Journal articles on the topic 'Predictive Spectral Analysis'
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Robert, P., D. Bertrand, M. Crochon, and J. Sabino. "A New Mathematical Procedure for NIR Analysis: The Lattice Technique. Application to the Prediction of Sugar Content of Apples." Applied Spectroscopy 43, no. 6 (August 1989): 1045–49. http://dx.doi.org/10.1366/0003702894203723.
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Analytical applications of near-infrared spectroscopy require the determination of calibration equations linking chemical and spectral values. Such equations are difficult to update by including new calibration specimens. A new procedure for prediction which was not based on multiple linear regression has been investigated. This procedure could be included in a data base system. The proposed method consists of three steps: compression of the spectral data by applying principal component analysis, creation of a predictive lattice, and projection of the spectra of unknown specimens on to the predictive lattice. This enables the prediction of chemical data that are not perfectly linked to spectral data by a linear relationship. The procedure has been applied to the prediction of the refractive index of apples. A predictive lattice was designed with the use of 45 specimens of calibration. A prediction with 43 verification specimens gave a standard error of 0.8%, which appeared sufficient for grading apples in quality classes. Further studies are required in order to include the proposed method in spectral libraries specializing in analytical applications.
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Baishya, Nystha, Mohammad Mamouei, Karthik Budidha, Meha Qassem, Pankaj Vadgama, and Panayiotis A. Kyriacou. "Comparison of Dual Beam Dispersive and FTNIR Spectroscopy for Lactate Detection." Sensors 21, no. 5 (March 8, 2021): 1891. http://dx.doi.org/10.3390/s21051891.
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Near Infrared (800–2500 nm) spectroscopy has been extensively used in biomedical applications, as it offers rapid, in vivo, bed-side monitoring of important haemodynamic parameters, which is especially important in critical care settings. However, the choice of NIR spectrometer needs to be investigated for biomedical applications, as both the dual beam dispersive spectrophotomer and the FTNIR spectrometer have their own advantages and disadvantages. In this study, predictive analysis of lactate concentrations in whole blood were undertaken using multivariate techniques on spectra obtained from the two spectrometer types simultaneously and results were compared. Results showed significant improvement in predicting analyte concentration when analysis was performed on full range spectral data. This is in comparison to analysis of limited spectral regions or lactate signature peaks, which yielded poorer prediction models. Furthermore, for the same region, FTNIR showed 10% better predictive capability than the dual beam dispersive NIR spectrometer.
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Lilo, Taha, Camilo Morais, Kate Ashton, Ana Pardilho, Timothy Dawson, Nihal Gurusinghe, Charles Davis, and Frank Martin. "Predicting meningioma recurrence using spectrochemical analysis of tissues and subsequent predictive computational algorithms." Neuro-Oncology 21, Supplement_4 (October 2019): iv5. http://dx.doi.org/10.1093/neuonc/noz167.020.
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Abstract Introduction Meningioma recurrence remains a clinical dilemma. This has a significant clinical and huge financial implication. Hence, the search for predictors for meningioma recurrence has become an increasingly urgent research topic in recent years. Objective Using spectrochemical analytical methods such as attenuated total reflection Fourier-transform infrared (ATR-FTIR) and Raman spectroscopy, our primary objective is to compare the spectral fingerprint signature of WHO grade I meningioma vs. WHO grade I meningioma that recurred. Secondary objectives compare WHO grade I meningioma vs. WHO grade II meningioma and WHO grade II meningioma vs. WHO grade I meningioma recurrence. Materials and Methods Our selection criteria included convexity meningioma only restricted to Simpson grade I & II only and WHO grade I & grade II only with a minimum 5 years follow up. We obtained tissue from tumour blocks retrieved from the tissue bank. These were sectioned onto slides and de-waxed prior to ATR-FTIR or Raman spectrochemical analysis. Derived spectral datasets were then explored for discriminating features using computational algorithms in the IRootLab toolbox within MATLAB; this allowed for classification and feature extraction. Results After analysing the data using various classification algorithms with cross-validation to avoid over-fitting of the spectral data, we can readily and blindly segregate those meningioma samples that recurred from those that did not recur in the follow-up timeframe. The forward feature extraction classification algorithms generated results that exhibited excellent sensitivity and specificity, especially with spectra obtained following ATR-FTIR spectroscopy. Our secondary objectives remain to be fully developed.
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Collette, Timothy W., and Adam J. Szladow. "Use of Rough Sets and Spectral Data for Building Predictive Models of Reaction Rate Constants." Applied Spectroscopy 48, no. 11 (November 1994): 1379–86. http://dx.doi.org/10.1366/0003702944028047.
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A model for predicting the log of the rate constants for alkaline hydrolysis of organic esters has been developed with the use of gas-phase mid-infrared library spectra and a rule-building software system based on the mathematical theory of rough sets. A diverse set of 41 esters was used as training compounds. The model is an advance in the development of a generalized system for predicting environmentally important reactivity parameters based on spectroscopic data. By comparison to a previously developed model using the same training set with multiple linear regression (MLR), the rough-sets model provided better predictive power, was more widely applicable, and required less spectral data manipulation. [For the previous MLR model, a standard error of prediction (SEP) of 0.59 was calculated for 88% of the training set data under leave-one-out cross-validation. In the present study using rough sets, an SEP of 0.52 was calculated for 95% of the data set.] More importantly, analysis of the decision rules generated by rough-sets analysis can lead to a better understanding of both the reaction process under study and important trends in the spectral data, as well as underlying relationships between the two.
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Fischer, G., A. Neurauter, L. Wieser, H. U. Strohmenger, and C. N. Nowak. "Prediction of Countershock Success." Methods of Information in Medicine 48, no. 05 (2009): 486–92. http://dx.doi.org/10.3414/me0580.
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Summary Objectives: Spectral analysis of the ventricular fibrillation (VF) ECG has been used for predicting countershock success, where the Fast Fourier Transformation (FFT) is the standard spectral estimator. Autoregressive (AR) spectral estimation should compute the spectrum with less computation time. This study compares the predictive power and computational performance of features obtained by the FFT and AR methods. Methods: In an animal model of VF cardiac arrest, 41 shocks were delivered in 25 swine. For feature parameter analysis, 2.5 s signal intervals directly before the shock and directly before the hands-off interval were used, respectively. Invasive recordings of the arterial pressure were used for assessing the outcome of each shock. For a proof of concept, a micro-controller program was implemented. Results: Calculating the area under the receiver operating characteristic (ROC) curve (AUC), the results of the AR-based features called spectral pole power (SPP) and spectral pole power with dominant frequency (DF) weighing (SPPDF) yield better outcome prediction results (85 %; 89 %) than common parameters based on FFT calculation method (centroid frequency (CF), amplitude spectrum area (AMSA)) (72%; 78%) during hands-off interval. Moreover, the predictive power of the feature parameters during ongoing CPR was not invalidated by closed-chest compressions. The calculation time of the AR-based parameters was nearly 2.5 times faster than the FFT-based features. Conclusion: Summing up, AR spectral estimators are an attractive option compared to FFT due to the reduced computational speed and the better outcome prediction. This might be of benefit when implementing AR prediction features on the microprocessor of a semi-automatic defibrillator.
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Vrazhnov, Denis, Anastasia Knyazkova, Maria Konnikova, Oleg Shevelev, Ivan Razumov, Evgeny Zavjalov, Yury Kistenev, Alexander Shkurinov, and Olga Cherkasova. "Analysis of Mouse Blood Serum in the Dynamics of U87 Glioblastoma by Terahertz Spectroscopy and Machine Learning." Applied Sciences 12, no. 20 (October 19, 2022): 10533. http://dx.doi.org/10.3390/app122010533.
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In this research, an experimental U87 glioblastoma small animal model was studied. The association between glioblastoma stages and the spectral patterns of mouse blood serum measured in the terahertz range was analyzed by terahertz time-domain spectroscopy (THz-TDS) and machine learning. The THz spectra preprocessing included (i) smoothing using the Savitsky–Golay filter, (ii) outlier removing using isolation forest (IF), and (iii) Z-score normalization. The sequential informative feature-selection approach was developed using a combination of principal component analysis (PCA) and a support vector machine (SVM) model. The predictive data model was created using SVM with a linear kernel. This model was tested using k-fold cross-validation. Achieved prediction accuracy, sensitivity, specificity were over 90%. Also, a relation was established between tumor size and the THz spectral profile of blood serum samples. Thereby, the possibility of detecting glioma stages using blood serum spectral patterns in the terahertz range was demonstrated.
