Vintage analysis of Chinese Baijiu by GC and 1H NMR combined with multivariable analysis

A novel quantum dot-based ratiometric fluorescence sensor array: For reducing substances detection and Baijiu quality discrimination

BACKGROUND

Discriminating the quality of baijiu is critical for fostering the growth of the China baijiu market and safeguarding customers' rights. However, establishing a small-scale and rapid baijiu discriminating sensor assay still remains a challenge.

RESULTS

Here, we first introduced ratiometric fluorescence sensor array for the detection of reducing substances in baijiu to achieve baijiu discrimination. A ratiometric fluorescence sensor array is built using 2,3-diaminophenazine (oxidized-state OPD, oxOPD) to quench three distinct fluorescence signals of quantum dots while reducing interference from background signals. The reducing chemicals in baijiu can react with Ag+, weakening the quenching effect and changing the ratio. The discriminating of 12 types of organic small molecules which were presented in baijiu was achieved with 97.2 % accuracy by using machine learning classification methods. Meanwhile, 0.1 μM limit of detection (LOD) for ascorbic acid shows that our methods have the potential to quantitative detect reducing substances. In real sample detection, our methods can discriminate 10 distinct qualities of baijiu with 100 % accuracy. We also encoded the fingerprints of different varieties of baijiu for quality control and information reading.

SIGNIFICANCE AND NOVELTY

Overall, our easy but robust sensing array not only overcomes the problem of background signal interference but also gives an ideal way for discriminating different qualities of baijiu, food and other areas.

Classifiability Analysis of Spectroscopic Profiling Datasets in Food Safety-related Discriminative Tasks

Discriminative tasks, i.e., the identification of different food materials, brands, and origins, have become an essential part of food safety control. In recent years, spectroscopic profiling combined with machine learning is becoming popular for food-related discriminative tasks, but finding an appropriate classification model can be challenging. Compared to the current "trial-and-error" practice, this paper proposes a dedicated two-step classifiability analysis framework to address this issue. The first step collects more than 90 diversified metrics to measure the dataset separability from different perspectives. The second step synthesizes these metrics into a quantitative score using meta-learner and decomposition-based strategies. Finally, two Raman spectroscopic profiling case studies were conducted to validate the method, demonstrating higher scores for the easily separable liquor dataset (around 1.0) compared to the more challenging table salt dataset (<0.5). This score can guide researchers to determine the required model complexity and assess the adequacy of the current physio-chemical profiling instrument. We expected the classifiability analysis framework proposed in this research to be generalized to a wide range of machine learning applications within the realm of food, where data-driven classification or discriminative tasks are involved.

A two-step framework integrating lasso and Relaxed Lasso for resolving multidimensional collinearity in Chinese baijiu aging research

The aging process is crucial for Chinese Baijiu production, significantly enhancing the spirit's flavor, aroma and quality. However, aging involves a complex interplay of numerous compounds, and the extensive duration required for aging leads to a scarcity of samples available for scientific research. These limitations pose a challenge in analyzing high-dimensional data with collinearity, complicating the understanding of the intricate chemical processes at play. In this article, a two-step framework was proposed that integrated Relaxed Lasso regression models with Lasso-selected predictors to address this issue. Baijiu samples subjected to various aging conditions were analyzed using direct GC-MS and HS-GC-MS, and the obtained data was processed by this approach. The results demonstrate significantly superior performance compared to other methods, including PLSR and Gradient Boosting. Analyses were also performed on a previously documented dataset, yielding enhanced results and underscoring the method's advantage in processing high dimensional data with multicollinearity. Moreover, this method proved effective in screening of potential indicative compounds, highlighting its utility in Baijiu aging research.

A Novel Data Fusion Strategy of GC-MS and 1H NMR Spectra for the Identification of Different Vintages of Maotai-flavor Baijiu

Counterfeit Baijiu has been emerging because of the price variances of real-aged Chinese Baijiu. Accurate identification of different vintages is of great interest. In this study, the combination of gas chromatography-mass spectrometry (GC-MS) and proton nuclear magnetic resonance (1H NMR) spectroscopy was applied for the comprehensive analysis of chemical constituents for Maotai-flavor Baijiu. Furthermore, a novel data fusion strategy combined with machine learning algorithms has been established. The results showed that the midlevel data fusion combined with the random forest algorithm were the best and successfully applied for classification of different Baijiu vintages. A total of 14 differential compounds (belonging to fatty acid ethyl esters, alcohols, organic acids, and aldehydes) were identified, and used for evaluation of commercial Maotai-flavor Baijiu. Our results indicated that both volatiles and nonvolatiles contributed to the vintage differences. This study demonstrated that GC-MS and 1H NMR spectra combined with a data fusion strategy are practical for the classification of different vintages of Maotai-flavor Baijiu.

