Spectral Points Research Articles

Analyzing the spectrum characteristics of a double-grating transmission scheme

In this paper, the spectral characteristics formed by a double-grating transmission scheme are discussed based on the scalar diffraction theory. First, considering two different grating periods, the angular spectrum at the back of the second grating is obtained. Then, the spectral characteristics are discussed with three different periodic conditions, while it is proved that the spatial distribution and spacing of the spectrum are absolutely determined by the grating constants and angles. The theoretical results manifest that the spatial distribution range of the spectrum becomes larger as the angle increases, unlike the spectral points, which are distributed on a single line when the grating lines of the two gratings are parallel. Finally, for the sake of proving that our theoretical analysis is correct and reasonable, three sets of experiments are conducted with three different combinations of Ronchi gratings. As expected, the experimental and theoretical results are in perfect agreement. In a word, the involved results could be useful for expanding the application of moiré deflectometry.

Optical solitons and their propagations in a time-varying coefficient modified nonlinear Schrödinger equation with non-zero boundary conditions via Riemann–Hilbert method

In this article, a time-varying coefficient modified nonlinear Schrödinger equation (tvcmNLSE) with non-zero boundary conditions (NZBCs) at infinity, phase term of which contains time-varying coefficients, is proposed to describe the nonlinear propagation behavior of optical pulses in non-uniform optical fibre. Meanwhile, the Riemann–Hilbert method (RHM) for the tvcmNLSE with NZBCs is presented for the first time. Specifically, a special transformation is constructed to convert the tvcmNLSE with NZBCs into the case with constant NZBCs, and the associated asymptotic spectral problem (ASP) is obtained. By introducing a suitable two-piece Riemann surface to map the original spectral parameter into a single-valued one, and analyzing the ASP, we construct the corresponding Jost solutions and spectral matrix, and sequentially analyze their analytical, symmetric, and asymptotic properties. Then, a generalized Riemann–Hilbert problem (RHP) is established that relates to the converted tvcmNLSE with constant NZBCs. Using the reconstruction formula, we further derive N-soliton solution with NZBCs in the absence of reflection potential, and analyze the dynamic characteristics of single and double solitons. Most notably, by modulating discrete spectral points (DSPs) and time-varying coefficients, we discover some previously unreported new waveforms, such as parabolic and periodic solitons with NZBCs. These waveforms have important implications in both theory and practice. In addition, the influences of different time-varying coefficients, NZBCs, and DSPs on soliton solutions are also discussed and analyzed. This study shows that the selection of time-varying coefficients, distribution of DSPs, and constraints from NZBCs all have significant impacts on the properties of soliton solutions.

Measurement of particle size in suspension based on VIS-NIR-RGB sensors and gradient-boosted regression tree

To better utilize the spectral features of near-infrared and visible light and address issues in traditional suspension particle size detection, such as long measurement cycles, high costs, and complex operation, we propose a new method that combines multispectral sensor characterization technology with machine learning algorithms for rapid particle size measurement. By integrating a lab-designed light source driving circuit and spectral data acquisition device, we obtained 15 spectral data points for visible light, near-infrared, and red–blue–green (RGB) values to build a prediction model. The near-infrared-based model obtained a coefficient of determination (R2) of 0.9130, whereas models combining RGB-near-infrared and visible-near-infrared features performed best, with R2 values of 0.9670 and 0.9845. These models improved performance by 5.91% and 7.83%, effectively overcoming the limitations of single spectral features in different suspensions. This combination of visible and near-infrared light with machine learning proves effective for detecting average particle size in suspensions.

Variable Iron Mineralogy and Redox Conditions Recorded in Ancient Rocks Measured by In Situ Visible/Near‐Infrared Spectroscopy at Jezero Crater, Mars

