathogenesis of AML, the development of novel detection strategies is still poorly explored. This is particularly important for patients that check the criteria for complete remission after treatment, since they can relapse through the persistence of some leukemic stem cells. This condition, recently named as measurable residual disease (MRD), has severe consequences for disease progression. Hence, an early and accurate diagnosis of MRD would allow an appropriate therapy to be tailored, improving a patient's prognosis. Many novel techniques with high potential in disease prevention and early detection are being explored. Among them, microfluidics has flourished in recent years due to its ability at processing complex samples as well as its demonstrated capacity to isolate rare cells from biological fluids. In parallel, surface-enhanced Raman scattering (SERS) spectroscopy has shown outstanding sensitivity and capability for multiplex quantitative detection of disease biomarkers. Together, these technologies can allow early and cost-effective disease detection as well as contribute to monitoring the efficiency of treatments. In this review, we aim to provide a comprehensive overview of AML disease, the conventional techniques currently used for its diagnosis, classification (recently updated in September 2022), and treatment selection, and we also aim to present how novel technologies can be applied to improve the detection and monitoring of MRD.

Chemometric Analysis of a Ternary Mixture of Caffeine, Quinic Acid, and Nicotinic Acid by Terahertz Spectroscopy

Caffeine, quinic acid, and nicotinic acid are among the significant chemical determinants of coffee quality. This study develops a chemometric model to quantify these compounds in ternary mixtures analyzed by terahertz time-domain spectroscopy (THz-TDS). A data set of 480 THz spectra was obtained from 80 samples. Combinations of data preprocessing methods, including normalization (Z-score, min-max scaling, Mie baseline removal) and dimensionality reduction (principal component analysis (PCA), factor analysis (FA), independent component analysis (ICA), locally linear embedding (LLE), non-negative matrix factorization (NMF), isomap), and prediction models (partial least-squares regression (PLSR), support vector regression (SVR), multilayer perceptron (MLP), convolutional neural network (CNN), gradient boosting) were analyzed for their prediction performance (totaling to 4,711,685 combinations). Results show that the highest quantification performance was achieved at a root-mean-square error of prediction (RMSEP) of 0.0254 (dimensionless mass ratio), using min-max scaling and factor analysis for data preprocessing and multilayer perceptron for prediction. Effects of preprocessing, comparison of prediction models, and linearity of data are discussed.

Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra

The grading of cancer tissues is still one of the main challenges for pathologists. The development of enhanced analysis strategies hence becomes crucial to accurately identify and further deal with each individual case. Raman spectroscopy (RS) is a promising tool for the classification of tumor tissues as it allows us to obtain the biochemical maps of the tissues under analysis and to observe their evolution in terms of biomolecules, proteins, lipid structures, DNA, vitamins, and so on. However, its potential could be further improved by providing a classification system which would be able to recognize the sample tumor category by taking as input the raw Raman spectroscopy signal; this could provide more reliable responses in shorter time scales and could reduce or eliminate false-positive or -negative diagnoses. Deep Learning techniques have become ubiquitous in recent years, with models able to perform classification with high accuracy in most diverse fields of research, e.g., natural language processing, computer vision, medical imaging. However, deep models often rely on huge labeled datasets to produce reasonable accuracy, otherwise occurring in overfitting issues when the training data is insufficient. In this paper, we propose a chondrogenic tumor CLAssification through wavelet transform of RAman spectra (CLARA), which is able to classify with high accuracy Raman spectra obtained from bone tissues. CLARA recognizes and grades the tumors in the evaluated dataset with 97% accuracy by exploiting a classification pipeline consisting of the division of the original task in two binary classification steps, where the first is performed on the original RS signals while the latter is accomplished through the use of a hybrid temporal-frequency 2D transform.

Toward in-process technology-aided automation for enhanced microbial food safety and quality assurance in milk and beverages processing

Ensuring the safety of food products is critical to food production and processing. In food processing and production, several standard guidelines are implemented to achieve acceptable food quality and safety. This notwithstanding, due to human limitations, processed foods are often contaminated either with microorganisms, microbial byproducts, or chemical agents, resulting in the compromise of product quality with far-reaching consequences including foodborne diseases, food intoxication, and food recall. Transitioning from manual food processing to automation-aided food processing (smart food processing) which is guided by artificial intelligence will guarantee the safety and quality of food. However, this will require huge investments in terms of resources, technologies, and expertise. This study reviews the potential of artificial intelligence in food processing. In addition, it presents the technologies and methods with potential applications in implementing automated technology-aided processing. A conceptual design for an automated food processing line comprised of various operational layers and processes targeted at enhancing the microbial safety and quality assurance of liquid foods such as milk and beverages is elaborated.

