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Evaluating quality of tomato during storage using fusion information of computer vision and electronic nose
Author(s) -
Huang Xingyi,
Pan Sihui,
Sun Zhaoyan,
Ye Weitao,
Aheto Joshua Harrington
Publication year - 2018
Publication title -
journal of food process engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.507
H-Index - 45
eISSN - 1745-4530
pISSN - 0145-8876
DOI - 10.1111/jfpe.12832
Subject(s) - electronic nose , artificial intelligence , support vector machine , computer science , pattern recognition (psychology) , linear discriminant analysis , ripeness , mean squared error , correlation coefficient , partial least squares regression , mathematics , machine learning , statistics , ripening , chemistry , food science
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 ( R p ) was 94.09% and the root mean square error of prediction ( RMSEP ) was 2.33 N; to the SVR model, the R p 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.