Mixed Spectrum Research Articles

Comparative characterization of mixed spectra and thermal neutron shielded irradiated tungsten

Comparative characterization of mixed spectra and thermal neutron shielded irradiated tungsten

Quantitative analysis of acetamiprid and thiacloprid in green tea using enhanced SERS and neural networks

Neonicotinoids (NEOs) are widely used in agricultural cultivation, and the presence of residual NEOs poses a serious threat to consumer health. Therefore, trace detection of NEOs is particularly important. In this study, a response surface experimental design was employed to develop AgNP with high enhancement factor and surface height anisotropy, effectively enhancing the Raman signals of acetamiprid (ACE) and thiacloprid (THI) through densely populated hotspots on the surface. Gaussian simulation calculations were used to assign peaks in the ACE and THI spectra. Subsequently, convolutional neural network, backpropagation neural network, and AlexNet neural network were employed to identify the ACE, THI, and mixed component spectra, successfully distinguishing the spectra of the three compounds and achieving qualitative analysis of unknown spectra. Furthermore, genetic algorithm-partial least squares was used to model the spectral data, achieving a correlation coefficient of 0.97. The method was applied for the detection of spiked tea samples, achieving a precision RSD of 4.84 %. Therefore, this sensor combined with intelligent algorithms can be used for spectral identification and trace detection of mixed pesticide samples with structurally similar compounds.

Non-Negative Matrix Factorization with Averaged Kurtosis and Manifold Constraints for Blind Hyperspectral Unmixing

The Nonnegative Matrix Factorization (NMF) algorithm and its variants have gained widespread popularity across various domains, including neural networks, text clustering, image processing, and signal analysis. In the context of hyperspectral unmixing (HU), an important task involving the accurate extraction of endmembers from mixed spectra, researchers have been actively exploring different regularization techniques within the traditional NMF framework. These techniques aim to improve the precision and reliability of the endmember extraction process in HU. In this study, we propose a novel HU algorithm called KMBNMF, which introduces an average kurtosis regularization term based on endmember spectra to enhance endmember extraction, additionally, it integrates a manifold regularization term into the average kurtosis-constrained NMF by constructing a symmetric weight matrix. This combination of these two regularization techniques not only optimizes the extraction process of independent endmembers but also improves the part-based representation capability of hyperspectral data. Experimental results obtained from simulated and real-world hyperspectral datasets demonstrate the competitive performance of the proposed KMBNMF algorithm when compared to state-of-the-art algorithms.

High-throughput single cell analysis using multi-focus Raman spectroscopy under randomly interleaved scattering projection

Raman microspectroscopy is a powerful tool for label-free monitoring of single-cell dynamics. However, the traditional single-point acquisition mode is extremely inefficient because it only analyzes one cell at a time. We propose a method that combines multi-focus Raman excitation with random interleaving of scattering projections. The approach uses a time-sharing multi-focus array to simultaneously excite multiple biological cells and synchronously projects their Raman spectra into a single spectral channel with varying shifts. The spectral shifts of the cells are randomly interleaved during data acquisition, resulting in a sequence of mixed spectra from which a compressive sensing method is utilized to reconstruct the time-lapse Raman spectra of the individual cells. The method’s feasibility and performance are validated by numerical modeling and experimental investigations into biological spore germination. The results indicated that the throughput of single cell analysis can be increased by up to a factor of 15. The reconstructed spectra of the individual cells demonstrated exceptional fidelity, faithfully capturing the cellular changes in the individual cells. The developed technology paves the way towards high-throughput, label-free monitoring of living single cells, which has promising applications in cell biology.

Interpretation of complex x-ray photoelectron peak shapes. I. Case study of Fe 2p3/2 spectra

Analyzing transition metal XPS peaks is widely used to determine surface composition and chemistry. However, these peaks have a complex structure, which is still the subject of investigation. Fe 2p analysis is a case in point where the multiplet structure and many-electron-effects lead to peak shapes that cannot be analyzed using standard approaches. Examination of the literature reveals that one of the most widely used approaches to data reduction when processing Fe 2p3/2 spectra involves using symmetric two- or three-component peak fitting with each peak effectively acting to capture a single chemical species (chemistry fit) in the complex spectra. Herein, this approach is compared to an envelope fit approach using Biesinger multiplet components of known iron oxides to determine how effective these methods are in reproducing iron oxide composition. Mixed oxide and metal XPS Fe 2p spectra were synthesized using reference spectra collected experimentally. For the first time, the accuracy and differences between the two approaches are reported. It is demonstrated that no meaningful conclusions can be drawn using single symmetric peaks to analyze complex Fe 2p3/2 spectra, implying that a large portion of the literature is flawed. The envelope fit approach, however, is shown to provide useful information regarding oxide ratios in mixed iron oxide materials, though limitations do exist. A methodology for evaluating the quality of XPS analysis of Fe 2p3/2 spectra is proposed for benchmarking new submissions so that reviewers, authors, and editors can assess these submissions.