Progress on aptamer-based SERS sensors for food safety and quality assessment: methodology, current applications and future trends

It is well known that food safety has aroused extensive attentions from governments to researchers and to food industries. As a versatile technology based on molecular interactions, aptamer sensors which could specifically identify a wide range of food contaminants have been extensively studied in recent years. Surface-enhanced Raman spectroscopy integrated aptamer combines the advantages of both technologies, not only in the ability to specifically identify a wide range of food contaminants, but also in the ultra-high sensitivity, simplicity, portable and speed. To provide beneficial insights into the evaluation techniques in the field of food safety, we offer a comprehensive review on the design strategies for aptamer-SERS sensors in different scenarios, including non-nucleic acid amplification methods ("on/off" mode, sandwich mode, competition model and catalytic model) and nucleic acid amplification methods (hybridization chain reaction, rolling circle amplification, catalytic hairpin assembly). Meanwhile, a special attention is paid to the application of aptamer-SERS sensors in biological (foodborne pathogenic, bacteria and mycotoxins) and chemical contamination (drug residues, metal ions, and food additives) of food matrix. Finally, the challenges and prospects of developing reliable aptamer-SERS sensors for food safety were discussed, which are expected to offer a strong guidance for further development and extended applications.

The role of 5-hydroxymethylfurfural in food and recent advances in analytical methods

The thermal processing, storage, and transportation of foodstuffs (e.g., fruit juices, coffee, honey, and vinegar) generate 5-hydroxymethylfurfural (HMF). The food industry uses this compound as a quality marker, thus increasing the demand for fast and reliable analytical methods for its determination. This review focuses on the formation of HMF in food, its desirable and toxic effects, and recent advances in analytical methods for its determination in foodstuffs. The advantages and limitations of these analytical approaches are discussed relative to the main analytical features.

Highly Sensitive Flexible SERS-Based Sensing Platform for Detection of COVID-19

COVID-19 continues to spread and has been declared a global emergency. Individuals with current or past infection should be identified as soon as possible to prevent the spread of disease. Surface-enhanced Raman spectroscopy (SERS) is an analytical technique that has the potential to be used to detect viruses at the site of therapy. In this context, SERS is an exciting technique because it provides a fingerprint for any material. It has been used with many COVID-19 virus subtypes, including Deltacron and Omicron, a novel coronavirus. Moreover, flexible SERS substrates, due to their unique advantages of sensitivity and flexibility, have recently attracted growing research interest in real-world applications such as medicine. Reviewing the latest flexible SERS-substrate developments is crucial for the further development of quality detection platforms. This article discusses the ultra-responsive detection methods used by flexible SERS substrate. Multiplex assays that combine ultra-responsive detection methods with their unique biomarkers and/or biomarkers for secondary diseases triggered by the development of infection are critical, according to this study. In addition, we discuss how flexible SERS-substrate-based ultrasensitive detection methods could transform disease diagnosis, control, and surveillance in the future. This study is believed to help researchers design and manufacture flexible SERS substrates with higher performance and lower cost, and ultimately better understand practical applications.

Recent Advances in the Use of Surface-Enhanced Raman Scattering for Illicit Drug Detection

The rapid increase in illicit drug use and its adverse health effects and socio-economic consequences have reached alarming proportions in recent years. Surface-enhanced Raman scattering (SERS) has emerged as a highly sensitive analytical tool for the detection of low dosages of drugs in liquid and solid samples. In the present article, we review the state-of-the-art use of SERS for chemical analysis of illicit drugs in aqueous and complex biological samples, including saliva, urine, and blood. We also include a review of the types of SERS substrates used for this purpose, pointing out recent advancements in substrate fabrication towards quantitative and qualitative detection of illicit drugs. Finally, we conclude by providing our perspective on the field of SERS-based drug detection, including presently faced challenges. Overall, our review provides evidence of the strong potential of SERS to establish itself as both a laboratory and in situ analytical method for fast and sensitive drug detection and identification.

Liquid Surface-Enhanced Raman Spectroscopy (SERS) Sensor-Based Au-Ag Colloidal Nanoparticles for Easy and Rapid Detection of Deltamethrin Pesticide in Brewed Tea

Deltamethrin pesticides can cause inflammation, nephrotoxicity and hepatotoxicity as well as affect the activity of antioxidant enzymes in tissues. As a result of this concern, there is a rising focus on the development of fast and reliable pesticide residue testing to minimise potential risks to humans. The goal of this study is to use Au-Ag colloid nanoparticles as liquid surface-enhanced Raman spectroscopy (SERS) to improve the Raman signal in the detection of deltamethrin pesticide in a brewed tea. The liquid SERS system is fascinating to study due to its ease of use and its unlikeliness to cause several phenomena, such as photo-bleaching, combustion, sublimation and even photo-catalysis, which can interfere with the Raman signal, as shown in the SERS substrate. Our liquid SERS system is simpler than previous liquid SERS systems that have been reported. We performed the detection of pesticide analyte directly on brewed tea, without diluting it with ethanol or centrifuging it. Femtosecond laser-induced photo-reduction was employed to synthesise the liquid SERS of Au, Au-Ag, and Ag colloidal nanoparticles. The SERS was utilised to detect deltamethrin pesticide in brewed tea. The result showed that liquid SERS-based Ag NPs significantly enhance the Raman signal of pesticides compared with liquid SERS-based Au NPs and Au-Ag Nanoalloys. The maximum residue limits (MRLs) in tea in Indonesia are set at 10 ppm. Therefore, this method was also utilised to detect and improve, to 0.01 ppm, the deltamethrin pesticide Limit of Detection (LOD).

