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Abstract Confirmatory identification of dyes in the physical pieces of evidence, such as hair and fabric, is critically important in forensics. This information can be used to demonstrate the link between a person of interest and a crime scene. High performance liquid chromatography is broadly used for dye analysis. However, this technique is destructive and laborious. This problem can be overcome by near-Infrared excitation Raman spectroscopy (NIeRS), non-invasive and non-destructive technique that can be used to determine chemical structure of highly fluorescent dyes. Analyzed fabric materials often possess body fluid stains, which may obscure the accuracy of NIeRS-based identification of dyes. In this study, we investigate the extent to which fabric contamination with body fluids can alter the accuracy of NIeRS. Our results showed that NIeRS coupled with partial-least squared discriminant analysis (PLS-DA) enabled on average 97.6% accurate identification of dyes on fabric contaminated with dry blood, urine and semen. We also found that NIeRS could be used to identify blood, urine and semen on such fabric with 99.4% accuracy. Furthermore, NIeRS could be used to differentiate between wet and dry blood, as well as reveal the presence of blood on washed fabric. These results indicate that NIeRS coupled with PLS-DA could be used as a robust and reliable analytical approach in forensic analysis of fabric.

Plant pathogens, including viruses, bacteria, and fungi, cause massive crop losses around the world. Abiotic stresses, such as drought, salinity and nutritional deficiencies are even more detrimental. Timely diagnostics of plant diseases and abiotic stresses can be used to provide site- and doze-specific treatment of plants. In addition to the direct economic impact, this “smart agriculture” can help minimizing the effect of farming on the environment. Mounting evidence demonstrates that vibrational spectroscopy, which includes Raman (RS) and infrared spectroscopies (IR), can be used to detect and identify biotic and abiotic stresses in plants. These findings indicate that RS and IR can be used for in-field surveillance of the plant health. Surface-enhanced RS (SERS) has also been used for direct detection of plant stressors, offering advantages over traditional spectroscopies. Finally, all three of these technologies have applications in phenotyping and studying composition of crops. Such non-invasive, non-destructive, and chemical-free diagnostics is set to revolutionize crop agriculture globally. This review critically discusses the most recent findings of RS-based sensing of biotic and abiotic stresses, as well as the use of RS for nutritional analysis of foods.

An unprecedented extension of life expectancy observed during the past century drastically increased the number of patients diagnosed with Parkinson’s diseases (PD) worldwide. Estimated costs of PD alone reached $52 billion per year, making effective neuroprotective treatments an urgent and unmet need. Current treatments of both AD and PD focus on mitigating the symptoms associated with these pathologies and are not neuroprotective. In this review, we discuss the most advanced therapeutic strategies that can be used to treat PD. We also critically review the shift of the therapeutic paradigm from a small molecule-based inhibition of protein aggregation to the utilization of natural degradation pathways and immune cells that are capable of degrading toxic amyloid deposits in the brain of PD patients.

α-Synuclein (α-syn) is a small protein that is involved in cell vesicle trafficking in neuronal synapses. A progressive aggregation of this protein is the expected molecular cause of Parkinson's disease, a disease that affects millions of people around the world. A growing body of evidence indicates that phospholipids can strongly accelerate α-syn aggregation and alter the toxicity of α-syn oligomers and fibrils formed in the presence of lipid vesicles. This effect is attributed to the presence of high copies of lysines in the N-terminus of the protein. In this study, we performed site-directed mutagenesis and replaced one out of two lysines at each of the five sites located in the α-syn N-terminus. Using several biophysical and cellular approaches, we investigated the extent to which six negatively charged fatty acids (FAs) could alter the aggregation properties of K10A, K23A, K32A, K43A, and K58A α-syn. We found that FAs uniquely modified the aggregation properties of K43A, K58A, and WT α-syn, as well as changed morphology of amyloid fibrils formed by these mutants. At the same time, FAs failed to cause substantial changes in the aggregation rates of K10A, K23A, and K32A α-syn, as well as alter the morphology and toxicity of the corresponding amyloid fibrils. Based on these results, we can conclude that K10, K23, and K32 amino acid residues play a critical role in protein-lipid interactions since their replacement on non-polar alanines strongly suppressed α-syn-lipid interactions.

