Laser NIR Irradiation Enhances Antimicrobial Photodynamic Inactivation of Biofilms of Staphylococcus aureus.

OBJECTIVES: Photodynamic inactivation (PDI) is a powerful technique for eradicating microorganisms, and our group previously demonstrated its effectiveness against planktonic cultures of Staphylococcus aureus bacteria using 5,10,15,20-tetrakis[4-(3-N,N-dimethylaminopropoxy)phenyl]porphyrin (TAPP) and visible light irradiation. However, biofilms exhibit a lower sensitivity to PDI, mainly due to limited penetration of the photosensitizer (PS). In the context of emerging antibacterial strategies, near-infrared treatments (NIRTs) have shown promise, especially for combating resistant strains. NIRT can act either through photon absorption by water, causing a thermal effect on bacteria, or by specific chromophores without a significant temperature increase. Our objective was to enhance biofilm sensitivity to TAPP-PDI by pretreatment with NIRT. This combined approach aims to disrupt biofilms and increase the efficacy of TAPP-PDI against bacterial biofilms. MATERIALS AND METHODS: In vitro biofilm models of S. aureus RN6390 were utilized. NIRTs involved a 980 nm laser (continuous mode, 7.5 W/cm2, 30 s, totaling 225 J/cm2) post-TAPP exposure to enhance photosensitizer accumulation. Subsequent visible light irradiation at 180 J/cm2 was employed to perform PDI. Colony-forming unit counts evaluated the synergistic effect on bacterial viability. Scanning electron microscopy visualized the architectural changes in the biofilm structure. TAPP was extracted from bacteria to estimate the impact of NIRT on biofilm penetration. RESULTS: Using in vitro biofilm models, NIRT application following biofilm exposure to TAPP increased PS accumulation per bacteria. Under these conditions, NIRT induced a transient increase in the temperature of PBS to 46.0 ± 2.6°C (ΔT = 21.5°C). Following exposure to visible light, a synergistic effect emerged, yielding a substantial 4.4 ± 0.1-log CFU reduction. In contrast, the PDI and NIRT treatments individually caused a decrease in viability of 0.9 ± 0.1 and 0.8 ± 0.2-log respectively. Interestingly, preheating TAPP-PBS to 46°C had no significant impact on TAPP-PDI efficacy, suggesting the involvement of thermal and nonthermal effects of NIR action. In addition to the enhanced TAPP penetration, NIRT dispersed the biofilms and induced clefts in the biofilm matrix. CONCLUSION: Our findings suggest that NIR irradiation serves as a complementary treatment to PDI. This combined strategy reduces bacterial numbers at lower PS concentrations than standalone PDI treatment, highlighting its potential as an effective and resource-efficient antibacterial approach.

Fluorescence and Hyperspectral Sensors for Nondestructive Analysis and Prediction of Biophysical Compounds in the Green and Purple Leaves of Tradescantia Plants.

The application of non-imaging hyperspectral sensors has significantly enhanced the study of leaf optical properties across different plant species. In this study, chlorophyll fluorescence (ChlF) and hyperspectral non-imaging sensors using ultraviolet-visible-near-infrared shortwave infrared (UV-VIS-NIR-SWIR) bands were used to evaluate leaf biophysical parameters. For analyses, principal component analysis (PCA) and partial least squares regression (PLSR) were used to predict eight structural and ultrastructural (biophysical) traits in green and purple Tradescantia leaves. The main results demonstrate that specific hyperspectral vegetation indices (HVIs) markedly improve the precision of partial least squares regression (PLSR) models, enabling reliable and nondestructive evaluations of plant biophysical attributes. PCA revealed unique spectral signatures, with the first principal component accounting for more than 90% of the variation in sensor data. High predictive accuracy was achieved for variables such as the thickness of the adaxial and abaxial hypodermis layers (R2 = 0.94) and total leaf thickness, although challenges remain in predicting parameters such as the thickness of the parenchyma and granum layers within the thylakoid membrane. The effectiveness of integrating ChlF and hyperspectral technologies, along with spectroradiometers and fluorescence sensors, in advancing plant physiological research and improving optical spectroscopy for environmental monitoring and assessment. These methods offer a good strategy for promoting sustainability in future agricultural practices across a broad range of plant species, supporting cell biology and material analyses.

