Literature DB >> 33430393

NIR Analysis of Intact Grape Berries: Chemical and Physical Properties Prediction Using Multivariate Analysis.

Teodora Basile1, Antonio Domenico Marsico1, Rocco Perniola1.   

Abstract

Texture characteristics are valuable parameters in the perceived quality and overall acceptability of fresh fruit. The characterization of grape texture attributes, such as firmness and crunchiness, is usually performed by sensory analysis or instrumental texture analysis. Both methodologies are destructive. Hence, it is not possible to test multiple times or perform any other analysis on the same sample. In this article, near-infrared (NIR) spectroscopy was applied to intact berries of table grape cv. Regal Seedless. NIR spectra were employed to predict both the physical parameter "hardness", which is correlated with the crunchiness of berry flesh and the sweetness, which is correlated with the total soluble solids content (TSS, as °Brix). The chemometric analysis was carried out exclusively based on an open-source software environment, producing results readily usable for any operator, besides the specific level of experience with NIR spectroscopy.

Entities:  

Keywords:  NIR; R software; TSS; crunchiness; grape; hardness; iPLS

Year:  2021        PMID: 33430393      PMCID: PMC7827816          DOI: 10.3390/foods10010113

Source DB:  PubMed          Journal:  Foods        ISSN: 2304-8158


  9 in total

1.  Quality control and peak finding for proteomics data collected from nipple aspirate fluid by surface-enhanced laser desorption and ionization.

Authors:  Kevin R Coombes; Herbert A Fritsche; Charlotte Clarke; Jeng-Neng Chen; Keith A Baggerly; Jeffrey S Morris; Lian-Chun Xiao; Mien-Chie Hung; Henry M Kuerer
Journal:  Clin Chem       Date:  2003-10       Impact factor: 8.327

2.  Evaluation of anticancer drug-loaded nanoparticle characteristics by nondestructive methodologies.

Authors:  David Awotwe-Otoo; Ahmed S Zidan; Ziyaur Rahman; Muhammad J Habib
Journal:  AAPS PharmSciTech       Date:  2012-04-26       Impact factor: 3.246

3.  Comparison of NIRS approach for prediction of internal quality traits in three fruit species.

Authors:  Gabrieli Alves de Oliveira; Sylvie Bureau; Catherine Marie-Geneviève Claire Renard; Adaucto Bellarmino Pereira-Netto; Fernanda de Castilhos
Journal:  Food Chem       Date:  2013-08-02       Impact factor: 7.514

4.  Tailoring noise frequency spectrum to improve NIR determinations.

Authors:  Shaofei Xie; Bingren Xiang; Liyan Yu; Haishan Deng
Journal:  Talanta       Date:  2009-08-18       Impact factor: 6.057

5.  Influence of grape density and harvest date on changes in phenolic composition, phenol extractability indices, and instrumental texture properties during ripening.

Authors:  Luca Rolle; Susana Río Segade; Fabrizio Torchio; Simone Giacosa; Enzo Cagnasso; Fabio Marengo; Vincenzo Gerbi
Journal:  J Agric Food Chem       Date:  2011-08-02       Impact factor: 5.279

6.  Detection of Outliers in Projection-Based Modeling.

Authors:  Oxana Ye Rodionova; Alexey L Pomerantsev
Journal:  Anal Chem       Date:  2020-01-13       Impact factor: 6.986

7.  Dataset of Near-infrared spectroscopy measurement for amylose determination using PLS algorithms.

Authors:  P Sampaio; A Soares; A Castanho; A S Almeida; J Oliveira; C Brites
Journal:  Data Brief       Date:  2017-10-06

8.  On-The-Go VIS + SW - NIR Spectroscopy as a Reliable Monitoring Tool for Grape Composition within the Vineyard.

Authors:  Juan Fernández-Novales; Javier Tardáguila; Salvador Gutiérrez; María Paz Diago
Journal:  Molecules       Date:  2019-07-31       Impact factor: 4.411

9.  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
  9 in total
  3 in total

1.  Dataset of adulteration with water in coconut milk using FTIR spectroscopy.

Authors:  Agustami Sitorus; Muhamad Muslih; Irwin Syahri Cebro; Ramayanty Bulan
Journal:  Data Brief       Date:  2021-04-20

2.  Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy.

Authors:  Sergio Ghidini; Luca Maria Chiesa; Sara Panseri; Maria Olga Varrà; Adriana Ianieri; Davide Pessina; Emanuela Zanardi
Journal:  Foods       Date:  2021-04-18

3.  Use of Artificial Neural Networks and NIR Spectroscopy for Non-Destructive Grape Texture Prediction.

Authors:  Teodora Basile; Antonio Domenico Marsico; Rocco Perniola
Journal:  Foods       Date:  2022-01-20
  3 in total

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