Machine learning-guided REIMS pattern recognition of non-dairy cream, milk fat cream and whipping cream for fraudulence identification

The illegal adulteration of non-dairy cream in milk fat cream during the manufacturing process of baked goods has significantly hindered the robust growth of the dairy industry. In this study, a method based on rapid evaporative ionization mass spectrometry (REIMS) lipidomics pattern recognition integrated with machine learning algorithms was established. A total of 26 ions with importance were picked using multivariate statistical analysis as salient contributing features to distinguish between milk fat cream and non-dairy cream. Furthermore, employing discriminant analysis, decision trees, support vector machines, and neural network classifiers, machine learning models were utilized to classify non-dairy cream, milk fat cream, and minute quantities of non-dairy cream adulterated in milk fat cream. These approaches were enhanced through hyperparameter optimization and feature engineering, yielding accuracy rates at 98.4-99.6%. This artificial intelligent method of machine learning-guided REIMS pattern recognition can accurately identify adulteration of whipped cream and might help combat food fraud.

Investigation of Solid Phase Microextraction Gas Chromatography–Mass Spectrometry, Fourier Transform Infrared Spectroscopy and 1H qNMR Spectroscopy as Potential Methods for the Authentication of Baijiu Spirits

The baijiu spirit is often the focus of fraudulent activity due to the widely varying prices of the products. In this work, Solid Phase Microextraction Gas Chromatography (SPME GCMS), Fourier Transform Infrared (FTIR) Spectroscopy and 1H qNMR spectroscopy were evaluated as potential methods to authenticate baijiu samples. Data were collected for 30 baijiu samples produced by seven different distilleries. The data from the SPME GCMS and FTIR methods were treated by a Principal Component Analysis to identify clusters that would suggest chemical differences in the products from different distilleries. The results suggest that SPME GCMS has the potential to be a fully portable method for baijiu authentication. FTIR did not appear suitable for authentication but can be used to find the %ABV range of the sample. 1H quantitative NMR (1H qNMR) was utilized to quantify the ethanol concentrations and calculate the observable congener chemistry comprising ester, ethanol, methanol, fusel alcohol, and organic acids. Discrepancies in ethanol content were observed in three samples, and a lack of major congeners in two samples indicates the possible presence of a counterfeit product. Detailed and quantitative congener chemistry is obtainable by NMR and provides a possible fingerprint analysis for the authentication and quality control of baijiu style, producer, and the length of the ageing process.

Machine learning based age-authentication assisted by chemo-kinetics: Case study of strong-flavor Chinese Baijiu

The aged Chinese liquor, Baijiu, is highly valued for its superior organoleptic qualities. However, since age-authentication method and aging-mechanism elucidation of Baijiu is still in the exploratory stage, high-quality aged Baijiu is often replaced by lower-quality, less-aged product with fraudulent mislabeling. Authentic high-quality strong-flavor Baijiu was analyzed by gas chromatography-mass spectrometry. Total esters decreased with aging, while acids, alcohols, aldehydes, ketones, terpenes, pyrazines increased. Although concentrations of partial compounds showed non-monotonic profiling during aging, a close positive linear correlation (R² = 0.7012) of Baijiu Evenness index (0.55-0.59) with aging time was observed, indicating a more balanced composition in aged Baijiu. The reaction quotient (Qc) of each esterification, calculated by the corresponding reactant and product concentration, approached to the corresponding thermodynamic equilibrium constant Kc. This result demonstrated that the spontaneous transformation driven by thermodynamics explained part of the aging compositional profiling. Furthermore, an aging-related feature selection and an age-authentication method were established based on three models combined with five ranking algorithms. Forty-one key features, including thirty-six compound concentrations, four esterification Qc values and the Evenness index were selected out. The age-authentication based on neural network using forty-one input features accurately predicted the age group of Baijiu samples (F1 = 100 %). These findings have deepened understanding of the Baijiu aging mechanism and provided a novel, effective approach for age-authentication of Baijiu and other liquors.