Nondestructive Detection of Codling Moth Infestation in Apples Using Pixel-Based NIR Hyperspectral Imaging with Machine Learning and Feature Selection

Codling moth (CM) (Cydia pomonella L.), a devastating pest, creates a serious issue for apple production and marketing in apple-producing countries. Therefore, effective nondestructive early detection of external and internal defects in CM-infested apples could remarkably prevent postharvest losses and improve the quality of the final product. In this study, near-infrared (NIR) hyperspectral reflectance imaging in the wavelength range of 900–1700 nm was applied to detect CM infestation at the pixel level for three organic apple cultivars, namely Gala, Fuji and Granny Smith. An effective region of interest (ROI) acquisition procedure along with different machine learning and data processing methods were used to build robust and high accuracy classification models. Optimal wavelength selection was implemented using sequential stepwise selection methods to build multispectral imaging models for fast and effective classification purposes. The results showed that the infested and healthy samples were classified at pixel level with up to 97.4% total accuracy for validation dataset using a gradient tree boosting (GTB) ensemble classifier, among others. The feature selection algorithm obtained a maximum accuracy of 91.6% with only 22 selected wavelengths. These findings indicate the high potential of NIR hyperspectral imaging (HSI) in detecting and classifying latent CM infestation in apples of different cultivars.

Fluorescence-based characterisation of selected edible insect species: Excitation emission matrix (EEM) and parallel factor (PARAFAC) analysis

Fluorescence spectroscopy coupled with chemometric tools is a powerful analytical method, largely used for rapid food quality and safety evaluations. However, its potential has not yet been explored in the novel food sector. In the present study, excitation emission matrices (EEMs) of 15 insect powders produced by milling insects belonging to 5 Orthoptera species (Acheta domesticus, Gryllus assimilis, Gryllus bimaculatus, Locusta migratoria, Schistocerca gregaria) from 3 different origins were investigated. Parallel factor (PARAFAC) analysis performed on the overall averaged dataset was validated for five components, highlighting the presence of five different fluorescence peaks. The presence of these peaks was confirmed on each species, suggesting that fluorescence compounds of edible insects are the same in several species. PARAFAC analysis performed on the overall averaged dataset after alternatively adding the EEM recorded from one standard compound allowed to speculate that edible insects fluorescence raises from mixtures of: tryptophan + tyrosine (PARAFAC component-1), tryptophan + tyrosine + tocopherol (PARAFAC component-2), collagen + pyridoxine + pterins (PARAFAC component-3). This study suggests that fluorescence spectroscopy may represent a powerful method for investigating composition and quality of insect-based foods.

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Fluorescence landscape of edible insects comprises of 5 different peaks.

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Similar fluorescence compounds are present among several Orthoptera species.

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Fluorescence peaks of edible insects result from several chemical molecules.

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Fluorescence intensity of edible insects depends on their species and origin.

Raman Scattering-Based Biosensing: New Prospects and Opportunities

The growing interest in the development of new platforms for the application of Raman spectroscopy techniques in biosensor technologies is driven by the potential of these techniques in identifying chemical compounds, as well as structural and functional features of biomolecules. The effect of Raman scattering is a result of inelastic light scattering processes, which lead to the emission of scattered light with a different frequency associated with molecular vibrations of the identified molecule. Spontaneous Raman scattering is usually weak, resulting in complexities with the separation of weak inelastically scattered light and intense Rayleigh scattering. These limitations have led to the development of various techniques for enhancing Raman scattering, including resonance Raman spectroscopy (RRS) and nonlinear Raman spectroscopy (coherent anti-Stokes Raman spectroscopy and stimulated Raman spectroscopy). Furthermore, the discovery of the phenomenon of enhanced Raman scattering near metallic nanostructures gave impetus to the development of the surface-enhanced Raman spectroscopy (SERS) as well as its combination with resonance Raman spectroscopy and nonlinear Raman spectroscopic techniques. The combination of nonlinear and resonant optical effects with metal substrates or nanoparticles can be used to increase speed, spatial resolution, and signal amplification in Raman spectroscopy, making these techniques promising for the analysis and characterization of biological samples. This review provides the main provisions of the listed Raman techniques and the advantages and limitations present when applied to life sciences research. The recent advances in SERS and SERS-combined techniques are summarized, such as SERRS, SE-CARS, and SE-SRS for bioimaging and the biosensing of molecules, which form the basis for potential future applications of these techniques in biosensor technology. In addition, an overview is given of the main tools for success in the development of biosensors based on Raman spectroscopy techniques, which can be achieved by choosing one or a combination of the following approaches: (i) fabrication of a reproducible SERS substrate, (ii) synthesis of the SERS nanotag, and (iii) implementation of new platforms for on-site testing.

