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

Abstract

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.

Reliable e-nose for air toxicity monitoring by filter diagonalization method

“…To prevent products' adulteration, Świgło and Chmielewski [5] proposed an e-nose to assist in the authenticity testing of products such as meat, honey, milk, and plant oils. To discourage meat dealers from committing food fraud, Laga and Sarno [6] presented an e-nose discriminating pork from beef. Wang et al [7] deployed an e-nose inside a domestic refrigerator to assess the food freshness level of fruits, vegetables, and meat.…”

Freshness assessment of tilapia fish in traditional market based on an electronic nose

“…Since aroma still becomes the main parameter in food products quality control, the use of aroma meters such as an e-nose will increase in the future, including in the determination of fish products quality. Currently e-nose is widely used in various fields such as quality control of food products [9][10], assessment and classification of meat freshness [11]- [13], determination of fish freshness [8], classification of fruits [14], characterization of tea aroma [15]- [16], identification and classification of coffee aroma [17]- [25], identification of environmental quality [26], and medical purposes such as identification of respiratory disease [27]. E-nose produces a specific response when a sample of aroma compound is exposed on the sensor array headspace.…”

Naive Bayes modification for intrusion detection system classification with zero probability

“…This algorithm can be used for simple classification with fixed Y variable and also for text classification [14]- [16]. Laga and Sarno [17] showed that Naïve Bayes gave the best accuracy from other classification methods, such as KNN, SVM, and random forest. However, the Naïve Bayes algorithm still has a drawback, that is, if the probability value from one of the variables is 0, it can make the final comparison result 0, which can lead to inaccurate prediction results [15], [17]- [20].…”

“…Laga and Sarno [17] showed that Naïve Bayes gave the best accuracy from other classification methods, such as KNN, SVM, and random forest. However, the Naïve Bayes algorithm still has a drawback, that is, if the probability value from one of the variables is 0, it can make the final comparison result 0, which can lead to inaccurate prediction results [15], [17]- [20]. Research [15], [17] overcomes zero probability with RB-Bayes, while research [20] uses Hybrid N-gram, and research [19], [20] uses multinomial Naïve Bayes.…”

“…Based on the previous research [15], [17]- [20], it can be seen if the prediction results from the testing data are not found due to the opportunity 0. Therefore, it is necessary to modify the Naïve Bayes algorithm to overcome the existing problems.…”