Abstract The discovery of clandestine burials poses unique challenges for forensic specialists, requiring diverse expertise to analyze remains in various states. Bones, teeth, and hair often endure the test of time, with hair particularly exposed to the external environment. While existing studies focus on the degradation of virgin hair influenced by soil pH and decomposition fluids, the interaction between artificial dyes on hair and soil remains underexplored. This paper introduces a novel approach to forensic hair analysis that is based on high-throughput, nondestructive, and non-invasive surface-enhanced Raman spectroscopy (SERS) and machine learning. Using this approach, we investigated the reliability of the detection and identification of artificial dyes on hair buried in three distinct soil types for up to eight weeks. Our results demonstrated that SERS enabled the correct prediction of 97.9% of spectra for five out of the eight dyes used within the 8 weeks of exposure. We also investigated the extent to which SERS and machine learning can be used to predict the number of weeks since burial, as this information may provide valuable insights into post-mortem intervals. We found that SERS enabled highly accurate exposure intervals to soils for specific dyes. The study underscores the high achievability of SERS in extrapolating colorant information from dyed hairs buried in diverse soils, with the suggestion that further model refinement could enhance its reliability in forensic applications.

Abstract Background Ticks and tick-borne diseases pose significant challenges to cattle production, thus the species identification of ticks and knowledge on their presence, abundance, and dispersal are necessary for the development of effective control measures. The standard method of inspection for the presence of ticks is the visual and physical examination of restrained animals, but the limitations of human sight and touch can allow larval, nymphal, and unfed adult ticks to remain undetected due to their small size and site of attachment. However, Raman spectroscopy, an analytical tool widely used in agriculture and other sectors, shows promise for the identification of tick species in infested cattle. Raman spectroscopy is a non-invasive and efficient method that employs the interaction between molecules and light for the identification of the molecular constituents of specimens. Methods Raman spectroscopy was employed to analyze the structure and composition of tick feces deposited on host skin and hair during blood-feeding. Feces of 12 species from a total of five genera and one subgenus of ixodid ticks were examined. Spectral data were subjected to partial least squares discriminant analysis, a machine-learning model. We also used Raman spectroscopy and the same analytical procedures to compare and evaluate feces of the horn fly Haematobia irritans after it fed on cattle. Results Five genera and one sub-genus at overall true prediction rates ranging from 92.3 to 100% were identified from the Raman spectroscopy data of the tick feces. At the species level, Dermacentor albipictus, Dermacentor andersoni and Dermacentor variabilis at overall true prediction rates of 100, 99.3 and 100%, respectively, were identified. There were distinct differences between horn fly and tick feces with respect to blood and guanine vibrational frequencies. The overall true prediction rate for the separation of tick and horn fly feces was 98%. Conclusions Our findings highlight the utility of Raman spectroscopy for the reliable identification of tick species from their feces, and its potential application for the identification of ticks from infested cattle in the field. Graphical Abstract

Colored textiles are valuable physical evidence often found at crime scenes. Analysis of the chemical structure of textiles could be used to establish a connection between fabric found at a crime scene and suspect cloths. High-performance liquid chromatography (HPLC) and mass spectroscopy coupled HPLC are traditionally used for the identification of dyes in fabric. However, these techniques are invasive and destructive. A growing body of evidence indicates that near-infrared excitation (λ = 830 nm) Raman spectroscopy (NIeRS) could be used to probe the chemical signature of such colorants. At the same time, it remains unclear whether environmental factors, such as solar light could lower the accuracy of NIeRS-based identification of dyes in textiles. In this study, we exposed cotton fabric colored with six different dyes to light and investigated the extent to which colorants fade during seven weeks using NIeRS. We found a decrease in the intensities of all vibrational bands in the acquired spectra as the time of the exposition of fabric to light increased. Nevertheless, utilization of partial least-squared discriminant analysis (PLS-DA) enabled identification of the colorants at all eight weeks. These results indicate that the effect of light exposure should be strongly considered by forensic experts upon the NIeRS-based analysis of colored fabric.

Currently, there is no reliable, non-destructive way to assess the maturity of cantaloupes (Cucumis melo L.). Here, we investigated the extent to which Raman spectroscopy can be used to determine cantaloupe maturity. Five cantaloupe cultivars were grown and harvested at 13, 26, and 39 days after anthesis. Raman spectra from cantaloupes were acquired and partial least-square discriminant analysis (PLS-DA) was used to predict maturity based on the collected spectra. The PLS-DA model predicted maturity with up to 100 % accuracy in the cultivars studied. HPLC analysis of lutein and β-carotene in cantaloupe rind showed an increase in the concentration of carotenoids with maturity. The same trend was observed in the vibrational bands originating from carotenoids in the acquired Raman spectra. Based on this, Raman spectroscopy can primarily detect the changes of carotenoids in different cultivars of cantaloupe rind, which can be used for non-invasive and non-destructive assessment of fruit maturity.

