Predicting pectin performance strength using near‐infrared spectroscopic data: A comparative evaluation of 1‐D convolutional neural network, partial least squares, and ridge regression modeling-Reference-Cited by-同舟云学术

Predicting pectin performance strength using near‐infrared spectroscopic data: A comparative evaluation of 1‐D convolutional neural network, partial least squares, and ridge regression modeling

Published:2021-05-18 Issue: Volume: Page:
ISSN:0886-9383
Container-title:Journal of Chemometrics
language:en
Short-container-title:Journal of Chemometrics

Author:

Einarson Kasper A.¹,Baum Andreas¹^ORCID,Olsen Terkel B.¹,Larsen Jan¹²,Armagan Ibrahim²,Santacoloma Paloma A.²,Clemmensen Line K. H.¹

Affiliation:

1. Applied Mathematics and Computer Science Technical University of Denmark Kgs Lyngby Denmark

2. Innovation CP Kelco Aps Lille Skensved Danmark Denmark

Funder

Innovationsfonden

Publisher

Wiley

Subject

Applied Mathematics,Analytical Chemistry

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cem.3348

Reference29 articles.

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2. Prediction of Pectin Yield and Quality by FTIR and Carbohydrate Microarray Analysis

3. Comparative vibrational spectroscopy for determination of quality parameters in amidated pectins as evaluated by chemometrics

4. Using deep learning to evaluate peaks in chromatographic data;Risum AB;Talanta,2019

5. Deep convolutional neural networks for Raman spectrum recognition: a unified solution

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