Monitoring industrial hydrogenation of soybean oil using self-organizing maps

Sánchez, Jorge; Pereira, Sérgio Benedito Gonçalves; Tanamati, Augusto; Tanamati, Ailey Aparecida Coelho

doi:10.9755/ejfa.2019.v31.i10.2019

Cited by 8 publications

(9 citation statements)

References 37 publications

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“…Earlier, several authors reported SOM for describing the features of food materials particularly in the field of sensory analysis. Sanchez et al, 2019 andMilovanovic et al, 2019 had studied the hydrogenation of soybean oil and the classification of different wine products based on organic acid respectively. [36][37]3] Though it is the first kind of approach, in terms of classification by food compositional analysis, sensory attributes and physical characterization like textural and color analysis through SOM.…”

Section: 𝑊 = 𝛽𝑦 − 𝛾𝑊[𝑊 𝑈 𝑦]mentioning

confidence: 99%

Comparative Classification Efficiency of Self-organizing Map, Principal Component Analysis, and Hierarchical Cluster Analysis for Normal Dairy and Differently Pineapple Fortified Rasgulla

Sarkar¹,

Salauddin²,

Hazra³

et al. 2020

ES Food Agrofor.

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Rasgulla, the popular Indian dairy-based dessert, was fortified by using pineapple to improve its nutritional and different physical parameters like color, texture and sensory attributes. Being a highly nutritious, palatable fruit with excellent flavor and visual color characteristics, pineapple fortification provides an edge over normal dairy rasgulla. Comparative analysis of nutritional, X-ray diffraction, texture and sensory characteristics for normal rasgulla sample and pineapple with different drying process (pulp (PP), convective (PH), microwave (PMW), microwave convective (PMWC), and freeze-dried (PF)) fortified rasgulla samples were done. PH was found with maximum carbohydrate (40.60±2.04) and minimum protein content (5.91±0.95). PMWC was found with the maximum total color difference (ΔE = 48.79±3.33). Pineapple fortification did not alter the crystal characteristics of the rasgulla samples. Normal rasgulla showed the lowest hardness value while PMWC was the hardest one. The results obtained from the analysis were further comparatively segregated with the help of principal component analysis, hierarchical cluster analysis and self-organizing map to optimize the analysis frequency towards a more accurate result. It was found that more or less the same trends in results were obtained by all three analysis tools. It can be concluded that machine learning-based self-organizing map can be more sophistically used for classification between different parameters even in case of complex non-linear problems also. Among the entire samples microwave, convective dried pineapple rasgullawas found to be the best in terms of all the observed parameters.

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Section: 𝑊 = 𝛽𝑦 − 𝛾𝑊[𝑊 𝑈 𝑦]mentioning

confidence: 99%

Comparative Classification Efficiency of Self-organizing Map, Principal Component Analysis, and Hierarchical Cluster Analysis for Normal Dairy and Differently Pineapple Fortified Rasgulla

Sarkar¹,

Salauddin²,

Hazra³

et al. 2020

ES Food Agrofor.

View full text Add to dashboard Cite

show abstract

“…It is also possible to visualize the results by the weight maps or weight plan through a level contour graph, representing each variable’s influence for the sample segmentation. Together with the topological map, they observe behavior rules for each group formed and infer each variable’s influence on the result obtained [ 25 ]. For the weight maps build, the w j values for each variable were interpolated by “nearest” and “spline” functions (in Matlab) to regularize the hexagonal grid, using the automatic arrangement of the samples obtained from the topological map.…”

Section: Methodsmentioning

confidence: 99%

The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks

et al. 2021

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Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country’s measures, which were implemented to contain the virus’ spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus’ spread in these cities, states, and regions.

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“…SOM has already been used successfully for data mining in several areas of knowledge [24,[26][27][28][29][30], like food science [26,28,29], fuels [27], monitoring chemical reactions [24,30], and other applications [25,31,32]. Recently, SOM was used to verify the spatial relationship of the COVID-19 spread in countries and states in Mexico [31].…”

Section: Of 16mentioning

confidence: 99%

Can Socioeconomic, Health, and Safety Data Explain the Spread of COVID-19 Outbreak on Brazilian Federative Units?

Galvan

Effting

Cremasco

et al. 2020

IJERPH

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Infinite factors can influence the spread of COVID-19. Evaluating factors related to the spread of the disease is essential to point out measures that take effect. In this study, the influence of 14 variables was assessed together by Artificial Neural Networks (ANN) of the type Self-Organizing Maps (SOM), to verify the relationship between numbers of cases and deaths from COVID-19 in Brazilian states for 110 days. The SOM analysis showed that the variables that presented a more significant relationship with the numbers of cases and deaths by COVID-19 were influenza vaccine applied, Intensive Care Unit (ICU), ventilators, physicians, nurses, and the Human Development Index (HDI). In general, Brazilian states with the highest rates of influenza vaccine applied, ICU beds, ventilators, physicians, and nurses, per 100,000 inhabitants, had the lowest number of cases and deaths from COVID-19, while the states with the lowest rates were most affected by the disease. According to the SOM analysis, other variables such as Personal Protective Equipment (PPE), tests, drugs, and Federal funds, did not have as significant effect as expected.

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Monitoring industrial hydrogenation of soybean oil using self-organizing maps

Cited by 8 publications

References 37 publications

Comparative Classification Efficiency of Self-organizing Map, Principal Component Analysis, and Hierarchical Cluster Analysis for Normal Dairy and Differently Pineapple Fortified Rasgulla

Comparative Classification Efficiency of Self-organizing Map, Principal Component Analysis, and Hierarchical Cluster Analysis for Normal Dairy and Differently Pineapple Fortified Rasgulla

The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks

Can Socioeconomic, Health, and Safety Data Explain the Spread of COVID-19 Outbreak on Brazilian Federative Units?

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