Rice (Oryza sativa) is the primary crop for nearly half of the world’s population. Groundwater in many rice-growing parts of the world often has elevated levels of arsenite and arsenate. At the same time, rice can accumulate up to 20 times more arsenic compared to other staple crops. This places an enormous amount of people at risk of chronic arsenic poisoning. In this study, we investigated whether Raman spectroscopy (RS) could be used to diagnose arsenic toxicity in rice based on biochemical changes that were induced by arsenic accumulation. We modeled arsenite and arsenate stresses in four different rice cultivars grown in hydroponics over a nine-day window. Our results demonstrate that Raman spectra acquired from rice leaves, coupled with partial least squares-discriminant analysis, enabled accurate detection and identification of arsenic stress with approximately 89% accuracy. We also performed high-performance liquid chromatography (HPLC)-analysis of rice leaves to identify the key molecular analytes sensed by RS in confirming arsenic poisoning. We found that RS primarily detected a decrease in the concentration of lutein and an increase in the concentration of vanillic and ferulic acids due to the accumulation of arsenite and arsenate in rice. This showed that these molecules are detectable indicators of biochemical response to arsenic accumulation. Finally, a cross-correlation of RS with HPLC and ICP-MS demonstrated RS’s potential for a label-free, non-invasive, and non-destructive quantification of arsenic accumulation in rice.

Tomato spotted wilt (TSW) disease caused by tomato spotted wilt orthotospovirus (TSWV, Orthotospovirus tomatomaculae) poses a significant threat to specialty and staple crops worldwide by causing over a billion dollars in crop losses annually. Current strategies for TSWV diagnosis heavily rely on nucleic acid or protein-based techniques which require significant technical expertise, and are invasive, time-consuming, and expensive, thereby catalyzing the search for better alternatives. In this study, we explored the potential of Raman spectroscopy (RS) in early detection of TSW in a non-invasive and non-destructive manner. Specifically, we investigated whether RS could be used to detect strain specific TSW symptoms associated with four TSWV strains infecting three differentially resistant tomato cultivars. In the acquired spectra, we observed notable reductions in the intensity of vibrational peaks associated with carotenoids. Using high-performance liquid chromatography (HPLC), we confirmed that TSWV caused a substantial decrease in the concentration of lutein that was detected by RS. Finally, we demonstrated that Partial Least Squares-Discriminant Analysis (PLS-DA) could be used to differentiate strain-specific TSW symptoms across all tested cultivars. These results demonstrate that RS can be a promising solution for early diagnosis of TSW, enabling timely disease intervention and thereby mitigating crop losses inflicted by TSWV.

Abstract Scalp hairs are readily present at most crime scenes because an average person sheds around 100 hairs a day. Forensic experts analyze hair found at crime scenes to identify suspects involved in a crime. Many people color their hair on a regular basis. Therefore, confirmatory analysis of hair colorants can be extremely useful in forensic investigation of hair evidence. However, most currently available methods for analysis of hair colorants are invasive, destructive, or not reliable. Surface enhanced Raman spectroscopy (SERS) is a minimally invasive, fast, and highly accurate technique that can be used to identify colorants present on hair. SERS is based on 106–108 enhancement of Raman scattering from molecules present in the close proximity to noble metal nanostructures. In this study, we investigate the extent to which SERS can be used to reveal coloration history of hair. We found that SERS enables nearly 100% identification of dyes of different color if those were applied on hair in the sequential order. The same accuracy was observed for colorants of different brand and type. Furthermore, SERS was capable of revealing the order in which two colorants were applied on hair. Finally, we demonstrated that SERS could be used to reveal hair coloration history if two randomly selected dyes of different color, brand and type were used to color the hair. These findings facilitate the need for forensic experts to account for hair that has been redyed and can be identified against a library of the same colorant combinations.

Abstract Food security is an emerging problem that is faced by our civilization. There are millions of people around the world suffering from various kinds of malnutrition. The number of people that starve will only increase considering the continuous growth of the world’s population. The problem of food security can be addressed by timely detection and identification biotic and abiotic stresses in plants that drastically reduce the crop yield. A growing body of evidence suggests that Raman spectroscopy (RS), an emerging analytical technique, can be used for the confirmatory and non-invasive diagnostics of plant stresses. However, it remains unclear whether RS can efficiently disentangle biotic and abiotic stresses, as well as detect both of them simultaneously in plants. In this work, we modeled a stalk rot disease in corn by inoculating the plant stalks with Colletotrichum graminicola. In parallel, we subjected plants to salt stress, as well as challenging plants with both stalk rot disease and salinity stress simultaneously. After the stresses were introduced, Raman spectra were collected from the stalks to reveal stress-specific changes in the plant biochemistry. We found that RS was able to differentiate between stalk rot disease and salinity stresses with 100% accuracy, as well as predict presence of both of those stresses in plants on early and late stages. These results demonstrate that RS is a robust and reliable approach that can be used for confirmatory, non-destructive and label-free diagnostics of biotic and abiotic stresses in plants.

