Optimal Sample Temperature of Electronic Nose For Detecting Beef and Pork Mixture

Laga, Sinarring Azi; Sarno, Riyanarto

doi:10.1109/icoiact46704.2019.8938492

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Strong demand and strong price of raw foodstuffs like beef was commonly used in conventional markets by beef dealers committing deception to gain more income. The fraud took the form of combining beef and pork [1]. In Indonesia, this has been a issue of food health in recent years.…”

Section: Introductionmentioning

confidence: 99%

Temperature effect of electronic nose sampling for classifying mixture of beef and pork

Laga

Sarno

2020

IJEECS

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Strong demand and strong price of raw foodstuffs like beef was commonly used in conventional markets by beef dealers to commit fraud in order to gain larger income. The fraud has been in the form of combining beef and pork. In Indonesia, this has been a issue of food health in recent years. Via scent, some food safety concerns can be expected. By using electronic nose that is equipped with electrochemical and air sensors such as temperature sensors, strain, and humidity to find the pure beef or mixed beef. According to its selectivity, the sensor can detect gas to make small icurrents that are the result of chemical sensor and gas interactions with oxygen .In this study, the classification method k-NN, SVM, Naïve Bayes, and Random Forest was used in 5 different meat variations with a ratio of 0%, 10%, 50%, 90% and 100% with temperatures of -22°C, Room Temp., And 55 ° C. The results showed the effect of temperature on increasing the accuracy, which is at a temperature of -22 ° C. The lower the temperature, the more stable the value obtained by electronic nose. At a temperature of -22 ° C, the method that produces the highest accuracy is the Random Forest method.

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Section: Introductionmentioning

confidence: 99%

Temperature effect of electronic nose sampling for classifying mixture of beef and pork

Laga

Sarno

2020

IJEECS

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“…The coefficient of determination (R2) between sensor data and minced pork ratios is 0.9762. To study and find the optimal temperature required for the measurement sample to be tested using E-nose for discriminating beef–pork mixtures, a sensory array made of eight MOS sensors with temperature and humidity sensors is designed [ 45 , 51 ]. Five beef–pork mixture classes are discriminated in this study using a support vector machine (SVM), k-nearest neighbors (KNN), random forest, and naive Bayes techniques at −22 °C, room temperature, and 55 °C, respectively.…”

Section: Introductionmentioning

confidence: 99%

Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose

Pulluri

Kumar

2022

Sensors

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Food adulteration is the most serious problem found in the food industry as it harms people’s healths and undermines their beliefs. The present study is focused on designing and developing a smart electronic nose (SE-Nose) for the qualitative and quantitative fast-track detection of food adulteration. The SE-Nose methodology is comprised of a dataset, sample slicing window protocol, normalization, pattern recognition, and output blocks. The dataset pork adulteration in beef is used to validate the SE-Nose methodology. The sample slicing window protocol extracts the early part of the signal. The sample slicing window protocol and pattern recognition models (classification and regression models) together achieved the high-performance and fast-track detection of pork adulteration in beef. With classification models, the qualitative analysis of adulteration is measured, and with regression models, the quantitative analysis of adulteration is measured. An accuracy of 99.996% and an RMSE of 0.02864 were achieved with the SVM classification and regression model. The recognition time in detecting pork adulteration in beef with SVM models is 40 s. With the proposed SE-Nose methodology, the recognition time is reduced by one-third. To validate the classification and regression models, a 10-fold cross-validation method was used.

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The effect of gas concentration on detection and classification of beef and pork mixtures using E-nose

Wakhid

Sarno

Sabilla

2022

Computers and Electronics in Agriculture

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Optimal Sample Temperature of Electronic Nose For Detecting Beef and Pork Mixture

Cited by 4 publications

References 8 publications

Temperature effect of electronic nose sampling for classifying mixture of beef and pork

Temperature effect of electronic nose sampling for classifying mixture of beef and pork

Qualitative and Quantitative Detection of Food Adulteration Using a Smart E-Nose

The effect of gas concentration on detection and classification of beef and pork mixtures using E-nose

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