Quantitative detection of 6-thioguanine in body fluids based on a free-standing liquid membrane SERS substrate

The adverse reactions caused by 6-thioguanine (6-TG) in anti-cancer treatment are closely related to the dose, leading to the urgent need for clinical monitoring of its concentration. In this work, a highly reproducible free-standing liquid membrane (FLM) surface-enhanced Raman spectroscopy (SERS) substrate was developed to detect 6-TG in human urine and serum quantitatively. Briefly, a prepared sample was adjusted to pH 2 and mixed with concentrated core-shell bimetallic nanoparticle (Ag_coreAu_shell NP) suspension. The Au/Ag ratio of the Ag_coreAu_shell NPs was optimized. Then the mixture was formed into an FLM using a custom mold. The relative standard deviation (RSD) of the experimental results can be stabilized below 10% (n ≥ 10). The R² of the calibration curve in the range of 10 ~ 100 μg kg^-1 was 0.988. In addition, the limit of detection (LOD) (3σ/k) of 6-TG was 5 μg kg^-1. The FLM SERS platform has been successfully applied to the rapid and reliable analysis of 6-TG spiked in body fluids.

Dynamic confocal Raman spectroscopy of flowing blood in bionic blood vessel

How to quickly and safely identify blood species has always been an urgent problem for scientists. Smear test method has the risk of blood contamination, and the blood itself may carry some unknown viruses or pathogens, which will bring health risks to the testing personnel. Therefore, in order to meet the urgent needs of rapid and safe detection of blood, a technology which can detect dynamic confocal Raman spectroscopy of flowing blood in bionic blood vessel was proposed. The blood, which was sealed in the bionic blood vessel, flowed through the focus gaze area of laser by the microfluidic pump, to detect the dynamic blood Raman spectrum. Human blood and cattle blood were selected as experimental objects, and the experiments were carried out under the same parameters. Combined with PCA-LDA (principal component analysis and linear discriminate analysis) classification model, the predictive classification of the two species without error recognition was realized. The hidden weak Raman signals were mined by derivative spectra, and the fundamental differences of Raman spectra of two species were compared. Then the biochemical information that caused the differences was also analyzed. The results show the method can meet the detection requirements of sealed blood, and the Raman spectra of flowing blood is more representative than those of static blood.

Polymers in pharmaceutical additive manufacturing: A balancing act between printability and product performance

Materials and manufacturing processes share a common purpose of enabling the pharmaceutical product to perform as intended. This review on the role of polymeric materials in additive manufacturing of oral dosage forms, focuses on the interface between the polymer and key stages of the additive manufacturing process, which determine printability. By systematically clarifying and comparing polymer functional roles and properties for a variety of AM technologies, together with current and emerging techniques to characterize these properties, suggestions are provided to stimulate the use of readily available and sometimes underutilized pharmaceutical polymers in additive manufacturing. We point to emerging characterization techniques and digital tools, which can be harnessed to manage existing trade-offs between the role of polymers in printer compatibility versus product performance. In a rapidly evolving technological space, this serves to trigger the continued development of 3D printers to suit a broader variety of polymers for widespread applications of pharmaceutical additive manufacturing.

Raman spectroscopy and surface-enhanced Raman spectroscopy (SERS) spectra of salivary glands carcinoma, tumor and healthy tissues and their homogenates analyzed by chemometry: Towards development of the novel tool for clinical diagnosis

