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1.
Sensors (Basel) ; 22(1)2022 Jan 04.
Artigo em Inglês | MEDLINE | ID: mdl-35009907

RESUMO

Hyperspectral imaging is a popular tool used for non-invasive plant disease detection. Data acquired with it usually consist of many correlated features; hence most of the acquired information is redundant. Dimensionality reduction methods are used to transform the data sets from high-dimensional, to low-dimensional (in this study to one or a few features). We have chosen six dimensionality reduction methods (partial least squares, linear discriminant analysis, principal component analysis, RandomForest, ReliefF, and Extreme gradient boosting) and tested their efficacy on a hyperspectral data set of potato tubers. The extracted or selected features were pipelined to support vector machine classifier and evaluated. Tubers were divided into two groups, healthy and infested with Meloidogyne luci. The results show that all dimensionality reduction methods enabled successful identification of inoculated tubers. The best and most consistent results were obtained using linear discriminant analysis, with 100% accuracy in both potato tuber inside and outside images. Classification success was generally higher in the outside data set, than in the inside. Nevertheless, accuracy was in all cases above 0.6.


Assuntos
Solanum tuberosum , Análise Discriminante , Imageamento Hiperespectral , Análise dos Mínimos Quadrados , Tubérculos
2.
Food Chem ; 372: 131246, 2022 Mar 15.
Artigo em Inglês | MEDLINE | ID: mdl-34818727

RESUMO

Maize mildew is a common phenomenon and it is essential to detect the mildew of a single maize kernel and prevent mildew from spreading around. In this study, a line-scanning Raman hyperspectral imaging system was applied to detect fungal spore quantity of a single maize kernel. Raman spectra were extracted while textural features were obtained to depict the maize mildew. Three kinds of modeling algorithms were used to establish the quantitative model to determine the fungal spore quantity of a single maize kernel. Then competitive adaptive reweighted sampling (CARS) was used to optimize characteristic variables. The optimal detection model was established with variables selected from the combination of Raman spectra and textural variance feature by PLSR. Results indicated that it was feasible to detect the fungal spore quantity of a single maize kernel by Raman hyperspectral technique. The study provided an in-situ and nondestructive alternative to detect fungal spore quantity.


Assuntos
Imageamento Hiperespectral , Zea mays , Algoritmos , Fungos , Espectroscopia de Luz Próxima ao Infravermelho
3.
J Hazard Mater ; 421: 126706, 2022 01 05.
Artigo em Inglês | MEDLINE | ID: mdl-34325290

RESUMO

The toxicity impacts of herbicides on crop, animals, and human are big problems global wide. The rapid and non-invasive ways for assessing herbicide-responsible effects on crop growth regarding types and levels still remain unexplored. In this study, visible/near infrared hyperspectral imaging (Vis/NIR HSI) coupled with SCNN was used to reveal the different characteristics in the spectral reflectance of 2 varieties of wheat seedling leaves that were subjected to 4 stress levels of 3 herbicide types during 4 stress durations and make early herbicide stress prediction. The first-order derivative results showed the spectral reflectance exhibited obvious differences at 518-531 nm, 637-675 nm and the red-edge. A SCNN model with attention mechanism (SCNN-ATT) was proposed for herbicide type and level classification of different stress durations. Further, a SCNN-based feature selection model (SCNN-FS) was proposed to screen out the characteristic wavelengths. The proposed methods achieved 96% accuracy of herbicide type classification and around 80% accuracy of stress level classification for both wheat varieties after 48 h. Overall, this study illustrated the potential of using Vis/NIR HSI to rapidly distinguish different herbicide types and serial levels in wheat at an early stage, which held great value for developing on-line herbicide stress recognizing methods in the field.


