A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety-Reference-Cited by-同舟云学术

A Review of Optical Nondestructive Visual and Near-Infrared Methods for Food Quality and Safety

Published:2013-03-24 Issue: Volume:2013 Page:1-36
ISSN:1687-9449
Container-title:International Journal of Spectroscopy
language:en
Short-container-title:International Journal of Spectroscopy

Author:

Alander Jarmo T.¹,Bochko Vladimir¹,Martinkauppi Birgitta¹,Saranwong Sirinnapa²,Mantere Timo¹

Affiliation:

1. Department of Electrical and Energy Engineering, University of Vaasa, 65101 Vaasa, Finland

2. Bruker Optics KK, 1-4-1 Shinkawa, Chuo-ku, Tokyo 104-0033, Japan

Abstract

This paper is a review of optical methods for online nondestructive food quality monitoring. The key spectral areas are the visual and near-infrared wavelengths. We have collected the information of over 260 papers published mainly during the last 20 years. Many of them use an analysis method called chemometrics which is shortly described in the paper. The main goal of this paper is to provide a general view of work done according to different FAO food classes. Hopefully using optical VIS/NIR spectroscopy gives an idea of how to better meet market and consumer needs for high-quality food stuff.

Funder

Finnish Funding Agency for Technology and Innovation

Publisher

Hindawi Limited

Subject

Rehabilitation,Physical Therapy, Sports Therapy and Rehabilitation,General Medicine

Link

http://downloads.hindawi.com/archive/2013/341402.pdf

Reference254 articles.

1. Near infrared spectroscopy for on/in-line monitoring of quality in foods and beverages: A review

2. Near infrared and fluorescence spectroscopic methods and electronic nose technology for monitoring foods

3. How the NIR technology came to and spread in Europe for quality assessment and control in the food industry

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