scholarly journals Rapid Discrimination of Cheese Products Based on Probabilistic Neural Network and Raman Spectroscopy

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Zheng-Yong Zhang
Keyword(s):  
Neural Network ◽  
Raman Spectroscopy ◽  
Control Method ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Molecular Vibration ◽  
Characteristic Peak ◽  
Statistical Process ◽  
Cheese Products ◽  
Raman Spectral Data

The aim of this work is to solve the practical problem that there are relatively few fast, intelligent, and objective methods to distinguish dairy products and to further improve the quality control methods of them. Therefore, an approach of cheese product brand discrimination method based on Raman spectroscopy and probabilistic neural network algorithm was developed. The experimental results show that the spectrum contains abundant molecular vibration information of carbohydrates, fats, proteins, and other components, and the Raman spectral data collection time of a single sample is only 100 s. Due to the high spectral similarity between samples, it is impossible to identify them with naked eyes. Characteristic peak intensity combined with statistical process control method was employed to study the fluctuation characteristics of samples. The results show that the characteristic peak of experimental samples fluctuates within a certain control limit. However, due to the high similarity between the Raman spectra of different brand samples, they cannot be effectively identified as well. This paper further studied and established the analytical approach based on Raman spectroscopy, including wavelet denoising, normalization, principal component analysis, and probabilistic neural network discrimination. In db1 wavelet processing, [−1, 1] normalization, 74 principal components (cumulative contribution rate of 100%) can realize the effective discrimination of different brands of cheese products in 1 s, with the average recognition accuracy of 96%. The discriminant method established in this work has the advantages of simple operation, rapid analysis, and accurate results. It provides a technical reference for the fight against counterfeit products and has a broad application prospect.

scholarly journals Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Li-li Li ◽  
Kun Chen ◽  
Jian-min Gao ◽  
Hui Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Control Chart ◽  
Control Charts ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Original Data ◽  
Improved Genetic Algorithm ◽  
Training Time ◽  
Mixed Pattern

Aiming at the problems of the lack of abnormal instances and the lag of quality anomaly discovery in quality database, this paper proposed the method of recognizing quality anomaly from the quality control chart data by probabilistic neural network (PNN) optimized by improved genetic algorithm, which made up deficiencies of SPC control charts in practical application. Principal component analysis (PCA) reduced the dimension and extracted the feature of the original data of a control chart, which reduced the training time of PNN. PNN recognized successfully both single pattern and mixed pattern of control charts because of its simple network structure and excellent recognition effect. In order to eliminate the defect of experience value, the key parameter of PNN was optimized by the improved (SGA) single-target optimization genetic algorithm, which made PNN achieve a higher rate of recognition accuracy than PNN optimized by standard genetic algorithm. Finally, the above method was validated by a simulation experiment and proved to be the most effective method compared with traditional BP neural network, single PNN, PCA-PNN without parameters optimized, and SVM optimized by particle swarm optimization algorithm.

Raman Spectroscopy Combined with Principal Component Analysis for Screening Nasopharyngeal Cancer in Human Blood Sera

2017 ◽  
Vol 71 (11) ◽  
pp. 2497-2503 ◽  
Author(s):  
Saranjam Khan ◽  
Rahat Ullah ◽  
Samina Javaid ◽  
Shaheen Shahzad ◽  
Hina Ali ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Raman Spectroscopy ◽  
Human Blood ◽  
Nasopharyngeal Cancer ◽  
Principal Component ◽  
Component Analysis ◽  
Data Sets ◽  
Analysis Technique ◽  
Raman Spectral Data ◽  
Raman Spectral

This study demonstrates the analysis of nasopharyngeal cancer (NPC) in human blood sera using Raman spectroscopy combined with the multivariate analysis technique. Blood samples of confirmed NPC patients and healthy individuals have been used in this study. The Raman spectra from all these samples were recorded using 785 nm laser for excitation. Important Raman bands at 760, 800, 815, 834, 855, 1003, 1220–1275, and 1524 cm−1, have been observed in both normal and NPC samples. A decrease in the lipids content, phenylalanine, and β-carotene, whereas increases in amide III, tyrosine, and tryptophan have been observed in the NPC samples. The two data sets were well separated using principal component analysis (PCA) based on Raman spectral data. The spectral variations between the healthy and cancerous samples have been further highlighted by plotting loading vectors PC1 and PC2, which shows only those spectral regions where the differences are obvious.

