Blood species identification for forensic purposes using Raman spectroscopy combined with advanced statistical analysis

Detection of Oral Fluid Stains on Common Substrates Using SEM and ATR-FTIR Spectroscopy for Forensic Purposes

Attenuated total reflectance (ATR) Fourier-transform infrared (FTIR) spectroscopy has been pursued as a novel approach to detect and differentiate biological materials with high specificity owing to its ability to record unique spectral patterns corresponding to the biochemical composition of a specimen. This study expands the application of ATR-FTIR for detecting oral fluid (OF) stains on various common substrates, including four porous and six nonporous substrates. For nonporous substrates, the spectral contribution from the substrate was minimal, and no background subtraction from the substrate bands was required (except for mirrors). For porous substrates, the contribution from the surface was pronounced and was addressed via background subtraction. The results indicated that major OF bands were detected on all the surfaces, even six months after OF deposition. Furthermore, scanning electron microscopy (SEM) was used to probe the morphologies of OF stains on various substrates. SEM micrographs revealed characteristic salt crystals and protein aggregates formed by the dried OF, which were observed for fresh samples and samples after six months post-deposition. Overall, this study demonstrated the great potential of SEM and ATR-FTIR spectroscopy for detecting OF traces on porous and nonporous substrates for up to six months for forensic purposes.

Raman hyperspectroscopy of saliva and machine learning for Sjögren's disease diagnostics

Sjögren's disease is an autoimmune disorder affecting exocrine glands, causing dry eyes and mouth and other morbidities. Polypharmacy or a history of radiation to the head and neck can also lead to dry mouth. Sjogren's disease is often underdiagnosed due to its non-specific symptoms, limited awareness among healthcare professionals, and the complexity of diagnostic criteria, limiting the ability to provide therapy early. Current diagnostic methods suffer from limitations including the variation in individuals, the absence of a single diagnostic marker, and the low sensitivity and specificity, high cost, complexity, and invasiveness of current procedures. Here we utilized Raman hyperspectroscopy combined with machine learning to develop a novel screening test for Sjögren's disease. The method effectively distinguished Sjögren's disease patients from healthy controls and radiation patients. This technique shows potential for development of a single non-invasive, efficient, rapid, and inexpensive medical screening test for Sjögren's disease using a Raman hyper-spectral signature.

[Vibrational spectroscopy use for forensic purposes combined with machine learning]

Vibrational spectroscopy combined with machine learning has a great potential for forensic research. Portable Raman spectrometers are already being used by law-enforcement agencies to identify drugs. Several new technologies based on vibrational spectroscopy, that can be used in forensic science to analyze documents, gunshot traces, cloths, soil, hair, nails and lacquer, are being developed nowadays. The article considers the use of vibrational spectroscopy in forensic practice for conducting serological studies with an emphasis on the development of a universal method of identifying the main secretions of the body. The method allows to determine the time elapsed since the trace was made, as well as the phenotypic profile of host, including sex, race and age.

Non-Destructive Identification of Dyes on Fabric Using Near-Infrared Raman Spectroscopy

Fabric is a commonly found piece of physical evidence at most crime scenes. Forensic analysis of fabric is typically performed via microscopic examination. This subjective approach is primarily based on pattern recognition and, therefore, is often inconclusive. Most of the fabric material found at crime scenes is colored. One may expect that a confirmatory identification of dyes can be used to enhance the reliability of the forensic analysis of fabric. In this study, we investigated the potential of near-infrared Raman spectroscopy (NIRS) in the confirmatory, non-invasive, and non-destructive identification of 15 different dyes on cotton. We found that NIRS was able to resolve the vibrational fingerprints of all 15 colorants. Using partial-squared discriminant analysis (PLS-DA), we showed that NIRS enabled ~100% accurate identification of dyes based on their vibrational signatures. These findings open a new avenue for the robust and reliable forensic analysis of dyes on fabric directly at crime scenes. Main conclusion: a hand-held Raman spectrometer and partial least square discriminant analysis (PLS-DA) approaches enable highly accurate identification of dyes on fabric.

Forensic analysis of biological fluid stains on substrates by spectroscopic approaches and chemometrics: A review

BACKGROUND

Bodily fluid stains are one of the most relevant evidence that can be found at the crime scene as it provides a wealth of information to the investigators. They help to report on the individuals involved in the crime, to check alibis, or to determine the type of crime that has been committed. They appear as stains in different types of substrates, some of them porous, which can interfere in the analysis. The spectroscopy techniques combined with chemometrics are showing increasing potential for their use in the analysis of such samples due to them being fast, sensitive, and non-destructive.

FINDINGS

This is a comprehensive review of the studies that used different spectroscopic techniques followed by chemometrics for analysing biological fluid stains on several surfaces, and under various conditions. It focuses on the bodily fluid stains and the most suitable spectroscopic techniques to study forensic scientific problems such as the substrate's characteristics, the influence of ambient conditions, the aging process of the bodily fluids, the presence of animal bodily fluids and non-biological fluids (interfering substances), and the bodily fluid mixtures. The most widely used techniques were Raman spectroscopy and attenuated total reflection Fourier transform infrared spectroscopy (ATR FTIR). Nonetheless, other non-destructive techniques have been also used, like near infrared hyperspectral imaging (HSI-NIR) or surface enhanced Raman spectroscopy (SERS), among others. This work provides the criteria for the selection of the most promising non-destructive techniques for the effective in situ detection of biological fluid stains at crime scene investigations.

