Texture Quality Analysis of Rainbow Trout Using Hyperspectral Imaging Method

Abstract

In this study a Hyperspectral Imaging System (Short Wave Infrared range from 1000 to 2500 nm) was used to model fish texture by experimental compression test. Partial Least SquareDiscriminate Analysis modeling technique was used for classifying the samples by linking the hyperspectral information and their measured texture. The R 2 of cross validation and prediction were 0.97 and 0.96, respectively. The Root Mean Squared Errors for cross validation and prediction were 0.07 and 0.09, respectively. Sensitivity and specificity for both class I and II were 1.00. Results indicated that hyperspectral imaging in SWIR range has ability to detect texture stiffness of rainbow trouts which is affected by freshness.

Development and Optimization of Cold Plasma Pretreatment for Drying on Corn Kernels

“…The regression model of drying rate has a coefficient of determination ( R 2 ) of 0.9876, adjusted R 2 of 0.9717 and RMSE of 0.0775. The good statistical indexes ( R 2 and RMSE ) suggests the experimental data conform to the model (Abasi, Minaei, & Khoshtaghaza, ; Khoshtaghaza, Khojastehnazhand, Mojaradi, Goodarzi, & Saeys, ). Drying rate is influenced by the cold plasma pretreatment time, cold plasma pretreatment power, and drying temperature.…”

Perspective Chapter: Hyperspectral Imaging for the Analysis of Seafood

“…Wang et al developed multivariate regression models based on the spectral data from commercial crisp grass carp (Ctenopharyngodon idellus) fillets to predict their hardness attributes using the spectral information in the VNIR spectral range [43]. Another research study demonstrated that the use of hyperspectral images in the SWIR spectral range is also suitable for the estimation of texture features in rainbow trout (Oncorhynchus mykiss) fillets [44]. The results of these studies showed high correlation between the predicted texture values fromthespectraldataandthetexturemeasurements.…”