Principles and Applications of Vibrational Spectroscopic Imaging in Plant Science: A Review

High-sensitivity miniaturized spectrometers using photonic crystal slab filters

Miniaturized spectrometers have emerged as pivotal tools in numerous scientific and industrial applications, offering advantages such as portability, cost-effectiveness, and the capability for onsite analysis. Despite these significant benefits, miniaturized spectrometers face critical challenges, particularly in sensitivity. Reduced dimensions often lead to compromises in optical path length and component quality, which can diminish detection limits and limit their applications in areas such as low-light-level measurements. Here we developed a compact spectrometer that integrates an array of photonic crystal slab filters with band-stop spectral transmission characteristics into an image sensor. Compared to traditional gratings or bandpass filter strategies, where each detector can only read light of a single wavelength component, our band-stop strategy allows each detector to read the light of all wavelengths except the band-stop wavelength. This maximizes energy extraction from incident signals, significantly improving the sensitivity of the spectrometer. Spectral reconstruction is achieved mathematically using pre-calibrated band-stop responses combined with a single coded image. Our spectrometer delivers a spectral resolution of 1.9 nm and demonstrates sensitivity more than ten times greater than that of conventional grating spectrometers during fluorescence spectroscopy of Ascaris lumbricoides. The design is fully compatible with complementary metal-oxide-semiconductor (CMOS) technology, allowing for mass production at low costs and thus promising broad deployment in sensitive applications.

Raman spectroscopy - a visit to the literature on plant, food, and agricultural studies

Raman spectroscopy, a fast, non-invasive, and label-free optical technique, has significantly advanced plant and food studies and precision agriculture by providing detailed molecular insights into biological tissues. Utilizing the Raman scattering effect generates unique spectral fingerprints that comprehensively analyze tissue composition, concentration, and molecular structure. These fingerprints are obtained without chemical additives or extensive sample preparation, making Raman spectroscopy particularly suitable for in-field applications. Technological enhancements such as surface-enhanced Raman scattering, Fourier-transform-Raman spectroscopy, and chemometrics have increased Raman spectroscopy sensitivity and precision. These and other advancements enable real-time monitoring of compound translocation within plants and improve the detection of chemical and biological contaminants, essential for food safety and crop optimization. Integrating Raman spectroscopy into agronomic practices is transformative and marks a shift toward more sustainable farming activities. It assesses crop quality - as well as the quality of the food that originated from crop production - early plant stress detection and supports targeted breeding programs. Advanced data processing techniques and machine learning integration efficiently handle complex spectral data, providing a dynamic and detailed view of food conditions and plant health under varying environmental and biological stresses. As global agriculture faces the dual challenges of increasing productivity and sustainability, Raman spectroscopy stands out as an indispensable tool, enhancing farming practices' precision, food safety, and environmental compatibility. This review is intended to select and briefly comment on outstanding literature to give researchers, students, and consultants a reference for works of literature in Raman spectroscopy mainly focused on plant, food, and agronomic sciences. © 2024 Society of Chemical Industry.

Reflectance spectroscopy as a promising tool for 'sensing' metals in hyperaccumulator plants

MAIN CONCLUSION

The VNIR reflectance spectra of nickel hyperaccumulator plant leaves have spectral variations due to high nickel concentrations and this property could potentially be used for discovery of these plants.  Hyperaccumulator plants accumulate high concentrations of certain metals, including manganese, cobalt, or nickel. Of these metals, the divalent ions of nickel have three absorption bands in the visible to near-infrared region which may cause variations in the spectral reflectance of nickel hyperaccumulator plant leaves, but this has not been investigated previously. In this shortproof-of-concept study, the spectral reflectance of eight different nickel hyperaccumulator plant species leaves were subjected to visible and near-infrared and shortwave infrared (VNIR-SWIR) reflectance spectrum measurements in dehydrated state, and for one species, it was also assessed in hydrated state. Nickel concentrations in the plant leaves were determined with other methods and then correlated to the spectral reflectance data. Spectral variations centred at 1000 ± 150 nm were observed and had R-values varying from 0.46 to 0.96 with nickel concentrations. The extremely high nickel concentrations in nickel hyperaccumulator leaves reshape their spectral reflectance features, and the electronic transition of nickel-ions directly contributes to absorption at ~ 1000 nm. Given that spectral variations are correlated with nickel concentrations it make VNIR-SWIR reflectance spectrometry a potential promising technique for discovery of hyperaccumulator plants, not only in the laboratory or herbarium, but also in the field using drone-based platforms. This is a preliminary study which we hope will instigate further detailed research on this topic to validate the findings and to explore possible applications.

