The effect of data fusion on improving the accuracy of olive oil quality measurement

Highlights

• •Olive oil samples classified and their quality evaluated by ultrasound and E-Nose.
• •Seven different machine learning algorithms applied.
• •Fusion of ultrasound and E-nose data, leads to improving the quality evaluation.
• •The impact of ultrasonic data was higher than that of the odor data.
• •Artificial neural network (with 95.51% accuracy) was the best algorithm.

Abstract

Olive oil is one of the healthiest and most nutritious edible oils, and it has a great potential to be adulterated. In this research, fraud samples of olive oil were detected with six different classification models by fusion of two methods of E-nose and ultrasound. The samples were prepared in six categories of adulteration. The E-nose system included eight various sensors. 2 MHz probes were used in through transmission ultrasound system. Principal Component Analysis method was used to reduce features and six classification models were used for classification. Feature with the greatest influence in the classification was “percentage of ultrasonic amplitude loss.” It was found that the ultrasound system’s data had worked more effectively than the E-nose system. Results showed that the ANN method was recognized as the most effective classifier with the highest accuracy (95.51%). The accuracy of classification in all the classification models significantly increased with data fusion.

Introduction

The olive (Olea europaea L.) is a species of small tree or shrub in the family Oleaceae, found traditionally in the Mediterranean Basin. The healthy fats of olives are extracted to produce olive oil. This is one of the main components of a healthy Mediterranean diet, where 90% of olives are used to make olive oil.

Many studies show that it prevents cardiovascular diseases, obesity, diabetes and various types of cancer (Battino et al., 2019, Borzi et al., 2019, Escrich et al., 2006, Gill et al., 2005, Owen et al., 2004).

Regarding edible oils, an E-nose has been used to classify different types of oils according to other varieties, detect adulterations and evaluate the extent of oxidation (Majchrzak et al., 2018).

There are different methods to evaluate the quality of edible oils, among which can be mentioned: thermometry (Rodríguez et al., 2020, Van Wetten et al., 2015), machine vision (Gila et al., 2020, Jafari et al., 2014), spectroscopy (El Orche et al., 2020, Ok, 2017), electronic nose machine (Ordukaya and Karlik, 2017, Sanaeifar et al., 2014), sound and ultrasound (Zarezadeh et al., 2021, Fathizadeh et al., 2020, Zarezadeh et al., 2022), the sensory method by trained people called Taster (Fuentes et al., 2015).

Electronic nose systems have advantages in comparison to other mentioned systems, including fast analysis, non-destructiveness, application in portable devices, and user-friendliness. There are some studies for classifying and fraud detection in olive fruit using electronic nose (Sánchez et al., 2021, Sánchez et al., 2022, Gila et al., 2020) (Table 1).

Table 1.

Examples of food quality measurement by combining data from two or more methods.

Sample	Evaluation Method	Description	Reference
Extra virgin olive oil	Ultrasound & E-nose	Quality evaluation & fraud detection	Zarezadeh et al., 2022
Meat	Spectroscopy & E-nose	quantitation of fat and peroxide value of pork meat	Aheto et al. 2020
Sesame oil	Dielectric spectroscopy & chemometry	Rapid identification and quantification of sesame oils adulteration	Firouz et al. 2020
Saffron	IRMS & GC–MS & E-Nose	discrimination of saffron samples with different origin, process and age	Rocchi et al. 2019
Olive oil	Dielectric spectroscopy & Machine vision	quality characterization of olive oil during storage	Sanaeifar et al., 2014
Extra virgin olive oil	Mid infrared & Raman spectroscopy	Adulteration detection	Georgouli et al. 2017
Olive oil	Electronic nose and tongue	Oil characterization	Apetrei et al. 2016
Tea	Electronic nose and tongue	Adulteration detection	Roy et al. 2014
Edible oil	Electronic nose and tongue	Detection of Mixed Edible-Oil	Men et al. 2014
Olive oil	visible spectroscopic & fingerprints & chemical descriptors	Classification of oils	Pizarro et al. 2013
Wine	Electronic nose and tongue and vision	Monitoring of evolution during red wine aging	Apetrei et al. 2012

Blending oils of mean and low nutritional value with extra virgin olive oil causes changes in physical properties such as density and homogeneity, which has a direct effect on the velocity, attenuation and refractive index of waves. A diagnostic ultrasound system with a machine learning algorithm can easily detect different purity of extra virgin olive oil.

