Multivariate Analysis Coupled with M-SVM Classification for Lard Adulteration Detection in Meat Mixtures of Beef, Lamb, and Chicken Using FTIR Spectroscopy

Adulteration of meat products is a delicate issue for people around the globe. The mixing of lard in meat causes a significant problem for end users who are sensitive to halal meat consumption. Due to the highly similar lipid profiles of meat species, the identification of adulteration becomes more difficult. Therefore, a comprehensive spectral detailing of meat species is required, which can boost the adulteration detection process. The experiment was conducted by distributing samples labeled as “Pure (80 samples)” and “Adulterated (90 samples)”. Lard was mixed with the ratio of 10–50% v/v with beef, lamb, and chicken samples to obtain adulterated samples. Functional groups were discovered for pure pork, and two regions of difference (RoD) at wavenumbers 1700–1800 cm−1 and 2800–3000 cm−1 were identified using absorbance values from the FTIR spectrum for all samples. The principal component analysis (PCA) described the studied adulteration using three principal components with an explained variance of 97.31%. The multiclass support vector machine (M-SVM) was trained to identify the sample class values as pure and adulterated clusters. The acquired overall classification accuracy for a cluster of pure samples was 81.25%, whereas when the adulteration ratio was above 10%, 71.21% overall accuracy was achieved for a group of adulterated samples. Beef and lamb samples for both adulterated and pure classes had the highest classification accuracy value of 85%, whereas chicken had the lowest value of 78% for each category. This paper introduces a comprehensive spectrum analysis for pure and adulterated samples of beef, chicken, lamb, and lard. Moreover, we present a rapid M-SVM model for an accurate classification of lard adulteration in different samples despite its low-level presence.

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Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

Mathematical Problems in Engineering ◽

10.1155/2014/814593 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 8

Author(s):

Jia Uddin ◽

Myeongsu Kang ◽

Dinh V. Nguyen ◽

Jong-Myon Kim

Keyword(s):

Classification Accuracy ◽

Induction Motors ◽

Feature Vector ◽

Texture Features ◽

Fault Classification ◽

High Dimensional ◽

Support Vector ◽

Fault Features ◽

Multiclass Support Vector Machine

This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D) texture features and a multiclass support vector machine (MCSVM). The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns), and extracts these texture features by generating the dominant neighborhood structure (DNS) map. The principal component analysis (PCA) is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA) multiclass support vector machines (MCSVMs) to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.

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Detection of Sulfite Dioxide Residue on the Surface of Fresh-Cut Potato Slices Using Near-Infrared Hyperspectral Imaging System and Portable Near-Infrared Spectrometer

Molecules ◽

10.3390/molecules25071651 ◽

2020 ◽

Vol 25 (7) ◽

pp. 1651 ◽

Cited By ~ 2

Author(s):

Xiulin Bai ◽

Qinlin Xiao ◽

Lei Zhou ◽

Yu Tang ◽

Yong He

Keyword(s):

Sulfur Dioxide ◽

Hyperspectral Imaging ◽

Classification Accuracy ◽

Near Infrared ◽

Imaging System ◽

Principal Component ◽

Support Vector ◽

Svm Model ◽

Fresh Cut ◽

Potato Slices

Sodium pyrosulfite is a browning inhibitor used for the storage of fresh-cut potato slices. Excessive use of sodium pyrosulfite can lead to sulfur dioxide residue, which is harmful for the human body. The sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentrations of sodium pyrosulfite solution was classified by near-infrared hyperspectral imaging (NIR-HSI) system and portable near-infrared (NIR) spectrometer. Principal component analysis was used to analyze the object-wise spectra, and support vector machine (SVM) model was established. The classification accuracy of calibration set and prediction set were 98.75% and 95%, respectively. Savitzky–Golay algorithm was used to recognize the important wavelengths, and SVM model was established based on the recognized important wavelengths. The final classification accuracy was slightly less than that based on the full spectra. In addition, the pixel-wise spectra extracted from NIR-HSI system could realize the visualization of different samples, and intuitively reflect the differences among the samples. The results showed that it was feasible to classify the sulfur dioxide residue on the surface of fresh-cut potato slices immersed in different concentration of sodium pyrosulfite solution by NIR spectra. It provided an alternative method for the detection of sulfur dioxide residue on the surface of fresh-cut potato slices.

