Summary
This paper presents a new approach for estimating antioxidant activity and anthocyanin content at ripening stages of sweet cherry by combining image processing and artificial neural network (ANN) techniques. The system was consisted of a CCD camera, fluorescent lights, capture card and MATLAB software. Anthocyanin content and antioxidant activity were determined by pH differential and 2, 2‐diphenyl‐1‐picrylhydrazyl methods, respectively. It was found that anthocyanin content was constantly increased during ripening stages, and antioxidant activity decreased during the early stages of development but increased from stage five. Several ANN models were designed and tested. Among these networks, a two hidden layer network with 11‐6‐20‐1 architecture had the highest correlation coefficient (R = 0.965) and the lowest value of mean square error (MSE) (215.4) for modelling anthocyanin content. Similarly, a two hidden layer network with 11‐14‐9‐1 architecture had the highest correlation coefficient (R = 0.914) and the lowest value of MSE (0.070) for modelling antioxidant activity.
Comparison of the models showed that ANN outperformed ANFIS for this case. By considering the advantages of the applied system and the accuracy obtained in somewhat similar studies, it can be concluded that both techniques presented here have good potential to be used as estimators of proposed attributes.
The quality and composition of bitter orange essential oils (EOs) strongly depend on the ripening stage of the citrus fruit. The concentration of volatile compounds and consequently its organoleptic perception varies. While this can be detected by trained humans, we propose an objective approach for assessing the bitter orange from the volatile composition of their EO. The method is based on the combined use of headspace gas chromatography–mass spectrometry (HS-GC-MS) and artificial neural networks (ANN) for predictive modeling. Data obtained from the analysis of HS-GC-MS were preprocessed to select relevant peaks in the total ion chromatogram as input features for ANN. Results showed that key volatile compounds have enough predictive power to accurately classify the EO, according to their ripening stage for different applications. A sensitivity analysis detected the key compounds to identify the ripening stage. This study provides a novel strategy for the quality control of bitter orange EO without subjective methods.
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