Hyperspectral imaging (HSI) emerges as a non-destructive and rapid analytical tool for assessing food quality, safety, and authenticity. This work aims to investigate the potential of combining the spectral and spatial features of HSI data with the aid of deep learning approach for the pixel-wise classification of food products. We applied two strategies for extracting spatial-spectral features: (1) directly applying three-dimensional convolution neural network (3-D CNN) model; (2) first performing principal component analysis (PCA) and then developing 2-D CNN model from the first few PCs. These two methods were compared in terms of efficiency and accuracy, exemplified through two case studies, i.e., classification of four sweet products and differentiation between white stripe (“myocommata”) and red muscle (“myotome”) pixels on salmon fillets. Results showed that combining spectral-spatial features significantly enhanced the overall accuracy for sweet dataset, compared to partial least square discriminant analysis (PLSDA) and support vector machine (SVM). Results also demonstrated that spectral pre-processing techniques prior to CNN model development can enhance the classification performance. This work will open the door for more research in the area of practical applications in food industry.

中文翻译：

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食品的像素级分类的近红外高光谱图像的深光谱空间特征。

高光谱成像（HSI）成为一种用于评估食品质量，安全性和真实性的非破坏性快速分析工具。这项工作旨在研究借助深度学习方法将HSI数据的光谱和空间特征相结合以对食品进行像素分类的潜力。我们采用了两种提取空间光谱特征的策略：（1）直接应用三维卷积神经网络（3-D CNN）模型；（2）首先执行主成分分析（PCA），然后从前几台PC中开发二维CNN模型。比较了这两种方法的效率和准确性，并通过两个案例研究进行了举例说明，即四种甜品的分类以及鲑鱼片上白色条纹（“横纹肌瘤”）和红色肌肉（“肌节”）像素之间的区别。结果表明，与偏最小二乘判别分析（PLSDA）和支持向量机（SVM）相比，光谱空间特征的组合显着提高了甜数据集的整体准确性。结果还表明，在CNN模型开发之前进行光谱预处理可以增强分类性能。这项工作将为食品工业实际应用领域的更多研究打开大门。