| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 2086805 | Innovative Food Science & Emerging Technologies | 2013 | 9 Pages |
This study was conducted to explore the potential of hyperspectral imaging (HSI) technique in visible and near-infrared (VIS–NIR) region (400–1700 nm) for determining the spatial distribution of moisture content (MC) in farmed Atlantic salmon fillets rapidly and non-destructively. The quantitative relationship between spectral data and the reference MC values was successfully established by partial least squares regression (PLSR). Three spectral ranges of 400–1000 nm (Spectral Range I), 900–1700 nm (Spectral Range II) and 400–1700 nm (Spectral Range III) were considered, and their results were compared to choose the best spectral range. The established PLSR models had coefficients of determination (RP2) of 0.893, 0.902 and 0.849, and root-mean-square errors of prediction (RMSEP) of 1.513%, 1.450% and 1.800% for three spectral ranges, respectively. Important wavelengths were then selected by using regression coefficients of PLSR models for three spectral ranges, and optimised PLSR models were built using only the important wavelengths, resulting in RP2 of 0.893, 0.888 and 0.884 with RMSEP of 1.517%, 1.553% and 1.578% for three spectral ranges, respectively. PLSR model with eight important wavelengths (420, 445, 545, 585, 635, 870, 925 and 955 nm) selected from Spectral Range I was considered as the best model for MC determination and was transferred to each pixel within the image for visualising MC in all locations of salmon fillets with an aid of a developed image processing algorithm. The results revealed that hyperspectral imaging technique has a great potential to predict the MC distribution of salmon fillets non-destructively and accurately. In addition to realising the MC difference within salmon fillets, it could be possible for hyperspectral imaging to classify and grade salmon fillets based on different MC levels.Industrial RelevanceThe results revealed that hyperspectral imaging technique has a great potential to predict the MC distribution of salmon fillets non-destructively and accurately for the food industry.
► We present hyperspectral imaging to determine moisture distribution in salmon fillets. ► We compare the predictive abilities of PLSR models with different spectral ranges. ► We develop multivariate models for predicting moisture with good accuracy. ► We select some important wavelengths to develop multispectral imaging system. ► We develop image processing algorithm to obtain moisture distribution maps.