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Tang, Siyu, Chong Du, and Tangzhe Nie. "Inversion Estimation of Soil Organic Matter in Songnen Plain Based on Multispectral Analysis." Land 11, no. 5 (April 21, 2022): 608. http://dx.doi.org/10.3390/land11050608.
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Sentinel-2A multi-spectral remote sensing image data underwent high-efficiency differential processing to extract spectral information, which was then matched to soil organic matter (SOM) laboratory test values from field samples. From this, multiple-linear stepwise regression (MLSR) and partial least square (PLSR) models were established based on a differential algorithm for surface SOM modeling. The original spectra were subjected to basic transformations with first- and second-derivative processing. MLSR and PLSR models were established based on these methods and the measured values, respectively. The results show that Sentinel-2A remote sensing imagery and SOM content correlated in some bands. The correlation between the spectral value and SOM content was significantly improved after mathematical transformation, especially square-root transformation. After differential processing, the multi-band model had better predictive ability (based on fitting accuracy) than single-band and unprocessed multi-band models. The MLSR and PLSR models of SOM had good prediction functionality. The reciprocal logarithm first-order differential MLSR regression model had the best prediction and inversion results (i.e., most consistent with the real-world data). The MLSR model is more stable and reliable for monitoring SOM content, and provides a feasible method and reference for SOM content-mapping of the study area.
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Dean, Roger T., Andrew J. Milne, and Freya Bailes. "Spectral Pitch Similarity is a Predictor of Perceived Change in Sound- as Well as Note-Based Music." Music & Science 2 (January 1, 2019): 205920431984735. http://dx.doi.org/10.1177/2059204319847351.
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Spectral pitch similarity (SPS) is a measure of the similarity between spectra of any pair of sounds. It has proved powerful in predicting perceived stability and fit of notes and chords in various tonal and microtonal instrumental contexts, that is, with discrete tones whose spectra are harmonic or close to harmonic. Here we assess the possible contribution of SPS to listeners’ continuous perceptions of change in music with fewer discrete events and with noisy or profoundly inharmonic sounds, such as electroacoustic music. Previous studies have shown that time series of perception of change in a range of music can be reasonably represented by time series models, whose predictors comprise autoregression together with series representing acoustic intensity and, usually, the timbral parameter spectral flatness. Here, we study possible roles for SPS in such models of continuous perceptions of change in a range of both instrumental (note-based) and sound-based music (generally containing more noise and fewer discrete events). In the first analysis, perceived change in three pieces of electroacoustic and one of piano music is modeled, to assess the possible contribution of (de-noised) SPS in cooperation with acoustic intensity and spectral flatness series. In the second analysis, a broad range of nine pieces is studied in relation to the wider range of distinctive spectral predictors useful in previous perceptual work, together with intensity and SPS. The second analysis uses cross-sectional (mixed-effects) time series analysis to take advantage of all the individual response series in the dataset, and to assess the possible generality of a predictive role for SPS. SPS proves to be a useful feature, making a predictive contribution distinct from other spectral parameters. Because SPS is a psychoacoustic “bottom up” feature, it may have wide applicability across both the familiar and the unfamiliar in the music to which we are exposed.
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Burke, Harry B. "Proteomics: Analysis of Spectral Data." Cancer Informatics 1 (January 2005): 117693510500100. http://dx.doi.org/10.1177/117693510500100102.
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The goal of disease-related proteogenomic research is a complete description of the unfolding of the disease process from its origin to its cure. With a properly selected patient cohort and correctly collected, processed, analyzed data, large scale proteomic spectra may be able to provide much of the information necessary for achieving this goal. Protein spectra, which are one way of representing protein expression, can be extremely useful clinically since they can be generated from blood rather than from diseased tissue. At the same time, the analysis of circulating proteins in blood presents unique challenges because of their heterogeneity, blood contains a large number of different abundance proteins generated by tissues throughout the body. Another challenge is that protein spectra are massively parallel information. One can choose to perform top-down analysis, where the entire spectra is examined and candidate peaks are selected for further assessment. Or one can choose a bottom-up analysis, where, via hypothesis testing, individual proteins are identified in the spectra and related to the disease process. Each approach has advantages and disadvantages that must be understood if protein spectral data are to be properly analyzed. With either approach, several levels of information must be in tegrated into a predictive model. This model will allow us to detect disease and it will allow us to discover therapeutic interventions that reduce the risk of disease in at-risk individuals and effectively treat newly diagnosed disease.
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Zhang, Xianglin, Jie Xue, Yi Xiao, Zhou Shi, and Songchao Chen. "Towards Optimal Variable Selection Methods for Soil Property Prediction Using a Regional Soil Vis-NIR Spectral Library." Remote Sensing 15, no. 2 (January 12, 2023): 465. http://dx.doi.org/10.3390/rs15020465.
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Soil visible and near-infrared (Vis-NIR, 350–2500 nm) spectroscopy has been proven as an alternative to conventional laboratory analysis due to its advantages being rapid, cost-effective, non-destructive and environmentally friendly. Different variable selection methods have been used to deal with the high redundancy, heavy computation, and model complexity of using full spectra in spectral modelling. However, most previous studies used a linear algorithm in the variable selection, and the application of a non-linear algorithm remains poorly explored. To address the current knowledge gap, based on a regional soil Vis-NIR spectral library (1430 soil samples), we evaluated seven variable selection algorithms together with three predictive algorithms in predicting seven soil properties. Our results showed that Cubist overperformed partial least squares regression (PLSR) and random forests (RF) in most soil properties (R2 > 0.75 for soil organic matter, total nitrogen and pH) when using the full spectra. Most of variable selection can greatly reduce the number of spectral bands and therefore simplified predictive models without losing accuracy. The results also showed that there was no silver bullet for the optimal variable selection algorithm among different predictive algorithms: (1) competitive adaptive reweighted sampling (CARS) always performed best for the PLSR algorithm, followed by forward recursive feature selection (FRFS); (2) recursive feature elimination (RFE) and genetic algorithm (GA) generally had better accuracy than others for the Cubist algorithm; and (3) FRFS had the best model performance for the RF algorithm. In addition, the performance was generally better when the algorithm used in the variable selection matched the predictive algorithm. The outcome of this study provides a valuable reference for predicting soil information using spectroscopic techniques together with variable selection algorithms.
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Tylová, L., J. Kukal, and O. Vyšata. "SPECTRAL ANALYSIS OF PREDICTIVE ERROR IN ALZHEIMER'S DISEASE DIAGNOSTICS." Neural Network World 23, no. 5 (2013): 427–34. http://dx.doi.org/10.14311/nnw.2013.23.026.
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Crozier, P. M., B. M. G. Cheetham, C. Holt, and E. Munday. "Speech enhancement employing spectral subtraction and linear predictive analysis." Electronics Letters 29, no. 12 (1993): 1094. http://dx.doi.org/10.1049/el:19930730.
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Konopásková, Jana, and V. Červený. "Predictive spectral analysis of short records using maximum entropy." Studia Geophysica et Geodaetica 29, no. 3 (September 1985): 228–37. http://dx.doi.org/10.1007/bf01638434.
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Tziolas, Nikolaos, Stella A. Ordoudi, Apostolos Tavlaridis, Konstantinos Karyotis, George Zalidis, and Ioannis Mourtzinos. "Rapid Assessment of Anthocyanins Content of Onion Waste through Visible-Near-Short-Wave and Mid-Infrared Spectroscopy Combined with Machine Learning Techniques." Sustainability 13, no. 12 (June 9, 2021): 6588. http://dx.doi.org/10.3390/su13126588.
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A sustainable process for valorization of onion waste would need to entail preliminary sorting out of exhausted or suboptimal material as part of decision-making. In the present study, an approach for monitoring red onion skin (OS) phenolic composition was investigated through Visible Near-Short-Wave infrared (VNIR-SWIR) (350–2500 nm) and Fourier-Transform-Mid-Infrared (FT-MIR) (4000–600 cm−1) spectral analyses and Machine-Learning (ML) methods. Our stepwise approach consisted of: (i) chemical analyses to obtain reference values for Total Phenolic Content (TPC) and Total Monomeric Anthocyanin Content (TAC); (ii) spectroscopic analysis and creation of OS spectral libraries; (iii) generation of calibration and validation datasets; (iv) spectral exploratory analysis and regression modeling via several ML algorithms; and (v) model performance evaluation. Among all, the k-nearest neighbors model from 1st derivative VNIR-SWIR spectra at 350–2500 nm resulted promising for the prediction of TAC (R2 = 0.82, RMSE = 0.52 and RPIQ = 3.56). The 2nd derivative FT-MIR spectral fingerprint among 600–900 and 1500–1600 cm−1 proved more informative about the inherent phenolic composition of OS. Overall, the diagnostic value and predictive accuracy of our spectral data support the perspective of employing non-destructive spectroscopic tools in real-time quality control of onion waste.
