Spectral Fingerprints Research Articles

Directly printed plasmonic substrates of gold micro-flake array for plasmonically-enhanced infrared absorption-spectroscopic biodetection

Infrared absorption-spectroscopy (IRAS) is an essential tool for identifying molecular species with specific spectral fingerprints and widely used to characterize and identify known or unknown chemical substances in scientific research and industry. Recently, plasmonic substrates that can dramatically enhance local light field have greatly boosted the performance of IRAS technology, which is generally called plasmonically-enhanced infrared absorption-spectroscopy (PEIRAS), for high-sensitivity biochemical detection. However, the fabrication of micro/nano-structures of noble metals, such as gold and silver, is still challenging and costly, which thus hinders the broad use and further development of PEIRAS technologies. Here, we present a precision photoreduction and replacement reaction combined technology to directly print plasmonic substrates of periodic gold micro-flake array for PEIRAS biodetection. Such a periodic gold micro-flake array exhibits a strong plasmonic surface-lattice resonance whose reflection spectra can be precisely tailored to desired wavelength for PEIRAS biodetection. It has been experimentally demonstrated that the directly printed plasmonic substrate of PEIRAS can be utilized to detect bovine serum albumin via IRAS at the limit of concentration level at 10 nM. The additive printing nature of its fabrication technology makes such a plasmonic substrate promising for widespread IRAS biodetection applications.

Rapid Determination of Soil Horizons and Suborders Based on VIS-NIR-SWIR Spectroscopy and Machine Learning Models

In an effort to improve the efficiency of soil classification, traditional methods are being combined with analytical and computational techniques. This integration has strengthened the connection between conventional classification and the application of machine-learning (ML) models to interpret soil spectral reflectance data. Due to the time and computational cost, several studies are limited to testing the classification performance of a few algorithms and do not always explore the best parameters for model optimization. The study aims to assess the efficiency of combining soil spectral reflectance with prevalent ML models for classifying pedogenetic horizons and soil suborders, enhancing traditional classification methods. We collected seven soil monoliths, previously classified according to the Brazilian Soil Classification System (SiBCS) and soil taxonomy. Using the ASD Fieldspec spectroradiometer, we obtained spectral reflectance samples along each monolith (n = 800 per monolith) to classify horizons and n = 5600 for suborder classification. Spectral fingerprints were obtained and explored by Principal Component Analysis (PCA). The spectral data were subdivided into training (70%) and test (30%) sets and submitted to the Logistic Regression (LR), Artificial Neural Network (NN), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting (GB) models for the classification of horizons and suborders, considering the model’s adjustment parameters. Accuracy and F-Score were used to verify the performance of the models. There was a significant influence of particle size and soil organic carbon on the spectral fingerprint of the soils. The PCA indicated that topsoil horizons clustered in most of the monoliths analyzed, while most of the subsoil horizons showed data overlap. The NN model showed the highest accuracy in the classification of horizons (97%), while the SVM showed the lowest performance (52% accuracy). The classification of soil suborders presented accuracies between 95% and 98%. Therefore, our study concludes that spectral data combined with ML models can enhance the discrimination and classification of soil horizons and suborders, improving upon traditional methods.

Infrared and Raman Diagnostic Modeling of Phosphate Adsorption on Ceria Nanoparticles.

