scholarly journals A Classification for Electronic Nose Based on Broad Learning System

2020 ◽  
Author(s):  
Yu Wang ◽  
Xiaoyan Peng ◽  
Hao Cui ◽  
Pengfei Jia
Keyword(s):  
Electronic Nose ◽  
Storage Period ◽  
Learning System ◽  
Training Time ◽  
Linear Discriminant ◽  
Citrus Juice ◽  
Citrus Juices ◽  
Current Stage ◽  
Simple Network

The odor of citrus juice changes during the storing process. We use an electronic nose (E-nose) to detect the volatile odors released by citrus juice and use the detect results to classify citrus juices from different storage periods. In this article, a novel classifier of E-nose, namely broad learning system (BLS) is introduced. BLS is different from traditional classifier. It has a simple network model, which can greatly reduce the training time of the model. BLS can effectively combine feature extraction and classification recognition to make the model more efficient. We apply BLS to the analysis of valencia citrus juice data. The experimental results show that BLS can effectively identify the current stage of the stored valencia citrus juice. Compared with traditional classifier such as radical basis function neural network (RBFNN) and linear discriminant analysis (LDA), the results show that BLS has better performance for the storage period classification of valencia citrus juice.

scholarly journals Five Typical Stenches Detection Using an Electronic Nose

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2514 ◽  
Author(s):  
Wei Jiang ◽  
Daqi Gao
Keyword(s):  
Electronic Nose ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Odor Concentration ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Components Analysis ◽  
Olfactory Organs

This paper deals with the classification of stenches, which can stimulate olfactory organs to discomfort people and pollute the environment. In China, the triangle odor bag method, which only depends on the state of the panelist, is widely used in determining odor concentration. In this paper, we propose a stenches detection system composed of an electronic nose and machine learning algorithms to discriminate five typical stenches. These five chemicals producing stenches are 2-phenylethyl alcohol, isovaleric acid, methylcyclopentanone, γ-undecalactone, and 2-methylindole. We will use random forest, support vector machines, backpropagation neural network, principal components analysis (PCA), and linear discriminant analysis (LDA) in this paper. The result shows that LDA (support vector machine (SVM)) has better performance in detecting the stenches considered in this paper.

scholarly journals Digitization of Broccoli Freshness Integrating External Color and Mass Loss

Foods ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1305
Author(s):  
Yoshio Makino ◽  
Genki Amino
Keyword(s):  
Mass Loss ◽  
Retention Rate ◽  
Vision System ◽  
Color Space ◽  
Storage Period ◽  
Evaluation Index ◽  
Linear Discriminant ◽  
Green Color ◽  
Color Retention

Yellowing of green vegetables due to chlorophyll decomposition is a phenomenon indicating serious deterioration of freshness, and it is evaluated by measuring color space values. In contrast, mass reduction due to water loss is a deterioration of freshness observed in all horticultural crops. Therefore, in this study, we propose a novel freshness evaluation index for green vegetables that combines the degree of greenness and mass loss. The green color retention rate was measured using a computer vision system, and the mass retention rate was measured by weighing. Linear discriminant analysis (LDA) was performed using both variables (greenness and mass) as covariates to obtain a single freshness evaluation value (first canonical variable). The correct classification of storage period length by LDA was 96%. Green color retention alone allowed for classification of storage durations between 0 day and 10 days, whereas LDA could classify storage durations between 0 day and 12 days. The novel freshness evaluation index proposed by this research, which integrates greenness and mass, has been shown to be more accurate than the conventional evaluation index that uses only greenness.

