scholarly journals Variety Identification of Raisins Using Near-Infrared Hyperspectral Imaging

Molecules ◽  
2018 ◽  
Vol 23 (11) ◽  
pp. 2907 ◽  
Author(s):  
Lei Feng ◽  
Susu Zhu ◽  
Chu Zhang ◽  
Yidan Bao ◽  
Pan Gao ◽  
...  
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Network Models ◽  
Principal Component ◽  
Component Analysis ◽  
Variety Identification ◽  
Support Vector ◽  
Neural Network Models ◽  
K Nearest Neighbors ◽  
Standard Normal Variate

Different varieties of raisins have different nutritional properties and vary in commercial value. An identification method of raisin varieties using hyperspectral imaging was explored. Hyperspectral images of two different varieties of raisins (Wuhebai and Xiangfei) at spectral range of 874–1734 nm were acquired, and each variety contained three grades. Pixel-wise spectra were extracted and preprocessed by wavelet transform and standard normal variate, and object-wise spectra (sample average spectra) were calculated. Principal component analysis (PCA) and independent component analysis (ICA) of object-wise spectra and pixel-wise spectra were conducted to select effective wavelengths. Pixel-wise PCA scores images indicated differences between two varieties and among different grades. SVM (Support Vector Machine), k-NN (k-nearest Neighbors Algorithm), and RBFNN (Radial Basis Function Neural Network) models were built to discriminate two varieties of raisins. Results indicated that both SVM and RBFNN models based on object-wise spectra using optimal wavelengths selected by PCA could be used for raisin variety identification. The visualization maps verified the effectiveness of using hyperspectral imaging to identify raisin varieties.

scholarly journals Variety Identification of Orchids Using Fourier Transform Infrared Spectroscopy Combined with Stacked Sparse Auto-Encoder

Molecules ◽  
2019 ◽  
Vol 24 (13) ◽  
pp. 2506 ◽  
Author(s):  
Yunfeng Chen ◽  
Yue Chen ◽  
Xuping Feng ◽  
Xufeng Yang ◽  
Jinnuo Zhang ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Spectroscopic Technique ◽  
Variety Identification ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Discriminant Models ◽  
The Fourier Transform ◽  
Better Than

The feasibility of using the fourier transform infrared (FTIR) spectroscopic technique with a stacked sparse auto-encoder (SSAE) to identify orchid varieties was studied. Spectral data of 13 orchids varieties covering the spectral range of 4000–550 cm−1 were acquired to establish discriminant models and to select optimal spectral variables. K nearest neighbors (KNN), support vector machine (SVM), and SSAE models were built using full spectra. The SSAE model performed better than the KNN and SVM models and obtained a classification accuracy 99.4% in the calibration set and 97.9% in the prediction set. Then, three algorithms, principal component analysis loading (PCA-loading), competitive adaptive reweighted sampling (CARS), and stacked sparse auto-encoder guided backward (SSAE-GB), were used to select 39, 300, and 38 optimal wavenumbers, respectively. The KNN and SVM models were built based on optimal wavenumbers. Most of the optimal wavenumbers-based models performed slightly better than the all wavenumbers-based models. The performance of the SSAE-GB was better than the other two from the perspective of the accuracy of the discriminant models and the number of optimal wavenumbers. The results of this study showed that the FTIR spectroscopic technique combined with the SSAE algorithm could be adopted in the identification of the orchid varieties.

