Abstract
A review is given of different non-destructive techniques for fruit quality classification. As a hot research topic in the field of International Agricultural and Food Engineering, fruit quality classification has great influence on meeting consumers’ requirements for food quality and safety. Recently, a range of non-destructive techniques has been used for evaluating fruit quality. The main non-destructive techniques for fruit classification include using electrical properties, acoustic properties, optical properties, sonic vibration properties of fruits or THz, NMR, x-rays, electronic nose, machine vision technology, near-infrared spectroscopy and hyperspectral imaging to evaluate fruits without destruction. This review focus on summing up different types of non-destructive fruit classification techniques, including their pros and cons. Hyperspectral imaging techniques which were developed in recent years has been involved.
Highlights
China becomes the great producer of fruit in the world because of the vast territory, but in the same time our exports did not match the huge production
Novel and emerging non-destructive techniques for fruit quality measurement include near infrared spectroscopy, hyperspectral imaging, thermal imaging, nuclear magnetic resonance imaging, etc
Compared to those traditional methods, these new non-destructive techniques can be used to classify fruit according to external features (Oranusi and Wesley, 2012; Liu et al, 2014; Yang and Li, 2015) and some internal features (Lu et al, 2000; Ying and Yu, 2007)
Summary
China becomes the great producer of fruit in the world because of the vast territory, but in the same time our exports did not match the huge production. Novel and emerging non-destructive techniques for fruit quality measurement include near infrared spectroscopy, hyperspectral imaging, thermal imaging, nuclear magnetic resonance imaging, etc. Compared to those traditional methods, these new non-destructive techniques can be used to classify fruit according to external features (e.g., color, texture, size, shape, surface defects, etc.) (Oranusi and Wesley, 2012; Liu et al, 2014; Yang and Li, 2015) and some internal features (e.g., sugar content, acidity, firmness, soluble solids content, starch content, moisture, maturity, etc.) (Lu et al, 2000; Ying and Yu, 2007)
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