Seeing plants as never before

Imaging has long supported our ability to understand the inner life of plants, their development, and response to a dynamic environment. While optical microscopy remains the core tool for imaging, a suite of novel technologies is now beginning to make a significant contribution to visualize plant metabolism. The purpose of this review was to provide the scientific community with an overview of current imaging methods, which rely variously on either nuclear magnetic resonance (NMR), mass spectrometry (MS) or infrared (IR) spectroscopy, and to present some examples of their application in order to illustrate their utility. In addition to providing a description of the basic principles underlying these technologies, the review discusses their various advantages and limitations, reveals the current state of the art, and suggests their potential application to experimental practice. Finally, a view is presented as to how the technologies will likely develop, how these developments may encourage the formulation of novel experimental strategies, and how the enormous potential of these technologies can contribute to progress in plant science.

Scytonemin redox status in a filamentous cyanobacterium visualized by an excitation-laser-line-scanning spontaneous Raman scattering spectral microscope

Some cyanobacteria produce a UVA-absorbing pigment, scytonemin, at extracellular sheaths. Although scytonemin-containing dark sheaths are recognizable through optical microscopes and its redox changes have been known for decades, there has been no report to obtain images of both oxidized and reduced scytonemins at subcellular resolution. Here, we show that a spontaneous Raman scattering spectral microscopy based on an excitation-laser-line-scanning method unveil 3D subcellular distributions of both the oxidized and reduced scytonemins in a filamentous cyanobacterium. The redox changes of scytonemin were supported by comparison in the Raman spectra between the cyanobacterial cells, solid-state scytonemin and quantum chemical normal mode analysis. Distributions of carotenoids, phycobilins, and the two redox forms of scytonemin were simultaneously visualized with an excitation wavelength at 1064 nm that is virtually free from the optical screening by the dark sheaths. The redox differentiation of scytonemin will advance our understanding of the redox homeostasis and secretion mechanisms of individual cyanobacteria as well as microscopic chemical environments in various microbial communities. The line-scanning Raman microscopy based on the 1064 nm excitation is thus a promising tool for exploring hitherto unreported Raman spectral features and distribution of nonfluorescent molecules embedded below nontransparent layers for visible light, while avoiding interference by autofluorescence.

Accurate quantification, naked eyes detection and bioimaging of nitrite using a colorimetric and near-infrared fluorescent probe in food samples and Escherichia coli

Nitrite (NO₂^-) is an inorganic contaminant that exists widely in the environment including water and food products, excessive amounts of NO₂^- would threaten humans and aquatic life. Developing a rapid and convenient sensing method for NO₂^- remains a great challenge. Herein, a colorimetric and near-infrared fluorescent probe (TBM) was synthesized and applied for sensitively and selectively detecting NO₂^- in water, food samples and Escherichia coli (E. coli). With the addition of NO₂^-, the probe TBM solution has a distinct visual color changed from red to colorless and fluorescence intensity at 620 nm quickly decreased. The probe TBM could detect NO₂^- quantitatively with a detection limit of 85 nM based on a 3σ/slope. Under optimum conditions, TBM has been successfully used to detect NO₂^- in real-world environmental and dietary samples, with positive results. Besides, paper strips loaded with TBM have been used to visually determine NO₂^- levels. Most importantly, TBM has also been proven to be able to discriminate from different concentrations of NO₂^- in E. coli by fluorescence imaging. In summary, the probe TBM was successfully developed for the accurate quantification, naked eyes detection and bioimaging of NO₂^- in water, food samples and E. coli, which provides a useful tool to better guarantee the quality and safety of daily life and food industry.