SIGNIFICANCE AND NOVELTY

The use of the proper spectroscopic and chemometric approaches on the crime scene is expected to improve the support of forensic sciences to criminal investigations. Evidence may be analysed in a non-destructive manner and kept intact for further analysis. They will also speed up forensic investigations by allowing the selection of relevant samples from occupational ones.

Classification of bloodstains deposited at different times on floor tiles using hierarchical modelling and a handheld NIR spectrometer

Bloodstains are commonly encountered at crime scenes, especially on floor tiles, and can be deposited over different periods and intervals. Therefore, it is crucial to develop techniques that can accurately identify bloodstains deposited at different times. This study builds upon a previous investigation and aims to enhance the performance of three distinct hierarchical models (HMs) designed to differentiate and identify stains of human blood (HB), animal blood (AB), and common false positives (CFPs) on nine different types of floor tiles. Soft Independent Modeling Class Analogies (SIMCA), and Partial Least Squares-Discriminant Analysis (PLS-DA) were employed as decision rules in this process. The originally published model was constructed using a training set that included samples with a known time of deposit of six days. This model was then tested to predict samples with various deposition times, including human blood samples aged for 0, 1, 9, 20, 30, and 162 days, as well as animal blood samples aged for 0, 1, 10, 13, 20, 29, 105, and 176 days. To improve the identification of human blood, the models were modified by adding zero-day and one-day-old bloodstains to the original training set. All models showed improvement when fresher samples were included in the training set. The best results were achieved with the hierarchical model that used partial least squares-discriminant analysis as the second decision rule and incorporated one-day-old samples in the training set. This model yielded sensitivity values above 0.92 and specificity values above 0.7 for samples aged between zero and 30 days.

Applications of Raman spectroscopy in the analysis of biological evidence

During the past few decades, Raman spectroscopy has progressed and captivated added attention in the field of science. However, the application of Raman spectroscopy is not limited to the field of forensic science and analytical chemistry; it is one of the emerging spectroscopic techniques, utilized in the field of forensic science which in turn could be a supporting tool in the law and justice system. The advantage of Raman spectroscopy over the other conventional techniques is that it is rapid, reliable, and non-destructive in nature with minimal or no sample preparation. The quantitative and qualitative analysis of evidence from biological and non-biological origins could easily be performed by using Raman spectroscopy. The forensic domain is highly complex with multidisciplinary branches, and therefore a plethora of techniques are utilized for the detection, identification, and differentiation of innumerable pieces of evidence for the purpose of law and justice. Herein, a systematic review is carried out on the application of Raman spectroscopy in the realm of forensic biology and serology considering its usefulness in practical perspectives. This review paper highlights the significance of modern techniques, including micro-Raman spectroscopy, confocal Raman spectroscopy, surface-enhanced Raman spectroscopy, and paper-based surface-enhanced Raman spectroscopy, in the field of Raman spectroscopy. These techniques have demonstrated notable advancements in terms of their applications and capabilities. Furthermore, to comprehensively capture the progress in the development of Raman spectroscopy, all the published papers which could be retrieved from the available databases from the year 2007 to 2022 were incorporated.

Raman ConvMSANet: A High-Accuracy Neural Network for Raman Spectroscopy Blood and Semen Identification

Animal blood and semen analysis plays a significant role in national biological resource management, wildlife conservation, and customs security quarantine. Traditional blood analysis methods have disadvantages, such as complex sample preparation, time consumption, and false positives. Therefore, proposing a rapid and highly accurate analysis method is highly valuable. Raman spectroscopy has been widely used in blood analysis, and efficient and accurate analysis results can be obtained through the machine learning algorithm feature extraction. Recently, the transformer network structure was applied to Raman spectroscopy recognition. However, the multihead self-attention mechanism does not perform well in extracting local feature peaks, although it obtains global feature relations. This paper proposes a neural network based on the combination of one-dimensional convolution and multihead self-attention mechanism (Raman ConvMSANet) to identify 52 species of blood and semen Raman spectra. The network can achieve reliable identification effects in multiclassification and sample imbalance situations, and the average identification accuracy of blood and semen can reach more than 98.5%. The proposed network model can be applied not only to blood and semen identification but also to other biological fields.

Classification method based on Siamese-like neural network for inter-species blood Raman spectra similarity measure

Analysis of blood species is an extremely important part in customs inspection, forensic investigation, wildlife protection and other fields. In this study, a classification method based on Siamese-like neural network (SNN) for interspecies blood (22 species) was proposed to measure Raman Spectra similarity. The average accuracy was above 99.20% in the test set of spectra (known species) that did not appear in the training set. This model could detect species not represented in the dataset underlying the model. After adding new species to the training set, we can update the training based on the original model without retraining the model from scratch. For species with lower accuracy, SNN model can be trained intensively in the form of enriched training data for that species. A single model can achieve both multiple-classification and binary classification functions. Moreover, SNN showed higher accuracy rates when trained with smaller datasets compared to other methods.