AbstractUsing relative reflectance measurements from the Mastcam‐Z and SuperCam instruments on the Mars 2020 Perseverance rover, we assess the variability of Fe mineralogy in Noachian/Hesperian‐aged rocks at Jezero crater. The results reveal diverse Fe3+ and Fe2+ minerals. The igneous crater floor, where small amounts of Fe3+‐phyllosilicates and poorly crystalline Fe3+‐oxyhydroxides have been reported, is spectrally similar to most oxidized basalts observed at Gusev crater. At the base of the western Jezero sedimentary fan, new spectral type points to an Fe‐bearing mineral assemblage likely dominated by Fe2+. By contrast, most strata exposed at the fan front show signatures of Fe3+‐oxides (mostly fine‐grained crystalline hematite), Fe3+‐sulfates (potentially copiapites), strong signatures of hydration, and among the strongest signatures of red hematite observed in situ, consistent with materials having experienced vigorous water‐rock interactions and/or higher degrees of diagenesis under oxidizing conditions. The fan top strata show hydration but little to no signs of Fe oxidation likely implying that some periods of fan construction occurred either during a reduced atmosphere era or during short‐lived aqueous activity of liquid water in contact with an oxidized atmosphere. We also report the discovery of alternating cm‐scale bands of red and gray layers correlated with hydration and oxide variability, which has not yet been observed elsewhere on Mars. This could result from syn‐depositional fluid chemistry variations, possibly as seasonal processes, or diagenetic overprint of oxidized fluids percolating through strata having variable permeability.

Terahertz Fingerprint Metasurface Sensor Based on Temperature Variation for Trace Molecules.

Terahertz (THz) spectroscopy has demonstrated significant potential for substance detection due to its low destructiveness and due to the abundance of molecular fingerprint absorption signatures that it contains. However, there is limited research on the fingerprint detection of substances at different temperatures. Here, we propose a THz metamaterial slit array sensor that exploits localized surface plasmons to enhance the electric field within the slit. The transmission peak frequency can be modulated via temperature adjustments. This method enables the detection of molecular absorption characteristics at multiple spectral frequency points, thereby achieving a specific and highly sensitive detection of characteristic analyte fingerprint spectra. Additionally, the sensor supports the detection of substances at multiple temperatures and sensitively identifies changes in their absorption properties as a function of temperature. Our research has employed temperature variation to achieve a highly sensitive and specific detection of trace analytes, offering a new solution for THz molecular detection.

An efficient method for tracing the geographic origin of Enshi Yulu fresh tea leaves based on near infrared spectroscopy combined with synergy interval PLS and genetic algorithm

This paper applies near-infrared spectroscopy (NIRS) and multiple chemometrics to efficiently distinguish the origins of fresh tea leaves. The key components were obtained using the partial least squares discriminant analysis (PLS-DA) method. PLS, synergy interval PLS (siPLS), principal component analysis (PCA), genetic algorithm (GA), and their combination methods were used to establish NIRS non-destructive discrimination models. Then, the practical application was examined using external samples. The study identified nine key components (variable importance for the projection (VIP) > 1): epigallocatechin, epicatechin, total sugar, water extracts, total catechins, gallocatechin gallate, tea polyphenols, gallocatechin, and epigallocatechin gallate. Of the six NIRS models, the siPLS-GA model that used 37 spectral data points produced the best results (Rp2 = 0.9706, RMSEP = 0.0772, RPD = 6.59). This model had a prediction accuracy of 96.67% for the prediction set samples and 93.33% for the external samples. It offers a rapid, precise, and non-invasive approach to monitor and regulate the illicit trade of fresh tea leaves, thereby guaranteeing the authenticity of Enshi Yulu products from the processing source and fostering the long-term prosperity and stability of the Enshi Yulu tea industry.

Research on a Data Preprocessing Method for a Vehicle-Mounted Solar Occultation Flux–Fourier Transform Infrared Spectrometer

A vehicle-mounted solar occultation flux–Fourier transform infrared spectrometer uses the sun as an infrared light source to quantify molecular absorption in the atmosphere. It can be used for the rapid three-dimensional monitoring of pollutant emissions and the column concentration monitoring of greenhouse gases. The system has the advantages of high mobility and a capacity for noncontact measurement and measurement over long distances. However, in vehicle-mounted applications, vehicle bumps and obstacles introduce aberrations in the measured spectra, affecting the accuracy of gas concentration inversion results and flux calculations. In this paper, we propose a spectral data preprocessing method that combines a self-organizing mapping neural network and correlation analysis to reject anomalous spectral data measured by the solar occultation flux–Fourier transform infrared spectrometer during mobile observations. Compared to the traditional method, this method does not need to adjust the comparison threshold and obtain the training spectra in advance and has the advantage of automatically updating the weights without the need to set fixed correlation comparison coefficients. The accurate identification of all anomalous simulated spectra in the simulation experiments proved the effectiveness of the method. In the vehicle-mounted application experiment, 342 anomalous spectra were successfully screened from 1739 spectral data points. The experimental results show that the method can improve the accuracy of gas concentration measurement results and can be applied to a vehicle-mounted solar occultation flux–Fourier transform infrared spectrometer system to meet the preprocessing needs of a high number of spectral data in mobile monitoring.

