Liu Renhao, Li Qingxu, Zhang Hongzhou
College of Mechanical and Electrical Engineering, Tarim University, Alar 843300, China.
College of Computer Science, Anhui University of Finance & Economics, Bengbu 233030, China.
Foods. 2024 Oct 10;13(20):3226. doi: 10.3390/foods13203226.
The Chinese mitten crab () is highly valued by consumers for its delicious taste and high nutritional content, including proteins and trace elements, giving it significant economic value. However, variations in taste and nutritional value among crabs from different regions lead to considerable price differences, fueling the prevalence of counterfeit crabs in the market. Currently, there are no rapid detection methods to verify the origin of Chinese mitten crabs, making it crucial to develop fast and accurate detection techniques to protect consumer rights. This study focused on Chinese mitten crabs from different regions, specifically Hongze Lake, Tuo Lake, and Weishan Lake, by collecting near-infrared (NIR) diffuse reflectance spectral data from both the abdomen and carapace regions of the crabs. To eliminate noise from the spectral data, pretreatment was performed using Savitzky-Golay (SG) smoothing, Standard Normal Variate (SNV) transformation, and Multiplicative Scatter Correction (MSC). Key wavelengths reflecting the origin of Chinese mitten crabs were selected using Competitive Adaptive Reweighted Sampling (CARS), Bootstrap Soft Shrinkage (BOSS), and Uninformative Variable Elimination (UVE) algorithms. Finally, Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Back Propagation Neural Network (BP) models were developed for rapid detection of crab origin. The results demonstrated that MSC provided the best preprocessing performance for NIR spectral data from both the abdomen and back of the crabs. For abdomen data, the SVM model developed using feature wavelengths selected by the CARS algorithm after MSC preprocessing achieved the highest accuracy () of 90.00%, with precision (), recall (), and F1-score for crabs from Weishan Lake at 89.29%, 86.21%, and 87.72%, respectively; for crabs from Tuo Lake at 86.96%, 95.24%, and 90.91%; and for crabs from Hongze Lake at 90.00%, 93.10%, and 91.53%. For carapace data, the SVM model based on wavelengths selected by the BOSS algorithm after MSC pretreatment achieved the best performance, with an of 87.50%, and , , and 1 for crabs from Weishan Lake at 77.14%, 93.10%, and 84.38%; for Tuo Lake crabs at 100%, 90.47%, and 95.00%; and for Hongze Lake crabs at 92.31%, 80.00%, and 85.71%. In conclusion, NIR spectroscopy can effectively detect the origin of Chinese mitten crabs, providing technical support for developing rapid detection instruments and thereby safeguarding consumer rights.
中华绒螯蟹因其美味的口感和丰富的营养成分，包括蛋白质和微量元素，深受消费者喜爱，具有很高的经济价值。然而，不同地区的螃蟹在口感和营养价值上存在差异，导致价格差异较大，这使得市场上假冒螃蟹的现象盛行。目前，尚无快速检测方法来验证中华绒螯蟹的产地，因此开发快速准确的检测技术对于保护消费者权益至关重要。本研究聚焦于来自不同地区的中华绒螯蟹，特别是洪泽湖、沱湖和微山湖的螃蟹，通过收集螃蟹腹部和背甲区域的近红外（NIR）漫反射光谱数据进行研究。为了消除光谱数据中的噪声，使用Savitzky-Golay（SG）平滑、标准正态变量（SNV）变换和多元散射校正（MSC）进行预处理。利用竞争性自适应重加权采样（CARS）、自助软收缩（BOSS）和无信息变量消除（UVE）算法选择反映中华绒螯蟹产地的关键波长。最后，开发了支持向量机（SVM）、卷积神经网络（CNN）和反向传播神经网络（BP）模型用于快速检测螃蟹产地。结果表明，MSC对螃蟹腹部和背部的近红外光谱数据提供了最佳的预处理性能。对于腹部数据，在MSC预处理后使用CARS算法选择的特征波长开发的SVM模型达到了最高准确率（）90.00%，微山湖螃蟹的精确率（）、召回率（）和F1分数分别为89.29%、86.21%和87.72%；沱湖螃蟹的精确率、召回率和F1分数分别为86.96%、95.24%和90.91%；洪泽湖螃蟹的精确率、召回率和F1分数分别为90.00%、93.10%和91.53%。对于背甲数据，在MSC预处理后基于BOSS算法选择的波长的SVM模型表现最佳，准确率为87.50%，微山湖螃蟹的精确率、召回率和F1分数分别为77.14%、93.10%和84.38%；沱湖螃蟹的精确率、召回率和F1分数分别为100%、90.47%和95.00%；洪泽湖螃蟹的精确率、召回率和F1分数分别为92.31%、80.00%和85.71%。总之，近红外光谱技术可以有效地检测中华绒螯蟹的产地，为开发快速检测仪器提供技术支持，从而保障消费者权益。
