- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Simultaneous Determination of Clarithromycin, Tinidazole and Omeprazole in Helicure Tablets Using Reflectance Near-Infrared Spectroscopy with the Aid of Chemometry
摘要： A near infrared spectroscopic method for the simultaneous determination of the active principles clarithromycin, tinidazole and omeprazole in a pharmaceutical preparation was developed. The three active principles are quantified using partial least-squares regression methods. The proposed method is applicable over a wide analyte concentration range (80–120%) of labeled content, so it requires careful selection of the calibration set and to ensure thorough homogenization of the product. The method was validated in accordance with the ICH standard validation guidelines for NIR spectroscopy by determining its selectivity, linearity, accuracy, precision and stability. Based on the results, it is an effective alternative to the existing choice (HPLC) for the same purpose.
关键词： Partial least squares,Clarithromycin,Helicure,Near Infrared Spectroscopy,Preprocessing,Genetic algorithm,Multivariate calibration
更新于2025-09-23 15:23:52
-
Prediction model optimization using full model selection with regression trees demonstrated with FTIR data from bovine milk
摘要： Predictive modeling is the development of a model that is best able to predict an outcome based on given input variables. Model algorithms are different processes that are used to define functions that transform the data within models. Common algorithms include logistic regression (LR), linear discriminant analysis (LDA), classification and regression trees (CART), na?ve Bayes (NB), and k-nearest neighbor (KNN). Data preprocessing option, such as feature extraction and reduction, and model algorithms are commonly selected empirically in epidemiological studies even though these decisions can significantly affect model performance. Accordingly, full model selection (FMS) methods were developed to provide a systematic approach to select predictive modeling methods; however, current limitations of FMS, such as its dependency on user-selected hyperparameters, have prevented their routine incorporation into analyses for model performance optimization. Here we present the use of regression trees as an innovative method to apply FMS. Regression tree FMS (rtFMS) requires the development of a model for every combination of predictive modeling method options under consideration. The iterated, cross-validation performances of these models are then passed through a regression tree for selection of a final model. We demonstrate the benefits of rtFMS using a milk Fourier transform infrared spectroscopy dataset, wherein we build prediction models for two blood metabolic health parameters in dairy cows, nonesterified fatty acids (NEFA) and β-hydroxybutyrate acid (BHBA). The goal for building NEFA and BHBA prediction models is to provide a milk-based screening tool for metabolic health in dairy cattle that can be incorporated automatically in milk analysis routines. These models could be used in conjunction with physical exams, cow side tests, and other indications to initiate medical intervention. In contrast to previously reported FMS methods, rtFMS is not a black box, is simple to implement and interpret, it does not have hyperparameters, and it illustrates the relative importance of modeling options. Additionally, rtFMS allows for indirect comparisons among models developed using different datasets. Finally, rtFMS eliminates user bias due to personal preference for certain methods and rtFMS removes the dependency on published comparisons of methods. Thus, rtFMS provides clear benefits over the empirical selection of data preprocessing options and model algorithms.
关键词： Prediction model,Fourier-transform infrared spectra,Regression tree,Preprocessing,Full model selection
更新于2025-09-23 15:23:52
-
OPTICAL CHARACTER RECOGNITION MENGGUNAKAN ALGORITMA TEMPLATE MATCHING CORRELATION
摘要： OCR (Optical Character Recognition) adalah suatu solusi yang efektif untuk proses konversi dokumen cetak ke dokumen digital. Permasalahan yang timbul dalam proses pengenalan dokumen komputer adalah bagaimana teknik pengenalan untuk mengidentifikasi berbagai jenis karakter dengan berbagai ukuran dan bentuk. Metode pengenalan yang digunakan dalam tugas akhir ini adalah metode Template Matching Correlation. Sebelum proses pengenalan, citra masukan dengan format * bmp atau jpg * diolah terlebih dahulu di proses preprocessing, yang meliputi binerisasi, segmentasi, dan normalisasi gambar. Rata-rata tingkat keberhasilan pengenalan yang dihasilkan oleh sistem ini adalah 92,90%. Hasil akhir menunjukkan bahwa penggunaan metode Template Matching Correlation cukup untuk membangun sebuah sistem OCR dengan akurasi yang baik efektif.
