Nondestructive determination of IMP content in chilled chicken based on hyperspectral data combined with chemometrics

Yangyang Wang,Hanjun Ma,Shengqi Jiang,Hongju He

doi:10.25165/j.ijabe.20221501.6612

Abstract

This study was conducted to investigate the potential of hyperspectral imaging technique (900-1700 nm) for nondestructive determination of inosinic acid (IMP) in chicken. Hyperspectral images of chicken flesh samples were acquired, and their mean spectra within the images were extracted. The quantitative relationship between the mean spectra and reference IMP value was fitted by partial least squares (PLS) regression algorithm. A PLS model (MAS-PLS) built with moving average smoothing (MAS) spectra showed better performance in predicting IMP content, leading to correlation coefficients (RP) of 0.951, root mean square error (RMSEP) of 0.046 mg/g, and residual predictive deviation (RPD) of 3.152. Regression coefficient (RC), successive projections algorithm (SPA), stepwise, competitive adaptive reweighted sampling (CARS), and uninformative variable elimination (UVE) were used to select the optimal wavelengths to optimize the MAS-PLS model. Based on the 18 optimal wavelengths (907.14, 917.02, 918.67, 926.90, 930.20, 936.78, 956.54, 1004.28, 1135.89, 1211.56, 1302.07, 1367.94, 1397.60, 1488.31, 1680.17, 1683.49, 1686.80 and 1695.10 nm) selected from MAS spectra by SPA, the MAS-SPA-PLS model was built with RP of 0.920, RMSEP of 0.056 mg/g and RPD of 3.220, which was similar to the MAS-PLS model. The overall study indicated that hyperspectral imaging in the 900-1700 nm range combined with PLS and SPA could be used to predict the IMP content in chicken flesh. Keywords: near-infrared hyperspectral imaging, chicken, inosinic acid, partial least squares, successive projections algorithm DOI: 10.25165/j.ijabe.20221501.6612 Citation: Wang Y Y, He H J, Jiang S Q, Ma H J. Nondestructive determination of IMP content in chilled chicken based on hyperspectral data combined with chemometrics. Int J Agric & Biol Eng, 2022; 15(1): 277–284.

Highlights

Compared with pork, beef, and mutton, chicken meat has become one of the most popular products for consumption because of its high protein content, low content of fat, heat energy, and cholesterol[1]
The potential of 900-1700 nm high-resolution Hyperspectral imaging (HSI) spectra were investigated for the non-destructive prediction of Inosine Monophosphate (IMP) content in chicken breast
partial least squares (PLS) algorithm was applied to mine the linear relationship between Raw, moving average smoothing (MAS), Gaussian filter smoothing (GFS), Savitzky-Golay smoothing (SGS), NOR, multiplicative scatter correction (MSC), standard normal variate (SNV), Gap-Segment derivatives (GSD), Norris gap derivatives (NGD) spectra and IMP values, respectively

Summary

Introduction

Beef, and mutton, chicken meat has become one of the most popular products for consumption because of its high protein content, low content of fat, heat energy, and cholesterol[1]. Studies have found that the umami taste of meat and its products mainly depends on two types of substances, one is amino acids, and the other is nucleotides, and the strongest umami taste is inosinic acid (IMP)[6]. IMP, named hypoxanthine nucleotide, the main component of the umami taste of meat, plays an important role in the water holding capacity, physical properties, and sensory properties[7]. Umami can be enriched with the accumulation of IMP and other breakdown products in meat, and IMP can produce a unique umami flavor when heated in water or fat[8,9]. The ability to increase the umami taste of food using IMP is 40 times stronger than sodium

Methods

Results

Conclusion