Next Article in Journal
Beam Grouping Based RS Resource Reuse and De-Contamination in Large Scale MIMO Systems
Next Article in Special Issue
Nondestructive Estimation of Moisture Content, pH and Soluble Solid Contents in Intact Tomatoes Using Hyperspectral Imaging
Previous Article in Journal
Analysis of MPPT Failure and Development of an Augmented Nonlinear Controller for MPPT of Photovoltaic Systems under Partial Shading Conditions
Previous Article in Special Issue
Early Detection of Aspergillus parasiticus Infection in Maize Kernels Using Near-Infrared Hyperspectral Imaging and Multivariate Data Analysis
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Appl. Sci. 2017, 7(1), 97; doi:10.3390/app7010097

Measurement of Soy Contents in Ground Beef Using Near-Infrared Spectroscopy

1
College of Engineering, China Agricultural University, Beijing 100083, China
2
Quality & Safety Assessment Research Unit, U.S. National Poultry Research Center, USDA-ARS, 950 College Station Rd., Athens, GA 30605, USA
3
Department of Chemistry, the University of Georgia, Athens, GA 30602, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Kuanglin Kevin Chao and Costas Balas
Received: 23 November 2016 / Revised: 30 December 2016 / Accepted: 13 January 2017 / Published: 19 January 2017
(This article belongs to the Special Issue Applications of Hyperspectral Imaging for Food and Agriculture)
View Full-Text   |   Download PDF [1565 KB, uploaded 19 January 2017]   |  

Abstract

Models for determining contents of soy products in ground beef were developed using near-infrared (NIR) spectroscopy. Samples were prepared by mixing four kinds of soybean protein products (Arconet, toasted soy grits, Profam and textured vegetable protein (TVP)) with ground beef (content from 0%–100%). NIR spectra of meat mixtures were measured with dispersive (400–2500 nm) and Fourier transform NIR (FT-NIR) spectrometers (1000–2500 nm). Partial least squares (PLS) regression with full leave-one-out cross-validation was used to build prediction models. The results based on dispersive NIR spectra revealed that the coefficient of determination for cross-validation (Rcv2) ranged from 0.91 for toasted soy grits to 0.99 for Arconet. The results based on FT-NIR spectra exhibited the best prediction for toasted soy grits (Rcv2 = 0.99) and Rcv2 > 0.98 for the other three soy types. For identification of different types of soy products, support vector machine (SVM) classification was used and the total accuracy for dispersive NIR and FT-NIR was 95% and 83.33%, respectively. These results suggest that either dispersive NIR or FT-NIR spectroscopy could be used to predict the content and the discrimination of different soy products added in ground beef products. In application, FT-NIR spectroscopy methods would be recommended if time is a consideration in practice. View Full-Text
Keywords: PLS; SVM; quantification; classification; dispersive NIR; FT-NIR PLS; SVM; quantification; classification; dispersive NIR; FT-NIR
Figures

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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Jiang, H.; Zhuang, H.; Sohn, M.; Wang, W. Measurement of Soy Contents in Ground Beef Using Near-Infrared Spectroscopy. Appl. Sci. 2017, 7, 97.

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