Literature DB >> 32041126

Rapid Identification and Visualization of Jowl Meat Adulteration in Pork Using Hyperspectral Imaging.

Hongzhe Jiang1, Fengna Cheng1, Minghong Shi1.   

Abstract

Minced pork jowl meat, also called the sticking-piece, is commonly used to be adulterated in minced pork, which influences the overall product quality and safety. In this study, hyperspectral imaging (HSI) methodology was proposed to identify and visualize this kind of meat adulteration. A total of 176 hyperspectral images were acquired from adulterated meat samples in the range of 0%-100% (w/w) at 10% increments using a visible and near-infrared (400-1000 nm) HSI system in reflectance mode. Mean spectra were extracted from the regions of interests (ROIs) and represented each sample accordingly. The performance comparison of established partial least square regression (PLSR) models showed that spectra pretreated by standard normal variate (SNV) performed best with Rp2 = 0.9549 and residual predictive deviation (RPD) = 4.54. Furthermore, functional wavelengths related to adulteration identification were individually selected using methods of principal component (PC) loadings, two-dimensional correlation spectroscopy (2D-COS), and regression coefficients (RC). After that, the multispectral RC-PLSR model exhibited the most satisfactory results in prediction set that Rp2 was 0.9063, RPD was 2.30, and the limit of detection (LOD) was 6.50%. Spatial distribution was visualized based on the preferred model, and adulteration levels were clearly discernible. Lastly, the visualization was further verified that prediction results well matched the known distribution in samples. Overall, HSI was tested to be a promising methodology for detecting and visualizing minced jowl meat in pork.

Entities:  

Keywords:  hyperspectral imaging; jowl meat; meat adulteration; minced pork; visualization

Year:  2020        PMID: 32041126     DOI: 10.3390/foods9020154

Source DB:  PubMed          Journal:  Foods        ISSN: 2304-8158


  7 in total

Review 1.  Hyperspectral Imaging (HSI) for meat quality evaluation across the supply chain: Current and future trends.

Authors:  Wenyang Jia; Saskia van Ruth; Nigel Scollan; Anastasios Koidis
Journal:  Curr Res Food Sci       Date:  2022-06-03

Review 2.  Quality Assessment of Fruits and Vegetables Based on Spatially Resolved Spectroscopy: A Review.

Authors:  Wan Si; Jie Xiong; Yuping Huang; Xuesong Jiang; Dong Hu
Journal:  Foods       Date:  2022-04-20

3.  Integration of Partial Least Squares Regression and Hyperspectral Data Processing for the Nondestructive Detection of the Scaling Rate of Carp (Cyprinus carpio).

Authors:  Huihui Wang; Kunlun Wang; Xinyu Zhu; Peng Zhang; Jixin Yang; Mingqian Tan
Journal:  Foods       Date:  2020-04-16

4.  A Machine Learning Method for the Quantitative Detection of Adulterated Meat Using a MOS-Based E-Nose.

Authors:  Changquan Huang; Yu Gu
Journal:  Foods       Date:  2022-02-20

5.  Nondestructive Testing of Pear Based on Fourier Near-Infrared Spectroscopy.

Authors:  Zhaohui Lu; Ruitao Lu; Yu Chen; Kai Fu; Junxing Song; Linlin Xie; Rui Zhai; Zhigang Wang; Chengquan Yang; Lingfei Xu
Journal:  Foods       Date:  2022-04-08

6.  Evaluation of Mutton Adulteration under the Effect of Mutton Flavour Essence Using Hyperspectral Imaging Combined with Machine Learning and Sparrow Search Algorithm.

Authors:  Binbin Fan; Rongguang Zhu; Dongyu He; Shichang Wang; Xiaomin Cui; Xuedong Yao
Journal:  Foods       Date:  2022-07-30

Review 7.  Application of Visible/Infrared Spectroscopy and Hyperspectral Imaging With Machine Learning Techniques for Identifying Food Varieties and Geographical Origins.

Authors:  Lei Feng; Baohua Wu; Susu Zhu; Yong He; Chu Zhang
Journal:  Front Nutr       Date:  2021-06-17
  7 in total

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