关键词： Template Matching Correlation,preprocessing,Optical Character Recognition,OCR
更新于2025-09-23 15:22:29
-
[IEEE 2018 IEEE 3rd International Conference on Integrated Circuits and Microsystems (ICICM) - Shanghai, China (2018.11.24-2018.11.26)] 2018 IEEE 3rd International Conference on Integrated Circuits and Microsystems (ICICM) - Image Preprocessing of Iris Recognition
摘要： The aim of this paper is to propose the methods for image preprocessing of image enhancement and boundary detection. Iris recognition has been widely considered as one of the most dependable identification method. However, the iris systems are still not widespread due to many factors, for example, the production cost, the processing time and the recognition rate. The problems of production cost and the processing time will be resolved with the development of integrate circuit technology. The problem of recognition rate mentioned here is not about the iris itself, but the acquisition of the effective image of the iris. The quality of the iris image has become the key point of the current iris system. The preprocessing of iris recognition involves hardware and software design of the system and in this paper both of the designs are discussed.
关键词： Hough transform,iris recognition,image preprocessing,histogram equalization
更新于2025-09-23 15:22:29
-
Open-Source Python Module for Automated Preprocessing of Near Infrared Spectroscopic Data
摘要： Near infrared spectroscopy (NIRS) is an analytical technique for determining the chemical composition or structure of a given sample. For several decades, NIRS has been a frequently used analysis tool in agriculture, pharmacology, medicine, and petrochemistry. The popularity of NIRS is constantly growing as new application areas are discovered. Contrary to mid infrared spectral region, the absorption bands in near infrared spectral regions are often non-specific, broad, and overlapping. Analysis of NIR spectra requires multivariate methods which are highly subjective to noise arising from instrumentation, scattering effects, and measurement setup. NIRS measurements are also frequently performed outside of a laboratory which further contributes to the presence of noise. Therefore, preprocessing is a critical step in NIRS as it can vastly improve the performance of multivariate models. While extensive research regarding various preprocessing methods exists, selection of the best preprocessing method is often determined through trial-and-error. A more powerful approach for optimizing preprocessing in NIRS models would be to automatically compare a large number of preprocessing techniques (e.g., through grid-search or hyperparameter tuning). To enable this, we present, nippy, an open-source Python module for semi-automatic comparison of NIRS preprocessing methods (available at https://github.com/uef-bbc/nippy). We provide here a brief overview of the capabilities of nippy and demonstrate the typical usage through two examples with public datasets.
关键词： Near infrared spectroscopy,Chemometrics,Preprocessing
更新于2025-09-23 15:19:57
-
Fault diagnosis method of photovoltaic array based on support vector machine
摘要： Photovoltaic (PV) arrays are prone to various faults due to the hostile working environment. This paper presents the fault diagnosis algorithm based on support vector machine (SVM) to detect short circuit, open circuit, and lack of irradiation faults that occurred in PV arrays. By analyzing these faults and I–V characteristic curves of PV arrays, the short-circuit current, open-circuit voltage, maximum-power current, and maximum-power voltage are chosen as input parameters of SVM-based fault diagnosis algorithm. The data pre-processing methods are used to improve the quality of fault data set considering the effects of the quality on the performance of SVM-based fault diagnosis algorithm. The grid search and k-fold cross-validation methods are proposed to optimize the parameters of the SVM-based fault diagnosis algorithm. It gets test accuracy of 97% by testing the trained SVM-based fault diagnosis algorithm with 400 data. The experimental results indicate that the SVM-based fault diagnosis algorithm has higher accuracy and generalization ability than other algorithm for fault diagnosis of PV arrays.
关键词： k-fold cross-validation,PV arrays,data preprocessing,grid search,SVM-based fault diagnosis algorithm
更新于2025-09-12 10:27:22
-
[IEEE 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI, USA (2018.7.18-2018.7.21)] 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Introducing a Novel Layer in Convolutional Neural Network for Automatic Identification of Diabetic Retinopathy
摘要： Convolutional neural networks have been widely used for identifying diabetic retinopathy on color fundus images. For such application, we proposed a novel framework for the convolutional neural network architecture by embedding a preprocessing layer followed by the first convolutional layer to increase the performance of the convolutional neural network classifier. Two image enhancement techniques i.e. 1- Contrast Enhancement 2- Contrast-limited adaptive histogram equalization were separately embedded in the proposed layer and the results were compared. For identification of exudates, hemorrhages and microaneurysms, the proposed framework achieved the total accuracy of 87.6%, and 83.9% for the contrast enhancement and contrast-limited adaptive histogram equalization layers, respectively. However, the total accuracy of the convolutional neural network alone without the prreprocessing layer was found to be 81.4%. Consequently, the new convolutional neural network architecture with the proposed preprocessing layer improved the performance of convolutional neural network.