Red and near-infrared light-activated photoelectrochemical nanobiosensors for biomedical target detection.

Photoelectrochemical (PEC) nanobiosensors integrate molecular (bio)recognition elements with semiconductor/plasmonic photoactive nanomaterials to produce measurable signals after light-induced reactions. Recent advancements in PEC nanobiosensors, using light-matter interactions, have significantly improved sensitivity, specificity, and signal-to-noise ratio in detecting (bio)analytes. Tunable nanomaterials activated by a wide spectral radiation window coupled to electrochemical transduction platforms have further improved detection by stabilizing and amplifying electrical signals. This work reviews PEC biosensors based on nanomaterials like metal oxides, carbon nitrides, quantum dots, and transition metal chalcogenides (TMCs), showing their superior optoelectronic properties and analytical performance for the detection of clinically relevant biomarkers. Furthermore, it highlights the innovative role of red light and NIR-activated PEC nanobiosensors in enhancing charge transfer processes, protecting them from biomolecule photodamage in vitro and in vivo applications. Overall, advances in PEC detection systems have the potential to revolutionize rapid and accurate measurements in clinical diagnostic applications. Their integration into miniaturized devices also supports the development of portable, easy-to-use diagnostic tools, facilitating point-of-care (POC) testing solutions and real-time monitoring.

NIR-Based Electronic Platform for Glucose Monitoring for the Prevention and Control of Diabetes Mellitus.

The glucose level in the blood is measured through invasive methods, causing discomfort in the patient, loss of sensitivity in the area where the sample is obtained, and healing problems. This article deals with the design, implementation, and evaluation of a device with an ESP-WROOM-32D microcontroller with the application of near-infrared photospectroscopy technology that uses a diode array that transmits between 830 nm and 940 nm to measure glucose levels in the blood. In addition, the system provides a webpage for the monitoring and control of diabetes mellitus for each patient; the webpage is hosted on a local Linux server with a MySQL database. The tests are conducted on 120 people with an age range of 35 to 85 years; each person undergoes two sample collections with the traditional method and two with the non-invasive method. The developed device complies with the ranges established by the American Diabetes Association: presenting a measurement error margin of close to 3% in relation to traditional blood glucose measurement devices. The purpose of the study is to design and evaluate a device that uses non-invasive technology to measure blood glucose levels. This involves constructing a non-invasive glucometer prototype that is then evaluated in a group of participants with diabetes.

Near infrared spectroscopy for cooking time classification of cassava genotypes.

Cooking time is a crucial determinant of culinary quality of cassava roots and incorporating it into the early stages of breeding selection is vital for breeders. This study aimed to assess the potential of near-infrared spectroscopy (NIRS) in classifying cassava genotypes based on their cooking times. Five cooking times (15, 20, 25, 30, and 40 minutes) were assessed and 888 genotypes evaluated over three crop seasons (2019/2020, 2020/2021, and 2021/2022). Fifteen roots from five plants per plot, featuring diameters ranging from 4 to 7 cm, were randomly chosen for cooking analysis and spectral data collection. Two root samples (15 slices each) per genotype were collected, with the first set aside for spectral data collection, processed, and placed in two petri dishes, while the second set was utilized for cooking assessment. Cooking data were classified into binary and multiclass variables (CT4C and CT6C). Two NIRs devices, the portable QualitySpec® Trek (QST) and the benchtop NIRFlex N-500 were used to collect spectral data. Classification of genotypes was carried out using the K-nearest neighbor algorithm (KNN) and partial least squares (PLS) models. The spectral data were split into a training set (80%) and an external validation set (20%). For binary variables, the classification accuracy for cassava cooking time was notably high ( R C a l 2 ranging from 0.72 to 0.99). Regarding multiclass variables, accuracy remained consistent across classes, models, and NIR instruments (~0.63). However, the KNN model demonstrated slightly superior accuracy in classifying all cooking time classes, except for the CT4C variable (QST) in the NoCook and 25 min classes. Despite the increased complexity associated with binary classification, it remained more efficient, offering higher classification accuracy for samples and facilitating the selection of the most relevant time or variables, such as cooking time ≤ 30 minutes. The accuracy of the optimal scenario for classifying samples with a cooking time of 30 minutes reached R C a l 2   = 0.86 and R V a l 2 = 0.84, with a Kappa value of 0.53. Overall, the models exhibited a robust fit for all cooking times, showcasing the significant potential of NIRs as a high-throughput phenotyping tool for classifying cassava genotypes based on cooking time.

