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
Open AccessArticle

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

College of Engineering, China Agricultural University, Beijing 100083, China
Quality & Safety Assessment Research Unit, U.S. National Poultry Research Center, USDA-ARS, 950 College Station Rd., Athens, GA 30605, USA
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
Appl. Sci. 2017, 7(1), 97;
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)
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
Show Figures

Graphical abstract

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.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop