Journal of Near Infrared Spectroscopy最新文献

{"title":"Non-linear machine learning coupled near infrared spectroscopy enhanced model performance and insights for coffee origin traceability","authors":"Joy Sim, Cushla McGoverin, Indrawati Oey, Russell Frew, Biniam Kebede","doi":"10.1177/09670335241269014","DOIUrl":"https://doi.org/10.1177/09670335241269014","url":null,"abstract":"Over the past decade, there has been overwhelming interest in rapid and routine origin tracing and authentication methods, such as near infrared (NIR) spectroscopy. In a systematic and comprehensive approach, this study coupled NIR with advanced machine learning models to explore the origin classification of coffee at various scales (continental to regional level). Speciality green coffee beans were sourced from three continents, eight countries, and 22 regions. The dispersive bulk NIR spectra were used for spectral registration in the reflectance mode, and the obtained spectra were preprocessed with extended multiplicative scatter correction and mean centering. The classical linear partial least squares-discriminant analysis (PLS-DA) adequately predicted origin at the continental and country level, and showed promise at the regional level. Non-linear machine learning models improved predictions further, with the best accuracy found using random forest with accuracies up to 0.99. Discriminating wavelength regions and constituents were identified at each origin scale, with more minor wavelength regions selected by random forest. This proof of concept work demonstrated the potential of NIR spectroscopy coupled with machine learning for rapid origin classification of coffee from the continental to the regional level.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using visible and near infrared spectroscopy and machine learning for estimating total petroleum hydrocarbons in contaminated soils","authors":"Fereshteh Karimian, Shamsollah Ayoubi, Banafsheh Khalili, Seyed Ahmad Mireei, Yaseen Al-Mulla","doi":"10.1177/09670335241269168","DOIUrl":"https://doi.org/10.1177/09670335241269168","url":null,"abstract":"Petroleum pollution in soil is very damaging to the areas affected by the accidental release of petroleum hydrocarbons and has destructive impacts on natural resources and environmental health. Therefore, its monitoring and analysis are critical, however, due to the cost and time associated with chemical approaches, finding a quick and cost-effective analytical method is valuable. This study was conducted to evaluate the potential of using visible near infrared (Vis-NIR) spectroscopy to predict total petroleum hydrocarbons (TPH) in polluted soils around the Shadegan ponds, in southern Iran. One hundred soil samples showing various degrees of pollution were randomly collected from topsoil (0–10 cm). The soil samples were analyzed for TPH using Vis-NIR reflectance spectroscopy in the laboratory and then following application of preprocessing transformation, partial least squares PLS regression as well as two machine learning models including random forest (RF), and support vector machine (SVM) were examined. The results showed that the reflectance values at 1725 nm and 2311 nm, respectively, served as indicative TPH reflectance features, exhibiting weaker reflection with rising TPH. Among the preprocessing methods, the baseline correction method indicated the highest performance than the others. According to the evaluation model criteria in the validation dataset, the efficiency of the three selected models was observed in the following order SVM &gt; RF &gt; PLS regression. The SVM model provided the best performance in the validation dataset with r<jats:sup>2</jats:sup> = 0.85, root mean of square (RMSEP = 1.59 %, and the ratio of prediction to deviation (RPD) = 2.6. Overall, this study provided strong evidence supporting the considerable potential of Visible-NIR spectroscopy as a rapid and cost-effective technique for estimating TPH levels in oil-contaminated soils, surpassing traditional chemical analytical methods. Applying the mid-infrared spectrum (MIR) in combination with Visible-NIR data is expected to provide more comprehensive and accurate results when assessing soils in polluted areas, thereby enhancing the accuracy and reliability of the results across a diverse range of soil types.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection and classification of spongy tissue disorder in mango fruit during ripening by using visible-near infrared spectroscopy and multivariate analysis","authors":"Patil R Kiran, Parth Jadhav, G Avinash, Pramod Aradwad, Arunkumar TV, Rakesh Bhardwaj, Roaf A Parray","doi":"10.1177/09670335241269005","DOIUrl":"https://doi.org/10.1177/09670335241269005","url":null,"abstract":"The esteemed Alphonso mango, cherished in India for its taste, saffron color, texture, and extended shelf life, holds global commercial appeal. Unfortunately, the prevalent spongy tissue disorder in Alphonso mangoes results in a soft and corky texture, with up to 30% of mangoes within a single batch affected. This issue leads to outright rejection during export due to delayed disorder identification. The current assessment method involves destructive sampling, causing substantial fruit loss, and lacks assurance for overall batch