Graphene-integrated microring cavity for electronically controlled molecular fingerprinting

Microring cavities supporting whispering-gallery modes (WGMs) have an exceptionally high quality factor (Q) and a small mode volume, greatly improving the interaction between light and matter, which has attracted great attention in various microscale/nanoscale photonic devices and potential applications. Recently, two-dimensional van der Waals (vdW) materials such as graphene have emerged as a potential platform for next-generation biosensing by enabling the confinement of light fields at the nanoscale. Here, we propose what we believe to be a novel approach to achieve molecular fingerprint retrieval by integrating graphene into a microring cavity and conducting numerical simulations using the finite-difference time-domain (FDTD) method. The hybrid cavity exhibits high-quality WGMs with a high Q factor of up to 800. Moreover, the resonant wavelength can be electronically controlled through modulation of graphene’s Fermi level, enabling coverage of the entire free spectral range at infrared frequencies. By depositing a thin layer of biomolecular material (e.g., CBP) onto the surface of our hybrid cavity, we are able to accurately read out the absorption spectrum at multiple spectral points, thereby achieving broadband fingerprint retrieval for the targeted biomolecule. Our results pave the way for highly sensitive, chip-integrated, miniaturized, and electrically modulated infrared spectroscopy biosensing.

Discrimination of ivory from extant and extinct elephant species using Raman spectroscopy: A potential non-destructive technique for combating illegal wildlife trade.

The use of elephant ivory as a commodity is a factor in declining elephant populations. Despite recent worldwide elephant ivory trade bans, mammoth ivory trade remains unregulated. This complicates law enforcement efforts, as distinguishing between ivory from extant and extinct species requires costly, destructive and time consuming methods. Elephant and mammoth ivory mainly consists of dentine, a mineralized connective tissue that contains an organic collagenous component and an inorganic component of calcium phosphate minerals, similar in structure to hydroxyapatite crystals. Raman spectroscopy is a non-invasive laser-based technique that has previously been used for the study of bone and mineral chemistry. Ivory and bone have similar biochemical properties, making Raman spectroscopy a promising method for species identification based on ivory. This study aimed to test the hypothesis that it is possible to identify differences in the chemistry of mammoth and elephant ivory using Raman spectroscopy. Mammoth and elephant tusks were obtained from the Natural History Museum in London, UK. Included in this study were eight samples of ivory from Mammuthus primigenius, two samples of carved ivory bangles from Africa (Loxodonta species), and one cross section of a tusk from Elephas maximus. The ivory was scanned using an inVia Raman micro spectrometer equipped with a x50 objective lens and a 785nm laser. Spectra were acquired using line maps and individual spectral points were acquired randomly or at points of interest on all samples. The data was then analysed using principal component analysis (PCA) with use of an in-house MATLAB script. Univariate analysis of peak intensity ratios of phosphate to amide I and III peaks, and carbonate to phosphate peaks showed statistical differences (p<0.0001) in the average peak intensity ratios between Mammuthus primigenius, Loxodonta spp. and Elephas maximus. Full width at half maximum hight (FWHM)analysis of the phosphate peak demonstrated higher crystal maturity of Mammuthus primigenius compared to living elephant species. The results of the study have established that spectra acquired by Raman spectroscopy can be separated into distinct classes through PCA. In conclusion, this study has shown that well-preserved mammoth and elephant ivory has the potential to be characterized using Raman spectroscopy, providing a promising method for species identification. The results of this study will be valuable in developing quick and non-destructive methods for the identification of ivory, which will have direct applications in archaeology and the regulation of international trade.

Geographical origin discrimination of lemon myrtle (Backhousia citriodora) leaf powder using near-infrared hyperspectral imaging

Lemon myrtle (LM), Backhousia citriodora, is a popular flavouring agent and herbal tea from Australia. To ensure traceability and consumer trust in global food supply chain, rapid and non-destructive tools are crucial. In this study, hyperspectral images were acquired from 91 L M samples sourced from three different origins (Malaysia, Queensland, and New South Wales (Australia)), within 950–2500 nm range. Classification models were developed using linear partial least squares-discriminant analysis (PLS-DA) with two approaches, pixel-based (trained by all spectral data points) and sample-based (trained by average spectrum). All models achieved classification accuracies above 96%. The sample-based PLS-DA model, trained by mean-centring transformed data, demonstrated the highest discriminatory performance. Both approaches show potential for LM origin classification, but the sample-based model is more suitable for automated and rapid industry applications due to its shorter calculation time. However, additional spectral data acquisition is necessary to improve the model and fully explore its capabilities and limitations.