Abstract During bloating and active decay, human remains begin to deform and warp their physical identity. After the skin and muscles loosen and detach from their skeletal structuration, everything but bones, teeth, and hair will fully disintegrate into the soil that surrounds the body. Nearly half of people in the world dye their hair with a variety of permanent and semi-permanent colorants. Expanding upon this, we hypothesized that confirmatory analysis of hair colorants can be used to facilitate and advance forensic analysis of human remains. A growing body of evidence suggests that hair colorants can be identified directly on hair using surface-enhanced Raman spectroscopy (SERS). In this study, we investigate the extent to which SERS can be used to detect black and blue permanent and semi-permanent dyes on hair exposed to sunlight. Our results showed that although substantial photodegradation of all dyes was observed by week 7, SERS enabled highly accurate detection and identification of hair colorants during all 10 weeks of hair exposure to the sunlight with on average 99.2% accuracy. We also found that SERS could be used to predict fading rates of hair colorants. This information can shed light on the exposure of human remains to the exterior environment.

Herbicide application is a critical component of modern horticulture. Misuse of herbicides can result in damage to economically important plants. Currently, such damage can be detected only at symptomatic stages by subjective visual inspection of plants, which requires substantial biological expertise. In this study, we investigated the potential of Raman spectroscopy (RS), a modern analytical technique that allows sensing of plant health, for pre-symptomatic diagnostics of herbicide stresses. Using roses as a model plant system, we investigated the extent to which stresses caused by Roundup (Glyphosate) and Weed-B-Gon (2, 4-D, Dicamba and Mecoprop-p (WBG), two of the most commonly used herbicides world-wide, can be diagnosed at pre- and symptomatic stages. We found that spectroscopic analysis of rose leaves enables ~90% accurate detection of Roundup- and WBG-induced stresses one day after application of these herbicides on plants. Our results also show that the accuracy of diagnostics of both herbicides at seven days reaches 100%. Furthermore, we show that RS enables highly accurate differentiation between the stresses induced by Roundup- and WBG. We infer that this sensitivity and specificity arises from the differences in biochemical changes in plants that are induced by both herbicides. These findings suggest that RS can be used for a non-destructive surveillance of plant health to detect and identify herbicide-induced stresses in plants.

Potato is the most consumed vegetable worldwide. Potato tubers contain water, starch, proteins, minerals, and vitamins. The amounts of these chemicals depend on the cultivar and growing location. When potatoes are exposed to high temperatures during the growing period, tuber yield and quality are detrimentally affected; however, there is limited knowledge about the influence of high temperatures on tuber chemical composition. With temperatures rising around the globe, the reaction of potato cultivars to high temperatures is increasingly important, and heat-induced changes, including changes in the chemical composition of tubers, should be considered. The Texas A&M University Potato Breeding Program has been selecting potato clones under high-temperature conditions for many years. Several released cultivars are considered heat-tolerant based on high marketable yields and low internal and external tuber defects. In this study, we used Raman spectroscopy (RS), an analytical tool, to determine whether heat stress causes changes in the chemical composition of tubers of ten potato cultivars. RS is a non-invasive method that requires less time and labor than conventional chemical analysis. We found drastic changes in the intensities of vibrational bands that originate from carbohydrates in the spectra acquired from tubers of heat-stressed plants compared to tubers produced by potato plants grown under normal conditions. These results demonstrate that RS could be used as a replacement or complement to conventional chemical analysis to inspect the effect of heat stress on tuber chemical composition.

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.

