@article {869,
	title = {Non-destructive Estimation of Chlorophyll a Content in Red Delicious Apple Cultivar Based on Spectral and Color Data},
	journal = {Journal of Agricultural Sciences},
	volume = {26},
	year = {2020},
	pages = {339{\textendash}348},
	abstract = {Non-destructive estimation of the chemical properties of fruit is an important goal of researchers in the food industry, since online operations, such as fruit packaging based on the amount of different chemical properties and determining different stages of handling, are done based on these estimations. In this study, chlorophyll a content in Red Delicious apple cultivar is predicted as a chemical property that is altered by apple ripening stage, using non-destructive spectral and color methods combined. Two artificial intelligence methods based on hybrid Multilayer Perceptron Neural Network - Artificial Bee Colony Algorithm (ANN-ABC) and Partial least squares regression (PLSR) were used in order to obtain a non-destructive estimation of chlorophyll a content. In application of the PLSR method, various pre-processing algorithms were used. In order to statistically properly validate the hybrid ANN-ABC predictive method, 20 runs were performed. Results showed that the best regression coefficient of the PLSR method in predicting chlorophyll a content using spectral data alone was 0.918. At the same time, the average determination coefficient over 20 repetitions in hybrid ANN-ABC in the estimation of chlorophyll a content, using spectral data and color features were higher than 0.92{\textpm}0.040 and 0.89{\textpm}0.045, respectively, which to our knowledge is a remarkable non-intrusive estimation result.},
	doi = {https://doi.org/10.15832/ankutbd.523574},
	url = {https://dergipark.org.tr/en/pub/ankutbd/issue/56429/523574},
	author = {Yousef Abbaspour-Gilandeh and Sabzi, Sajad and Azadshahraki, Farzad and Karimzadeh, Rouhollah and Ilbeygi, Elham and J I Arribas}
}
@article {870,
	title = {Non-destructive visible and short-wave near-infrared spectroscopic data estimation of various physicochemical properties of Fuji apple (Malus pumila) fruits at different maturation stages},
	journal = {Chemometrics and Intelligent Laboratory Systems},
	year = {2020},
	pages = {104147},
	abstract = {measurement of physicochemical properties of fruits during maturation stages can help having proper fruit management. Spectroscopy data analyzing and processing is among the commonly used methods that enable non-destructive accurate property estimation. Non-destructive linear (partial least squares regression, PSLR) and non-linear (artificial neural network, ANN) regression estimation of different physicochemical properties including firmness, acidity (pH) and starch content of 160 Fuji (Malus pumila) apple fruit samples at various maturity stages using visible and short wave near infrared (VSWIR) spectroscopic data in wavelength range 400{\textendash}1000 nm is investigated with the following steps: (1) harvesting 160 Fuji apple samples at four different maturation levels; (2) extracting spectral data in wavelength range of 400{\textendash}1000 nm; extracting physicochemical properties of tissue firmness, acidity (pH) and starch content; (3) pre-processing the reflectance spectra from each sample; (4) selecting effective wavelength values for each chemical property; and (5) non-destructive estimation of tissue firmness, acidity (pH) and starch content using spectral data range 400{\textendash}1000 nm and spectral data based on effective wavelengths, by means of an ensemble average artificial neural network method. Results show that the neural ensemble reached similar results when using VSWIR spectral data content (wavelength range) and fixed effective selected NIR wavelengths. Correlation coefficients estimating tissue firmness, acidity (pH), and starch content were 0.800, 0.919, and 0.940 for VSWIR spectral data (linear PLS regression), 0.826, 0.947, and 0.969 for VSWIR spectral data (non-linear ANN), 0.827, 0.946, and 0.969 for fixed NIR effective wavelengths (non-linear ANN). Mean {\textpm} std. Regression coefficients for tissue firmness, acidity (pH), and starch content were 0.717 {\textpm} 0.113, 0.786 {\textpm} 0.131, and 0.941 {\textpm} 0.013 for Vis/NIR spectral data (linear PLS regression), 0.849 {\textpm} 0.017, 0.930 {\textpm} 0.017, and 0.967 {\textpm} 0.007 for Vis/NIR spectral data (non-linear ANN), 0.852 {\textpm} 0.016, 0.929 {\textpm} 0.015, and 0.966 {\textpm} 0.006 for fixed effective NIR wavelengths (non-linear ANN).},
	doi = {https://doi.org/10.1016/j.chemolab.2020.104147},
	url = {https://www.sciencedirect.com/science/article/pii/S016974392030304X},
	author = {Pourdarbani, Razieh and Sabzi, Sajad and Kalantari, Davood and J I Arribas}
}
@conference {871,
	title = {Non-intrusive image processing Thompson orange grading methods},
	booktitle = {2017 56th FITCE Congress},
	year = {2017},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {A key issue in fruit export is classification and sorting for marketing. In this work image processing techniques are used to grad Thompson orange fruit. For this purpose, fourteen parameters were extracted, comprising area, eccentricity, perimeter, length/area, blue value, green value, red value, width, contrast, texture, width/area, width/length, roughness, and length. Adaptive neuro fuzzy inference system (ANFIS), linear, and nonlinear regression methods were used. Based on results, mean square error (MSB), sum squared error (SSE) and coefficient of determination (R 2 ) were 3.47e-08, 3.47e-07, 0.988 (ANFIS), 51.33, 4927.59, 0.866 (linear reg.) and 64.85, 6092.5, 0.832 (non-linear reg.), respectively. ANFIS model was shown as the best fit model based on previously listed performance evaluation criteria.},
	doi = {https://doi.org/10.1109/FITCE.2017.8093004},
	url = {https://ieeexplore.ieee.org/abstract/document/8093004},
	author = {Sabzi, Sajad and Yousef Abbaspour-Gilandeh and J I Arribas}
}
@conference {872,
	title = {A new method based on computer vision for non-intrusive orange peel sorting},
	booktitle = {2017 56th FITCE Congress},
	year = {2017},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {As it is well-known, orange peel is used for making jam and oil. For this purpose, orange samples with high peel thickness are best. In order to predict peel thickness in orange fruit, we present a system based in image features, comprising: area, eccentricity, perimeter, length/area, blue value, green value, red value, wide, contrast, texture, wide/area, wide/length, roughness, and length. A novel identification solution based on the hybrid of particle swarm optimization (PSO), genetic algorithm (GA) and artificial neural network (ANN) is proposed. In addition, principal component analysis (PCA) has been applied to reduce the number of dimensions, without much loss of information. Taguchi{\textquoteright}s robust optimization technique has been applied to determine the optimal setting for parameters of PSO, GA, and ANN. The optimal level of factors were: Number of Neuron in first layer=7, Number of Neuron in second layer=2, Maximum Iteration=400, Crossover probability=0.7, Mutation probability=0.1, and Swarm (Population) Size=200. Results for prediction of orange peel thickness based on levels that are achieved by Taguchi method were evaluated by five performance measures: the coefficient of determination (R 2 ), mean squared error (MSE), mean absolute error (MAE), sum square error (SSE), and root mean square error (RMSE), reaching values of 0.8571, 0.0123, 0.0924, 1.392, and 0.1109, respectively.},
	doi = {https://doi.org/10.1109/FITCE.2017.8093001},
	url = {https://ieeexplore.ieee.org/abstract/document/8093001},
	author = {Sabzi, Sajad and Yousef Abbaspour-Gilandeh and J I Arribas}
}