In silico NIR spectroscopy - A review. Molecular fingerprint, interpretation of calibration models, understanding of matrix effects and instrumental difference

Quantum mechanical calculations are routinely used as a major support in mid-infrared (MIR) and Raman spectroscopy. In contrast, practical limitations for long time formed a barrier to developing a similar synergy between near-infrared (NIR) spectroscopy and computational chemistry. Recent advances in theoretical methods suitable for calculation of NIR spectra opened the pathway to modeling NIR spectra of various molecules. Accurate theoretical reproduction of NIR spectra of molecules reaching the size of long-chain fatty acids was accomplished so far. In silico NIR spectroscopy, where the spectra are calculated ab initio, provides substantial improvement in our understanding of the overtones and combination bands that overlap in staggering numbers and create complex lineshape typical for NIR spectra. This improves the comprehension of the spectral information enabling access to rich and detail molecular footprint, essential for fundamental research and useful in routine analysis by NIR spectroscopy and chemometrics. This review article summarizes the most recent accomplishments in the emerging field with examples of simulated NIR spectra of molecules reaching long-chain fatty acids and polymers. In addition to detailed NIR band assignments and new physical insights, simulated spectra enable innovative support in applications. Understanding of the difference in the performance observed between miniaturized NIR spectrometers and chemical interpretation of the chemometric models are noteworthy here. These new elements integrated into NIR spectroscopy framework enable a knowledge-based design of the analysis with comprehension of the processed chemical information.

Micro(nano)plastics in food system: potential health impacts on human intestinal system

Micro(nano)plastics (MNPs) in human food system have been broadly recognized by researchers and have drawn an increasing public attention to their potential health risks, particularly the risk to the intestinal system regarding the long-term exposure to MNPs through food consumption. This study aims to review the environmental properties (formation and composition) of MNPs and MNPs pollution in human food system following the order of food production, food processing and food consumption. The current analytic and identical technologies utilized by researchers are also summarized in this review. In fact, parts of commonly consumed food raw materials, processed food and the way to take in food all become the possible sources for human MNPs ingestion. In addition, the available literatures investigating MNPs-induced intestinal adverse effect are discussed from in vitro models and in vivo mammalian experiments, respectively. Particle translocation, cytotoxicity, damaged gut barrier, intestinal inflammation as well as microbial alteration are mostly reported. Moreover, the practical remediation strategies for MNPs pollution are also illustrated in the last section. This review is expected to provide a research insight for foodborne MNPs and arouse more public awareness of MNPs pollution in food and potential risk for human intestinal health.

Practical Qualitative Evaluation and Screening of Potential Biomarkers for Different Parts of Wolfiporia cocos Using Machine Learning and Network Pharmacology

Wolfiporia cocos is a widely used traditional Chinese medicine and dietary supplement. Artificial intelligence algorithms use different types of data based on the different strategies to complete multiple tasks such as search and discrimination, which has become a trend to be suitable for solving massive data analysis problems faced in network pharmacology research. In this study, we attempted to screen the potential biomarkers in different parts of W. cocos from the perspective of measurability and effectiveness based on fingerprint, machine learning, and network pharmacology. Based on the conclusions drawn from the results, we noted the following: (1) exploratory analysis results showed that differences between different parts were greater than those between different regions, and the partial least squares discriminant analysis and residual network models were excellent to identify Poria and Poriae cutis based on Fourier transform near-infrared spectroscopy spectra; (2) from the perspective of effectiveness, the results of network pharmacology showed that 11 components such as dehydropachymic acid and 16α-hydroxydehydrotrametenolic acid, and so on had high connectivity in the “component-target-pathway” network and were the main active components. (3) From a measurability perspective, through orthogonal partial least squares discriminant analysis and the variable importance projection > 1, it was confirmed that three components, namely, dehydrotrametenolic acid, poricoic acid A, and pachymic acid, were the main potential biomarkers based on high-performance liquid chromatography. (4) The content of the three components in Poria was significantly higher than that in Poriae cutis. (5) The integrated analysis showed that dehydrotrametenolic acid, poricoic acid A, and pachymic acid were the potential biomarkers for Poria and Poriae cutis. Overall, this approach provided a novel strategy to explore potential biomarkers with an explanation for the clinical application and reasonable development and utilization in Poria and Poriae cutis.

