In this study, two distinct hyperspectral imaging systems, the Visible and Near-Infrared Spectroscopy (VNIR) and the Short-Wave Infrared Spectroscopy (SWIR), were employed in combine with chemometric techniques to examine the alterations in the texture properties (hardness, resilience, chewiness, and cohesiveness) of tilapia during the crispy process. Partial least squares regression (PLSR) was used to establish a linear relationship between the spectral reflectance values and the different texture parameters of tilapia, and various preprocessing, variable selection, and data fusion (Low-Level Fusion (LLF) and Mid-Level Fusion (MLF)) methods were considered to optimize the calibration model. For hardness, with R, RMSEP, %RMSEP and RPD values of 0.63, 5.84, 21.65% and 1.26 respectively. For resilience, the LLF presented superior performance, with R, RMSEP, %RMSEP and RPD values of 0.92, 0.03, 6.8% and 2.52 respectively. For chewiness, the MLF presented superior performance, with R, RMSEP, %RMSEP and RPD values of 0.68, 4.03, 23.05% and 1.36. For cohesiveness, the MLF exhibited the best performance, with R, RMSEP, %RMSEP and RPD values of 0.82, 0.03, 4.85% and 1.65 respectively. It was demonstrated that the fusion of both hyperspectral datasets provides a promising method for non-destructive texture measurement of tilapia at different stages of crispiness.

中文翻译：

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利用高光谱成像和数据融合快速无损评估脆皮罗非鱼脆化过程中质地特性的变化

在这项研究中，采用两种不同的高光谱成像系统，即可见光和近红外光谱（VNIR）以及短波红外光谱（SWIR），与化学计量技术相结合，以检查纹理特性（硬度、弹性）的变化。罗非鱼在酥脆过程中的口感、咀嚼性和粘着性）。采用偏最小二乘回归（PLSR）建立罗非鱼光谱反射率值与不同纹理参数之间的线性关系，并进行各种预处理、变量选择和数据融合（Low-Level Fusion（LLF）和Mid-Level Fusion） (MLF))方法被认为是优化校准模型。对于硬度，R、RMSEP、%RMSEP 和 RPD 值分别为 0.63、5.84、21.65% 和 1.26。在弹性方面，LLF 表现出优异的性能，R、RMSEP、%RMSEP 和 RPD 值分别为 0.92、0.03、6.8% 和 2.52。对于咀嚼性，MLF 表现出优越的性能，R、RMSEP、%RMSEP 和 RPD 值分别为 0.68、4.03、23.05% 和 1.36。对于内聚性，MLF表现出最好的性能，R、RMSEP、%RMSEP和RPD值分别为0.82、0.03、4.85%和1.65。事实证明，两个高光谱数据集的融合为罗非鱼不同脆度阶段的无损纹理测量提供了一种有前景的方法。