Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety

doi:10.3390/books978-3-0365-9325-8

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Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety

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November 2023

348 pages

ISBN978-3-0365-9324-1 (Hardback)
ISBN978-3-0365-9325-8 (PDF)

https://doi.org/10.3390/books978-3-0365-9325-8

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This book is a reprint of the Special Issue Fast Non-destructive Detection Technology and Equipment for Food Quality and Safety that was published in

Biology & Life Sciences

Chemistry & Materials Science

Engineering

Public Health & Healthcare

Summary

Fast, non-destructive detection technology and equipment for food quality and safety is a powerful technical support tool to ensure the development of food industry informatization and intelligence, with the advantages of fast speed, convenient operation, and easy online inspection. During the past two decades, such technologies have found numerous successful applications for food and agricultural product detection and processing. Owing to improvements in the manufacturing of photoelectric sensor pieces and progress in artificial intelligence and software algorithms, fast non-destructive detection technologies are able to provide more accurate, reliable, and stable solutions for food quality and safety detection and processing. They are closely integrated with the Internet of Things and intelligent manufacturing, promoting a new wave of innovation in intelligent manufacturing in the food industry. The application of new sensing technology and equipment in the fast, non-destructive detection of food has always been at the forefront of scientific and technological research. This Special Issue aims to focus on the latest research progress of this application and jointly discuss the focus of development of this research direction.

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Keywords

maize; moldy level; catalase activity; hyperspectral image; data fusion; feature selection; fruit quality monitoring; room-temperature ethylene sensor; density functional theory; adsorption energy; band energy alignment; apple; NIR; size correction; extinction coefficient; fruit diameter difference; acceptability; benchtop NMR; mandarins; NMR; successive projective algorithm; uninformative variable elimination; support vector regression; Korla fragrant pear; stone cell content; intelligent evaluation; cultivation; visible/near infrared spectrum; fresh jujube; model update; variable fusion; defective apples; apple grading; deep learning; object detection; semantic segmentation; shrimp; hot air drying; quality change; hyperspectral images; low field magnetic resonance; micro Raman; microfluidic chip; fungal spores; crop disease; numerical simulation; degree of milling; multi-scale information fusion; residual network model; Bayesian optimization algorithm; hyperspectral imaging; maize seeds; defect detection; feature selection; convolutional neural network; tomato; leaf mildew; terahertz time-domain spectroscopy; near infrared hyperspectral technology; multi-source information fusion; YOLOv5; walnut kernels; impurities detection; small object detection; liposomes; high stability; freshness; bi-layer indicator; light penetration depth; apple; spatial-frequency domain imaging; depth-resolved; bruise; scattering; near infrared spectroscopy; vegetables; anthocyanidins; fast determination; Curcumae Longae Rhizoma; volatile oil; ⁶⁰Co; GC–IMS; SERS detection; chromium contamination; tea sample; carbimazole hydrolysate; Au@Ag nanoparticles; PAEs; Raman; DFT; HF; theoretical study; gas sensor; spoilage monitoring; early warning; logistics control; simulated annealing; apple; surface-enhanced Raman spectroscopy; flexible substrate; polycyclic aromatic hydrocarbons; in situ detection; deep learning; common carp; hyperspectral imaging; texture; machine learning; visualization; n/a