This paper proposes the combination of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and chemometrics as a method to detect the age of Chinese liquor (Baijiu). Headspace conditions were optimized through single-factor optimization experiments. The optimal sample preparation involved diluting Baijiu with saturated brine to 15% alcohol by volume. The sample was equilibrated at 70 °C for 30 min, and then analyzed with 200 μL of headspace gas. A total of 39 Baijiu samples from different vintages (1998–2019) were collected directly from pottery jars and analyzed using HS-GC-IMS. Partial least squares regression (PLSR) analysis was used to establish two discriminant models based on the 212 signal peaks and the 93 identified compounds. Although both models were valid, the model based on the 93 identified compounds discriminated the ages of the samples more accurately according to the goodness of fit value (R2) and the root mean square error of prediction (RMSEP), which were 0.9986 and 0.244, respectively. Nineteen compounds with variable importance for prediction (VIP) scores > 1, including 11 esters, 4 alcohols, and 4 aldehydes, played vital roles in the model established by the 93 identified compounds. Overall, we determined that HS-GC-IMS combined with PLSR could serve as a rapid and accurate method for detecting the age of Baijiu.
Electronic nose (E-nose) technology is frequently attempted
to
simulate the human olfactory system to recognize complex odors. Metal
oxide semiconductors (MOSs) are E-noses’ most popular sensor
materials. However, these sensor responses to different scents were
poorly understood. This study investigated the characteristic responses
of sensors to volatile compounds in a MOS-based E-nose platform, using
baijiu as an evaluation system. The results showed that the sensor
array had distinctive responses for different volatile compounds,
and the response intensities varied depending on the sensors and the
volatile compounds. Some sensors had dose–response relationships
in a specific concentration range. Among all the volatiles investigated
in this study, fatty acid esters had the greatest contribution to
the overall sensor response of baijiu. Different aroma types of Chinese
baijiu and different brands of strong aroma-type baijiu were successfully
classified using the E-nose. This study provided an understanding
of detailed MOS sensor response with volatile compounds, which could
be further applied to improve the E-nose technology and its practical
application in food and beverages.
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