scholarly journals A Novel Method for the Discrimination of Semen Arecae and Its Processed Products by Using Computer Vision, Electronic Nose, and Electronic Tongue

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Min Xu ◽  
Shi-Long Yang ◽  
Wei Peng ◽  
Yu-Jie Liu ◽  
Da-Shuai Xie ◽  
...  
Keyword(s):  
Computer Vision ◽  
Electronic Nose ◽  
Electronic Tongue ◽  
Principal Component ◽  
Accurate Method ◽  
Color Parameters ◽  
Hydroxymethyl Furfural ◽  
Novel Method ◽  
Sensory Responses ◽  
Processed Products

Areca nut, commonly known locally as Semen Arecae (SA) in China, has been used as an important Chinese herbal medicine for thousands of years. The raw SA (RAW) is commonly processed by stir-baking to yellow (SBY), stir-baking to dark brown (SBD), and stir-baking to carbon dark (SBC) for different clinical uses. In our present investigation, intelligent sensory technologies consisting of computer vision (CV), electronic nose (E-nose), and electronic tongue (E-tongue) were employed in order to develop a novel and accurate method for discrimination of SA and its processed products. Firstly, the color parameters and electronic sensory responses of E-nose and E-tongue of the samples were determined, respectively. Then, indicative components including 5-hydroxymethyl furfural (5-HMF) and arecoline (ARE) were determined by HPLC. Finally, principal component analysis (PCA) and discriminant factor analysis (DFA) were performed. The results demonstrated that these three instruments can effectively discriminate SA and its processed products. 5-HMF and ARE can reflect the stir-baking degree of SA. Interestingly, the two components showed close correlations to the color parameters and sensory responses of E-nose and E-tongue. In conclusion, this novel method based on CV, E-nose, and E-tongue can be successfully used to discriminate SA and its processed products.

scholarly journals A Comprehensive Method for Assessing Meat Freshness Using Fusing Electronic Nose, Computer Vision, and Artificial Tactile Technologies

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xiaohui Weng ◽  
Xiangyu Luan ◽  
Cheng Kong ◽  
Zhiyong Chang ◽  
Yinwu Li ◽  
...  
Keyword(s):  
Computer Vision ◽  
Electronic Nose ◽  
Prediction Models ◽  
Meat Products ◽  
Principal Component ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Pattern Recognition Methods ◽  
Fusion Data ◽  
Objective Detection

The traditional methods cannot be used to meet the requirements of rapid and objective detection of meat freshness. Electronic nose (E-Nose), computer vision (CV), and artificial tactile (AT) sensory technologies can be used to mimic humans’ compressive sensory functions of smell, look, and touch when making judgement of meat quality (freshness). Though individual E-Nose, CV, and AT sensory technologies have been used to detect the meat freshness, the detection results vary and are not reliable. In this paper, a new method has been proposed through the integration of E-Nose, CV, and AT sensory technologies for capturing comprehensive meat freshness parameters and the data fusion method for analysing the complicated data with different dimensions and units of six odour parameters of E-Nose, 9 colour parameters of CV, and 4 rubbery parameters of AT for effective meat freshness detection. The pork and chicken meats have been selected for a validation test. The total volatile base nitrogen (TVB-N) assays are used to define meat freshness as the standard criteria for validating the effectiveness of the proposed method. The principal component analysis (PCA) and support vector machine (SVM) are used as unsupervised and supervised pattern recognition methods to analyse the source data and the fusion data of the three instruments, respectively. The experimental and data analysis results show that compared to a single technology, the fusion of E-Nose, CV, and AT technologies significantly improves the detection performance of various freshness meat products. In addition, partial least squares (PLS) is used to construct TVB-N value prediction models, in which the fusion data is input. The root mean square error predictions (RMSEP) for the sample pork and chicken meats are 1.21 and 0.98, respectively, in which the coefficient of determination (R2) is 0.91 and 0.94. This means that the proposed method can be used to effectively detect meat freshness and the storage time (days).