Hyperspectral and Chlorophyll Fluorescence Analyses of Comparative Leaf Surfaces Reveal Cellular Influences on Leaf Optical Properties in Tradescantia Plants.

The differential effects of cellular and ultrastructural characteristics on the optical properties of adaxial and abaxial leaf surfaces in the genus Tradescantia highlight the intricate relationships between cellular arrangement and pigment distribution in the plant cells. We examined hyperspectral and chlorophyll a fluorescence (ChlF) kinetics using spectroradiometers and optical and electron microscopy techniques. The leaves were analysed for their spectral properties and cellular makeup. The biochemical compounds were measured and correlated with the biophysical and ultrastructural features. The main findings showed that the top and bottom leaf surfaces had different amounts and patterns of pigments, especially anthocyanins, flavonoids, total phenolics, chlorophyll-carotenoids, and cell and organelle structures, as revealed by the hyperspectral vegetation index (HVI). These differences were further elucidated by the correlation coefficients, which influence the optical signatures of the leaves. Additionally, ChlF kinetics varied between leaf surfaces, correlating with VIS-NIR-SWIR bands through distinct cellular structures and pigment concentrations in the hypodermis cells. We confirmed that the unique optical properties of each leaf surface arise not only from pigmentation but also from complex cellular arrangements and structural adaptations. Some of the factors that affect how leaves reflect light are the arrangement of chloroplasts, thylakoid membranes, vacuoles, and the relative size of the cells themselves. These findings improve our knowledge of the biophysical and biochemical reasons for leaf optical diversity, and indicate possible implications for photosynthetic efficiency and stress adaptation under different environmental conditions in the mesophyll cells of Tradescantia plants.

Comparison of hyperspectral imaging and spectrometers for prediction of cheeses composition.

Artisanal cheeses are part of the heritage and identity of different countries or regions. In this work, we investigated the spectral variability of a wide range of traditional Brazilian cheeses and compared the performance of different spectrometers to discriminate cheese types and predict compositional parameters. Spectra in the visible (vis) and near infrared (NIR) region were collected, using imaging (vis/NIR-HSI and NIR-HSI) and conventional (NIRS) spectrometers, and it was determined the chemical composition of seven types of cheeses produced in Brazil. Principal component analysis (PCA) showed that spectral variability in the vis/NIR spectrum is related to differences in color (yellowness index) and fat content, while in NIR there is a greater influence of productive steps and fat content. Partial least squares discriminant analysis (PLSDA) models based on spectral information showed greater accuracy than the model based on chemical composition to discriminate types of traditional Brazilian cheeses. Partial least squares (PLS) regression models based on vis/NIR-HSI, NIRS, NIR-HSI data and HSI spectroscopic data fusion (vis/NIR + NIR) demonstrated excellent performance to predict moisture content (RPD > 2.5), good ability to predict fat content (2.0 < RPD < 2.5) and can be used to discriminate between high and low protein values (∼1.5 < RPD < 2.0). The results obtained for imaging and conventional equipment are comparable and sufficiently accurate, so that both can be adapted to predict the chemical composition of the Brazilian traditional cheeses used in this study according to the needs of the industry.

Light-Responsive and Dual-Targeting Liposomes: From Mechanisms to Targeting Strategies.