Ultra-sensitive, rapid detection of dried bloodstains by surface enhanced Raman scattering on Ag substrates

A simple water extraction and transfer procedure is found to result in reproducible and highly sensitive 785 nm excited SERS spectra of 24 h dried bloodstains on Ag nanoparticle substrates. This protocol allows confirmatory detection and identification of dried stains of blood that have been diluted by up to 10⁵ in water on Ag substrates. While previous SERS results demonstrated similar performance on Au substrates when a 50% acetic acid extraction and transfer procedure was used, the water/Ag methodology avoids any potential DNA damage when the sample size is extremely small (≤∼1 μL) due to low pH exposure. The water only procedure is not effective on Au SERS substrates. This metal substrate difference results from the efficient red blood cell lysis and hemoglobin denaturation effects of the Ag nanoparticle surfaces as compare to that of Au nanoparticles. Consequently, the 50% acetic acid exposure is required for the acquisition of 785 nm SERS spectra of dried bloodstains on Au substrates.

Specific fluorescent signatures for body fluid identification using fluorescence spectroscopy

Non-invasive, rapid, on-site detection and identification of body fluids is highly desired in forensic investigations. The use of fluorescence-based methods for body fluid identification, have so far remain relatively unexplored. As such, the fluorescent properties of semen, serum, urine, saliva and fingermarks over time were investigated, by means of fluorescence spectroscopy, to identify specific fluorescent signatures for body fluid identification. The samples were excited at 81 different excitation wavelengths ranging from 200 to 600 nm and for each excitation wavelength the emission was recorded between 220 and 700 nm. Subsequently, the total emitted fluorescence intensities of specific fluorescent signatures in the UV-visible range were summed and principal component analysis was performed to cluster the body fluids. Three combinations of four principal components allowed specific clustering of the body fluids, except for fingermarks. Blind testing showed that 71.4% of the unknown samples could be correctly identified. This pilot study shows that the fluorescent behavior of ageing body fluids can be used as a new non-invasive tool for body fluid identification, which can improve the current guidelines for the detection of body fluids in forensic practice and provide the robustness of methods that rely on fluorescence.

Identification of blood species based on surface-enhanced Raman scattering spectroscopy and convolutional neural network

The identification of blood species is of great significance in many aspects such as forensic science, wildlife protection, and customs security and quarantine. Conventional Raman spectroscopy combined with chemometrics is an established method for identification of blood species. However, the Raman spectrum of trace amount of blood could hardly be obtained due to the very small cross-section of Raman scattering. In order to overcome this limitation, surface-enhanced Raman scattering (SERS) was adopted to analyze trace amount of blood. The 785 nm laser was selected as the optimal laser to acquire the SERS spectra, and the blood SERS spectra of 19 species were measured. The convolutional neural network (CNN) was used to distinguish the blood of 19 species including human. The recognition accuracy of the blood species was obtained with 98.79%. Our study provides an effective and reliable method for identification and classification of trace amount of blood.

Determination of blood species using echelle Raman spectrometer and surface enhanced Raman spectroscopy

Blood species identification of human and animals has attracted much attention in the areas of customs inspection and forensic science. The combination of vibrational spectroscopy and machine learning has been proven to be feasible and effective for this purpose. However, the popularization of this technology needs instrument which is compact, robust and more suitable for field application. Besides the quantity of the blood sample should be as little as possible. In this study, we proposed a system using echelle Raman spectrometer combined with surface enhanced Raman spectroscopy (SERS), which protocol combines the advantages of broadband and high resolution of echelle Raman spectrometer with the advantages of high SERS spectral sensitivity. The SERS spectra of 26 species including human were collected with echelle Raman spectrometer, and the convolutional neural network was used for species identification, with an accuracy rate of over 94%. The feasibility, validity and reliability of the combination of echelle Raman spectrometer and SERS for blood species identification were realized.

Attenuated total reflection-Fourier transform infrared spectroscopy: a universal analytical technique with promising applications in forensic analyses

Contemporary criminal investigations are based on the statements made by the victim and the eyewitnesses. They also rely on the physical evidences found in the crime scene. These evidences, and more particularly biological ones, have a great judicial value in the courtroom. They are usually used to revoke the suspect's allegations and confirm or refute the statements made by the victim and the witnesses. Stains of body fluids are biological evidences highly sought by forensic investigators. In many criminal cases, the success of the investigation relies on the correct identification and classification of these stains. Therefore, the adoption of reliable and accurate forensic analytical methods seems to be of vital importance to attain this objective. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) is a modern and universal analytical technique capable of fingerprint recognition of the analyte using minimal amount of the test sample. The current systematic review aims to through light on the fundamentals of this technique and to illustrate its wide range of applications in forensic investigations. ATR-FTIR is a nondestructive technique which has demonstrated an exceptional efficiency in detecting, identifying and discriminating between stains of various types of body fluids usually encountered in crime scenes. The ATR-FTIR spectral data generated from bloodstains can be used to deduce a wealth of information related to the donor species, age, gender, and race. These data can also be exploited to discriminate between stains of different types of bloods including menstrual and peripheral bloods. In addition, ATR-FTIR has a great utility in the postmortem investigations. More particularly, in estimating the postmortem interval and diagnosing death caused by extreme weather conditions. It is also useful in diagnosing some ambiguous death causes such as fatal anaphylactic shock and diabetic ketoacidosis.

