Sun Xudong, Li Jiajun, Shen Yun, Li Wenping
School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang, China.
Institute of Space Science and Technology, Nanchang University, Nanchang, China.
Front Nutr. 2021 Oct 18;8:757491. doi: 10.3389/fnut.2021.757491. eCollection 2021.
Non-destructive testing of low-density and organic foreign bodies is the main challenge for food safety control. Terahertz time-domain spectroscopy (THz-TDS) and imaging technologies were applied to explore the feasibility of detection for insect foreign bodies in the finishing tea products. THz-TDS of tea leaves and foreign bodies of insects demonstrated significant differences in terms of time domain and frequency signals in the range of 0.3-1.0 THz. These signals were corrected by the use of adaptive iteratively reweighted penalized least squares (AirPLS), asymmetric least squares (AsLS), and baseline estimation and de-noising using sparsity (BEADS) for reducing baseline drift and enhancing effective spectral information. The K-nearest neighbor (KNN) and partial least squares discrimination analysis (PLS-DA) models showed the best performance after AirPLS correction with the prediction accuracy of 98 and 100%, respectively. In addition, the locations and outlines of insect bodies could be clearly presented the THz-TDS image. These results suggested that THz-TDS spectroscopy and imaging provide an alternative tool for the detection of insect foreign bodies in finishing tea products.
低密度和有机异物的无损检测是食品安全控制的主要挑战。太赫兹时域光谱(THz-TDS)和成像技术被用于探索检测成品茶中昆虫异物的可行性。茶叶和昆虫异物的太赫兹时域光谱在0.3 - 1.0太赫兹范围内的时域和频域信号方面表现出显著差异。通过使用自适应迭代重加权惩罚最小二乘法(AirPLS)、非对称最小二乘法(AsLS)以及基于稀疏性的基线估计和去噪(BEADS)对这些信号进行校正,以减少基线漂移并增强有效光谱信息。K近邻(KNN)和偏最小二乘判别分析(PLS-DA)模型在经过AirPLS校正后表现最佳,预测准确率分别为98%和100%。此外,在太赫兹时域光谱图像中可以清晰呈现昆虫身体的位置和轮廓。这些结果表明,太赫兹时域光谱和成像为检测成品茶中的昆虫异物提供了一种替代工具。