Use of microscale heterogeneity in samples for spectral factorization—A strategy to build robust prediction models for nondestructive analyses

Nondestructive spectroscopic analysis is widely used to evaluate food composition. However, distinguishing analytes of interest from other compounds remains challenging. Since most foods are heterogeneous when viewed under a microscope, we hypothesized that spectra measured at microscopic points would be “purer” than spectra acquired from a larger area. By coupling this data with nonnegative matrix factorization (NMF), the analytes of interest can be separated. This preliminary study discusses the quantification of glucose in mixtures of different sugars. Samples were made by mixing glucose with other powders in different ratios and Raman spectra were measured at 200 micro-points for each sample. NMF was applied to factorize the mixed spectra into spectra of pure compounds and their concentrations, leading to the accurate quantification of glucose, while eliminating the effects of other compounds. While this study targets simple powders, separation of analytes using microscale heterogeneity is applicable for measuring more complex foods.

A novel vegetation-water resistant soil moisture index for remotely assessing soil surface moisture content under the low-moderate wheat cover

Soil surface moisture content (SMC) is one of the most critical and fundamental indicators of crop drought because water is vital for vegetation growth. In recent decades, satellite high-spatial-resolution optical multi-spectral remote-sensing sensors have entered a new stage. However, current optical remote sensing SMC indices (SMIs) are sensitive to cropland SMC and vegetation water content (VWC). From dry to saturated, the soil spectra decreased quickly as the SMC increased. For the vegetation spectra, the short-wave infrared (SWIR) bands decreased as the VWC increased. The differences in the responses of near-infrared (NIR) and SWIR bands to water may help distinguish soil moisture from mixed soil-vegetation spectra. This study focuses on (a) analyzing how soil and vegetation reflectance are affected by VWC and SMC, (b) designing a new vegetation-water resistant soil moisture index (VRSMI) based on the differences in the responses of NIR bands and SWIR bands to water, and (c) evaluating the potential of using VRSMI to remotely estimate SMC in low-moderate crop-covered regions. VRSMI uses SWIR1-SWIR2 as the numerator to detect the water absorption features and NIR×(SWIR1)0.5 as the denominator to weaken the vegetation effect. VRSMI and soil-VRSMI (VRSMIs) were tested using laboratory- and field-based spectra datasets. Our study presents the following conclusions: (1) The differences in the responses of NIR and SWIR bands to water help distinguish the effects of soil moisture on mixed soil-vegetation spectra. (2) VRSMIs can provide high-performance SMC estimates under low-moderate vegetation cover (NDVI < 0.55). (3) VRSMIs can be used for cropland SMC evaluation without considering crop VWC (laboratory-based spectra dataset: RMSE = 0.07–0.11, R2 = 0.96–0.98; field-based spectra dataset: RMSE = 0.17–0.18, R2 = 0.63–0.79). Our study indicates that VRSMI and VRSMIs can provide high-performance relative SMC estimates under low-to-moderate vegetation cover.

Investigation of Lunar Shallow Subsurface Water Content and Soil Elemental Composition via Active Neutron Detection

The detection of planetary water and soil elements is a pivotal area of research due to its implications for understanding celestial bodies. Within the realm of planetary sampling missions, attention is predominantly directed toward the shallow surface layers, typically to a depth of 1 m. This paper examines the Moon as a case study, employing Monte Carlo simulations to introduce an active detection methodology that integrates high-energy neutron pulse generators with neutron and gamma detectors. Simulations were made of the albedo neutrons and prompt gamma counts after mitigating the interference of secondary neutrons and gamma rays, which result from the interaction between galactic cosmic rays and the lunar surface. The depth limit of active neutron detection on the shallow surface is about 100 cm. The cadmium ratio (CdR), the ratio between total neutron counts and counts caused by nonthermal neutrons, facilitates the rapid and accurate water content calculation using a fitted CdR curve. Standard gamma spectra of the associated elements, derived through Monte Carlo simulations, along with the mixed gamma spectra requiring resolution, form the foundation for the spectral analysis. Utilizing the weighted least-squares method to invert gamma spectra facilitates the identification of the content of associated elements. Integrating the analysis of albedo neutron energy spectra with prompt gamma spectra allows for the rapid assessment of the region’s water content and soil conditions. Moreover, this study also explores the impact of variations in the content of associated elements on the determination of water content.

