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ARS Home » Midwest Area » East Lansing, Michigan » Sugarbeet and Bean Research » Research » Publications at this Location » Publication #337299

Research Project: Nondestructive Quality Assessment and Grading of Fruits and Vegetables

Location: Sugarbeet and Bean Research

Title: Innovative hyperspectral imaging-based techniques for quality evaluation of fruits and vegetables: a review

item LU, YUZHEN - Michigan State University
item HUANG, YUPING - Michigan State University
item Lu, Renfu

Submitted to: Applied Sciences
Publication Type: Review Article
Publication Acceptance Date: 2/8/2017
Publication Date: 2/15/2017
Citation: Lu, Y., Huang, Y., Lu, R. 2017. Innovative hyperspectral imaging-based techniques for quality evaluation of fruits and vegetables: a review. Applied Sciences. 7:189.

Interpretive Summary:

Technical Abstract: New, non-destructive sensing techniques for fast and more effective quality assessment of fruits and vegetables are needed to meet the ever-increasing consumer demand for better, more consistent and safer food products. Over the past 15 years, hyperspectral imaging has emerged as a new generation of sensing technology for non-destructive food quality and safety evaluation, because it integrates the major features of imaging and spectroscopy, thus enabling to acquire both spectral and spatial information from an object simultaneously. This paper first provides a brief overview of hyperspectral imaging configurations and common sensing modes used for food quality and safety evaluation. It then introduces the three innovative hyperspectral imaging-based techniques or sensing platforms, i. e., spectral scattering, integrated reflectance and transmittance, and spatially-resolved spectroscopy, that have been developed in our laboratory for property and quality evaluation of fruits, vegetables and other food products. The basic principle and instrumentation of each technique are covered, followed by the mathematical methods for processing and extracting critical information from the acquired data. Also presented are applications of these techniques for property and quality evaluation of fruits and vegetables. Finally, concluding remarks are given on future research needs to move forward these hyperspectral imaging techniques.