Rapid qualitative and quantitative analysis of strong aroma base liquor based on SPME-MS combined with chemometrics

Fast and non-invasive identification of Baijiu based on Tyndall effect and chemometrics

The value of Baijiu is affected by its flavor, age, and adulteration. Therefore, a simple and rapid identification method is crucial for the market. In this study, we present a rapid, non-intrusive identification technique for Baijiu utilizing the Tyndall effect combined with chemometrics analysis. Our experiment begins illuminating Baijiu with a 405 nm wavelength laser and recording the resulting bright light path due to the Tyndall effect. To further analyze the color and brightness information, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Hierarchical Cluster Analysis (HCA), and Multilayer Perceptron (MLP) were employed. This study establishes correlations between the brightness of the Tyndall light path and seven trace flavor compounds in Baijiu. The findings demonstrate that this method effectively identifies the flavor, age cellar, and adulteration of Baijiu and also quantitatively detects the concentrations of flavor compounds. Additionally, an analysis platform was developed to enable the rapid identification of Baijiu.

Recent advances and applications of ionic covalent organic frameworks in food analysis

Ionic covalent organic frameworks with both crystallinity and charged sites have attracted significant attention from the scientific community. The versatile textural structures, precisely defined channels, and abundant charged sites of ionic COFs offer immense potential in various areas such as separation, sample pretreatment, ion conduction mechanisms, sensing applications, catalytic reactions, and energy storage systems. This review presents a comprehensive overview of facile preparation methods for ionic covalent organic frameworks (iCOFs), along with their applications in food sample pretreatment techniques such as solid-phase extraction (SPE), magnetic solid-phase extraction (MSPE), and dispersive solid-phase extraction (DSPE). Furthermore, it highlights the extensive utilization of iCOFs in detecting various food contaminants including pesticides, contaminants from food packaging, veterinary drugs, perfluoroalkyl substances, and poly-fluoroalkyl substances. Specifically, this review critically discusses the limitations, challenges, and future prospects associated with employing iCOF materials to ensure food safety.

A Machine Learning Method for the Quality Detection of Base Liquor and Commercial Liquor Using Multidimensional Signals from an Electronic Nose

Chinese liquor is a world-famous beverage with a long history. Base liquor, a product of liquor brewing, significantly affects the flavor and quality of commercial liquor. In this study, a machine learning method consisting of a deep residual network (ResNet)18 backbone with a light gradient boosting machine (LightGBM) classifier (ResNet-GBM) is proposed for the quality identification of base liquor and commercial liquor using multidimensional signals from an electronic nose (E-Nose). Ablation experiments are conducted to analyze the contribution of the framework’s components. Five evaluation metrics (accuracy, sensitivity, precision, F1 score, and kappa score) are used to verify the performance of the proposed method, and six other frameworks (support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), extreme gradient boosting (XGBoost), multidimensional scaling-support vector machine (MDS-SVM), and back-propagation neural network (BPNN)) on three datasets (base liquor, commercial liquor, and mixed base and commercial liquor datasets). The experimental results demonstrate that the proposed ResNet-GBM model achieves the best performance for identifying base liquor and commercial liquors with different qualities. The proposed framework has the highest F1 score for the identification of commercial liquor in the mixed dataset due to the contribution of similar microconstituents from the base liquor. The proposed method can be used for the quality control of Chinese liquor and promotes the practical application of E-nose devices.

Comprehensive identification of key compounds in different quality grades of soy sauce-aroma type baijiu by HS-SPME-GC-MS coupled with electronic nose

In the production of soy sauce-aroma type baijiu (SSAB), the quality of base liquor significantly affects the finished liquor's quality. Moreover, low-quality liquor may cause health problems. The different quality grades of base liquor were analyzed to investigate the relationship between the quality and the key compounds in SSAB. In this study, samples were evaluated by the sensory and further analyzed by headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS) coupled with electronic nose (E-nose). First, by sensory evaluation, the sauce, floral and fruity, fermented aromas and taste indicators (softness, fullness, harmony, purity and persistence) were positively correlated with the quality grade of the base liquor. The E-nose could distinguish the different quality grades of base liquor well. Second, differential compounds were identified via untargeted metabolome based on the HS-SPME-GC-MS. 16 common differential compounds were shared in the base liquor from different fermentation rounds, including 11 esters, 1 alcohol, 2 aldehydes and 2 ketones. It was found that the higher the quality grade of the base liquor, the richer the content of aromatics, alcohols, aldehydes and ketones. The principal component analysis (PCA) biplots of the differential compounds in the different quality grades of base liquor indicated that the superior-grade base liquor has a strong fruity aroma. By correlation analysis of the differential compounds and sensors responses of E-nose, furfuryl ethyl ether, butanoic acid ethyl ester, isopentyl hexanoate, nonanoic acid ethyl ester and 3-methyl-1-butanol had a significant effect on the response intensity of E-nose sensors. In the present study, the key differential compounds between the different quality grades of base liquor were identified, and the sensory differences between the base liquor were digitized.

The Discrimination and Characterization of Volatile Organic Compounds in Different Areas of Zanthoxylum bungeanum Pericarps and Leaves by HS-GC-IMS and HS-SPME-GC-MS

The pericarps of Zanthoxylum bungeanum (ZBP) and leaves of Zanthoxylum bungeanum (ZBL) are popular spices in China, and they have pharmacological activities as well. In this experiment, the volatile organic compounds (VOCs) of the pericarps of Zanthoxylum bungeanum in Sichuan (SJ) and its leaves (SJY) and the pericarps of Zanthoxylum bungeanum in Shaanxi (SHJ) and its leaves (SHJY) were analyzed by headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). The fingerprint of HS-GC-IMS and the heat maps of HS-SPME-GC-MS were established. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed. The results showed that a total of 95 components were identified, 62 components identified by HS-SPME-GC-MS and 40 components identified by HS-GC-IMS, of which 7 were the same. The analysis found that SJ and SHJ were obviously distinguished, while SJY and SHJY were not. There were considerably fewer VOCs in the leaves than in the pericarps. In the characterization of the VOCs of ZBL and ZBP, the flavor of ZBP was more acrid and stronger, while the flavor of ZBL was lighter and slightly acrid. Thirteen and eleven differential markers were identified by HS-GC-IMS and HS-SPME-GC-MS, respectively. This is helpful in distinguishing between SHJ and SJ, which contributes to their quality evaluation.