Abstract Background Fucoidans are sulfated polysaccharides from the cell-wall of brown algae. They have a wide range of applications in medicine, including regenerative medicine, ophthalmology, cancer, and autoimmune disease. Biological activity of fucoidans directly depends on their structure, which remains poorly understood. This is primarily because the polymeric nature of these molecules limits the use of nuclear magnetic resonance and mass spectrometry, classical tools of structural biology for their structural characterization. Raman and Infrared spectroscopies are non-invasive and non-destructive techniques that can be used to probe the structural organization of biological specimens. In this study, we investigate the potential of Raman and Infrared spectroscopy for structural analysis of several fucoidan extracts. Results Our results show that Infrared and Raman provide different but complimentary information about the structure of crude extracts of fucoidans, revealing the presence of minor impurities from co-extractants. We also found that at high extraction temperatures acidic conditions limit formation of melanoidins, while also yielding relatively high sulfate ester fucoidan. However, at high temperatures, water extraction may potentially result in formation of advanced glycation end products. Their presence could be problematic for fucoidan extracts intended for medicinal use, as advanced glycation end products have been linked to endocrine interruption mechanisms in vivo by crosslinking to and permanently altering extracellular matrix proteins. Conclusion Raman and Infrared can be used as complementary tools for rapid screening of crude fucoidan extracts, which can be a valuable tool for assessing impurities that remain after extraction.

Lyme disease (LD), the leading tick-borne disease in the Northern hemisphere, is caused by spirochetes of several genospecies of the Borreliella burgdorferi sensu lato complex. LD is a multi-systemic and highly debilitating illness that is notoriously challenging to diagnose. The main drawbacks of the two-tiered serology, the only approved diagnostic test in the United States, include poor sensitivity, background seropositivity, and cross-reactivity. Recently, Raman spectroscopy (RS) was examined for its LD diagnostic utility by our earlier proof-of-concept study. The previous investigation analyzed the blood from mice that were infected with 297 and B31 strains of Borreliella burgdorferi sensu stricto (s.s.). The selected strains represented two out of the three major clades of B. burgdorferi s.s. isolates found in the United States. The obtained results were encouraging and prompted us to further investigate the RS diagnostic capacity for LD in this study. The present investigation has analyzed blood of mice infected with European genospecies, Borreliella afzelii or Borreliella garinii, or B. burgdorferi N40, a strain of the third major class of B. burgdorferi s.s. in the United States. Moreover, 90 human serum samples that originated from LD-confirmed, LD-negative, and LD-probable human patients were also analyzed by RS. The overall results demonstrated that blood samples from Borreliella-infected mice were identified with 96% accuracy, 94% sensitivity, and 100% specificity. Furthermore, human blood samples were analyzed with 88% accuracy, 85% sensitivity, and 90% specificity. Together, the current data indicate that RS should be further explored as a potential diagnostic test for LD patients.

Wheat is one of the oldest and most widely cultivated staple food crops worldwide. Wheat encounters an array of biotic and abiotic stresses during its growth that significantly impact the crop yield and consequently global food security. Molecular and imaging methods that can be used to detect such stresses are laborious and have numerous limitations. This catalyzes the search for alternative techniques that can be used to monitor plant health. Raman spectroscopy (RS) is a modern analytical technique that is capable of probing structure and composition of samples non-invasively and non-destructively. In this study, we investigate the accuracy of RS in confirmatory diagnostics of biotic and abiotic stresses in wheat. Specifically, we modelled nitrogen deficiency (ND) and drought, key abiotic stresses, and Russian wheat aphid (Diuraphis noxia) infestation and viral diseases: wheat streak mosaic virus (WSMV) and Triticum mosaic virus (TriMV), economically significant biotic stresses in common bread wheat. Raman spectra as well as high pressure liquid chromatography (HPLC)-based analyses revealed drastically distinct changes in the intensity of carotenoid vibration (1185 cm-1) and in the concentration of lutein, chlorophyll, and pheophytin biomolecules of wheat, triggered in response to aforementioned biotic and abiotic stresses. The biochemical changes were reflected in unique vibrational signatures in the corresponding Raman spectra, which, in turn could be used for ~100% accurate identification of biotic and abiotic stresses in wheat. These results demonstrate that a hand-held Raman spectrometer could provide an efficient, scalable, and accurate diagnosis of both biotic as well as abiotic stresses in the field.

Abstract 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.

