Untargeted Headspace-Gas Chromatography-Ion Mobility Spectrometry in Combination with Chemometrics for Detecting the Age of Chinese Liquor (Baijiu)

Abstract

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.

Chemical Composition and Flavor Characteristics of Cider Fermented with Saccharomyces cerevisiae and Non-Saccharomyces cerevisiae

“…There were some volatile compounds that were detected only by GC-IMS, such as ethyl lactate, isoamyl acetate, ethyl butanoate, 2-methylpropyl acetate, 2-pentanone, ethyl propanoate, hexanoic acid, ethyl ester, and 1-hexanol. This was due to the high sensitivity of GC-IMS [30]. Furthermore, some volatile compounds could not be identified due to the incomplete database.…”

Synergy of physicochemical reactions occurred during aging for harmonizing and improving flavor

“…Based on the acquired database, two discriminant models were established and verified for Baijiu aging time identification according to two hundred and twelve signal peaks (model A) and ninety-three identified compounds (model B) respectively, through partial least squares regression analysis. Herein, model B was more accurate to distinguish age-unknown Baijiu samples, exhibiting the goodness of fit value (R 2 ) of 0.9986 and root mean square error of prediction (RMSEP) of 0.244 ( Chen et al, 2021 ). Besides, a prediction model on account of forty-five characteristic molecules of aged Feng -flavor Baijiu which were analyzed through ultrahigh performance liquid chromatography mass spectrometry combined with various statistical analysis and artificial neural networks was established to discriminate Baijiu vintage and the prediction accuracy was up to 100 % ( Jia et al, 2021a ).…”

“…The four backbone components of Strong -flavor Baijiu, ethyl acetate, ethyl butanoate, ethyl hexanoate and ethyl lactate gradually decreased, while acetic, hexanoic, and lactic acid significantly increased ( Huang et al, 2022 ). In another study, seven compounds were considered as potential aging markers of Gujinggong Baijiu ( Strong -flavor) through headspace gas chromatography-ion mobility spectrometry combined with chemometrics ( Chen et al, 2021 ). The concentration of ethyl hexanoate, propyl hexanoate, ethyl pentanoate and ethyl heptanoate were positively correlated with aging time, while ethyl acetate, 2-methyl-1-propanol and methylpropane were inverse.…”

Extracting Information and Enhancing the Quality of Separation Data: A Review on Chemometrics-Assisted Analysis of Volatile, Soluble and Colloidal Samples

“…Chen et al [83] used GC-IMS with PCA and CA to evaluate the change in rice VOCs during storage. A following work [84] instead applied PLS to GC-IMS data to study the aging of Baijiu, a Chinese liquor. Yang et al [85] used GC-IMS, GC-MS and GC-e-nose to follow the change in the volatile fraction of green tea during the manufacturing process, paying particular attention to the products of Maillard reaction (Figure 3); analyses were elaborated by PLS and OPLS.…”