Cerium dioxide (CeO2; ceria) nanoparticles (CeNPs) are promising nanozymes that show a variety of biological activity. Effective nanozymes need to retain their activity in the face of surface speciation in biological environments, and characterizing surface speciation is therefore critical to understanding and controlling the therapeutic capabilities of CeNPs. In particular, adsorbed phosphates can impact the enzymatic activity exploited to convert phosphate prodrugs into therapeutics in vivo and also define the early stages of the phosphate-scavenging processes that lead to the transformation of active CeO2 into inactive CePO4. In this work, we utilize ab initio lattice-dynamics calculations to study the interaction of phosphates with the three major surfaces of ceria and to predict the infrared (IR) and Raman spectral signatures of adsorbed phosphate species. We find that phosphates adsorb strongly to CeO2 surfaces in a range of stable binding configurations, of which 5-fold coordinated P species in a trigonal bipyramidal coordination may represent a stable intermediate in the early stages of phosphate scavenging. We find that the phosphate species show characteristic spectral fingerprints between 500 and 1500 cm-1, whereas the bare CeO2 surfaces show no active modes above 600 cm-1, and the 5-fold coordinated P species in particular show potential diagnostic P-O stretching modes between 650 and 700 cm-1 in both IR and Raman spectra. This comprehensive exploration of different binding modes for phosphates on CeO2 and the set of reference spectra provides an important step toward the experimental characterization of phosphate speciation and, ultimately, control of its impact on the performance of ceria nanozymes.

Compressed AFM-IR hyperspectral nanoimaging

Infrared (IR) hyperspectral imaging is a powerful approach in the field of materials and life sciences. However, for the extension to modern sub-diffraction nanoimaging it still remains a highly inefficient technique, as it acquires data via inherent sequential schemes. Here, we introduce the mathematical technique of low-rank matrix reconstruction to the sub-diffraction scheme of atomic force microscopy-based infrared spectroscopy (AFM-IR), for efficient hyperspectral IR nanoimaging. To demonstrate its application potential, we chose the trypanosomatid unicellular parasites Leishmania species as a realistic target of biological importance. The mid-IR spectral fingerprint window covering the spectral range from 1300 to 1900 cm−1 was chosen and a distance between the data points of 220 nm was used for nanoimaging of single parasites. The method of k-means cluster analysis was used for extracting the chemically distinct spatial locations. Subsequently, we randomly selected only 10% of an originally gathered data cube of 134 (x) × 50 (y) × 148 (spectral) AFM-IR measurements and completed the full data set by low-rank matrix reconstruction. This approach shows agreement in the cluster regions between full and reconstructed data cubes. Furthermore, we show that the results of the low-rank reconstruction are superior compared to alternative interpolation techniques in terms of error-metrics, cluster quality, and spectral interpretation for various subsampling ratios. We conclude that by using low-rank matrix reconstruction the data acquisition time can be reduced from more than 14 h to 1–2 h. These findings can significantly boost the practical applicability of hyperspectral nanoimaging in both academic and industrial settings involving nano- and bio-materials.

Exploring Suspended Sediment Dynamics Using a Novel Indexing Framework Based on X‐Ray Diffraction Spectral Fingerprinting

AbstractUnderstanding the origins and hotspots of suspended sediments is crucial for safeguarding the biotic communities and for management of water quality in river‐reservoir systems. This study is an attempt to develop a new analytical framework to deconvolve the suspended sediment sources contribution using a novel index (i.e., erosion susceptibility index, ESI). The ESI is computed using sediment fingerprinting information, sediment yield, and the areal coverage of the sediment sources (land use). We have used X‐ray diffraction (XRD) and Partial least square regression (PLSR) based spectral fingerprinting technique to apportion the land use classes as sources of sediments in a representative catchment of Chotanagpur Plateau (CP), India (Konar reservoir catchment). PLSR model on the XRD spectra has performed well with R2 > 0.88 and identified agricultural areas as the major contributor of suspended sediments by delivering 28%–43%, varying seasonally (in the period of 2018–2019). However, based on the ESI values barren lands and human settlements were found to be the most crucial land use classes (with highest ESI value of 1.31 and 1.08 respectively) in terms of sediment generation (22%–29% and 14%–23% respectively) with only 14% and 12% areal coverage. The results have demonstrated that the state of critical conservation urgency of different land uses can be quantified more effectively when combining spectral tracers based knowledge with ESI.