Fusarium oxysporum f. sp. cubense (FOC) causes Fusarium wilt, one of the most concerning diseases in banana (Musa spp.), compromising global banana production. There are limited curative management options after FOC infections, and early Fusarium wilt symptoms are similar with other abiotic stress factors such as drought. Therefore, finding a reliable and timely form of early detection and proper diagnostics is critical for disease management for FOC. In this study, Portable Raman spectroscopy (handheld Raman spectrometer equipped with 830 nm laser source) was applied for developing a confirmatory diagnostic tool for early infection of FOC on asymptomatic banana. Banana plantlets were inoculated with FOC; uninoculated plants exposed to a drier condition were also prepared compared to well-watered uninoculated control plants. Subsequent Raman readings from the plant leaves, without damaging or destroying them, were performed weekly. The conditions of biotic and abiotic stresses on banana were modeled to examine and identify specific Raman spectra suitable for diagnosing FOC infection. Our results showed that Raman spectroscopy could be used to make highly accurate diagnostics of FOC at the asymptomatic stage. Based on specific Raman spectra at vibrational bands 1,155, 1,184, and 1,525 cm−1, Raman spectroscopy demonstrated nearly 100% accuracy of FOC diagnosis at 40 days after inoculation, differentiating FOC-infected plants from uninoculated plants that were well-watered or exposed to water deficit condition. This study first reported that Raman spectroscopy can be used as a rapid and non-destructive tool for banana Fusarium wilt diagnostics.

A non-invasive and non-destructive technique, Raman spectroscopy, was explored to distinguish different maturity stages (20, 30, 40, and 50 days after anthesis) of watermelon (Citrullus lanatus) fruits from four cultivars: Fascination, Orange Crisp, Amarillo and Crimson Sweet. Spectral acquisition from the fruit surface was carried out at the wavelength range of 400–2,000 cm−1 using a handheld Raman spectrometer equipped with 830 nm laser excitation source. The spectra were normalized at 1,438 cm−1 which was assigned to CH2 and CH3 vibration. Detecting changes in the spectral features of carotenoids on the surface of watermelon fruits can be used as a marker to monitor the maturity of the fruit. The spectral analysis confirmed the presence of two major carotenoids, lutein and β-carotene, and their intensity decreased upon maturity on the fruit surface. Identification of these pigments was further confirmed by resonance Raman spectra and high-performance liquid chromatography analysis. Results of partial least square discriminant analysis of pre-processed spectra have demonstrated that the method can successfully predict the maturity of watermelon samples with more than 85% accuracy. Analysis of Variance of individual Raman bands has revealed a significant difference among the stages as the level of carotenoids was declined during the ripening of the fruits. Thus, Raman spectral signatures can be used as a versatile tool for the non-invasive determination of carotenoid changes on the watermelon fruits’ surface during ripening, thereby enabling effective monitoring of nutritional quality and maturity indices before harvesting the watermelon.

Metal toxicities can be detrimental to a plant health, as well as to the health of animals and humans that consume such plants. Metal content of plants can be analyzed using colorimetric, atomic absorption- or mass spectroscopy-based methods. However, these techniques are destructive, costly and laborious. In the current study, we investigate the potential of Raman spectroscopy (RS), a modern spectroscopic technique, for detection and identification of metal toxicities in rice. We modeled medium and high levels of iron and aluminum toxicities in hydroponically grown plants. Spectroscopic analyses of their leaves showed that both iron and aluminum toxicities can be detected and identified with ∼100% accuracy as early as day 2 after the stress initiation. We also showed that diagnostics accuracy was very high not only on early, but also on middle (day 4–day 8) and late (day 10–day 14) stages of the stress development. Importantly this approach only requires an acquisition time of 1 s; it is non-invasive and non-destructive to plants. Our findings suggest that if implemented in farming, RS can enable pre-symptomatic detection and identification of metallic toxins that would lead to faster recovery of crops and prevent further damage.

A growing body of evidence suggests that Raman spectroscopy (RS) can be used for diagnostics of plant biotic and abiotic stresses. RS can be also utilized for identification of plant species and their varieties, as well as assessment of the nutritional content and commercial values of seeds. The power of RS in such cases to a large extent depends on chemometric analyses of spectra. In this work, we critically discuss three major approaches that can be used for advanced analyses of spectroscopic data: summary statistics, statistical testing and chemometric classification. On the example of Raman spectra collected from roses, we demonstrate the outcomes and the potential of all three types of spectral analyses. We anticipate that our findings will help to design the most optimal spectral processing and preprocessing that is required to achieved the desired results. We also expect that reported collection of results will be useful to all researchers who work on spectroscopic analyses of plant specimens.

