Quality control of fresh strawberries by a random forest model

BACKGROUND Strawberry quality is one of the most important factors that guarantees consistent commercialization of the fruit and ensures the consumer's satisfaction. This work makes innovative use of random forest (RF) to predict sensory measures of strawberries using physical and physical‐chem...

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Bibliographic Details
Published in:Journal of the science of food and agriculture 2021-08, Vol.101 (11), p.4514-4522
Main Authors: Ribeiro, Michele N, Carvalho, Iago A, Fonseca, Gabriel A, Lago, Rafael C, Rocha, Lenízy CR, Ferreira, Danton D, Vilas Boas, Eduardo VB, Pinheiro, Ana CM
Format: Article
Language:English
Subjects:
classification
machine learning
random forests
regression
sensory response
strawberry
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Summary:BACKGROUND Strawberry quality is one of the most important factors that guarantees consistent commercialization of the fruit and ensures the consumer's satisfaction. This work makes innovative use of random forest (RF) to predict sensory measures of strawberries using physical and physical‐chemical variables. Furthermore, it also employs these same physical and physical‐chemical variables to classify strawberries in the classes "satisfied" or "not satisfied" and "would pay more" or "wouldn't pay more. The RF‐based model predicts the acceptance, expectation, ideal of sweetness, ideal of acidity, and the ideal of succulence based on the physical and physical‐chemical data. Then, the predicted parameters are used as input for the RF‐based classification model. RESULTS The RF achieved a coefficient of determination R2 > 0.72 and a root‐mean‐squared error (RMSE) smaller than 0.17 for the prediction task, which indicates that one can estimate the sensory measures of strawberries using physical and physical–chemical data. Furthermore, the RF was able to classify 87.95% of the strawberry samples correctly into the classes ‘satisfied’ and ‘not satisfied’ and 78.99% in the classes ‘would pay more’ or ‘would not pay more’. A two‐step RF model, which employed both physical and physical–chemical data to classify strawberry samples regarding the consumer's response also correctly classified 100% and 90.32% of the samples with respect to consumers’ satisfaction and their willingness to pay more, respectively. CONCLUSION The results indicate that the developed models can be used in the quality control of strawberries, supporting the establishment of quality standards that consider the consumer's response. The proposed methodology can be extended to control the sensory quality of other fruits. © 2021 Society of Chemical Industry
ISSN:0022-5142
1097-0010
DOI:10.1002/jsfa.11092