quality. In light of the mentioned challenges, this current study focuses on utilizing visible-near infrared (Vis-NIR) spectroscopy as a non-invasive method to assess the internal quality of mangoes. It also enables innovative classification models for automated binary categorization (healthy vs spongy tissue-affected). Through preprocessing and principal component analysis of spectral reflectance data, wavelength ranges of 670–750 nm, 900–970 nm, and 1100–1170 nm were identified for distinguishing healthy and damaged mangoes. Soft independent modelling of class analogy (SIMCA) modelling is a novel approach that can be used to classify mango into healthy and spongy tissue-affected categories for better postharvest management. The accuracy of SIMCA models for classifying mangoes into healthy and spongy tissue-affected classes was highest in the wavelength regions of 670–750 nm and 900–970 nm, being 94.4% and 96.7%, respectively. The spectral reflectance between wavelength region 650–970 nm gave significant and visible differentiation between all stages of spongy tissue, that is, mild, medium, and severe. Furthermore, the application of Vis-NIR spectroscopy alongside SIMCA modelling offers a viable avenue for examining internal abnormalities resulting from diseases or injuries in fruits, broadening its utility for diverse inspection needs.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A method to standardize the temperature for near infrared spectra of the indigo pigment in non-dairy cream based on symbolic regression","authors":"Yun Zhang, Jun Liu, Zheng lin Tan, Ming Yi Jiang","doi":"10.1177/09670335241268928","DOIUrl":"https://doi.org/10.1177/09670335241268928","url":null,"abstract":"Near infrared (NIR) spectroscopy is sensitive to physical conditions such as sample temperature, meaning that rapid detection methods based on NIR spectroscopy are significantly influenced by temperature. To address this challenge, symbolic regression was employed to mitigate the effects of temperature. The Weighted Windowed Adaptive Optimization algorithm was proposed and combined with the Sequential Projection Algorithm to extract temperature-related feature points and remove redundant data. Subsequent 3D modeling of these feature points revealed that absorbance alterations due to temperature comprised two distinct segments. Consequently, based on symbolic regression, the temperature standardization algorithm was devised to generate piecewise equations. This algorithm surpassed genetic programming and non-segmented methods in performance metrics. The piecewise function equations generated by the algorithm were used to regress the absorbance at different temperatures to the standard temperature. Non-dairy cream, with different indigo pigment contents, was temperature standardized using a piecewise function to obtain spectra at two standard temperatures; 18°C and 28°C. The r<jats:sup>2</jats:sup> for the quantitative regression model improved from 0.71 to 0.95 at 18°C and from 0.63 to 0.85 at 28°C. The temperature standardization method offers interpretable equations for spectra that model the complex changes with temperature, factoring out the temperature variation, thereby facilitating the practical use of NIR spectroscopy in rapid detection applications.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142175122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Moisture content of Panax notoginseng taproot predicted using near infrared spectroscopy","authors":"Fujie Zhang, Shanshan Li, Lei Shi, Lixia Li, Xiuming Cui","doi":"10.1177/09670335241242644","DOIUrl":"https://doi.org/10.1177/09670335241242644","url":null,"abstract":"The rapid determination of moisture content in Panax notoginseng taproot (PNT) was determined using a portable near infrared spectrometer (900∼1700 nm). First, to reduce baseline offset of the spectra Savitzky-Golay and standard normal variate transformation were combined to preprocess the original spectral data. Then, competitive adaptive reweighting sampling and bootstrapping soft shrinkage (BOSS) were employed to extract feature wavelengths that could characterize the moisture content information of PNT respectively. Finally, the least square support vector regression (LSSVR) model was established based on feature spectra and full spectra. To improve the prediction accuracy of the model, a LSSVR model based on the arithmetic optimization algorithm (AOA) was proposed, and the optimization results were compared with those of the snake optimizer and particle swarm optimization. The results indicated that the best prediction model was BOSS-AOA-LSSVR, with r<jats:sup>2</jats:sup> and RMSEP values of 0.96 and 0.03%, respectively. Thus, it is feasible to predict the moisture content of Panax notoginseng taproot by portable near infrared spectroscopy in combination with BOSS-AOA-LSSVR. The results show that portable near infrared spectroscopy can be used to predict the moisture content of Panax notoginseng taproot, which provides a theoretical basis for the rapid and non-destructive detection of the moisture content of Panax notoginseng taproots.