scholarly journals Qualitative detection of pesticide residues using mass spectral data based on convolutional neural network

2021 ◽  
Vol 3 (7) ◽  
Author(s):  
Jian Wei ◽  
Xuemei Wang ◽  
Zhenyu Wang ◽  
Jin Cao
Keyword(s):  
Neural Network ◽  
Mass Spectrometry ◽  
Convolutional Neural Network ◽  
Pesticide Residue ◽  
Pesticide Residues ◽  
Human Life ◽  
Channel Structure ◽  
Mass Spectrometry Data ◽  
Rapid Screening ◽  
Mixture Analysis

AbstractExcessive pesticide residues in crops directly threaten human life and health, so rapid screening and effective measurements of agricultural pesticides residues have important application significance in the field of food safety. It is imperative to detect different pesticide residue types in actual complex crop samples cause mixture analysis can provide more information than individual components. However, the accuracy of mixture analysis can be obviously affected by the impurities and noise disturbances. Purification and denoising will cost a lot of algorithm time. In this work, we used the problem transformation method to convert pesticide residues prediction into multi-label classification problem. In addition, a new convolutional neural network structure Pesticide Residues Neural Network (PRNet) was proposed to solve the problem of multi-label organophosphate pesticide residue prediction. The method of binary correlation and label energy set was used to adapt 35 pesticide residues labels. The Cross Entropy were used as loss functions for PRNet. The comprehensive comparison performances (e.g. 97% optimal accuracy rate) of PRNet is better than the other four models. By comparing the ROC curves of the five models, PRNet performs the best. The PRNet can separate the independent mass spectrometry data by different collision energy applied to phosphorus pesticide compounds through a three-channel structure. No complicated data preprocessing is required, the PRNet can extract the characteristics of different compounds more efficiently and presents high detecting accuracy and good model performance of multi-label mass spectrometry data classification. By inputting MS data of different instruments and adding more offset MS data, the model will be more transplantable and could lay the foundation for the wide application of PRNet model in rapid, on-site, accurate and broad-spectrum screening of pesticide residues in the future.

Multi-pesticide residue analysis by high resolution mass spectrometry in complementary matrices: wheat flour, lettuce and apple samples

2018 ◽  
Vol 10 (32) ◽  
pp. 3958-3967 ◽  
Author(s):  
Marilda Chiarello ◽  
Sidnei Moura
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
Human Health ◽  
Pesticide Residue ◽  
Pesticide Residues ◽  
High Resolution Mass Spectrometry ◽  
Residue Analysis ◽  
Food Contamination ◽  
Pesticide Residue Analysis ◽  
Resolution Mass

Nowadays, food contamination with pesticide residues is prevalent, which can cause problems to human health.

Optimized parallel architecture of evolutionary neural network for mass spectrometry data processing

2017 ◽  
Vol 08 (01) ◽  
pp. 1750016 ◽  
Author(s):  
Amin Jarrah ◽  
Bashar Haddad ◽  
Mohammad A. Al-Jarrah ◽  
Muhammad Bassam Obeidat
Keyword(s):  
Neural Network ◽  
Mass Spectrometry ◽  
Parallel Processing ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Function Optimization ◽  
Mass Spectrometry Data ◽  
Adaptive Optimization ◽  
Graphic Processing Units ◽  
Evolutionary Neural Network

Evolutionary neural network (ENN) shows high performance in function optimization and in finding approximately global optima from searching large and complex spaces. It is one of the most efficient and adaptive optimization techniques used widely to provide candidate solutions that lead to the fitness of the problem. ENN has the extraordinary ability to search the global and learning the approximate optimal solution regardless of the gradient information of the error functions. However, ENN requires high computation and processing which requires parallel processing platforms such as field programmable gate arrays (FPGAs) and graphic processing units (GPUs) to achieve a good performance. This work involves different new implementations of ENN by exploring and adopting different techniques and opportunities for parallel processing. Different versions of ENN algorithm have also been implemented and parallelized on FPGAs platform for low latency by exploiting the parallelism and pipelining approaches. Real data form mass spectrometry data (MSD) application was tested to examine and verify our implementations. This is a very important and extensive computation application which needs to search and find the optimal features (peaks) in MSD in order to distinguish cancer patients from control patients. ENN algorithm is also implemented and parallelized on single core and GPU platforms for comparison purposes. The computation time of our optimized algorithm on FPGA and GPU has been improved by a factor of 6.75 and 6, respectively.

scholarly journals Detection and Classification of White Blood Cells through Deep Learning Techniques

2020 ◽  
Vol 16 (15) ◽  
pp. 94
Author(s):  
Samir Abou El-Seoud ◽  
Muaad Hammuda Siala ◽  
Gerard McKee
Keyword(s):  
Neural Network ◽  
Blood Cell ◽  
Convolutional Neural Network ◽  
White Blood Cell ◽  
Blood Cells ◽  
Human Life ◽  
White Blood Cells ◽  
Learning Techniques ◽  
Normal White Blood Cell ◽  
High Level

Leukemia is one of the deadliest diseases in human life, it is a type of cancer that hits blood cells. The task of diagnosing Leukemia is time consuming and tedious for doctors; it is also challenging to determine the level and type of Leukemia. The diagnoses of Leukemia are achieved through identifying the changes on the White blood Cells (WBC). WBCs are divided into five types: Neutrophils, Eosinophils, Basophils, Monocytes, and Lymphocytes. In this paper, the authors propose a Convolutional Neural Network to detect and classify normal white blood cells. The program will learn about the shape and type of normal WBC by performing the following two tasks. The first task is identifying high level features of a normal white blood cell. The second task is classifying the normal white blood cell according to its type. Using a Convolutional Neural Network CNN, the system will be able to detect normal WBCs by comparing them with the high-level features of normal WBC. This process of identifying and classifying WBC can be vital for doctors and medical staff to make a decision. The proposed network achieves an accuracy up to 96.78% with a dataset including 10,000 blood cell images.

scholarly journals Human activity recognition and embedded application based on convolutional neural network

2020 ◽  
Author(s):  
Yang Xu ◽  
Ting Ting Qiu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Human Life ◽  
Extraction Methods ◽  
Human Activity Recognition ◽  
Stochastic Gradient Descent ◽  
Average Accuracy ◽  
Gradient Descent Algorithm

With the improvement of people's living standards, the demand for health monitoring and exercise detection is increasing. It is of great significance to study human activity recognition methods that are different from traditional feature extraction methods. This article uses convolutional neural network algorithms in deep learning to automatically extract features of activities related to human life. It uses a stochastic gradient descent algorithm to optimize the parameters of the convolutional neural network. The trained network model is compressed on STM32CubeMX-AI. Finally, this article introduces the use of neural networks on embedded devices to recognize six human activities of daily life, such as sitting, standing, walking, jogging, upstairs and downstairs. The acceleration sensor related to human activity information is used to obtain the relevant characteristics of the activity, thereby solving the human activity recognition (HAR) problem. The network structure of the constructed CNN model is shown in Figure 1, including an input layer, two convolutional layers and two pooling layers. After comparing the average accuracy of each set of experiments and the test set of the best model obtained from it, the best model is then selected.

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