Potential application of electronic nose coupled with chemometric tools for authentication assessment in tomato paste

Abstract

In this study, adulteration in tomato paste was investigated using a gas sensors array. For this purpose, olfactory machine system was evaluated based on five gas sensors to investigate pumpkin, potato, and starch adulteration (0, 5, 10, 15, and 20%). Principal component analysis (PCA), linear discriminant analysis (LDA), support vector machine (SVM), and partial least square (PLS) methods were used for classification and analysis of sensors response. The use of PCA method in the classification of pumpkin, potato, and starch adulteration has explained 99% of the variance between the data. Also, TGS2610, MQ3, TGS2620, and TGS2600 sensors are widely used for classification. LDA method precision for pumpkin, potato, and starch adulteration classification was 79.07, 83.87, and 92.22%, respectively. In SVM method, C-SVM function as polynomial function and Nu-SVM as radial base function were used for pumpkin classification with 98.84 and 88.14% precision, respectively. In the classification of potato adulteration, the polynomial function had 90.32 and 83.87% precision in C-SVM and 96.77 and 86.02% precision in Nu-SVM. Finally, the two TGS2610 and MQ3 sensors had the best results for classification by PLS. Among the methods of classification, PCA method for categorizing pumpkin adulteration and PLS method in the classification of potato and starch adulteration had the best results. Practical applicationsIn this study, an electronic nose system was examined based on five metal oxide semiconductor sensors to recognize pumpkin, potato, and starch adulteration in tomato paste in combination with pattern recognition methods including PCA, LDA, SVM, and PLS.According to results, it is recommended to use TGS2610 and MQ-3 sensors combined with PCA and SVM method in the design and development of the electronic nose system. Also, potential of the electronic nose system uses for food adulteration monitoring.

Nondestructive Technique for Identifying Adulteration and Additives in Lemon Juice Based on Analyzing Volatile Organic Compounds (VOCs)

“…Additionally, the AUC and F values were 0.992 and 0.983, respectively. The results obtained in this research were consistent with other products, such as different oil types and adulterated safflower seed oil [58], minced mutton mixed with pork [59], oranges [60], and tomato paste [61]. Moreover, in another study, the electronic nose was used by Gomez, et al [62] to identify the ripening state of tomatoes, and the results revealed that the electronic nose was able to discriminate the ripening states of tomatoes.…”

A Green Analytical Method Combined with Chemometrics for Traceability of Tomato Sauce Based on Colloidal and Volatile Fingerprinting

“…Lo Feudo et al [ 71 ], for example, evaluated its origin (Italian, Italian regions, and non-Italian) by ICP-MS, quantifying the concentration of 32 elements. Two more studies focused their attention on quantifying possible tomato sauce adulterations by NIR spectroscopy and electronic tongue [ 72 ], or electronic nose [ 73 ]. Finally, Boukid et al [ 74 ] evaluated the effect of thermal treatments and the addition of ingredients in the physical properties of tomato double concentrate, another tomato product similar to tomato sauce.…”

“…Moreover, as already stated, no chemical reagents were used in the current work, making the analyses cheaper and cleaner. The untargeted methods coupled with chemometrics [ 72 , 73 ] are able to extract useful information with a lower analysis effort, optimizing times and costs without losing effectiveness. In addition, as shown for GC-IMS analysis in this work, and in line with the work of Vitalis et al [ 72 ], an untargeted method can be carried out to obtain a fingerprint of the samples; then, with the help of chemometrics, some particularly interesting variables can be highlighted and deeply studied, without the need of an in-depth quantification of all the possible analytes.…”

A Low Cost Electronic Nose System for Classification of Gayo Arabica Coffee Roasting Levels Using Stepwise Linear Discriminant and K-Nearest Neighbor

“…E-Nose able to classify and determine aroma through statistical analysis based on sensor output [9], such as principal component analysis (PCA), linear discriminant analysis [10], support vector machine (SVM), and partial least squares (PLS) [11]. A considerable amount of literature has been published on stepwise linear discriminant analysis [12,13], these studies provide important insight into E-Nose classification.…”