This study innovatively proposes a feature fusion technique to determine fatty acid content during rice storage. Firstly, a self-developed olfactory visualization sensor was used to capture the odor information of rice samples at different storage periods and a portable spectroscopy system was employed to collect the near-infrared (NIR) spectra during rice storage. Then, principal component analysis (PCA) was performed on the pre-processed olfactory visualization sensor data and the NIR spectra, and the number of the best principal components (PCs) based on the single technique model was optimized during the backpropagation neural network (BPNN) modeling. Finally, the optimal PCs were fused at the feature level, and a BPNN detection model based on the fusion feature was established to achieve rapid measurement of fatty acid content during rice storage. The experimental results showed that the best BPNN model based on the fusion feature had a good predictive performance where the correlation coefficient (R_P) was 0.9265, and the root mean square error (RMSEP) was 1.1005 mg/100 g. The overall results demonstrate that the detection accuracy and generalization performance of the feature fusion model are an improvement on the single-technique data model; and the results of this study can provide a new technical method for high-precision monitoring of grain storage quality.

中文翻译：

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嗅觉可视化数据与近红外光谱特征融合的大米中脂肪酸含量测定

这项研究创新地提出了一种特征融合技术来确定稻米储藏期间的脂肪酸含量。首先，使用自行开发的嗅觉可视传感器来捕获不同存储时间段的大米样品的气味信息，并使用便携式光谱系统收集大米存储期间的近红外（NIR）光谱。然后，对预处理后的嗅觉可视化传感器数据和近红外光谱进行主成分分析（PCA），并在反向传播神经网络（BPNN）中优化基于单一技术模型的最佳主成分（PC）的数量。 ）建模。最后，在功能级别融合了最佳PC，建立了基于融合特征的BPNN检测模型，以实现水稻储藏过程中脂肪酸含量的快速测量。实验结果表明，基于融合特征的最佳BPNN模型具有良好的预测性能，其中相关系数（R_P）为0.9265，均方根误差（RMSEP）为1.1005 mg / 100 g。总体结果表明，特征融合模型的检测精度和泛化性能是对单技术数据模型的改进。研究结果为高精度监测粮食储藏质量提供了一种新的技术手段。