To achieve a complete and comprehensive understanding of a subject, researchers are responsible for observing all aspects and dimensions of the phenomenon under study. Therefore, the subject of data fusion is presented as an essential methodological strategy the purpose of which is to combine and entangle collected data and information around the practitioner under study. The fusion of information leads to synergy of data, determination of cognitive gaps and shortages and reaching the status of complete overlook on the subject. The data fusion technique can be applied to the data collected from one sensor over time with different sampling or from various simultaneous sensors. In many cases, one sensor alone cannot provide enough information about the surrounding environment, so by using several sensors, it is possible to obtain more accurate and complete information from the surrounding world. Data combination was first used in military industries such as automatic target identification and tracking systems. Then, due to its many advantages, especially in the last two decades, it was also used in civilian industries (Castanedo, 2013).

When two or more characteristics of a food are measured and evaluated, it allows the researcher to have more control over the subject and the results can be presented with more confidence. This way, the problems and weaknesses of one method are solved by the use of the data of another approach. In 2020, Aheto and colleagues by combining the two forms of hyperspectral imaging and electronic nose, presented a powerful way for rapid prediction of intramuscular fat and the peroxide value in pork meat treated with salt and temperature. They used the support vector machine regression method for classification and found that accuracy improves when fusing data (Aheto et al., 2020). In 2020, Firouz and colleagues estimated the amount of adulteration in sesame oil samples by combining low-frequency dielectric spectroscopy methods (40 kHz to 20 MHz) and chemical methods. They used principal component analysis for feature reduction and artificial neural network models and support vector regression for classification (Firouz et al., 2020).

In this research, two powerful methods, ultrasound and an E-nose machine, were used as powerful and non-destructive methods for classifying extra virgin olive oil. By fusion of the data of both systems, the accuracy of classification increases in addition to the results being validated. Also, by using the two techniques, the weaknesses of one method are compensated by the other method. In this study, first, the ultrasonic method and then the E-Nose system were evaluated. Then the data and results of mentioned systems are combined and considered and finally, the effect of fusing the data in improving the classification accuracy has been investigated and analyzed.

Materials and methods

The extra virgin olive oil used in this research was obtained from Danzeh Food Industry (a prominent factory located in Lowshan, a city in the Iranian province of Gilan) which is extracted from the “Mary” variety of olive. Cold pressing was applied to olive oil extraction according to European Union (EU) legislation (EC Reg. 29/2012).

To ensure that the samples are extra virgin olive oil (According to COI/T.15/NC No 3/Rev 2021), the characteristics and percentages of fatty acids were determined by gas chromatography (GC) of the Agilent Technologies model (7890B) in a laboratory (an Iranian laboratory, Rahpooyan Danesh Koolak).

GC results showed that the control treatment (pure EVOO), has 66.1% oleic acid, 12.6% Linoleic acid and 15.7% palmitic acid. The olive oil used in this research was mixed with the frying oil available in the market, which is a mixture of sunflower, canola and corn oil (Including 40.6% oleic acid, 46.1% Linoleic acid and 5.9% palmitic acid), with weight of percentages of 5, 10, 20, 35 and 50. The samples were mixed with a simple mixer and there was no need for high-speed mixers. From each category of fraud, seven pieces of one hundred grams were prepared. Samples and data acquisition was repeated seven times for each sample. This way, 49 experiments were performed for each category.