Assuntos
Herbicidas , Triticum , Animais , Herbicidas/toxicidade , Humanos , Imageamento Hiperespectral , Redes Neurais de Computação , Folhas de Planta
4.
Food Chem ; 371: 131159, 2022 Mar 01.
Artigo em Inglês | MEDLINE | ID: mdl-34598115

RESUMO

Coffee aroma is critical for consumer liking and enables price differentiation of coffee. This study applied hyperspectral imaging (1000-2500 nm) to predict volatile compounds in single roasted coffee beans, as measured by Solid Phase Micro Extraction-Gas Chromatography-Mass Spectrometry and Gas Chromatography-Olfactometry. Partial least square (PLS) regression models were built for individual volatile compounds and chemical classes. Selected key aroma compounds were predicted well enough to allow rapid screening (R2 greater than 0.7, Ratio to Performance Deviation (RPD) greater than 1.5), and improved predictions were achieved for classes of compounds - e.g. aldehydes and pyrazines (R2 âˆ¼ 0.8, RPD âˆ¼ 1.9). To demonstrate the approach, beans were successfully segregated by HSI into prototype batches with different levels of pyrazines (smoky) or aldehydes (sweet). This is industrially relevant as it will provide new rapid tools for quality evaluation, opportunities to understand and minimise heterogeneity during production and roasting and ultimately provide the tools to define and achieve new coffee flavour profiles.


Assuntos
Café , Compostos Orgânicos Voláteis , Cromatografia Gasosa-Espectrometria de Massas , Imageamento Hiperespectral , Odorantes/análise , Sementes/química , Compostos Orgânicos Voláteis/análise
5.
Food Chem ; 370: 131047, 2022 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-34626928

RESUMO

Rapidly and non-destructively predicting the oil content of single maize kernel is crucial for food industry. However, obtaining a large number of oil content reference values of maize kernels is time-consuming and expensive, and the limited data set also leads to low generalization ability of the model. Here, hyperspectral imaging technology and deep convolutional generative adversarial network (DCGAN) were combined to predict the oil content of single maize kernel. DCGAN was used to simultaneously expand their spectral data and oil content data. After many iterations, fake data that was very similar to the experimental data was generated. Partial least squares regression (PLSR) and support vector regression (SVR) models were established respectively, and their performance was compared before and after data augmentation. The results showed that this method not only improved the performance of two regression models, but also solved the problem of requiring a large amount of training data.


Assuntos
Imageamento Hiperespectral , Zea mays , Análise dos Mínimos Quadrados , Redes Neurais de Computação , Tecnologia
6.
Environ Pollut ; 292(Pt B): 118405, 2022 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-34710518

RESUMO

Cadmium (Cd) is a toxic metal that can accumulate in soils and negatively impact crop as well as human health. Amendments like biochar have potential to address these challenges by reducing Cd bioavailability in soil, though reliance on post-harvest wet chemical methods to quantify Cd uptake have slowed efforts to identify the most effective amendments. Hyperspectral imaging (HSI) is a novel technology that could overcome this limitation by quantifying symptoms of Cd stress while plants are still growing. The goals of this study were to: 1) determine whether HSI can detect Cd stress in two distinct leafy green crops, 2) quantify whether a locally sourced biochar derived from hardwoods can reduce Cd stress and uptake in these crops, and 3) identify vegetative indices (VIs) that best quantify changes in plant stress responses. Experiments were conducted in a tightly controlled automated phenotyping facility that allowed all environmental factors to be kept constant except Cd concentration (0, 5 10 and 15 mg kg-1). Symptoms of Cd stress were stronger in basil (Ocimum basilicum) than kale (Brassica oleracea), and were easier to detect using HSI. Several VIs detected Cd stress in basil, but only the anthocyanin reflectance index (ARI) detected all levels of Cd stress in both crop species. The biochar amendment did reduce Cd uptake, especially at low Cd concentrations in kale which took up more Cd than basil. Again, the ARI index was the most effective in quantifying changes in plant stress mediated by the biochar. These results indicate that the biochar evaluated in this study has potential to reduce Cd bioavailability in soil, and HSI could be further developed to identify rates that can best achieve this benefit. The technology also may be helping in elucidating mechanisms mediating how biochar can influence plant growth and stress responses.


