The study assessed the feasibility of merging data acquired from hyperspectral imaging (HSI) and electronic nose (e-nose) to develop a robust method for the rapid prediction of intramuscular fat (IMF) and peroxide value (PV) of pork meat affected by temperature and NaCl treatments. Multivariate calibration models for prediction of IMF and PV using median spectra features (MSF) and image texture features (ITF) from HSI data and mean signal values (MSV) from e-nose signals were established based on support vector machine regression (SVMR). Optimum wavelengths highly related to IMF and PV were selected from the MSF and ITF. Next, recurring optimum wavelengths from the two feature groups were manually obtained and merged to constitute "combined attribute features" (CAF) which yielded acceptable results with (Rc2 = 0.877, 0.891; RMSEC = 2.410, 1.109; Rp2 = 0.790, 0.858; RMSEP = 3.611, 2.013) respectively for IMF and PV. MSV yielded relatively low results with (Rc2 = 0.783, 0.877; RMSEC = 4.591, 0.653; Rp2 = 0.704, 0.797; RMSEP = 3.991, 0.760) respectively for IMF and PV. Finally, data fusion of CAF and MSV was performed which yielded relatively improved prediction results with (Rc2 = 0.936, 0.955; RMSEC = 1.209, 0.997; Rp2 = 0.895, 0.901; RMSEP = 2.099, 1.008) respectively for IMF and PV. The results obtained demonstrate that it is feasible to mutually integrate spectral and image features with volatile information to quantitatively monitor IMF and PV in processed pork meat. Graphical abstract.

中文翻译：

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基于高光谱成像和电子鼻的多传感器集成方法用于猪肉脂肪和过氧化物的定量分析。

这项研究评估了合并从高光谱成像（HSI）和电子鼻（e-nose）获得的数据以开发一种可靠的方法来快速预测受温度影响的猪肉的肌内脂肪（IMF）和过氧化物值（PV）的可行性和氯化钠处理。基于支持向量机回归（SVMR），使用HSI数据的中值光谱特征（MSF）和图像纹理特征（ITF）以及电子鼻信号的平均信号值（MSV）建立了用于预测IMF和PV的多元校准模型。从MSF和ITF中选择了与IMF和PV高度相关的最佳波长。接下来，手动获得了来自两个特征组的重复最佳波长，并将其合并以构成“组合属性特征”（CAF），得出的可接受结果为（Rc2 = 0.877，0.891; RMSEC = 2.410，1.109; Rp2 = 0.790，0.858; 对于IMF和PV，RMSEP = 3.611，2.013）。MSV对IMF和PV的结果相对较低，分别为（Rc2 = 0.783，0.877; RMSEC = 4.591，0.653; Rp2 = 0.704，0.797; RMSEP = 3.991，0.760）。最后，对CAF和MSV进行数据融合，得到的IMF和PV的预测结果相对有所改善，分别为（Rc2 = 0.936，0.955; RMSEC = 1.209，0.997; Rp2 = 0.895，0.901; RMSEP = 2.099，1.008）。获得的结果表明，将光谱和图像特征与挥发性信息相互整合以定量监测加工猪肉中的IMF和PV是可行的。图形概要。IMF和PV分别为0.760）。最后，对CAF和MSV进行数据融合，得到的IMF和PV的预测结果相对有所改善，分别为（Rc2 = 0.936，0.955; RMSEC = 1.209，0.997; Rp2 = 0.895，0.901; RMSEP = 2.099，1.008）。获得的结果表明，将光谱和图像特征与挥发性信息相互整合以定量监控加工猪肉中的IMF和PV是可行的。图形概要。IMF和PV分别为0.760）。最后，对CAF和MSV进行数据融合，得到的IMF和PV的预测结果相对有所改善，分别为（Rc2 = 0.936，0.955; RMSEC = 1.209，0.997; Rp2 = 0.895，0.901; RMSEP = 2.099，1.008）。获得的结果表明，将光谱和图像特征与挥发性信息相互整合以定量监控加工猪肉中的IMF和PV是可行的。图形概要。获得的结果表明，将光谱和图像特征与挥发性信息相互整合以定量监控加工猪肉中的IMF和PV是可行的。图形概要。获得的结果表明，将光谱和图像特征与挥发性信息相互整合以定量监控加工猪肉中的IMF和PV是可行的。图形概要。