Non-invasive lactate- and pH-monitoring in porcine meat using Raman spectroscopy and chemometrics
Non-invasive lactate- and pH-monitoring in porcine meat using Raman spectroscopy and chemometrics
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DOI：
10.1016/j.chemolab.2015.02.002
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发表时间：
2015-03-15
影响因子：
3.9
通讯作者：
Hitzmann, Bernd
中科院分区：
文献类型：
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作者：
Nache, Marius;Scheier, Rico;Hitzmann, Bernd
研究了利用化学计量学和拉曼光谱作为一种快速、非侵入性的方法来监测宰后早期猪肉中乳酸积累和pH下降的可行性。对于这一应用，尚未建立在线监测方法。基于猪半膜肌的原始拉曼光谱，研究了一系列光谱预处理和多元校正技术，以建立和检验猪肉品质参数的在线预测模型。比较了预处理方法对线性和非线性预测算法的预测速度、鲁棒性和准确性的影响。最有效的化学计量学评价程序的识别进行使用最小二乘线性回归与局部加权回归和元启发式数据优化方法，如遗传算法和蚁群优化。本文提出的分析表明，应用于标准正态变量（SNV）归一化拉曼光谱的局部加权回归提供了最准确和稳健的模型，pH和乳酸的交叉验证决定系数（r（cv）（2））为0.97，乳酸预测的交叉验证均方根误差（RMSECV）为4.5 mmol/kg，pH预测的交叉验证均方根误差（RMSECV）为0.06 pH单位。这些结果表明，结合化学计量学和拉曼光谱在线肉类质量控制应用的巨大潜力。(C)2015爱思唯尔B. V.保留所有权利。
The feasibility of using chemometrics and Raman spectroscopy as a fast and non-invasive method to monitor the early postmortem lactate accumulation and pH decline in pork meat has been investigated. For this application, an on-line monitoring methodology has not yet been established. Based on raw Raman spectra of porcine semimembranosus muscles, a range of spectral pre-processing and multivariate calibration techniques were investigated to develop and test on-line prediction models for the meat quality parameters. The influence of the pre-processing methods on the prediction speed, robustness and accuracy performance of the employed linear and non-linear algorithms was compared. Identification of the most effective chemometric evaluation procedure was performed using least square linear regression together with locally weighted regression and metaheuristic data optimization methods such as the genetic algorithm and the ant colony optimization. The herein presented analysis suggests that the locally weighted regression applied to the standard normal variate (SNV) normalized Raman spectra provides the most accurate and robust models with a cross-validated coefficient of determination (r(cv)(2)) of 0.97 for pH and lactate, a cross-validated root mean square error (RMSECV) of 4.5 mmol/kg for the lactate prediction and 0.06 pH-units for the pH prediction. These results demonstrate the great potential of combining chemometrics and Raman spectroscopy for on-line meat quality control applications. (C) 2015 Elsevier B.V. All rights reserved.