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An evaluation of biochemical, structural and volatile changes of dry-cured pork using a combined ion mobility spectrometry, hyperspectral and confocal imaging approach.
Tian, Xiao-Yu; Aheto, Joshua H; Huang, Xingyi; Zheng, Kaiyi; Dai, Chunxia; Wang, Chengquan; Bai, Jun-Wen.
Afiliação
  • Tian XY; School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China.
  • Aheto JH; School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China.
  • Huang X; School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China.
  • Zheng K; School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China.
  • Dai C; School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China.
  • Wang C; School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, PR China.
  • Bai JW; School of Food and Biological Engineering, Jiangsu University, Zhenjiang, PR China.
J Sci Food Agric ; 101(14): 5972-5983, 2021 Nov.
Article em En | MEDLINE | ID: mdl-33856705
ABSTRACT

BACKGROUND:

Food processing induces various modifications that affect the structure, physical and chemical properties of food products and hence the acceptance of the product by the consumer. In this work, the evolution of volatile components, 2-thiobarbituric acid reactive substances (TBARS), moisture content (MC) and microstructural changes of pork was investigated by hyperspectral (HSI) and confocal imaging (CLSM) techniques in synergy with gas chromatography-ion mobility spectrometry (GC-IMS). Models based on partial least squares regression (PLSR) were developed using the full HSI spectrum variables as well as optimum variables selected through a competitive adaptive reweighted sampling algorithm.

RESULTS:

Prediction results for MC and TBARS using multiplicative scatter correction pre-processed spectra models demonstrated greater efficiency and predictability with determination coefficient of prediction of 0.928, 0.930 and root mean square error of prediction of 0.114, 1.002, respectively. Major structural changes were also observed during CLSM imaging, which were greatly pronounced in pork samples oven cooked for 15 and 20 h. These structural changes could be related to the denaturation of the major meat components, which could explain the loss of moisture and the formation of TBARS visualized from the HSI chemical distribution maps. GC-IMS identified 35 volatile components, including hexanal and pentanal, which are also known to have a higher lipid oxidation specificity.

CONCLUSION:

The synergistic application of HSI, CLSM and GC-IMS enhanced data mining and interpretation and provided a convenient way for analyzing the chemical, structural and volatile changes occurring in meat during processing. © 2021 Society of Chemical Industry.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Espectrometria de Mobilidade Iônica / Carne de Porco / Imageamento Hiperespectral / Cromatografia Gasosa-Espectrometria de Massas / Produtos da Carne Tipo de estudo: Evaluation_studies / Prognostic_studies Limite: Animals Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Espectrometria de Mobilidade Iônica / Carne de Porco / Imageamento Hiperespectral / Cromatografia Gasosa-Espectrometria de Massas / Produtos da Carne Tipo de estudo: Evaluation_studies / Prognostic_studies Limite: Animals Idioma: En Ano de publicação: 2021 Tipo de documento: Article