scholarly journals TRIGONOMETRIC SERIES IN ORTHOGONAL EXPANSIONS FOR DENSITY ESTIMATES OF DEEP IMAGE FEATURES

2018 ◽  
Vol 42 (1) ◽  
pp. 149-158 ◽  
Author(s):  
A. V. Savchenko
Keyword(s):  
Neural Network ◽  
Trigonometric Series ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Image Features ◽  
Small Samples ◽  
Deep Convolutional Neural Networks ◽  
Orthogonal Expansions ◽  
Computational Speed ◽  
Reference Images

In this paper we study image recognition tasks in which the images are described by high dimensional feature vectors extracted with deep convolutional neural networks and principal component analysis. In particular, we focus on the problem of high computational complexity of a statistical approach with non-parametric estimates of probability density implemented by the probabilistic neural network. We propose a novel statistical classification method based on the density estimators with orthogonal expansions using trigonometric series. It is shown that this approach makes it possible to overcome the drawbacks of the probabilistic neural network caused by the memory-based approach of instance-based learning. Our experimental study with Caltech-101 and CASIA WebFace datasets demonstrates that the proposed approach reduces the error rate by 1–5 % and increases the computational speed by 1.5 – 6 times when compared to the original probabilistic neural network for small samples of reference images.

EMG-Based Control of a Multi-Joint Robot for Operating a Glovebox

2015 ◽  
pp. 36-52 ◽  
Author(s):  
Toshio Tsuji ◽  
Taro Shibanoki ◽  
Keisuke Shima
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Probabilistic Neural Network ◽  
Robotic Manipulator ◽  
Control Command ◽  
Emg Patterns

This chapter describes a control method for a multi-joint robotic manipulator using Electromyogram (EMG) signals for operating a glovebox. The system uses a Probabilistic Neural Network (PNN) to estimate the user's intended motion from EMG patterns, and generates a control command for the glovebox and robotic manipulator corresponding to the estimated motions. The user can therefore control the manipulator as well as various functions of the glovebox system through his/her EMG signals while performing some manual operations through gloves. With this system, the authors produce intuitive control of the glovebox with the robotic manipulator. The authors confirm the effectiveness of the proposed system with an experiment using the developed prototype.

scholarly journals Establishment of Prediction Models of Asphalt Pavement Performance based on a Novel Data Calibration Method and Neural Network

2019 ◽  
Vol 2673 (1) ◽  
pp. 66-82 ◽  
Author(s):  
Linyi Yao ◽  
Qiao Dong ◽  
Jiwang Jiang ◽  
Fujian Ni
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Control Method ◽  
Principal Component ◽  
Skid Resistance ◽  
Data Quality Control ◽  
Transverse Cracks ◽  
Transverse Cracking ◽  
Pavement Surface ◽  
Surface Distress

This paper aims to develop models to forecast the deterioration of pavement conditions including rutting, roughness, skid-resistance, transverse cracking, and pavement surface distress. A data quality control method was proposed to rebuild the performance data based on the idea of longest increasing or decreasing subsequences. Neural network (NN) was used to develop the five models, and principal component analysis (PCA) was applied to reduce the dimension of traffic variables. The influence of different input variables on the model outputs was discussed respectively by comparing their mean impact values (MIV). Results show that the proposed NN models demonstrated great potential for accurate prediction of pavement conditions, with an average testing R-square of 0.8692. The results of sensitivity analysis revealed that recent pavement conditions may influence the future pavement conditions significantly. Rutting and roughness were sensitive to pavement age and maintenance type. The materials of original pavement asphalt layer were highly relevant to the prediction of pavement roughness, skid-resistance, and pavement surface distress. Moreover, traffic loads obviously affected the pavement skid-resistance and transverse cracking. Pavement and bridge had different effect on surface distress. The material of the base has a remarkable impact on the initiation and development of transverse cracks. Disease treatment in terms of pavement cracking—such as sticking the cracks, excavating and filling the cracks—shows a high MIV in the prediction model of transverse cracking and pavement surface distress.

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