Freshness Assessment of Broccoli using Electronic Nose

Ezhilan, Madeshwari; Nesakumar, Noel; Babu, K. Jayanth; Srinandan, C. S.; Rayappan, John Bosco Balaguru

doi:10.1016/j.measurement.2019.06.005

Cited by 39 publications

(19 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Some researches did not calculate these values [6,16,32,33], which could actually improve performance measurement. SVM produced an average TPR and TNR of 92% and 96%, as can be seen in Table 7.…”

Section: Discussionmentioning

confidence: 99%

“…The e-nose is an electronic instrument that has the ability to emulate the human nose in detecting smells. This tool can be used in many fields, for example in biomedicine for detecting diabetes [1][2][3], in the foodindustry [4][5][6], for environmental monitoring [7], in agriculture [8], for detecting mixtures of food ingredients [9,10], and detecting the methane gas content of chili [11]. An e-nose consists of a nonselective chemical gas sensor array to capture and transform smell into electric signals or sensor responses.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing Threshold using Pearson Correlation for Selecting Features of Electronic Nose Signals

Sabilla¹,

Sarno²,

Triyana³

2019

IJIES

View full text Add to dashboard Cite

This research studied the use of sensors in an electronic-nose (e-nose) system to select the most efficient features to detect three classes of data signals. The data signals were collected from sensors that were assembled specifically for recognition of meat types. The development of e-noses has been the subject of many researches. However, researchers tend to install multiple sensors without optimizing their utilization, leading to higher costs. This research proposes three significant contributions to optimize the number of sensors to be used in e-noses, i.e. (i) tuning the threshold of the correlation coefficient; (ii) obtaining significant features by determining the optimal threshold of independent features; (iii) optimizing efficiency by considering the significant features and the accuracy of the results. The proposed efficiency method suggested that the number of sensors in the system could be reduced from 10 to 5. The optimal threshold suggested that 12 out of 20 features were correlated to each other. The highest accuracy obtained in this research was 92% for three classes of meats.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing Threshold using Pearson Correlation for Selecting Features of Electronic Nose Signals

Sabilla¹,

Sarno²,

Triyana³

2019

IJIES

View full text Add to dashboard Cite

show abstract

“…During PCA, orthogonal transformation is used to convert the original data set into a set of values of linearly uncorrelated variables called principal components (Collins et al, 2013). In this study, electronic nose data software was used to conduct the PCA (Ezhilan et al, 2019). (Wang, He, et al, 2020).…”

Section: Electronic Nose Analysismentioning

confidence: 99%

“…Research has proved that electronic nose combined with PCA could distinguish different types of damage in tea plants (Sun et al, 2018). Ezhilan et al (2019) In addition to the sensor analysis, we also analyzed the volatile components in each sample (Figure 4). Through the radar chart analysis of nine samples, we found that the volatile components of the nine samples were mainly concentrated on sensors W5S and W1W.…”

Section: Electronic Nose Technology Analysismentioning

confidence: 99%

Optimization of celery leaf tea processing and the volatile components analysis

Liu

Cheng

Zhang

et al. 2021

J. Food Process. Preserv.

View full text Add to dashboard Cite

In this study, celery leaves were used as materials to study the effects of different processing techniques on the quality of celery leaf tea. Sensory evaluation, GC-MS, and electronic nose analysis were used to identify the main aroma components of nine different processing techniques of tea. In sensory evaluation, sample No. 2 with green tea processing technology (fragrance enhancement at 80°C for 6 hr) was better popular in the processing of green tea. While the tea sample of No. 7 with oolong tea processing technology (fragrance enhancement at 80°C for 6 hr) was most suitable for the sense of the majority of the population. In the electronic nose measurement, the contents of aroma components in samples No. 2 and No. 7 were better than other samples. A total of 51 key aroma compounds were detected by GC-MS that could basically reflect the aroma characteristics of celery leaf tea.

show abstract

“…Electronic noses are systems that can contain different types of chemical gas sensors. In recent years, this kind of device has received considerable attention for its potentialities in various fields, with excellent results [8][9][10][11][12][13]. Furthermore, compared with such classical chemical techniques as gas chromatography (GC), they are more rapid, cheaper, more portable, and more user-friendly.…”

Section: Introductionmentioning

confidence: 99%

An Array of MOX Sensors and ANNs to Assess Grated Parmigiano Reggiano Cheese Packs’ Compliance with CFPR Guidelines

et al. 2020

View full text Add to dashboard Cite

Parmigiano Reggiano cheese is one of the most appreciated Italian foods on account of its high nutrient content and taste. Due to its high cost, these characteristics make this product subject to counterfeiting in different forms. In this study, an approach based on an array of gas sensors has been employed to assess if it was possible to distinguish different samples based on their aroma. Samples were characterized in terms of rind percentage, seasoning, and rind working process. From the responses of the sensors, five features were extracted and the capability of these parameters to recognize target classes was tested with statistical analysis. Hence, the performance of the sensors’ array was quantified using artificial neural networks. To simplify the problem, a hierarchical approach has been used: three steps of classification were performed, and in each step one parameter of the grated cheese was identified (firstly, seasoning; secondly, rind working process; finally, rind percentage). The accuracies ranged from 88.24% to 100%.

show abstract

Freshness Assessment of Broccoli using Electronic Nose

Cited by 39 publications

References 31 publications

Optimizing Threshold using Pearson Correlation for Selecting Features of Electronic Nose Signals

Optimizing Threshold using Pearson Correlation for Selecting Features of Electronic Nose Signals

Optimization of celery leaf tea processing and the volatile components analysis

An Array of MOX Sensors and ANNs to Assess Grated Parmigiano Reggiano Cheese Packs’ Compliance with CFPR Guidelines

Contact Info

Product

Resources

About