Assuntos
Cádmio , Poluentes do Solo , Cádmio/análise , Carvão Vegetal , Humanos , Imageamento Hiperespectral , Solo , Poluentes do Solo/análise , Tecnologia
7.
Spectrochim Acta A Mol Biomol Spectrosc ; 268: 120722, 2022 Mar 05.
Artigo em Inglês | MEDLINE | ID: mdl-34902690

RESUMO

The quality of wheat kernels is critical to ensure crop yields. However, in actual breeding work, unsound kernels are scarce compared to healthy kernels. Limited data sets or unbalanced data sets make it difficult for many algorithms to accurately identify kernels in different states. A novel method based on deep convolutional generative adversarial network (DCGAN) and near-infrared hyperspectral imaging technology was proposed to identify unsound wheat kernels in this paper. Three classifiers, convolutional neural network (CNN), support vector machine (SVM) and decision tree (DT) were used. After expanding the samples, the results showed that the accuracy of the test set of the DT model increased from 51.67% to 80.83%, a total increase of 29.16%. And the CNN and SVM models increased by 8.34% and 14.17% respectively. This demonstrated that the DCGAN method had the ability to generate reliable data samples for unbalanced data sets for improving the performance of the classifier. On this basis, the training samples are further expanded for improving the performance of the classifier. The results showed that CNN model gained the most from incremental data, and its accuracy rate had been continuously improved from 79.17% to 96.67%, a total increase of 17.50%. This also demonstrated that the DCGAN method had the ability to expand a limited data set. In general, the joint model based on DCGAN and CNN combined with hyperspectral imaging technology had a good prospect in the identification of unsound kernels.


Assuntos
Imageamento Hiperespectral , Triticum , Redes Neurais de Computação , Máquina de Vetores de Suporte , Tecnologia
8.
Sci Total Environ ; 805: 150423, 2022 Jan 20.
Artigo em Inglês | MEDLINE | ID: mdl-34818810

RESUMO

Cyanobacteria are notorious for producing harmful algal blooms that present an ever-increasing serious threat to aquatic ecosystems worldwide, impacting the quality of drinking water and disrupting the recreational use of many water bodies. Remote sensing techniques for the detection and quantification of cyanobacterial blooms are required to monitor their initiation and spatiotemporal variability. In this study, we developed a novel semi-analytical approach to estimate the concentration of cyanobacteria-specific pigment phycocyanin (PC) and common phytoplankton pigment chlorophyll a (Chl a) from hyperspectral remote sensing data. The PC algorithm was derived from absorbance-concentration relationship, and the Chl a algorithm was devised based on a conceptual three-band structure model. The developed algorithms were applied to satellite imageries obtained by the Hyperspectral Imager for the Coastal Ocean (HICO™) sensor and tested in Lake Kinneret (Israel) during strong cyanobacterium Microcystis sp. bloom and out-of-bloom times. The sensitivity of the algorithms to errors was evaluated. The Chl a and PC concentrations were estimated with a mean absolute percentage difference (MAPD) of 16% and 28%, respectively. Sensitivity analysis shows that the influences of backscattering and other water constituents do not affect the estimation accuracy of PC (~2% MAPD). The reliable PC/Chl a ratios can be obtained at PC concentrations above 10 mg m-3. The computed PC/Chl a ratio depicts the contribution of cyanobacteria to the total phytoplankton biomass and permits investigating the role of ambient factors in the formation of a complex planktonic community. The novel algorithms have extensive practical applicability and should be suitable for the quantification of PC and Chl a in aquatic ecosystems using hyperspectral remote sensing data as well as data from future multispectral remote sensing satellites, if the respective bands are featured in the sensor.


Assuntos
Cianobactérias , Ecossistema , Algoritmos , Clorofila/análise , Clorofila A , Monitoramento Ambiental , Imageamento Hiperespectral , Lagos , Tecnologia de Sensoriamento Remoto
9.
Food Chem ; 367: 130732, 2022 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-34384980

RESUMO

Saccharin was determined based on a new molecularly imprinted solid-phase extraction (MISPE) procedure. The polymer was synthesized with a hybrid monomer of metacrylic acid and 3-amino propril tetraethoxysilane and saccharin as template. After the synthesis, the saccharin removal from the MIP was verified by the UV analysis of the solutions used in the template removal procedure, as well as by the direct MIP analysis using FTIR hyperspectral image and chemometrics. The residual saccharin concentrations observed in the image analysis revealed a narrow concentration distribution consistent with a homogenous material. The MISPE was performed with homemade cartridges containing 200 mg of the MIP. The results obtained with standards and diet tea samples confirmed high affinity, adsorption capacity and selectivity of the MIP. The MISPE cartridge exhibited recoveries of 100 ± 3% in six extraction cycles. The diet tea analysis showed a significant reduction of the interferences, which can considerable simplifies the HPLC-UV analysis.