Achieving Molecular Recognition of Structural Analogues in Surface‐Enhanced Raman Spectroscopy: Inducing Charge and Geometry Complementarity to Mimic Molecular Docking

AbstractMolecular recognition of complex isomeric biomolecules remains challenging in surface‐enhanced Raman scattering (SERS) spectroscopy due to their small Raman cross‐sections and/or poor surface affinities. To date, the use of molecular probes has achieved excellent molecular sensitivities but still suffers from poor spectral specificity. Here, we induce “charge and geometry complementarity” between probe and analyte as a key strategy to achieve high spectral specificity for effective SERS molecular recognition of structural analogues. We employ 4‐mercaptopyridine (MPY) as the probe, and chondroitin sulfate (CS) disaccharides with isomeric sulfation patterns as our proof‐of‐concept study. Our experimental and in silico studies reveal that “charge and geometry complementarity” between MPY's binding pocket and the CS sulfation patterns drives the formation of site‐specific, multidentate interactions at the respective CS isomerism sites, which “locks” each CS in its analogue‐specific complex geometry, akin to molecular docking events. Leveraging the resultant spectral fingerprints, we achieve > 97 % classification accuracy for 4 CSs and 5 potential structural interferences, as well as attain multiplex CS quantification with < 3 % prediction error. These insights could enable practical SERS differentiation of biologically important isomers to meet the burgeoning demand for fast‐responding applications across various fields such as biodiagnostics, food and environmental surveillance.

Achieving Molecular Recognition of Structural Analogues in Surface-Enhanced Raman Scattering Spectroscopy: Inducing Charge and Geometry Complementarity to Mimic Molecular Docking.

Molecular recognition of complex isomeric biomolecules remains challenging in surface-enhanced Raman scattering (SERS) spectroscopy due to their small Raman cross-sections and/or poor surface affinities. To date, the use of molecular probes has achieved excellent molecular sensitivities but still suffers from poor spectral specificity. Here, we induce 'charge and geometry complementarity' between probe and analyte as a key strategy to achieve high spectral specificity for effective SERS molecular recognition of structural analogues. We employ 4-mercaptopyridine (MPY) as the probe, and chondroitin sulfate (CS) disaccharides with isomeric sulfation patterns as our proof-of-concept study. Our experimental and in silico studies reveal that 'charge and geometry complementarity' between MPY's binding pocket and the CS sulfation patterns drives the formation of site-specific, multidentate interactions at the respective CS isomerism sites, which 'locks' each CS in its analogue-specific complex geometry, akin to molecular docking events. Leveraging the resultant spectral fingerprints, we achieve > 97% classification accuracy for 4 CSs and 5 potential structural interferences, as well as attain multiplex CS quantification with < 3 % prediction error. These insights could enable practical SERS differentiation of biologically important isomers to meet the burgeoning demand for fast-responding applications across various fields such as biodiagnostics, food and environmental surveillance.

Analysis of the response mechanisms of Pinellia ternata to terahertz wave stresses using transcriptome and metabolic data.

Pinellia ternata (Thunb.) Breit. (Araceae), a significant medicinal plant, has been used to treat various diseases for centuries. Terahertz radiation (THZ) is located between microwaves and infrared rays on the electromagnetic spectrum. THZ possesses low single-photon energy and a spectral fingerprint, but its effects on plant growth have not yet been investigated. The study's primary objective was to examine the transcriptome and metabolome databases of the SY line to provide a new perspective for identifying genes associated with resistance and growth promotion and comprehending the underlying molecular mechanism. Variations in the biological characteristics of P. ternata grown under control and experimental conditions were analyzed to determine the effect of THZ. Compared with the control group, phenotypic variables such as leaf length, petiole length, number of leaves, leaf petiole diameter, and proliferation coefficient exhibited significant differences. P. ternata response to THZ was analyzed regarding the effects of various coercions on root exudation. The experimental group contained considerably more sugar alcohol than the control group. The transcriptome analysis revealed 1,695 differentially expressed genes (DEGs), including 509 upregulated and 1,186 downregulated genes. In the KEGG-enriched plant hormone signaling pathway, there were 19 differentially expressed genes, 13 of which were downregulated and six of which were upregulated. In the metabolomic analysis, approximately 416 metabolites were uncovered. There were 112 DEMs that were downregulated, whereas 148 were upregulated. The P. ternata leaves displayed significant differences in phytohormone metabolites, specifically in brassinolide (BR) and abscisic acid (ABA). The rise in BR triggers alterations in internal plant hormones, resulting in faster growth and development of P. ternata. Our findings demonstrated a link between THZ and several metabolic pathway processes, which will enhance our understanding of P. ternata mechanisms.

