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| Comparative Application of Hyperspectral Imaging and Near Infrared Spectroscopy in Nondestructive Detection of Chicken Quality |
| XING Suxia, WANG Rui, GUO Peiyuan, LIU Yanfang, WANG Jiuqing, XU Pan |
| Beijing Key Laboratory of Big Data Technology for Food Safety, College of Computer and Information Engineering,Beijing Technology and Business University, Beijing 100048, China |
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Abstract Hyperspectral imaging and near infrared spectroscopy (NIR) are two important techniques in modern food detection. This study intended to study the prediction accuracy of the two techniques for non-destructive chicken quality detection. Totally 62 chicken breast samples with different freshness were selected for hyperspectral imaging. Spectral data were extracted from the region of interest (ROI). Total volatile base nitrogen (TVB-N) content and total viable count (TVC) were measured. The optimal combination of spectral pretreatment and band was searched by OPUS software (version 6.0). A predictive model to quantify TVB-N and TVC was established by means of partial least squares (PLS) regression, respectively. Moreover, another 30 samples with different freshness were used to develop a PLS model for predicting TVB-N content and TVC by NIR spectroscopy, respectively. The performance of each model was evaluated using cross-validation. The results showed that the correlation coefficients (R2) of the TVB-N content and TVC prediction models developed from the ROI average spectra from hyperspectral images were 0.965 and 0.919 with a root mean square error of cross validation (RMSECV) of 0.121 and 0.215, respectively, while those of the prediction models established from NIR spectra were 0.801 and 0.780 with a RMSECV of 0.232 and 0.312, respectively. It can be concluded that the model based on ROI spectra from hyperspectral images has higher prediction accuracy for chicken quality compared with the NIR model.
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2017, Vol. 31 
