Pattern-based colorimetric sensor array chip for discrimination of Baijiu aromas

Gas mixtures are comprised of numerous complex components, making the accurate identification a continuing challenge due to the significant limitations of existing detection methods. Herein, we developed a low-cost and sensitive pattern-based colorimetric sensor array chip for the identification of typical gas mixtures - Baijiu aroma. Specifically, three nanomaterials (AuNPs, MoS2 and ZIF-8) were prepared to adsorb gas molecules and enhance the reaction of trace gases with sensor arrays. The colorimetric sensor array chip took only 5 min to complete the recognition of Baijiu aromas and effectively avoided recognition errors caused by sommelier olfactory fatigue. Notably, the hierarchical cluster analysis (HCA) revealed no confusion or errors in the results of 80 tests across the five trials involving 16 commercial Baijius. Even fake Baijius with similar ingredients could be easily identified, demonstrating the excellent analytical capabilities of the system in Baijiu identification and its significant potential for quality control of Baijius.

Sensing the quality parameters of Chinese traditional Yao-meat by using a colorimetric sensor combined with genetic algorithm partial least squares regression

Yao-meat is a traditional Chinese salted meat. Total volatile basic nitrogen content (TVB-N), total viable bacterial count (TVC), and residual nitrite (RN) level are important indexes of freshness for Yao-meat. This paper attempted the feasibility to determine TVB-N content, TVC and RN level in Yao-meat by a colorimetric sensor array chip. A color change profile for each sample was obtained by differentiating the image of sensor array before and after exposure to Yao-meat's volatile organic compounds. Genetic algorithm partial least squares regression (GA-PLS) was proposed to build the relationship between the TVB-N content, TVC, RN and the color change profiles of sensor array, and to select informative chemically responsive dyes for the three quality parameters. The GA-PLS models were achieved with RTVB-N=0.812, RTVC=0.856, RRN=0.855, in prediction set. This study demonstrated that colorimetric sensory array with GA-PLS algorithm could be used successfully to analyze the quality of Chinese traditional Yao-meat.