《可見(jiàn)/近紅外高光譜成像技術(shù)快速評(píng)估魚(yú)肉品質(zhì)Rapid Fish Meat Quality Evluation by Visible/Near-infrared》
作者：
何鴻舉
出版日期：
2018-05-01
字?jǐn)?shù)：
137000
開(kāi)本：
異16
頁(yè)數(shù)：
148
分類(lèi)：
食品科技
ISBN：
978-7-5184-1908-1
定價(jià)：
￥80.00
官網(wǎng)優(yōu)惠價(jià)格：
￥64
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圖書(shū)目錄
Chapter 1 Recent Progresses on Spectroscopy and Imaging for Fish Meat Quality Evaluation and
Assessment
1.1 Fish meat quality
1.2 Spectroscopy and Computer/Machine vision
1.2.1 VIS/NIR spectrosco……Chapter 1 Recent Progresses on Spectroscopy and Imaging for Fish Meat Quality Evaluation and
Assessment
1.1 Fish meat quality
1.2 Spectroscopy and Computer/Machine vision
1.2.1 VIS/NIR spectroscopy
1.2.2 Computer/Machine vision
1.2.3 Hyperspectral Imaging (HSI)
1.3 VIS/NIR spectroscopy for fish meat quality evaluation
1.3.1 Chemical composition measurement
1.3.2 Quality inspection and differentiation
1.3.3 Microbial spoilage detection
1.4 Computer/machine vision for fish meat quality evaluation
1.4.1 Physical attributes measurement
1.4.2 Chemical component determination
1.4.3 Classification/sorting
1.5 HSI technique for fish meat quality evaluation
1.5.1 Chemical and physical attributes prediction
1.5.2 Parasites detection
1.5.3 Differentiation and classification
Chapter 2 Materials and Methods
2.1 Sample collection and preparation
2.2 Measurement of quality attributes
2.2.1 Moisture
2.2.2 Drip loss
2.2.3 pH
2.2.4 Tenderness (Warner–Bratzler shear force, WBSF)
2.2.5 Lactic acid bacteria (LAB)
2.2.6 Pseudomonas
2.2.7 Enterobacteriaceae
2.3 HSI system
2.4 Hyperspectral image acquisition
2.5 Hyperspectral image pre-processing
2.5.1 Image calibration
2.5.2 ROI identification and spectral extraction
2.6 Multivariate data analysis
2.6.1 Partial least squares (PLS) regression
2.6.2 Multiple linear regression (MLR)
2.6.3 Least squares-support vector machine (LS-SVM)
2.7 Validation of calibration models
2.8 Performance evaluation of calibration models
2.9 Selection of important wavelengths
2.9.1 Regression coefficients (RC)
2.9.2 Successive projections algorithm (SPA)
2.9.3 Competitive adaptive reweighted sampling (CARS)
2.10 Image post-processing (Visualisation)
Chapter 3 Quantitative Prediction of Moisture Distribution in Salmon Meat Fillets Using VIS/NIR HSI
3.1 Materials and methods
3.1.1 Sample preparation
3.1.2 HSI system and image acquisition
3.1.3 Reference moisture analysis
3.1.4 Spectra extraction
3.1.5 PLS calibration
3.1.6 Evaluation of PLS models
3.1.7 Selection of important wavelengths
3.1.8 MC distribution map
3.2 Results and discussion
3.2.1 Spectral features
3.2.2 Prediction of MC using full spectral range
3.2.3 Modelling of MC by PLS using only important wavelengths
3.2.4 Distribution map of MC
3.3 Conclusions
Chapter 4 Evaluation of Drip Loss and pH Distribution in Salmon Meat Fillets Using VIS/NIR HSI
4.1 Materials and methods
4.1.1 Sample preparation
4.1.2 HSI system and image acquisition
4.1.3 Reference drip loss and pH analysis
4.1.4 Spectra extraction
4.1.5 PLS calibration
4.1.6 Validation of PLS models
4.1.7 Selection of important wavelengths
4.1.8 Drip loss and pH distribution map
4.2 Results and discussion
4.2.1 Spectral characteristics
4.2.2 Prediction of drip loss and pH by PLS using full wavelength
4.2.3 Important wavelength selection and establishment of new PLS models
4.2.4 Distribution map of drip loss and pH
4.3 Conclusions
Chapter 5 HSI Combined with Chemometric Analysis for Determining Tenderness in Raw Salmon
Meat Fillets
5.1 Materials and methods
5.1.1 Sample preparation
5.1.2 HSI system and image acquisition
5.1.3 Reference tenderness analysis
5.1.4 Spectra extraction
5.1.5 PLS calibration
5.1.6 Model evaluation
5.1.7 Selection of important wavelengths
5.1.8 Visualisation of WBSF distribution
5.2 Results and discussion
5.2.1 Spectral features of salmon meat fillets
5.2.2 Modelling based on full wavelengths
5.2.3 Modelling based on optimal wavelengths
5.2.4 Distribution map of WBSF
5.3 Conclusions
Chapter 6 Prediction of Lactic Acid Bacteria (LAB) in Salmon Meat Using NIR HSI and Chemometrics
6.1 Materials and methods
6.1.1 Sample preparation
6.1.2 HSI system and image acquisition
6.1.3 Reference LAB analysis
6.1.4 Spectra extraction
6.1.5 Model calibration
6.1.6 Model evaluation
6.1.7 Selection of important wavelengths
6.1.8 Visualisation of LAB
6.2 Results and discussion
6.2.1 Spectral features of samples
6.2.2 Spectral analysis based on full wavelength
6.2.3 MIWs selection by CARS
6.2.4 Calibration with MIWs selected by CARS
6.2.5 Maps of LAB distribution
6.3 Conclusions
Chapter 7 Prediction of Pseudomonas Counts in Salmon Meat Fillets Using NIR HSI
7.1 Materials and methods
7.1.1 Sample preparation
7.1.2 HSI system and image acquisition
7.1.3 Reference PC analysis
7.1.4 Spectra extraction
7.1.5 PLS calibration
7.1.6 Validation of PLS models
7.1.7 Selection of important wavelengths
7.1.8 Visualisation of PC spoilage
7.2 Results and discussion
7.2.1 Spectral features of samples
7.2.2 PLS analysis with full wavelengths
7.2.3 MEW selection and model optimisation
7.2.4 Maps of PC distribution
7.3 Conclusions
Chapter 8 Prediction of Enterobacteriaceae Contamination in Salmon Meat by Applying Informative
Spectral Wavelengths
8.1 Materials and methods
8.1.1 Sample preparation
8.1.2 HSI system and image acquisition
8.1.3 Reference Enterobacteriaceae loads
8.1.4 Spectra extraction
8.1.5 PLS calibration
8.1.6 Evaluation of PLS models
8.1.7 Selection of informative wavelengths
8.1.8 Visualisation of Enterobacteriaceae distribution
8.2 Results and discussion
8.2.1 Spectral profiles of salmon samples
8.2.2 PLS analysis based on full range spectra
8.2.3 Wavelength selection and model optimisation
8.2.4 Distribution of Enterobacteriaceae loads
8.3 Conclusions
References
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