Advances, limitations, and prospects of biosensing technology for detecting phytopathogenic bacteria

Phytopathogenic bacteria cause severe economic losses in agricultural production worldwide. The spread rates, severity, and emerging plant bacterial diseases have become serious threat to the sustainability of food sources and the fruit industry. Detection and diagnosis of plant diseases are imperative in order to manage plant diseases in field conditions, greenhouses, and food storage conditions as well as to maximize agricultural productivity and sustainability. To date, various techniques including, serological, observation-based, and molecular methods have been employed for plant disease detection. These methods are sensitive and specific for genetic identification of bacteria. However, these methods are specific for genetic identification of bacteria. Currently, the innovative biosensor-based disease detection technique is an attractive and promising alternative. A biosensor system involves biological recognition and transducer active receptors based on sensors used in plant-bacteria diagnosis. This system has been broadly used for the rapid diagnosis of plant bacterial pathogens. In the present review, we have discussed the conventional methods of bacterial-disease detection, however, the present review mainly focuses on the applications of different biosensor-based techniques along with point-of-care (POC), robotics, and cell phone-based systems. In addition, we have also discussed the challenges and limitations of these techniques.

Raman-based identification of tick species (Ixodidae) by spectroscopic analysis of their feces

Ticks are blood-feeding parasites that vector a large number of pathogens of medical and veterinary importance. There are strong connections between tick and pathogen species. Timely detection of certain tick species on cattle can cease the spread of numerous devastating diseases such as Bovine babiesiosis and anaplasmosis. Detection of ticks is currently performed by slow and laborious scout-based inspection of cattle. In this study, we investigated the possibility of identification of tick species (Ixodidae) based on spectroscopic signatures of their feces. We collected Raman spectra from individual grains of feces of seven different species of ticks. Our results show that Raman spectroscopy (RS) allows for highly accurate (above 90%) differentiation between tick species. Furthermore, RS can be used to predict the tick developmental stage and differentiate between nymphs, meta-nymphs and adult ticks. We have also demonstrated that diagnostics of tick species present on cattle can be achieved using a hand-held Raman spectrometer. These findings show that RS can be used for non-invasive, non-destructive and confirmatory on-site analysis of tick species present on cattle.

Raman spectroscopy enables highly accurate differentiation between young male and female hemp plants

Hand-held Raman spectroscopy can be used for highly accurate differentiation between young male and female hemp plants. This differentiation is based on significantly different concentration of lutein in these plants. Last year, a global market of only industrial hemp attained the value of USD 4.7 billion. It is by far the fastest growing market with projected growth of 22.5% between 2021 and 2026. Hemp (Cannabis sativa L.) is a dioecious species that has separate male and female plants. In hemp farming, female plants are strongly preferred because male plants do not produce sufficient amount of cannabinoids. Male plants are also eliminated to minimize a possibility of uncontrolled cross-fertilization of plants. Silver treatments can induce development of male flowers on genetically female plants in order to produce feminized seed. Resulting cannabinoid hemp production fields should contain 100% female plants. However, any unintended pollination from male plants can produce unwanted males in production fields. Therefore, there is a growing demand for a label-free, non-invasive, and confirmatory approach that can be used to differentiate between male and female plants before flowering. In this study, we examined the extent to which Raman spectroscopy, an emerging optical technique, can be used for the accurate differentiation between young male and female hemp plants. Our findings show that Raman spectroscopy enables differentiation between male and female plants with 90% and 94% accuracy on the level of young and mature plants, respectively. Such analysis is entirely non-invasive and non-destructive to plants and can be performed in seconds using a hand-held spectrometer. High-performance liquid chromatography (HPLC) analysis and collected Raman spectra demonstrate that this spectroscopic differentiation is based on significantly different concentrations of carotenoids in male vs female plants. These findings open up a new avenue for quality control of plants grown in both field and a greenhouse.

Hierarchical classification models and Handheld NIR spectrometer to human blood stains identification on different floor tiles

One of the most important types of evidence in certain criminal investigations is traces of human blood. For a detailed investigation, blood samples must be identified and collected at the crime scene. The present study aimed to evaluate the potential of the identification of human blood in stains deposited on different types of floor tiles (five types of ceramics and four types of porcelain tiles) using a portable NIR instrument. Hierarchical models were developed by combining multivariate analysis techniques capable of identifying traces of human blood (HB), animal blood (AB) and common false positives (CFP). The spectra of the dried stains were obtained using a portable MicroNIR spectrometer (Viavi). The hierarchical models used two decision rules, the first to separate CFP and the second to discriminate HB from AB. The first decision rule, used to separate the CFP, was based on the Q-Residual criterion considering a PCA model. For the second rule, used to discriminate HB and AB, the Q-Residual criterion were tested as obtained from a PCA model, a One-Class SIMCA model, and a PLS-DA model. The best results of sensitivity and specificity, both equal to 100%, were obtained when a PLS-DA model was employed as the second decision rule. The hierarchical classification models built for these same training sets using a PCA or SIMCA model also obtained excellent sensitivity results for HB classification, with values above 94% and 78% of specificity. No CFP samples were misclassified. Hierarchical models represent a significant advance as a methodology for the identification of human blood stains at crime scenes.

Raman spectrum matching with contrastive representation learning

An effective contrastive representation learning method for spectra identification with a frequentist guarantee of including the correct class prediction on two Raman datasets (Mineral and Organic) and one SERS dataset (Bacteria).

Recent advances of vibrational spectroscopy and chemometrics for forensic biological analysis

Biological materials found at a crime scene are crucially important evidence for forensic investigation because they provide contextual information about a crime and can be linked to the donor-individuals through combination with DNA analysis. Applications of vibrational spectroscopy to forensic biological analysis have been emerging because of its advantageous characteristics such as the non-destructivity, rapid measurement, and quantitative evaluation, compared to most current methods based on histological observation or biochemical techniques. This review presents an overview of recent developments in vibrational spectroscopy for forensic biological analysis. We also emphasize chemometric techniques, which can elicit reliable and advanced analytical outputs from highly complex spectral data from forensic biological materials. The analytical subjects addressed herein include body fluids, hair, soft tissue, bones, and bioagents. Promising applications for various analytical purposes in forensic biology are presented. Simultaneously, future avenues of study requiring further investigation are discussed.