Assuntos
Impressão Molecular , Cromatografia Líquida de Alta Pressão , Dieta , Imageamento Hiperespectral , Sacarina , Extração em Fase Sólida , Chá
10.
Spectrochim Acta A Mol Biomol Spectrosc ; 264: 120250, 2022 Jan 05.
Artigo em Inglês | MEDLINE | ID: mdl-34391991

RESUMO

Botanical drugs hold great potential to prevent and treat complex diseases. Quality control is essential in ensuring the safety, efficacy, and therapeutic consistency of these drug products. The quality of a botanical drug product can be assessed using a variety of analytical methods based on criteria that judge the identity, strength, purity, and potency. However, most of these methods are developed on separate analytical platforms, and few approaches are available for in-process monitoring of multiple quality properties in a non-destructive manner. Here, we present a hyperspectral imaging-based strategy for online measurement of physical, chemical, and biological properties of botanical drugs using artificial intelligence algorithms. An end-to-end convolutional neural network (CNN) model was established to accurately determine phytochemicals and bioactivities based on the spectra. Besides, a new dual-scale anomaly (DSA) detection algorithm was proposed for visible particle inspection based on the images. The strategy was exemplified on Shuxuening Injection, a Ginkgo biloba-derived drug used in the treatment of cerebrovascular and cardiovascular diseases. Four quality metrics of the injection, including total flavonol, total ginkgolides, antioxidant activity, and anticoagulant activity, were successfully predicted by the CNN model with validation R2 of 0.922, 0.921, 0.880, and 0.913 respectively, showing better performance than the other models. Unqualified samples with visible particles could be detected by DSA with a low false alarm rate of 9.38 %. Chromaticity results indicated that the inter-company variations of color were significant, while intra-company variations were relatively small. This demonstrates a real application of integrating hyperspectral imaging with artificial intelligence to provide a rapid, accurate, and non-destructive approach for process analysis of botanical drugs.


Assuntos
Inteligência Artificial , Imageamento Hiperespectral , Algoritmos , Redes Neurais de Computação , Controle de Qualidade
11.
Chemosphere ; 286(Pt 3): 131861, 2022 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-34399269

RESUMO

Contamination by microplastics (MP) represents a critical environmental challenge with potential consequences at ecosystem, economic and societal levels. As the marine system is the final sink for MP, there is an urgent need to develop methods for the monitoring of synthetic particles in different marine compartments and sample matrices. Extensive evaluations are hindered by time and costs associated with to conventional MP spectroscopic analyses. The potential of near infrared hyperspectral imaging (NIR-HSI) has been recently evaluated. However, NIR-HSI has been poorly studied so far, limitedly to the detection of large particles (>300 µm), and its capability for direct characterization of MP in real marine matrices has not been considered yet. In the present study, a rapid near infrared hyperspectral imaging (NIR-HSI) method, coupled with a customised normalised difference image (NDI) strategy for data processing, is presented and used to detect MP down to 50 µm in environmental matrices. The proposed method is largely automated, without the need for extensive data processing, and enabled a successful identification of different polymers, both in surface water and mussel soft tissue samples, as well as on real field samples with environmentally occurring MP. NIR-HSI is applied directly on filters, without the need for particles pre-sorting or multiple sample purifications, avoiding time consuming procedures, airborne contaminations, particle degradation and loss. Thanks to the time and cost effectiveness, a large-scale implementation of this method would enable to extensively monitor the MP presence in natural environments for assessing the ecological risk related to MP contamination.


Assuntos
Microplásticos , Poluentes Químicos da Água , Ecossistema , Monitoramento Ambiental , Imageamento Hiperespectral , Plásticos , Polímeros , Poluentes Químicos da Água/análise
12.
Food Chem ; 370: 131013, 2022 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-34509150

RESUMO

Malus micromalus Makino has great commercial and nutritional value. The regression and classification models were investigated by using near-infrared hyperspectral imaging (NIR-HSI) combined with chemometrics to improve the efficiency of non-destructive detection. The successive projections algorithm (SPA), interval random frog, and competitive adaptive reweighted sampling were employed to extract effective wavelengths sensitive to changes of soluble solid content (SSC) and firmness index (FI) information. Two types of assessment models based on full spectrum and effective wavelengths, namely partial least squares regression and extreme learning machine, were established to predict SSC and FI. In addition, the classification models based on the support vector machine improved by the grey wolf optimizer (GWO-SVM) and partial least squares discrimination analysis were constructed to differentiate maturity stage. The SPA-ELM and SPA-GWO-SVM models achieved satisfactory performance. The results illustrate that NIR-HSI is feasible for evaluation of the quality of Malus micromalus Makino.


