scholarly journals Online Feature Selection for Robust Classification of the Microbiological Quality of Traditional Vanilla Cream by Means of Multispectral Imaging

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4071 ◽  
Author(s):  
Alexandra Lianou ◽  
Arianna Mencattini ◽  
Alexandro Catini ◽  
Corrado Di Natale ◽  
George-John E. Nychas ◽  
...  
Keyword(s):  
Feature Selection ◽  
Multispectral Imaging ◽  
Dairy Product ◽  
Real Life ◽  
Microbiological Quality ◽  
Support Vector ◽  
Isothermal Conditions ◽  
Online Feature Selection ◽  
Model Training

The performance of an Unsupervised Online feature Selection (UOS) algorithm was investigated for the selection of training features of multispectral images acquired from a dairy product (vanilla cream) stored under isothermal conditions. The selected features were further used as input in a support vector machine (SVM) model with linear kernel for the determination of the microbiological quality of vanilla cream. Model training (n = 65) was based on two batches of cream samples provided directly by the manufacturer and stored at different isothermal conditions (4, 8, 12, and 15 °C), whereas model testing (n = 132) and validation (n = 48) were based on real life conditions by analyzing samples from different retail outlets as well as expired samples from the market. Qualitative analysis was performed for the discrimination of cream samples in two microbiological quality classes based on the values of total viable counts [TVC ≤ 2.0 log CFU/g (fresh samples) and TVC ≥ 6.0 log CFU/g (spoiled samples)]. Results exhibited good performance with an overall accuracy of classification for the two classes of 91.7% for model validation. Further on, the model was extended to include the samples in the TVC range 2–6 log CFU/g, using 1 log step to define the microbiological quality of classes in order to assess the potential of the model to estimate increasing microbial populations. Results demonstrated that high rates of correct classification could be obtained in the range of 2–5 log CFU/g, whereas the percentage of erroneous classification increased in the TVC class (5,6) that was close to the spoilage level of the product. Overall, the results of this study demonstrated that the UOS algorithm in tandem with spectral data acquired from multispectral imaging could be a promising method for real-time assessment of the microbiological quality of vanilla cream samples.

scholarly journals Prediction of Diabetic Nephropathy from the Relationship between Fatigue, Sleep and Quality of Life

2020 ◽  
Vol 10 (9) ◽  
pp. 3282
Author(s):  
Angela Shin-Yu Lien ◽  
Yi-Der Jiang ◽  
Jia-Ling Tsai ◽  
Jawl-Shan Hwang ◽  
Wei-Chao Lin
Keyword(s):  
Quality Of Life ◽  
Feature Selection ◽  
Diabetic Nephropathy ◽  
Sleep Quality ◽  
Support Vector ◽  
Svm Classifier ◽  
Poor Sleep ◽  
Poor Sleep Quality ◽  
The Relationship

Fatigue and poor sleep quality are the most common clinical complaints of people with diabetes mellitus (DM). These complaints are early signs of DM and are closely related to diabetic control and the presence of complications, which lead to a decline in the quality of life. Therefore, an accurate measurement of the relationship between fatigue, sleep status, and the complication of DM nephropathy could lead to a specific definition of fatigue and an appropriate medical treatment. This study recruited 307 people with Type 2 diabetes from two medical centers in Northern Taiwan through a questionnaire survey and a retrospective investigation of medical records. In an attempt to identify the related factors and accurately predict diabetic nephropathy, we applied hybrid research methods, integrated biostatistics, and feature selection methods in data mining and machine learning to compare and verify the results. Consequently, the results demonstrated that patients with diabetic nephropathy have a higher fatigue level and Charlson comorbidity index (CCI) score than without neuropathy, the presence of neuropathy leads to poor sleep quality, lower quality of life, and poor metabolism. Furthermore, by considering feature selection in selecting representative features or variables, we achieved consistence results with a support vector machine (SVM) classifier and merely ten representative factors and a prediction accuracy as high as 74% in predicting the presence of diabetic nephropathy.

scholarly journals Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis

Foods ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 2723
Author(s):  
Evgenia D. Spyrelli ◽  
Christina Papachristou ◽  
George-John E. Nychas ◽  
Efstathios Z. Panagou
Keyword(s):  
Support Vector Machines ◽  
Discriminant Analysis ◽  
Microbiological Quality ◽  
Poultry Meat ◽  
Support Vector ◽  
Classification Models ◽  
Pseudomonas Spp ◽  
Vector Machines ◽  
Ft Ir

