Liu Henan, Ma Sijia, Liang Ni, Wang Xin
School of Physical Science and Technology, Tiangong University, Tianjin 300387, China.
Foods. 2024 Dec 23;13(24):4182. doi: 10.3390/foods13244182.
The fast and accurate quantitative detection of camellia oil products is significant for multiple reasons. In this study, rice bran oil and corn oil, whose Raman spectra both hold great similarities with camellia oil, are blended with camellia oil, and the concentration of each composition is predicted by models with varying feature extraction methods and regression algorithms. Back propagation neural network (BPNN), which has been rarely investigated in previous work, is used to construct regression models, the performances of which are compared with models using random forest (RF) and partial least squares regression (PLSR). Independent component analysis (ICA), competitive adaptive reweighing sampling (CARS), and their dual combinations served to extract spectral features. In camellia oil adulteration with rice bran oil, both the ICA-BPNN and ICA-PLSR models are found to achieve satisfactory performances. For camellia oil adulteration with rice bran oil and corn oil, on the other hand, the performances of BPNN-based models are substantially deteriorated, and the best prediction accuracy is achieved by a PLSR model coupled with CARS-ICA. In addition to performance fluctuations with varying regression algorithms, the output for feature extraction method also played a vital role in ultimate prediction performance.
对山茶油产品进行快速、准确的定量检测具有多重意义。在本研究中，将拉曼光谱与山茶油极为相似的米糠油和玉米油与山茶油混合，并通过采用不同特征提取方法和回归算法的模型预测每种成分的浓度。以往研究很少涉及的反向传播神经网络（BPNN）被用于构建回归模型，并将其性能与使用随机森林（RF）和偏最小二乘回归（PLSR）的模型进行比较。独立成分分析（ICA）、竞争性自适应重加权采样（CARS）及其双重组合用于提取光谱特征。在山茶油掺假米糠油的情况下，发现ICA - BPNN和ICA - PLSR模型均取得了令人满意的性能。另一方面，对于山茶油掺假米糠油和玉米油的情况，基于BPNN的模型性能大幅下降，而结合CARS - ICA的PLSR模型实现了最佳预测精度。除了不同回归算法导致的性能波动外，特征提取方法的输出对最终预测性能也起着至关重要的作用。