Ultrasonic test method

In this study, a diagnostic ultrasound system was used to extract and evaluate the ultrasonic properties of extra virgin olive oil samples were mixed with common frying oil in the market. This system included a DIO 1000 STARMANS diagnostic ultrasound device, a pair of transmitter and receiver probes with a nominal frequency of two MHz (model 57745, series B2S), a system for guiding the probes, and an oil sample chamber. This chamber was made of thin glass with a thickness of one millimeter to have the most negligible impact on the raw data in terms of attenuation of ultrasonic signals and for the wave to reach the receiver probe in the shortest way.

Fig. 1-b shows an example of the output signal of the ultrasonic system. The different peaks in this figure are caused by the impact and reflections of the transmitted ultrasound waves experiencing different environments. The raw data were transferred to a computer through a USB port and converted into processable data using MATLAB R2014a software. Regarding the difference between the transmission and reception signals (each transmitted wave passes through different environments, that is, from the transmitter probe to the wall of the sample glass container, from the glass to the olive oil, from the olive oil to the glass, and finally from the glass to the receiving probe). Four features were extracted from these ultrasound signals:

Fig. 1.

a. Reaction of an olfactory sensor in different operation stages of the electronic nose machine b. Different peaks of a sample of ultrasonic wave extracted from the ultrasonic system (horizontal axis showing time and vertical axis showing amplitude).

The percentage of amplitude reduction from the transmitter probe to the receiver probe (as an indicator of signal attenuation), 2. The time the wave passes from the transmitter probe to the receiver (time of flight, TOF) (which is an indicator of the wave velocity and is obtained by the distance between the transmitter and receiver probes divided by the time of flight), 3. The difference between the first and second peaks of the amplitude in the time–amplitude graph (Fig. 1-b) and 4. The ratio of the first and second peaks of the amplitude in the time–amplitude diagram.

Olfaction machine method (E-Nose)

Olive oil has 26 volatile compounds in 4 groups of aldehydes, ketones, alcohols and esters (Garrido-Delgado, Del Mar Dobao-Prieto, Arce, & Valcárcel, 2015). Therefore, it is possible to perform quality measurements with the help of a system that reacts to the gases around it. In this research, an E-nose machine system was used to obtain the olfactory features of extra virgin olive oil and fraud samples. These features act like an identity card and typical trace of each sample, which is unique (Ghasemi-Varnamkhasti, Mohtasebi, Siadat, Ahmadi, & Razavi, 2015).

Eight semiconductor metal oxide gas sensors were used in this system which are in three types: 1. Two types of Taguchi sensors (Figaro Engineering Inc., Japan) (TGS813 and TGS822) 2. Five kinds of MQ sensors (MQ3, MQ4, MQ8, MQ135, MQ136) (Hanwei Electronics Group Corporation, China) and one FIS sensor (NISSHA Co., Japan). Each of these sensors reacted to a range of gases.

In the olfactory system, an oxygen capsule (equipped with a pressure gauge at its outlet to display the pressure of the exhaust gas), solenoid valves, a sensor chamber, a sample chamber with an air pump on the chamber, a data acquisition card, and electric circuits were used along with a computer to observe and record data.

The electronic nose system has a sample chamber with enough volume to place the olive oil sample and collect the volatile substances emitted from the samples. In this research, a plastic container with a volume of 5 L was experimentally used as a container for collecting the volatile gases of oil samples. In the tests, the oil sample is locked inside the sample chamber for a particular time (20 min). This specific time depends on various parameters. It differs depending on the amount of volatile compounds on the sample and is determined experimentally. For example, this time is less for substances such as rose water or saffron, which have more volatile compounds, or if a smaller sample container is used, this specific time can be reduced. (Of course, in this case, the quality of the test will drop due to less volatile compounds). During twenty minutes considered for this stage, the volatile compounds of the oil are gradually separated from the material and the sample chamber is saturated with volatile compounds.