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

Numerous counterfeit vintage Baijiu are widely distributed in the market driven by economic interest which disturb the market economic rules and damage the reputation of particular Baijiu brand. Found on the situation, the Baijiu system variation during aging period, aging mechanisms and discrimination strategies for vintage Baijiu are systematically illuminated. The aging mechanisms of Baijiu cover volatilization, oxidation, association, esterification, hydrolysis, formation of colloid molecules and catalysis by metal elements or other raw materials dissolved from storage vessels. The discrimination of aged Baijiu has been performed by electrochemical method, colorimetric sensor array or component characterization coupled with multivariate analysis. Nevertheless, the characterization of non-volatile compounds in aged Baijiu is deficient. Further research on the aging principles, more easy-operation and low-cost discrimination strategies for aged Baijiu are imperative. The above information is favorable to better understand the aging process and mechanisms of Baijiu, and promote the development of artificial aging techniques.

Application progress of intelligent flavor sensing system in the production process of fermented foods based on the flavor properties

Fermented foods are sensitive to the production conditions because of microbial and enzymatic activities, which requires intelligent flavor sensing system (IFSS) to monitor and optimize the production process based on the flavor properties. As the simulation system of human olfaction and gustation, IFSS has been widely used in the field of food with the characteristics of nondestructive, pollution-free, and real-time detection. This paper reviews the application of IFSS in the control of fermentation, ripening, and shelf life, and the potential in the identification of quality differences and flavor-producing microbes in fermented foods. The survey found that electronic nose (tongue) is suitable to monitor fermentation process and identify food authenticity in real time based on the changes of flavor profile. Gas chromatography-ion mobility spectrometry and nuclear magnetic resonance technology can be used to analyze the flavor metabolism of fermented foods at various production stages and explore the correlation between flavor substances and microorganisms.

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.

Use of ATR-FTIR Spectroscopy and Chemometrics for the Variation of Active Components in Different Harvesting Periods of Lonicera japonica

Lonicera japonica Thunb is a commonly used Chinese herbal medicine, which belongs to the family Caprifoliaceae. The active components varied greatly during bud development. Research on the variation of the main active components is significant for the timely harvesting and quality control of Lonicera japonica. In this study, the attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) combined with the chemometric method was performed to investigate the variability of different harvesting periods of Lonicera japonica. The preliminary characterization from ATR-FTIR fingerprints showed various characteristic absorption peaks of the main active components from the different harvesting times, such as flavonoids, organic acids, iridoids, and volatile oils. Additionally, principal component analysis (PCA) scatter plots showed that there was a clear clustering trend in the samples of the same harvesting period, and the samples of the different harvesting periods could be well distinguished. Finally, further analysis by the orthogonal partial least-squares discriminant analysis (OPLS-DA) showed that there were regular changes in flavonoids, phenolic acids, iridoids, and volatile oils in different harvesting periods. Therefore, ATR-FTIR, as a novel and convenient analytical method, could be applied to evaluate the quality of Lonicera japonica.

Age Discrimination of Chinese Baijiu Based on Midinfrared Spectroscopy and Chemometrics

Baijiu is a traditional and popular Chinese liquor which is affected by the storage time. The longer the storage time of Baijiu is, the better its quality is. In this paper, the raw and mellow Baijiu samples from different storage time are discriminated accurately throughout midinfrared (MIR) spectroscopy and chemometrics. Firstly, changing regularities of the substances in Chinese Baijiu are discussed by gas chromatography-mass spectrometry (GC-MS) during the aging process. Then, infrared spectrums of Baijiu samples are processed by smoothing, multivariate baseline correction, and the first and second derivative processing, but no significant variation can be observed. Next, the spectral date pretreatment methods are constructively introduced, and principal component analysis (PCA) and discriminant analysis (DA) are developed for data analyses. The results show that the accuracy rates of samples by the DA method in calibration and validation sets are 91.7% and 100%, respectively. Consequently, an identification model based on support vector machine (SVM) and PCA is established combined with the grid search strategy and cross-validation methods to discriminate the age of Chinese Baijiu validly, where 100% classification accuracy rate is obtained in both training and test sets.