In recent years, nanocarriers have played an ever-increasing role in clinical and biomedical applications owing to their unique physicochemical properties and surface functionalities. Lately, much effort has been directed towards the development of smart, stimuli-responsive nanocarriers that are capable of releasing their cargos in response to specific stimuli. These intelligent-responsive nanocarriers can be further surface-functionalized so as to achieve active tumor targeting in a sequential manner, which can be simply modulated by the stimuli. By applying this methodological approach, these intelligent-responsive nanocarriers can be directed to different target-specific organs, tissues, or cells and exhibit on-demand controlled drug release that may enhance therapeutic effectiveness and reduce systemic toxicity. Light, an external stimulus, is one of the most promising triggers for use in nanomedicine to stimulate on-demand drug release from nanocarriers. Light-triggered drug release can be achieved through light irradiation at different wavelengths, either in the UV, visible, or even NIR region, depending on the photophysical properties of the photo-responsive molecule embedded in the nanocarrier system, the structural characteristics, and the material composition of the nanocarrier system. In this review, we highlighted the emerging functional role of light in nanocarriers, with an emphasis on light-responsive liposomes and dual-targeted stimuli-responsive liposomes. Moreover, we provided the most up-to-date photo-triggered targeting strategies and mechanisms of light-triggered drug release from liposomes and NIR-responsive nanocarriers. Lastly, we addressed the current challenges, advances, and future perspectives for the deployment of light-responsive liposomes in targeted drug delivery and therapy.

Assessing Polymorphic Purity of Rifampicin in Double and Triple-Drug Fixed-Dose Combination Products.

First-line tuberculostatic agents, Rifampicin (RIF), Isoniazid (ISH), Ethambutol (ETB), and Pyrazinamide (PZA) are generally administered as a fixed-dose combination (FDC) for improving patient adherence. The major quality challenge of these FDC products is their variable bioavailability, where RIF and its solid state are key factors. In this work, the analysis of the impact of the polymorphism in the performance of RIF in RIF-ISH and PZA-RIF-ISH combined products was carried out by an overall approach that included the development and validation of two methodologies combining near-infrared (NIR) spectroscopy and partial least squares (PLS) to the further evaluation of commercial products. For NIR-PLS methods, training and validation sets were prepared with mixtures of Form I/Form II of RIF, and the appropriate amount of ISH (for double associations) or ISH-PZA (for triple associations). The corresponding matrix of the excipients was added to the mixture of APIs to simulate the environment of each FDC product. Four PLS factors, reduced spectral range, and the combination of standard normal variate and Savitzky-Golay 1st derivative (SNV-D') were selected as optimum data pre-treatment for both methods, yielding satisfactory recoveries during the analysis of validation sets (98.5±2.0%, and 98.7±1.8% for double- and triple-FDC products, respectively). The NIR-PLS model for RIF-ISH successfully estimated the polymorphic purity of Form II in double-FDC capsules (1.02 ± 0.02w/w). On the other hand, the NIR-PLS model for RIF-ISH-PZA detected a low purity of Form II in triple FDC tablets (0.800 ± 0.021w/w), these results were confirmed by X-ray powder diffraction. Nevertheless, the triple-FDC tablets showed good performance in the dissolution test (Q=99-102%), implying a Form II purity about of 80% is not low enough to affect the safety and efficacy of the product.

Electrochemical Diselenation of BODIPY Fluorophores for Bioimaging Applications and Sensitization of 1 O2.

We report a rapid, efficient, and scope-extensive approach for the late-stage electrochemical diselenation of BODIPYs. Photophysical analyses reveal red-shifted absorption - corroborated by TD-DFT and DLPNO-STEOM-CCSD computations - and color-tunable emission with large Stokes shifts in the selenium-containing derivatives compared to their precursors. In addition, due to the presence of the heavy Se atoms, competitive ISC generates triplet states which sensitize 1 O2 and display phosphorescence in PMMA films at RT and in a frozen glass matrix at 77 K. Importantly, the selenium-containing BODIPYs demonstrate the ability to selectively stain lipid droplets, exhibiting distinct fluorescence in both green and red channels. This work highlights the potential of electrochemistry as an efficient method for synthesizing unique emission-tunable fluorophores with broad-ranging applications in bioimaging and related fields.