Additive patterns in near-infrared diffuse reflectance spectra: Implications for product formulation and analysis

In this study, we investigated the additive patterns observed in near-infrared (NIR) diffuse reflectance spectra within the context of food and pharmaceutical product formulation. Employing the Kubelka–Munk theory, we examined the linear correlation between spectra and concentration in polymer materials and tobacco powder samples. Our findings confirm the principle of spectral additivity in diffuse reflectance spectra, demonstrating a linear relationship when the sample scattering coefficient remains constant. Moreover, our results validate the feasibility of substituting actual mixed spectra with NIR additive spectra in tobacco leaf systems. This approach can potentially enhance formulation design, thereby improving efficiency and accuracy while expanding the scope and combination of formulation materials. Furthermore, this study offers a rapid, information-rich, and environmentally friendly alternative to traditional methods, with significant implications for the future of product formulation.

Genotypic cluster analysis of Neisseria gonorrhoeae reveals a spectrum of sexual mixing including among HIV-serodiscordant men who have sex with men

Background Men who have sex with men (MSM) are more vulnerable to acquiring sexually transmitted infections (STIs). In 2019, for instance, 74% of European Neisseria gonorrhoeae (Ng) cases among males affected MSM. A recent report by the World Health Organization showed that most of the 2020’ interim targets to end STIs by 2030 had not been met. A broadened understanding of STI transmission networks could guide future elimination strategies and reduce the STI burden. Therefore, we used whole-genome sequencing (WGS) to determine Ng-clusters and assess sexual mixing. Methods WGS was performed on Ng-isolates collected at the Medical University of Vienna, Austria and was used for core genome multi-locus sequencing typing cluster analysis. Epidemiologic and infection-specific details were extracted from medical records. Results Genomic analysis and demographic data were available for 415 isolates, and 43.9% (182/415) were allocated to 31 Ng-clusters. Nine clusters comprised samples from heterosexual individuals only (women N = 4, human immunodeficiency virus (HIV)-negative men N = 49, HIV-positive man N = 1), nine clusters included MSM only (HIV-negative N = 22, HIV-positive N = 13) and 13 clusters included both heterosexuals and MSM (HIV-negative N = 75, HIV-positive N = 18). Current use of HIV pre-exposure prophylaxis (PrEP) was reported by 22.8% of MSM. In multivariate analysis, only ‘MSM’ predicted clustering with isolates from HIV-positive individuals (adjusted odds ratio 10.24 (95% CI 5.02–20.90)). Conclusions Sexual mixing of HIV-positive, HIV-negative MSM and non-MSM was frequently observed. Furthermore, HIV-serodiscordant clustering highlights the importance of PrEP rollout to avert HIV transmission. Our findings can inform future STI prevention strategies and continuous surveillance efforts are required to keep up with transmission dynamics.

Rapid nuclide identification algorithm based on self-attention mechanism neural network

Nuclide identification technology plays a critical role in various fields such as nuclear energy, medicine, environmental monitoring, and defense. However, traditional nuclide identification algorithms face challenges, such as high computational complexity and long response time, when handling large-scale data. The adaptability and universality of these algorithms for different domains and tasks are also confronted with certain challenges. In recent years, numerous nuclide identification methods based on convolutional neural networks have been proposed, to some extent, addressing the issues present in traditional nuclide identification algorithms. Yet, in the context of unmanned nuclear emergencies, nuclear counterterrorism, and post-nuclear contamination processing, there is a widespread need for rapid nuclide identification methods suitable for deployment on edge computing devices. Therefore, this paper combines the advantages of self-attention mechanisms and convolutional neural networks to design a novel network architecture which can meet the aforementioned application requirements. We simulated and measured energy spectrum data for radioactive sources, including 241Am, 57Co, 60Co, 99mTc, 131I, 133Ba, 137Cs, 226Ra, 232Th, and 40K, as the training dataset. In experiments with a training set containing only individual radioactive source energy spectra, the model achieves 99.49% accuracy, with an average inference time of 0.14 ms per sample. It has 0.27 million parameters and 1.55 billion FLOPs. When the training set includes both individual and mixed radioactive source energy spectra, the model achieves 99.84% accuracy, with an average inference time of 0.25 ms per sample. It has 0.83 million parameters and 1.58 billion FLOPs.

