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.