Using Vis-NIR Spectroscopy for Predicting Quality Compounds in Foods

Edited by
May 2023
236 pages
  • ISBN978-3-0365-7501-8 (Hardback)
  • ISBN978-3-0365-7500-1 (PDF)

This book is a reprint of the Special Issue Using Vis-NIR Spectroscopy for Predicting Quality Compounds in Foods that was published in

Chemistry & Materials Science
Environmental & Earth Sciences

Near-Infrared reflectance spectroscopy (NIRS) has become one of the most attractive and used technique for analysis as it allows a fast and simultaneous qualitative and quantitative characterization of a wide variety of food samples. NIR spectroscopy is essential in various other fields, e.g., pharmaceuticals, petrochemical, textiles, cosmetics, medical applications, and chemicals such as polymers. The high level of interest in NIR spectroscopy among scientific and professional sectors demonstrates its relevance. We feel that the Special Issue's scope has facilitated the interchange of ideas and thereby aided in expanding the new development in this field of knowledge. Furthermore, we aimed to provide the readership with a comprehensive summary of present state-of-the-art NIR spectroscopy, current development trends, and future possibilities. We also believe that by doing so, we will be able to provide an accceptable opportunity for all contributors to make their results and methodologies more visible, as well as to highlight their recent achievements in their respective fields which have been made possible by the use of NIR spectroscopy. The Special Issue had a resoundingly enthusiastic response, with several submissions from academics and professional spectroscopists, resulting in the collection of 13 papers, including 1 exhaustive review paper. The articles submitted well represent the variety of the application field. These articles cover a wide range of topics related to NIR spectroscopy in a broad sense. The majority of the papers concentrate on applied qualitative and quantitative analysis in a variety of fields.

  • Hardback
© 2022 by the authors; CC BY-NC-ND license
hyperspectral; spatial-spectral features; classification; principal component analysis; convolutional neural network; near infrared spectra; chemometry; dry meat; artificial neural networks; organoleptic parameters; prediction; protected geographical indication distinguishing; near infrared; vitamin C; ellagic acid; wild harvest; Kakadu plum; chemometrics; proximal sensing; precision agriculture; E. coli; S. typhimurium; biofilm; hyperspectral imaging; discriminant analysis; pesticide residues; spectroscopy; PLS; soft computing; algorithm; NIRS; muscle; bovine; chemometrics; MUFA; PUFA; SFA; NIR spectrometer; intact potato; dry matter; reducing sugars; chemometrics; MPLS; pepper leaf; SPAD value; hyperspectral inversion; characteristic waveband selection; NIR; calibration models; PLS-R; volatile phenols; aged wine spirit; breast milk quality control; chemometrics; handheld; spectroscopy; chemometrics; olive oil; near-infrared spectroscopy; quality parameters; mangetout; pea pod; near-infrared reflectance spectroscopy; quality parameters; n/a