Automatic Fruit Sorting by Non-Destructive Determination of Quality Parameters Using Visible-near Infrared to Improve Wine Quality: II. Regression Analysis

NIR news ◽  
2014 ◽  
Vol 25 (1) ◽  
pp. 4-6 ◽  
Author(s):  
Christian Negara ◽  
Kai-Uwe Vieth ◽  
Magali Lafontaine ◽  
Maximilian Freund
Keyword(s):  
Regression Analysis ◽  
Near Infrared ◽  
Quality Parameters ◽  
Wine Quality ◽  
Fruit Sorting ◽  
Non Destructive

Rapid and non-destructive determination of quality parameters in the ‘Tommy Atkins’ mango using a novel handheld near infrared spectrometer

2016 ◽  
Vol 197 ◽  
pp. 1207-1214 ◽  
Author(s):  
Emanuel José Nascimento Marques ◽  
Sérgio Tonetto de Freitas ◽  
Maria Fernanda Pimentel ◽  
Celio Pasquini
Keyword(s):  
Near Infrared ◽  
Quality Parameters ◽  
Infrared Spectrometer ◽  
Near Infrared Spectrometer ◽  
Non Destructive

scholarly journals Non-Destructive Micromagnetic Determination of Hardness and Case Hardening Depth Using Linear Regression Analysis and Artificial Neural Networks

Metals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 18
Author(s):  
Rahel Jedamski ◽  
Jérémy Epp
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Case Hardening ◽  
Artificial Neural ◽  
Non Destructive

Non-destructive determination of workpiece properties after heat treatment is of great interest in the context of quality control in production but also for prevention of damage in subsequent grinding process. Micromagnetic methods offer good possibilities, but must first be calibrated with reference analyses on known states. This work compares the accuracy and reliability of different calibration methods for non-destructive evaluation of carburizing depth and surface hardness of carburized steel. Linear regression analysis is used in comparison with new methods based on artificial neural networks. The comparison shows a slight advantage of neural network method and potential for further optimization of both approaches. The quality of the results can be influenced, among others, by the number of teaching steps for the neural network, whereas more teaching steps does not always lead to an improvement of accuracy for conditions not included in the initial calibration.

Simultaneous Determination of the Main Constituents of Viscose Spinning Solutions by Visible near Infrared Spectroscopy

1997 ◽  
Vol 5 (3) ◽  
pp. 135-148 ◽  
Author(s):  
S. Sollinger ◽  
M. Voges
Keyword(s):  
Near Infrared ◽  
Quality Parameters ◽  
Spectroscopic Technique ◽  
Cellulose Content ◽  
Careful Monitoring ◽  
Cellulose Fibres ◽  
Chemical Determination ◽  
Analytical Procedures ◽  
Product Control

The production of cellulose fibres by wet fibres spinning requires a careful monitoring of the spinning bath and especially the spinning solution in terms of product control and for quality assurance purposes. The chemical composition as well as the ripening of the spinning solution are of major importance for maintaining a constant product quality. The conventional determination of the quality parameters of the viscose spinning solution are rather time- and labour-intensive due to the fact that several independent analytical procedures are involved which score low in time efficiency themselves. Briefly, the complete characterisation of the spinning solution requires an iodometric titration of the cellulose xanthogenate (γ-number), an acidimetric titration of the sodium hydroxide (NaOH) content, a UV-VIS spectroscopic determination of the trithiocarbonate (TTC) content and a time-consuming gravimetric cellulose content determination. Sometimes, also, a colloid chemical determination of the degree of ripening (Hottenroth number) is performed in the plant control laboratory. With this work, an approach will be demonstrated, which enables the substitution of these numerous analytical procedures by a single and time-efficient method—a VIS-NIR spectroscopic technique. Therefore, it is possible to determine the parameters: NaOH, TTC, cellulose xanthogenate content and the cellulose content of the viscose spinning solution simultaneously with a reasonable precision within a few minutes.

scholarly journals Determination of soybean routine quality parameters using near-infrared spectroscopy

2018 ◽  
Vol 6 (4) ◽  
pp. 1109-1118 ◽  
Author(s):  
Zhenying Zhu ◽  
Shangbing Chen ◽  
Xueyou Wu ◽  
Changrui Xing ◽  
Jian Yuan
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Quality Parameters
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