Machine learning supported ground beef freshness monitoring based on near-infrared and paper chromogenic array

IF 7.4 Q1 FOOD SCIENCE & TECHNOLOGY Food frontiers Pub Date : 2024-06-24 DOI:10.1002/fft2.438
Yihang Feng, Yi Wang, Burcu Beykal, Zhenlei Xiao, Yangchao Luo
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Abstract

Maintaining freshness and quality is crucial in the meat industry, as lipid oxidation can lead to undesirable odors, flavors, and potential health risks. Traditional methods for assessing meat freshness often involve time-consuming and destructive techniques, highlighting the need for rapid, noninvasive approaches. Recent advancements in spectroscopic and chromogenic sensor array technologies have opened up new avenues for monitoring meat quality parameters, offering the potential for real-time, accurate, and cost-effective solutions. As thiobarbituric acid reactive substances (TBARS) value is a classic indicator of meat lipid oxidation, this study investigated the data fusion of near-infrared spectroscopy (NIR) and paper chromogenic array (PCA) for monitoring ground beef TBARS. A standardized PCA was fabricated by photolithography with nine chemoresponsive dyes. Changes in ground beef volatile organic compounds during storage were captured in the shifts of PCA color patterns. Nippy, an open-source Python module, was used for automated NIR spectra preprocessing. The optimal preprocessing pipeline was found by 10-fold cross-validation in machine learning model development. Among optimized models, partial least square regression showed the best performance in coefficient of determination (R2) of .9477, root mean squared error of prediction of 0.0545 mg malondialdehyde/kg meat, and residual prediction deviation of 4.3717. The promising result of this study indicated the potential for NIR and PCA combinations to monitor TBARS values for ground beef freshness assessment.

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基于近红外和纸质色原阵列的机器学习支持碎牛肉新鲜度监测
保持肉类的新鲜度和质量对肉类行业至关重要,因为脂质氧化会导致不良气味、味道和潜在的健康风险。评估肉类新鲜度的传统方法通常涉及耗时且具有破坏性的技术,因此需要快速、无创的方法。光谱和色度传感器阵列技术的最新进展为监测肉类质量参数开辟了新途径,提供了实时、准确和经济高效的解决方案。由于硫代巴比妥酸活性物质(TBARS)值是肉类脂质氧化的典型指标,本研究探讨了近红外光谱(NIR)与纸质色原阵列(PCA)的数据融合,以监测碎牛肉的 TBARS。通过光刻技术用九种化学显色染料制作了标准化的 PCA。通过 PCA 颜色图案的变化来捕捉碎牛肉在贮藏过程中挥发性有机化合物的变化。Nippy 是一个开源 Python 模块,用于自动近红外光谱预处理。在机器学习模型开发过程中,通过 10 倍交叉验证找到了最佳预处理管道。在优化模型中,偏最小二乘法回归表现最佳,其判定系数(R2)为 0.9477,预测均方根误差为 0.0545 毫克丙二醛/千克肉,预测残差为 4.3717。这项研究的良好结果表明,近红外和 PCA 组合具有监测 TBARS 值以评估碎牛肉新鲜度的潜力。
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10.50
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10 weeks
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