Blood identification at the single-cell level based on a combination of laser tweezers Raman spectroscopy and machine learning

Laser tweezers Raman spectroscopy (LTRS) combines optical tweezers technology and Raman spectroscopy to obtain biomolecular compositional information from a single cell without invasion or destruction, so it can be used to "fingerprint" substances to characterize numerous types of biological cell samples. In the current study, LTRS was combined with two machine learning algorithms, principal component analysis (PCA)-linear discriminant analysis (LDA) and random forest, to achieve high-precision multi-species blood classification at the single-cell level. The accuracies of the two classification models were 96.60% and 96.84%, respectively. Meanwhile, compared with PCA-LDA and other classification algorithms, the random forest algorithm is proved to have significant advantages, which can directly explain the importance of spectral features at the molecular level.

Detection of bloodstains using attenuated total reflectance-Fourier transform infrared spectroscopy supported with PCA and PCA-LDA

The most important task in a criminal investigation is to detect and identify the recovered biological stains beyond reasonable scientific doubt and preserve the sample for further DNA analysis. In the light of this fact, many presumptive and confirmatory tests are routinely employed in the forensic laboratories to determine the type of body fluid. However, the currently used techniques are specific to one type of body fluid and hence it cannot be utilized to differentiate multiple body fluids. Moreover, these tests consume the samples in due process, and thus it becomes a great limitation especially considering the fact that samples are recovered in minute quantity in forensic cases. Therefore, such limitations necessitate the use of non-destructive techniques that can be applied simultaneously to all types of bodily fluids and allow sample preservation for further analysis. In the current work, attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy has been used to circumvent the aforementioned limitations. The important factors which could influence the detection of blood such as the effect of substrates, washing/chemical treatment, ageing, and dilution limits on the analysis of blood have been analysed. In addition, blood discrimination from non-blood substance (biological and non-biological in nature) has also been studied. Chemometric technique that is PCA-LDA has been used to discriminate blood from other body fluids and it resulted in 100% accurate classification. Furthermore, blood and non-blood substances including fake blood have also been classified into separate clusters with a 100% accuracy, sensitivity, and specificity. All-inclusive, this preliminary study substantiates the potential application of ATR-FTIR spectroscopy for the non-destructive identification of blood traces in simulated forensic casework conditions with 0% rate of false classification.

Surface enhanced Raman scattering specificity for detection and identification of dried bloodstains

Surface enhanced Raman spectroscopy (SERS) provides highly specific vibrational signatures identifying dried blood for a variety of forensic applications. SERS spectra on Au nanoparticle substrates excited at 785 nm are found to identify dried stains of human and nonhuman blood from seven animals, and distinguish stains due to menstrual and peripheral blood. In addition, the unique SERS bloodstain spectrum is distinct from the SERS spectra of thirty red-brown stains of potential household substances that could be visually mistaken for bloodstains and from food stains that have been shown to give positive results with presumptive colorimetric blood tests. Finally, a SERS swab procedure has been developed and demonstrates that the substrates that a blood sample dried on does not offer any Raman or fluorescence interference for the SERS identification of dried blood. Such bloodstains on porous and nonporous materials are all identical and exclusively due to the heme moiety of hemoglobin. Optimized selection of the extraction solvent is found to control the chemical composition of molecular components appearing in the SERS spectrum of complex, multicomponent biological mixtures, such as body fluids.

Biochemical Origin of Raman-Based Diagnostics of Huanglongbing in Grapefruit Trees

Biotic and abiotic stresses cause substantial changes in plant biochemistry. These changes are typically revealed by high-performance liquid chromatography (HPLC) and mass spectroscopy-coupled HPLC (HPLC-MS). This information can be used to determine underlying molecular mechanisms of biotic and abiotic stresses in plants. A growing body of evidence suggests that changes in plant biochemistry can be probed by Raman spectroscopy, an emerging analytical technique that is based on inelastic light scattering. Non-invasive and non-destructive detection and identification of these changes allow for the use of Raman spectroscopy for confirmatory diagnostics of plant biotic and abiotic stresses. In this study, we couple HPLC and HPLC-MS findings on biochemical changes caused by Candidatus Liberibacter spp. (Ca. L. asiaticus) in citrus trees to the spectroscopic signatures of plant leaves derived by Raman spectroscopy. Our results show that Ca. L. asiaticus cause an increase in hydroxycinnamates, the precursors of lignins, and flavones, as well as a decrease in the concentration of lutein that are detected by Raman spectroscopy. These findings suggest that Ca. L. asiaticus induce a strong plant defense response that aims to exterminate bacteria present in the plant phloem. This work also suggests that Raman spectroscopy can be used to resolve stress-induced changes in plant biochemistry on the molecular level.

