FOOD SCIENCE ›› 2025, Vol. 46 ›› Issue (16): 345-352.doi: 10.7506/spkx1002-6630-20250221-097

• Safety Detection • Previous Articles     Next Articles

Prediction of Pork TVB-N Content and pH Using Broad Learning System Based on Hyperspectral Imaging with Hybrid Wavelength Selection

LUO Yizhi, TANG Shuqi, JIN Qingting, QIU Guangjun, QI Haijun, MENG Fanming, LI Peng   

  1. (1. Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China; 2. National Key Laboratory of Agricultural Equipment Technology, Guangzhou 510642, China; 3. National Key Laboratory of Swine and Poultry Breeding, Guangzhou 510645, China; 4. College of Engineering, South China Agricultural University, Guangzhou 510642, China; 5. College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China; 6. Institute of Animal Science, Guangdong Academy of Agricultural Sciences, Guangzhou 510645, China; 7. School of Computer and Information Engineering, Fuyang Normal University, Fuyang 236041, China)
  • Online:2025-08-25 Published:2025-07-22

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Abstract: This study proposed a non-destructive and accurate method for the detection of pork freshness based on hyperspectral imaging (HSI) and broad learning system (BLS). BLS models were developed and evaluated for their ability to predict the total volatile basic nitrogen (TVB-N) content and pH in pork samples based on hyperspectral images. Four different preprocessing methods (Savitzky-Golay (SG) smoothing, normalization, baseline correction, and standard normal variate) were applied to optimize the spectral data, and feature extraction was performed using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and interval variable iterative space shrinking approach (iVISSA). The results indicated that SG was the best preprocessing method, and combining iVISSA with SPA for feature extraction effectively removed redundant features and reduced interference from irrelevant information, achieving optimal prediction performance in the BLS regression models. Specifically, for TVB-N prediction, the iVISSA-SPA-BLS model exhibited excellent performance with correlation coefficient of prediction (RP) of 0.942 2, root mean square error of prediction (RMSEP) of 3.007 2, and residual prediction deviation (RPD) of 2.803 8. For pH prediction, the RP, RMSEP and RPD were 0.817 3, 0.367 9, and 1.716 4, respectively. The developed method not only enables efficient and non-destructive prediction of pork freshness, but also provides a new non-destructive approach for food safety detection.

Key words: pork freshness, hyperspectral imaging technique, broad learning system, feature extraction

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