用高光谱成像+机器学习无损识别牡蛎品种，准确率达100%
Non-destructive Identification of Oyster Species is possible from Hyperspectral Images with Machine Learning
- 用高光谱图像结合偏最小二乘判别分析，从牡蛎壳表面反射光谱区分物种
- 模型对左右壳分类准确率分别达100%和96%，显著优于卷积神经网络
- 适合水产养殖与供应链溯源，可快速无损检测活体牡蛎
区分牡蛎品种对培育适配生产系统的新型商业品种及保障海产品供应链可追溯性至关重要。传统方法如DNA分析具有破坏性且耗时。本研究探讨了利用高光谱成像（HSI）区分黑唇岩牡蛎（BL）与悉尼岩牡蛎（SR）的可能性。对156个活体样本的左右壳进行950–2515nm波段扫描，采用偏最小二乘判别分析（PLS-DA）与卷积神经网络（CNN）结合蒙特卡洛交叉验证训练模型。结果表明，PLS-DA在左右壳上的测试集分类准确率分别为100%和96%，优于CNN的83%和96%。电子显微镜分析显示，黑唇牡蛎右壳层数为4层，悉尼牡蛎为2层；其外层碳、氧浓度差异显著，黑唇富碳，悉尼富氧，可能反映几丁质与糖蛋白含量或组成的区别。模型识别出的关键波长对应于这些化合物的振动模式。透射分析表明光线可透过壳边缘，提示光谱信号可能受另一壳或肉质影响。研究证明了一种高效、快速、非破坏性的牡蛎物种识别方法。
原文摘要 · Abstract (English)
Differentiating between oyster species is important for developing new commercial oyster species suited to production systems and is critical for traceability in seafood supply chains. Common methods, such as DNA profiling, are destructive and time consuming. The possibility of using hyperspectral imaging (HSI) for discriminating between Black-Lip rock (BL) and Sydney rock (SR) oysters was investigated. Live BL and SR samples (N = 156) were scanned with a HSI camera (950-2515nm). Partial Least Square Discriminant Analysis and Convolutional Neural Networks were trained with Monte Carlo Cross Validation to distinguish BL and SR oysters from the spectral reflectance of their left and rights valves. The PLS-DA model successfully distinguished between the species from both the left and right valves with a median test set classification accuracy of 100%, out performing the CNN with 83% and 96% respectively. Elemental and mineralogical composition in the surface and cross-section of oyster valves were measured with electron microscopy. Analysis of the right valve revealed a greater number of layers in BL compared to SR (4 vs 2). The concentrations of carbon and oxygen varied in the outer layer of the right valves, with BL being rich in carbon and SR being rich in oxygen. The variation in carbon and oxygen concentrations observed between BL and SR right valves may reflect differences in the relative abundance or composition of chitin and glycoproteins. This is supported by model-derived wavelength importance corresponding to vibrational modes of functional groups characteristic of these compounds. Transmittance analysis revealed that light was transmitted through the valves, around the valve edges, indicating that the spectral signatures may have been influenced by the other valve or the meat. Ultimately, the findings highlight an effective rapid, non-destructive methodology for oyster species.
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