Assuntos
Malus , Algoritmos , Imageamento Hiperespectral , Análise dos Mínimos Quadrados , Espectroscopia de Luz Próxima ao Infravermelho , Máquina de Vetores de Suporte
13.
Food Chem ; 366: 130559, 2022 Jan 01.
Artigo em Inglês | MEDLINE | ID: mdl-34289440

RESUMO

In order to realize rapid and non-destructive detection of hardness for maize kernels, a method for quantitative hardness measurement was proposed based on hyperspectral imaging technology. Firstly, the regression model of hardness and moisture content was established. Then, based on reflectance hyperspectral imaging at wavelengths within 399.75-1005.80 nm, the prediction model of the moisture content was studied by the partial least squares regression (PLSR) based on the characteristic wavelengths, which was selected through successive projection algorithm (SPA). Finally, the hardness prediction model was validated by combing the prediction model of moisture content with the regression model of hardness. The coefficient of determination (R2), the root mean square error (RMSE) the ratio of performance-to-deviation (RPD) and the ratio of error range (RER) of hardness prediction were 0.912, 17.76 MPa, 3.41 and 14, respectively. Therefore, this study provided a method for rapid and non-destructive detection of hardness of maize kernels.


Assuntos
Imageamento Hiperespectral , Zea mays , Dureza , Análise dos Mínimos Quadrados , Espectroscopia de Luz Próxima ao Infravermelho
14.
Spectrochim Acta A Mol Biomol Spectrosc ; 267(Pt 1): 120537, 2022 Feb 15.
Artigo em Inglês | MEDLINE | ID: mdl-34740002

RESUMO

The geographical origin and processing month of green tea greatly affect its economic value and consumer acceptance. This study investigated the feasibility of combining near-infrared hyperspectral imaging (NIR-HSI) with chemometrics for the identification of green tea. Tea samples produced in three regions of Chongqing (southeastern Chongqing, northeastern Chongqing, and western Chongqing) for four months (from May to August 2020) were collected. Principal component analysis (PCA) was used to reduce data dimensionality and visualize the clustering of samples in different categories. Linear partial least squares-discriminant analysis (PLS-DA) and nonlinear support vector machine (SVM) algorithms were used to develop discriminant models. The PCA-SVM models based on the first four and first five principal components (PCs) achieved the best accuracies of 97.5% and 95% in the prediction set for geographical origin and processing month of green tea, respectively. This study demonstrated the feasibility of HSI in the identification of green tea species, providing a rapid and nondestructive method for the evaluation and control of green tea quality.


Assuntos
Chá , Imageamento Hiperespectral , Análise dos Mínimos Quadrados , Análise de Componente Principal , Espectroscopia de Luz Próxima ao Infravermelho , Máquina de Vetores de Suporte
15.
Anal Chem ; 93(46): 15323-15330, 2021 11 23.
Artigo em Inglês | MEDLINE | ID: mdl-34766751

RESUMO

Vibrational microscopy methods based on Raman scattering or infrared absorption provide a label-free approach for chemical-contrast imaging, but employ point-by-point scanning and impose a compromise between the imaging speed and field-of-view (FOV). Optothermal microscopy has been proposed as a promising imaging modality to avoid this compromise, although at restrictively small FOVs capable of imaging only few cells. Here, we present wide-field optothermal mid-infrared microscopy (WOMiM) for wide-field chemical-contrast imaging based on snapshot pump-probe detection of optothermal signal, using a custom-made condenser-free phase contrast microscopy to capture the phase change of samples after mid-infrared irradiation. We achieved chemical contrast for FOVs up to 180 µm in diameter, yielding 10-fold larger imaging areas than the state-of-the-art, at imaging speeds of 1 ms/frame. The maximum possible imaging speed of WOMiM was determined by the relaxation time of optothermal heat, measured to be 32.8 µs in water, corresponding to a frame rate of ∼30 kHz. This proof-of-concept demonstrates that vibrational imaging can be achieved at an unprecedented imaging speed and large FOV with the potential to significantly facilitate label-free imaging of cellular dynamics.