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A Study of Movement Classification of the Lower Limb Based on up to 4-EMG Channels

Electronics ◽

10.3390/electronics8030259 ◽

2019 ◽

Vol 8 (3) ◽

pp. 259 ◽

Cited By ~ 8

Author(s):

Diana Toledo-Pérez ◽

Miguel Martínez-Prado ◽

Roberto Gómez-Loenzo ◽

Wilfrido Paredes-García ◽

Juvenal Rodríguez-Reséndiz

Keyword(s):

Lower Limb ◽

Classification Accuracy ◽

Research Work ◽

Principal Component ◽

Support Vector ◽

Semg Signal ◽

Zero Crossings ◽

The Difference ◽

The Right

The number and position of sEMG electrodes have been studied extensively due to the need to improve the accuracy of the classification they carry out of the intention of movement. Nevertheless, increasing the number of channels used for this classification often increases their processing time as well. This research work contributes with a comparison of the classification accuracy based on the different number of sEMG signal channels (one to four) placed in the right lower limb of healthy subjects. The analysis is performed using Mean Absolute Values, Zero Crossings, Waveform Length, and Slope Sign Changes; these characteristics comprise the feature vector. The algorithm used for the classification is the Support Vector Machine after applying a Principal Component Analysis to the features. The results show that it is possible to reach more than 90% of classification accuracy by using 4 or 3 channels. Moreover, the difference obtained with 500 and 1000 samples, with 2, 3 and 4 channels, is not higher than 5%, which means that increasing the number of channels does not guarantee 100% precision in the classification.

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Chemometrics in Tandem with Hyperspectral Imaging for Detecting Authentication of Raw and Cooked Mutton Rolls

Foods ◽

10.3390/foods10092127 ◽

2021 ◽

Vol 10 (9) ◽

pp. 2127

Author(s):

Hongzhe Jiang ◽

Yi Yang ◽

Minghong Shi

Keyword(s):

Hyperspectral Imaging ◽

Meat Products ◽

Principal Component ◽

Support Vector ◽

Meat Industry ◽

Successive Projections Algorithm ◽

Classification Rate ◽

Meat Species ◽

Spectral Principal Component Analysis ◽

Cooked Meat

Authentication assurance of meat or meat products is critical in the meat industry. Various methods including DNA- or protein-based techniques are accurate for assessing meat authenticity, however, they are destructive, expensive, or laborious. This study explores the feasibility of chemometrics in tandem with hyperspectral imaging (HSI) for identifying raw and cooked mutton rolls substitution by pork and duck rolls. Raw or cooked samples (n = 180) of three meat species were prepared to collect hyperspectral images in range of 400–1000 nm. Spectra were extracted from representative regions of interest (ROIs), and spectral principal component analysis (PCA) revealed that PC1 and PC2 were effective for the identification. Different methods including standard normal variable (SNV), first and second derivatives, and normalization were individually employed for spectral preprocessing, and modeling methods of partial least squares-discriminant analysis (PLS-DA) and support vector machines (SVM) were also individually applied to develop classification models for both the raw and the cooked. Results showed that PLS-DA model developed by raw spectra presented the highest 100% correct classification rate (CCR) of success in all sets. After that, effective wavelengths selected by successive projections algorithm (SPA) built optimal simplified models which didn’t influence the modeling results compared with full spectra regardless of the meat roll states. Therefore, SPA-PLS-DA models were subsequently used to visualize the raw and cooked meat rolls classification. As a consequence, the general meat species of both raw and cooked meat rolls were readily discernible in pixel-wise manner by generating classification maps. The results showed that HSI combined with chemometrics can be used to identify the authentication of raw and cooked mutton rolls substituted by pork and duck rolls accurately. This promising methodology provides a reference which can be extended to the classification or grading of other meat rolls.

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WBC-CNN: Efficient CNN-Based Models to Classify White Blood Cells Subtypes

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v17i13.27373 ◽

2021 ◽

Vol 17 (13) ◽

pp. 135-150

Author(s):

Najla Alofi ◽

Wafa Alonezi ◽

Wedad Alawad

Keyword(s):

Feature Extraction ◽

Blood Cells ◽

Classification Accuracy ◽

White Blood Cells ◽

Hybrid Models ◽

Learning Technologies ◽

Support Vector ◽

Svm Model ◽

Vector Machines

Blood is essential to life. The number of blood cells plays a significant role in observing an individual’s health status. Having a lower or higher number of blood cells than normal may be a sign of various diseases. Thus it is important to precisely classify blood cells and count them to diagnose different health conditions. In this paper, we focused on classifying white blood cells subtypes (WBC) which are the basic parts of the immune system. Classification of WBC subtypes is very useful for diagnosing diseases, infections, and disorders. Deep learning technologies have the potential to enhance the process and results of WBC classification. This study presented two fine-tuned CNN models and four hybrid CNN-based models to classify WBC. The VGG-16 and MobileNet are the CNN architectures used for both feature extraction and classification in fine-tuned models. The same CNN architectures are used for feature extraction in hybrid models; however, the Support Vector Machines (SVM) and the Quadratic Discriminant Analysis (QDA) are the classifiers used for classification. Among all models, the fine-tuned VGG-16 performs best, its classification accuracy is 99.81%. Our hybrid models are efficient in detecting WBC as well. 98.44% is the classification accuracy of the VGG-16+SVM model, and 98.19% is the accuracy of the MobileNet+SVM.

