Trends in Near Infrared Spectroscopy and Multivariate Data Analysis from an Industrial Perspective

被引：16

作者：

Wiesner, Kerstin ^{[1
]}

Fuchs, Karen ^{[1
,2
]}

Gigler, Alexander M. ^{[1
]}

Pastusiak, Remigiusz ^{[1
]}

机构：

[1] Siemens AG, Corp Technol, Otto Hahn Ring 6, D-81739 Munich, Germany

[2] LMU, Dept Stat, D-80039 Munich, Germany

来源：

28TH EUROPEAN CONFERENCE ON SOLID-STATE TRANSDUCERS (EUROSENSORS 2014) | 2014年 / 87卷

关键词：

Infrared Spectroscopy; Multivariate Data Analysis; Chemometrics; Functional Data Analysis; REGRESSION;

D O I：

10.1016/j.proeng.2014.11.292

中图分类号：

TB3 [工程材料学];

学科分类号：

0805 ; 080502 ;

摘要：

Mid-and Near-Infrared (MIR, NIR) spectra convey characteristic information on material type and sample composition. Intense developments towards more robust and reliable IR spectrometers have made this technique an important chemical analytical method for industrial quality control and in-line process monitoring. New trends in miniaturization of spectrometers facilitate a wide range of possible applications in fields such as food & beverage, healthcare, fuel/media quality control, and environmental analytics. Multivariate data analysis is mandatory for data evaluation of NIR spectra. The development of novel chemometric tools also plays an important role in promoting new applications of IR spectroscopy in the near future. (C) 2014 The Authors. Published by Elsevier Ltd.

引用

页码：867 / 870

页数：4

共 13 条

[1] Interpolation and extrapolation problems of multivariate regression in analytical chemistry: benchmarking the robustness on near-infrared (NIR) spectroscopy data [J].

Balabin, Roman M. ;

Smirnov, Sergey V. .

ANALYST, 2012, 137 (07) :1604-1610

[2] NTR calibration in non-linear systems:: different PLS approaches and artificial neural networks [J].

Blanco, M ;

Coello, J ;

Iturriaga, H ;

Maspoch, S ;

Pagès, J .

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS, 2000, 50 (01) :75-82

[3] Random forests [J].

Breiman, L .

MACHINE LEARNING, 2001, 45 (01) :5-32

[4]

Burns D.A., 2007, Handbook of near-infrared analysis

[5]

Ferraty F., 2011, RECENT ADV FUNCTIONA

[6] Statistics for functional data [J].

Gonzalez Manteiga, Wenceslao ;

Vieu, Philippe .

COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2007, 51 (10) :4788-4792

[7]

Otto M., 2007, Chemometrics: Statistics and Computer Application in Analytical Chemistry

[8] Classification of dry-cured hams according to the maturation time using near infrared spectra and artificial neural networks [J].

Prevolnik, M. ;

Andronikov, D. ;

Zlender, B. ;

Font-i-Furnols, M. ;

Novic, M. ;

Skorjanc, D. ;

Candek-Potokar, M. .

MEAT SCIENCE, 2014, 96 (01) :14-20

[9]

Ramsay J.O., 2005, FUNCTIONAL DATA ANAL

[10] Investigation of support vector machines and Raman spectroscopy for lymph node diagnostics [J].

Sattlecker, Martina ;

Bessant, Conrad ;

Smith, Jennifer ;

Stone, Nick .

ANALYST, 2010, 135 (05) :895-901

← 1 2 →