Reviewing the Impact of Raman Spectroscopy on Crop Quality Assessment: An Interview with Miri Park

Raman spectroscopy is a nondestructive analytical technique that is commonly used in crop analysis because of its ability to measure molecular structures efficiently. By utilizing laser light to probe molecular vibrations within plant tissues, Raman spectroscopy provides insights into the chemical composition of crops, including information about pigments, nutrients, and structural components like cellulose and lignin. This technique allows researchers and agricultural experts to swiftly analyze the quality, maturity, and disease status of crops, aiding in early detection of stressors such as nutrient deficiencies or pathogen attacks. But where is all this heading?

Miri Park of the Fraunhofer Institute for Environmental, Safety, and Energy Technologies is examining how Raman spectroscopy could aid non-destructive sensing in agricultural science. Recently, Park sat down with Spectroscopy to discuss micro-Raman spectroscopy's role in assessing crop quality, particularly secondary metabolites, across different contexts (in vitro, in vivo, and in situ), while suggesting future research for broader application possibilities.

Miri Park of the Fraunhofer Institute for Environmental, Safety, and Energy Technologies | Photo Credit: © Fraunhofer UMSICHT/Felix Homann

What specific crop quality metabolites are the most important for developing fast and non-destructive analytical techniques, such as Raman spectroscopy? The review emphasizes the assessment of secondary metabolites in crop quality assessment (1). How were these specific metabolites selected, and how does micro-Raman spectroscopy facilitate their analysis?

Plant metabolites are broadly classified into primary metabolites, such as carbohydrates, lipids, and proteins, which serve as essential energy sources for humans, and secondary metabolites, which include carotenoids and polyphenols (both of which are known for their antioxidant properties), generating significant interest in healthcare applications. Both primary and secondary metabolites are pivotal considerations in crop cultivation, influencing nutritional quality, plant health, and potential benefits for human well-being. Both metabolites can be measured using fast and non-destructive analytical techniques such as Raman spectroscopy. Therefore, the choice of metabolites for observation may vary depending on the specific goals and purposes of the analysis.

Our review placed particular emphasis on secondary metabolites because of their designation as stress factors. Generally, secondary metabolites exhibit greater sensitivity to both biotic and abiotic environmental stressors compared to primary metabolites. In other words, primary metabolites are crucial for crop growth, yet they exhibit more stability and do not respond immediately to environmental changes. In contrast, secondary metabolites are frequently synthesized by plants in reaction to various environmental stressors, encompassing biotic challenges like pathogen attacks, as well as abiotic stressors such as drought, UV radiation, or nutrient deficiencies. The production of secondary metabolites can be induced rapidly in response to stress as part of the plant's adaptive strategy. Raman spectroscopy involves directing a laser onto the surface of a crop and capturing the re-emitted signal, revealing the molecular structure of the surface materials. This allows for the immediate identification of the content of specific components without the need for pre-treatment. We believe that the merits of non-destructive analytical methods can be optimized when continuously monitoring crop health in real-time within agricultural systems. Consequently, our focus in the study centers on secondary metabolites, as they are particularly responsive indicators of the plant's stress and health status.

What are the primary advantages of utilizing Raman spectroscopy as a crop quality sensor compared to other methods, for example near-infrared spectroscopy?

In addition to Raman spectroscopy, several technologies, including RGB color analysis, Fourier transform infrared spectroscopy (FT-IR), Ultraviolet–visible (UV–vis) spectrometry, and near infrared (NIR) spectroscopy, have been embraced as promising methods for evaluating crop quality. Although each technology possesses various unique advantages in contributing to the assessment of crop quality, all these approaches are commonly favored for their time efficiency and potential for development as on-site remote sensing instruments. In my perspective, Raman spectroscopy stands out as a fascinating tool for crop sensing because of two distinctive strengths. First, its molecular specificity provides detailed information for identifying and quantifying individual components within crops. Additionally, Raman spectroscopy exhibits water content insensitivity, a notable advantage compared to NIR spectroscopy. Unlike the latter, which is sensitive to water content and requires intricate calibration for accuracy, Raman spectroscopy proves particularly impressive when dealing with samples containing a high proportion of water, such as plants. This characteristic ensures more reliable and accurate results, even in samples with varying moisture levels.

Could you elaborate on the factors that affect the sensitivity of Raman spectra to target metabolites in plant samples? How do these factors influence the accuracy of the analysis?

In the application of Raman spectroscopy for measuring target metabolites, the accuracy of the measurements is subject to various factors that depend on the specific environmental conditions. In this context, I would recommend focusing attention on two fundamental factors to ensure a more precise measurement.