A Spatial-Spectral Classification Method Based on Deep Learning for Controlling Pelagic Fish Landings in Chile.

Fishing has provided mankind with a protein-rich source of food and labor, allowing for the development of an important industry, which has led to the overexploitation of most targeted fish species. The sustainable management of these natural resources requires effective control of fish landings and, therefore, an accurate calculation of fishing quotas. This work proposes a deep learning-based spatial-spectral method to classify five pelagic species of interest for the Chilean fishing industry, including the targeted Engraulis ringens, Merluccius gayi, and Strangomera bentincki and non-targeted Normanichthtys crockeri and Stromateus stellatus fish species. This proof-of-concept method is composed of two channels of a convolutional neural network (CNN) architecture that processes the Red-Green-Blue (RGB) images and the visible and near-infrared (VIS-NIR) reflectance spectra of each species. The classification results of the CNN model achieved over 94% in all performance metrics, outperforming other state-of-the-art techniques. These results support the potential use of the proposed method to automatically monitor fish landings and, therefore, ensure compliance with the established fishing quotas.

An improved successive projections algorithm version to variable selection in multiple linear regression.

The aim of the successive projections algorithm (SPA) is to enhance the accuracy of multiple linear regressions (MLR) by minimizing the impact of collinearity effects in the calibration data set. Combining SPA with MLR as a variable selection approach has resulted in the SPA-MLR method, which has been reported in literature to produce models with good prediction ability compared to conventional full-spectrum models obtained with partial-least-squares (PLS) in some cases. This paper proposes the addition of a filter step to the current version of the SPA algorithm to reduce the number of uninformative variables before the projection phase and assist the algorithm in selecting the best variables on subsequent steps. The proposed fSPA-MLR algorithm is evaluated in two case studies involving the near-infrared spectrometric analysis of pharmaceutical tablet and diesel/biodiesel mixture samples. Compared to PLS, the fSPA-MLR models demonstrate similar or better performance. Moreover, the fSPA-MLR models outperform the original SPA-MLR in both cross-validation and external prediction. The fSPA-MLR models deliver superior results regardless of the pre-processing algorithm tested, including first-derivative Savitzky-Golay (SG) and Standard Normal Variate (SNV), or even in raw spectra data.

Human Exposure to Non-Ionizing Radiation from Indoor Distributed Antenna System: Shopping Mall Measurement Analysis.

It is crucial to monitor the levels of Non-Ionizing Radiation (NIR) to which the general population may be exposed and compare them to the limits defined in the current standards, in view of the rapid rise of communication services and the prospects of a connected society. A high number of people visits shopping malls and since these locations usually have several indoor antennas close to the public, it is therefore a kind of place that must be evaluated. Thus, this work presents measurements of the electric field in a shopping mall located in Natal, Brazil. We proposed a set of six measurement points, following two criteria: places with great the flow of people and the presence of one or more Distributed Antenna System (DAS), co-sited or not with WiFi access points. Results are presented and discussed in terms of the distance to DAS (conditions: near and far) and flow density of people in the mall (scenarios: low and high number of people). The highest peaks of electric field measured were 1.96 and 3.26 V/m, respectively corresponding to 5% and 8% of the limits defined by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and the Brazilian National Telecommunication Agency (ANATEL).

Synthesis of gold decorated silica nanoparticles and their photothermal properties.

Silica-Gold Nanostructures (SGNs), composed of a silica core decorated by gold nanoparticles, have the photothermal capacity to transform near-infrared (NIR) wavelengths into heat. This work presents a simple, efficient, and replicable method of synthesis of SGNs and their characterization by: (1) transmission electron microscopy to obtain micrographs of the particles and their corresponding diameter distribution; (2) diffraction patterns showing the amorphous atomic arraignment of the silica and the crystalline atomic arrangement of the gold nanoparticles; (3) zeta potential confirming the stability of the SGNs in a colloidal solution; and (4) thermal images displaying the capacity of SGNs to convert NIR irradiation into heat and their respective increment in temperature. SGNs were synthesized over silica cores with diameters of 63, 83, and 132 nm and decorated with a partial gold shell. They were heated with a coherent light intensity of 340 mW/cm2 with a wavelength of 852 nm. This wavelength is within the range of the optical window of the human body; therefore, SGNs may be used for the photothermal ablation of tumors with no damage to the tissue. The heating of different dimensions of SGNs took 6-8 min of NIR radiation, and their cooling, once the laser was turned off, was in the order of 2-3 min. It was found that SGNs, with a core diameter of 132 nm, have a notable photothermal capacity. That enables them to increase the temperature of their surroundings by 4.4 ºC. This increment in temperature is sufficient to induce cellular necrosis, which makes SGNs a good option for photothermal treatments.