After this certain time, the practical steps of the olfactory system begin. As mentioned before, in the first step, the sensors must be preheated. After getting heated, the three practical steps of the system start including: 1- baseline correction, 2- Injecting the saturated smell of the sample chamber into the sensor chamber and 3- Cleaning the sensor chamber and the sample chamber. In the first step, pure oxygen gas is passed over all the sensors with a certain flow rate and a certain time to calibrate the sensors. As pure oxygen passes over the sensors, the output voltage of all sensors decreases (except the FIS sensor going through an upward procedure unlike the others). Any change in the output comes with a change in voltage. Fig. 1-a shows an example of the reaction of the applied sensors in different stages.

In this figure, the horizontal axis shows the time, and the vertical axis shows the sensor voltage change. The timing for the baseline correction, the time for the smell over the sensors to go away and the cleaning time of the sensors are entirely experimental. In this study, after 20 min, when the sample was placed in the sample chamber and the chamber was almost saturated with the smell of olive oil, 150 s were allowed to clean the sensors with oxygen (baseline correction), 250 s for sampling time or the time of passing the volatile component near the sensors and 100 s were considered for cleaning the sensors chamber and the sample chamber.

The data acquisition rate from the sensors' outputs was one second; That is, every second, the output voltage value of each gas sensor was recorded and kept. The pressure of the gas passing through the sensors, fed by an oxygen capsule, was considered to be about 2 bar using a barometer built into its outlet. Since the sensors applied are metal oxide semiconductors and work better at temperatures higher than room temperature, the sensors were preheated before data acquisition. The tests used at standard room temperature (about 20 degrees Celsius) but the sensors were at about 30 degrees Celsius. Each of these sensors reacts to a range of gases (in this case the aromatic compounds of olive oil) and not to a specific type.

For each test, four features were extracted for each sensor: 1. Sensor output (mV) at the starting point of the sampling stage (when pure oxygen has passed over the sensors and reached its initial state and is ready to start data acquisition); 2. Sensor output at the end of the sampling stage; 3. The difference between the sensor output at the beginning and the end of sampling and 4. The average of the sensor output at the sampling stage.

Thus, with eight sensors, the output in each stage of data acquisition will include eight curves similar to the curves of the diagram in Fig. 1-a therefore, 32 olfactory characteristics were extracted for each test. The outputs of the mentioned eight sensors were entered into the computer and the LabView software environment using the analog channels of the data acquisition system (DAQ) and transferred through the USB port.

Also, the opening and closing of the solenoid valve and the operation of the pump and their timing were done using the program written with LabView software and through the digital channels of the data acquisition card.

Quality measurement by fusion of olfactory and ultrasound data

To obtain the best results in fusing the data of the two ultrasonic and E-nose systems mentioned above, it is necessary to take data for the two methods with a minimum time gap. Considering this principle, a system (Fig. 2) was designed so that the two approaches could be installed and data collected simultaneously in one group. This led to an increasing the accuracy and precision of the tests and in addition, it helped to consolidate the system.

Fig. 2.

Schematic of the fused ultrasonic-E-nose system - liable to collect simultaneous data from both systems (1. Oxygen capsule, 2. Solenoid valves, 3. The Gas sensor chamber, 4. The chamber for collecting the aromatic compounds of oil sample, 5. Oil sample chamber, 6. A couple of ultrasound probes, 7. The mechanism for fixing the probes and 8. Processing unit.

To find out the dispersion of the data, all 36 features (including four ultrasonic features and 32 olfactory features) were analyzed using box plots and histograms.

In this study, pre-processing codes, principal component analysis (PCA) and data classification operations were written and executed using PYTHON software, version 3.7. The principal component analysis method was used to reduce the features.

