Hybrid wavelength selection strategy combined with ATR-FTIR spectroscopy for preliminary exploration of vintage labeling traceability of sauce-flavor baijiu

The allure of substantial profits has perpetuated the illicit trade of counterfeit vintage labels for baijiu. While various approaches have been employed to intelligently ascertain the vintage of baijiu, many of them are both cost-intensive and time-consuming. This work pioneered the use of Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometric analysis, offering a non-destructive and economically viable method for discriminating sauce-flavor baijiu across different aging periods (1-, 2-, and 3-year). In this research, principal component analysis (PCA) was first conducted to explore clustering trends among distinct vintage groups. Subsequently, the effect of spectral pre-processing on modeling performance was explored. For wavelength selection, four wavelength selection methods (ReliefF, random forest variable importance (RFVI), variable importance in projection (VIP), and Venn) were first used to identify the subset of candidate features that potentially best mapped the vintage labels. Immediately following this, to explore the possibility of further improving the identification capabilities of the model as well as to reduce the redundant data that may still be present, sequential backward selection (SBS) was utilized for secondary feature reduction within the subset of candidates. The amalgamation of these two techniques is termed a "hybrid wavelength selection strategy." Additionally, the dimensionality reduction effects of PCA and kernel principal component analysis (KPCA) were compared to demonstrate the robustness of the proposed method. Finally, classification models such as partial least squares discriminant analysis (PLS-DA), random forest (RF), and grasshopper optimization algorithm-based support vector machine (GOA-SVM) were developed. The results show that the spectral data need not be pre-processed, and the proposed hybrid wavelength selection strategy can further improve the identification ability of the model. Among the many models developed, ReliefF-SBS-GOA-SVM emerged as the most proficient classification model, yielding accuracy, sensitivity, and specificity rates of 94.44%, 95.23%, and 94.44%, respectively. This method not only holds promise for the discrimination of baijiu class attributes such as brand, origin, flavor, and vintage but also exhibits potential applicability in other non-targeted identification studies involving spectroscopy methodologies.

Authenticity identification of high - Temperature Daqu Baijiu through multi-channel visual array sensor of organic dyes combined with smart phone App

High - temperature Daqu Baijiu faces a challenge from illegal adulteration of high-grade Baijiu bottles with low-grade Baijiu, affecting its quality and value. This study developed a rapid identification method for high temperature Daqu Baijiu with the same aroma type using a four-channel visual array sensor and detection of color changes caused by competition coordination with Zn2+ and color-changing organic dyes. The array sensor demonstrated high stability and repeatability in targeting flavor components and achieved 97.78 % or more accuracy combined with DD-SIMCA model in detecting adulteration across the Baijiu with same aroma type. The results of GC-MS and Quantum Chemical Calculation showed that esters, acids, and pyrazines played a crucial role. The smart phone App could quickly identify the authenticity of Baijiu with accuracy achieved 93 %. This research provides a foundation for rapid and reliable assessment of Baijiu quality and authenticity, enabling the industry to combat fraudulent practices effectively.

Volatile Compound Abundance Correlations Provide a New Insight into Odor Balances in Sauce-Aroma Baijiu

Sauce-aroma Baijiu (SAB) is one of the most famous Baijius in China; SAB has more than 500 aroma compounds in it. However, the key aroma compound in SAB flavor remains unclear. Volatiles play an important role in SAB aroma and are highly correlated to SAB quality. In the present study, 63 volatile compounds were quantified among 66 SAB samples using gas chromatography with flame ionization detector (GC-FID). The authors analyzed odor contributions and volatile compound correlations in two quality groups of SAB samples. Moreover, an odor activity value (OAV) ratio-based random forest classifier was used to explain the volatile compound relationship differentiations between the two quality groups. Our results proved higher quality SABs had richer aromas and indicated a set of fruity-like ethyl valerate, green- and malt-like isobutyraldehyde and malt-like 3-methylbutyraldehyde and sweet-like furfural, had closer co-abundance correlations in higher quality SABs. These results indicated that the aroma and contributions of volatile compounds in SABs should be analyzed not only with compound odor activity values, but also the correlations between different aroma compounds.

Chinese Baijiu: The Perfect Works of Microorganisms

Chinese Baijiu is one of the famous distilled liquor series with unique flavors in the world. Under the open environment, Chinese Baijiu was produced by two solid-state fermentation processes: jiuqu making and baijiu making. Chinese Baijiu can be divided into different types according to the production area, production process, starter type, and product flavor. Chinese Baijiu contains rich flavor components, such as esters and organic acids. The formation of these flavor substances is inseparable from the metabolism and interaction of different microorganisms, and thus, microorganisms play a leading role in the fermentation process of Chinese Baijiu. Bacteria, yeasts, and molds are the microorganisms involved in the brewing process of Chinese Baijiu, and they originate from various sources, such as the production environment, production workers, and jiuqu. This article reviews the typical flavor substances of different types of Chinese Baijiu, the types of microorganisms involved in the brewing process, and their functions. Methods that use microbial technology to enhance the flavor of baijiu, and for detecting flavor substances in baijiu were also introduced. This review systematically summarizes the role and application of Chinese Baijiu flavor components and microorganisms in baijiu brewing and provides data support for understanding Chinese Baijiu and further improving its quality.