Path analysis and near-infrared spectroscopy in canola crop / Análise de trilha e espectroscopia no infravermelho próximo na cultura da canola

ABSTRACT: This study measured the effect of the association between agronomic traits related to the yield of canola grains grown at different sowing dates through path analysis. Another objective was to obtain a method to predict the oil content in the grains, fitting a multivariate model through near-infrared (NIR) spectroscopy analysis. The experiment was conducted in the field using a randomized block design in plots subdivided by time, with four plots (sowing dates), six subplots (canola hybrids), and four replicates. In each hybrid, phenological observations were performed, and the grain yield was determined. The data were subjected to analysis of variance in the R environment using the F test at 5% probability. The oil content in the grains was determined by the traditional chemical method, and based on the NIR spectral signature of the grain samples, partial least squares regression (PLS-R) was established to estimate the oil content in the canola grains. The sowing dates influenced the production components and oil content of the grains of all hybrids. The trait number of grains in five plants (0.6857) and their height (0.4943) had greater estimates of positive correlations with grain yield, as well as higher values of positive direct effects on yield (0.2494 and 0.1595, respectively). The NIR technique combined with PLS-R was able to predict the oil content in the grains, resulting in good predictive models (R2 of 0.86 and root mean square error (RMSE) of 1.56 in external validation).

RESUMO: Objetivou-se mensurar o efeito da associação entre caracteres agronômicos relacionados à produtividade de grãos de canola cultivada em diferentes épocas de semeadura, através da análise de trilha. Assim como também objetivou-se obter um método para predizer o teor de óleo nos grãos, ajustando um modelo multivariado através da análise por espectroscopia na região do infravermelho próximo. O experimento foi conduzido em campo, utilizando-se o delineamento de blocos ao acaso, em parcelas subdivididas no tempo, sendo quatro parcelas (épocas de semeadura) e seis subparcelas (híbridos de canola), com quatro repetições. Em cada híbrido foram realizadas observações fenológicas e determinada a produtividade de grãos. Os dados foram submetidos à análise de variância em ambiente R pelo teste F, a cinco de probabilidade. O teor de óleo nos grãos foi determinado pelo método químico tradicional, e com base na assinatura espectral no infravermelho próximo de amostras dos grãos foi estabelecida regressão dos mínimos quadrados parciais (PLS-R) para estimar o teor de óleo nos grãos de canola. As épocas de semeadura influenciaram os componentes de produção e o teor de óleo dos grãos de todos híbridos. Os caracteres número de grãos em cinco plantas (0,6857) e altura (0,4943) apresentaram maiores estimativas de correlação positiva com a produtividade de grãos, assim como os maiores valores de efeito direto positivo sobre a produtividade, 0,2494 e 0,1595 respectivamente. Entretanto, o ciclo total (-0,7848), juntamente com dias em florescimento (-0,4520) apresentou correlação significativa negativa com a produtividade. A técnica NIR associada à PLS-R foi capaz de predizer o teor de óleo nos grãos, resultando em bons modelos preditivos (R2 de 0,86 e RMSE de 1,56 na validação externa) que podem ser usados com sucesso na análise da qualidade das amostras após colheita e nos programas de melhoramento genético.