Fourier transform infrared spectroscopy (FT-IR) and multispectral imaging (MSI) were evaluated for the prediction of the microbiological quality of poultry meat via regression and classification models. Chicken thigh fillets (n = 402) were subjected to spoilage experiments at eight isothermal and two dynamic temperature profiles. Samples were analyzed microbiologically (total viable counts (TVCs) and Pseudomonas spp.), while simultaneously MSI and FT-IR spectra were acquired. The organoleptic quality of the samples was also evaluated by a sensory panel, establishing a TVC spoilage threshold at 6.99 log CFU/cm2. Partial least squares regression (PLS-R) models were employed in the assessment of TVCs and Pseudomonas spp. counts on chicken’s surface. Furthermore, classification models (linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVMs), and quadratic support vector machines (QSVMs)) were developed to discriminate the samples in two quality classes (fresh vs. spoiled). PLS-R models developed on MSI data predicted TVCs and Pseudomonas spp. counts satisfactorily, with root mean squared error (RMSE) values of 0.987 and 1.215 log CFU/cm2, respectively. SVM model coupled to MSI data exhibited the highest performance with an overall accuracy of 94.4%, while in the case of FT-IR, improved classification was obtained with the QDA model (overall accuracy 71.4%). These results confirm the efficacy of MSI and FT-IR as rapid methods to assess the quality in poultry products.

scholarly journals Implementation of Multispectral Imaging (MSI) for Microbiological Quality Assessment of Poultry Products

2020 ◽  
Vol 8 (4) ◽  
pp. 552
Author(s):  
Evgenia D. Spyrelli ◽  
Agapi I. Doulgeraki ◽  
Anthoula A. Argyri ◽  
Chrysoula C. Tassou ◽  
Efstathios Z. Panagou ◽  
...  
Keyword(s):  
Multispectral Imaging ◽  
Microbiological Quality ◽  
Chicken Breast ◽  
Microbiological Analysis ◽  
Least Squares Regression ◽  
Burger Model ◽  
Factors Affecting ◽  
Poultry Products ◽  
Msi Analysis

The aim of this study was to investigate on an industrial scale the potential of multispectral imaging (MSI) in the assessment of the quality of different poultry products. Therefore, samples of chicken breast fillets, thigh fillets, marinated souvlaki and burger were subjected to MSI analysis during production together with microbiological analysis for the enumeration of Total Viable Counts (TVC) and Pseudomonas spp. Partial Least Squares Regression (PLS-R) models were developed based on the spectral data acquired to predict the “time from slaughter” parameter for each product type. Results showed that PLS-R models could predict effectively the time from slaughter in all products, while the food matrix and variations within and between batches were identified as significant factors affecting the performance of the models. The chicken thigh model showed the lowest RMSE value (0.160) and an acceptable correlation coefficient (r = 0.859), followed by the chicken burger model where RMSE and r values were 0.285 and 0.778, respectively. Additionally, for the chicken breast fillet model the calculated r and RMSE values were 0.886 and 0.383 respectively, whereas for chicken marinated souvlaki, the respective values were 0.934 and 0.348. Further improvement of the provided models is recommended in order to develop efficient models estimating time from slaughter.

COMPARISON OF MICROBIOLOGICAL QUALITY OF MILK PRODUCTS FROM ORGANIC AND CONVENTIONAL PRODUCTION

2018 ◽  
Vol XX (6) ◽  
pp. 147-153
Author(s):  
Anita Kukułowicz
Keyword(s):  
Escherichia Coli ◽  
Staphylococcus Aureus ◽  
Dairy Product ◽  
Microbiological Quality ◽  
Organic Production ◽  
Microbiological Analysis ◽  
Milk Products ◽  
Significant Difference ◽  
Conventional Production

The aim of the research was to compare the microbiological quality of dairy products derived from organic and conventional farming. The test material consisted of milk and milk products (natural yoghurt, cream, curd cheese, rennet ripening cheese, milk). Investigated products were subjected to microbiological analysis and marked for the number of Enterococcus sp., Staphylococcus aureus, yeast and moulds, Escherichia coli as well as to state the presence of Salmonella sp. in them. All samples were free of Salmonella sp. Escherichia coli occurred in 2 samples of curd cheese from organic production. Products derived from organic and conventional production didn’t differ in the average number of microorganisms depending on the type of dairy product, with the exception of ripening rennet cheese, which showed a significant difference in the amount of Staphylococcus aureus.

scholarly journals Differential Evolution and Multiclass Support Vector Machine for Alzheimer’s Classification

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Jhansi Rani Kaka ◽  
K. Satya Prasad
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Differential Evolution ◽  
Optimization Method ◽  
Support Vector ◽  
Local Optima ◽  
Whale Optimization ◽  
Multiclass Support Vector Machine ◽  
Poor Convergence