Compressive hyperspectral microscopy for cancer detection.

Hyperspectral microscopy grants the ability to characterize unique properties of tissues based on their spectral fingerprint. The ability to label and measure multiple molecular probes simultaneously provides pathologists and oncologists with a powerful tool to enhance accurate diagnostic and prognostic decisions. As the pathological workload grows, having an objective tool that provides companion diagnostics is of immense importance. Therefore, fast whole-slide spectral imaging systems are of immense importance for automated cancer prognostics that meet current and future needs. We aim to develop a fast and accurate hyperspectral microscopy system that can be easily integrated with existing microscopes and provide flexibility for optimizing measurement time versus spectral resolution. The method employs compressive sensing (CS) and a spectrally encoded illumination device integrated into the illumination path of a standard microscope. The spectral encoding is obtained using a compact liquid crystal cell that is operated in a fast mode. It provides time-efficient measurements of the spectral information, is modular and versatile, and can also be used for other applications that require rapid acquisition of hyperspectral images. We demonstrated the acquisition of breast cancer biopsies hyperspectral data of the whole camera area within . This means that a typical biopsy can be measured in . The hyperspectral images with 250 spectral bands are reconstructed from 47 spectrally encoded images in the spectral range of 450 to 700nm. CS hyperspectral microscopy was successfully demonstrated on a common lab microscope for measuring biopsies stained with the most common stains, such as hematoxylin and eosin. The high spectral resolution demonstrated here in a rather short time indicates the ability to use it further for coping with the highly demanding needs of pathological diagnostics, both for cancer diagnostics and prognostics.

In vitro hyperspectral biomarkers of human chondrosarcoma cells in nanoparticle-mediated radiosensitization using carbon ions

New therapeutic approaches are needed for the management of the highly chemo- and radioresistant chondrosarcoma (CHS). In this work, we used polyethylene glycol-encapsulated iron oxide nanoparticles for the intracellular delivery of the chemotherapeutic doxorubicin (IONPDOX) to augment the cytotoxic effects of carbon ions in comparison to photon radiation therapy. The in vitro biological effects were investigated in SW1353 chondrosarcoma cells focusing on the following parameters: cell survival using clonogenic test, detection of micronuclei (MN) by cytokinesis blocked micronucleus assay and morphology together with spectral fingerprints of nuclei using enhanced dark-field microscopy (EDFM) assembled with a hyperspectral imaging (HI) module. The combination of IONPDOX with ion carbon or photon irradiation increased the lethal effects of irradiation alone in correlation with the induction of MN. Alterations in the hyperspectral images and spectral profiles of nuclei reflected the CHS cell biological modifications following the treatments, highlighting possible new spectroscopic markers of cancer therapy effects. These outcomes showed that the proposed combined treatment is promising in improving CHS radiotherapy.