Resolution Limit in Infrared Chemical Imaging

Chemical imaging combines the spatial specificity of optical microscopy with the spectral selectivity of vibrational spectroscopy. Mid-infrared (IR) absorption imaging instruments are now able to capture high-quality spectra with microscopic spatial detail, but the limits of their ability to resolve spatial and spectral objects remain less understood. In particular, the sensitivity of measurements to chemical and spatial changes and rules for optical design have been presented, but the influence of spectral information on spatial sensitivity is as yet relatively unexplored. We report an information theory-based approach to quantify the spatial localization capability of spectral data in chemical imaging. We explicitly consider the joint effects of the signal-to-noise ratio and spectral separation that have significance in experimental settings to derive resolution limits in IR spectroscopic imaging.

Miniaturized NIR Spectroscopy in Food Analysis and Quality Control: Promises, Challenges, and Perspectives

The ongoing miniaturization of spectrometers creates a perfect synergy with the common advantages of near-infrared (NIR) spectroscopy, which together provide particularly significant benefits in the field of food analysis. The combination of portability and direct onsite application with high throughput and a noninvasive way of analysis is a decisive advantage in the food industry, which features a diverse production and supply chain. A miniaturized NIR analytical framework is readily applicable to combat various food safety risks, where compromised quality may result from an accidental or intentional (i.e., food fraud) origin. In this review, the characteristics of miniaturized NIR sensors are discussed in comparison to benchtop laboratory spectrometers regarding their performance, applicability, and optimization of methodology. Miniaturized NIR spectrometers remarkably increase the flexibility of analysis; however, various factors affect the performance of these devices in different analytical scenarios. Currently, it is a focused research direction to perform systematic evaluation studies of the accuracy and reliability of various miniaturized spectrometers that are based on different technologies; e.g., Fourier transform (FT)-NIR, micro-optoelectro-mechanical system (MOEMS)-based Hadamard mask, or linear variable filter (LVF) coupled with an array detector, among others. Progressing technology has been accompanied by innovative data-analysis methods integrated into the package of a micro-NIR analytical framework to improve its accuracy, reliability, and applicability. Advanced calibration methods (e.g., artificial neural networks (ANN) and nonlinear regression) directly improve the performance of miniaturized instruments in challenging analyses, and balance the accuracy of these instruments toward laboratory spectrometers. The quantum-mechanical simulation of NIR spectra reveals the wavenumber regions where the best-correlated spectral information resides and unveils the interactions of the target analyte with the surrounding matrix, ultimately enhancing the information gathered from the NIR spectra. A data-fusion framework offers a combination of spectral information from sensors that operate in different wavelength regions and enables parallelization of spectral pretreatments. This set of methods enables the intelligent design of future NIR analyses using miniaturized instruments, which is critically important for samples with a complex matrix typical of food raw material and shelf products.

Leaf microscopy applications in photosynthesis research: identifying the gaps

Leaf imaging via microscopy has provided critical insights into research on photosynthesis at multiple junctures, from the early understanding of the role of stomata, through elucidating C4 photosynthesis via Kranz anatomy and chloroplast arrangement in single cells, to detailed explorations of diffusion pathways and light utilization gradients within leaves. In recent decades, the original two-dimensional (2D) explorations have begun to be visualized in three-dimensional (3D) space, revising our understanding of structure-function relationships between internal leaf anatomy and photosynthesis. In particular, advancing new technologies and analyses are providing fresh insight into the relationship between leaf cellular components and improving the ability to model net carbon fixation, water use efficiency, and metabolite turnover rate in leaves. While ground-breaking developments in imaging tools and techniques have expanded our knowledge of leaf 3D structure via high-resolution 3D and time-series images, there is a growing need for more in vivo imaging as well as metabolite imaging. However, these advances necessitate further improvement in microscopy sciences to overcome the unique challenges a green leaf poses. In this review, we discuss the available tools, techniques, challenges, and gaps for efficient in vivo leaf 3D imaging, as well as innovations to overcome these difficulties.