关键词： contrast-limited adaptive histogram equalization,contrast enhancement,preprocessing layer,diabetic retinopathy,Convolutional neural networks,image enhancement
更新于2025-09-10 09:29:36
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Hyperspectral Endmember Extraction Preprocessing Using Combination of Euclidean and Geodesic Distances
摘要： Combination the spatial-contextual information in spectral unmixing as a preprocessing of endmember extraction algorithms (EEAs) has been an important issue in hyperspectral image analysis. Particularly, this paper performs a new preprocessing framework using combination of spectral Geodesic and spatial Euclidean distances prior to classical spectral-based EEAs. It exploits both spatial and spectral features of image pixels in order to look for high spectrally correlated and spatially homogenous regions where pure spectral signatures are more likely to be found. For this purpose, it exerts a new correlation coefficient quantity on spatially homogenous pixels designated by spectral weighting determination and appraising the cluster label of spatial neighbours of pure pixels. The novel preprocessing hampers from useless computation of a great number of mixed pixels executed by EEAs. Additionally, two new spectral Geodesic and spatial Euclidean distances are presented to specify the final mean vector which exploits in correlation coefficient computations. The validation of our preprocessing is deliberated on two real hyperspectral datasets from the viewpoints of RMSE and SAD based errors in comparison with other schemes. Experimental consequences declare that such preprocessing can amend figures of unmixing accuracy without intensifying the complexity and with no requirement of changing EEAs.
关键词： endmember,spectral,preprocessing,Euclidean,spatial,Geodesic
更新于2025-09-10 09:29:36
-
High Quality - Low Computational Cost Technique for Automated Principal Object Segmentation Applied in Solar and Medical Imaging
摘要： The objective of this paper is to introduce a fully computerized, simple and low-computational cost technique that can be used in the preprocessing stages of digital images. This technique is specially designed to detect the principal (largest) closed shape object that embody the useful information in certain image types and neglect and avoid other noisy objects and artifacts. The detection process starts by calculating certain statistics of the image to estimate the amount of bit-plane slicing required to exclude the non-informative and noisy background. A simple closing morphological operation is then applied and followed by circular filter applied only on the outer coarse edge to finalize the detection process. The proposed technique takes its importance from the huge explosion of images that need accurate processing in real time speedy manner. The proposed technique is implemented using MATLAB and tested on many solar and medical images; it was shown by the quantitative evaluation that the proposed technique can handle real-life (e.g. solar, medical fundus) images and shows very good potential even under noisy and artifacts conditions. Compared to the publicly available datasets, 97% and 99% of similarity detection is achieved in medical and solar images, respectively. Although it is well-know, the morphological bit-plane slicing technique is hoped to be used in the preprocessing stages of different applications to ease the subsequent image processing stages especially in real time applications where the proposed technique showed dramatic (~100 times) saving in processing time.
关键词： solar images,image preprocessing,medical fundus images,morphological bit-plane slicing
更新于2025-09-09 09:28:46
-
Determination of Nitrogen Concentration in Fresh Pear Leaves by Visible/Near-Infrared Reflectance Spectroscopy
摘要： A rapid and reliable method is required to determine the N status of pear (Pyrus communis L.) leaves during the growing season for timely fertilization to improve the yields and fruit quality. In the present study, we evaluated visible and near-infrared reflectance (Vis/NIR) spectra of fresh pear leaves using partial least squares (PLS) regression to determine the N concentration of fresh pear leaves. In addition, we studied the performance of modified spectra generated using different preprocessing techniques. A total of 450 leaf samples were collected from 6-yr-old pear trees of two cultivars, and randomly separated into two subsets (calibration subset [294 samples] and validation subset [180 samples]) after excluding outliers by using principle component analysis. Results showed that the model built using full spectra performed better than that developed using characteristic wavelength segments. In addition, we found that original spectral proved to provide better accuracy than derivative spectra. Among the studied preprocessing techniques, moving average smoothing (MAS) technique improved accuracy the most. Overall results suggested that PLS regression with preprocessing of full spectra using MAS is optimal method for modeling N concentration of fresh pear leaves which yielded 0.961 and 0.953 coefficient of determination (R2) for calibration and cross-validation, respectively. The validation of this method resulted high R2 value (0.847) and low mean relative error (4.48%). In conclusion, this model could provide a rapid and more reliable method to determine the total N concentration in fresh pear leaves and could be useful for fertilization management in pear orchards.
关键词： partial least squares regression,preprocessing techniques,pear leaves,Nitrogen concentration,visible/near-infrared reflectance spectroscopy
更新于2025-09-09 09:28:46