Rotation and noise invariant near-infrared face recognition by means of Zernike moments and spectral regression discriminant analysis

2013 ◽  
Vol 22 (1) ◽  
pp. 013030 ◽  
Author(s):  
Sajad Farokhi ◽  
Siti Mariyam Shamsuddin ◽  
Jan Flusser ◽  
Usman Ullah Sheikh ◽  
Mohammad Khansari ◽  
...  
2015 ◽  
Vol 316 ◽  
pp. 234-245 ◽  
Author(s):  
Sajad Farokhi ◽  
Usman Ullah Sheikh ◽  
Jan Flusser ◽  
Bo Yang

2014 ◽  
Vol 31 ◽  
pp. 13-27 ◽  
Author(s):  
Sajad Farokhi ◽  
Siti Mariyam Shamsuddin ◽  
U.U. Sheikh ◽  
Jan Flusser ◽  
Mohammad Khansari ◽  
...  

Author(s):  
Ilaria Lanza ◽  
Daniele Conficoni ◽  
Stefania Balzan ◽  
Marco Cullere ◽  
Luca Fasolato ◽  
...  

Abstract Near-infrared (NIR) spectroscopy is a rapid technique able to assess meat quality even if its capability to determine the shelf life of chicken fresh cuts is still debated, especially for portable devices. The aim of the study was to compare bench-top and portable NIR instruments in discriminating between four chicken breast refrigeration times (RT), coupled with multivariate classifier models. Ninety-six samples were analysed by both NIR tools at 2, 6, 10 and 14 days post-mortem. NIR data were subsequently submitted to partial least squares discriminant analysis (PLS-DA) and canonical discriminant analysis (CDA). The latter was preceded by double feature selection based on Boruta and Stepwise procedures. PLS-DA sorted moderate separation of RT theses, while shelf life assessment was more accurate on application of Stepwise-CDA. Bench-top tool had better performance than portable one, probably because it captured more informative spectral data as shown by the variable importance in projection (VIP) and restricted pool of Stepwise-CDA predictive scores (SPS). NIR tools coupled with a multivariate model provide deep insight into the physicochemical processes occurring during storage. Spectroscopy showed reliable effectiveness to recognise a 7-day shelf life threshold of breasts, suitable for routine at-line application for screening of meat quality.


2021 ◽  
pp. 096703352098731
Author(s):  
Adenilton C da Silva ◽  
Lívia PD Ribeiro ◽  
Ruth MB Vidal ◽  
Wladiana O Matos ◽  
Gisele S Lopes

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.


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