Full
Outline
To meet the practical industrial demand for real-time, batch detection of freshness in white feather broiler meat, this study explores a multidimensional rapid method for assessing meat freshness and validates its validity.
This study focuses on the breast meat of white-feathered broilers, with freshness assessment as its central theme. Under refrigerated storage at 4 ℃, a gradient of storage durations from 0 to 144 hours was established.The pH and moisture content values obtained via rapid detection methods were validated in accordance with GB 5009.237—2016 National food safety standard-Determination of pH in foods and GB 5009.3—2016 National food safety standard-Determination of moisture in foods, respectively, to analyze their variation patterns as freshness deteriorates. Concurrently, hyperspectral imaging technology was employed to collect spectral data of the samples.Spectral preprocessing was conducted using Savitzky-Golay (S-G) smoothing combined with first-derivative transformation to extract characteristic wavelengths related to physicochemical indices. Subsequently, a freshness discriminant model for chicken breast meat was constructed based on the partial least squares regression (PLSR)algorithm.
The results of the rapid determination method showed no significant difference from those determined by GB 5009.237—2016 and GB 5009.3—2016 for pH and moisture content (P>0.05). Among them, the P values of pH and moisture content were 0.8141 and 0.2586 respectively. At the same time, the model constructed based on PLSR had good predictive performance for various physicochemical indicators, with the correlation coefficient (R2p)of the prediction set ranging from 0.74 to 0.87, and the predicted root mean square error (RMSEP) ranging from 0.12 to 1.80.
Spectral pre-processing can effectively reduce instrument noise and external interference and highlight characteristic absorption peaks. There is a significant difference in the characteristic wavelength spectral responses between fresh meat and spoiled meat. The constructed PLSR model can achieve accurate discrimination between fresh meat and spoiled meat. The multi-index collaborative detection system established in this study provides effective and reliable technical support for the monitoring of chicken breast meat quality and safety, and enriches the theoretical and practical research on non-destructive detection of chicken freshness.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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