Preliminary investigation of Terahertz spectroscopy to predict pork freshness non-destructively

QI Liang,ZHAO Maocheng,ZHAO Jie,TANG Yuweiyi

doi:10.1590/fst.25718

QI Liang, ZHAO Maocheng + Show 2 more

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https://doi.org/10.1590/fst.25718

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Abstract

Abstract Freshness, a very important criterion for pork quality control, is normally assessed by the index of K value. In this paper, Terahertz (THz) spectroscopy was employed to predict K value of pork nondestructively. The THz spectra (0.2~2.0THz) of 80 pork samples with different freshness in the attenuated total reflectance (ATR) mode were acquired. Simultaneously, their K values were determined by high performance liquid chromatography (HPLC). A back propagation artificial neural network (BP-ANN) prediction model of K value was established. The precision of BP-ANN was further improved after optimization by the algorithm of Adaptive boosting (AdaBoost), whose root mean square error of prediction (RMSEP) and correlation coefficient (RP) were 9.89% and 0.84 respectively in the prediction set, indicating that the non-linear models (BP-ANN and BP-AdaBoost) were superior to the linear principal component regression (PCR) model. The topological neural network architecture was much more suitable for analyzing complicated regression relationship between K value and THz spectra. It can be concluded that the THz spectral coupled with BP-AdaBoost algorithm is capable of predicting the pork K value.

Highlights

Pork is delicious and rich in nutrients
The THz spectra acquisitions were performed in the attenuated total reflectance (ATR) mode using the TAS7500 spectrometer (Advantest Co., Kitakyushu, Japan)
Each spectrum was the average of 2048 automatical scans to improve spectral signal noise ratio (SNR)

Summary

Introduction

Pork is delicious and rich in nutrients. It is the main consumption type of meat products, accounting for 37% (Food and Agriculture Organization of the United Nations, 2014). There are two main ways to assess meat freshness: Sensory evaluation and physical or chemical analysis (Gil et al, 2011). The former is subjective and boring, while the latter is accurate and reliable with high repeatability. The K value has attracted wide attention as an index of meat freshness, and is proved to be feasible in pork freshness detection in recent years (Cheng et al, 2016; Gil et al, 2011)

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