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Adedipe, Oluwatosin Emmanuel, and Ben Dawson-Andoh. "Prediction of Yellow-Poplar (Liriodendron Tulipifera) Veneer Stiffness and Bulk Density Using near Infrared Spectroscopy and Multivariate Calibration." Journal of Near Infrared Spectroscopy 16, no. 5 (January 1, 2008): 487–96. http://dx.doi.org/10.1255/jnirs.812.
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This study investigated the feasibility of using near infrared (NIR) spectroscopy and multivariate calibration to predict bulk density and stiffness of 3.2 mm thick yellow poplar veneer strips. Full-range (800–2500 nm) raw NIR spectra and spectra pre-treated using the first derivative method, along with spectra from three other different wavelength windows of 1200–2400 nm, 1800–2400 nm and 1400–2000 nm were regressed against the bulk density (kg m−3) values and the dynamic modulus of elasticity (stiffness; GPa) of the veneers using the projection to latent structures (PLS) method to develop calibration models. All predictive models developed performed well in the prediction of bulk density and stiffness of new test samples that were not included in the calibration models. R2 values ranged from 0.67-0.78 and 0.56-0.72, respectively, for bulk density and stiffness. There was significant improvement in models developed with first derivative spectra over models developed with raw spectra. The models developed using the first derivative used fewer latent variables to achieve predictive models with higher R2 values, lower root mean square errors of prediction (RMSEP) and standard errors of prediction (SEP). Models developed using the full NIR spectral range (800–2500 nm) and the NIR spectral region of 1200–2400 nm performed better than models developed using the restricted NIR wavelength regions of 1800–2400 nm and 1400–2000 nm. However, there was no clear distinction between models developed using the full NIR spectral range and the NIR spectral region of 1200–2400 nm. Overall, models developed with the first derivative pre-processed spectra using the whole NIR spectrum performed best in predictability. The results of this study show the potential of using multivariate data analysis coupled with NIR spectroscopy for on-line sorting and assessment of veneer stiffness prior to the lay-up process in the manufacturing of veneer-based engineered wood products such as plywood, Parallam and laminated veneer lumber.
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Semella, Sebastian, Christopher Hutengs, Michael Seidel, Mathias Ulrich, Birgit Schneider, Malte Ortner, Sören Thiele-Bruhn, Bernard Ludwig, and Michael Vohland. "Accuracy and Reproducibility of Laboratory Diffuse Reflectance Measurements with Portable VNIR and MIR Spectrometers for Predictive Soil Organic Carbon Modeling." Sensors 22, no. 7 (April 2, 2022): 2749. http://dx.doi.org/10.3390/s22072749.
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Soil spectroscopy in the visible-to-near infrared (VNIR) and mid-infrared (MIR) is a cost-effective method to determine the soil organic carbon content (SOC) based on predictive spectral models calibrated to analytical-determined SOC reference data. The degree to which uncertainty in reference data and spectral measurements contributes to the estimated accuracy of VNIR and MIR predictions, however, is rarely addressed and remains unclear, in particular for current handheld MIR spectrometers. We thus evaluated the reproducibility of both the spectral reflectance measurements with portable VNIR and MIR spectrometers and the analytical dry combustion SOC reference method, with the aim to assess how varying spectral inputs and reference values impact the calibration and validation of predictive VNIR and MIR models. Soil reflectance spectra and SOC were measured in triplicate, the latter by different laboratories, for a set of 75 finely ground soil samples covering a wide range of parent materials and SOC contents. Predictive partial least-squares regression (PLSR) models were evaluated in a repeated, nested cross-validation approach with systematically varied spectral inputs and reference data, respectively. We found that SOC predictions from both VNIR and MIR spectra were equally highly reproducible on average and similar to the dry combustion method, but MIR spectra were more robust to calibration sample variation. The contributions of spectral variation (ΔRMSE < 0.4 g·kg−1) and reference SOC uncertainty (ΔRMSE < 0.3 g·kg−1) to spectral modeling errors were small compared to the difference between the VNIR and MIR spectral ranges (ΔRMSE ~1.4 g·kg−1 in favor of MIR). For reference SOC, uncertainty was limited to the case of biased reference data appearing in either the calibration or validation. Given better predictive accuracy, comparable spectral reproducibility and greater robustness against calibration sample selection, the portable MIR spectrometer was considered overall superior to the VNIR instrument for SOC analysis. Our results further indicate that random errors in SOC reference values are effectively compensated for during model calibration, while biased SOC calibration data propagates errors into model predictions. Reference data uncertainty is thus more likely to negatively impact the estimated validation accuracy in soil spectroscopy studies where archived data, e.g., from soil spectral libraries, are used for model building, but it should be negligible otherwise.
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Vasenin, A. B., S. E. Stepanov, and O. V. Kryukov. "COMPARATIVE ASSESSMENT OF METHODS FOR PREDICTING THE TECHNICAL CONDITION OF ELECTRIC DRIVES OF HAZARDOUS PRODUCTION FACILITIES." Kontrol'. Diagnostika, no. 269 (November 2020): 54–62. http://dx.doi.org/10.14489/td.2020.11.pp.054-062.
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The paper deals with the design of efficient and reliable systems for operational diagnostics and forecasting of the technical condition of adjustable megawatt electric drives. The statistics of failure of the most critical components of AC electrical machines – stator windings, bearings and ACS are presented. The methodology and architecture of artificial neural networks have been developed for obtaining predictive models of high-voltage synchronous machines. Examples of neuro-fuzzy prediction of the technical state and resource of stator windings and analysis of the spectral composition of the supply voltage by the series method are given. Tests of selected networks, the Box–Jenkins fuzzy model, models of the method for analyzing the dynamics of spectral components with predicting the values of current and stator temperatures are obtained. The comparative results of the analysis of the expected states of electric machines of high power, based on the consideration of various operational factors of the operation of electric drives, made it possible to develop recommendations for the use of new predictive methods.
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Vasenin, A. B., S. E. Stepanov, and O. V. Kryukov. "COMPARATIVE ASSESSMENT OF METHODS FOR PREDICTING THE TECHNICAL CONDITION OF ELECTRIC DRIVES OF HAZARDOUS PRODUCTION FACILITIES." Kontrol'. Diagnostika, no. 269 (November 2020): 54–62. http://dx.doi.org/10.14489/td.2020.11.pp.054-062.
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The paper deals with the design of efficient and reliable systems for operational diagnostics and forecasting of the technical condition of adjustable megawatt electric drives. The statistics of failure of the most critical components of AC electrical machines – stator windings, bearings and ACS are presented. The methodology and architecture of artificial neural networks have been developed for obtaining predictive models of high-voltage synchronous machines. Examples of neuro-fuzzy prediction of the technical state and resource of stator windings and analysis of the spectral composition of the supply voltage by the series method are given. Tests of selected networks, the Box–Jenkins fuzzy model, models of the method for analyzing the dynamics of spectral components with predicting the values of current and stator temperatures are obtained. The comparative results of the analysis of the expected states of electric machines of high power, based on the consideration of various operational factors of the operation of electric drives, made it possible to develop recommendations for the use of new predictive methods.
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Taleb Bendiab, Anis, Maxime Ryckewaert, Daphné Heran, Raphaël Escalier, Raphaël K. Kribich, Caroline Vigreux, and Ryad Bendoula. "Coupling Waveguide-Based Micro-Sensors and Spectral Multivariate Analysis to Improve Spray Deposit Characterization in Agriculture." Sensors 19, no. 19 (September 26, 2019): 4168. http://dx.doi.org/10.3390/s19194168.