Early diagnosis of Alzheimer’s helps a doctor to decide the treatment for the patient based on the stages. The existing methods involve applying the deep learning methods for Alzheimer’s classification and have the limitations of overfitting problems. Some researchers were involved in applying the feature selection based on the optimization method, having limitations of easily trapping into local optima and poor convergence. In this research, Differential Evolution-Multiclass Support Vector Machine (DE-MSVM) is proposed to increase the performance of Alzheimer’s classification. The image normalization method is applied to enhance the quality of the image and represent the features effectively. The AlexNet model is applied to the normalized images to extract the features and also applied for feature selection. The Differential Evolution method applies Pareto Optimal Front for nondominated feature selection. This helps to select the feature that represents the characteristics of the input images. The selected features are applied in the MSVM method to represent in high dimension and classify Alzheimer’s. The DE-MSVM method has accuracy of 98.13% in the axial slice, and the existing whale optimization with MSVM has 95.23% accuracy.

scholarly journals Evaluating the Performance of Machine Learning Approaches to Predict the Microbial Quality of Surface Waters and to Optimize the Sampling Effort

Water ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 2457
Author(s):  
Manel Naloufi ◽  
Françoise S. Lucas ◽  
Sami Souihi ◽  
Pierre Servais ◽  
Aurélie Janne ◽  
...  
Keyword(s):  
Machine Learning ◽  
Escherichia Coli ◽  
Random Forest ◽  
Microbiological Quality ◽  
Support Vector ◽  
Sampling Effort ◽  
Temperature Conductivity ◽  
Adaptive Boosting ◽  
Paris Area

Exposure to contaminated water during aquatic recreational activities can lead to gastrointestinal diseases. In order to decrease the exposure risk, the fecal indicator bacteria Escherichia coli is routinely monitored, which is time-consuming, labor-intensive, and costly. To assist the stakeholders in the daily management of bathing sites, models have been developed to predict the microbiological quality. However, model performances are highly dependent on the quality of the input data which are usually scarce. In our study, we proposed a conceptual framework for optimizing the selection of the most adapted model, and to enrich the training dataset. This frameword was successfully applied to the prediction of Escherichia coli concentrations in the Marne River (Paris Area, France). We compared the performance of six machine learning (ML)-based models: K-nearest neighbors, Decision Tree, Support Vector Machines, Bagging, Random Forest, and Adaptive boosting. Based on several statistical metrics, the Random Forest model presented the best accuracy compared to the other models. However, 53.2 ± 3.5% of the predicted E. coli densities were inaccurately estimated according to the mean absolute percentage error (MAPE). Four parameters (temperature, conductivity, 24 h cumulative rainfall of the previous day the sampling, and the river flow) were identified as key variables to be monitored for optimization of the ML model. The set of values to be optimized will feed an alert system for monitoring the microbiological quality of the water through combined strategy of in situ manual sampling and the deployment of a network of sensors. Based on these results, we propose a guideline for ML model selection and sampling optimization.

Efficient Semi-Supervised Learning and Sparse Structural Learning for Feature Selection of Leukemia Dataset

2020 ◽  
Vol 10 (8) ◽  
pp. 1815-1824
Author(s):  
S. Nithya Roopa ◽  
N. Nagarajan
Keyword(s):  
Feature Selection ◽  
Supervised Learning ◽  
Health Informatics ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Research Work ◽  
Real Life ◽  
Support Vector ◽  
Structural Learning ◽  
Huge Amount

The amount of data produced in health informatics growing large and as a result analysis of this huge amount of data requires a great knowledge which is to be gained. The basic aim of health informatics is to take in real world medical data from all levels of human existence to help improve our understanding of medicine and medical practices. Huge amount of unlabeled data are obtainable in lots of real-life data-mining tasks, e.g., uncategorized messages in an automatic email categorization system, unknown genes functions for doing gene function calculation, and so on. Labelled data is frequently restricted and expensive to produce, while labelling classically needs human proficiency. Consequently, semi-supervised learning has become a topic of significant recent interest. This research work proposed a new semi-supervised grouping, where the performance of unsupervised clustering algorithms is enhanced with restricted numbers of supervision in labels form on constraints or data. The previous system designed a Clustering Guided Hybrid support vector machine based Sparse Structural Learning (CGHSSL) for feature selection. However, it does not produce a satisfactory accuracy results. In this research, proposed clustering-guided with Convolution Neural Network (CNN) based sparse structural learning clustering algorithm. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is progressed for learning cluster labels of input samples having more accuracy guiding features election at same time. Concurrently, prediction of cluster labels is as well performed by CNN by means of using hidden structure which is shared by various characteristics. The parameters of CNN are then optimized maximizing Multi-objective Bee Colony (MBO) algorithm that can unravel feature correlations to render outcomes with additional consistency. Row-wise sparse designs are then balanced to yield design depicted to suit for feature selection. This semi supervised algorithm is utilized to choose important characteristics from Leukemia1 dataset additional resourcefully. Therefore dataset size is decreased significantly utilizing semi supervised algorithm prominently. As well proposed Semi Supervised Clustering-Guided Sparse Structural Learning (SSCGSSL) technique is utilized to increase the clustering performance in higher. The experimental results show that the proposed system achieves better performance compared with the existing system in terms of Accuracy, Entropy, Purity, Normalized Mutual Information (NMI) and F-measure.