Differentiation between Cerebrospinal Fluid and Serum with Electronic Nose

2005 ◽  
Vol 133 (1) ◽  
pp. 16-19 ◽  
Author(s):  
Anna Aronzon ◽  
C. William Hanson ◽  
Erica R. Thaler
Keyword(s):  
Principal Component Analysis ◽  
Cerebrospinal Fluid ◽  
Electronic Nose ◽  
Clinical Setting ◽  
Sensor Array ◽  
Low Cost ◽  
Principal Component ◽  
Rapid Diagnosis ◽  
Novel Method ◽  
Csf Leaks

OBJECTIVE: The study investigates the ability of the electronic nose to differentiate between cerebrospinal fluid (CSF) and serum and to identify an unknown specimen as CSF or serum. STUDY DESIGN AND SETTING: CSF and serum specimens were heated and tested with an organic semiconductor-based Cyranose 320 electronic nose (Cyrons Sciences, Pasadena, CA). Data from the 32-element sensor array were subjected to principal component analysis to depict differences in odorant patterns. RESULTS: The electronic nose was able to distinguish between CSF and serum isolates with Mahalanobis distance >5. Furthermore, the electronic nose was able to place unknown specimens in the appropriate class of CSF or serum. CONCLUSIONS: The electronic nose is a novel method that may allow rapid, low cost, and reliable distinction between CSF and serum in a clinical setting. SIGNIFICANCE: Because the results are available almost immediately, the electronic nose is a powerful tool that in the future may allow for rapid diagnosis of CSF leaks in the office setting.

scholarly journals Effective discrimination of flavours and tastes of Chinese traditional fish soups made from different regions of the silver carp using an electronic nose and electronic tongue

2020 ◽  
Vol 38 (No. 2) ◽  
pp. 84-93
Author(s):  
Zhengyi Hu ◽  
Yao Tong ◽  
Anne Manyande ◽  
Hongying Du
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Electronic Nose ◽  
Theoretical Basis ◽  
Silver Carp ◽  
Electronic Tongue ◽  
Principal Component ◽  
Chemical Components ◽  
Sds Page ◽  
Carp Fish

Silver carp is a one of the most important freshwater fish species in China, and is popular when making soup in the Chinese dietary culture. In order to investigate the profile of fish soup tastes and flavours cooked using different regions of the same fish, the silver carp was cut into four different regions: head, back, abdomen, and tail. The differences in taste and flavour of the four kinds of homemade fish soup were investigated by an electronic nose and electronic tongue. The basic chemical components of the different fish regions and the SDS-PAGE profile of the fish soup samples were investigated. Two chemometrics methods (principal component analysis and discriminant factor analysis) were used to classify the odour and taste of the fish soup samples. The results showed that the electronic tongue and nose performed outstandingly in discriminating the four fish soups even though the samples were made from different regions of the same fish. The taste and flavour information of different regions of the silver carp fish could provide the theoretical basis for food intensive processing.<br /><br />

scholarly journals Comparison of Sensory Qualities in Eggs from Three Breeds Based on Electronic Sensory Evaluations

Foods ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1984
Author(s):  
Xiaoguang Dong ◽  
Libing Gao ◽  
Haijun Zhang ◽  
Jing Wang ◽  
Kai Qiu ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Classification Accuracy ◽  
Laying Hens ◽  
Electronic Tongue ◽  
Principal Component ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Sensory Qualities ◽  
Egg Yolks

The present study was conducted on three commercial laying breeder strains to evaluate differences of sensory qualities, including texture, smell, and taste parameters. A total of 140 eggs for each breed were acquired from Beinong No.2 (B) laying hens, Hy-Line Brown (H) laying hens, and Wuhei (W) laying hens. Sensory qualities of egg yolks and albumen from three breeds were detected and discriminated based on different algorithms. Texture profile analysis (TPA) showed that the eggs from three breeds had no differences in hardness, adhesiveness, springiness, and chewiness other than cohesiveness. The smell profiles measured by electronic nose illustrated that differences existed in all 10 sensors for albumen and 8 sensors for yolks. The taste profiles measured by electronic tongue found that the main difference of egg yolks and albumen existed in bitterness and astringency. Principal component analysis (PCA) successfully showed grouping of three breeds based on electronic nose data and failed in grouping based on electronic tongue data. Based on electronic nose data, linear discriminant analysis (LDA), fine k-nearest neighbor (KNN) and linear support vector machine (SVM) were performed to discriminate yolks, albumen, and the whole eggs with 100% classification accuracy. While based on electronic tongue data, the best classification accuracy was 96.7% for yolks by LDA and fine tree, 88.9% for albumen by LDA, and 87.5% for the whole eggs by fine KNN. The experiment results showed that three breeds’ eggs had main differences in smells and could be successfully discriminated by LDA, fine KNN, and linear SVM algorithms based on electronic nose.