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The leaf coverage surface is a key measurement of the spraying process to maximize spray efficiency. To determine leaf coverage surface, the development of optical micro-sensors that, coupled with a multivariate spectral analysis, will be able to measure the volume of the droplets deposited on their surface is proposed. Rib optical waveguides based on Ge-Se-Te chalcogenide films were manufactured and their light transmission was studied as a response to the deposition of demineralized water droplets on their surface. The measurements were performed using a dedicated spectrophotometric bench to record the transmission spectra at the output of the waveguides, before (reference) and after drop deposition, in the wavelength range between 1200 and 2000 nm. The presence of a hollow at 1450 nm in the relative transmission spectra has been recorded. This corresponds to the first overtone of the O–H stretching vibration in water. This result tends to show that the optical intensity decrease observed after droplet deposition is partly due to absorption by water of the light energy carried by the guided mode evanescent field. The probe based on Ge-Se-Te rib optical waveguides is thus sensitive throughout the whole range of volumes studied, i.e., from 0.1 to 2.5 μL. Principal Component Analysis and Partial Least Square as multivariate techniques then allowed the analysis of the statistics of the measurements and the predictive character of the transmission spectra. It confirmed the sensitivity of the measurement system to the water absorption, and the predictive model allowed the prediction of droplet volumes on an independent set of measurements, with a correlation of 66.5% and a precision of 0.39 μL.
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Wang, Fuxiang, and Chunguang Wang. "Improved Model for Starch Prediction in Potato by the Fusion of Near-Infrared Spectral and Textural Data." Foods 11, no. 19 (October 8, 2022): 3133. http://dx.doi.org/10.3390/foods11193133.
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In this study, visible-near-infrared (VIS-NIR) hyperspectral imaging was combined with a data fusion strategy for the nondestructive assessment of the starch content in intact potatoes. Spectral and textural data were extracted from hyperspectral images and transformed principal component (PC) images, respectively, and a partial least squares regression (PLSR) prediction model was then established. The results revealed that low-level data fusion could not improve accuracy in predicting starch content. Therefore, to improve prediction accuracy, key variables were selected from the spectral and textural data through competitive adaptive reweighted sampling (CARS) and correlation analysis, respectively, and mid-level data fusion was performed. With a residual predictive deviation (RPD) value > 2, the established PLSR model achieved satisfactory prediction accuracy. Therefore, this study demonstrated that appropriate data fusion can effectively improve the prediction accuracy for starch content and thus aid the sorting of potato starch content in the production line.
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Liang, Long, Guigan Fang, Yongjun Deng, Zhixin Xiong, and Ting Wu. "Determination of Moisture Content and Basic Density of Poplar Wood Chips under Various Moisture Conditions by Near-Infrared Spectroscopy." Forest Science 65, no. 5 (May 20, 2019): 548–55. http://dx.doi.org/10.1093/forsci/fxz007.
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AbstractThe potential of near-infrared (NIR) spectroscopy coupled with partial least-squares (PLS) regression was used to determine the moisture content and basic density of poplar wood chips. NIR spectra collected from the surface of wood chips were used to develop calibration models for moisture content and basic density predication, and various spectral preprocessing techniques were applied to improve the accuracy and robustness of the prediction models. The models were tested using totally independent sample sets and exhibited acceptable predictive performance for moisture content (coefficient of determination for prediction [R2p] = 0.98 and standard error of prediction [SEP] = 2.51 percent) and basic density (R2p = 0.87 and SEP = 17.61 kg m–3). In addition, the effect of moisture variations on prediction of basic density was investigated based on NIR spectra from wood chips under various moisture levels. The results demonstrated that broad absorption bands from water molecules, especially when free water exists in the cell lumen, overlap with informative signals related to wood properties and weaken the calibration relation between spectral features and basic density. Thus, maintaining wood chips in a low and even moisture state would help achieve reliable estimates of wood density by NIR analysis models.
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Markgraf, Wenke, Jannis Lilienthal, Philipp Feistel, Christine Thiele, and Hagen Malberg. "Algorithm for Mapping Kidney Tissue Water Content during Normothermic Machine Perfusion Using Hyperspectral Imaging." Algorithms 13, no. 11 (November 10, 2020): 289. http://dx.doi.org/10.3390/a13110289.
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The preservation of kidneys using normothermic machine perfusion (NMP) prior to transplantation has the potential for predictive evaluation of organ quality. Investigations concerning the quantitative assessment of physiological tissue parameters and their dependence on organ function lack in this context. In this study, hyperspectral imaging (HSI) in the wavelength range of 500–995 nm was conducted for the determination of tissue water content (TWC) in kidneys. The quantitative relationship between spectral data and the reference TWC values was established by partial least squares regression (PLSR). Different preprocessing methods were applied to investigate their influence on predicting the TWC of kidneys. In the full wavelength range, the best models for absorbance and reflectance spectra provided Rp2 values of 0.968 and 0.963, as well as root-mean-square error of prediction (RMSEP) values of 2.016 and 2.155, respectively. Considering an optimal wavelength range (800–980 nm), the best model based on reflectance spectra (Rp2 value of 0.941, RMSEP value of 3.202). Finally, the visualization of TWC distribution in all pixels of kidneys’ HSI image was implemented. The results show the feasibility of HSI for a non-invasively and accurate TWC prediction in kidneys, which could be used in the future to assess the quality of kidneys during the preservation period.
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Zhou, Yan, and Hui Cao. "An Augmented Classical Least Squares Method for Quantitative Raman Spectral Analysis against Component Information Loss." Scientific World Journal 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/306937.
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We propose an augmented classical least squares (ACLS) calibration method for quantitative Raman spectral analysis against component information loss. The Raman spectral signals with low analyte concentration correlations were selected and used as the substitutes for unknown quantitative component information during the CLS calibration procedure. The number of selected signals was determined by using the leave-one-out root-mean-square error of cross-validation (RMSECV) curve. An ACLS model was built based on the augmented concentration matrix and the reference spectral signal matrix. The proposed method was compared with partial least squares (PLS) and principal component regression (PCR) using one example: a data set recorded from an experiment of analyte concentration determination using Raman spectroscopy. A 2-fold cross-validation with Venetian blinds strategy was exploited to evaluate the predictive power of the proposed method. The one-way variance analysis (ANOVA) was used to access the predictive power difference between the proposed method and existing methods. Results indicated that the proposed method is effective at increasing the robust predictive power of traditional CLS model against component information loss and its predictive power is comparable to that of PLS or PCR.
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Askari, Mohammad Sadegh, Sharon M. O'Rourke, and Nicholas M. Holden. "A comparison of point and imaging visible-near infrared spectroscopy for determining soil organic carbon." Journal of Near Infrared Spectroscopy 26, no. 2 (April 2018): 133–46. http://dx.doi.org/10.1177/0967033518766668.
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This study evaluated whether the accuracy of soil organic carbon measurement by laboratory hyperspectral imaging can match that of standard point spectroscopy operating in the visible–near infrared. Hyperspectral imaging allows a greater amount of spectral information to be collected from the soil sample compared to standard spectroscopy, accounting for greater sample representation. A total of 375 representative Irish soils were scanned by two-point spectrometers (a Foss NIR Systems 6500 labelled S-1 and a Varian FT-IR 3100 labelled S-2) and two laboratory hyperspectral imaging systems (two push broom line-scanning hyperspectral imaging systems manufactured by DV optics and Spectral Imaging Ltd, respectively, labelled S-3 and S-4). The objectives were (a) to compare the predictive ability of spectral datasets for soil organic carbon prediction for each instrument evaluated and (b) to assess the impact of imposing a common wavelength range and spectral resolution on soil organic carbon model accuracy. These objectives examined the predictive ability of spectral datasets for soil organic carbon prediction based on optimal settings of each instrument in (a) and introduced a constraint in wavelength range and spectral resolution to achieve common settings for instruments in (b). Based on optimal settings for each instrument, the deviation (root-mean square error of prediction) from the best fit line between laboratory measured and predicted soil organic carbon, ranked the instruments as S-1 (26.3 g kg−1) < S-2 (29.4 g kg−1) < S-3 (34.3 g kg−1) < S-4 (41.1 g kg−1). The S-1 model outperformed in all partial least squares regression performance indicators, and across all spectral ranges, and produced the most favourable outcomes in means testing, variance testing and identification of significant variables. It is assumed that a larger wavelength range produced more accurate soil organic carbon predictions for S-1 and S-2. Under common instrument settings, the prediction accuracy for S-3 that was almost equal to S-1. It is concluded that under standard operating procedures, greater soil sample representation captured by hyperspectral imaging can equal the quality of the spectra from point spectroscopy. This result is important for the development of laboratory hyperspectral imaging for soil image analysis.
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Ge, Zhengfang, Kevin T. Schomacker, and Norman S. Nishioka. "Identification of Colonic Dysplasia and Neoplasia by Diffuse Reflectance Spectroscopy and Pattern Recognition Techniques." Applied Spectroscopy 52, no. 6 (June 1998): 833–39. http://dx.doi.org/10.1366/0003702981944571.
