scholarly journals Rapid Microbial Quality Assessment of Chicken Liver Inoculated or Not With Salmonella Using FTIR Spectroscopy and Machine Learning

2021 ◽  
Vol 11 ◽  
Author(s):  
Dimitra Dourou ◽  
Athena Grounta ◽  
Anthoula A. Argyri ◽  
George Froutis ◽  
Panagiotis Tsakanikas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spectral Analysis ◽  
Quantitative Estimation ◽  
Meat Product ◽  
Microbiological Quality ◽  
Viable Count ◽  
Chicken Liver ◽  
Support Vector ◽  
Microbiological Analysis ◽  
Analysis Workflow

Chicken liver is a highly perishable meat product with a relatively short shelf-life and that can get easily contaminated with pathogenic microorganisms. This study was conducted to evaluate the behavior of spoilage microbiota and of inoculated Salmonella enterica on chicken liver. The feasibility of Fourier-transform infrared spectroscopy (FTIR) to assess chicken liver microbiological quality through the development of a machine learning workflow was also explored. Chicken liver samples [non-inoculated and inoculated with a four-strain cocktail of ca. 103 colony-forming units (CFU)/g Salmonella] were stored aerobically under isothermal (0, 4, and 8°C) and dynamic temperature conditions. The samples were subjected to microbiological analysis with concomitant FTIR measurements. The developed FTIR spectral analysis workflow for the quantitative estimation of the different spoilage microbial groups consisted of robust data normalization, feature selection based on extra-trees algorithm and support vector machine (SVM) regression analysis. The performance of the developed models was evaluated in terms of the root mean square error (RMSE), the square of the correlation coefficient (R2), and the bias (Bf) and accuracy (Af) factors. Spoilage was mainly driven by Pseudomonas spp., followed closely by Brochothrix thermosphacta, while lactic acid bacteria (LAB), Enterobacteriaceae, and yeast/molds remained at lower levels. Salmonella managed to survive at 0°C and dynamic conditions and increased by ca. 1.4 and 1.9 log CFU/g at 4 and 8°C, respectively, at the end of storage. The proposed models exhibited Af and Bf between observed and predicted counts within the range of 1.071 to 1.145 and 0.995 to 1.029, respectively, while the R2 and RMSE values ranged from 0.708 to 0.828 and 0.664 to 0.949 log CFU/g, respectively, depending on the microorganism and chicken liver samples. Overall, the results highlighted the ability of Salmonella not only to survive but also to grow at refrigeration temperatures and demonstrated the significant potential of FTIR technology in tandem with the proposed spectral analysis workflow for the estimation of total viable count, Pseudomonas spp., B. thermosphacta, LAB, Enterobacteriaceae, and Salmonella on chicken liver.

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.

Irradiation of Chewable Tobacco Mixes for Improvement in Microbiological Quality

1999 ◽  
Vol 62 (6) ◽  
pp. 678-681 ◽  
Author(s):  
RAHUL G. WARKE ◽  
ANU S. KAMAT ◽  
MADHUSUDAN Y. KAMAT
Keyword(s):  
Room Temperature ◽  
Microbiological Quality ◽  
Viable Count ◽  
Microbiological Analysis ◽  
Total Viable Count ◽  
Salmonella Spp ◽  
Chewable Tobacco ◽  
Sterilization Dose ◽  
Mold Count

Microbiological quality of chewable tobacco mixes traditionally known as “Gutkha” was studied. The microbiological analysis of 15 samples analyzed revealed high bacterial and fungal counts. The total viable counts were in the range of 1.8 × 104 to 7.2 × 104 CFU g−1 and the yeast and mold count from 3.6 × 103 to 7.1 × 104 CFU g−1. The proteolytic and lipolytic counts were 9 × 102 to 2.6 × 103 CFU g−1 and 2.6 × 103 CFU g−1, on an average, respectively. Lecithinase-positive Staphylococcus aureus was found in 2 of the 15 samples analyzed; the counts were up to 3.4 × 103 CFU g−1. Coliform and Salmonella spp. were found to be absent. Aflatoxins B1, B2, and G2 were found to be present in all the samples. These samples were exposed to gamma radiation (60Co) at 1-, 2-, 3-, 5-, 10-, and 25-kGy doses. The decrease in total viable count and fungal count was noticed with increase of radiation dose. The 3-kGy dose was observed to be the sterilization dose for Gutkha. At this dose no survival of organisms was noticed and no revival was observed during postirradiation storage at room temperature for 6 months.

scholarly journals Microbiological analysis of pasteurized and fresh fruit juice sold in Rawalpindi of Pakistan

2013 ◽  
Vol 48 (3) ◽  
pp. 185-192 ◽  
Author(s):  
SA Batool ◽  
SS Tahir ◽  
N Rauf ◽  
R Kalsoom
Keyword(s):  
Fruit Juice ◽  
Microbiological Quality ◽  
Total Coliform ◽  
Viable Count ◽  
Fruit Juices ◽  
Local Market ◽  
Microbiological Analysis ◽  
E Coli ◽  
Bacteria And Fungi

Freshly prepared and pasteurized fruit juices sold by vendors in local market of Rawalpindi city from 10 locations were analyzed for the microbiological quality. Total viable count (TVC), total coliform, faecal coliform, molds and the presence of pathogenic microorganisms such as E. coli, Pseudomonas, Staphylococcus aureus, Salmonella, and fungi like Aspergillus, Pencillum, Rhizopus were determined .In open fruit juices available in city were highly contaminated with bacteria and fungi. E. coli, Salmonella, Staphylococcus, and Pseudomonas were isolated with different frequency. Aspergillus, Pencillum and Rhizopus were also found in juices especially Aspergillus was with high percentages. The pasteurized juices have less contamination as compared to the fresh juices samples. The number and type of microorganisms recovered from the freshly squeezed fruit juices made them unsafe for drinking. The results of this study demonstrate the unhygienic quality of popular types of market vended freshly squeezed fruit juices and their risk to the consumers. DOI: http://dx.doi.org/10.3329/bjsir.v48i3.17329 Bangladesh J. Sci. Ind. Res. 48(3), 185-192, 2013

scholarly journals Comparative microbiological analysis of raw fishes and sun-dried fishes collected from the Kawran bazaar in Dhaka city, Bangladesh