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141863218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cellulose crystalline structure and thermal properties under heat treatment: An investigation by 2D correlation of near infrared spectroscopy and differential scanning calorimetry","authors":"Siti Hanifah Mahdiyanti, Masaki Asaoka, T. Inagaki, S. Tsuchikawa","doi":"10.1177/09670335241257649","DOIUrl":"https://doi.org/10.1177/09670335241257649","url":null,"abstract":"This study investigated the thermal behavior and crystallinity of microcrystalline cellulose (MCC) under heat treatment using near infrared (NIR) spectroscopy and differential scanning calorimetry (DSC). The results showed that heat treatment reduced the crystallinity and thermal stability of MCC at a certain point, and that the changes in the chemical components and structure of MCC were correlated with the heat flow measured by DSC. The analysis was performed using two-dimensional correlation spectroscopy (2DCOS), which revealed the simultaneous changes in the NIR second-derivative spectra and the DSC thermograms of heat-treated MCC. Linear regression analysis showed a high r2 value of 0.90 between the DSC enthalpy change at 270 °C–400°C and the PC1 score of NIR second-derivative spectra at 7500–4100 cm−1. The 2DCOS synchronous map showed a positive correlation at 6656–6229 cm−1 with a r value of 0.70–0.98 for the endothermic reaction, and a negative correlation at 6229; 5620; 5401; 4844; 4535 cm−1 with a r value of −0.90 to −0.99 for the exothermic reaction. This study extended the knowledge on the thermal behavior and decomposition mechanisms of heat-treated MCC, and provided a useful method for cellulose characterization and identification.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141336127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Near infrared spectroscopy models to predict sensory and texture traits of sweetpotato roots","authors":"JS Nantongo, Edwin Serunkuma, Fabrice Davrieux, Mariam Nakitto, Gabriela Burgos, Zum Felde Thomas, Porras Eduardo, Ted Carey, Jolien Swankaert, Robert OM Mwanga, E. Alamu, R. Ssali","doi":"10.1177/09670335241259901","DOIUrl":"https://doi.org/10.1177/09670335241259901","url":null,"abstract":"High-throughput phenotyping technologies successfully employed in plant breeding and precision agriculture could facilitate the screening process for developing consumer-preferred traits. The current study evaluated the potential of near infrared (NIR) spectroscopy to predict visual, aromatic, flavor, taste and texture traits of sweetpotatoes. The focus was to develop predicting models that would be cost-effective, efficient and high throughput. The roots of 207 sweetpotato genotypes from six agroecological zones of Uganda were collected from breeding trials. The spectra were collected in the wavelengths of 400 – 2500 nm at 2 nm intervals. Using the plsR package, the calibrations were carried out using external validation models. The best calibration equation between the sensory and texture reference values (10-point scales) and spectral data was identified based on the highest coefficient of determination (R2) and smallest RMSE in calibration and validation. Of the visual traits, orange color intensity was well calibrated using NIR spectroscopy (R2val = 0.92, SEP = 0.92), and the model is sufficient for field application. Pumpkin aroma (R2val = 0.67, SEP = 0.33) was the highest predicted among the aromas. The pumpkin flavour model exhibited the highest coefficient of determination in the calibration (R2val = 0.52, SEP = 0.45) for the traits considered under flavor and taste. Different models for textural traits exhibited moderate calibration coefficients: mealiness (chalky/floury) by hand (R2val = 0.75; SEP = 1.31), crumbliness (R2val = 0.73, SEP = 1.21), moisture in mass (R2val = 0.73, SEP = 1.26), fracturability (R2val = 0.60, SEP = 1.52), hardness by hand (R2val = 0.61, SEP = 1.27) and dry matter (R2val = 0.70, SEP = 3.10). The range error ratio (RER) values were mostly >6.0. These models could be used for preliminary screening. The predictability of the traits varied among different modes of samples. Models could be improved with an increased range of reference values and/or exploiting the correlations between chemical compounds and sensory traits.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141344758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fraud detection in the fishing sector using hyperspectral imaging","authors":"Paula Luri Esplandiú, Juan-Jesús Marín-Méndez, Miriam Alonso-Santamaría, Berta Remírez-Moreno, M. Sáiz-Abajo","doi":"10.1177/09670335241258667","DOIUrl":"https://doi.org/10.1177/09670335241258667","url":null,"abstract":"Currently, more and more consumers are interested in the quality, safety, and authenticity of food products. The fishing sector is the second food category with the highest risk of fraud and the greatest presence of authentication problems. There are non-destructive, fast and accurate techniques for real-time authentication, with hyperspectral imaging (HSI) standing out among these. In this context, the main aim of this study is to explore the viability of HSI in the visible and near infrared (VIS-NIR) and near infrared (NIR) ranges for the detection of fraud by origin and by non-declaration of the previous freezing process, in anchovies. The spectral pretreatment methods used were the standard normal variate