This comprehensive review (open access) covers methods such as near-infrared spectroscopy (NIR), Fourier-transform near-infrared spectroscopy (FT-NIR), mid-infrared spectroscopy (MIR), ultraviolet-visible spectroscopy (UV-Vis), Raman spectroscopy, laser-induced breakdown spectroscopy (LIBS), hyperspectral imaging (HSI), and digital and thermal imaging techniques.
The authors consider that HSI and other imaging systems are best suited for solid samples measured in reflectance mode. These techniques are ideal for analyzing products like eggs, meat, fish, seafood, and milk powder. On the other hand, spectroscopy methods such as Raman, NIR, and FTIR spectroscopy can be adapted for both liquid (e.g., milk) and solid samples. These methods allow measurements in reflectance, transmittance, or absorbance modes.
Spectroscopic methods provide detailed chemical composition analysis for precise identification of changes in food samples that could signal loss of freshness or adulteration. However, detailed preprocessing steps are required, and some methods, like FTIR and NIR, are affected by scattering phenomena in turbid samples. In contrast, HSI and other imaging systems are highly effective for providing spatial information. This makes them valuable for visualizing structural differences, such as changes in surface texture or temperature caused by microbial activity, improper storage, or the presence of adulterants.
The authors consider that digital imaging is the most cost-effective method, making it accessible for routine inspections. However, it requires good lighting and environmental conditions for optimal results. Additionally, digital imaging is limited to surface-level analysis and cannot detect internal defects, such as egg freshness. For such applications, thermal imaging is required, though it comes at an additional cost.
Denaturation, spoilage, or adulteration can impact animal protein-based food quality and cause changes in protein conformation and composition, as well as high absorbance and reflectance signals.
CNN-based models can further automate the extraction of high-level features from images. In cases where limited datasets are available, data augmentation techniques, such as rotation, flipping, and scaling, are employed to increase dataset diversity and improve model performance. Additionally, resampling techniques like SMOTE can be applied to address class imbalances by generating synthetic samples of minority classes, enhancing model predictability without overfitting.
Often, selecting the optimal Machine Learning and modeling approach is not straightforward, leading to the application of multiple methods to achieve the desired analytical outcome. Models may perform poorly on new data due to model complexity, sample size and effect size. K-fold cross-validation is a common approach used in the studies reviewed in this paper. However, K-fold cross-validation assumes data point independence, which can lead to variability in results across different data splits. To mitigate this limitation, techniques such as stratified K-fold cross-validation or Leave-One-Out cross-validation can enhance model generalizability. Similarly, mechanisms like ECA, LRN, conjugate gradient, and sequential minimal optimization methods can be applied to improve the robustness and generalizability of CNN-based models.
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