Franco Allegrini
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    Explore the profile of Franco Allegrini including associated specialties, affiliations and a list of published articles.
          
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              Articles
              12
            
            
              Citations
              61
            
            
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  Recent Articles
          1.
        
    
    Allegrini F, Olivieri A
  
  
    Anal Chim Acta
    . 2022 Sep;
          1226:340248.
    
    PMID: 36068054
  
  
          Concepts from data science, machine learning, deep learning and artificial neural networks are spreading in many disciplines. The general idea is to exploit the power of statistical tools to interpret...
      
2.
        
    
    Chiappini F, Allegrini F, Goicoechea H, Olivieri A
  
  
    Anal Chem
    . 2020 Aug;
          92(18):12265-12272.
    
    PMID: 32812757
  
  
          The use of machine learning for multivariate spectroscopic data analysis in applications related to process monitoring has become very popular since non-linearities in the relationship between signal and predicted variables...
      
3.
        
    
    Allegrini F, Braga J, Moreira A, Olivieri A
  
  
    Anal Chim Acta
    . 2018 Feb;
          1011:20-27.
    
    PMID: 29475481
  
  
          A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the...
      
4.
        
    
    Pellegrino Vidal R, Allegrini F, Olivieri A
  
  
    Anal Chim Acta
    . 2018 Jan;
          1003:10-15.
    
    PMID: 29317024
  
  
          Multivariate curve resolution-alternating least-squares (MCR-ALS) is the model of choice when dealing with some non-trilinear arrays, specifically when the data are of chromatographic origin. To drive the iterative procedure to...
      
5.
        
    
    Fragoso W, Allegrini F, Olivieri A
  
  
    Anal Chim Acta
    . 2016 Aug;
          933:43-9.
    
    PMID: 27496995
  
  
          Generalized analytical sensitivity (γ) is proposed as a new figure of merit, which can be estimated from a multivariate calibration data set. It can be confidently applied to compare different...
      
6.
        
    
    Allegrini F, Olivieri A
  
  
    Anal Chem
    . 2016 Jul;
          88(15):7807-12.
    
    PMID: 27363813
  
  
          With the proliferation of multivariate calibration methods based on artificial neural networks, expressions for the estimation of figures of merit such as sensitivity, prediction uncertainty, and detection limit are urgently...
      
7.
        
    
    Allegrini F, Wentzell P, Olivieri A
  
  
    Anal Chim Acta
    . 2015 Dec;
          903:51-60.
    
    PMID: 26709298
  
  
          Most of the current expressions used to calculate figures of merit in multivariate calibration have been derived assuming independent and identically distributed (iid) measurement errors. However, it is well known...
      
8.
        
    
    Ruckebusch C, Bernex R, Allegrini F, Sliwa M, Hofkens J, Dedecker P
  
  
    Anal Chem
    . 2015 Apr;
          87(9):4675-82.
    
    PMID: 25844921
  
  
          Recent advances in fluorescence bioimaging with single-molecule sensitivity have relied on the analysis and visualization of single-molecule data obtained on smart fluorophores. We describe an alternative method to enhance the...
      
9.
        
    
    Allegrini F, Olivieri A
  
  
    Anal Chem
    . 2014 Jul;
          86(15):7858-66.
    
    PMID: 25008998
  
  
          There is currently no well-defined procedure for providing the limit of detection (LOD) in multivariate calibration. Defining an estimator for the LOD in this scenario has shown to be more...
      
10.
        
    
    Allegrini F, Olivieri A
  
  
    Talanta
    . 2013 Sep;
          115:755-60.
    
    PMID: 24054659
  
  
          A new optimization strategy for multivariate partial-least-squares (PLS) regression analysis is described. It was achieved by integrating three efficient strategies to improve PLS calibration models: (1) variable selection based on...