Prediction of Glycogen and Moisture Contents in Japanese Wagyu Beef by Fourier Transform Near-infrared Spectroscopy for Quality Evaluation

Zhifeng Yao, Toshiaki Oe, Xiao Ye, Rui Yatabe, Yusuke Tahara, Takeshi Onodera, Kiyoshi Toko, Masami Nishimura, Ken Iwao, Naruhiko Tanaka

Research output: Contribution to journalArticlepeer-review

Abstract

Glycogen and moisture contents are associated with palatability, especially the flavor of Japanese wagyu beef samples. In this study, near-infrared (NIR) absorbance spectroscopy (12500–4000 cm−1) was carried out to predict the glycogen and moisture contents in Japanese wagyu beef. Calibration and prediction models were established by partial least-squares regression (PLSR) between the measured reference glycogen and moisture contents and the spectral data. Different spectral preprocessing methods were used, and the loading coefficient obtained from PLSR models was employed to select feature spectra. As a result, the prediction model of glycogen with the selected spectral region of 9000–4300 cm−1 after smoothing-standard normalized variate (SNV) yielded optimum results with a determination coefficient (Rp 2) of 0.415, a root-mean-squared error of prediction (RMSEP) set of 0.386 mg/g, and a ratio of prediction to deviation (RPD) of 1.218. In addition, the prediction model of moisture content in the full spectral region of 12500–4000 cm−1 after smoothing-multiplicative scatter correction (MSC) yielded optimum results with Rp 2 of 0.795, RMSEP of 2.669%, and RPD of 2.008. The results of this study demonstrated that NIR spectroscopy offers great potential for the prediction of glycogen and moisture contents in Japanese wagyu beef samples.

Original languageEnglish
Pages (from-to)2381-2391
Number of pages11
JournalSensors and Materials
Volume31
Issue number7
DOIs
Publication statusPublished - 2019

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation

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