Principal component analysis, simply put, is a method for extracting essential variables (in the form of components) from a large group of variables in a data set. Principal component analysis extracts a low-dimensional set of features from a high-dimensional set to help record more information with fewer variables. In this way, data visualization also becomes more meaningful. Application of principal component analysis happens when the data have three or more dimensions. PCA is always applied to the covariance or correlation matrix. This means that the data must be numerical and standardized.

Six different classification models were used for quality measurement and evaluation of samples. Before data analysis, pre-processing operations were done on the raw data and outliers were identified and normalized.

The artificial neural network (ANN) showed the highest accuracy in the classification of samples. ANNs are biologically inspired computational networks. Among the various types of ANNs, in this study, we used multilayer perceptrons (MLPs). It is most commonly used for a wide variety of problems, is based on a supervised procedure and comprise three layers: input, hidden, and output.

Results and discussion

According to the International Olive Council (IOC, 2021) for olive oil, the amount of oleic acid, which is its main fatty acid, should be between 55 and 83 percent (de Mendoza et al., 2013). For our pure sample, the value was 66.1%. The amount of oleic acid in the olive oil sample of the Kallel and colleagues’ study (Kallel et al., 2020) was between 54.97 and 63.19 percent. The results of the chromatography tests on the oil samples showed that the percentage of this fatty acid (C18:1) decreases with the increase in the adulterated percentage of olive oil, so that for the 5% adulterated sample, it was 63.8% for the 50% adulterated sample, it was 55.5%. This result was not unexpected.

Due to a large number of features of this research (32 olfactory and four ultrasonic features), to reduce the features, it is necessary to determine the dominant factor with statistical methods and focus on them. Therefore, in this study, the method of principal components analysis was used to determine the most important features that are effective in classifying and reducing data dimensions (Fig. 3).

Fig. 3.

The result of pca analysis to extract the most important features.

Reducing the features will increase the speed of fraud detection by reducing the number of calculation operations and result in faster classification.

Determination of important features

The top features applied (features of the highest correlation with the desired category) are shown in Fig. 4.

Fig. 4.

Important features in classification.

To extract the order of importance of the features, the extra tree classifier method (ETC) was used. The random forest algorithm is an ensemble algorithm that uses decision trees for its simple and weak algorithms.

Fig. 4 shows the most important features (features with the highest correlation with the category) in percentage. This figure shows that the feature of “reducing the ultrasonic amplitude from the transmitter to the receiver” with a 15.69% of impact in classification is the most effective feature in this machine learning method and shows that the slightest change in the amount of olive oil sample adulteration will bring about the most significant impact and change in the output of this feature.

The features “time of flight” and “the difference between maximum and minimum of the ultrasonic signal amplitude” and “average output of the TGS 2620 gas sensor“ are three features in order (after the feature ”ultrasonic amplitude loss from the transmitter to the receiver“) having a high effect on the accuracy of classification. The first three features are related to the ultrasonic system. It can be concluded that the ultrasonic properties of olive oil have a better and greater ability to detect fraud than the olfactory properties and work more effectively. Considering that the ultrasonic method is affected by the internal characteristics of olive oil such as density, texture and composition, and the olfactory system judges the substance based on the volatile compounds of the sample. Therefore, it can be concluded that the structure and texture of the substance are more effective than the gases emitted from it in detecting a liquid substance such as olive oil.

The attenuation coefficient (α) was calculated in different samples of olive oil with equation 1 which is in the unit of Neper per meter (Np/m). The attenuation coefficients of the samples were obtained from 40 to 50. In the study of Chanamai and McClements, the attenuation coefficient of olive oil was 61.9 (Chanamai and McClements, 1998). Its difference from the value obtained in this study is due to the measurement method, while in the present study is the method of transmitter–receiver (In this method, the passage of the wave through the wall of the sample chamber causes an error) but in their research the pulse-echo method was used (where the probe is directly connected to the oil sample). Fig. 5 shows the relationship between the attenuation coefficient of samples and the percentage of fraud. As the graph shows, the attenuation coefficient increases with the increase in fraud percentage.

