Next Article in Journal
Learning Behavior Trees for Autonomous Agents with Hybrid Constraints Evolution
Previous Article in Journal
A Study on Important Issues for Estimating the Effectiveness of the Proposed Piezoelectric Energy Harvesters under Volume Constraints
Previous Article in Special Issue
Fusion of Spectra and Texture Data of Hyperspectral Imaging for the Prediction of the Water-Holding Capacity of Fresh Chicken Breast Filets
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(7), 1076; https://doi.org/10.3390/app8071076

Evaluation of Near-Infrared Hyperspectral Imaging for Detection of Peanut and Walnut Powders in Whole Wheat Flour

1
College of Engineering, China Agricultural University, Beijing 100083, China
2
Crop Genetics and Breeding Research Unit, USDA-ARS, 2747 Davis Road, Tifton, GA 31793, USA
3
Multidisciplinary Initiative Center, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
4
Academy of State Administration of Grain P.R.C, No. 11 Baiwanzhuang Avenue, Xicheng District, Beijing 100037, China
*
Authors to whom correspondence should be addressed.
Received: 22 May 2018 / Revised: 14 June 2018 / Accepted: 15 June 2018 / Published: 3 July 2018
(This article belongs to the Special Issue Hyperspectral Chemical Imaging for Food Authentication)
View Full-Text   |   Download PDF [2411 KB, uploaded 4 July 2018]   |  

Abstract

The general utilization of processing equipment in industry has increased the risk of foreign material contamination. For example, peanut and walnut contaminants in whole wheat flour, which typically a healthy food, are a threat to people who are allergic to nuts. The feasibility of utilizing near-infrared hyperspectral imaging to inspect peanut and walnut powder in whole wheat flour was evaluated herein. Hyperspectral images at wavelengths 950–1700 nm were acquired. A standard normal variate combined with the Savitzky–Golay first derivative spectral transformation was adopted for the development of a partial least squares regression (PLSR) model to predict contamination concentrations. A successive projection algorithm (SPA) and uninformative variable elimination (UVE) for feature wavelength selection were compared. Two individual prediction models for peanut or walnut-contaminated flour, and a general multispectral model for both peanut-contaminated flour and walnut-contaminated flour, were developed. The optimal general multispectral model had promising results, with a determination coefficient of prediction (Rp2) of 0.987, and a root mean square error of prediction (RMSEP) of 0.373%. Visualization maps based on multispectral PLSR models reflected the contamination concentration variations in a spatial manner. The results demonstrated that near-infrared hyperspectral imaging has the potential to inspect peanut and walnut powders in flour for rapid quality control. View Full-Text
Keywords: near-infrared hyperspectral imaging; peanut and walnut powders; whole wheat flour; visualization near-infrared hyperspectral imaging; peanut and walnut powders; whole wheat flour; visualization
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhao, X.; Wang, W.; Ni, X.; Chu, X.; Li, Y.-F.; Sun, C. Evaluation of Near-Infrared Hyperspectral Imaging for Detection of Peanut and Walnut Powders in Whole Wheat Flour. Appl. Sci. 2018, 8, 1076.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top