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

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...

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Bibliographic Details
Other Authors: Kharbach, Mourad (Editor), Urpelainen, Samuli (Editor)
Format: Electronic Book Chapter
Language:English
Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
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Online Access:DOAB: download the publication
DOAB: description of the publication
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520 |a 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. 
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650 7 |a Technology: general issues  |2 bicssc 
653 |a hyperspectral imaging 
653 |a pesticide residue 
653 |a table grape 
653 |a deep learning 
653 |a non-destructive detection 
653 |a Salvia spp. 
653 |a GC/Q-ToF analysis 
653 |a chemometrics 
653 |a quality evaluation 
653 |a chemical fingerprints 
653 |a 1H-NMR 
653 |a carbohydrates 
653 |a fruits 
653 |a PCA 
653 |a LDA 
653 |a laser-induced breakdown spectroscopy 
653 |a brown rice flour adulteration 
653 |a time-resolved spectra 
653 |a machine learning 
653 |a red pepper powder 
653 |a multivariate analysis 
653 |a moisture adjustment 
653 |a Theobroma cacao L. 
653 |a dry matter 
653 |a fermentation index 
653 |a protein content 
653 |a meliponine honey 
653 |a physicochemical properties 
653 |a biomes 
653 |a antioxidant potential 
653 |a mineral profile 
653 |a mass spectrometry analysis 
653 |a spatial frequency domain imaging (SFDI) 
653 |a optical properties 
653 |a absorption 
653 |a reduced scattering 
653 |a long short-term memory (LSTM) 
653 |a FTIR 
653 |a mid-infrared 
653 |a caprine milk 
653 |a milk absorbance spectra 
653 |a variance components 
653 |a sources of variation 
653 |a Fritillaria thunbergii 
653 |a heavy metals 
653 |a variable selection 
653 |a food analysis 
653 |a food authenticity 
653 |a food chemicals 
653 |a spectroscopy techniques 
653 |a n/a 
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