Active control of Alfvén eigenmodes by external magnetic perturbations with different spatial spectra

Alfvén eigenmodes have been suppressed and excited in tokamak plasmas by (just) modifying the poloidal spectra of externally applied static magnetic perturbations. This effect is observed experimentally when toroidal spectra of n = 2, n = 4 as well as a mixed spectrum of n = 2 and n = 4 is applied. Under the n = 2 magnetic perturbations, the modes are excited or suppressed by modifying the coil phasing between the upper and the lower set of coils. Regardless of the absolute rotation, an even parity for the n = 4 perturbation is observed to reduce the amplitude of the Alfvénic instabilities, while an odd parity amplifies it. To combine the stabilizing (and destabilizing) effect of n = 2 and n = 4, a mixed spectrum is applied, finding similar reduction (and amplification) trends. However, the impact on the mode amplitude is more subtle, due to the reduced coil current required for a mixed spectrum. The signal level on the fast-ion loss detector is sensitive to the applied poloidal spectrum, which is consistent with Hamiltonian full-orbit modelling of an edge resonant transport layer activated by the 3D perturbative fields. An internal redistribution of the fast-ion population is induced, modifying the phase-space gradients driving the Alfvénic instabilities, and ultimately determining their existence. The calculated edge resonant layers for both n = 2 and n = 4 toroidal spectra are consistent with the observed suppressed and excited phases. Moreover, hybrid kinetic-magnetohydrodynamic (MHD) simulations reveal that this edge resonant transport layer overlaps in phase-space with the population responsible for the fast-ion drive. The results presented here may help to control fast-ion driven Alfvénic instabilities in future burning plasmas with a significant fusion born alpha particle population.

Detection of moisture content of polyester fabric based on hyperspectral imaging and BP neural network

To validate the feasibility and improve the accuracy of water content detection in polyester fabrics using hyperspectral imaging, 150 sets of hyperspectral images of polyester fabrics with varying thicknesses and moisture contents were obtained, and the characteristics of the spectral curves and impact of moisture content were elucidated. In addition, the area and full width at half maximum of the characteristic peaks around 1363 and 1890 nm were determined as spectral characteristic variables. Furthermore, the models of polyester fabric moisture content detection were developed using backpropagation neural networks, and their accuracy was evaluated using correlation coefficient and mean squared error. It was observed that the change in the moisture content of polyester fabrics not only affected the reflectance of the overall spectral curve of polyester fabrics but also altered the position and overall shape of the characteristic peaks. As the moisture content increased, the proportion of pure water spectra in the mixed spectra of water-containing polyester fabrics also increased, leading to a change in the overall shape of the characteristic peaks of polyester fabrics. Because of the overlap between the near-infrared absorption bands of pure water and the polyester fabric around 1363 and 1890 nm, the area and full width at half maximum of the characteristic peaks were considered to be more representative than the reflection for modeling. The established backpropagation neural network–based moisture content quantitative detection model has shown extremely high detection accuracy, with the correlation coefficient for the test set being higher than 0.999 and the root mean square error being lower than 0.3 %, indicating that the detection error of moisture content was only about 0.3 wt%.

Characterization of initial CLABSI culture results to support antimicrobial de-escalation in pediatric GI inpatients.

Central Line-associated Bloodstream Infections (CLABSIs) pose a serious mortality and morbidity risk. An institutional protocol was developed for the evaluation and empirical antibiotic treatment of possible CLABSIs. The potential impact of de-escalating antimicrobial therapy based on initial Gram stain and molecular identification was assessed. All positive blood cultures from patients admitted to the gastroenterology service at a large pediatric medical center were collected from 1/1/14 to 12/31/20. Cultures that were negative, repeated, or causative organisms that were unable to be identified with susceptibility data were excluded. Timepoints and organism(s) from each culture were recorded. Polymicrobial cultures were classified as containing only gram-positive organisms (polymicrobial GP), only gram-negative organisms (polymicrobial GN), or mixed spectrum. During the 6-year period, 361 positive blood cultures were included in the study. Single isolates were identified in 79.5% (287/361) of cultures. Polymicrobial cultures from confirmed central line source accounted for 15.0% (54/361), with 6.4% (23/361) Polymicrobial GP, 4.4% (16/361) Polymicrobial GN, and 4.2% (15/361) being mixed-spectrum cultures. Both organism types were detected on initial gram-stain in 40% (6/15) of the mixed-spectrum cultures, another 26.7% (4/15) had the opposite-spectrum organism identified within an average of <3 h and the remaining 33.3% (5/15) had the opposite-spectrum organism identified by culture growth. Polymicrobial mixed-spectrum cultures accounted for <5% of positive blood cultures and most isolates were identified within 3 h of first positivity. This may allow for further investigation of early de-escalation of therapy for this population and limit antimicrobial exposure.

