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<p>Colorimetric sensor arrays (CSA) are quickly becoming popular in food authentication, owing to their ease of use, easy application, and quick response. Because these sensors can detect color changes in response to certain chemical components, they are very helpful in determining the quality, freshness, and authenticity of food. Current research has employed solid-phase microextraction gas chromatography-mass spectrometry (SPME–GC–MS) in conjunction with a colorimetric sensor array to discover the volatile chemical molecules that give wheat cultivars their unique aroma. Detectable amounts of acids, esters, and ketones were present in addition to the many other prevalent chemicals in the analysis. Among these were terpenes, alcohols, aldehydes, alkenes, and alkanes. Data from mobile-based CSA and SPME–GC–MS were used to differentiate wheat varieties using principal component analysis (PCA), hierarchical cluster analysis (HCA), and k-nearest neighbors (KNN) models. PCA successfully distinguished between wheat varieties, and the first three principal components (PCs) accounted for 73.82% of the variance, with PC1 contributing 33.41%, PC2 contributing 25.95%, and PC3 contributing 14.06%. The KNN model performed better, with 94% calibration and 93% prediction rates. The proposed smart sensing technique successfully differentiated between wheat varieties while remaining simple, quick, and inexpensive.</p>

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Application of colorimetric sensor arrays and GC–MS for discrimination of wheat varieties coupled with multivariate analysis

  • 期刊论文

  • 发表日期：

    2025年10月27日

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  • Muhammad Zareef,
  • Muhammad Arslan,
  • Md Mehedi Hassan,
  • Waqas Ahmad,
  • Muhammad Shoaib,
  • Malik Muhammad Hashim,
  • Suleiman A. Haruna,
  • Sadaf Javaria,
  • Quansheng Chen

摘要

Colorimetric sensor arrays (CSA) are quickly becoming popular in food authentication, owing to their ease of use, easy application, and quick response. Because these sensors can detect color changes in response to certain chemical components, they are very helpful in determining the quality, freshness, and authenticity of food. Current research has employed solid-phase microextraction gas chromatography-mass spectrometry (SPME–GC–MS) in conjunction with a colorimetric sensor array to discover the volatile chemical molecules that give wheat cultivars their unique aroma. Detectable amounts of acids, esters, and ketones were present in addition to the many other prevalent chemicals in the analysis. Among these were terpenes, alcohols, aldehydes, alkenes, and alkanes. Data from mobile-based CSA and SPME–GC–MS were used to differentiate wheat varieties using principal component analysis (PCA), hierarchical cluster analysis (HCA), and k-nearest neighbors (KNN) models. PCA successfully distinguished between wheat varieties, and the first three principal components (PCs) accounted for 73.82% of the variance, with PC1 contributing 33.41%, PC2 contributing 25.95%, and PC3 contributing 14.06%. The KNN model performed better, with 94% calibration and 93% prediction rates. The proposed smart sensing technique successfully differentiated between wheat varieties while remaining simple, quick, and inexpensive.