Raman-Based Diagnostics of Stalk Rot Disease of Maize Caused by Colletotrichum graminicola

Stalk rot caused by Colletotrichum graminicola is a disease of worldwide importance. Stalk rot is difficult to detect at the early stages of infection because the fungus colonizes the tissues inside the maize stem. Current diagnostic methods are time-consuming, laborious, and destructive to the stem tissue. We utilized Raman spectroscopy to follow the development of stalk rot in three different maize genotypes grown either in the field or the greenhouse. We then used the acquired spectra to calibrate statistical models to differentiate amongst the different disease timepoints and the genotypes themselves. This non-invasive spectroscopic method enabled high-accuracy identification of stalk rot based on both stalk and leaf spectra. We additionally found that leaf spectra were favorable for identifying maize by genotype. Finally, we identified Raman bands that showed correlation with the sizes of stalk rot-associated lesions in the stems. We demonstrated that Raman spectroscopy is a viable tool for detection of stalk rot disease, as well as potent for the differentiation of maize genotypes.

Raman spectroscopy enables phenotyping and assessment of nutrition values of plants: a review

Our civilization has to enhance food production to feed world's expected population of 9.7 billion by 2050. These food demands can be met by implementation of innovative technologies in agriculture. This transformative agricultural concept, also known as digital farming, aims to maximize the crop yield without an increase in the field footprint while simultaneously minimizing environmental impact of farming. There is a growing body of evidence that Raman spectroscopy, a non-invasive, non-destructive, and laser-based analytical approach, can be used to: (i) detect plant diseases, (ii) abiotic stresses, and (iii) enable label-free phenotyping and digital selection of plants in breeding programs. In this review, we critically discuss the most recent reports on the use of Raman spectroscopy for confirmatory identification of plant species and their varieties, as well as Raman-based analysis of the nutrition value of seeds. We show that high selectivity and specificity of Raman makes this technique ideal for optical surveillance of fields, which can be used to improve agriculture around the world. We also discuss potential advances in synergetic use of RS and already established imaging and molecular techniques. This combinatorial approach can be used to reduce associated time and cost, as well as enhance the accuracy of diagnostics of biotic and abiotic stresses.

Raman-Based Diagnostics of Biotic and Abiotic Stresses in Plants. A Review

Digital farming is a novel agricultural philosophy that aims to maximize a crop yield with the minimal environmental impact. Digital farming requires the development of technologies that can work directly in the field providing information about a plant health. Raman spectroscopy (RS) is an emerging analytical technique that can be used for non-invasive, non-destructive, and confirmatory diagnostics of diseases, as well as the nutrient deficiencies in plants. RS is also capable of probing nutritional content of grains, as well as highly accurate identification plant species and their varieties. This allows for Raman-based phenotyping and digital selection of plants. These pieces of evidence suggest that RS can be used for chemical-free surveillance of plant health directly in the field. High selectivity and specificity of this technique show that RS may transform the agriculture in the US. This review critically discusses the most recent research articles that demonstrate the use of RS in diagnostics of abiotic and abiotic stresses in plants, as well as the identification of plant species and their nutritional analysis.

Discrimination between human and animal blood by attenuated total reflection Fourier transform-infrared spectroscopy

Forensic chemistry is an important area of analytical chemistry. This field has been rapidly growing over the last several decades. Confirmation of the human origins of bloodstains is important in practical forensics. Current serological blood tests are destructive and often provide false positive results. Here, we report on the development of a nondestructive method that could potentially be applied at the scene for differentiation of human and animal blood using attenuated total reflection Fourier transform-infrared (ATR FT-IR) spectroscopy and statistical analysis. The following species were used to build statistical models for binary human-animal blood differentiation: cat, dog, rabbit, horse, cow, pig, opossum, and raccoon. Three other species (deer, elk, and ferret) were used for external validation. A partial least squares discriminant analysis (PLSDA) was used for classification purposes and showed excellent performance in internal cross-validation (CV). The method was externally validated first using blood samples from new donors of species used in the training data set, and second using donors of new species that were not used to construct the model. Both validations showed excellent results demonstrating potential of the developed approach for nondestructive, rapid, and statistically confident discrimination between human and animal blood for forensic purposes.

Ribose-induced Maillard Reaction as an Analytical Method for Detection of Adulteration and Differentiation of Chilled and Frozen-thawed Minced Veal

Quality control of meat products is one of the main concerns of consumers, governmental control authorities, and retailers. The purpose of this study was to employ ribose-induced Maillard reaction in detection of meat adulteration and differentiation of fresh-chilled from frozen-thawed minced veal. The browning intensity was assessed through measuring the absorbance at 420 nm with a spectrophotometer as well as the direct analysis of the color and pH. The results showed that CIE b*, CIE a*, and A420* values in the extract of fresh-chilled veal were significantly (p<0.05) higher than frozen-thawed samples. The extract of frozen meat samples stored at -18°C became significantly darker and more yellowish compared to -4°C. The results showed that the A420* value in the frozen-thawed veal stored at -4°C and -18°C was reduced by approximately 17.22±3.53% and 11.68±2.49%, respectively, compared with fresh-chilled veal. The findings also showed that the storage temperature of minced veal and the heating time in this reaction had a significant effect on all tested variables (p<0.0001). The proposed method can be considered as an easy, quick, and inexpensive test for differentiating between the fresh-chilled and frozen-thawed minced veal.