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Diffuse reflectance spectroscopy of colonic tissue was employed to determine whether the spectra can be used to distinguish between neoplastic and non-neoplastic tissue in vivo. A total of 224 spectra were obtained in the wavelength range of 350–800 nm from 107 non-neoplastic tissue samples (84 normal mucosa, 23 hyperplastic polyps) and 53 neoplastic tissue samples (44 adenomatous polyps, 9 adenocarcinomas). Pattern recognition algorithms including multiple linear regression (MLR), linear discriminant analysis (LDA), and backpropagating neural network (BNN) were used to distinguish between the two tissue classes. The spectra were randomly separated into training and prediction sets for data analyses. The mean predictive accuracies of distinguishing neoplastic tissue from non-neoplastic tissue with MLR, LDA, and BNN were 85, 82, and 85%, respectively. In a similar fashion, the more clinically relevant problem of distinguishing adenomatous polyps from hyperplastic polyps was assessed. The mean predictive accuracies of distinguishing adenomatous polyps from hyperplastic polyps with MLR, LDA, and BNN were 85, 81, and 82%, respectively. The major spectral differences between tissues were attributed to changes in blood volume, oxygen saturation of hemoglobin, mean vessel depth within tissue, and tissue scattering.
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Robert, P., M. F. Devaux, and D. Bertrand. "Beyond Prediction: Extracting Relevant Information from near Infrared Spectra." Journal of Near Infrared Spectroscopy 4, no. 1 (January 1996): 75–84. http://dx.doi.org/10.1255/jnirs.78.
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With the increase of near infrared (NIR) applications, numerous chemometric methods have been developed. Among the mathematical treatments available, principal comoponent analysis (PCA) is certainly the most well-known when considering highly correlated data. In the field of near infrared spectroscopy, it allows the study of spectra without deleting wavelengths and without making any preliminary assumptions on the data. One advantage of PCA lies in the graphical displays obtained and, more precisely, on the similarity maps and spectral patterns. While the maps reveal clusters of the samples, the spectral patterns make a spectral interpretation possible. The present paper reviews our contribution to the development and application of PCA to NIR spectroscopy. It shows that PCA is the core of various mathematical treatments such as principal component regression (PCR), factorial discriminant analysis (FDA) and canonical correlation analysis (CCA). One advantage of using PCA in the prediction techniques lies in the use of all the wavelengths in the predictive model. The extraction of relevant and comprehensive wavelengths can be guided by CCA which allows the description of the samples by taking both mid- and near infrared data into account. Besides a comprehensive presentation of the mathematical treatements, examples are given.
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Bian, Xi-Hui, Shu-Juan Li, Meng-Ran Fan, Yu-Gao Guo, Na Chang, and Jiang-Jiang Wang. "Spectral quantitative analysis of complex samples based on the extreme learning machine." Analytical Methods 8, no. 23 (2016): 4674–79. http://dx.doi.org/10.1039/c6ay00731g.
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Costes, Jean-Philippe. "A predictive surface profile model for turning based on spectral analysis." Journal of Materials Processing Technology 213, no. 1 (January 2013): 94–100. http://dx.doi.org/10.1016/j.jmatprotec.2012.08.009.
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LIU, YI, LAIJUN SUN, ZHIYONG RAN, XUYANG PAN, SHUANG ZHOU, and SHUANGCAI LIU. "Prediction of Talc Content in Wheat Flour Based on a Near-Infrared Spectroscopy Technique." Journal of Food Protection 82, no. 10 (September 17, 2019): 1655–62. http://dx.doi.org/10.4315/0362-028x.jfp-18-582.
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ABSTRACT A procedure for the prediction of talc content in wheat flour based on radial basis function (RBF) neural network and near-infrared spectroscopy (NIRS) data is described. In this study, 41 wheat flour samples adulterated with different concentrations of talc were used. The diffuse reflectance spectra of all samples were collected by NIRS analyzer in the spectral range of 400 to 2,500 nm. A sample of outliers was eliminated by Mahalanobis distance based on near-infrared spectral scanning, and the remaining 40 wheat flour samples were used for spectral characteristic analysis. A calibration set of 26 samples and a prediction set of 14 samples of wheat flour were built as a result of sample set partitioning based on joint x–y distances division. A comparison of Savitzky-Golay smoothing, multiplicative scatter correction (MSC), first derivation, second derivation, and standard normal variation in the modeling showed that MSC has the best preprocessing effect. To develop a simpler, more efficient prediction model, the correlation coefficient method (CCM) was used to reduce spectral redundancy and determine the maximum correlation informative wavelength (MIW). From the full 1,050 wavelengths, 59 individual MIWs were finally selected. The optimal combined detection model was CCM-MSC-RBF based on the selected MIWs, with a determination of prediction coefficients of prediction (Rp) of 0.9999, root-mean-square error of prediction of 0.0765, and residual predictive deviation of 65.0909. The study serves as a proof of concept that NIRS technology combined with multivariate analysis has the potential to provide a fast, nondestructive and reliable assay for the prediction of talc content in wheat flour.
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Ma, Xiaoyan, Yanbin Zhang, Hui Cao, Shiliang Zhang, and Yan Zhou. "Nonlinear Regression with High-Dimensional Space Mapping for Blood Component Spectral Quantitative Analysis." Journal of Spectroscopy 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/2689750.
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Accurate and fast determination of blood component concentration is very essential for the efficient diagnosis of patients. This paper proposes a nonlinear regression method with high-dimensional space mapping for blood component spectral quantitative analysis. Kernels are introduced to map the input data into high-dimensional space for nonlinear regression. As the most famous kernel, Gaussian kernel is usually adopted by researchers. More kernels need to be studied for each kernel describes its own high-dimensional feature space mapping which affects regression performance. In this paper, eight kernels are used to discuss the influence of different space mapping to the blood component spectral quantitative analysis. Each kernel and corresponding parameters are assessed to build the optimal regression model. The proposed method is conducted on a real blood spectral data obtained from the uric acid determination. Results verify that the prediction errors of proposed models are more precise than the ones obtained by linear models. Support vector regression (SVR) provides better performance than partial least square (PLS) when combined with kernels. The local kernels are recommended according to the blood spectral data features. SVR with inverse multiquadric kernel has the best predictive performance that can be used for blood component spectral quantitative analysis.
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Xu, Hanyi, Dongyun Xu, Songchao Chen, Wanzhu Ma, and Zhou Shi. "Rapid Determination of Soil Class Based on Visible-Near Infrared, Mid-Infrared Spectroscopy and Data Fusion." Remote Sensing 12, no. 9 (May 9, 2020): 1512. http://dx.doi.org/10.3390/rs12091512.
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Wise soil management requires detailed soil information, but conventional soil class mapping in a rather coarse spatial resolution cannot meet the demand for precision agriculture. With the advantages of non-destructiveness, rapid cost-efficiency, and labor savings, the spectroscopic technique has proved its high potential for success in soil classification. Previous studies mainly focused on predicting soil classes using a single sensor. In this study, we attempted to compare the predictive ability of visible near infrared (vis-NIR) spectra, mid-infrared (MIR) spectra, and their fused spectra for soil classification. A total of 146 soil profiles were collected from Zhejiang, China, and the soil properties and spectra were measured by their genetic horizons. Along with easy-to-measure auxiliary soil information (soil organic matter, soil texture, color and pH), four spectral data, including vis-NIR, MIR, their simple combination (vis-NIR-MIR), and outer product analysis (OPA) fused spectra, were used for soil classification using a multiple objectives mixed support vector machine model. The independent validation results showed that the classification model using MIR (accuracy of 64.5%) was slightly better than that using vis-NIR (accuracy of 64.2%). The predictive model built on vis-NIR-MIR did not improve the classification accuracy, having the lowest accuracy of 61.1%, which likely resulted from an over-fitting problem. The model based on OPA fused spectra performed best with an accuracy of 68.4%. Our results prove the potential of fusing vis-NIR and MIR using OPA for improving prediction ability for soil classification.
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Bekiaris, Georgios, Dimitra Tagkouli, Georgios Koutrotsios, Nick Kalogeropoulos, and Georgios I. Zervakis. "Pleurotus Mushrooms Content in Glucans and Ergosterol Assessed by ATR-FTIR Spectroscopy and Multivariate Analysis." Foods 9, no. 4 (April 24, 2020): 535. http://dx.doi.org/10.3390/foods9040535.
