Table 6.

k-Nearest Neighbor (k-NN) applications in hyperspectral image analysis of food products.

Study	Wavelength range (nm)	Spectral pre-processing	Image processing	k-NN characteristics		k-NN Computational software	Classification accuracy	References
				Value of k	Training set: Validation set
Assessment of packaged cod	400–1000	Area Normalization; 1st derivate	Image thresholding	3	75:25	R	100%	Washburn et al. (2017)
Classification of coffee species	900–1700	Standard Normal Variate; 1st derivative; mean centering	Image thresholding	5	73:27	MATLAB 7.0	100%	Calvini et al. (2015)
Detection of aflatoxin in maize	400–1000	Multiplicative signal correction (MSC)	Image thresholding	–	82:18	MATLAB R2018b	99%	Gao et al. (2020)
Detection of pesticide residue on spinach leaves	900–1700	Multiplicative signal correction (MSC)	Image thresholding	–	80:20	MATLAB R2016b; PYTHON 3.6	99%	Zhan-qi et al. (2018)
Classification of fat and lean tissue in packed salmon	400–1000	Mean-centering and unit variance normalization	Image thresholding	17	80:20	MATLAB R2012a	100%	Ivorra et al. (2016)
Evaluation of sugar content in different potato varieties	400–1000	Weighted baseline	Image thresholding	3 and 5	75:25	MATLAB 7.0	86%	Rady et al. (2015)
Classification of contaminants in wheat	900–1700	Standard Normal Variate (SNV)	Image thresholding, background separation	–	75:25	MATLAB 8.1	>90%	Ravikanth et al. (2015)
Classification of fresh Atlantic salmon fillets	400–1000	Standard Normal Variate (SNV)	Image thresholding	3	80:20	Interactive Data Language (IDL) 7.1	88%	Sone et al. (2011)
Classification of black beans	400–1000	Standard Normal Variate (SNV) Successive projections algorithm (SPA)	Textural attributes extraction: Gray level co-occurrence matrix	–	80:20	MATLAB 2009	98%	Sun et al. (2016)
Identification of states of wheat grain	900–1700	Standardization and multiple scattering correction	Image thresholding	7 (for reverse side of wheat grain); 6 (ventral side of wheat grain)	75:25	MATLAB R2018b	95%	Zhang et al. (2019)