Assuntos
Imageamento Hiperespectral , Microscopia , Microscopia de Contraste de Fase , Análise Espectral Raman , Vibração
16.
Sensors (Basel) ; 21(21)2021 Nov 04.
Artigo em Inglês | MEDLINE | ID: mdl-34770640

RESUMO

Hyperspectral imaging and reflectance spectroscopy in the range from 200-380 nm were used to rapidly detect and characterize copper oxidation states and their layer thicknesses on direct bonded copper in a non-destructive way. Single-point UV reflectance spectroscopy, as a well-established method, was utilized to compare the quality of the hyperspectral imaging results. For the laterally resolved measurements of the copper surfaces an UV hyperspectral imaging setup based on a pushbroom imager was used. Six different types of direct bonded copper were studied. Each type had a different oxide layer thickness and was analyzed by depth profiling using X-ray photoelectron spectroscopy. In total, 28 samples were measured to develop multivariate models to characterize and predict the oxide layer thicknesses. The principal component analysis models (PCA) enabled a general differentiation between the sample types on the first two PCs with 100.0% and 96% explained variance for UV spectroscopy and hyperspectral imaging, respectively. Partial least squares regression (PLS-R) models showed reliable performance with R2c = 0.94 and 0.94 and RMSEC = 1.64 nm and 1.76 nm, respectively. The developed in-line prototype system combined with multivariate data modeling shows high potential for further development of this technique towards real large-scale processes.


Assuntos
Cobre , Imageamento Hiperespectral , Análise dos Mínimos Quadrados , Óxidos , Análise de Componente Principal
17.
PLoS One ; 16(10): e0254542, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34648508

RESUMO

The purposes are to solve the isomorphism encountered while processing hyperspectral remote sensing data and improve the accuracy of hyperspectral remote sensing data in extracting and classifying lithological information. Taking rocks as the research object, Backpropagation Neural Network (BPNN) is introduced. After the hyperspectral image data are normalized, the lithological spectrum and spatial information are the feature extraction targets to construct a deep learning-based lithological information extraction model. The performance of the model is analyzed using specific instance data. Results demonstrate that the overall accuracy and the Kappa coefficient of the lithological information extraction and classification model based on deep learning were 90.58% and 0.8676, respectively. This model can precisely distinguish the properties of rock masses and provide better performance compared with the state of other analysis models. After introducing deep learning, the recognition accuracy and the Kappa coefficient of the proposed BPNN model increased by 8.5% and 0.12, respectively, compared with the traditional BPNN. The proposed extraction and classification model can provide some research values and practical significances for the hyperspectral rock and mineral classification.


Assuntos
Sedimentos Geológicos/análise , Sedimentos Geológicos/química , Imageamento Hiperespectral/métodos , Armazenamento e Recuperação da Informação/métodos , Aprendizado Profundo , Minerais/química , Redes Neurais de Computação
18.
Sensors (Basel) ; 21(20)2021 Oct 19.
Artigo em Inglês | MEDLINE | ID: mdl-34696147

RESUMO

Thermal ablation is an acceptable alternative treatment for primary liver cancer, of which laser ablation (LA) is one of the least invasive approaches, especially for tumors in high-risk locations. Precise control of the LA effect is required to safely destroy the tumor. Although temperature imaging techniques provide an indirect measurement of the thermal damage, a degree of uncertainty remains about the treatment effect. Optical techniques are currently emerging as tools to directly assess tissue thermal damage. Among them, hyperspectral imaging (HSI) has shown promising results in image-guided surgery and in the thermal ablation field. The highly informative data provided by HSI, associated with deep learning, enable the implementation of non-invasive prediction models to be used intraoperatively. Here we show a novel paradigm "peak temperature prediction model" (PTPM), convolutional neural network (CNN)-based, trained with HSI and infrared imaging to predict LA-induced damage in the liver. The PTPM demonstrated an optimal agreement with tissue damage classification providing a consistent threshold (50.6 ± 1.5 °C) for the damage margins with high accuracy (~0.90). The high correlation with the histology score (r = 0.9085) and the comparison with the measured peak temperature confirmed that PTPM preserves temperature information accordingly with the histopathological assessment.


