Yu Guowei, Li Huihui, Li Yujie, Hu Yating, Wang Gang, Ma Benxue, Wang Huting
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
Analysis and Testing Center, Xinjiang Academy of Agricultural and Reclamation Sciences, Shihezi 832000, China.
Foods. 2023 Apr 22;12(9):1742. doi: 10.3390/foods12091742.
The problem of pyrethroid residues has become a topical issue, posing a potential food safety concern. Pyrethroid pesticides are widely used to prevent and combat pests in Hami melon cultivation. Due to its high sensitivity and accuracy, gas chromatography (GC) is used most frequently for detecting pyrethroid pesticide residues. However, GC has a high cost and complex operation. This study proposed a deep-learning approach based on the one-dimensional convolutional neural network (1D-CNN), named Deepspectra network, to detect pesticide residues on the Hami melon based on visible/near-infrared (380-1140 nm) spectroscopy. Three combinations of convolution kernels were compared in the single-scale Deepspectra network. The convolution group of "5 × 1" and "3 × 1" kernels obtained a better overall performance. The multiscale Deepspectra network was compared to three single-scale Deepspectra networks on the preprocessing spectral data and obtained better results. The coefficient of determination (R2) for lambda-cyhalothrin and beta-cypermethrin was 0.758 and 0.835, respectively. The residual predictive deviation (RPD) for lambda-cyhalothrin and beta-cypermethrin was 2.033 and 2.460, respectively. The Deepspectra networks were compared with two conventional regression models: partial least square regression (PLSR) and support vector regression (SVR). The results showed that the multiscale Deepspectra network outperformed the other models. It was found that the multiscale Deepspectra network could be a novel approach for the quantitative estimation of pyrethroid pesticide residues on the Hami melon. These findings can also provide an effective strategy for spectral analysis.
拟除虫菊酯残留问题已成为一个热门话题，引发了潜在的食品安全担忧。拟除虫菊酯类农药被广泛用于哈密瓜种植中的害虫防治。由于其高灵敏度和准确性，气相色谱法（GC）是最常用于检测拟除虫菊酯类农药残留的方法。然而，GC成本高且操作复杂。本研究提出了一种基于一维卷积神经网络（1D-CNN）的深度学习方法，即深度光谱网络（Deepspectra network），用于基于可见/近红外（380 - 1140 nm）光谱检测哈密瓜上的农药残留。在单尺度深度光谱网络中比较了三种卷积核组合。“5×1”和“3×1”核的卷积组获得了更好的整体性能。在预处理光谱数据上，将多尺度深度光谱网络与三个单尺度深度光谱网络进行了比较，得到了更好的结果。氯氟氰菊酯和高效氯氰菊酯的决定系数（R2）分别为0.758和0.835。氯氟氰菊酯和高效氯氰菊酯的残留预测偏差（RPD）分别为2.033和2.460。将深度光谱网络与两种传统回归模型进行了比较：偏最小二乘回归（PLSR）和支持向量回归（SVR）。结果表明，多尺度深度光谱网络优于其他模型。发现多尺度深度光谱网络可能是一种用于定量估计哈密瓜上拟除虫菊酯类农药残留的新方法。这些发现也可为光谱分析提供一种有效策略。