A novel method for rapid discrimination of bulbus of Fritillaria by using electronic nose and electronic tongue technology

2015 ◽  
Vol 7 (3) ◽  
pp. 943-952 ◽  
Author(s):  
Shilong Yang ◽  
Shaopeng Xie ◽  
Min Xu ◽  
Chao Zhang ◽  
Na Wu ◽  
...  
Keyword(s):  
Electronic Nose ◽  
Electronic Tongue ◽  
Novel Method ◽  
Rapid Discrimination

E-nose and E-tongue coupled with the chemometrics were employed to discriminate the bulbus of fritillaria in the form of powder.

scholarly journals Data Fusion of Electronic Nose and Electronic Tongue for Detection of Mixed Edible-Oil

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hong Men ◽  
Donglin Chen ◽  
Xiaoting Zhang ◽  
Jingjing Liu ◽  
Ke Ning
Keyword(s):  
Data Fusion ◽  
Electronic Nose ◽  
Food Processing ◽  
Electronic Tongue ◽  
Principal Component ◽  
Edible Oil ◽  
Frying Oil ◽  
Using Data ◽  
The Cost ◽  
Detection Effect

For the problem of the waste of the edible-oil in the food processing, on the premise of food security, they often need to add new edible-oil to the old frying oil which had been used in food processing to control the cost of the production. Due to the fact that the different additive proportion of the oil has different material and different volatile gases, we use fusion technology based on the electronic nose and electronic tongue to detect the blending ratio of the old frying oil and the new edible-oil in this paper. Principal component analysis (PCA) is used to distinguish the different proportion of the old frying oil and new edible-oil; on the other hand we use partial least squares (PLS) to predict the blending ratio of the old frying oil and new edible-oil. Two conclusions were proposed: data fusion of electronic nose and electronic tongue can be used to detect the blending ratio of the old frying oil and new edible-oil; in contrast to single used electronic nose or single used electronic tongue, the detection effect has increased by using data fusion of electronic nose and electronic tongue.

scholarly journals Hyperspectral Image Classification with Localized Graph Convolutional Filtering

2021 ◽  
Vol 13 (3) ◽  
pp. 526
Author(s):  
Shengliang Pu ◽  
Yuanfeng Wu ◽  
Xu Sun ◽  
Xiaotong Sun
Keyword(s):  
Hyperspectral Image ◽  
Principal Component ◽  
Representation Learning ◽  
Classification Performance ◽  
Hyperspectral Data ◽  
Spectral Graph Theory ◽  
Feature Reduction ◽  
Graph Representation ◽  
Novel Method ◽  
Local Graph

The nascent graph representation learning has shown superiority for resolving graph data. Compared to conventional convolutional neural networks, graph-based deep learning has the advantages of illustrating class boundaries and modeling feature relationships. Faced with hyperspectral image (HSI) classification, the priority problem might be how to convert hyperspectral data into irregular domains from regular grids. In this regard, we present a novel method that performs the localized graph convolutional filtering on HSIs based on spectral graph theory. First, we conducted principal component analysis (PCA) preprocessing to create localized hyperspectral data cubes with unsupervised feature reduction. These feature cubes combined with localized adjacent matrices were fed into the popular graph convolution network in a standard supervised learning paradigm. Finally, we succeeded in analyzing diversified land covers by considering local graph structure with graph convolutional filtering. Experiments on real hyperspectral datasets demonstrated that the presented method offers promising classification performance compared with other popular competitors.

scholarly journals Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 916 ◽  
Author(s):  
Wen Cao ◽  
Chunmei Liu ◽  
Pengfei Jia
Keyword(s):  
Feature Extraction ◽  
Kernel Function ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Object Function ◽  
Optimal Value ◽  
Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

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