Identification of peanut cultivars for distinct phenotypic or genotypic traits whether using visual characterization or laboratory analysis requires substantial expertise, time, and resources. A less subjective and more precise method is needed for identification of peanut germplasm throughout the value chain. In this proof-of-principle study, the accuracy of Raman spectroscopy (RS), a non-invasive, non-destructive technique, in peanut phenotyping and identification is explored. We show that RS can be used for highly accurate peanut phenotyping via surface scans of peanut leaves and the resulting chemometric analysis: On average 94% accuracy in identification of peanut cultivars and breeding lines was achieved. Our results also suggest that RS can be used for highly accurate determination of nematode resistance and susceptibility of those breeding lines and cultivars. Specifically, nematode-resistant peanut cultivars can be identified with 92% accuracy, whereas susceptible breeding lines were identified with 81% accuracy. Finally, RS revealed substantial differences in biochemical composition between resistant and susceptible peanut cultivars. We found that resistant cultivars exhibit substantially higher carotenoid content compared to the susceptible breeding lines. The results of this study show that RS can be used for quick, accurate, and non-invasive identification of genotype, nematode resistance, and nutrient content. Armed with this knowledge, the peanut industry can utilize Raman spectroscopy for expedited breeding to increase yields, nutrition, and maintaining purity levels of cultivars following release.

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.

Abstract Water deficit and salinity are two major abiotic stresses that have tremendous effect on crop yield worldwide. Timely identification of these stresses can help limit associated yield loss. Confirmatory detection and identification of water deficit stress can also enable proper irrigation management. Traditionally, unmanned aerial vehicle (UAV)‐based imaging and satellite‐based imaging, together with visual field observation, are used for diagnostics of such stresses. However, these approaches can only detect salinity and water deficit stress at the symptomatic stage. Raman spectroscopy (RS) is a noninvasive and nondestructive technique that can identify and detect plant biotic and abiotic stress. In this study, we investigated accuracy of Raman‐based diagnostics of water deficit and salinity stresses on two greenhouse‐grown peanut accessions: tolerant and susceptible to water deficit. Plants were grown for 76 days prior to application of the water deficit and salinity stresses. Water deficit treatments received no irrigation for 5 days, and salinity treatments received 1.0 L of 240‐mM salt water per day for the duration of 5‐day sampling. Every day after the stress was imposed, plant leaves were collected and immediately analyzed by a hand‐held Raman spectrometer. RS and chemometrics could identify control and stressed (either water deficit or salinity) susceptible plants with 95% and 80% accuracy just 1 day after treatment. Water deficit and salinity stressed plants could be differentiated from each other with 87% and 86% accuracy, respectively. In the tolerant accessions at the same timepoint, the identification accuracies were 66%, 65%, 67%, and 69% for control, combined stresses, water deficit, and salinity stresses, respectively. The high selectivity and specificity for presymptomatic identification of abiotic stresses in the susceptible line provide evidence for the potential of Raman‐based surveillance in commercial‐scale agriculture and digital farming.

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.

The non-judicious use of herbicides has led to a widespread evolution of herbicide resistance in various weed species including Palmer amaranth, one of the most aggressive and troublesome weeds in the United States. Early detection of herbicide resistance in weed populations may help growers devise alternative management strategies before resistance spreads throughout the field. In this study, Raman spectroscopy was utilized as a rapid, non-destructive diagnostic tool to distinguish between three different glyphosate-resistant and four -susceptible Palmer amaranth populations. The glyphosate-resistant populations used in this study were 11-, 32-, and 36-fold more resistant compared to the susceptible standard. The 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) gene copy number for these resistant populations ranged from 86 to 116. We found that Raman spectroscopy could be used to differentiate herbicide-treated and non-treated susceptible populations based on changes in the intensity of vibrational bands at 1156, 1186, and 1525 cm–1 that originate from carotenoids. The partial least squares discriminant analysis (PLS-DA) model indicated that within 1 day of glyphosate treatment (D1), the average accuracy of detecting herbicide-treated and non-treated susceptible populations was 90 and 73.3%, respectively. We also found that glyphosate-resistant and -susceptible populations of Palmer amaranth can be easily detected with an accuracy of 84.7 and 71.9%, respectively, as early as D1. There were relative differences in the concentration of carotenoids in plants with different resistance levels, but these changes were not significant. The results of the study illustrate the utility of Raman spectra for evaluation of herbicide resistance and stress response in plants under field conditions.

Cannabis (Cannabis sativa L.) is a dioecious plant that produces both male and female inflorescences. In nature, male and female plants can be found with nearly equal frequency, which determines species out-crossing. In cannabis farming, only female plants are preferred due to their high yield of cannabinoids. In addition to unfavorable male plants, commercial production of cannabis faces the appearance of hermaphroditic inflorescences, species displaying both pistillate flowers and anthers. Such plants can out-cross female plants, simultaneously producing undesired seeds. The problem of hermaphroditic cannabis triggered a search for analytical tools that can be used for their rapid detection and identification. In this study, we investigate the potential of Raman spectroscopy (RS), an emerging sensing technique that can be used to probe plant biochemistry. Our results show that the biochemistry of male, female and hermaphroditic cannabis plants is drastically different which allows for their confirmatory identification using a hand-held Raman spectrometer. Furthermore, the coupling of machine learning approaches enables the identification of hermaphrodites with 98.7% accuracy, whereas both male and female plants can be identified with 100% accuracy. Considering the label-free, non-invasive and non-destructive nature of RS, the developed optical sensing approach can transform cannabis farming in the U.S. and overseas.