Bankruptcy Prediction Using Principal Component Analysis Based Neural Network Models

2010 ◽  
pp. 138-156
Author(s):  
G. A. Rekha Pai ◽  
G. A. Vijayalakshmi Pai
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Prediction Models ◽  
Network Models ◽  
Principal Component ◽  
Component Analysis ◽  
Bankruptcy Prediction ◽  
Financial Ratios ◽  
Neural Network Models ◽  
The Individual

Industrial bankruptcy is a rampant problem which does not occur overnight and when it occurs can cause acute financial embarrassment to Governments and financial institutions as well as threaten the very viability of the firms. It is therefore essential to help industries identify the impending trouble early. Several statistical and soft computing based bankruptcy prediction models that make use of financial ratios as indicators have been proposed. Majority of these models make use of a selective set of financial ratios chosen according to some appropriate criteria framed by the individual investigators. In contrast, this study considers any number of financial ratios irrespective of the industrial category and size and makes use of Principal Component Analysis to extract their principal components, to be used as predictors, thereby dispensing with the cumbersome selection procedures used by its predecessors. An Evolutionary Neural Network (ENN) and a Backpropagation Neural Network with Levenberg Marquardt’s training rule (BPN) have been employed as classifiers and their performance has been compared using Receiver Operating Characteristics (ROC) analyses. Termed PCA-ENN and PCA-BPN models, the predictive potential of the two models have been analyzed over a financial database (1997-2000) pertaining to 34 sick and 38 non sick Indian manufacturing companies, with 21 financial ratios as predictor variables.

Functional principal component analysis for near-infrared spectral data: a case study on Tricholoma matsutakeis

2020 ◽  
Vol 16 (8) ◽  
Author(s):  
Haoran Li ◽  
Tianhong Pan ◽  
Yuqiang Li ◽  
Shan Chen ◽  
Guoquan Li
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Component Analysis ◽  
Detection Algorithm ◽  
Functional Principal Component Analysis ◽  
Support Vector ◽  
Edible Fungi ◽  
Functional Principal Component

AbstractTricholoma matsutakeis (TM) is the most expensive edible fungi in China. Given its price and exclusivity, some dishonest merchants will sell adulterated TM by combining it with cheaper fungi in an attempt to earn more profits. This fraudulent behavior has broken food laws and violated consumer trust. Therefore, there is an urgent need to develop a rapid, accurate, and nondestructive tool to discriminate TM from other edible fungi. In this work, a novel detection algorithm combined with near-infrared spectroscopy (NIR) and functional principal component analysis (FPCA) is proposed. Firstly, the raw NIR data were pretreated by locally weighted scatterplot smoothing (LOWESS) and multiplication scatter correction (MSC). Then, FPCA was used to extract valuable information from the preprocessed NIR data. Then, a classifier was designed by using the least-squares support-vector machine (LS-SVM) to distinguish categories of edible fungi. Furthermore, the one-versus-one (OVO) strategy was included and the binary LS-SVM was extended to a multi-class classifier. The 166 samples of four varieties of fungi were used to validate the proposed method. The results show that the proposed method has great capability in near infrared spectra classification, and the average accurate of FPCA-LSSVM is 97.3% which is greater than that of PCA-LSSVM (93.5%).

scholarly journals Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition

2019 ◽  
Vol 3 (2) ◽  
pp. 80-84 ◽  
Author(s):  
Mustafa H. Mohammed Alhabib ◽  
Mustafa Zuhaer Nayef Al-Dabagh ◽  
Firas H. AL-Mukhtar ◽  
Hussein Ibrahim Hussein
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Wavelet Transform ◽  
Principal Component ◽  
Component Analysis ◽  
Input Image ◽  
Personal Identification ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
The Face

Facial analysis has evolved to be a process of considerable importance due to its consequence on the safety and security, either individually or generally on the society level, especially in personal identification. The paper in hand applies facial identification on a facial image dataset by examining partial facial images before allocating a set of distinctive characteristics to them. Extracting the desired features from the input image is achieved by means of wavelet transform. Principal component analysis is used for feature selection, which specifies several aspects in the input image; these features are fed to two stages of classification using a support vector machine and K-nearest neighborhood to classify the face. The images used to test the strength of the suggested method are taken from the well-known (Yale) database. Test results showed the eligibility of the system when it comes to identify images and assign the correct face and name.