Path analysis and near-infrared spectroscopy in canola crop / Análise de trilha e espectroscopia no infravermelho próximo na cultura da canola

This study measured the effect of the association between agronomic traits related to the yield of canola grains grown at different sowing dates through path analysis. Another objective was to obtain a method to predict the oil content in the grains, fitting a multivariate model through near-infrared (NIR) spectroscopy analysis. The experiment was conducted in the field using a randomized block design in plots subdivided by time, with four plots (sowing dates), six subplots (canola hybrids), and four replicates. In each hybrid, phenological observations were performed, and the grain yield was determined. The data were subjected to analysis of variance in the R environment using the F test at 5% probability. The oil content in the grains was determined by the traditional chemical method, and based on the NIR spectral signature of the grain samples, partial least squares regression (PLS-R) was established to estimate the oil content in the canola grains. The sowing dates influenced the production components and oil content of the grains of all hybrids. The trait number of grains in five plants (0.6857) and their height (0.4943) had greater estimates of positive correlations with grain yield, as well as higher values of positive direct effects on yield (0.2494 and 0.1595, respectively). The NIR technique combined with PLS-R was able to predict the oil content in the grains, resulting in good predictive models (R2 of 0.86 and root mean square error (RMSE) of 1.56 in external validation).

Objetivou-se mensurar o efeito da associação entre caracteres agronômicos relacionados à produtividade de grãos de canola cultivada em diferentes épocas de semeadura, através da análise de trilha. Assim como também objetivou-se obter um método para predizer o teor de óleo nos grãos, ajustando um modelo multivariado através da análise por espectroscopia na região do infravermelho próximo. O experimento foi conduzido em campo, utilizando-se o delineamento de blocos ao acaso, em parcelas subdivididas no tempo, sendo quatro parcelas (épocas de semeadura) e seis subparcelas (híbridos de canola), com quatro repetições. Em cada híbrido foram realizadas observações fenológicas e determinada a produtividade de grãos. Os dados foram submetidos à análise de variância em ambiente R pelo teste F, a cinco de probabilidade. O teor de óleo nos grãos foi determinado pelo método químico tradicional, e com base na assinatura espectral no infravermelho próximo de amostras dos grãos foi estabelecida regressão dos mínimos quadrados parciais (PLS-R) para estimar o teor de óleo nos grãos de canola. As épocas de semeadura influenciaram os componentes de produção e o teor de óleo dos grãos de todos híbridos. Os caracteres número de grãos em cinco plantas (0,6857) e altura (0,4943) apresentaram maiores estimativas de correlação positiva com a produtividade de grãos, assim como os maiores valores de efeito direto positivo sobre a produtividade, 0,2494 e 0,1595 respectivamente. Entretanto, o ciclo total (-0,7848), juntamente com dias em florescimento (-0,4520) apresentou correlação significativa negativa com a produtividade. A técnica NIR associada à PLS-R foi capaz de predizer o teor de óleo nos grãos, resultando em bons modelos preditivos (R2 de 0,86 e RMSE de 1,56 na validação externa) que podem ser usados com sucesso na análise da qualidade das amostras após colheita e nos programas de melhoramento genético.

Near-Infrared Spectroscopy with Supervised Machine Learning as a Screening Tool for Neutropenia.

The use of non-invasive tools in conjunction with artificial intelligence (AI) to detect diseases has the potential to revolutionize healthcare. Near-infrared spectroscopy (NIR) is a technology that can be used to analyze biological samples in a non-invasive manner. This study evaluated the use of NIR spectroscopy in the fingertip to detect neutropenia in solid-tumor oncologic patients. A total of 75 patients were enrolled in the study. Fingertip NIR spectra and complete blood counts were collected from each patient. The NIR spectra were pre-processed using Savitzky-Golay smoothing and outlier detection. The pre-processed data were split into training/validation and test sets using the Kennard-Stone method. A toolbox of supervised machine learning classification algorithms was applied to the training/validation set using a stratified 5-fold cross-validation regimen. The algorithms included linear discriminant analysis (LDA), logistic regression (LR), random forest (RF), multilayer perceptron (MLP), and support vector machines (SVMs). The SVM model performed best in the validation step, with 85% sensitivity, 89% negative predictive value (NPV), and 64% accuracy. The SVM model showed 67% sensitivity, 82% NPV, and 57% accuracy on the test set. These results suggest that NIR spectroscopy in the fingertip, combined with machine learning methods, can be used to detect neutropenia in solid-tumor oncology patients in a non-invasive and timely manner. This approach could help reduce exposure to invasive tests and prevent neutropenic patients from inadvertently undergoing chemotherapy.