Single Pixel Spectral Color Constancy

Color constancy is still one of the biggest challenges in camera color processing. Convolutional neural networks have been able to improve the situation but there are still problems in many conditions, especially in scenes where a single color is dominating. In this work, we approach the problem from a slightly different setting. What if we could have some other information than the raw RGB image data. What kind of information would help to bring significant improvements while still be feasible in a mobile device. These questions sparked an idea for a novel approach for computational color constancy. Instead of raw RGB images used by the existing algorithms to estimate the scene white points, our approach is based on the scene’s average color spectra-single pixel spectral measurement. We show that as few as 10–14 spectral channels are sufficient. Notably, the sensor output has five orders of magnitude less data than in raw RGB images of a 10MPix camera. The spectral sensor captures the “spectral fingerprints” of different light sources and the illuminant white point can be accurately estimated by a standard regressor. The regressor can be trained with generated measurements using the existing RGB color constancy datasets. For this purpose, we propose a spectral data generation pipeline that can be used if the dataset camera model is known and thus its spectral characterization can be obtained. To verify the results with real data, we collected a real spectral dataset with a commercial spectrometer. On all datasets the proposed Single Pixel Spectral Color Constancy obtains the highest accuracy in the both single and cross-dataset experiments. The method is particularly effective for the difficult scenes for which the average improvements are 40–70% compared to state-of-the-arts. The approach can be extended to multi-illuminant case for which the experimental results also provide promising results.

A sensitized ratiometric fluorescence probe based on N/S doped carbon dots and mercaptoacetic acid capped CdTe quantum dots for the highly selective detection of multiple tetracycline antibiotics in food.

A ratiometric fluorescent probe based on N/S doped carbon dots (N/S-CQDs) and mercaptoacetic acid capped CdTe quantum dots (TGA-CdTe QDs) with sensitized and self-calibration functions was constructed to sensitively detect multiple tetracycline antibiotics (TCs). N/S-CQDs could attach stably to TGA-CdTe QDs and form a new composite ratiometric fluorescent probe that had a more than tenfold increase in sensitivity to TCs compared with each single QD. The probe could detect four common TCs as the color of the probe changed from bright red to dark red, and the limit of detection (LOD) was 1.47×10-2-1.78×10-2 mg/L. Practical applications of the probe in food and urine were also verified with recovery rates of 95.21%-104.97%. Due to the abundant spectral fingerprints provided by both QDs, this novel probe could accurately recognize not only different single TCs but also mixed TC samples even in actual samples combined with chemometrics.

Hyperspectral imaging as a new diagnostic tool for cervical intraepithelial neoplasia

PurposeCervical cancer screening by visual inspection with acetic acid (VIA) during colposcopy can be challenging and is highly dependent on the clinical experience of the examiner. Health-care systems lack qualified physicians able to perform the examination in both industrialized and low- and middle-income countries. Previous work has shown the general potential of hyperspectral imaging (HSI) to discriminate CIN from normal tissue, but clinical translation has been limited due to the lack of medically approved HSI systems.MethodsIn this study, we evaluate the feasibility of a commercially available HSI system for CIN detection in a prospective monocentric clinical trial.ResultsBy obtaining spectral fingerprints of 41 patients with CIN 1–3 we show that HSI-based differentiation between CIN and normal tissue is possible with high statistical significance. Major spectral differences were seen in the 555–585 wavelength area.ConclusionHSI advances tissue differentiation by associating each pixel with high-dimensional spectra and thereby obtains morphological and biochemical information of the observed tissue. Currently available and medically approved HSI systems may represent a contact- and marker-free examiner-independent method for the diagnosis of CIN.

Exotic Electronic Properties of 2D Nanosheets Isolated from Liquid Phase Exfoliated Phyllosilicate Minerals.

Spectrally inactive, electrically insulating, and chemically inert are adjectives broadly used to describe phyllosilicate minerals like mica and chlorite. Here, the above is disproved by demonstrating aqueous suspensions of liquid exfoliated nanosheets from five bulk mica types and chlorite schist. Nanosheet quality is confirmed via transmission electron and X-ray photoelectron spectroscopies, as well as electron diffraction. Through Raman spectroscopy, a previously unreported size- and layer-dependent spectral fingerprint is observed. When analyzing the high-yield suspensions (≈1mgmL-1 ) through UV-vis spectroscopy, all phyllosilicates present bandgap (Eg ) narrowing from ≈7eV in the bulk to ≈4eV for monolayers. Unusually, the bandgap is inversely proportional to the areal size (A) of the nanosheets, measured via atomic force microscopy. Due to an unrecorded quantum confinement effect, nanosheet electronic properties scale toward semiconducting behavior (bandgap ≈3eV) as nanosheet area increases. Furthermore, modeling X-ray diffraction spectra shows that the root cause of the initial bandgap narrowing is lattice relaxation. Finally, with their broad range of isomorphically substituted ions, phyllosilicate nanosheets show remarkable catalytic properties for hydrogen production.

