Table 2. Summary of technologies for detection abnormal eggs

Detection method	Target (Measurement index)	Sample egg	Prediction algorithm	Accuracy	References
Machine vision	Dirty egg	350 white eggs	Image processing algorithm developed in the study	85.7%	[26]
	Cracked egg	400 white eggs	FLI model	94.5%	[4]
	Cracked egg	150 eggs	Negative LoG -LFI model	91.3%	[24]
	Broken egg	67 white eggs	Image pre-processing and CNN model	100.0%	[1]
	Bloody egg / cracked egg / dirty egg	400 white eggs	SMI-CNN-BiLSTM model	99.2%	[27]
	Cracked egg	130 white eggs	Image pre-processing and CNN model	95.4%	[28]
	Bloody egg	200 white eggs	Image processing algorithm developed in the study	-	[3]
	Stale egg (HU and albumen pH)	210 eggs	Image processing - LM algorithm	93.3%	[39]
Machine vision + line laser	Cracked egg	200 brown eggs	Image pre-processing and ANN model	97.5%	[29]
Machine vision + density measurement	Stale egg (storage time)	87 brown eggs	Calibration model developed in the study	99% or more	[38]
Machine vision + dielectric measurement	Stale egg (HU)	287 white eggs	ANN model	99% or more	[40]
Acoustic response	Cracked egg	203 brown eggs	FFT-DSP-calibration model developed in the study	98.0%	[35]
	Cracked egg	693 brown eggs	PCA-FFT-QDA model	99.6%	[33]
Negative pressure	Cracked egg	160 white eggs	Image processing algorithm developed in the study	98.7%	[9]
	Cracked egg	201 white eggs	Image processing algorithm developed in the study	94.5%	[36]
Vis-NIR spectroscopy	Stale egg (albumen pH)	96 white eggs and 96 brown eggs	MSC-SBC model (brown egg as a reference variety)	90.8%	[17]
	Bloody egg	200 brown eggs	MSC-PLSDA model	97.9% (0.1 mL)	[20]
	Bloody egg	194 brown eggs	MSC-1st derivative-BLR model	96.9%	[49]
NIR spectroscopy	Stale egg (HU)	185 white eggs	SNV-1st SG derivative-iPLS-SVMR model	88.0%	[19]
	Stale egg (freshness grade)	185 white eggs	SNV-1st SG derivative-PLSDA model	87.0%
	Stale egg	176 eggs	ICA-GA-ANN model	91.4%	[47]
	Stale egg (storage time)	66 brown eggs	SG-ANN model	87.3%	[48]
Hyper-spectral imaging	Stale egg (HU)	100 white eggs	SPA-SVMR model	84.0%	[13]
	Bubble in egg	80 white eggs	PCA-GLCM-SVMC model	90% (90°)
	Scattered yolk in egg	80 white eggs	image-processing algorithm-SVMC model	96.3%
	Stale egg (HU)	33 white eggs	SNV-PLSR	85.0%	[14]
	Stale egg (HU)	150 brown eggs	0° scattering MSC-SPA model	100.0%	[15]
	Bloody egg	34 brown eggs	Normalization-SPA-SVM model	96.4% (Input 4)	[16]

FLI, fuzzy logic inference; Log, laplacian of gaussian; LFI, local fitting image; CNN, convolutional neural network; SMI, sequential multiple image; BiLSTM, bidirectional long-short-term-memory; HU, haugh unit; LM, levenberg-marquardt; ANN, artificial neural network; FFT, fast Fourier transform; DSP, digital signal processing; PCA, principal component analysis; QDA, quadratic discriminant analysis; MSC, multiplicative scatter correction; SBC, slope/bias correction; PLSDA, partial least square discriminant analysis; BLR, binary logistic regression; SNV, standard normal variate; SG, Savitzky Golay; iPLS, interval partial least square; SVMR, support vector machine regression; ICA, independent component analysis; GA, genetic algorithm; SPA, successive projection algorithm; GLCM, gray level co-occurrence matrix; SVMC, support vector machine classification; PLSR, partial least square regression; SVMC, support vector.