Electronic Nose: A Non-destructive Technology to Screen Tomato Fruit with Internal Bruising

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 489c-489
Author(s):  
Celso L. Moretti ◽  
Steven A. Sargent ◽  
Rolf Puschmann
Keyword(s):  
Electronic Nose ◽  
Mahalanobis Distance ◽  
Tomato Fruit ◽  
System Stability ◽  
Linear Discriminant ◽  
Polymer Sensors ◽  
Ripe Stage ◽  
Multivariate Discriminant ◽  
Non Destructive

Tomato (Lycopersicon esculentum Mill) fruit, cv. Solar Set, were harvested at the mature-green stage and gassed with 100 mg·kg–1 of ethylene at 20 °C. At the breaker stage, fruit were held by vacuum to avoid fruit rotation and dropped from a 40 cm height on a metallic, solid, smooth surface. Following impact, fruit were stored at 20 °C and 85% to 95% relative humidity until table-ripe stage. Bruised and unbruised fruit were then placed individually inside the electronic nose-sampling vessel and the 12 conducting polymer sensors were lowered into the vessel and exposed to the volatile given off by the fruit. Data were analyzed employing multivariate discriminant analysis (MVDA), which maximizes the variance between treatments. The degree of dissimilarity was defined using the Mahalanobis distance and posterior probabilities were calculated to accurate re-classification of cases. The differences found between bruised and unbruised fruit were highly significant (P < 0.0041). The Mahalanobis distance between groupings (28.19 units) was a dramatic indicative of the differences between the two treatments. The re-classification of bruised and unbruised fruit using a single linear discriminant function was highly accurate, being 1.0 for both bruised and unbruised fruit. The electronic nose proved to be a useful tool to nondestructively identify and classify tomato fruit exposed to harmful postharvest practices such as mechanical injuries. However, there are still some factors that must be investigated, including system stability and the development of specific sensors for specific commodities.

scholarly journals Detection of adulteration in freshly squeezed orange juice by electronic nose and infrared spectroscopy

2016 ◽  
Vol 34 (No. 3) ◽  
pp. 224-232 ◽  
Author(s):  
F. Shen ◽  
Q. Wu ◽  
A. Su ◽  
P. Tang ◽  
X. Shao ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Electronic Nose ◽  
Orange Juice ◽  
Principal Component ◽  
Rapid Screening ◽  
Gas Chromatography Mass Spectrometry ◽  
Attenuated Total Reflectance ◽  
Linear Discriminant ◽  
Total Reflectance

The use of electronic nose and attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) as rapid tools for detection of orange juice adulteration has been preliminarily investigated and compared. Freshly squeezed orange juices were tentatively adulterated with 100% concentrated orange juices at levels ranging from 0% to 30% (v/v). Then the E-nose response signals and FTIR spectra collected from samples were subjected to multivariate analysis by principal component analysis (PCA) and linear discriminant analysis (LDA). PCA indicated that authentic juices and adulterated ones could be approximately separated. For the classification of samples with different adulteration levels, the overall accuracy obtained by LDA in prediction was 91.7 and 87.5% for E-nose and ATR-FTIR, respectively. Gas chromatography-mass spectrometry (GC-MS) results verified that there existed an obvious holistic difference in flavour characteristics between fresh squeezed and concentrated juices. These results demonstrated that both E-nose and FTIR might be used as rapid screening techniques for the detection of this type of juice adulteration.

Classification of vegetable oils by linear discriminant analysis of Electronic Nose data

1999 ◽  
Vol 384 (1) ◽  
pp. 83-94 ◽  
Author(s):  
Yolanda González Martı́n ◽  
José Luis Pérez Pavón ◽  
Bernardo Moreno Cordero ◽  
Carmelo Garcı́a Pinto
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Vegetable Oils ◽  
Electronic Nose ◽  
Linear Discriminant

scholarly journals A hybrid CNN-LSTM model for pre-miRNA classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abdulkadir Tasdelen ◽  
Baha Sen
Keyword(s):  
Evaluation Criteria ◽  
Mature Mirnas ◽  
Structural Features ◽  
Linear Discriminant ◽  
Sequential Structure ◽  
Precursor Mirnas ◽  
Lstm Network ◽  
Future Work ◽  
Cumulative Evidence