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.

Proper management of nutrients in agricultural systems is critically important for maximizing crop yields while simultaneously minimizing the health and environmental impacts of pollution from fertilizers. These goals can be achieved by timely confirmatory diagnostics of nutrient deficiencies in plants, which enable precise administration of fertilizers and other supplementation in fields. Traditionally, nutrient diagnostics are performed by wet-laboratory analyses, which are both time- and labor-consuming. Unmanned aerial vehicle (UAV) and satellite imaging have offered a non-invasive alternative. However, these imaging approaches do not have sufficient specificity, and they are only capable of detecting symptomatic stages of nutrient deficiencies. Raman spectroscopy (RS) is a non-invasive and non-destructive technique that can be used for confirmatory detection and identification of both biotic and abiotic stresses on plants. Herein, we show the use of a hand-held Raman spectrometer for highly accurate pre-symptomatic diagnostics of nitrogen, phosphorus, and potassium deficiencies in rice (Oryza sativa). Moreover, we demonstrate that RS can also be used for pre symptomatic diagnostics of medium and high salinity stresses. A Raman-based analysis is fast (1 s required for spectral acquisition), portable (measurements can be taken directly in the field), and label-free (no chemicals are needed). These advantages will allow RS to transform agricultural practices, enabling precision agriculture in the near future.

Plant diseases can reduce crop yield by up to 100%. Therefore, timely and confirmatory diagnosis of plant diseases is strongly desired. Typical pathogen assaying methods include polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA). These approaches are quite useful but are also time-consuming and destructive to the sample. Raman spectroscopy (RS) is a modern analytical technique that enables non-invasive plant disease detection. In this study, we report on Raman-based detection of wheat diseases caused by wheat streak mosaic virus (WSMV) and barley yellow dwarf virus (BYDV). Our results show that RS can be used to differentiate between healthy wheat and wheat infected by these two viruses. We also show that RS can be used to identify whether wheat is infected by these individual viruses or by a combination of WSMV and BYDV, as well as WSMV, BYDV, and Triticum mosaic virus (TriMV). We found that wheat spectra showed non-linear spectroscopic responses to coinfection by different viruses. These results suggest that RS can be used to probe pathogen-specific changes in plant metabolism. The portable nature of this approach opens the possibility of RS directly in the field for confirmatory diagnostics of viral diseases.

Digital farming is a modern agricultural concept that aims to maximize the crop yield while simultaneously minimizing the environmental impact of farming. Successful implementation of digital farming requires development of sensors to detect and identify diseases and abiotic stresses in plants, as well as to probe the nutrient content of seeds and identify plant varieties. Experimental evidence of the suitability of Raman spectroscopy (RS) for confirmatory diagnostics of plant diseases was previously provided by our team and other research groups. In this study, we investigate the potential use of RS as a label-free, non-invasive and non-destructive analytical technique for the fast and accurate identification of nutrient components in the grains from 15 different rice genotypes. We demonstrate that spectroscopic analysis of intact rice seeds provides the accurate rice variety identification in ~86% of samples. These results suggest that RS can be used for fully automated, fast and accurate identification of seeds nutrient components.

Amyloid fibrils are β-sheet-rich protein aggregates commonly found in the organs and tissues of patients with various amyloid-associated diseases. Understanding the structural organization of amyloid fibrils can be beneficial for the search of drugs to successfully treat diseases associated with protein misfolding. The structure of insulin fibrils was characterized by deep ultraviolet resonance Raman (DUVRR) and Nuclear Magnetic Resonance (NMR) spectroscopy combined with hydrogen-deuterium exchange. The compositions of the fibril core and unordered parts were determined at single amino acid residue resolution. All three disulfide bonds of native insulin remained intact during the aggregation process, withstanding scrambling. Three out of four tyrosine residues were packed into the fibril core, and another aromatic amino acid, phenylalanine, was located in the unordered parts of insulin fibrils. In addition, using all-atom MD simulations, the disulfide bonds were confirmed to remain intact in the insulin dimer, which mimics the fibrillar form of insulin.