scholarly journals Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2403
Author(s):  
Jakub Browarczyk ◽  
Adam Kurowski ◽  
Bozena Kostek
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Mental States ◽  
Extraction Methods ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbors ◽  
Vector Machines

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch’s method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

Detection of Aflatoxin B1 in Pistachio Kernels Using Visible/Near-Infrared Hyperspectral Imaging

2019 ◽  
Vol 62 (5) ◽  
pp. 1065-1074
Author(s):  
Qifang Wu ◽  
Huirong Xu
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Nutrient Content ◽  
Aflatoxin Contamination ◽  
Quantitative Prediction ◽  
Spectra Analysis ◽  
Linear Discriminant ◽  
Standard Normal Variate

Abstract. Pistachios are susceptible to aflatoxin contamination because of their rich nutrient content. Hyperspectral imaging (HSI), a new method for collecting spectral and image information, has been successfully employed in contamination research to classify staple agricultural products, such as maize, that are contaminated with aflatoxins. However, only a few studies have been conducted on the nondestructive discrimination among contaminated nuts using HSI for both qualitative and quantitative purposes. Thus, the feasibility of directly detecting aflatoxin B1 (AFB1) in individual pistachio kernels using visible/near-infrared HSI (VNIR HSI) was explored in this study. A total of 300 pistachio kernels were randomly selected to prepare target samples that were artificially contaminated with 5, 10, 20, 30, or 50 ppb (parts per billion) of AFB1. Principal component analysis (PCA) showed an overall separation trend between the control and all contaminated kernels. Accuracies greater than 90.0% were obtained by linear discriminant analysis (LDA) for samples that were artificially contaminated with different concentrations of AFB1 based on spectra at 694 to 988 nm that had been preprocessed with standard normal variate (SNV) and Savitzky-Golay (SG) smoothing. The correlation coefficients of calibration and validation (rc and rv) from stepwise multiple linear regression (SMLR) models were all >0.9100. Moreover, five key wavelengths (708, 771, 892, 915, and 941 nm) closely associated with AFB1 contamination were identified using principal component spectra analysis. Generally, the results indicated that VNIR HSI could be employed for preliminary screening of pistachio kernels that were artificially contaminated with AFB1, even at the 5 ppb level. However, the quantitative prediction of the specific AFB1 concentration needed to be further improved. Keywords: Aflatoxins, Detection analysis, Hyperspectral information, Pistachios, Visible/near-infrared.

scholarly journals Identifying Freshness of Spinach Leaves Stored at Different Temperatures Using Hyperspectral Imaging

Foods ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 356 ◽  
Author(s):  
Zhu ◽  
Feng ◽  
Zhang ◽  
Bao ◽  
He
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Principal Component ◽  
Imaging Systems ◽  
Support Vector ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Different Temperatures ◽  
Learning Machine ◽  
Spinach Leaves

Spinach is prone to spoilage in the course of preservation. Spinach leaves stored at different temperatures for different durations will have varying degrees of freshness. In order to monitor the freshness of spinach leaves during storage, a rapid and non-destructive method—hyperspectral imaging technology—was applied in this study. Visible near-infrared reflectance (Vis-NIR) (380–1030 nm) and near-infrared reflectance (NIR) (874–1734 nm) hyperspectral imaging systems were used. Spinach leaves preserved at different temperatures with different durations (0, 3, 6, 9 days at 4 °C and 0, 1, 2 days at 20 °C) were studied. Principal component analysis (PCA) was adopted as a qualitative analysis method. The second-order derivative spectra were utilized to select effective wavelengths. Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and extreme learning machine (ELM) were used to build models based on full spectra and effective wavelengths. All three models achieved good results, with accuracies above 92% for both Vis-NIR spectra and NIR spectra. ELM obtained the best results, with all accuracies reaching 100%. The overall results indicate the possibility of the freshness identification of spinach preserved at different temperatures for different durations using two kinds of hyperspectral imaging systems.

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