Skull fractures in forensic putrefied/skeletonised cases: The challenge of estimating the post-traumatic interval

The objective of our study was to assess the reliability of the estimation of posttraumatic survival time (PTST) in forensic cases based on microCT and histology of putrefied/dry bone samples with comparison of initial macroscopic fracture classification performed during autopsy. Macroscopic morphological patterns of bone fracture are routinely used in forensic pathology and anthropology to distinguish between antemortem, perimortem and postmortem injuries. Based on macroscopic and microscopic analysis of six craniofacial fractures, our study results illustrate the need to complete macroscopical findings and initial fracture classification with microscopic analysis to avoid any inaccuracy. MicroCT has become a powerful technique to identify early bone healing signs but histology remains the gold standard to estimate the PTST and determine vital fracture based on hemorrhage marker. Raman microspectroscopy can identify a blood clot in the fracture line.

Surface enhanced Raman scattering for robust, sensitive detection and confirmatory identification of dried bloodstains

785 nm SERS spectra provide rapid, sensitive confirmatory identification of dried bloodstains due to a ferric, high spin heme moiety.

Blood species identification based on deep learning analysis of Raman spectra

Blood analysis is an indispensable means of detection in criminal investigation, customs security and quarantine, anti-poaching of wildlife, and other incidents. Detecting the species of blood is one of the most important analyses. In order to classify species by analyzing Raman spectra of blood, a recognition method based on deep learning principle is proposed in this paper. This method can realize multi-identification blood species, by constructing a one-dimensional convolution neural network and establishing a Raman spectra database containing 20 kinds of blood. The network model is obtained through training, and then is employed to predict the testing set data. The average accuracy of blind detection is more than 97%. In this paper, we try to increase the diversity of data to improve the robustness of the model, optimize the network and adjust the hyperparameters to improve the recognition ability of the model. The evaluation results show that the deep learning model has high recognition performance to distinguish the species of blood.

Minimalist strategies applied to analysis of forensic samples using elemental and molecular analytical techniques - A review

Forensic science is an emerging field driven by a number of factors, and the development of different methods of analyses, instruments, and techniques is of great help to experts in the field. Sampling and sample preparation in forensic cases are of utmost importance, and therefore, the methods for processing (or not) the samples are critical for acquiring accurate results. Some alternatives for attaining the minimalist concept, i.e. little or no sample treatment, are discussed in this review. For elemental analysis, analytical techniques, such as X-ray spectrometry, laser-ablation mass spectrometry, laser-induced breakdown spectrometry, inductively coupled plasma mass spectrometry and optical emission spectrometry, and Mössbauer spectrometry are overviewed. Molecular analysis, such as Raman spectroscopy, and ambient ionization mass spectrometry are discussed. Some representative examples are presented that involve in situ analysis, counterfeit bank notes and documents, post-mortem and bone analyses, and forensic analysis of drugs, glass, fingerprints, biological fluids and explosives.

Rapid and noninvasive diagnostics of Huanglongbing and nutrient deficits on citrus trees with a handheld Raman spectrometer

Huanglongbing (HLB) or citrus greening is a devastating disease of citrus trees that is caused by the gram-negative Candidatus Liberibacter spp. bacteria. The bacteria are phloem limited and transmitted by the Asian citrus psyllid, Diaphorina citri, and the African citrus psyllid, Trioza erytreae, which allows for a wider dissemination of HLB. Infected trees exhibit yellowing of leaves, premature leaf and fruit drop, and ultimately the death of the entire plant. Polymerase chain reaction (PCR) and antibody-based assays (ELISA and/or immunoblot) are commonly used methods for HLB diagnostics. However, they are costly, time-consuming, and destructive to the sample and often not sensitive enough to detect the pathogen very early in the infection stage. Raman spectroscopy (RS) is a noninvasive, nondestructive, analytical technique which provides insight into the chemical structures of a specimen. In this study, by using a handheld Raman system in combination with chemometric analyses, we can readily distinguish between healthy and HLB (early and late stage)-infected citrus trees, as well as plants suffering from nutrient deficits. The detection rate of Raman-based diagnostics of healthy vs HLB infected vs nutrient deficit is ~ 98% for grapefruit and ~ 87% for orange trees, whereas the accuracy of early- vs late-stage HLB infected is 100% for grapefruits and ~94% for oranges. This analysis is portable and sample agnostic, suggesting that it could be utilized for other crops and conducted autonomously. Graphical abstract.

Noninvasive and Nondestructive Detection of Cowpea Bruchid within Cowpea Seeds with a Hand-Held Raman Spectrometer

Insect damage to crops is a serious issue, in particular when the pest dwells within its host. The cowpea bruchid ( Callosobruchus maculatus) is an herbivore of legumes including beans and peas. The bruchid lays its eggs on the seeds themselves; after hatching, the larvae burrow into and develop inside the seed, complicating detection and treatment. Left unchecked, two insects could destroy up to 50% of 1 ton of harvest cowpea ( Vigna unguiculata) after several months of storage. In this study, we investigated the possibility of using a hand-held Raman spectrometer to detect the pest during its development within intact cowpeas. Our results show that Raman spectroscopy can detect chemical signatures of bruchid larvae as well as their excrements inside the intact seeds. Additionally, using chemometric methods, we distinguished between healthy and infested seeds as well as among seeds hosting developmentally early or late-stage larvae with high accuracy. This study demonstrates Raman spectroscopy's efficacy in not only detection of pathogens and pests present on the surface of plant leaves and the grain but also inside the seeds. This Raman-based method may prove useful as a rapid means of screening crops for internal pests.

An evaluation of monovalent, divalent, and trivalent cations as aggregating agents for surface enhanced Raman spectroscopy (SERS) analysis of synthetic cannabinoids

Monovalent, divalent and trivalent chloride, sulfate and nitrate salts were examined to determine the critical coagulation concentration (CCC) for each salt and its corresponding effect on detection limits for SERS analysis of synthetic cannabinoids.

