Table 3.

Support Vector Machines (SVM) applications in hyperspectral image analysis of food products.

Study	Wavelength range (nm)	Spectral pre-processing	Image processing	SVM characteristics				SVM Computational software	Classification accuracy	References
				Kernel function	Parameter values/Pertinent particulars	Cross validation	Training set: Validation set
Detection of early decay in strawberry through prediction of total water-soluble solids	1000–2500	Standard normal variate correction (SNV); successive projection algorithm (SPA)	Image masking	Radial basis function	Gamma (γ): 1 Penalty factor (c): 3.16	Five-fold	70:30	MATLAB R2014	94%	Liu et al. (2019)
Detection and identification of fungal infection in cereals	400–1000	Successive projection algorithm (SPA)	Image cropping and thresholding	Radial basis function	Grid search optimization method: Kernel parameter values	Five-fold	67:33	–	99%	Lu et al. (2020)
Classification of foodborne bacterial pathogens grown on agar plates	400–1000	Standard Normal Variate (SNV); CARS (Competitive Adaptive Weighted sampling)	Image thresholding	Radial basis function	Optimization algorithm: Particle Swarm Optimization; Kernel parameter (γ): 46.20; Penalty factor (c): 1.45	Five-fold	70:30	MATLAB R2018a	98%	Bonah et al. (2020)
Classification of infected maize kernels	900–1700	Successive projection algorithm (SPA) Image cropping	Ostu segmentation and watershed algorithms	Radial basis function	Grid search optimization method: Kernel parameter values	Five-fold	70:30	MATLAB R2013b	100%	Chu et al. (2020)
Degree of aflatoxin contamination in peanut kernels	400–720	Fisher method: obtaining narrow band spectrum	De-noising, contrast enhancement; Image thresholding	Radial basis function	Grid search optimization method: Kernel parameter values	Five-fold	70:30	MATLAB R2015b	96%	Zhongzhi et al. (2020)
Detection of black spot disease in pear	400–1000	1st order derivative, multiplicative signal correction (MSC), and mean centering	Image segmentation: Spectral angle mapper	Radial basis function	–	Five-fold	70:30	MATLAB R2017a	98%	Pan et al. (2019)
Identification of adulterated cooked millet flour	900–1700	CARS (Competitive Adaptive Weighted sampling)	Image thresholding	Radial basis function	Grid search optimization method: Kernel parameter values	Ten-fold	67:33	MATLAB R2011b	100%	Shao et al. (2018)
Determination and visualization of soluble solids content in winter jujubes	Spectral range 1: 400–1000; Spectral range 2: 900–1700	Wavelet transform and moving average smoothing; area normalization; successive projection algorithm (SPA)	Image segmentation: mask creation	Radial basis function on LS-SVM	Regularization parameter (γ): 5.750 ​× ​107; Kernel parameter (σ2): 9.760 ​× ​104	–	70:30	MATLAB R2017b	Spectral range 1: 89%; Spectral range 2: 87%	Zhao et al. (2020)
Classification of maize seed	400–1000	Normalization	Image segmentation: Adaptive threshold segmentation	Radial basis function	–	Ten-fold	50:50	MATLAB R2009b	94%	Xia et al. (2019)
Determination of viability of corn seed	1000–2500	Standard normal variate (SNV), Savitzky-Golay 2nd derivative	Image thresholding	Linear basis function	–	Ten-fold	70:30	–	100%	Wakholi et al. (2018)