期刊
FOOD ANALYTICAL METHODS
卷 11, 期 12, 页码 3481-3494出版社
SPRINGER
DOI: 10.1007/s12161-018-1320-0
关键词
Colony-counting method; Hyperspectral imaging; Selection variable method; Rainbow-trout fish; Total viable count (TVC)
资金
- Shiraz University of Iran [GR-AGR-56]
The aims of this study were to investigate the effect of different reference data extraction (colony-counting) and selection variable methods (Regression Coefficient: RC; Forward and Stepwise Multiple Regression: FMR and SMR) on the performance of PLSR and MLR model to predict TVC value in rainbow-trout fish fillets. TVC values were measured based on manual and digital image (OpenCFU, IMJ, and Photoshop) counting methods. The most and lowest prediction powers were obtained for Photoshop-PLSR and OpenCFU-PLSR, respectively (R-p(2)=0.873 and 0.815; RMSEP=0.761 and 0.884 Log(10)CFU/g). In simplified-model FMR-MLR has superior performance (R-p(2)=0.89 and RMSEP=0.65 Log(10)CFU/g). In simplified PLSR model group, RC-PLSR showed better performance (R-p(2)=0.866 and RSMEP=0.782). This distribution map of TVC load was generated by transferring the FMR-Photoshop-MLR model to each pixel of the images. HSI technique revealed a great potential to determine TVC of rainbow-trout fillets and the type of colony counting method influenced on prediction power of the model.
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