Application of the E‐nose machine system to detect adulterations in mixed edible oils using chemometrics methods

Abstract

Foodstuff adulteration involves addition of any low‐cost substances to the high‐price materials to reduce the content of the expensive components, and hence decrease the production cost and reach to the maximum profit. An electronic nose was used in this study to detect the adulterations in mixed edible oils. The acidity, peroxide, anisidine, and Totox values of the edible oil samples were measured according to the official American Oil Chemist Society (AOCS) standard. The results were analyzed by Cluster analysis (CA), principle component analysis (PCA), principal component regression (PCR), linear discriminant analysis (LDA), and artificial neural network (ANN) methods with accuracy of 95, 98, 98, 88, and 97.3%, respectively. According to the results, the ANN method with structure of 8‐7‐5 showed the highest accuracy in classification of oil adulteration. Its correct classification ratio, mean square errors, and correlation (r) were 97.3%, .117211, and .0963, respectively. The results also indicated that the proposed method can be used as an alternative of the official AOCS methods to innovatively detect the edible oil adulteration with high accuracy and speed. Practical applications Lipid oxidation is one of the major causes of food spoilage especially in those containing oil. AOCS has developed various methods to evaluate the oxidation status of the oil assets. However, these chemical tests are time‐consuming, destructive, and costly and require several glassware and reagents. E‐nose could be used for real‐time monitoring of the volatile components of the food to evaluate different features of the product. Generally, E‐nose evaluates mixture of smells released form a sample and is a reliable, nondestructive, cost‐effective, and portable method with high feasibility and speed as well as simple use. CA, PCA, and ANN methods were also applied for qualitative differentiation of different adulteration percentages in oxidized and nonoxidized oils.

Application of electronic nose with chemometrics methods to the detection of juices fraud

“…Application of the fraction method aimed to correct the baseline. In this method, the response of the sensors becomes dimensionless in addition to normalization to eliminate noise or possible deviation (Karami, Rasekh, & Mirzaee – Ghaleh, 2020; Karami et al, 2020; Karami Rasekh et al., 2020a):in which, Y s ( t ), X s (0), and X s ( t ) indicate the normalized sensor response, the baseline, and the sensor response, respectively.…”

“…Many supervised classification methods such as linear discriminant analysis (LDA), quadratic data analysis (QDA), support vector machines (SVM), and artificial neural networks (ANN) have been used successfully to analyze electronic nose data (Karami et al, 2020; Karami Rasekh, & Mirzaee‐Ghaleh, 2020a, 2020b). In general, supervised classification requires labeled data to fit good classification; in other words, labeled data can represent almost the basic structure of the entire data space.…”

“…successfully to analyze electronic nose data (Karami et al, 2020;Karami Rasekh, & Mirzaee-Ghaleh, 2020a, 2020b. In general, supervised classification requires labeled data to fit good classification; in other words, labeled data can represent almost the basic structure of the entire data space.…”

Application of a lab-made electronic nose for extra virgin olive oils commercial classification according to the perceived fruitiness intensity

“…The detection system includes nine MOS (Table 2), which electrical properties change according to the adsorption phenomena that occurs on the sensors surface when in contact with volatile compounds. The same or similar MOS have been previously used due to their capability to detect different aroma compounds of olive fruits [34] and vegetable oils [17,22,35,36]. The MOS sensors are porous layers heated by a filament that undergoes a redox reaction when it comes in contact with a reducing or oxidizing volatile compound, changing the electrical resistance across the circuit proportionally to the compound concentration.…”

“…Samples (0.5 mL) were then put into a glass vial (25 mL) and placed in the sampling chamber during 13 min (to allow obtaining a headspace with a volatile fraction representative of the sample) at 28 • C (temperature recommended by the International Olive Council for olive oils sensory analysis) [5]. Although several gas sample pre-concentration procedures exist, the static headspace technique was chosen due to its simplicity and rapid acquisition of a representative sample of the oil's volatile fraction, allowing an easy on-line and in-situ implementation, being widely implemented with both lab-made and commercial E-noses [30,35,36]. At the same time, the E-nose system was cleaned during 13 min using air flow (pumped under vacuum) or nitrogen flow (UN 1066, Linde 089 cyl 02/15) for the standard solutions or olive oils analysis, respectively, enabling reaching a stable signal baseline, indicative of a cleaned environment.…”