In this study, two approaches to salivary glands studies are presented: Raman imaging (RI) of tissue cross-section and surface-enhanced Raman spectroscopy (SERS) of tissue homogenates prepared according to elaborated protocol. Collected and analyzed data demonstrate the significant potential of SERS combined with multivariate analysis for distinguishing carcinoma or tumor from the normal salivary gland tissues as a rapid, label-free tool in cancer detection in oncological diagnostics. Raman imaging allows a detailed analysis of the cell wall's chemical composition; thus, the compound's distribution can be semi-quantitatively analyzed, while SERS of tissue homogenates allow for detailed analysis of all moieties forming these tissues. In this sense, SERS is more sensitive and reliable to study any changes in the area of infected tissues. Principal component analysis (PCA), as an unsupervised pattern recognition method, was used to identify the differences in the SERS salivary glands homogenates. The partial least squares-discriminant analysis (PLS-DA), the supervised pattern classification technique, was also used to strengthen further the computed model based on the latent variables in the SERS spectra. Moreover, the chemometric quantification of obtained data was analyzed using principal component regression (PCR) multivariate calibration. The presented data prove that the PCA algorithm allows for 91% in seven following components and the determination between healthy and tumor salivary gland homogenates. The PCR and PLS-DA methods predict 90% and 95% of the variance between the studied groups (in 6 components and 4 factors, respectively). Moreover, according to calculated RMSEC (RMSEP), R2C (R2P) values and correlation accuracy (based on the ROC curve), the PLS-DA model fits better for the studied data. Thus, SERS methods combined with PLS-DA analysis can be used to differentiate healthy, neoplastic, and mixed tissues as a competitive tool in relation to the commonly used method of histopathological staining of tumor tissue.

Preparation and characterization of a novel green tea essential oil nanoemulsion and its antifungal mechanism of action against Magnaporthae oryzae

Blast is one of the most devastating fungal diseases of rice caused by Magnaporthe oryzae. Plant essential oil (EO) can function as antifungal agents and are regarded as a safe and acceptable method for plant disease control. However, EOs are unstable and hydrophobic, which limits its use. In the present study, we aimed for the preparation and characterization of a nanoemulsion (NE) from green tea essential oil (GTO) by ultrasonication method and determined the antifungal activity of NE onM. oryzae. The particle size and zeta potential of the NE were 86.98 nm and -15.1 mV, respectively. The chemical composition and functional groups of GTO and NE were studied by using GC-MS analysis, portable Raman spectroscopy, and FTIR coupled with chemometric analysis. GC-MS analysis showed the major components in GTO and NE were n-Hexyl cinnamaldehyde and L-α-Terpineol. Both GTO and NE showed good antioxidant activity and total phenol content. Moreover, the NE showed good antifungal activity againstM. oryzae which was further confirmed by scanning electron microscopy (SEM) examination. Also, confocal Raman micro-spectroscopy (CRM) revealed the antifungal mechanism of GTO and NE on M. oryzae which proves the cell damage. To the best of our knowledge, this is the first study on the antifungal activity of GTO and NE against M. oryzae and also the use of CRM for the evaluation of the chemical changes in single fungal hyphae in a holistic approach. This study suggests that the prepared NE could be a potential candidate for use as a substitute for synthetic fungicides.

Data Science in the Food Industry

Food safety is one of the main challenges of the agri-food industry that is expected to be addressed in the current environment of tremendous technological progress, where consumers' lifestyles and preferences are in a constant state of flux. Food chain transparency and trust are drivers for food integrity control and for improvements in efficiency and economic growth. Similarly, the circular economy has great potential to reduce wastage and improve the efficiency of operations in multi-stakeholder ecosystems. Throughout the food chain cycle, all food commodities are exposed to multiple hazards, resulting in a high likelihood of contamination. Such biological or chemical hazards may be naturally present at any stage of food production, whether accidentally introduced or fraudulently imposed, risking consumers' health and their faith in the food industry. Nowadays, a massive amount of data is generated, not only from the next generation of food safety monitoring systems and along the entire food chain (primary production included) but also from the Internet of things, media, and other devices. These data should be used for the benefit of society, and the scientific field of data science should be a vital player in helping to make this possible.

SERS Sensors Based on Aptamer-Gated Mesoporous Silica Nanoparticles for Quantitative Detection of Staphylococcus aureus with Signal Molecular Release

This work describes a simple and novel biosensor for the quantitative determination of Staphylococcus aureus (S. aureus) based on target-induced release of signal molecules from aptamer-gated aminated mesoporous silica nanoparticles (MSNs) coupled with surface-enhanced Raman scattering (SERS) technology. MSNs were synthesized and then modified with amino groups by (3-aminopropyl) triethoxysilane to make them positively charged. Next, signal molecules (4-aminothiophenol, 4-ATP) were loaded into the pores of MSNs. Then, negatively charged aptamers of S. aureus were assembled on the surface of MSNs through electrostatic interactions. Upon the addition of S. aureus, the assembled aptamers were specifically bound to the bacteria. Consequently, the "gates" were opened, resulting in the release of 4-ATP from the pores of MSNs. The released molecules were measured by a Raman spectrometer, and the intensity of 4-ATP at 1071 cm^-1 was linearly related to the S. aureus concentration. A silver nanoflower silica core-shell structure (Ag NFs@SiO₂) was prepared and it served as a SERS substrate. Under optimized experimental conditions, a good linear relationship (y = 2107.93 + 1536.30x, R² = 0.9956) in the range from 4.7 × 10 to 4.7 × 10⁸ cfu/mL was observed with a limit of detection of 17 cfu/mL. The method was successfully applied for the analysis of S. aureus in fish samples and the recovery rate was 91.3-109%.