Prediction of sheep bulk milk coagulation properties from mid-infrared spectral data

The technological features of milk are essential for cheese manufacturing. This is particularly true for Italy, where most of the milk produced by sheep is intended for cheese production. The possibility to evaluate technological characteristics and coagulation aptitude of milk in advance, before any treatment, is crucial for decision-making at industry level. In the present study, we tested the ability of mid-infrared spectroscopy for prediction of coagulation traits (rennet coagulation time and curd firmness) by using more than 4000 bulk milk samples of 344 sheep herds. The models developed with a partial least square regression showed that spectral data points can be successfully used to predict the two traits. The coefficient of determination in external validation was 0.42 for rennet coagulation time and 0.28 for curd firmness, indicating that sheep milk delivered to dairies can undergo a preliminary screening only to assess the expected coagulation time. This finding will allow manufacturers to evaluate the milk received from farmers. Further investigation will be need to improve the prediction of rennet coagulation time that can be coupled with composition traits to define premiums or penalties in the payment system.

The collision-induced absorption of H2 near 1.20 µm: Subatmospheric measurements and validation tests of calculations

The weak binary collision-induced absorption (CIA) of molecular hydrogen is measured at room temperature in the first overtone region near 1.20 µm. Binary absorption coefficients are derived by cavity ring down spectroscopy (CRDS) at 28 selected spectral points sampling the (2–0) band between 7974 and 8650 cm−1. While all previous studies used high density samples, the sensitivity of the CRDS method allowed deriving accurate CIA by using pressure ramps of pure H2 limited to a maximum pressure of 1 atm. After subtraction of the Rayleigh contribution, a purely quadratic pressure dependence is obtained for the absorption coefficient at each measurement point and the CIA binary coefficients are derived with a 1.5 % accuracy. The comparison to theoretical values widely used for astronomical applications shows deviations values between 5 and 25 %.

The Impact of Radiative Transfer at Reduced Spectral Resolution in Large‐Eddy Simulations of Convective Clouds

AbstractMany radiative transfer schemes approximate the spectral integration over ∼105 to ∼106 wavelengths with correlated k‐distributions methods that typically require only 101–102 spectral integration points (g‐points). The exact number of g‐points is then chosen as an optimal balance between computational costs and accuracy, normally assessed in terms of a number of radiative quantities. How this radiative accuracy propagates to simulation accuracy, however, is not straightforward. In this study, we therefore explore the sensitivity of cloud properties in large‐eddy simulations (LES) to the accuracy of radiative fluxes and heating rates. We first generate smaller sets of g‐points from existing k‐distributions by repeatedly combining adjacent g‐points while maintaining the highest possible accuracy on a chosen set of radiative metrics. Next, we perform three sets of LES with varying cloud—radiation coupling pathways, and therefore different requirements for the accuracy of the radiative transfer computations, to investigate how these smaller and thus less accurate k‐distributions affect simulation characteristics. The decrease in radiative accuracy with 3–4 times smaller k‐distributions results in biases in cloud properties that are relative small compared to their temporal fluctuations. These results show potential for speeding up radiative transfer computations in cloud‐resolving models by reducing the resolved spectral detail. However, more statistically converged simulations and a wider set of case studies is required to fully assess the robustness of our results.

Sparsity for Ultrafast Material Identification

AbstractMid‐infrared spectroscopy is often used to identify material. Thousands of spectral points are measured in a time‐consuming process using expensive table‐top instrument. However, material identification is a sparse problem, which in theory can be solved with just a few measurements. Here the sparsity of the problem is exploited to develop an ultra‐fast, portable, and inexpensive method to identify materials. In a single‐shot, a mid‐infrared camera can identify materials based on their spectroscopic signatures. This method does not require prior calibration, making it robust and versatile in handling a broad range of materials.

Novel Time-Saving Technique to Measure the 2D Complex Profile of Spectral Transmittance of Linearly Variable Filters and Assessing Homogeneity of Optically Transparent Materials

Emerging technologies for coating new types of filters, such as linearly variable bandpass filters, urges new kind of measurement systems requiring accurate and time-saving spatially resolved transmittance measurements. On the other hand, one of the main uses of coated filters is standardization use; e.g., as certified reference materials (CRMs) for transmittance and absorbance standards. In this case, the homogeneity assessment is of great interest. To do such measurements with low uncertainty, spatial scanning methods using reference double-beam spectrophotometers are routinely used. High time consumption and relatively high-cost are disadvantages of this traditional method. In this work, we propose a new system based on an imaging technique. The newly developed system comprises a quartz tungsten halogen (QTH)-based illuminator, with collimator and coupling optics, a double-grating monochromator, customized integrating sphere, and scientific highly digitized, high-resolution and dynamic range Si-based CCD (monochrome) camera. The system is time-saving: in one-shot we may measure the spatial transmittance distribution of the samples (e.g. filters) without separately measuring the reference (air), which has many instability issues. Validation measurements using standard filters are presented. Systematic errors may be studied and corrected for. Many types of filters and liquids, for instance chemical reference materials (RMs), may be measured and reported. Uncertainty of spatial, spectral and radiometric components are studied carefully and estimated. The combined uncertainty evaluation has shown that relatively low uncertainty levels may be achieved, specially in the visible range (U &lt; 1 %, k=2 ). Spatially-resolved data with different curves at different positions (spatial points) are shown in the spectral range of 380 nm through 950 nm. The combined uncertainties at some spectral points are presented and proved to be sample- and wavelength-dependent at all the points in the field-of-view (FOV) of the CCD camera.

Sparse, Empirically Optimized Quadrature for Broadband Spectral Integration

AbstractBroadband (spectrally‐integrated) radiation calculations are dominated by the expense of spectral integration, and many applications require fast parameterizations for computing radiative flux. Here we describe a novel approach using a linear weighted sum of monochromatic calculations at a small set of optimally‐chosen frequencies. The empirically‐optimized quadrature method is used to compute atmospheric boundary fluxes, net flux profiles throughout the atmosphere, heating rate profiles, and top‐of‐the‐atmosphere forcing by CO2, in the longwave for clear skies. We evaluate the method against two modern correlated k‐distribution models and find that we can achieve comparable errors with 32 spectral points. We also examine the effect of minimizing different cost functions, and find that in order to accurately represent heating rates and CO2 forcing, these quantities must be included in the cost function.

Digital Prediction of the Purchase Price of Fresh Tea Leaves of Enshi Yulu Based on Near-Infrared Spectroscopy Combined with Multivariate Analysis.

In this study, near-infrared spectroscopy (NIRS) combined with a variety of chemometrics methods was used to establish a fast and non-destructive prediction model for the purchase price of fresh tea leaves. Firstly, a paired t-test was conducted on the quality index (QI) of seven quality grade fresh tea samples, all of which showed statistical significance (p < 0.05). Further, there was a good linear relationship between the QI, quality grades, and purchase price of fresh tea samples, with the determination coefficient being greater than 0.99. Then, the original near-infrared spectra of fresh tea samples were obtained and preprocessed, with the combination (standard normal variable (SNV) + second derivative (SD)) as the optimal preprocessing method. Four spectral intervals closely related to fresh tea prices were screened using the synergy interval partial least squares (si-PLS), namely 4377.62 cm-1-4751.74 cm-1, 4755.63 cm-1-5129.75 cm-1, 6262.70 cm-1-6633.93 cm-1, and 7386 cm-1-7756.32 cm-1, respectively. The genetic algorithm (GA) was applied to accurately extract 70 and 33 feature spectral data points from the whole denoised spectral data (DSD) and the four characteristic spectral intervals data (FSD), respectively. Principal component analysis (PCA) was applied, respectively, on the data points selected, and the cumulative contribution rates of the first three PCs were 99.856% and 99.852%. Finally, the back propagation artificial neural (BP-ANN) model with a 3-5-1 structure was calibrated with the first three PCs. When the transfer function was logistic, the best results were obtained (Rp2 = 0.985, RMSEP = 6.732 RMB/kg) by 33 feature spectral data points. The detection effect of the best BP-ANN model by 14 external samples were R2 = 0.987 and RMSEP = 6.670 RMB/kg. The results of this study have achieved real-time, non-destructive, and accurate evaluation and digital display of purchase prices of fresh tea samples by using NIRS technology.

Microwave absorption, physicochemical, elemental mapping, and high-frequency perspectives of the Co, Cu, Zn doped Ni-Ce absorbers for Ku band frequency

The excellent absorbing ability, low weight, and thin size are the key challenges for addressing the electromagnetic issues in military systems and stealth missile technology. Recently, we increased the absorption features of the Co, Cu, and Zn-doped Ni spinel ferrites to improve the absorption performance of the absorbers in a high-frequency regime. The two-step process was employed to synthesize Co, Cu, and Zn substituted Ni-Ce nano ferrites with a nominal composition of Ni0.5×0.3Ce0.2Fe2O4 (X = Co, Cu, Zn). Various tools for the characterizations, such as XRD, FESEM, FTIR, TG/DTA, XPS, VSM, and VNA, investigated these synthesized nanocrystalline ferrites from structural, spectral, thermal, morphological, elemental, magnetic, dielectric, and absorption points of view. XRD results revealed the existence of cubic phase structure in Ni-Ce ferrites. The crystallite size was enhanced from 43 to 54 nm. FTIR spectra showed the existence of tetrahedral and octahedral bands of the Co, Cu, and Zn doped circular-shaped Ni-Ce ferrites. FESEM images revealed the mixed particles of polygonal, circular shaped Co, Cu and Zn doped Ni-Ce ferrite sample. Electron mapping analysis reveals the presence of Ni, Ce, Co, Zn, Cu, Fe, and O elements. VSM analysis was used to calculate the magnetization and other parameters of the samples. Co-doped NiCe absorber depicted RL of -46.8 dB for 2.9 mm thickness at 16 GHz, whereas the Cu-doped NiCe absorber depicted RL of -43.5 dB for 2.8 mm at 14.9 GHz. In addition, Zn doped NiCe absorber shows RL of -51.5 dB is perceived for 2.8 mm thickness at 17.6 GHz, respectively. The Zn-doped NiCe absorber is an excellent absorber for Ku band absorption applications. The prepared absorbers could be used for EMI shielding materials in the next-generation absorbing devices for multipurpose applications.

Enhancing host-pathogen phenotyping dynamics: early detection of tomato bacterial diseases using hyperspectral point measurement and predictive modeling.

Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant disease diagnosis. This study aimed to investigate the potential of HS point-of-measurement (POM) data for in-situ, non-destructive diagnosis of tomato bacterial speck caused by Pseudomonas syringae pv. tomato (Pst), and bacterial spot, caused by Xanthomonas euvesicatoria (Xeu), on leaves (cv. cherry). Bacterial artificial infection was performed on tomato plants at the same phenological stage. A sensing system composed by a hyperspectral spectrometer, a transmission optical fiber bundle with a slitted probe and a white light source were used for spectral data acquisition, allowing the assessment of 3478 spectral points. An applied predictive classification model was developed, consisting of a normalizing pre-processing strategy allied with a Linear Discriminant Analysis (LDA) for reducing data dimensionality and a supervised machine learning algorithm (Support Vector Machine - SVM) for the classification task. The predicted model achieved classification accuracies of 100% and 74% for Pst and Xeu test set assessments, respectively, before symptom appearance. Model predictions were coherent with host-pathogen interactions mentioned in the literature (e.g., changes in photosynthetic pigment levels, production of bacterial-specific molecules, and activation of plants' defense mechanisms). Furthermore, these results were coherent with visual phenotyping inspection and PCR results. The reported outcomes support the application of spectral point measurements acquired in-vivo for plant disease diagnosis, aiming for more precise and eco-friendly phytosanitary approaches.

Research for accurate auxiliary diagnosis of lung cancer based on intracellular fluorescent fingerprint information.

The distinctions in pathological types and genetic subtypes of lung cancer have a direct impact on the choice of treatment choices and clinical prognosis in clinical practice. This study used pathological histological sections of surgically removed or biopsied tumor tissue from 36 patients. Based on a small sample size, millions of spectral data points were extracted to investigate the feasibility of employing intracellular fluorescent fingerprint information to diagnose the pathological types and mutational status of lung cancer. The intracellular fluorescent fingerprint information revealed the EGFR gene mutation characteristics in lung cancer, and the area under the curve (AUC) value for the optimal model was 0.98. For the classification of lung cancer pathological types, the macro average AUC value for the ensemble learning model was 0.97. Our research contributes new idea for pathological diagnosis of lung cancer and offers a quick, easy, and accurate auxiliary diagnostic approach. This article is protected by copyright. All rights reserved.