First of all, a thorough comprehension of the Raman spectra related to the chosen components for analysis is important. This is because each component produces peaks within corresponding Raman shift ranges. For instance, when assessing the carotenoid content in coleus lime, it is advisable to carefully examine Raman signals within the 1000–1200 cm⁻¹ range of Raman shift, which demonstrates a relative expression of carotenoids. In contrast, focusing on the 550–750 cm⁻¹ range of Raman shift may introduce signals associated with flavonoid molecules, potentially leading to a less accurate assessment (2).

Given the diverse chemical compositions and concentration variations among plants, understanding the specific concentration of each constituent in the measured plant sample could be another significant key to improving analytical precision. Raman spectroscopy presents a spectrum that encompasses all chemical bonds in a straightforward manner. Without knowledge of these compositions, determining the origin of certain Raman peaks related to a target component becomes challenging, leading to decreased accuracy.Moreover, when a plant contains a target substance in excessively low concentrations, the Raman scattering signal may exhibit weak intensity, establishing an observation limit that renders measurements either impossible or inaccurate. In such instances, it is advisable to contemplate the adoption of an appropriate measurement method (for example, surface-enhanced Raman spectroscopy [SERS]) capable of mitigating the limit of detection, thereby enhancing the accuracy of the analysis.

Taking into account these considerations, the development of individualized Raman profiles for each plant, which can be shared among analysts, could be considered a viable strategy for ensuring greater accuracy in future Raman analyses.

The study mentions various techniques like surface-enhanced Raman spectroscopy (SERS) and time-gated Raman spectroscopy (TGR) (1). Could you explain how these advanced techniques enhance the sensitivity and clarity of the crop spectra?

SERS involves localizing molecules on rough nanoscale metal surfaces, typically gold or silver nanoparticles, resulting in signal amplification primarily attributed to electromagnetic mechanisms. This technique, focusing on amplifying Raman signals with up to 10 times higher sensitivity than non-SERS methods (3), minimizes self-fluorescence in biological samples and enables analysis even at very low concentrations. It finds applications in various research areas, including chemistry, nanotechnology, biology, biomedicine, and food science. In agriculture, SERS detects toxic residues like pesticides on crop surfaces and expands to directly detect living plants by injecting nanoparticles into plant cells, enhancing selectivity for specific biotargets. This technique is promising, but challenges persist, necessitating ongoing research for a complex understanding of nanoscale biotargets and nanoprobes.

TGR is an advanced technique that enhances the clarity of crop spectra by exploiting differences in lifetimes between Raman scattering and fluorescence. Utilizing time-gated laser pulses to suppress fluorescence, TGR significantly improves signal-to-noise ratios, thereby enhancing signal quality and spectrum baseline accuracy. A notable advantage of TGR is its efficacy with shorter-wavelength lasers, specifically 532 nm, effectively addressing issues related to low peak intensity associated with longer-wavelength lasers. Despite its advantages, TGR's commercial application in plant and agricultural sciences is limited, due to insufficient evaluation of its analytical effectiveness. Some studies indicate an increase in TGR's utility for sample quantification, especially when combined with SERS for more comprehensive qualitative and quantitative analyses (4). Further research is necessary to fully assess the potential of TGR spectroscopy in the quality assessment of biological samples.

In discussing the challenges related to fluorescence effects in plant samples, the study (1) suggests using lasers with low energy, like 785 nm or near-infrared. How does the choice of laser wavelength affect the outcome of Raman spectra in analyzing plant materials?

Raman lasers come in different wavelengths, with commonly used options including visible lasers like 532 nm and near-infrared lasers such as 785 nm or 1064 nm, each offering specific advantages based on the sample and analysis requirements. The presence of inherent fluorophores, especially in biological samples—comprising aromatic amino acids, chlorophyll, flavonoids, and other organic compounds—results in fluorescence generation. This phenomenon has long posed a significant obstacle to detecting metabolites through Raman spectroscopy, particularly when a high-energy source, such as ultraviolet or blue light, is employed because the emitted fluorescence interferes with the Raman signals, making it troublesome to discern the specific spectral features associated with metabolites. Apart from the fluorescence issue, there is also the concern that using a high-energy laser may lead to the damaging (heating or burning) of the measured part of the plant sample. The use of lower-energy lasers, such as those in 785 nm or NIR range, minimizes the risk of inducing fluorescence, leading to more reliable Raman spectra. In addition, NIR lasers can penetrate deeper into plant tissues. This is advantageous for analyzing internal structures and obtaining representative spectra from various layers within the sample. It allows for a more comprehensive assessment of the plant's chemical composition. However, lower-energy lasers do not only have advantages. Since the Raman scattering efficiency is proportional to 1/λex, lower energy induces lower intensity of the Raman signal, making it challenging to detect low concentrations of metabolites. As a result, it is crucial for users not only to choose the best lasers, but also to combine advanced techniques with high sensitivity and minimal fluorescence interference.