Food Research ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 846-851
Author(s):  
I.T. Nur ◽  
B. Ghosh ◽  
M. Acharjee
Keyword(s):  
Pathogenic Bacteria ◽  
Microbiological Quality ◽  
Total Coliform ◽  
Viable Count ◽  
Quality Analysis ◽  
Microbiological Analysis ◽  
Total Viable Count ◽  
Salmonella Spp ◽  
E Coli ◽  
Coliform Count

Along with the raw fishes, dry fishes also have a huge contribution to meet up the demand of protein in our daily meal. The assay of microbiological quality is therefore needed to ensure the public health safety. The present study was emphasized on the existence of pathogenic bacteria in raw and dry fish. A total of 50 samples of raw fishes and sun-dried fishes was accumulated aseptically for microbiological quality analysis. Isolation of bacteria was done by spread plate method. All the samples including both (raw and dry) fishes harbored bacteria and fungi up to 106 CFU/g. E. coli was found in all samples as a specific pathogen. In case of raw fishes total viable count (TVC) and total coliform count (E. coli) were recorded up to 2.5x106 CFU/g and 5.2 x104 CFU/g respectively whereas a significant load of Salmonella spp. was observed in almost all samples. Staphylococcus spp. and Pseudomonas spp. were present up to 5 x102 CFU/g and 1.8 x 102 CFU/g respectively. Likewise, total viable count (TVC), total coliform count (E. coli) and fungal load were recorded in dry fish up 3.50 x 105 CFU/g, 1.2 x103 CFU/g respectively. Fungal growth was observed in all experimental raw and dried fishes. For most of the pathogenic isolates, higher rates of resistance were found against Ceftriaxone, Penicillin, Nalidixic acid, Neomycin. On the other hand, most of the isolates were found to retain higher sensitivity against Imipenem, Ciprofloxacin, Tetracyclin and Amoxicillin. This data suggested that the dry fish harbored fewer bacteria than raw fish and sun drying method is still a useful technique for the preservation of fish.

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

Structure-Based Virtual Screening of Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) as Endocrine Disruptors of Androgen Receptor Activity Using Molecular Docking and Machine Learning

2020 ◽  
Author(s):  
Azhagiya Singam Ettayapuram Ramaprasad ◽  
Phum Tachachartvanich ◽  
Denis Fourches ◽  
Anatoly Soshilov ◽  
Jennifer C.Y. Hsieh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Docking ◽  
Androgen Receptor ◽  
Endocrine Disruptors ◽  
Hormone Receptors ◽  
Steroid Hormone Receptors ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Polyfluoroalkyl Substances ◽  
Perfluoroalkyl And Polyfluoroalkyl Substances

Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) pose a substantial threat as endocrine disruptors, and thus early identification of those that may interact with steroid hormone receptors, such as the androgen receptor (AR), is critical. In this study we screened 5,206 PFASs from the CompTox database against the different binding sites on the AR using both molecular docking and machine learning techniques. We developed support vector machine models trained on Tox21 data to classify the active and inactive PFASs for AR using different chemical fingerprints as features. The maximum accuracy was 95.01% and Matthew’s correlation coefficient (MCC) was 0.76 respectively, based on MACCS fingerprints (MACCSFP). The combination of docking-based screening and machine learning models identified 29 PFASs that have strong potential for activity against the AR and should be considered priority chemicals for biological toxicity testing.

IMPLEMENTASI SUPPORT VECTOR MACHINE PADA PREDIKSI HARGA SAHAM GABUNGAN (IHSG)

2020 ◽  
Vol 25 (1) ◽  
pp. 24-38
Author(s):  
Eka Patriya
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector Regression ◽  
Support Vector

Saham adalah instrumen pasar keuangan yang banyak dipilih oleh investor sebagai alternatif sumber keuangan, akan tetapi saham yang diperjual belikan di pasar keuangan sering mengalami fluktuasi harga (naik dan turun) yang tinggi. Para investor berpeluang tidak hanya mendapat keuntungan, tetapi juga dapat mengalami kerugian di masa mendatang. Salah satu indikator yang perlu diperhatikan oleh investor dalam berinvestasi saham adalah pergerakan Indeks Harga Saham Gabungan (IHSG). Tindakan dalam menganalisa IHSG merupakan hal yang penting dilakukan oleh investor dengan tujuan untuk menemukan suatu trend atau pola yang mungkin berulang dari pergerakan harga saham masa lalu, sehingga dapat digunakan untuk memprediksi pergerakan harga saham di masa mendatang. Salah satu metode yang dapat digunakan untuk memprediksi pergerakan harga saham secara akurat adalah machine learning. Pada penelitian ini dibuat sebuah model prediksi harga penutupan IHSG menggunakan algoritma Support Vector Regression (SVR) yang menghasilkan kemampuan prediksi dan generalisasi yang baik dengan nilai RMSE training dan testing sebesar 14.334 dan 20.281, serta MAPE training dan testing sebesar 0.211% dan 0.251%. Hasil penelitian ini diharapkan dapat membantu para investor dalam mengambil keputusan untuk menyusun strategi investasi saham.

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

Sign in / Sign up

Export Citation Format

Share Document