Dual-model analysis for improving the discrimination performance of human and nonhuman blood based on Raman spectroscopy

The discrimination accuracy for human and nonhuman blood is important for customs inspection and forensic applications. Recently, Raman spectroscopy has shown effectiveness in analyzing blood droplets and stains with an excitation wavelength of 785 nm. However, the discrimination of liquid whole blood in a vacuum blood tube using Raman spectroscopy, which is a form of noncontact and nondestructive detection, has not been achieved. An excitation wavelength of 532 nm was chosen to avoid the fluorescent background of the blood tube, at the cost of reduced spectroscopic information and discrimination accuracy. To improve the accuracy and true positive rate (TPR) for human blood, a dual-model analysis method is proposed. First, model 1 was used to discriminate human-unlike nonhuman blood. Model 2 was then used to discriminate human-like nonhuman blood from the "human blood" obtained by model 1. A total of 332 Raman spectra from 10 species were used to build and validate the model. A blind test and external validation demonstrated the effectiveness of the model. Compared with the results obtained by the single partial least-squares model, the discrimination performance was improved. The total accuracy and TPR, which are highly important for practical applications, increased to 99.1% and 97.4% from 87.2% and 90.6%, respectively.

Assessment of the radiotherapy effect for nasopharyngeal cancer using plasma surface-enhanced Raman spectroscopy technology

Nasopharyngeal cancer (NPC) is a malignant tumor of the head and neck, which is extremely sensitive to radiotherapy. The aim of this study is to evaluate the feasibility of a label-free nanobiosensor based on plasma surface-enhanced Raman spectroscopy (SERS) to assess the radiotherapy effect in NPC. Here, SERS measurements were performed on plasma samples from 40 pre-treatment and post-treatment NPC as well as 30 healthy volunteers. Results demonstrate that the spectral characteristic of post-treatment samples is obviously different from that of pre-treatment ones, owing to the changes of biomolecules in plasma induced by radiotherapy. Classification sensitivities of 83.3%, 61.8% and 95.1%, and specificities of 91.2%, 67.4% and 93% can be achieved for separating pre- and post-treatment samples, post-treatment and normal samples, and pre-treatment and normal samples, respectively, suggesting the great potential of plasma SERS method as a rapid and convenient tool for radiotherapy assessment and cancer screening in NPC.

Detection and Identification of Fungal Infections in Intact Wheat and Sorghum Grain Using a Hand-Held Raman Spectrometer

Global population growth drives increasing food demand, which is anticipated to increase by at least 20% over the next 15 years. Rapid detection and identification of plant pathogens allows for up to a 50% increase in the total agricultural yield worldwide. Current molecular methods for pathogen diagnostics, such as polymerase chain reaction (PCR), are costly, time-consuming, and destructive. These limitations recently catalyzed a push toward developing minimally invasive and substrate general techniques that can be used in the field for confirmatory detection and identification of plant pathogens. Raman spectroscopy (RS) is a noninvasive, nondestructive, and label-free technique that can be used to determine chemical structure of analyzed specimens. In this study, we demonstrate that by using a hand-held Raman spectrometer, we can identify whether wheat or sorghum grains are healthy or not and identify present plant pathogens. We show that RS enables diagnosis of simple diseases, such as ergot, that are caused by one pathogen, as well as complex diseases, such as black tip or mold, which are induced by several different pathogens. The combination of chemometric analysis and RS allows for distinguishing between healthy and infected grains with high accuracy. We also show that RS can be used to determine states of disease development on grain. These results demonstrate that Raman-based approach for disease detection on plants is sample agnostic.

Detection and Identification of Plant Pathogens on Maize Kernels with a Hand-Held Raman Spectrometer

Rapid detection and identification of crop pathogens is essential for improving crop yield. Typical pathogen assaying methods, such as polymerase chain reaction (PCR) or enzyme-linked immunosorbent assay (ELISA), are time-consuming and destructive to the sample. Raman spectroscopy (RS) is a noninvasive nondestructive analytical technique that provides insight on the chemical structure of the specimen. In this study, we demonstrate that using a hand-held Raman spectrometer, in combination with chemometric analyses, we can distinguish between healthy and diseased maize ( Zea mays) kernels, as well as between different diseases with 100% accuracy. Our analysis is portable and sample-agnostic, suggesting that it could be retooled for other crops and conducted autonomously.

Infrared spectroscopy and spectroscopic imaging in forensic science

Infrared spectroscopy and spectroscopic imaging, are robust, label free and inherently non-destructive methods with a high chemical specificity and sensitivity that are frequently employed in forensic science research and practices. This review aims to discuss the applications and recent developments of these methodologies in this field. Furthermore, the use of recently emerged Fourier transform infrared (FT-IR) spectroscopic imaging in transmission, external reflection and Attenuated Total Reflection (ATR) modes are summarised with relevance and potential for forensic science applications. This spectroscopic imaging approach provides the opportunity to obtain the chemical composition of fingermarks and information about possible contaminants deposited at a crime scene. Research that demonstrates the great potential of these techniques for analysis of fingerprint residues, explosive materials and counterfeit drugs will be reviewed. The implications of this research for the examination of different materials are considered, along with an outlook of possible future research avenues for the application of vibrational spectroscopic methods to the analysis of forensic samples.