α = 1/x log (A/A₀) (1)

Fig. 5.

The relationship between attenuation coefficient and adulteration percentage of olive oil samples.

George and colleagues investigated the amount of adulteration in coconut oil using diagnostic ultrasound (George et al., 2017). They also concluded that the attenuation coefficient increases with the increase in the percentage of adulteration in pure coconut oil. They used sunflower oil and palm oil for fraud. With the increase in the percentage of adulteration, the increase in attenuation coefficient in the oil mixed with sunflower oil is more than when mixed with palm oil. In other words, coconut oil mixed with sunflower oil shows a greater reaction to the change in adulteration percentage.

The velocity of ultrasonic signals (V) in different olive oil samples was calculated by equation 2. For different samples, this value was obtained from 1285 to 1293 m/s. This velocity is not the velocity of waves in olive oil because it has passed through the wall of the container twice, but since the goal is to compare the samples, the results can be used with care.

V = x/t (2)

In Yan (2020), the average velocity for extra virgin olive oil samples was 1493.33 m/s. Of course, their measurement method is pulse-echo, in which case the signal does not pass through the wall of the container, and the value obtained for the velocity will be more reliable. Frequency and temperature are also effective in the obtained velocity. They also calculated the velocity at different temperatures.

Fig. 6 shows that the increase in the adulteration percentage of extra virgin olive oil samples with standard frying oil samples in the market, reduces the velocity of ultrasound; This issue is probably due to the change in density, because with the increase in liquid density, the velocity of the ultrasonic signal passing through the material increases (Yan, 2020) and in this study, the density of common frying oil in the market was lower than extra virgin olive oil. And therefore, it was expected that the rising percentage of fraud to lead to decreasing the velocity of the wave (time of flight).

Fig. 6.

The relationship between the velocity of the ultrasonic signals of the samples and the amount of fraud.

Yan and colleagues used the pulse-echo diagnostic ultrasound system to investigate the ultrasonic characteristics of several different types of oil found that there is a significant positive correlation between the velocity and density of the substance used (r = 0.75 and p < 0.05). They used oils such as extra virgin olive oil, refined olive oil, pomace oil, canola oil, sunflower oil and peanut oil. They also found that ultrasound waves in extra virgin olive oil are slower than other lower quality olive oils (olive pomace oil and refined olive oil), which is a result of the lower density of olive oil. (Yan, 2020). By comparing the results of Yan et al.'s research with the results of this research, we can conclude that the density of extra virgin olive oil is lower than other olive oils of lower quality (such as pomace oil and refined olive oil) and higher than common frying oil (the one used in this research) which makes the ultrasound velocity in extra virgin olive oil become lower than olive pomace oil but higher than the common frying oil.

In the E-nose system, TGS2620, MQ4 and FIS sensors are the most effective and important sensors in this test. The reason for the superiority of the TGS2620 sensor over other sensors in detecting adulteration can be due to this sensor’s further sensitivity to the volatile compounds in olive oil.

In summary, since the results of the principal components analysis showed greater effect of the TGS2620 sensor in detecting adulteration and classification of olive oils (among the olfactory sensors), and since the main application of this sensor refers to alcohol, toluene, and other organic vapors and alcohol with concentration range of 50 to 5000 ppm, it can be concluded that these aromatic compounds are probably more effective as detectors of fraud oils than extra virgin olive. Their percentage changes are significant with the increase in adulteration in extra virgin olive oil, and of course in the same range of 50–5000 ppm.

Six different classification models including logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), Naive Bayes (NB) and AdaBoost (AB) were used for classification and quality determination. To measure the classification accuracy of the applied models, a method called Train/Test was used. The model is trained using the data of the training set, and evaluated with the data of the test set of the model. With a general overview of the research, it can be seen that the percentage of training data is often used between 60 and 80. In this study, experimentally, 70% of the data was used for the training and 30% for the test.