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Detection of the Freshness State of Cooked Beef During Storage Using Hyperspectral Imaging

Applied Spectroscopy ◽

10.1177/0003702817718807 ◽

2017 ◽

Vol 71 (10) ◽

pp. 2286-2301 ◽

Cited By ~ 4

Author(s):

Dong Yang ◽

Dandan He ◽

Anxiang Lu ◽

Dong Ren ◽

Jihua Wang

Keyword(s):

Least Squares ◽

Hyperspectral Imaging ◽

Meat Products ◽

Principal Component ◽

Viable Count ◽

Classification Model ◽

Support Vector ◽

Meat Production ◽

Svm Classification ◽

Svm Model

The freshness of meat products during storage has received unprecedented attention. This study was conducted to investigate the feasibility of a hyperspectral imaging (HSI) technique to determine the freshness state of cooked beef during storage and identify the contaminated areas on the surface of spoiled samples. Hyperspectral images of cooked beef were acquired in the wavelength range of 400–1000 nm and the freshness state of all samples was divided into three classes (freshness, medium freshness, and spoilage) using the measured total viable count (TVC) of bacteria. Fifteen feature spectra variables were extracted by random frog (RF); based on this, six optimal wavelength variables were further selected by correlation analysis (CA). Partial least squares (PLS) and least squares–support vector machine (LS-SVM) classification models were established using different spectral variables. The results indicated that the performance of the RF-CA-LS-SVM classification model with a high overall classification accuracy of 97.14%, the results of sensitivity and specificity in the range of 0.92–1, and the κ coefficient of 0.9575 in the prediction set were obviously superior to other models. Spoiled samples were further obtained using a RF-CA-LS-SVM model, and then six feature images were extracted and further fused by principal component analysis (PCA). A PC3 image was used to segment successfully the contaminated areas from normal areas of cooked beef images using the Otsu threshold algorithm. The results demonstrated that HSI has great potential in classifying the freshness of cooked beef and identifying the contaminated areas. This current study provides a foundational basis for the classification and grading of meat production in further online detection.

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Construction of Classifier Based on MPCA and QSA and Its Application on Classification of Pancreatic Diseases

Computational and Mathematical Methods in Medicine ◽

10.1155/2013/713174 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Huiyan Jiang ◽

Di Zhao ◽

Tianjiao Feng ◽

Shiyang Liao ◽

Yenwei Chen

Keyword(s):

Principal Component Analysis ◽

Classification Accuracy ◽

Simulated Annealing Algorithm ◽

Principal Component ◽

Support Vector ◽

Pancreatic Diseases ◽

Novel Method ◽

Annealing Algorithm ◽

Multilinear Principal Component Analysis

A novel method is proposed to establish the classifier which can classify the pancreatic images into normal or abnormal. Firstly, the brightness feature is used to construct high-order tensors, then using multilinear principal component analysis (MPCA) extracts the eigentensors, and finally, the classifier is constructed based on support vector machine (SVM) and the classifier parameters are optimized with quantum simulated annealing algorithm (QSA). In order to verify the effectiveness of the proposed algorithm, the normal SVM method has been chosen as comparing algorithm. The experimental results show that the proposed method can effectively extract the eigenfeatures and improve the classification accuracy of pancreatic images.

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Classification of SSVEP-based BCIs using Genetic Algorithm

Journal Of Big Data ◽

10.1186/s40537-021-00478-y ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Hamideh Soltani ◽

Zahra Einalou ◽

Mehrdad Dadgostar ◽

Keivan Maghooli

Keyword(s):

Genetic Algorithm ◽

Dimension Reduction ◽

Classification Accuracy ◽

Bayesian Method ◽

Computer Interface ◽

Support Vector ◽

Svm Classifier ◽

Effective Dimension ◽

Effective Dimension Reduction

AbstractBrain computer interface (BCI) systems have been regarded as a new way of communication for humans. In this research, common methods such as wavelet transform are applied in order to extract features. However, genetic algorithm (GA), as an evolutionary method, is used to select features. Finally, classification was done using the two approaches support vector machine (SVM) and Bayesian method. Five features were selected and the accuracy of Bayesian classification was measured to be 80% with dimension reduction. Ultimately, the classification accuracy reached 90.4% using SVM classifier. The results of the study indicate a better feature selection and the effective dimension reduction of these features, as well as a higher percentage of classification accuracy in comparison with other studies.

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Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

Journal of Chemistry ◽

10.1155/2015/619685 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Hui Chen ◽

Zan Lin ◽

Chao Tan

Keyword(s):

Cancer Diagnosis ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Classification Model ◽

Support Vector ◽

Biomedical Analysis ◽

First Derivative ◽

Svm Model ◽

High Prediction

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

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