Table 1.

Artificial Neural Network (ANN) applications in hyperspectral image analysis of food products.

Study	Wavelength range (nm)	Spectral pre-processing	Image processing	ANN characteristics								ANN Computational software	Classification accuracy	References
				Network type	Network topology						Training set: Validation set
					Input layer		Hidden layer		Output layer
					Number	Nodes	Number	Nodes	Number	Nodes
Detection of mechanical damage in mushrooms	900–1700	Savitzky-Golay Second derivative	Harris corner detection algorithm	Polak–Ribie're conjugate gradient Back propagation	101	–	01	30	05	–	80:20	MATLAB R2012b	91%	Rojas-Moraleda et al. (2017)
Estimation of wheat hardness (single kernel)	1000–2500	Savitzky-Golay first derivates (SGD1); mean centering (MC) and orthogonal signal correction (OSC)	Image thresholding	Two-layer Back Propagation neural Network (BPNN)	01	–	02	03	01	–	80:20	MATLAB 8.2	90%	Erkinbaev et al. (2019)
Detection of cold injury in peaches	400–1000	–	–	Back-propagation feed-forward neural network	01	420	01	03	01	02	80:20	–	96%	Pan et al. (2015)
Detection of adulteration in honey	400–1000	Savitzky-Golay algorithm (2nd-order polynomial with 3-point window)	Otsu algorithm for image thresholding	Back-propagation feed-forward neural network	01	–	01	10	01	–	70:30	MATLAB	95%	Shafiee et al., 2016
Detection of mites in flour	400–800	Multiplication scatter correction (MSC); Successive projections algorithm (SPA) and ant colony optimization (ACO)	Image thresholding	Back Propagation Neural Network	01	–	01	05	01	03	67:33	MATLAB R2017b	98%	He et al. (2020)
Detection of stored insects in rice and maize	400–1000	Normalization	Otsu algorithm for image thresholding	Back Propagation Neural Network	01	–	03	–	01	–	60:40	MATLAB R2009b	98%	Cao et al. (2014)
Prediction of firmness in kiwi fruit	400–1000	Sawitzky–Golay algorithm with 2nd order polynomial	Image thresholding	Back-propagation feed-forward neural network	01	03	01	03	01	01	70:30	MATLAB	97%	Siripatrawan et al. (2011)
Detection of chilling injury in apple	400–1000	–	Global thresholding	Back-propagation feed-forward neural network	01	826	01	05	01	02	66:34	MATLAB 7.0	98.4%	Elmasry et al. (2009)
Differentiation of wheat classes	900–1700	–	Image cropping and statistical mean centering	BPNN Wardnet BPNN	01 01	75 75	01 01	79 78	01 01	08 08	60:40 for BPNN; 70:30 for Wardnet BPNN	MATLAB 7.0	90%	Mahesh et al. (2008)
Identification of wheat classes	900–1700	Normalization	Image cropping and thresholding	Back propagation neural network	01	100	01	–	01	08	60:40	MATLAB R2006a	92.1%	Choudhary et al. (2008)