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Non-Destructive Detection of the Freshness of Air-Modified Mutton Based on Near-Infrared Spectroscopy

Monitoring and identifying the freshness levels of meat holds significant importance in the field of food safety as it directly relates to human dietary safety. Traditional packaging methods for lamb meat quality assessment present issues such as cumbersome operations and irreversible damage. This r...

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Detalles Bibliográficos
Autores principales: Jin, Peilin, Fu, Yifan, Niu, Renzhong, Zhang, Qi, Zhang, Mingyue, Li, Zhigang, Zhang, Xiaoshuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10379075/
https://www.ncbi.nlm.nih.gov/pubmed/37509847
http://dx.doi.org/10.3390/foods12142756
Descripción
Sumario:Monitoring and identifying the freshness levels of meat holds significant importance in the field of food safety as it directly relates to human dietary safety. Traditional packaging methods for lamb meat quality assessment present issues such as cumbersome operations and irreversible damage. This research proposes a quality assessment method for modified atmosphere packaging lamb meat using near-infrared spectroscopy and multi-parameter fusion. Fresh lamb meat quality is taken as the research subject, comparing various physicochemical indicators and near-infrared spectroscopic information under different temperatures (4 °C and 10 °C) and different modified atmosphere packaging combinations. Through precision parameter comparison, rebound and TVB-N values are selected as the modeling parameters. Six spectral preprocessing methods (multi-scatter calibration, MSC; standard normal variate transformation, SNV; normalization; Savitzky–Golay smoothing, SG; Savitzky–Golay 1 derivative, SG-1st; and Savitzky–Golay 2 derivative, SG-2nd), and three feature wavelength selection methods (competitive adaptive reweighted sampling, CARS; successive projections algorithm, SPA; and uninformative variable elimination, UVE) are compared. Partial least squares (PLS) and support vector machine (SVM) are used to construct prediction models for chilled fresh lamb meat quality. The results show that when rebound is used as a parameter, the SG-2nd-SPA-PLSR model has the highest accuracy, with a determination coefficient R(2)p of 0.94 for the prediction set. When TVB-N is used as a parameter, the MSC-UVE-SVM model has the highest accuracy, with an R(2)p of 0.95 for the prediction set. In conclusion, the use of near-infrared spectroscopic analysis enables rapid and non-destructive prediction and evaluation of lamb meat freshness, including its textural characteristics and TVB-N content under different modified atmosphere packaging. This study provides a theoretical basis and technical support for further encapsulating the models into portable devices and developing portable near-infrared spectrometers to rapidly determine lamb meat freshness.