Surface-Enhanced Raman Scattering Spectroscopy Combined With Chemical Imaging Analysis for Detecting Apple Valsa Canker at an Early Stage

Apple Valsa canker (AVC) with early incubation characteristics is a severe apple tree disease, resulting in significant orchards yield loss. Early detection of the infected trees is critical to prevent the disease from rapidly developing. Surface-enhanced Raman Scattering (SERS) spectroscopy with simplifies detection procedures and improves detection efficiency is a potential method for AVC detection. In this study, AVC early infected detection was proposed by combining SERS spectroscopy with the chemometrics methods and machine learning algorithms, and chemical distribution imaging was successfully applied to the analysis of disease dynamics. Results showed that the samples of healthy, early disease, and late disease sample datasets demonstrated significant clustering effects. The adaptive iterative reweighted penalized least squares (air-PLS) algorithm was used as the best baseline correction method to eliminate the interference of baseline shifts. The BP-ANN, ELM, Random Forest, and LS-SVM machine learning algorithms incorporating optimal spectral variables were utilized to establish discriminative models to detect of the AVC disease stage. The accuracy of these models was above 90%. SERS chemical imaging results showed that cellulose and lignin were significantly reduced at the phloem disease-health junction under AVC stress. These results suggested that SERS spectroscopy combined with chemical imaging analysis for early detection of the AVC disease was feasible and promising. This study provided a practical method for the rapidly diagnosing of apple orchard diseases.

Applications of hyperspectral imaging in plant phenotyping

Our ability to interrogate and manipulate the genome far exceeds our capacity to measure the effects of genetic changes on plant traits. Much effort has been made recently by the plant science research community to address this imbalance. The responses of plants to environmental conditions can now be defined using a variety of imaging approaches. Hyperspectral imaging (HSI) has emerged as a promising approach to measure traits using a wide range of wavebands simultaneously in 3D to capture information in lab, glasshouse, or field settings. HSI has been applied to define abiotic, biotic, and quality traits for optimisation of crop management.

Recent Advances in Spectroscopic Techniques for the Analysis of Microplastics in Food

Microplastic pollution has become a worldwide concern in aquatic and terrestrial environments. Microplastics could also enter the food chain, causing potential harm to human health. To facilitate the risk assessment of microplastics to humans, it is critically important to have a reliable analytical technique to detect, quantify, and identify microplastics of various materials, sizes, and shapes from environmental, agricultural, and food matrices. Spectroscopic techniques, mainly vibrational spectroscopy (Raman and infrared), are commonly used techniques for microplastic analysis. This review focuses on recent advances of these spectroscopic techniques for the analysis of microplastics in food. The fundamental, recent technical advances of the spectroscopic techniques and their advantages and limitations were summarized. The food sample pretreatment methods and recent applications for detecting and quantifying microplastics in different types of food were reviewed. In addition, the current technical challenges and future research directions were discussed. It is anticipated that the advances in instrument development and methodology innovation will enable spectroscopic techniques to solve critical analytical challenges in microplastic analysis in food, which will facilitate the reliable risk assessment.

A Fast, Straightforward and Inexpensive Method for the Authentication of Baijiu Spirit Samples by Fluorescence Spectroscopy

The Chinese spirit baijiu is currently the world’s bestselling spirit, with more than ten billion liters sold in 2018. This is a figure that puts its sales higher than whiskey, vodka, gin, and tequila combined. The multitude of baijiu varieties available in the market differ in several ways ranging from aging to the traditional artisanship involved in producing the final spirit to several other features, including the rarity of the bottle. A result of these differences is a wide distribution of prices for the various baijiu products. Consequently, a single bottle of baijiu can cost anywhere from a few dollars, up to thousands of US dollars. The price differences among the various baijiu spirits necessitate the existence of reliable scientific methods that can efficiently differentiate and authenticate the qualities of baijiu spirits. In addition, the existence of such methods facilitates the prevention of counterfeit sales of the final product. Considering this, we introduce an analytical chemistry method that distinguishes amongst different baijiu spirits based on fluorescence spectroscopy. Its attributes include the low cost and convenience that allows analysis either before or while the spirit is in the market. Our work herein focuses on the analysis of thirty different varieties of baijiu spirits from six different distilleries from East Asia and North America by fluorescence emission spectroscopy, which is associated to the price of the product. For the analysis, we employed a HORIBA FLUOROLOG 3 (HORIBA—Jobin Yvon) spectrometer. Major advantages of this method include the low cost, as no consumables except a quartz reusable cuvette are required, the minimal waste, and finally the quick processing of data.

