Nondestructive determination of IMP content in chilled chicken based on hyperspectral data combined with chemometrics - Wang, Yangyang; He, Hongju; Jiang, Shengqi; Ma, Hanjun (International Journal of Agricultural and Biological Engineering, 2022)
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.