Prediction of soluble solid content of Agaricus bisporus during ultrasound-assisted osmotic dehydration based on hyperspectral imaging
Prediction of soluble solid content of Agaricus bisporus during ultrasound-assisted osmotic dehydration based on hyperspectral imaging
复制标题
基于高光谱成像的超声辅助渗透脱水过程中双孢蘑菇可溶性固形物含量预测
DOI：
10.1016/j.lwt.2020.109030
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发表时间：
2020-03-01
影响因子：
6
通讯作者：
Pei, Fei
中科院分区：
文献类型：
--
作者：
Xiao, Kunpeng;Liu, Qiang;Pei, Fei
可溶性固形物含量(SSC)是评价食品营养和风味质量的重要指标。这项研究提出了一种新的策略来预测超声辅助渗透脱水(UOD)过程中双孢菇切片中的SSC。通过高光谱成像(HSI)系统获取双孢菇的光谱特征，并使用不同的光谱预处理方法和模型来拟合和评价样品在UOD过程中的SSC行为。结果表明，经正交信号校正后的支持向量机对样品全波段光谱的拟合效果最好，具有较高的预测相关系数(R2 P，0.883)和剩余预测偏差(RPD，3.0 4)。此外，竞争自适应重加权采样(CARS)算法可以从复杂的原始全带波长中筛选出67个关键波长，而OSC-CARS-支持向量机模型对于简化的光谱显示出最好的SSC预测性能。此外，在伪彩色地图上显示了不同UOD时期样品的SSC分布，进一步揭示了UOD过程中样品的SSC分布。结果表明，HSI技术在快速、准确、无损地检测和预测双孢菇菌体悬浮物含量方面具有很大潜力。
Soluble solid content (SSC) is a critical index to evaluate the nutrition and flavor quality of food products. This study presents a novel strategy to predict the SSC in Agaricus bisporus slices during ultrasound-assisted osmotic dehydration (UOD). The spectral signatures of Agaricus bisporus were captured via a hyperspectral imaging (HSI) system and different spectral preprocessing methods and models were used to fit and evaluate the SSC behaviour of samples during UOD. The results showed that the support vector machine (SVM) preprocessed with orthogonal signal correction (OSC) provided the best fit for the full-band spectra of samples, with a higher correlation coefficient of prediction (R2 P, 0.883) and residual predictive deviation (RPD, 3.04). Moreover, the competitive adaptive reweighted sampling (CARS) algorithm can screen 67 key wavelengths from the complex original fullband wavelengths, and the OSC-CARS-SVM model showed the best predicted performance of SSC for the simplified spectra. In addition, the distribution of SSC in different UOD periods of the samples were demonstrated in a pseudo-colour map, which further revealed the SSC distribution of samples during UOD. The overall results showed the great potential of HSI to detect and predict the SSC of Agaricus bisporus rapidly, accurately, and non-destructively.