Reprint

Advanced Spectroscopy Techniques in Food Analysis: Qualitative and Quantitative Chemometric Approaches

Edited by
October 2023
204 pages
  • ISBN978-3-0365-8580-2 (Hardback)
  • ISBN978-3-0365-8581-9 (PDF)

This is a Reprint of the Special Issue Advanced Spectroscopy Techniques in Food Analysis: Qualitative and Quantitative Chemometric Approaches that was published in

Biology & Life Sciences
Chemistry & Materials Science
Engineering
Public Health & Healthcare
Summary

In today's global food market, ensuring both consumer satisfaction and the highest standards of safety is paramount. Food quality analysis covers chemical composition, physical properties, taste evaluation, and even traceability. Traditional methods are often slow, expensive, and eco-unfriendly due to their destructive nature. Here's the exciting part! Advanced spectroscopy techniques offer solutions. Imagine using non-destructive methods like X-rays, hyperspectral imaging, NMR, and Raman—quick, cost-effective, and eco-friendly, using less solvent. Now, let's demystify chemometrics—it extracts hidden info from spectra or image data, creating models for both qualitative and quantitative food analysis. This reprint presents recent advances in spectroscopy and chemometrics, focusing on their role in food analysis, quality evaluation, safety, and practical industry use. It's all about ensuring safe, delicious, and trustworthy food. Whether you're a curious consumer, food enthusiast, or industry insider, this reprint unveils cutting-edge methods for maintaining top food standards. With advanced spectroscopy and chemometrics, we're on track to boost consumer confidence in the food we love.

Format
  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
hyperspectral imaging; pesticide residue; table grape; deep learning; non-destructive detection; Salvia spp.; GC/Q-ToF analysis; chemometrics; quality evaluation; chemical fingerprints; 1H-NMR; carbohydrates; fruits; PCA; LDA; laser-induced breakdown spectroscopy; brown rice flour adulteration; time-resolved spectra; machine learning; deep learning; red pepper powder; hyperspectral imaging; multivariate analysis; moisture adjustment; Theobroma cacao L.; dry matter; chemometrics; fermentation index; protein content; meliponine honey; physicochemical properties; biomes; antioxidant potential; mineral profile; mass spectrometry analysis; chemometrics; spatial frequency domain imaging (SFDI); optical properties; absorption; reduced scattering; long short-term memory (LSTM); FTIR; mid-infrared; caprine milk; milk absorbance spectra; variance components; sources of variation; laser-induced breakdown spectroscopy; Fritillaria thunbergii; heavy metals; chemometrics; variable selection; machine learning; food analysis; food authenticity; food chemicals; spectroscopy techniques; chemometrics; multivariate analysis; n/a