Determination of the Geographical Origin of Coffee Beans Using Terahertz Spectroscopy Combined With Machine Learning Methods

Different geographical origins can lead to great variance in coffee quality, taste, and commercial value. Hence, controlling the authenticity of the origin of coffee beans is of great importance for producers and consumers worldwide. In this study, terahertz (THz) spectroscopy, combined with machine learning methods, was investigated as a fast and non-destructive method to classify the geographic origin of coffee beans, comparing it with the popular machine learning methods, including convolutional neural network (CNN), linear discriminant analysis (LDA), and support vector machine (SVM) to obtain the best model. The curse of dimensionality will cause some classification methods which are struggling to train effective models. Thus, principal component analysis (PCA) and genetic algorithm (GA) were applied for LDA and SVM to create a smaller set of features. The first nine principal components (PCs) with an accumulative contribution rate of 99.9% extracted by PCA and 21 variables selected by GA were the inputs of LDA and SVM models. The results demonstrate that the excellent classification (accuracy was 90% in a prediction set) could be achieved using a CNN method. The results also indicate variable selecting as an important step to create an accurate and robust discrimination model. The performances of LDA and SVM algorithms could be improved with spectral features extracted by PCA and GA. The GA-SVM has achieved 75% accuracy in a prediction set, while the SVM and PCA-SVM have achieved 50 and 65% accuracy, respectively. These results demonstrate that THz spectroscopy, together with machine learning methods, is an effective and satisfactory approach for classifying geographical origins of coffee beans, suggesting the techniques to tap the potential application of deep learning in the authenticity of agricultural products while expanding the application of THz spectroscopy.

Magneto-Plasmonic Nanoparticle Grid Biosensor with Enhanced Raman Scattering and Electrochemical Transduction for the Development of Nanocarriers for Targeted Delivery of Protected Anticancer Drugs

Safe administration of highly cytotoxic chemotherapeutic drugs is a challenging problem in cancer treatment due to the adverse side effects and collateral damage to non-tumorigenic cells. To mitigate these problems, promising new approaches, based on the paradigm of controlled targeted drug delivery (TDD), and utilizing drug nanocarriers with biorecognition ability to selectively target neoplastic cells, are being considered in cancer therapy. Herein, we report on the design and testing of a nanoparticle-grid based biosensing platform to aid in the development of new targeted drug nanocarriers. The proposed sensor grid consists of superparamagnetic gold-coated core-shell Fe2Ni@Au nanoparticles, further functionalized with folic acid targeting ligand, model thiolated chemotherapeutic drug doxorubicin (DOX), and a biocompatibility agent, 3,6-dioxa-octanethiol (DOOT). The employed dual transduction method based on electrochemical and enhanced Raman scattering detection has enabled efficient monitoring of the drug loading onto the nanocarriers, attaching to the sensor surface, as well as the drug release under simulated intracellular conditions. The grid's nanoparticles serve here as the model nanocarriers for new TDD systems under design and optimization. The superparamagnetic properties of the Fe2Ni@Au NPs aid in nanoparticles' handling and constructing a dense sensor grid with high plasmonic enhancement of the Raman signals due to the minimal interparticle distance.

Identification of methicillin-resistant Staphylococcus aureus bacteria using surface-enhanced Raman spectroscopy and machine learning techniques

To combat antibiotic resistance, it is extremely important to select the right antibiotic by performing rapid diagnosis of pathogens. Traditional techniques require complicated sample preparation and time-consuming processes which are not suitable for rapid diagnosis. To address this problem, we used surface-enhanced Raman spectroscopy combined with machine learning techniques for rapid identification of methicillin-resistant and methicillin-sensitive Gram-positive Staphylococcus aureus strains and Gram-negative Legionella pneumophila (control group). A total of 10 methicillin-resistant S. aureus (MRSA), 3 methicillin-sensitive S. aureus (MSSA) and 6 L. pneumophila isolates were used. The obtained spectra indicated high reproducibility and repeatability with a high signal to noise ratio. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and various supervised classification algorithms were used to discriminate both S. aureus strains and L. pneumophila. Although there were no noteworthy differences between MRSA and MSSA spectra when viewed with the naked eye, some peak intensity ratios such as 732/958, 732/1333, and 732/1450 proved that there could be a significant indicator showing the difference between them. The k-nearest neighbors (kNN) classification algorithm showed superior classification performance with 97.8% accuracy among the traditional classifiers including support vector machine (SVM), decision tree (DT), and naïve Bayes (NB). Our results indicate that SERS combined with machine learning can be used for the detection of antibiotic-resistant and susceptible bacteria and this technique is a very promising tool for clinical applications.