Assuntos
Aprendizado Profundo , Terapia a Laser , Imageamento Hiperespectral , Lasers , Redes Neurais de Computação
19.
Sensors (Basel) ; 21(17)2021 Aug 24.
Artigo em Inglês | MEDLINE | ID: mdl-34502595

RESUMO

Drought and water scarcity due to global warming, climate change, and social development have been the most death-defying threat to global agriculture production for the optimization of water and food security. Reflectance indices obtained by an Analytical Spectral Device (ASD) Spec 4 hyperspectral spectrometer from tomato growth in two soil texture types exposed to four water stress levels (70-100% FC, 60-70% FC, 50-60% FC, and 40-50% FC) was deployed to schedule irrigation and management of crops' water stress. The treatments were replicated four times in a randomized complete block design (RCBD) in a 2 × 4 factorial experiment. Water stress treatments were monitored with Time Domain Reflectometer (TDR) every 12 h before and after irrigation to maintain soil water content at the desired (FC%). Soil electrical conductivity (Ec) was measured daily throughout the growth cycle of tomatoes in both soil types. Ec was revealing a strong correlation with water stress at R2 above 0.95 p < 0.001. Yield was measured at the end of the end of the growing season. The results revealed that yield had a high correlation with water stress at R2 = 0.9758 and 0.9816 p < 0.01 for sandy loam and silty loam soils, respectively. Leaf temperature (LT °C), relative leaf water content (RLWC), leaf chlorophyll content (LCC), Leaf area index (LAI), were measured at each growth stage at the same time spectral reflectance data were measured throughout the growth period. Spectral reflectance indices used were grouped into three: (1) greenness vegetative indices; (2) water overtone vegetation indices; (3) Photochemical Reflectance Index centered at 570 nm (PRI570), and normalized PRI (PRInorm). These reflectance indices were strongly correlated with all four water stress indicators and yield. The results revealed that NDVI, RDVI, WI, NDWI, NDWI1640, PRI570, and PRInorm were the most sensitive indices for estimating crop water stress at each growth stage in both sandy loam and silty loam soils at R2 above 0.35. This study recounts the depth of 858 to 1640 nm band absorption to water stress estimation, comparing it to other band depths to give an insight into the usefulness of ground-based hyperspectral reflectance indices for assessing crop water stress at different growth stages in different soil types.


Assuntos
Lycopersicon esculentum , Solo , Desidratação , Imageamento Hiperespectral , Areia
20.
Anal Chim Acta ; 1180: 338852, 2021 Oct 02.
Artigo em Inglês | MEDLINE | ID: mdl-34538329

RESUMO

Controlling blending processes of solid material using advanced real-time sensing technologies tools is crucial to guarantee the quality attributes of manufactured products from diverse industries. The use of process analytical technology (PAT) tools based on chemical imaging systems are useful to assess heterogeneity information during mixing processes. Recently, a powerful procedure for heterogeneity assessment based on the combination of off-line acquired chemical images and variographic analysis has been proposed to provide specific heterogeneity indices related to global and distributional heterogeneity. This work proposes a novel PAT tool combining in situ chemical imaging and variogram-derived quantitative heterogeneity indices for the real-time monitoring of blending processes. The proposed method, so called sliding window variographic image analysis (SWiVIA), derives heterogeneity indices in real-time associated with a sliding image window that moves continuously until the full blending time interval is covered. The SWiVIA method is thoroughly assessed paying attention at the effect of relevant factors for continuous blending monitoring and heterogeneity description, such as the scale of scrutiny needed for heterogeneity definition or the blending period defined to set the sliding image window. SWiVIA is tested on blending runs of pharmaceutical and food products monitored with an in situ near-infrared chemical imaging system. The results obtained help to detect abnormal mixing phenomena and can be the basis to establish blending process control indicators in the future. SWiVIA is adapted to study blending behaviors of the bulk product or compound-specific blending evolutions.


Assuntos
Imageamento Hiperespectral , Tecnologia Farmacêutica , Processamento de Imagem Assistida por Computador
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