The Progress of Label-Free Optical Imaging in Alzheimer's Disease Screening and Diagnosis

As the major neurodegenerative disease of dementia, Alzheimer's disease (AD) has caused an enormous social and economic burden on society. Currently, AD has neither clear pathogenesis nor effective treatments. Positron emission tomography (PET) and magnetic resonance imaging (MRI) have been verified as potential tools for diagnosing and monitoring Alzheimer's disease. However, the high costs, low spatial resolution, and long acquisition time limit their broad clinical utilization. The gold standard of AD diagnosis routinely used in research is imaging AD biomarkers with dyes or other reagents, which are unsuitable for in vivo studies owing to their potential toxicity and prolonged and costly process of the U.S. Food and Drug Administration (FDA) approval for human use. Furthermore, these exogenous reagents might bring unwarranted interference to mechanistic studies, causing unreliable results. Several label-free optical imaging techniques, such as infrared spectroscopic imaging (IRSI), Raman spectroscopic imaging (RSI), optical coherence tomography (OCT), autofluorescence imaging (AFI), optical harmonic generation imaging (OHGI), etc., have been developed to circumvent this issue and made it possible to offer an accurate and detailed analysis of AD biomarkers. In this review, we present the emerging label-free optical imaging techniques and their applications in AD, along with their potential and challenges in AD diagnosis.

Darkfield and Fluorescence Macrovision of a Series of Large Images to Assess Anatomical and Chemical Tissue Variability in Whole Cross-Sections of Maize Stems

The proportion and composition of plant tissues in maize stems vary with genotype and agroclimatic factors and may impact the final biomass use. In this manuscript, we propose a quantitative histology approach without any section labelling to estimate the proportion of different tissues in maize stem sections as well as their chemical characteristics. Macroscopic imaging was chosen to observe the entire section of a stem. Darkfield illumination was retained to visualise the whole stem cellular structure. Multispectral autofluorescence images were acquired to detect cell wall phenolic compounds after UV and visible excitations. Image analysis was implemented to extract morphological features and autofluorescence pseudospectra. By assimilating the internode to a cylinder, the relative proportions of tissues in the internode were estimated from their relative areas in the sections. The approach was applied to study a series of 14 maize inbred lines. Considerable variability was revealed among the 14 inbred lines for both anatomical and chemical traits. The most discriminant morphological descriptors were the relative amount of rind and parenchyma tissues together with the density and size of the individual bundles, the area of stem and the parenchyma cell diameter. The rind, as the most lignified tissue, showed strong visible-induced fluorescence which was line-dependant. The relative amount of para-coumaric acid was associated with the UV-induced fluorescence intensity in the rind and in the parenchyma near the rind, while ferulic acid amount was significantly correlated mainly with the parenchyma near the rind. The correlation between lignin and the tissue pseudospectra showed that a global higher amount of lignin resulted in a higher level of lignin fluorescence whatever the tissues. We demonstrated here the potential of darkfield and autofluorescence imaging coupled with image analysis to quantify histology of maize stem and highlight variability between different lines.

NIR spectroscopy of natural medicines supported by novel instrumentation and methods for data analysis and interpretation

Near-infrared (NIR) spectroscopy is a powerful tool for qualitative and quantitative phytoanalysis. It is a rapid and high-throughput analytical method, with on-site capability, high chemical specificity, and no/minimal sample preparation. NIR spectroscopy is a powerful non-invasive and low-cost alternative with significant practical advantages compared to the conventional methods of analysis. These advantages are particularly exposed in the field of phytoanalysis. In contrast to synthetic medicines, natural products feature chemical diversity that can vary depending on the medicinal plant cultivation conditions, geographical origin or harvest time. The content of bioactive compounds and their derivatives, and thus, the quality parameters of the natural medicine need to be controlled with respect to a number of conditions. NIR spectroscopy has been proved to be particularly competitive in such difficult scenarios. In recent years, remarkable advances in the field of spectroscopic instrumentation and methods of analysis have appeared. Noteworthy was the appearance and dynamic continuing development of miniaturized, on-site capable NIR spectrometers. This was accompanied by application of new tools increasing the potential and reliability of NIR spectroscopy in phytoanalytical applications. The present review discussed the major principles of this technique and critically assesses its future application potential in phytoanalytical strategies. Major attention is given to the current development trends based on the most recent literature published in the field.