AbstractmiRNAs (or microRNAs) are small, endogenous, and noncoding RNAs construct of about 22 nucleotides. Cumulative evidence from biological experiments shows that miRNAs play a fundamental and important role in various biological processes. Therefore, the classification of miRNA is a critical problem in computational biology. Due to the short length of mature miRNAs, many researchers are working on precursor miRNAs (pre-miRNAs) with longer sequences and more structural features. Pre-miRNAs can be divided into two groups as mirtrons and canonical miRNAs in terms of biogenesis differences. Compared to mirtrons, canonical miRNAs are more conserved and easier to be identified. Many existing pre-miRNA classification methods rely on manual feature extraction. Moreover, these methods focus on either sequential structure or spatial structure of pre-miRNAs. To overcome the limitations of previous models, we propose a nucleotide-level hybrid deep learning method based on a CNN and LSTM network together. The prediction resulted in 0.943 (%95 CI ± 0.014) accuracy, 0.935 (%95 CI ± 0.016) sensitivity, 0.948 (%95 CI ± 0.029) specificity, 0.925 (%95 CI ± 0.016) F1 Score and 0.880 (%95 CI ± 0.028) Matthews Correlation Coefficient. When compared to the closest results, our proposed method revealed the best results for Acc., F1 Score, MCC. These were 2.51%, 1.00%, and 2.43% higher than the closest ones, respectively. The mean of sensitivity ranked first like Linear Discriminant Analysis. The results indicate that the hybrid CNN and LSTM networks can be employed to achieve better performance for pre-miRNA classification. In future work, we study on investigation of new classification models that deliver better performance in terms of all the evaluation criteria.

scholarly journals Preventing corneal blindness caused by keratitis using artificial intelligence

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhongwen Li ◽  
Jiewei Jiang ◽  
Kuan Chen ◽  
Qianqian Chen ◽  
Qinxiang Zheng ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Vision Loss ◽  
Learning System ◽  
Automated Classification ◽  
Slit Lamp ◽  
Resource Limited ◽  
The World ◽  
Different Types ◽  
Corneal Blindness

AbstractKeratitis is the main cause of corneal blindness worldwide. Most vision loss caused by keratitis can be avoidable via early detection and treatment. The diagnosis of keratitis often requires skilled ophthalmologists. However, the world is short of ophthalmologists, especially in resource-limited settings, making the early diagnosis of keratitis challenging. Here, we develop a deep learning system for the automated classification of keratitis, other cornea abnormalities, and normal cornea based on 6,567 slit-lamp images. Our system exhibits remarkable performance in cornea images captured by the different types of digital slit lamp cameras and a smartphone with the super macro mode (all AUCs>0.96). The comparable sensitivity and specificity in keratitis detection are observed between the system and experienced cornea specialists. Our system has the potential to be applied to both digital slit lamp cameras and smartphones to promote the early diagnosis and treatment of keratitis, preventing the corneal blindness caused by keratitis.

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.

scholarly journals A Machine Vision Approach for Bioreactor Foam Sensing

2021 ◽  
pp. 247263032110088
Author(s):  
Jonas Austerjost ◽  
Robert Söldner ◽  
Christoffer Edlund ◽  
Johan Trygg ◽  
David Pollard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
State Of The Art ◽  
Low Cost ◽  
High Accuracy ◽  
Consumer Electronics ◽  
Learning System ◽  
Automotive Applications ◽  
Fine Grained

Machine vision is a powerful technology that has become increasingly popular and accurate during the last decade due to rapid advances in the field of machine learning. The majority of machine vision applications are currently found in consumer electronics, automotive applications, and quality control, yet the potential for bioprocessing applications is tremendous. For instance, detecting and controlling foam emergence is important for all upstream bioprocesses, but the lack of robust foam sensing often leads to batch failures from foam-outs or overaddition of antifoam agents. Here, we report a new low-cost, flexible, and reliable foam sensor concept for bioreactor applications. The concept applies convolutional neural networks (CNNs), a state-of-the-art machine learning system for image processing. The implemented method shows high accuracy for both binary foam detection (foam/no foam) and fine-grained classification of foam levels.

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