Vibrationally Resolved Absorption and Fluorescence Cross Sections of Adamantane in the Far-ultraviolet Spectral Range on an Absolute Scale

High-resolution absorption, dispersed fluorescence emission, and photoionization cross sections are presented for gas-phase adamantane excited by synchrotron radiation in the exciting-photon energy range of 6–30 eV. Relative and absolute absorption cross sections of so-far unmatched resolution of down to 0.27 cm−1 line width in the region from 6.4–28 eV are shown along with newly discovered vibronic substructures around the HOMO–LUMO transition. Absorption line positions are provided with very high accuracy and listed in tabular form to be used as spectral fingerprints for the detection of adamantane in interstellar media, where its column density may be determined via the absolute cross sections. The fluorescence emission lies in the ultraviolet range from 190–250 nm and is excited starting at the HOMO–LUMO transition at 6.49 eV, which corresponds to the highest fluorescence emission energy. Hitherto unreported fluorescence in the same spectral range and relative photoionization cross sections in the exciting-photon energy range up to 30 eV are also presented along with lifetime measurements for differentiation of the involved electronic states.

Semi-distributed interferometers fiber-optic sensors for high-sensitivity refractive index detection: Design and sensitivity analysis

In this work, we present the design and analysis of fiber-optic refractive index (RI) sensors based on a simple semi-distributed interferometer (SDI). The SDI is a cavity formed between a cleaved fiber tip and the reflective interface between a single-mode fiber and a high-scattering fiber with core doped by Mg-silicate nanoparticles. The fabrication is rapid (∼1 min) and straightforward, as only a splice and cleave of the fibers are required; the resulting spectral fingerprint is an interferometer-shaped envelope, with a stochastic distribution due to the random scattering centers of the in-cavity fiber. We fabricated 18 sensors with 6 different enhanced-scattering fibers, having different drawing temperature and speed; by analyzing the RI sensitivity of each unit, we obtain sensitivity values up to 787.0 dB/RIU (refractive index units), whereas the optimal fiber allows achieving average sensitivity of 457.9 dB/RIU.

Spectral signatures of symmetry-breaking dynamical phase transitions.

Large deviation theory provides the framework to study the probability of rare fluctuations of time-averaged observables, opening new avenues of research in nonequilibrium physics. Some of the most appealing results within this context are dynamical phase transitions (DPTs), which might occur at the level of trajectories in order to maximize the probability of sustaining a rare event. While macroscopic fluctuation theory has underpinned much recent progress on the understanding of symmetry-breaking DPTs in driven diffusive systems, their microscopic characterization is still challenging. In this work we shed light on the general spectral mechanism giving rise to continuous DPTs not only for driven diffusive systems, but for any jump process in which a discrete Z_{n} symmetry is broken. By means of a symmetry-aided spectral analysis of the Doob-transformed dynamics, we provide the conditions whereby symmetry-breaking DPTs might emerge and how the different dynamical phases arise from the specific structure of the degenerate eigenvectors. In particular, we show explicitly how all symmetry-breaking features are encoded in the subleading eigenvectors of the degenerate subspace. Moreover, by partitioning configuration space into equivalence classes according to a proper order parameter, we achieve a substantial dimensional reduction which allows for the quantitative characterization of the spectral fingerprints of DPTs. We illustrate our predictions in several paradigmatic many-body systems, including (1) the one-dimensional boundary-driven weakly asymmetric exclusion process (WASEP), which exhibits a particle-hole symmetry-breaking DPT for current fluctuations, (2) the three- and four-state Potts model for spin dynamics, which displays discrete rotational symmetry-breaking DPTs for energy fluctuations, and (3) the closed WASEP which presents a continuous symmetry-breaking DPT into a time-crystal phase characterized by a rotating condensate.

