Literature DB >> 19084627

Rapid determination of sucrose in chocolate mass using near infrared spectroscopy.

Paulo Augusto da Costa Filho1.   

Abstract

This paper reports the results of a rapid method to determine sucrose in chocolate mass using near infrared spectroscopy (NIRS). We applied a broad-based calibration approach, which consists in putting together in one single calibration samples of various types of chocolate mass. This approach increases the concentration range for one or more compositional parameters, improves the model performance and requires just one calibration model for several recipes. The data were modelled using partial least squares (PLS) and multiple linear regression (MLR). The MLR models were developed using a variable selection based on the coefficient regression of PLS and genetic algorithm (GA). High correlation coefficients (0.998, 0.997, 0.998 for PLS, MLR and GA-MLR, respectively) and low prediction errors confirms the good predictability of the models. The results show that NIR can be used as rapid method to determine sucrose in chocolate mass in chocolate factories.

Entities:  

Year:  2008        PMID: 19084627     DOI: 10.1016/j.aca.2008.10.049

Source DB:  PubMed          Journal:  Anal Chim Acta        ISSN: 0003-2670            Impact factor:   6.558


  4 in total

1.  Monitoring of Water Spectral Pattern Reveals Differences in Probiotics Growth When Used for Rapid Bacteria Selection.

Authors:  Aleksandar Slavchev; Zoltan Kovacs; Haruki Koshiba; Airi Nagai; György Bázár; Albert Krastanov; Yousuke Kubota; Roumiana Tsenkova
Journal:  PLoS One       Date:  2015-07-02       Impact factor: 3.240

2.  Total lipid prediction in single intact cocoa beans by hyperspectral chemical imaging.

Authors:  Nicola Caporaso; Martin B Whitworth; Ian D Fisk
Journal:  Food Chem       Date:  2020-11-19       Impact factor: 7.514

3.  FT-NIR Analysis of Intact Table Grape Berries to Understand Consumer Preference Driving Factors.

Authors:  Teodora Basile; Antonio Domenico Marsico; Maria Francesca Cardone; Donato Antonacci; Rocco Perniola
Journal:  Foods       Date:  2020-01-17

4.  Chocolate Quality Assessment Based on Chemical Fingerprinting Using Near Infra-red and Machine Learning Modeling.

Authors:  Thejani M Gunaratne; Claudia Gonzalez Viejo; Nadeesha M Gunaratne; Damir D Torrico; Frank R Dunshea; Sigfredo Fuentes
Journal:  Foods       Date:  2019-09-20
  4 in total

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