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Open AccessArticle

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

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