Deep learning-based Raman spectroscopy qualitative analysis algorithm: A convolutional neural network and transformer approach

Raman spectroscopy is a general and non-destructive detection technique that can obtain detailed information of the chemical structure of materials. In the past, when using chemometric algorithms to analyze the Raman spectra of mixtures, the challenges of complex spectral overlap and noise often limited the accurate identification of components. The emergence of deep learning has introduced a novel approach to qualitative analysis of mixed Raman spectra. In this paper, we propose a deep learning-based Raman spectroscopy qualitative analysis algorithm (RST) by borrowing the ideas of convolutional neural network and Transformer. By transforming the Raman spectrum into 64 word vectors, the contribution weights of each word vector to the components are obtained. For the 75 spectral data used for validation, the positive identification rate can reach 100.00 %, the recall rate can reach 99.3 %, the average identification score can reach 9.51, and it is applicable to the fields of Raman and surface-enhanced Raman spectroscopy. Furthermore, compared with traditional CNN models, RST has excellent accuracy and robustness in identifying components in complex mixtures. The model's interpretability has been enhanced, aiding in a deeper understanding of spectroscopic learning patterns for future analysis of more complex mixtures.

Optimization of clonal micropropagation of stone fruit crops

The aim of the research was to optimize the clonal micropropagation of stone crops through the use of improved techniques. The objects of the research at the stage of introduction into culture in vitro were meristematic apexes, at the stage of micro-propagation itself – micro-shoots, at the rooting stage – rooted micro-shoots, at the adaptation stage – micro-plants. All experiments were carried out according to generally accepted methods using the example of the steppe cherry variety Shchedraya, the domestic plum variety Kazanskaya and the hybrid sweet cherry Fatezh. As a result of the research, it was found that the use of a 10 % chlorhexidine solution for sterilization of the initial plant material of stone crops increased the survival rate of apexes on average by 2.4 %; the combined use of growth regulators 6-benzylaminopurine 0.5 mg/l, gibberellic acid 0.2 mg/l, indo-lil-3-butyric acid (IBA) 0.2 mg/l in a nutrient medium and an experimental diode pulsed phytoirradiator in the last proliferation passage activated the proliferation of micro-shoots, increasing the reproduction coefficient of stone crops in 6 passages by an average of 0.9 pcs/explant; the addition of 1.0 mg/l (IBA ) to the nutrient medium provided an increase in the rooting capacity of micro-shoots of stone crops by an average of 9.6 %. The treatment of micro-plants of stone crops at the stage of adaptation by spraying with an 8 % solution of the extract of the products of the larvae of the large wax moth and the use of an experimental LED pulsed phytoirradiator with a mixed spectrum contributed to an increase in their survival rate by an average of 12.4 %. Compliance with the proposed improved methods of clonal micropropagation of stone crops using growth regulators and an experimental LED pulse phytoirradiator with a mixed spectrum made it possible to increase the yield of standard adapted micro-plants by 2 times. At the same time, the cost of one adapted micro-plant decreased by an average of 11.4 rubles, and the profitability of obtaining improved stone crop material increased by 33.7 % and amounted to 160.7 %.

Impact of increasing plasma-wall gap on plasma response to RMP fields in ITER

The impact of increasing plasma-wall gap on controlling edge-localized modes (ELMs) is numerically evaluated for the ITER 5 MA/2.65 T H-mode scenarios with full tungsten wall, based on the MARS-F computed plasma response to the applied n= 3–5 (n is the toroidal mode number) resonant magnetic perturbation (RMP) fields. Three new scenarios, referred to as standard, clearance and outergap, are considered assuming different plasma-wall gap sizes over a range on which vertically stability can be maintained by in-vessel coils in ITER. The latter are shown to have both direct and indirect effects on the plasma response and hence ELM control in ITER. The indirect and also primary influence occurs via change of the equilibrium edge safety factor q95 , which decreases with increasing the plasma-wall gap (at fixed plasma current and toroidal field), leading to a multi-peaking structure in the plasma response as measured by the plasma displacement near the X-point or the edge-localized resonant radial magnetic field perturbation. The direct, albeit secondary effect, is the reduction of local peak amplitudes with increasing the plasma-wall gap thus weakening the RMP field efficiency for ELM control with a given current in the control coils. A slight reduction of the plasma current, from 5 MA to 4.77, 4.92 and 4.65 MA for the standard, clearance and outergap scenarios, respectively, is found to be sufficient to access the q 95 window for the best ELM control with the n= 3 RMP. The n= 4 coil current configuration with the n= 5 sideband is also found favorable for ELM control in ITER, by producing RMP fields with mixed toroidal spectra compared to n = 3.

A Gaussian-mixture-model based peak splitting method in X-ray fluorescence for high concentration ratio solution

Simultaneous detection of multi-heavy metal ions in zinc sulfate solution facilitates the zinc hydrometallurgy process control. X-ray fluorescence spectrometry is widely used for composition detection due to its high detection speed and no sample pre-treatment. However, the high matrix ion-to-trace impurity ion ratio in zinc sulfate causes severe spectral overlap, hindering pure peak area acquisition—a vital feature in ion concentration detection. To address this, a peak-splitting method based on the Gaussian mixture model was proposed. Initially, two ion mixing spectra were obtained with optimized reagents. Subsequently, Gaussian models were established for the characteristic peak regions with varying distributions. Finally, the proposed model was combined with Chaotic Sparrow Search Algorithm to handle the overlapping peaks. The experimental results demonstrated superior stability and additivity compared to other methods. All coefficients of determination between single peak areas and ion concentrations exceeded 0.99, confirming the method's efficacy in resolving heavily masked overlapping peaks.

Research on energy spectrum anomaly detection method for state control radiation environmental monitoring based on LOF algorithm

The state-controlled radiation environment automatic monitoring stations are important facilities for monitoring the quality and trends of the radiation environment. However, the analysis and utilization of monitoring data from these stations have limitations. To address this issue, this paper proposes an approach that utilizes the high-dimensional data visualization method based on t-SNE and PCA-BIRCH algorithm's spectrum clustering model to classify historical spectrum data. Subsequently, a local outlier factor-based environmental spectrum abnormal fluctuation detection model is established to enhance discrimination of non-environmental factors causing spectrum anomalies. Results indicate significant differences in spectrum aggregation under different meteorological conditions. The clustering algorithm effectively separates clear and rainy weather spectra based on spectral features, with minimal time consumption and reduced interference from mixed spectrum data. By utilizing local density features, we establish an anomaly detection model, assigning an abnormal score to each spectrum. Comparative analysis demonstrates improved discriminability of the anomaly detection model for unclassified datasets. Our algorithm accurately identifies spectrum fluctuations caused by non-environmental factors amidst complex backgrounds, offering valuable technical support for environmental quality assurance and nuclear emergency decision-making.

Growth Light Quality Influences Leaf Surface Temperature by Regulating the Rate of Non-Photochemical Quenching Thermal Dissipation and Stomatal Conductance

Significant efforts have been made to optimise spectrum quality in indoor farming to maximise artificial light utilisation and reduce water loss. For such an improvement, green (G) light supplementation to a red–blue (RB) background was successfully employed in our previous studies to restrict both non-photochemical quenching (NPQ) and stomatal conductance (gs). At the same time, however, the downregulation of NPQ and gs had the opposite influence on leaf temperature (Tleaf). Thus, to determine which factor plays the most prominent role in Tleaf regulation and whether such a response is temporal or permanent, we investigated the correlation between NPQ and gs and, subsequently, Tleaf. To this end, we analysed tomato plants (Solanum lycopersicum L. cv. Malinowy Ozarowski) grown solely under monochromatic LED lamps (435, 520, or 662 nm; 80 µmol m−2 s−1) or a mixed RGB spectrum (1:1:1; 180 µmol m−2 s−1) and simultaneously measured gs and Tleaf with an infrared gas analyser and a thermocouple or an infrared thermal camera (FLIR) during thermal imaging analyses. The results showed that growth light quality significantly modifies Tleaf and that such a response is not temporal. Furthermore, we found that the actual adaxial leaf surface temperature of plants is more closely related to NPQ amplitude, while the temperature of the abaxial surface corresponds to gs.