method, the Savitzky-Golay 1st derivate and the Savitzky-Golay 2nd derivate, always followed by mean centering (MC). In addition, the impact of using a previous step of smoothing prior to pretreatment was also evaluated. Two classification algorithms: soft independent modeling of class analogy, and partial least squares discriminant analysis (PLS-DA) were used to build the classification model. After analysis, it was found that the modelling results using the VIS-NIR region were always better than those using the NIR region, and the best performing model was by PLS-DA with a recall of 0.97 for fresh and 0.98 for frozen-thawed anchovies and 0.98 for Cantabrian anchovies and 0.96 for Mediterranean anchovies. One advantage of the model obtained is the ability to classify the anchovies measuring on the skin side of fish without the need for sample preparation. Overall, the results showed that HSI combined with PLS-DA is a favorable solution for rapid, and non-destructive recognition of adulteration regarding freshness and origin in anchovies.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141366657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing quality control in emulsion-type sausage production: Predicting chemical composition of intact samples with near infrared spectroscopy","authors":"Pitiporn Ritthiruangdej, Kanithaporn Vangnai, Sumaporn Kasemsumran, Supapich Somboonying, Pimwaree Charoensin, Arisara Hiriotappa, Papawarin Lowleraha","doi":"10.1177/09670335241240518","DOIUrl":"https://doi.org/10.1177/09670335241240518","url":null,"abstract":"This research actively explores the potential of near infrared spectroscopy (NIR) for analyzing the chemical composition of emulsion-type sausages, focusing on critical factors like residual nitrite, moisture, protein, and fat content. To establish robust and generalizable models, we utilized a dataset of 100 experimentally prepared sausages encompassing a wide range of pork back fat replacement levels (5%, 15%, 30%, 45%, and 60%) and added sodium nitrite amounts (0, 80, 125, 250, and 375 ppm). An external validation set of 20 commercially sourced sausages further assessed the model’s real-world applicability. Partial least squares (PLS) regression calibration models with multiplicative scatter correction (MSC) pre-treatment demonstrated impressive accuracy for moisture (RMSECV = 0.57%, RPD = 9.8), fat (RMSECV = 1.17%, RPD = 9.5), and protein (RMSECV = 0.30%, RPD = 7.6). While residual nitrite prediction presented challenges due to its inherent complexity, the external validation yielded a competitive root mean square error of prediction (RMSEP) of 12.02 ppm, surpassing the average performance reported in similar studies (RMSEP ∼15 ppm) by 3 ppm. Importantly, sample homogenization did not significantly affect parameter prediction, highlighting the robustness of the NIR-based approach. These findings suggest that NIR spectroscopy, with its non-destructive, rapid, and cost-effective nature, could provide valuable tools for quality control and monitoring in the emulsion-type sausage industry. More importantly, improved nitrite prediction could pave the way for enhanced precision and control in sausage production, ultimately contributing to improved food safety and sustainability.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140311618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Near infrared spectroscopy for determination of moisture content in lyophilized formulation","authors":"Aruna Khanolkar, Pranita Pawale, Viraj Thorat, Bhaskar Patil, Gautam Samanta","doi":"10.1177/09670335241240309","DOIUrl":"https://doi.org/10.1177/09670335241240309","url":null,"abstract":"A non-invasive near infrared (NIR) spectroscopic method was developed for the quantitative moisture determination in a lyophilized injection formulation. The calibration samples were prepared by exposing lyophilized samples at different temperatures and relative humidity. The samples from different scales and different process parameters were considered for adding robustness to the model. The NIR spectra were collected using a Fourier- transform (FT) NIR with a diffuse reflectance probe and the same samples were further analyzed by the Karl Fisher (KF) method for moisture content. The pre-treated NIR spectra were used for quantitative method development for moisture content. Partial least squares (PLS) regression was used to develop calibrations in the 5600-4950 cm<jats:sup>−1</jats:sup> region with calibration coefficient of determination (R<jats:sup>2</jats:sup>) of 0.96 and root mean square error of calibration (RMSEC) of 0.149. The model was cross-validated internally using the Kernel algorithm with r<jats:sup>2</jats:sup> = 0.96 and RMSECV = 0.15. The accuracy of the NIR method against the KF method, precision, and reproducibility were good and the model was robust in predicting different external validation samples. This work allowed NIR as an alternative measurement for moisture analysis as well as facilitate 100% monitoring before packaging and save the cost of sample and time of KF analysis.","PeriodicalId":16551,"journal":{"name":"Journal of Near Infrared Spectroscopy","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140311597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}