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

• Published in: Foods Vol. 12; no. 14; p. 2756
• Main Authors: Jin, Peilin, Fu, Yifan, Niu, Renzhong, Zhang, Qi, Zhang, Mingyue, Li, Zhigang, Zhang, Xiaoshuan
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.07.2023; MDPI
• Subjects: Accuracy; Adaptive sampling; Algorithms; Atmosphere; Atmospheric models; Data collection; Efficiency; Food contamination; Food safety; Food science; Freshness; Infrared analysis; Infrared spectra; Infrared spectrometers; Infrared spectroscopy; Mathematical models; Meat; Meat processing; Meat products; Meat quality; modified atmosphere packaged; Modified atmospheres; Mutton; mutton quality detection; Near infrared radiation; near-infrared spectroscopy; Nondestructive testing; Packaging; Parameter modification; Portable equipment; Prediction models; Quality assessment; Quality control; Research methodology; Spectrometers; Spectrum analysis; Support vector machines; Technical services; Temperature; texture parameters; modified atmosphere packaged; near-infrared spectroscopy; texture parameters; mutton quality detection
• ISSN: 2304-8158; 2304-8158
• DOI: 10.3390/foods12142756

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 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 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.