Evaluating quality of tomato during storage using fusion information of computer vision and electronic nose

Abstract

A nondestructive method for evaluation of tomato quality was proposed to recognize the different ripening stages of tomatoes and to predict their hardness. Computer vision and electronic nose were used, and data fusion technology was used to establish the decision model of multi‐attribute information fusion. Fisher discriminant analysis (FDA) model and support vector classifier (SVC) model for tomato grading were established. Partial least squares (PLS) model and support vector regression (SVR) model were used for predicting the hardness. Comparing with the single detection technique, the combined system which fused computer vision and electronic nose, achieved better results. In the combined system: with respect to the SVC model, classification accuracy was 96.38% for the training set, 94.20% for the prediction set; while to the FDA model, classification accuracy was 93.48% for the training set, 85.51% for the prediction set; to the PLS model, the correlation coefficient of prediction (Rp) was 94.09% and the root mean square error of prediction (RMSEP) was 2.33 N; to the SVR model, the Rp was 95.14% and RMSEP was 0.03 N. It was observed that more robust and better prediction performance on tomato detection were achieved by using fusion information. Practical applications Computer vision, electronic nose, data fusion, and pattern recognition were used for the evaluation of tomato ripeness and hardness. Computer vision captured the color information of tomatoes and electronic nose responded to the smell of tomatoes. After data fusion, information became more abundant and the discrimination models also got better performance. It proved the possibility of nondestructive method for the evaluation of tomatoes quality during storage. So it is feasible to apply this method to detect tomato quality in factory.

Detection of submerged fermentation of Tremella aurantialba using data fusion of electronic nose and tongue

“…These TGS sensors have nonspecific sensitivity and wide cross-sensitivity toward VOCs of the fermentation broth of T. aurantialba. Electronic nose analysis of odors was carried out according to the previous literatures(Han, Huang, Teye, Gu, & Gu, 2013;Huang, Pan, Sun, Ye, & Aheto, 2018).…”

Quality detection of pomegranate fruit infected with fungal disease

“…[11][12][13] Composed of Metal Oxide Semiconductor (MOS) biosensors, the E-nose has been utilized for observing the changes in different food products during storage time and assessment of shelf life and spoilage. [14][15][16][17][18] Other studies have been conducted using E-nose for food products such as bamboo shoots [19] , tomato [20] , caraway [21] , cherry [22] , squid [23] , bitter chocolates [24] , stewed chicken [25] , pork necks [26] , beer [27] , rice [28] and saffron. [15] E-nose capability for identification or detection of fungal disease in fruits and vegetables has been evaluated also for cocoa [29] , potato [30] and peach.…”

Qualitative identification of the edible oil storage period using a homemade portable electronic nose combined with multivariate analysis

“…However, the oxidation reaction of edible oil during storage will lead to some differences in the odor information generated at different stages, which makes the electronic nose technology feasible with respect to the odor recognition of edible oil on technical principles. In recent years, electronic nose technology has been successfully used in the quality and grade analysis of food and agricultural products, [16][17][18][19][20][21][22][23] and also has made some progress for the odor quality analysis of edible oil. [24][25][26] However, research on qualitative identification of storage period is rarely reported in the literature.…”