Non-Ionizing Radiation Measurements for Trajectography Radars.

This work presents a Non-Ionizing Radiation (NIR) measurement campaign and proposes a specific measurement method for trajectography radars. This kind of radar has a high gain narrow beam antenna and emits a high power signal. Power density measurements from a C-band trajectography radar are carried out using bench equipment and a directional receiving antenna, instead of the commonly used isotropic probe. The measured power density levels are assessed for compliance test via comparison with the occupational and general public exposure limit levels of both the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and the Brazilian National Telecommunication Agency (Anatel). The limit for the occupational public is respected everywhere, evidencing the safe operation of the studied radar. However, the limit for the general public is exceeded at a point next to the radar's antenna, showing that preventive measures are needed.

Multivariate strategy for identifying and quantifying jet fuel contaminants by MCR-ALS/PLS models coupled to combined MIR/NIR spectra.

The investigation and control of jet fuel contamination for private aircrafts has gained attention due to the softer monitoring in comparison to commercial aviation. The possible contamination with kerosene solvent (KS) makes this investigation more challenging, since it has physicochemical similarities with jet fuel. To help solve this problem, a chemometric methodology was applied in this research combining multivariate curve resolution with alternating least squares (MCR-ALS) and partial least squares (PLS) models coupled to near- and mid-infrared spectroscopies (MIR/NIR) in order to detect and quantify KS in blends with JET-A1 using 23 samples (5-60% v/v). Additionally, 98 samples were stored for 60 days, and principal component analysis, genetic algorithm, and successive projections algorithm were coupled to linear discriminant analysis (PCA-LDA, GA-LDA, and SPA-LDA) in order to classify the blends according to the bands assigned to oxidation products, such as phenols and carboxylic acids. GA-LDA and SPA-LDA models were accurate and reached 100% sensitivity and specificity. Physicochemical analysis was not able to detect the presence of KS in contaminated jet fuel samples, even in high concentrations. The use of MIR-NIR combined spectra improved the quantification results, thus decreasing the experimental error from 5.22% (using only NIR) to 1.64%. PLS regression quantified the content of KS with high accuracy (RMSEP < 1.64%, R2 > 0.995). The MCR-ALS model stood out for recovering the spectral profile of kerosene solvent by segregating it from jet fuel spectra. The development of models using chemometric tools contributed to a fast, low-cost, and efficient process for quality control that can be applied in the fuel industry.

Spatiotemporal Temperature Distribution of NIR Irradiated Polypyrrole Nanoparticles and Effects of pH.

The spatiotemporal temperature distributions of NIR irradiated polypyrrole nanoparticles (PPN) were evaluated by varying PPN concentrations and the pH of suspensions. The PPN were synthesized by oxidative chemical polymerization, resulting in a hydrodynamic diameter of 98 ± 2 nm, which is maintained in the pH range of 4.2-10; while the zeta potential is significantly affected, decreasing from 20 ± 2 mV to -5 ± 1 mV at the same pH range. The temperature profiles of PPN suspensions were obtained using a NIR laser beam (1.5 W centered at 808 nm). These results were analyzed with a three-dimensional predictive unsteady-state heat transfer model that considers heat conduction, photothermal heating from laser irradiation, and heat generation due to the water absorption. The temperature profiles of PPN under laser irradiation are concentration-dependent, while the pH increase only induces a slight reduction in the temperature profiles. The model predicts a value of photothermal transduction efficiency (η) of 0.68 for the PPN. Furthermore, a linear dependency was found for the overall heat transfer coefficient (U) and η with the suspension temperature and pH, respectively. Finally, the model developed in this work could help identify the exposure time and concentration doses for different tissues and cells (pH-dependent) in photothermal applications.