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Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system

机译：在线高光谱成像系统快速预测脱水虾的水分含量

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摘要

Because the shape of prawn is not round, spectroscopy instruments cannot measure the spectra of the whole prawn without containing background information. In this study, an online hyperspectral imaging system in the spectral region of 380-1100 nm was developed to determine the moisture content of prawns at different dehydrated levels. Hyperspectral images of prawns were acquired at different dehydration periods. The spectra of prawns then were extracted from hyperspectral images based on 'Manual Prawn Mask' and 'Automatic Prawn Mask', respectively. Spectral data were analyzed using partial least squares regression (PLSR) and least-squares support vector machines (LS-SVM) to establish the calibration models, respectively. Successive projections algorithm (SPA) was first applied for the optimal wavelength selection in the hyperspectral image analysis. Out of 482 wavelengths, only twelve wavelengths (428,445, 544,569,629,672,697, 760, 827,917,958, and 999 nm) were selected by SPA as the optimum wavelengths for moisture prediction. Based on these optimum wavelengths, a multiple linear regression (MLR) calibration model was established and used to obtain the moisture distribution of each prawn. The overall results of this study revealed the potentiality of hyperspectral imaging as an objective and non-destructive method to obtain the content and distribution of moisture of prawns whose shapes are not round.
机译：由于虾的形状不是圆形，因此光谱仪器无法在不包含背景信息的情况下测量整个虾的光谱。在这项研究中，开发了一种在380-1100 nm光谱范围内的在线高光谱成像系统，以确定不同脱水水平下虾的水分含量。在不同的脱水时期采集大虾的高光谱图像。然后分别基于“手动对虾罩”和“自动对虾罩”从高光谱图像中提取对虾的光谱。分别使用偏最小二乘回归（PLSR）和最小二乘支持向量机（LS-SVM）分析光谱数据以建立校准模型。连续投影算法（SPA）首先用于高光谱图像分析中的最佳波长选择。通过SPA，在482个波长中，只有十二个波长（428,445、544,569,629,672,697、760、827,917,958和999 nm）被选为湿度预测的最佳波长。基于这些最佳波长，建立了多元线性回归（MLR）校准模型，并将其用于获得每个虾的水分分布。这项研究的总体结果表明，高光谱成像作为一种客观且非破坏性方法的潜力，可以用来获取形状不圆的虾的水分含量和分布。

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