Direct Identification and Quantitation of Protein Peptide Powders Based on Multi-Molecular Infrared Spectroscopy and Multivariate Data Fusion.

Given that protein peptide powders (PPPs) from different biological sources were inherited with diverse healthcare functions, which aroused adulteration of PPPs. A high-throughput and rapid methodology, united multi-molecular infrared (MM-IR) spectroscopy with data fusion, could determine the types and component content of PPPs from seven sources as examples. The chemical fingerprints of PPPs were thoroughly interpreted by tri-step infrared (IR) spectroscopy, and the defined spectral fingerprint region of protein peptide, total sugar, and fat was 3600-950 cm-1, which constituted MIR finger-print region. Moreover, the mid-level data fusion model was of great applicability in qualitative analysis, in which the F1-score reached 1 and the total accuracy was 100%, and a robust quantitative model was established with excellent predictive capacity (Rp: 0.9935, RMSEP: 1.288, and RPD: 7.97). MM-IR coordinated data fusion strategies to achieve high-throughput, multi-dimensional analysis of PPPs with better accuracy and robustness which meant a significant potential for the comprehensive analysis of other powders in food as well.

Characterisation of hybrid larch somatic embryo maturation by biochemical analyses and by a novel, fast mid-infrared method.

The morphology of somatic embryos (SE) is not a sufficient criterion to determine the level of maturation and the optimal stage to transfer embryos for germination, unlike the biochemical components. This composition characterization in the laboratory is too restrictive to be considered at each maturation cycle, as would be necessary. It is, therefore, essential to consider alternative methods. The objectives of this work were to achieve a complete biochemical characterization of the embryos during their development, to serve as a reference, and develop a characterization based on infrared spectrometry and chemometrics. During the precotyledonary stage (0-3 weeks of maturation), water content and glucose and fructose levels were high, which is consistent with SE development. After 4 weeks, the cotyledonary SE had a metabolism oriented towards the storage accumulation of lipids, proteins and starch, whereas raffinose only appeared from 8 weeks. Mid-infrared calibration models were developed for water, proteins, lipids, carbohydrates, glucose, fructose, inositols, raffinose, stachyose, and starch contents with an r2 average of 0.84. A model was also developed to discriminate the weeks of SE maturation. Different classes of age were discriminated with at least 72% of accuracy. Infrared analysis of the SE based on their full biochemical spectral fingerprint revealed a very slight variation in composition between 7 and 9 weeks, information that is very difficult to obtain by conventional analysis methods. These results provide novel insights into the maturation of conifer SE and indicate that mid-infrared spectrometry could be an easy and effective method for SE characterization. This article is protected by copyright. All rights reserved.

Recent Advances in THz Detection of Water.

The frequency range of terahertz waves (THz waves) is between 0.1 and 10 THz and they have properties such as low energy, penetration, transients, and spectral fingerprints, which are especially sensitive to water. Terahertz, as a frontier technology, have great potential in interpreting the structure of water molecules and detecting biological water conditions, and the use of terahertz technology for water detection is currently frontier research, which is of great significance. Firstly, this paper introduces the theory of terahertz technology and summarizes the current terahertz systems used for water detection. Secondly, an overview of theoretical approaches, such as the relaxation model and effective medium theory related to water detection, the relationship between water molecular networks and terahertz spectra, and the research progress of the terahertz detection of water content and water distribution visualization, are elaborated. Finally, the challenge and outlook of applications related to the terahertz wave detection of water are discussed. The purpose of this paper is to explore the research domains on water and its related applications using terahertz technology, as well as provide a reference for innovative applications of terahertz technology in moisture detection.