The study mentions that interpretations of plant quality can be influenced by measurement conditions and spectral analysis methods (1). Could you explain how the selected spectral analysis method affects these interpretations and how they impact the overall assessment of crop quality?

One of the trickiest aspects of interpreting Raman technology lies in the fact that Raman spectra depict the total sum of molecular structures constituting various substances within a sample. This implies that if different metabolites A and B share the same molecular bond within a molecule, the interpretation of whether the Raman signal originated from A or B may vary depending on the chosen interpretation method. This variability can occur at various stages, ranging from how the raw spectrum is processed (for example, peak identification or baseline correction) to how the interpreter analyzes the data (for example, principal component analysis [PCA], partial least squares regression [PLSR], or numerous statistical modeling approaches). The results of the interpretation, whether in terms of the content of specific substances or differences in composition of metabolites, can exhibit deviations. Ongoing research will systematically advance the development of more precise and universally applicable interpretation methods.

Regarding future research mentioned in the review paper, what key advancements or universal applications are anticipated for micro-Raman spectroscopy in crop quality assessment, particularly in considering in vitro, in vivo, and in situ analysis?

Micro-Raman spectroscopy holds significant promise in advancing crop quality assessment across various scenarios, including in vitro, in vivo, and in situ analyses, and even all stages from cultivating to post-harvest. The limitation associated with traditional bulky Raman systems to apply in the agricultural field have been addressed with the development of portable Raman spectroscopy. This advancement allows for cost-effective and on-site assessments, offering farmers (or even consumers) a practical tool for direct field evaluation of crop quality. The adaptation of this portable Raman system allows for non-destructive in vivo analysis by providing detailed chemical profiles of crops, aiding in the identification of key components such as carbohydrates, lipids, proteins, and secondary metabolites. Additionally, its capacity for early stress detection, whether abiotic or biotic, enables real-time monitoring of plant health, facilitating timely interventions to optimize crop yield. In terms of contamination, Raman spectroscopy proves valuable in distinguishing naturally occurring compounds from those introduced through fertilizers or artificial pesticides, ensuring the safety and quality of food products. Raman technology also enables instant verification of crop freshness in post-harvest storage management or distribution processes.

Do you recommend applying any specialized chemometric or machine learning techniques for this analysis?

As discussed in the previous question, it remains challenging for me to recommend specific chemometric techniques for Raman spectrum analysis. However, researchers commonly explore the application of tools like principal component analysis or partial least squares regression to interpret Raman data. It is worth noting that various methods are available for spectrum analysis, developed to analyze fluorescence absorption, electronic spectrum, sound spectrum, and more. Most of these methods can be universally adapted for analyzing diverse spectra. Consequently, analysts may be able to discover valuable insights by extending the application of spectrum analysis methods previously employed in various fields to Raman data, moving beyond the conventional framework of Raman spectrum analysis.

References

(1) Park, M.; Somborn, A.; Schlehuber, D.; Keuter, V.; Deerberg, G. Raman Spectroscopy in Crop Quality Assessment: Focusing on Sensing Secondary Metabolites: A Review. Hortic. Res. 2023, 10 (5), uhad074. DOI: 10.1093/hr/uhad074

(2) Altangerel, N.; Ariunbold, G. O.; Gorman, C.; Alkahtani, M. H.; Borrego, E. J.; Bohlmeyer, D.; Hemmer, P.; Kolomiets, M. V.; Yuan, J. S.; Scully, M. O. In vivo Diagnostics of Early Abiotic Plant Stress Response via Raman Spectroscopy. Proc Natl Acad Sci. 2017, 114, 3393–6. DOI: 10.1073/pnas.1701328114

(3) Numata, Y.; Tanaka, H. Quantitative Analysis of Quercetin Using Raman Spectroscopy. Food Chem. 2011, 126, 751–755. DOI: 10.1016/j.foodchem.2010.11.059

(4) Kögler, M.; Paul, A.; Anane, E.; Birkholz, M.; Bunker, A.; Viitala, T.; Maiwald, M.; Junne, S.; Neubauer, P. Comparison of Time-gated Surface-enhanced Raman Spectroscopy (TG-SERS) and Classical SERS-based Monitoring of Escherichia coli Cultivation Samples. Biotechnol. Progress. 2018, 34, 1533–1542. DOI: 10.1002/btpr.2665