Cancer Nanopharmaceuticals: Physicochemical Characterization and In Vitro/In Vivo Applications

Physicochemical, pharmacokinetic, and biopharmaceutical characterization tools play a key role in the assessment of nanopharmaceuticals' potential imaging analysis and for site-specific delivery of anti-cancers to neoplastic cells/tissues. If diagnostic tools and therapeutic approaches are combined in one single nanoparticle, a new platform called nanotheragnostics is generated. Several analytical technologies allow us to characterize nanopharmaceuticals and nanoparticles and their properties so that they can be properly used in cancer therapy. This paper describes the role of multifunctional nanoparticles in cancer diagnosis and treatment, describing how nanotheragnostics can be useful in modern chemotherapy, and finally, the challenges associated with the commercialization of nanoparticles for cancer therapy.

Comparison of Surface-Enhanced Raman Scattering Properties of Serum and Urine for the Detection of Chronic Kidney Disease in Patients

Chronic kidney disease (CKD) affects more than 10% of the global population and is associated with significant morbidity and mortality. In most cases, this disease is developed silently, and it can progress to the end-stage renal failure. Therefore, early detection becomes critical for initiating effective interventions. Routine diagnosis of CKD requires both blood test and urinalyses in a clinical laboratory, which are time-consuming and have low sensitivity and specificity. Surface-enhanced Raman scattering (SERS) is an emerging method for rapidly assessing kidney function or injury. This study was designed to compare the differences between the SERS properties of the serum and urine for easy and simple detection of CKD. Enrolled for this study were 126 CKD patients (Stages 2-5) and 97 healthy individuals. SERS spectra of both the serum and urine samples were acquired using a Raman spectrometer (785 nm excitation). The correlation of chemical parameters of kidney function with the spectra was examined using prinicpal component analysis (PCA) combined with linear discriminant analysis (LDA) and partial least squares (PLS) analysis. Here, we showed that CKD was discriminated from non-CKD controls using PCA-LDA with a sensitivity of 74.6% and a specificity of 93.8% for the serum spectra, and 78.0% and 86.0 % for the urine spectra. The integration area under the receiver operating characteristic curve was 0.937 ± 0.015 (p < 0.0001) for the serum and 0.886 ± 0.025 (p < 0.0001) for the urine. The different stages of CKD were separated with the accuracy of 78.0% and 75.4% by the serum and urine spectra, respectively. PLS prediction (R²) of the serum spectra was 0.8540 for the serum urea (p < 0.001), 0.8536 for the serum creatinine (p < 0.001), 0.7500 for the estimated glomerular filtration rate (eGFR) (p < 0.001), whereas the prediction (R²) of urine spectra was 0.7335 for the urine urea (p < 0.001), 0.7901 for the urine creatinine (p < 0.001), 0.4644 for the eGFR (p < 0.001) and 0.6579 for the urine microalbumin (p < 0.001). In conclusion, the accuracy of associations between SERS findings of the serum and urine samples with clinical conclusions of CKD diagnosis in this limited number of patients is similar, suggesting that SERS may be used as a rapid and easy-to-use method for early screening of CKD, which however needs further evaluation in a large cohort study.

The diagnostic tools for viable but nonculturable pathogens in the food industry: Current status and future prospects

Viable but nonculturable (VBNC) microorganisms have been recognized as pathogenic contaminants in foods and environments. The failure of VBNC cells to form the visible colonies hinders the ability to use conventional media for their detection. Efficient and rapid detection of pathogens in the VBNC state is a prerequisite to ensure the food safety and public health. Despite their nonculturability, VBNC cells have distinct characteristics, such as morphology, metabolism, chemical composition, and gene and protein expression, that have been used as the basis for the development of abundant diagnostic tools. This review covers the current status and advances in various approaches for examining microorganisms in the VBNC state, including but not limited to the methodological aspects, advantages, and drawbacks of each technique. Existing methods, such as direct viable count, SYTO/PI dual staining, and propidium monoazide quantitative polymerase chain reaction (PCR), as well as some techniques with potential to be applied in the future, such as digital PCR, enhanced-surface Raman spectroscopy, and impedance-based techniques, are summarized in depth. Finally, future prospects for the one-step detection of VBNC bacteria are proposed and discussed. We believe that this review can provide more optional methods for researchers and promote the development of rapid, accurate detecting methods, and for inspectors, the diagnostic tools can provide data to undertake risk analysis of VBNC cells.

FEAST of biosensors: Food, environmental and agricultural sensing technologies (FEAST) in North America

We review the challenges and opportunities for biosensor research in North America aimed to accelerate translational research. We call for platform approaches based on: i) tools that can support interoperability between food, environment and agriculture, ii) open-source tools for analytics, iii) algorithms used for data and information arbitrage, and iv) use-inspired sensor design. We summarize select mobile devices and phone-based biosensors that couple analytical systems with biosensors for improving decision support. Over 100 biosensors developed by labs in North America were analyzed, including lab-based and portable devices. The results of this literature review show that nearly one quarter of the manuscripts focused on fundamental platform development or material characterization. Among the biosensors analyzed for food (post-harvest) or environmental applications, most devices were based on optical transduction (whether a lab assay or portable device). Most biosensors for agricultural applications were based on electrochemical transduction and few utilized a mobile platform. Presently, the FEAST of biosensors has produced a wealth of opportunity but faces a famine of actionable information without a platform for analytics.

