Jin Peilin, Fu Yifan, Niu Renzhong, Zhang Qi, Zhang Mingyue, Li Zhigang, Zhang Xiaoshuan
College of Information Science and Technology, Shihezi University, Shihezi 832000, China.
Beijing Laboratory of Food Quality and Safety, College of Engineering, China Agricultural University, Beijing 100083, China.
Foods. 2023 Jul 20;12(14):2756. 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 Rp 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 Rp 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.
监测和识别肉类的新鲜度水平在食品安全领域具有重要意义，因为它直接关系到人类饮食安全。传统的羔羊肉品质评估包装方法存在操作繁琐和不可逆转的损害等问题。本研究提出了一种基于近红外光谱和多参数融合的气调包装羔羊肉品质评估方法。以新鲜羔羊肉品质为研究对象，比较了不同温度（4℃和10℃）和不同气调包装组合下的各种理化指标和近红外光谱信息。通过精确的参数比较，选择回弹值和TVB-N值作为建模参数。比较了六种光谱预处理方法（多元散射校正，MSC；标准正态变量变换，SNV；归一化；Savitzky-Golay平滑，SG；Savitzky-Golay一阶导数，SG-1st；和Savitzky-Golay二阶导数，SG-2nd）以及三种特征波长选择方法（竞争性自适应重加权采样，CARS；连续投影算法，SPA；和无信息变量消除，UVE）。采用偏最小二乘法（PLS）和支持向量机（SVM）构建冷却新鲜羔羊肉品质预测模型。结果表明，以回弹值为参数时，SG-2nd-SPA-PLSR模型精度最高，预测集的决定系数Rp为0.94。以TVB-N为参数时，MSC-UVE-SVM模型精度最高，预测集的Rp为0.95。总之，利用近红外光谱分析能够快速、无损地预测和评估羔羊肉新鲜度，包括其在不同气调包装下的质地特性和TVB-N含量。本研究为进一步将模型封装到便携式设备中并开发便携式近红外光谱仪以快速测定羔羊肉新鲜度提供了理论依据和技术支持。