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Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy was used to monitor the infrared absorption spectra of 79 mushroom samples from 29 Pleurotus ostreatus, P. eryngii and P. nebrodensis strains cultivated on wheat straw, grape marc and/or by-products of the olive industry. The spectroscopic analysis provided a chemical insight into the mushrooms examined, while qualitative and quantitative differences in regions related to proteins, phenolic compounds and polysaccharides were revealed among the species and substrates studied. Moreover, by using advanced chemometrics, correlations of the recorded mushrooms’ spectra versus their content in glucans and ergosterol, commonly determined through traditional analytical techniques, allowed the development of models predicting such contents with a good predictive power (R2: 0.80–0.84) and accuracy (low root mean square error, low relative error and representative to the predicted compounds spectral regions used for the calibrations). Findings indicate that FTIR spectroscopy could be exploited as a potential process analytical technology tool in the mushroom industry to characterize mushrooms and to assess their content in bioactive compounds.
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Nikolić, Saša, and Radoš Ćalasan. "Motor Current Signature Analysis in Predictive Maintenance." Journal of Energy - Energija 67, no. 4 (June 2, 2018): 3–6. http://dx.doi.org/10.37798/201867462.
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The aim of this paper is to draw attention to the possibilities offered by spectral analysis of current and voltage in the predictive maintenance of the electric motor. Motor Circuit analysis (MCA) and Motor Current Signature analysis (MCSA) are innovative and non-invasive methods that enable diagnostics and assessment of the condition of the electric motor. The main advantage of the method is that the test is carried out during the normal motor operation, without downtime. All motor defects can be detected at the earliest stage. This enable planning the overhaul according to the condition which can make significant savings. Advanced MCSA analysers enable diagnostics of electric motors that are powered either via a soft starter, frequency inverter or directly from mains. So, it is possible in a simple and reliable way make an condition assessment of frequency inverters. In addition, it is possible to detect faults of driven machine, like misalignment, imbalance, blade faults, belts, bearings issues etc. Theoretical basis and tests that are carried out are explained in the paper.
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Nemtanu, Florin, Ilona Madalina Costea, and Catalin Dumitrescu. "Spectral Analysis of Traffic Functions in Urban Areas." PROMET - Traffic&Transportation 27, no. 6 (December 17, 2015): 477–84. http://dx.doi.org/10.7307/ptt.v27i6.1686.
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The paper is focused on the Fourier transform application in urban traffic analysis and the use of said transform in traffic decomposition. The traffic function is defined as traffic flow generated by different categories of traffic participants. A Fourier analysis was elaborated in terms of identifying the main traffic function components, called traffic sub-functions. This paper presents the results of the method being applied in a real case situation, that is, an intersection in the city of Bucharest where the effect of a bus line was analysed. The analysis was done using different time scales, while three different traffic functions were defined to demonstrate the theoretical effect of the proposed method of analysis. An extension of the method is proposed to be applied in urban areas, especially in the areas covered by predictive traffic control.
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Khodasevich, M. A., E. A. Scorbanov, and M. V. Rogovaya. "Application of Multivariate Analysis of Broadband Transmission Spectra for Calibration of Physico-Chemical Parameters of Wines." Devices and Methods of Measurements 10, no. 2 (June 24, 2019): 198–206. http://dx.doi.org/10.21122/2220-9506-2019-10-2-198-206.
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The use of multivariate processing of spectral information has recently been favored due to the express nature of this method, the ease of use of mathematical packages, and the lack of the need to add chemical reagents. The aim of the work is using the methods of multivariate analysis of broadband transmission spectra to calibrate the physicochemical parameters of wines and to improve the accuracy of this calibration by selecting spectral variables.Using the interval projection to latent structures of the transmission spectra in the range of 220– 2500 nm, the physicochemical characteristics of the varietal unblended Moldovan wine are calibrated. Interval methods of multivariate data analysis allow signifi reducing the root mean square calibration error in comparison with the broadband multivariate methods. Residual predictive deviations exceed the threshold value of 2.5 for K, Ca, Mg, oxalic, malic and succinic acids, 2,3-butylene glycol, ash and phenolic compounds for red wines and Mg, tartaric, citric and lactic acids, 2,3-butylene glycol, ash, phenolic compounds and soluble salts for white wines. These values demonstrate good calibration quality.The application of the proposed method for calibrating the physicochemical parameters of wines makes it possible to replace traditional methods with spectral measurements, which are available not only in laboratory but also in the fi and characterized by small values of the root mean square error of calibration.
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Bian, Xihui, Caixia Zhang, Xiaoyao Tan, Michal Dymek, Yugao Guo, Ligang Lin, Bowen Cheng, and Xiaoyu Hu. "A boosting extreme learning machine for near-infrared spectral quantitative analysis of diesel fuel and edible blend oil samples." Analytical Methods 9, no. 20 (2017): 2983–89. http://dx.doi.org/10.1039/c7ay00353f.
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Gaonkar, Gopal H., and Ranjith Mohan. "Extracting Stochastic Airwake Models from a Database for Engineering Analysis and Simulation." Journal of the American Helicopter Society 57, no. 2 (April 1, 2012): 1–15. http://dx.doi.org/10.4050/jahs.57.022004.
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A framework is presented for extracting interpretive models of airwake autocorrelation and autospectrum as well as crosscorrelation and cross-spectrum from a database. These models have a simple analytical structure that aids routine simulation and application as a predictive tool. Airwake refers to turbulence shed from the ship superstructure, and the database, to a set of spectral (autospectral and cross-spectral) points of flow velocity data from experimental and computational fluid dynamics–based investigations. The framework is developed from first principles: It is based on perturbation theory; it addresses all three velocity components, and it is tested against a comprehensive database under different superstructure and wind-over-deck conditions. For each velocity component, the autocorrelation and cross-correlation are represented by separate perturbation series in which the first terms have a form of the von Karman longitudinal or lateral correlation function. These series are then transformed into equivalent perturbation series of autospectra and cross-spectra. The perturbation coefficients are evaluated by satisfying the algorithmic constraints and fitting a curve on a set of selected spectral data points in the low-frequency bandwidth (0≤f(Hz)≤1.6); the emphasis is on extracting spectral models for this bandwidth. Generally, no more than a second-order perturbation correction (a three-term perturbation series) is necessary, and the extracted models lend themselves well to construction of shaping filters driven by white noise. The framework's strengths and weaknesses are discussed as well.
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Abe, Y., A. Nakao, Y. Arikawa, A. Morace, T. Mori, Z. Lan, T. Wei, et al. "Predictive capability of material screening by fast neutron activation analysis using laser-driven neutron sources." Review of Scientific Instruments 93, no. 9 (September 1, 2022): 093523. http://dx.doi.org/10.1063/5.0099217.
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Bright, short-pulsed neutron beams from laser-driven neutron sources (LANSs) provide a new perspective on material screening via fast neutron activation analysis (FNAA). FNAA is a nondestructive technique for determining material elemental composition based on nuclear excitation by fast neutron bombardment and subsequent spectral analysis of prompt γ-rays emitted by the active nuclei. Our recent experiments and simulations have shown that activation analysis can be used in practice with modest neutron fluences on the order of 105 n/cm2, which is available with current laser technology. In addition, time-resolved γ-ray measurements combined with picosecond neutron probes from LANSs are effective in mitigating the issue of spectral interference between elements, enabling highly accurate screening of complex samples containing many elements. This paper describes the predictive capability of LANS-based activation analysis based on experimental demonstrations and spectral calculations with Monte Carlo simulations.
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Fu, Chengbiao, Heigang Xiong, and Anhong Tian. "Fractional Modeling for Quantitative Inversion of Soil-Available Phosphorus Content." Mathematics 6, no. 12 (December 14, 2018): 330. http://dx.doi.org/10.3390/math6120330.
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The study of field spectra based on fractional-order differentials has rarely been reported, and traditional integer-order differentials only perform the derivative calculation for 1st-order or 2nd-order spectrum signals, ignoring the spectral transformation details between 0th-order to 1st-order and 1st-order to 2nd-order, resulting in the problem of low-prediction accuracy. In this paper, a spectral quantitative analysis model of soil-available phosphorus content based on a fractional-order differential is proposed. Firstly, a fractional-order differential was used to perform a derivative calculation of original spectral data from 0th-order to 2nd-order using 0.2-order intervals, to obtain 11 fractional-order spectrum data. Afterwards, seven bands with absolute correlation coefficient greater than 0.5 were selected as sensitive bands. Finally, a stepwise multiple linear regression algorithm was used to establish a spectral estimation model of soil-available phosphorus content under different orders, then the prediction effect of the model under different orders was compared and analyzed. Simulation results show that the best order for a soil-available phosphorus content regression model is a 0.6 fractional-order, the coefficient of determination (), root mean square error (RMSE), and ratio of performance to deviation (RPD) of the best model are 0.7888, 3.348878, and 2.001142, respectively. Since the RPD value is greater than 2, the optimal fractional model established in this study has good quantitative predictive ability for soil-available phosphorus content.
