Identifying olive oil fraud and adulteration using machine learning algorithms
As olive oil (OO) is more expensive than other vegetable oils, it is usually adulterated by blending it with more economic edible oils such as cottonseed oil (CSO), canola oil (CO), and soybean oil (SO). This research aimed to determine the fatty acid compositions obtained as a result of blending different proportions of CSO, CO and SO with OO using a gas chromatograph and to reveal OO adulteration by evaluating the obtained data with different machine learning algorithms. The assessment of the OO consisted of two stages. The first step was extraction of the feature vector, while second step was the classification of feature vectors with regard to the data and computing the regression values. Features were extracted using the Relief method, classified with the Support Vector Machine (SVM) and the k-Nearest Neighbor (k-NN) and Decision Tree (DT) algorithms, and the neural network algorithm was used for regression. The highest accuracy values for classification were calculated as 0.946, 0.964 and 0.982 for OO-CO, OO-SO, and OO-CSO mixtures, using the SVM method, respectively. The errors in the regression analysis were computed as 0.005, 0.005 and 0.002 respectively using the neural network algorithm..
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:45 |
|---|---|
| Enthalten in: |
Química Nova - 45(2023), 10, Seite 1245-1250 |
| Sprache: |
Englisch ; Spanisch ; Portugiesisch |
|---|
| Beteiligte Personen: |
Yasin Yakar [VerfasserIn] |
|---|
| Links: |
doi.org [kostenfrei] |
|---|
| Themen: |
Adulteration |
|---|
| doi: |
10.21577/0100-4042.20170948 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
DOAJ08159710X |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | DOAJ08159710X | ||
| 003 | DE-627 | ||
| 005 | 20230310220522.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 230310s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.21577/0100-4042.20170948 |2 doi | |
| 035 | |a (DE-627)DOAJ08159710X | ||
| 035 | |a (DE-599)DOAJa6e1d3f8941e49f2a6a0975dcb263514 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng |a spa |a por | ||
| 050 | 0 | |a QD1-999 | |
| 100 | 0 | |a Yasin Yakar |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Identifying olive oil fraud and adulteration using machine learning algorithms |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a As olive oil (OO) is more expensive than other vegetable oils, it is usually adulterated by blending it with more economic edible oils such as cottonseed oil (CSO), canola oil (CO), and soybean oil (SO). This research aimed to determine the fatty acid compositions obtained as a result of blending different proportions of CSO, CO and SO with OO using a gas chromatograph and to reveal OO adulteration by evaluating the obtained data with different machine learning algorithms. The assessment of the OO consisted of two stages. The first step was extraction of the feature vector, while second step was the classification of feature vectors with regard to the data and computing the regression values. Features were extracted using the Relief method, classified with the Support Vector Machine (SVM) and the k-Nearest Neighbor (k-NN) and Decision Tree (DT) algorithms, and the neural network algorithm was used for regression. The highest accuracy values for classification were calculated as 0.946, 0.964 and 0.982 for OO-CO, OO-SO, and OO-CSO mixtures, using the SVM method, respectively. The errors in the regression analysis were computed as 0.005, 0.005 and 0.002 respectively using the neural network algorithm. | ||
| 650 | 4 | |a olive oil | |
| 650 | 4 | |a adulteration | |
| 650 | 4 | |a gas chromatography | |
| 650 | 4 | |a machine learning. | |
| 653 | 0 | |a Chemistry | |
| 700 | 0 | |a Kerim Karadağ |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i In |t Química Nova |d Sociedade Brasileira de Química, 2004 |g 45(2023), 10, Seite 1245-1250 |w (DE-627)DOAJ000063959 |x 16787064 |7 nnns |
| 773 | 1 | 8 | |g volume:45 |g year:2023 |g number:10 |g pages:1245-1250 |
| 856 | 4 | 0 | |u https://doi.org/10.21577/0100-4042.20170948 |z kostenfrei |
| 856 | 4 | 0 | |u https://doaj.org/article/a6e1d3f8941e49f2a6a0975dcb263514 |z kostenfrei |
| 856 | 4 | 0 | |u http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-40422022001001245&lng=en&tlng=en |z kostenfrei |
| 856 | 4 | 0 | |u http://www.scielo.br/pdf/qn/v45n10/0100-4042-qn-45-10-1245.pdf |z kostenfrei |
| 856 | 4 | 2 | |u https://doaj.org/toc/1678-7064 |y Journal toc |z kostenfrei |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_DOAJ | ||
| 951 | |a AR | ||
| 952 | |d 45 |j 2023 |e 10 |h 1245-1250 | ||