Detection of Chicken Freshness Utilizing VNIR, SWIR Spectroscopy, and Data Fusion

In light of the escalating demand for enhanced chicken quality and safety, there is an imperative need for an advanced methodology that can accurately and expeditiously ascertain the freshness of chicken. This study endeavors to harness hyperspectral imaging (HSI) technology, in synergy with machine learning and deep learning algorithms, to innovate a non-destructive method for the assessment of chicken freshness. In this study, chicken freshness was categorized into three distinct levels based on a comprehensive range of evaluation criteria specific to chicken freshness. Subsequent to preprocessing the spectra data, a discriminative model for chicken freshness predicated on visible and near-infrared (VNIR,400-1000 nm) and short-wave infrared (SWIR, 900-1700 nm) spectra was formulated utilizing both the raw and the preprocessed datasets. Consequently, key wavelengths were discerned via feature wavelengths selecting within the full spectra wavelengths, culminating in the establishment of the feature-wavelength model. The outcomes indicate that the VNIR-ResNet model, incorporating Normalization preprocessing, outperforms other full-spectra models, boasting an accuracy rate of 98.31%. Following feature wavelengths modeling, the precision of the feature selecting augmented by CARS and SPA was enhanced to 98.87%, respectively. Subsequently, a fusion model is developed through the application of a data fusion technique, the accuracy of data-layer fusion modeling was 98.87%, surpassing that derived from a singular data source, albeit the efficacy of feature-layer fusion modeling fell short of ideal. In summary, considering factors such as the cost and volume of hyperspectral data, the models such as MN-ResNet based on VNIR and MN-CARS-ResNet based on feature selecting emerge as more cost-effective and pragmatic solutions.

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