Prediction of Microbial Population in Meat Using Electronic Nose and Support Vector Regression Algorithm

Hibatulah, Rizky Pratama; Wijaya, Dedy Rahman; Wikusna, Wawa

doi:10.1109/icisit54091.2022.9872829

Cited by 3 publications

(2 citation statements)

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“…Because the boundary can be very complex, attention should be paid to the problem of overfitting. SVM was originally used for classification purposes, but its principles can be extended easily to the task of nonlinear regression by introducing an alternative loss and has already been applied successfully in other industry sectors, such as the food industry (Hibatulah et al, 2022; Upadhyay et al, 2022), but also for prediction of mobile phase behaviors in chromatographic analysis of proteins (Ladiwala et al, 2003; Rege et al, 2004; Yang et al, 2007). The basic idea of SVM regression is to map the original data set into the mapped data set in a high dimensional feature space via a nonlinear mapping function (so‐called kernel functions) and then perform a linear regression in this feature space.…”

Section: Statistical Modeling Conceptsmentioning

confidence: 99%

Sensors and chemometrics in downstream processing

Dürauer

Jungbauer

Scharl

2023

Biotech & Bioengineering

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The biopharmaceutical industry is still running in batch mode, mostly because it is highly regulated. In the past, sensors were not readily available and in‐process control was mainly executed offline. The most important product parameters are quantity, purity, and potency, in addition to adventitious agents and bioburden. New concepts using disposable single‐use technologies and integrated bioprocessing for manufacturing will dominate the future of bioprocessing. To ensure the quality of pharmaceuticals, initiatives such as Process Analytical Technologies, Quality by Design, and Continuous Integrated Manufacturing have been established. The aim is that these initiatives, together with technology development, will pave the way for process automation and autonomous bioprocessing without any human intervention. Then, real‐time release would be realized, leading to a highly predictive and robust biomanufacturing system. The steps toward such automated and autonomous bioprocessing are reviewed in the context of monitoring and control. It is possible to integrate real‐time monitoring gradually, and it should be considered from a soft sensor perspective. This concept has already been successfully implemented in other industries and requires relatively simple model training and the use of established statistical tools, such as multivariate statistics or neural networks. This review describes a scenario for integrating soft sensors and predictive chemometrics into modern process control. This is exemplified by selective downstream processing steps, such as chromatography and membrane filtration, the most common unit operations for separation of biopharmaceuticals.

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Section: Statistical Modeling Conceptsmentioning

confidence: 99%

Sensors and chemometrics in downstream processing

Dürauer

Jungbauer

Scharl

2023

Biotech & Bioengineering

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“…The Support Vector Machine Regression method was used in training and testing the data obtained from an e-nose. As a result of the experiments, they obtained high R 2 and RMSE values [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Beef Quality Classification with Reduced E-Nose Data Features According to Beef Cut Types

Feyzioğlu

Taşpınar

2023

Sensors

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Ensuring safe food supplies has recently become a serious problem all over the world. Controlling the quality, spoilage, and standing time for products with a short shelf life is a quite difficult problem. However, electronic noses can make all these controls possible. In this study, which aims to develop a different approach to the solution of this problem, electronic nose data obtained from 12 different beef cuts were classified. In the dataset, there are four classes (1: excellent, 2: good, 3: acceptable, and 4: spoiled) indicating beef quality. The classifications were performed separately for each cut and all cut shapes. The ANOVA method was used to determine the active features in the dataset with data for 12 features. The same classification processes were carried out by using the three active features selected by the ANOVA method. Three different machine learning methods, Artificial Neural Network, K Nearest Neighbor, and Logistic Regression, which are frequently used in the literature, were used in classifications. In the experimental studies, a classification accuracy of 100% was obtained as a result of the classification performed with ANN using the data obtained by combining all the tables in the dataset.

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