scholarly journals QUALITY AND TECHNOLOGY OF INDIGENOUS TRADITIONAL ‘’BIENO’’ CHEESE IN THE REGION OF MARIOVO, MACEDONIA

AGROFOR ◽  
2018 ◽  
Vol 2 (2) ◽  
Author(s):  
Gordana DIMITROVSKA ◽  
Sonja SRBINOVSKA ◽  
Elena JOSHEVSKA ◽  
Vangelica JOVANOVSKA
Keyword(s):  
Ottoman Empire ◽  
Milk Fat ◽  
Dry Matter ◽  
Dairy Product ◽  
Microbiological Quality ◽  
Milk Samples ◽  
Physico Chemical ◽  
Traditional Technologies ◽  
Traditional Production

“Bieno’’ cheese is an indigenous dairy product in Macedonia with autochthonoustraditional production technology which dates back from the Ottoman Empire. Theresearch includes and presents the results of the chemical composition and safety ofraw milk used for traditional production of ‘’Bieno’’ cheese, technology andphysico-chemical and microbiological quality of ‘’Bieno’’ cheese. The quality ofthe milk samples was determined within the permissible maximum according todata legislation. It is necessary to respect the hygienic-sanitary norms related tocultivation, preservation and care of the milking herds, and with the righttechnology of milking. After 45 days of ripening the researchers recorded theaverage results for the following parameters of the ‘’Bieno’’ cheese: moisture(38.63%), dry matter (61.37%), milk fat (26.89%), milk fat in dry matter (43.83%),proteins (26.53%), ash (9.25%), salt (5.21%) and the average of yield (9.36%). Theresearch specified the microbiological quality of “bieno” cheese after 45 days ofripening in accordance with the special requirements of food safety regarding themicrobiological criteria. Nowadays, there is a growing interest of consumers forcheeses produced with traditional technologies, usually based on handmadeproduction, characterized by piquant, unique and specific aromas, atypical forindustrial cheeses. The technology, physico-chemical and microbiological qualitycould be used in the protection of the origin and geographical labelling based on itsunique technology. The data obtained serve as the basis for creating standardizedproduction procedures, leading to the uniform quality of these products.

scholarly journals Feature Selection using K-Means Genetic Clustering To Predict Rheumatoid Arthritis Disease

2019 ◽  
Vol 8 (3) ◽  
pp. 7020-7023
Keyword(s):  
Rheumatoid Arthritis ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Learning Algorithm ◽  
Real Life ◽  
Common Disease ◽  
Rheumatoid Arthritis Disease

In our Society, Aging society plays serious problems in health and medical care. When compared to other diseases in the real life Rheumatoid Arthritis disease is a common disease, Rheumatoid Arthritis is a disease that causes pain in musculoskeletal system that affect the quality of the people. Rheumatoid Arthritis is onset at middle age, but can affect children and young adults. If the disease is not monitored and treated as early as possible, it can cause serious joint deformities. Cluster analysis is an unsupervised learning technique in data mining for identifying or exploring out the structure of data without known about class label. Many clustering algorithms were proposed to analyze high volume of data, but many of them not evaluate cluster’s quality because of inconvenient features presented in the dataset. Feature selection is a prime task in data analysis in case of high dimensional dataset. Optimal subsets of features are enough to cluster the data. In this study, Rheumatoid Arthritis clinical data were analyzed to predict the patient affected with Rheumatoid Arthritis disease. In this study, KMeans clustering algorithm was used to predict the patient affected with Rheumatoid Arthritis Disease. Genetic algorithm is used to filter the feature and at the end of the process it finds optimal clusters for k-Means clustering algorithm. Based on the initial centroid , K-Means algorithm may have the chance of producing empty cluster. K-means does not effectively handle the outliers or noisy data in the dataset. K-means algorithm when combined with Genetic Algorithm shows high performance quality of clustering and fast evolution process when compared with K-Means alone. In this paper, to diagnosis Rheumatoid Arthritis disease we use machine learning algorithm FSKG. A predictive FSKG model is explored that diagnoses rheumatoid arthritis. After completing data analysis and pre-processing operations, Genetic Algorithm and K-Means Clustering Algorithm are integrated to choose correct features among all the features. Experimental Results from this study imply improved accuracy when compared to k-means algorithm for rheumatoid disease prediction.

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