Prediction of soluble solid content of Agaricus bisporus during ultrasound-assisted osmotic dehydration based on hyperspectral imaging

• Published in: Food science & technology Vol. 122; p. 109030
• Main Authors: Xiao, Kunpeng, Liu, Qiang, Wang, Liuqing, Zhang, Bin, Zhang, Wei, Yang, Wenjian, Hu, Qiuhui, Pei, Fei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2020
• Subjects: Competitive adaptive reweighted sampling; Near-infrared; Partial least square regression; Support vector machine; Visualization; Visualization; Support vector machine; Competitive adaptive reweighted sampling; Near-infrared; Partial least square regression
• ISSN: 0023-6438; 1096-1127
• DOI: 10.1016/j.lwt.2020.109030

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 full-band 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. •The SSC of Agaricus bisporus during different UOD periods was measured and analysed.•The SSC of Agaricus bisporus was predicted via PLSR and SVM based on HSI.•The CARS algorithm screened 67 key wavelengths from the 510 full-band wavelengths.•The OSC-CARS-SVM model provided the best fit for the SSC profile of sample.•The pseudo color image revealed the SSC distribution of sample during UOD.