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Zhu, Chuanmei, Zipeng Zhang, Hongwei Wang, Jingzhe Wang, and Shengtian Yang. "Assessing Soil Organic Matter Content in a Coal Mining Area through Spectral Variables of Different Numbers of Dimensions." Sensors 20, no. 6 (March 24, 2020): 1795. http://dx.doi.org/10.3390/s20061795.
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Soil organic matter (SOM) is a crucial indicator for evaluating soil quality and an important component of soil carbon pools, which play a vital role in terrestrial ecosystems. Rapid, non-destructive and accurate monitoring of SOM content is of great significance for the environmental management and ecological restoration of mining areas. Visible-near-infrared (Vis-NIR) spectroscopy has proven its applicability in estimating SOM over the years. In this study, 168 soil samples were collected from the Zhundong coal field of Xinjiang Province, Northwest China. The SOM content (g kg−1) was determined by the potassium dichromate external heating method and the soil reflectance spectra were measured by the spectrometer. Two spectral feature extraction strategies, namely, principal component analysis (PCA) and the optimal band combination algorithm, were introduced to choose spectral variables. Linear models and random forests (RF) were used for predictive models. The coefficient of determination (R2), root mean square error (RMSE), and the ratio of the performance to the interquartile distance (RPIQ) were used to evaluate the predictive performance of the model. The results indicated that the variables (2DI and 3DI) derived from the optimal band combination algorithm outperformed the PCA variables (1DV) regardless of whether linear or RF models were used. An inherent gap exists between 2DI and 3DI, and the performance of 2DI is significantly poorer than that of 3DI. The accuracy of the prediction model increases with the increasing number of spectral variable dimensions (in the following order: 1DV < 2DI < 3DI). This study proves that the 3DI is the first choice for the optimal band combination algorithm to derive sensitive parameters related to SOM in the coal mining area. Furthermore, the optimal band combination algorithm can be applied to hyperspectral or multispectral images and to convert the spectral response into image pixels, which may be helpful for a soil property spatial distribution map.
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ZAMARRÓN, C., P. V. ROMERO, J. R. RODRIGUEZ, and F. GUDE. "Oximetry spectral analysis in the diagnosis of obstructive sleep apnoea." Clinical Science 97, no. 4 (August 24, 1999): 467–73. http://dx.doi.org/10.1042/cs0970467.
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Using spectral analysis of oximetry data, we prospectively evaluated the validity of this methodology in patients clinically suspected of suffering from obstructive sleep apnoea (OSA). A total of 233 outpatients were studied. Nocturnal oximetry was performed simultaneously with conventional polysomnography for all participants. The power density of oxygen saturation was analysed using Fast-Fourier transformation of the oximetric signal. Nocturnal oximetry test results were considered as abnormal (suspicion of OSA) if a peak in the spectrum between the period boundaries 30 and 70 s was observed. A normal test result was defined as the absence of the 30–70 s peak from the spectrum. Single-blind evaluation was performed by three independent observers, and agreement of two or more of these was considered definitive. The peak amplitude and the ratio of the area enclosed in the 30–70 s peak to the total area of the spectrum (rS) were measured. The presence of a peak has a sensitivity of 78%, a specificity of 89%, a positive predictive value of 89% and a negative predictive value of 78%. Apnoea–hypopnoea indexes were correlated significantly with peak amplitude (r = 0.74; P< 0.001) and with rS (r = 0.69; P< 0.001). For a peak amplitude threshold of 0.7%2, the sensitivity was 94% and the specificity was 65% for OSA diagnosis. Using a threshold for rS of 0.15, the sensitivity was 91% and the specificity was 67%. Thus the spectral analysis of nocturnal oximetry and identification of a peak at 30–70 s could be useful as a diagnostic technique for OSA subjects.
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Kawamura, Kensuke, Tomohiro Nishigaki, Andry Andriamananjara, Hobimiarantsoa Rakotonindrina, Yasuhiro Tsujimoto, Naoki Moritsuka, Michel Rabenarivo, and Tantely Razafimbelo. "Using a One-Dimensional Convolutional Neural Network on Visible and Near-Infrared Spectroscopy to Improve Soil Phosphorus Prediction in Madagascar." Remote Sensing 13, no. 8 (April 15, 2021): 1519. http://dx.doi.org/10.3390/rs13081519.
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As a proximal soil sensing technique, laboratory visible and near-infrared (Vis-NIR) spectroscopy is a promising tool for the quantitative estimation of soil properties. However, there remain challenges for predicting soil phosphorus (P) content and availability, which requires a reliable model applicable for different land-use systems to upscale. Recently, a one-dimensional convolutional neural network (1D-CNN) corresponding to the spectral information of soil was developed to considerably improve the accuracy of soil property predictions. The present study investigated the predictive ability of a 1D-CNN model to estimate soil available P (oxalate-extractable P; Pox) content in soils by comparing it with partial least squares (PLS) and random forest (RF) regressions using soil samples (n = 318) collected from natural (forest and non-forest) and cultivated (upland and flooded rice fields) systems in Madagascar. Overall, the 1D-CNN model showed the best predictive accuracy (R2 = 0.878) with a highly accurate prediction ability (ratio of performance to the interquartile range = 2.492). Compared to the PLS model, the RF and 1D-CNN models indicated 4.37% and 23.77% relative improvement in root mean squared error values, respectively. Based on a sensitivity analysis, the important wavebands for predicting soil Pox were associated with iron (Fe) oxide, organic matter (OM), and water absorption, which were previously known wavelength regions for estimating P in soil. These results suggest that 1D-CNN corresponding spectral signatures can be expected to significantly improve the predictive ability for estimating soil available P (Pox) from Vis-NIR spectral data. Rapid and accurate estimation of available P content in soils using our results can be expected to contribute to effective fertilizer management in agriculture and the sustainable management of ecosystems. However, the 1D-CNN model will require a large dataset to extend its applicability to other regions of Madagascar. Thus, further updates should be tested in future studies using larger datasets from a wide range of ecosystems in the tropics.
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Corrêdo, Lucas de Paula, Leonardo Felipe Maldaner, Helizani Couto Bazame, and José Paulo Molin. "Evaluation of Minimum Preparation Sampling Strategies for Sugarcane Quality Prediction by vis-NIR Spectroscopy." Sensors 21, no. 6 (March 21, 2021): 2195. http://dx.doi.org/10.3390/s21062195.
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Proximal sensing for assessing sugarcane quality information during harvest can be affected by various factors, including the type of sample preparation. The objective of this study was to determine the best sugarcane sample type and analyze the spectral response for the prediction of quality parameters of sugarcane from visible and near-infrared (vis-NIR) spectroscopy. The sampling and spectral data acquisition were performed during the analysis of samples by conventional methods in a sugar mill laboratory. Samples of billets were collected and four modes of scanning and sample preparation were evaluated: outer-surface (‘skin’) (SS), cross-sectional scanning (CSS), defibrated cane (DF), and raw juice (RJ) to analyze the parameters soluble solids content (Brix), saccharose (Pol), fibre, pol of cane and total recoverable sugars (TRS). Predictive models based on Partial Least Square Regression (PLSR) were built with the vis-NIR spectral measurements. There was no significant difference (p-value > 0.05) between the accuracy SS and CSS samples compared to DF and RJ samples for all prediction models. However, DF samples presented the best predictive performance values for the main sugarcane quality parameters, and required only minimal sample preparation. The results contribute to advancing the development of on-board quality monitoring in sugarcane, indicating better sampling strategies.
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Amorena, José Ignacio, Dolores María Eugenia Álvarez, and Elvira Fernández-Ahumada. "Development of Calibration Models to Predict Mean Fibre Diameter in Llama (Lama glama) Fleeces with Near Infrared Spectroscopy." Animals 11, no. 7 (July 4, 2021): 1998. http://dx.doi.org/10.3390/ani11071998.
