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Abstract：
The control of moisture content (MC) is essential in the drying of shrimp, directly impacting its quality and shelf life. This study aimed to develop an accurate method for determining shrimp MC by integrating hyperspectral imaging (HSI) with electronic nose (E-nose) technology. We employed three different data fusion approaches: pixel-, feature-, and decision-fusion, to combine HSI and E nose data for the prediction of shrimp MC. We developed partial least squares regression (PLSR) models for each method and compared their performance in terms of prediction accuracy. The decision fusion approach outperformed the other methods, producing the highest determination coefficients for both calibration (0.9595) and validation sets (0.9448). Corresponding root-mean square errors were the lowest for the calibration set (0.0370) and validation set (0.0443), indicating high prediction precision. Additionally, this approach achieved a relative percent deviation of 3.94, the highest among the methods tested. The findings suggest that the decision fusion of HSI and E nose data through a PLSR model is an effective, accurate, and efficient method for evaluating shrimp MC. The demonstrated capability of this approach makes it a valuable tool for quality control and market monitoring of dried shrimp products.
摘要：
水分含量（MC）的控制在虾的干燥中至关重要，直接影响其质量和保质期。本研究旨在通过将高光谱成像（HSI）与电子鼻（E-nose）技术相结合，开发一种准确的测定虾MC的方法。我们采用了三种不同的数据融合方法：像素-，feature-,和决策融合，结合HSI和E鼻数据进行对虾MC的预测。我们为每种方法开发了偏最小二乘回归（PLSR）模型，并在预测精度方面比较了它们的性能。决策融合方法优于其他方法，为校准集(0.9595)和验证集(0.9448)产生最高的测定系数。校准集（0.0370）和验证集（0.0443）的相应均方根误差最低，表明预测精度高。此外,这种方法实现了3.94的相对百分比偏差，在所测试的方法中最高。研究结果表明，通过PLSR模型对HSI和E鼻数据进行决策融合是一种有效的，准确,评价虾MC的有效方法。这种方法的证明能力使其成为干虾产品质量控制和市场监测的宝贵工具。