Response to "Comment on Bulk Nanobubbles or Not Nanobubbles: That is the Question"

Advanced techniques that combine high spatial resolution with chemical sensitivity to directly probe the observed nanoentities and provide direct evidence that they are truly gas-filled nanobubbles do not exist. Therefore, in our paper, we focused on providing, for the first time, multiple types of indirect evidence using a variety of physical and chemical techniques that the nanoentities are not due to contamination and, hence, they must be bulk nanobubbles (BNBs). It should be noted that such techniques require good experimental skills, sound protocols, good scientific expertise, and reliable equipment. While no single piece of indirect evidence on its own can be considered as conclusive proof, we estimate that our results combined provide strong evidence that bulk nanobubbles do exist and they are stable. The work presented in our paper is the culmination of a series of studies, and many authors have either directly or indirectly confirmed our findings. Nonetheless, in their Comment, Rak & Sedlak reject all of the work we reported. We here address their comments point by point and show that their criticisms are unwarranted and unfounded, as follows.

Scouting for strong light-matter coupling signatures in Raman spectra

Strong coupling between vibrational transitions and a vacuum field of a cavity mode leads to the formation of vibrational polaritons. These hybrid light-matter states have been widely explored because of their potential to control chemical reactivity. However, the possibility of altering Raman scattering through the formation of vibrational polaritons has been rarely reported. Here, we present the Raman scattering properties of different molecules under vibrational strong coupling conditions. The polariton states are clearly observed in the IR transmission spectra of the coupled system for benzonitrile and methyl salicylate in liquid phase and for polyvinyl acetate in a solid polymer film. However, none of the studied systems exhibits a signature of the polariton states in the Raman spectra. For the solid polymer film, we have used cavities with different layer structures to investigate the influence of vibrational strong coupling on the Raman spectra. The only scenario where alterations of the Raman spectra are observed is for a thin Ag layer being in direct contact with the polymer film. This shows that, even though the system is in the vibrational strong coupling regime, changes in the Raman spectra do not necessarily result from the strong coupling, but are caused by the surface enhancement effects.

Unplanned absorption of sunscreen ingredients: Impact of formulation and evaluation methods

Permeation of sunscreens agents reduces its effectiveness and safety, leading to systemic circulation and causing unknown adverse effects. In order to maintain the sunscreen efficacy and safety, the filters must stay on the skin surface, with minimum penetration through dermis. Even facing the possibility of filters permeation, the use of sunscreen is important to avoid skin damage as erythema, free-radicals formation, skin ageing and skin cancer, caused by ultraviolet radiation. Aiming potential side effects caused by topical absorption of sunscreens, studies are carried to improve formulation characteristics and stability, reduce skin permeation and evaluate sun protections factor (SPF). Current assays to detect the permeation of sunscreens involve in vivo or in vitro studies, to simulate physiological conditions of use. The aim of this review is to revisit sunscreen skin permeation data over the last decade and the factors that can enhance skin permeation or improve the sunscreen efficacy.

Monitoring Thermal and Non-Thermal Treatments during Processing of Muscle Foods: A Comprehensive Review of Recent Technological Advances

Muscle food products play a vital role in human nutrition due to their sensory quality and high nutritional value. One well-known challenge of such products is the high perishability and limited shelf life unless suitable preservation or processing techniques are applied. Thermal processing is one of the well-established treatments that has been most commonly used in order to prepare food and ensure its safety. However, the application of inappropriate or severe thermal treatments may lead to undesirable changes in the sensory and nutritional quality of heat-processed products, and especially so for foods that are sensitive to thermal treatments, such as fish and meat and their products. In recent years, novel thermal treatments (e.g., ohmic heating, microwave) and non-thermal processing (e.g., high pressure, cold plasma) have emerged and proved to cause less damage to the quality of treated products than do conventional techniques. Several traditional assessment approaches have been extensively applied in order to evaluate and monitor changes in quality resulting from the use of thermal and non-thermal processing methods. Recent advances, nonetheless, have shown tremendous potential of various emerging analytical methods. Among these, spectroscopic techniques have received considerable attention due to many favorable features compared to conventional analysis methods. This review paper will provide an updated overview of both processing (thermal and non-thermal) and analytical techniques (traditional methods and spectroscopic ones). The opportunities and limitations will be discussed and possible directions for future research studies and applications will be suggested.