According to the results of classification of ultrasound data, It can be concluded that the highest accuracy of classification refers to NaiveBayes method and the lowest accuracy is assigned to AdaBoost method with about 53%.

Also For E-nose data, the K-nearest neighbors method has the highest accuracy of about 90%, and the AdaBoost method has the lowest accuracy of 35%.

After the results and data of the two ultrasonic and olfactory methods were prepared in one table and given to the software for classification, a noticeable increase in classification accuracy was observed. The artificial neural network classification model showed the highest accuracy in the combined data with 95.5%. Of course, this method has spent more training time than other methods (Table 2).

Table 2.

Confusion matrix of ANN.

		ESTIMATED
		Pure	5%	10%	20%	35%	50%
REAL	Pure	17	0	0	0	0	0
	5%	0	18	0	0	0	0
	10%	0	0	14	1	0	0
	20%	0	0	1	11	0	0
	35%	0	2	0	0	9	0
	50%	0	0	0	0	0	16

The lowest accuracy was obtained by AdaBoost method with 86.52%. AdaBoost is an algorithm that is used along with other learning algorithms to improve performance and solve the problem of unbalanced categories. This method has been applied less in food products to classify their quality. In artificial neural networks, accuracy increases with the increase in the number of data. On the contrary, when the model is trained with a smaller number of data, the results will not be of good quality. In the present study, this model obtained the most accuracy, because the amount of data had increased by combining them (Table 3).

Table 3.

Comparison of results of classification before and after data combination.

Model	Ultrasound system accuracy (%)	E-nose system accuracy (%)		Combined data
Artificial neural network	86.27	67.52		95.5
Support vector machine	88.24	86.52		94.4
K-nearest neighbors	86.27	89.89		93.3
Naïve Bayes	90.2	82.02		93.3
Logistic regression	74.51	76.40		92.1
AdaBoost	52.94	34.83		86.5

Conclusion

In this research, a simple and low-cost E-nose system was used based on eight metal oxide semiconductor sensors (including two Taguchi gas sensors, five MQ gas sensors from Hanwei, China and one FIS sensor from NISSHA Japan) along with an ultrasonic diagnostic system having a pair of 2 MHz- ultrasonic transmitter and receiver to detect adulteration and classify different olive oil samples. The pre-processed and combined outputs of the two systems were classified. Six classification models including Artificial Neural Network, Support Vector Machine, K-Nearest Neighbors, NaiveBayes, Logistic Regression and Adaboost“. 4 features were extracted from ultrasound data and 32 odor features were extracted from oil samples.

The results showed that the ultrasonic system data were more effective than the E-nose data. The ultrasonic method measures the internal characteristics of a material such as the density, texture, and compounds of the material, but the E-nose system judges a material based on the volatile compounds of the sample. The ultrasonic characteristics were more effective in this research, therefore, it can be concluded that the effect of the structure and texture of the substance is more effective than the gases emitted from it in detecting a liquid substance such as olive oil; Of course, as mentioned in the above text, the combination of two different methods increases certainty and validates the results. The classification method along with artificial neural network was recognized the most effective method with 95.51% accuracy. This classification model provided poor results for the initial data, which were less in number, yet in the main and final data, due to the sufficient number, the classification accuracy improved. The support vector machine classification model and the gradient boosting classifier, after the artificial neural network method, were both known methods with the highest classification accuracy (94.38%).

The results showed that among the six models, the AdaBoost model with the lowest accuracy (86.52%) is not suitable for classification in this research. It is not recommended to use this method in similar research. TGS-2620 gas sensor shows a better response than other gas sensors against changes in the adulteration percentage of extra virgin olive oil. This sensor is suitable for classification of edible oils, especially olives, and is known suitable and recommended for similar applications. Also, the results showed that the ultrasonic wave velocity decreases but the attenuation coefficient increases with the increase in the percentage of adulteration in the oil sample.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.