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Llama fibre has the potential to become the most valuable textile resource in the Puna region of Argentina. In this study near infrared reflectance spectroscopy was evaluated to predict the mean fibre diameter in llama fleeces. Analyses between sets of carded and non-carded samples in combination with spectral preprocessing techniques were carried out and a total of 169 spectral signatures of llama samples in Vis and NIR ranges (400–2500 nm) were obtained. Spectral preprocessing consisted in wavelength selection (Vis–NIR, NIR and discrete ranges) and multiplicative and derivative pretreatments; spectra without pretreatments were also included, while modified partial least squares (M-PLS) regression was used to develop prediction models. Predictability was evaluated through R2: standard cross validation error (SECV), external validation error (SEV) and residual predictive value (RPD). A total of 54 calibration models were developed in which the best model (R2 = 0.67; SECV = 1.965; SEV = 2.235 and RPD = 1.91) was obtained in the Vis–NIR range applying the first derivative pretreatment. ANOVA analysis showed differences between carded and non-carded sets and the models obtained could be used in screening programs and contribute to valorisation of llama fibre and sustainable development of textile industry in the Puna territory of Catamarca. The data presented in this paper are a contribution to enhance the scarce information on this subject.
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Franzoi, Marco, Giovanni Niero, Mauro Penasa, and Massimo De Marchi. "Development of Infrared Prediction Models for Diffusible and Micellar Minerals in Bovine Milk." Animals 9, no. 7 (July 9, 2019): 430. http://dx.doi.org/10.3390/ani9070430.
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Milk and dairy products are major sources of minerals in human diet. Minerals influence milk technological properties; in particular, micellar and diffusible minerals differentially influence rennet clotting time, curd firmness and curd formation rate. The aim of the present study was to investigate the ability of mid-infrared spectroscopy to predict the content of micellar and diffusible mineral fractions in bovine milk. Spectra of reference milk samples (n = 93) were collected using Milkoscan™ 7 (Foss Electric A/S, Hillerød, Denmark) and total, diffusible and micellar content of minerals were quantified using inductively coupled plasma optical emission spectrometry. Backward interval partial least squares algorithm was applied to exclude uninformative spectral regions and build prediction models for total, diffusible and micellar minerals content. Results showed that backward interval partial least squares analysis improved the predictive ability of the models for the studied traits compared with traditional partial least squares approach. Overall, the predictive ability of mid-infrared prediction models was moderate to low, with a ratio of performance to deviation in cross-validation that ranged from 1.15 for micellar K to 2.73 for total P.
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Senger, Ryan S., and John L. Robertson. "The Rametrix™ PRO Toolbox v1.0 for MATLAB®." PeerJ 8 (January 6, 2020): e8179. http://dx.doi.org/10.7717/peerj.8179.
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Background Existing tools for chemometric analysis of vibrational spectroscopy data have enabled characterization of materials and biologicals by their broad molecular composition. The Rametrix™ LITE Toolbox v1.0 for MATLAB® is one such tool available publicly. It applies discriminant analysis of principal components (DAPC) to spectral data to classify spectra into user-defined groups. However, additional functionality is needed to better evaluate the predictive capabilities of these models when “unknown” samples are introduced. Here, the Rametrix™ PRO Toolbox v1.0 is introduced to provide this capability. Methods The Rametrix™ PRO Toolbox v1.0 was constructed for MATLAB® and works with the Rametrix™ LITE Toolbox v1.0. It performs leave-one-out analysis of chemometric DAPC models and reports predictive capabilities in terms of accuracy, sensitivity (true-positives), and specificity (true-negatives). Rametrix™PRO is available publicly through GitHub under license agreement at: https://github.com/SengerLab/RametrixPROToolbox. Rametrix™ PRO was used to validate Rametrix™ LITE models used to detect chronic kidney disease (CKD) in spectra of urine obtained by Raman spectroscopy. The dataset included Raman spectra of urine from 20 healthy individuals and 31 patients undergoing peritoneal dialysis treatment for CKD. Results The number of spectral principal components (PCs) used in building the DAPC model impacted the model accuracy, sensitivity, and specificity in leave-one-out analyses. For the dataset in this study, using 35 PCs in the DAPC model resulted in 100% accuracy, sensitivity, and specificity in classifying an unknown Raman spectrum of urine as belonging to a CKD patient or a healthy volunteer. Models built with fewer or greater number of PCs showed inferior performance, which demonstrated the value of Rametrix™ PRO in evaluating chemometric models constructed with Rametrix™ LITE.
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Ridley, Dennis, Willie Gist, Dennis Duke, and James C. Flagg. "The predictive ability of accounting operating cash flows: a moving window spectral analysis." American J. of Finance and Accounting 1, no. 2 (2008): 167. http://dx.doi.org/10.1504/ajfa.2008.019951.
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Wolak, Artur, Jarosław Molenda, Kamil Fijorek, and Bartosz Łankiewicz. "Prediction of the Total Base Number (TBN) of Engine Oil by Means of FTIR Spectroscopy." Energies 15, no. 8 (April 12, 2022): 2809. http://dx.doi.org/10.3390/en15082809.
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The objective of this study is to develop a statistical model to accurately estimate the total base number (TBN) value of diesel engine oils on the basis of the Fourier transform infrared spectroscopy (FTIR) analysis. The research sample consisted of oils used in the course of 14,820 km. The samples were collected after each 1000 km and both FTIR and TBN measurements were performed. By applying the measured absorbance values, five statistical models aimed at predicting TBN values were elaborated with the use of the following information: aggregated values of measured absorbance in defined spectral ranges, extremes at wavenumbers, or the surface area of spectral bands related to the vibrations of specific molecular structures. The obtained models may be considered a continuation and an extension of previous studies of this type described in the literature on the subject. The results of the study and the analysis of the obtained data have led to the development of two models with high predictive capabilities (R2 > 0.98, RMSE < 0.5). Another model, which had the smallest number of variables in comparison to other models, had markedly lower R2 value (0.9496) and the highest RMSE (0.5596). Yet another model, where the dimensionality of the pre-processed full spectra was reduced to four aggregates through averaging, turned out to be slightly worse than the best one (R2 = 0.9728). The study contributes to a more in-depth understanding of the FTIR-based TBN prediction tools that may be readily available to all interested parties.
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Saavedra, P. N., and D. E. Ramírez. "Vibration analysis of rotors for the identification of shaft misalignment Part 2: Experimental validation." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 218, no. 9 (September 1, 2004): 987–99. http://dx.doi.org/10.1243/0954406041991198.
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In Part 1 of this paper, a theoretical model to determine the dynamic behaviour of misaligned shaft rotors connected by a flexible coupling was developed. In this Part 2, experimental tests are performed to validate the model and simulation results obtained in Part 1. Vibratory characteristics, such as the frequency spectrum, waveform and phase shifts of the spectral components across the coupling are presented for different magnitudes of misalignment and types of coupling. Traditional vibration analysis rules used in practical predictive maintenance to diagnose shaft misalignment are evaluated. The influence of the frequency response functions on the amplitude of the spectral components and on the phase shifts of the spectral components across a coupling were studied. Finally, some practical conclusions for more reliable shaft misalignment identification using vibration analysis are suggested.
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Lin, L. X., Y. J. Wang, J. Y. Teng, and X. X. Xi. "Hyperspectral Analysis of Soil Total Nitrogen in Subsided Land Using the Local Correlation Mazimization-Complementary Superiority Method." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W4 (June 26, 2015): 87–97. http://dx.doi.org/10.5194/isprsarchives-xl-7-w4-87-2015.
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The measurement of soil total nitrogen (TN) by hyperspectral remote sensing provides an important tool for soil restoration programs in areas with subsided land caused by the extraction of natural resources. This study used the local correlation maximization-complementary superiority method (LCMCS) to establish TN prediction models by considering the relationship between spectral reflectance and TN based on spectral reflectance curves of soil samples collected from subsided land determined by synthetic aperture radar interferometry (InSAR) technology. Based on the 1655 selected effective bands of the optimal spectrum (OSP) of the first derivate differential of reciprocal logarithm ([log{1/R}]'), (correlation coefficients, P < 0.01), the optimal model of LCMCS method was obtained to determine the final model, which produced lower prediction errors (root mean square error of validation [RMSEV] = 0.89, mean relative error of validation [MREV] = 5.93%) when compared with models built by the local correlation maximization (LCM), complementary superiority (CS) and partial least squares regression (PLS) methods. The predictive effect of LCMCS model was optional in Cangzhou, Renqiu and Fengfeng District. Results indicate that the LCMCS method has great potential to monitor TN in subsided land caused by the extraction of natural resources including groundwater, oil and coal.