From Traditional to Intelligent, A Review of Application and Progress of Sensory Analysis in Alcoholic Beverage Industry.

Sensory analysis is an interdisciplinary field that combines multiple disciplines to analyze food qualitatively and quantitatively. At present, this analysis method has been widely used in product development, quality control, marketing, flavor analysis, safety supervision and inspection of alcoholic beverages. Due to the changing needs of analysis, new and more optimized methods are still emerging. Thereinto, intelligent and biometric technologies with growing attention have also been applied to sensory analysis. This work summarized the sensory analysis methods from three aspects, including traditional artificial sensory analysis, intelligent sensory technology, and innovative technologies. Meanwhile, the application sensory analysis in alcoholic beverages and its industrial production was scientifically emphasized. Moreover, the future tendency of sensory analysis in the alcoholic beverage industry is also highlights.

An advance in novel intelligent sensory technologies: From an implicit-tracking perspective of food perception.

Food sensory evaluation mainly includes explicit and implicit measurement methods. Implicit measures of consumer perception are gaining significant attention in food sensory and consumer science as they provide effective, subconscious, objective analysis. A wide range of advanced technologies are now available for analyzing physiological and psychological responses, including facial analysis technology, neuroimaging technology, autonomic nervous system technology, and behavioral pattern measurement. However, researchers in the food field often lack systematic knowledge of these multidisciplinary technologies and struggle with interpreting their results. In order to bridge this gap, this review systematically describes the principles and highlights the applications in food sensory and consumer science of facial analysis technologies such as eye tracking, facial electromyography, and automatic facial expression analysis, as well as neuroimaging technologies like electroencephalography, magnetoencephalography, functional magnetic resonance imaging, and functional near-infrared spectroscopy. Furthermore, we critically compare and discuss these advanced implicit techniques in the context of food sensory research and then accordingly propose prospects. Ultimately, we conclude that implicit measures should be complemented by traditional explicit measures to capture responses beyond preference. Facial analysis technologies offer a more objective reflection of sensory perception and attitudes toward food, whereas neuroimaging techniques provide valuable insight into the implicit physiological responses during food consumption. To enhance the interpretability and generalizability of implicit measurement results, further sensory studies are needed. Looking ahead, the combination of different methodological techniques in real-life situations holds promise for consumer sensory science in the field of food research.

Characterization of selected commercially available grilled lamb shashliks based on flavor profiles using GC-MS, GC × GC-TOF-MS, GC-IMS, E-nose and E-tongue combined with chemometrics.

HS-SPME-GC-MS, SPME-Arrow-GC × GC-TOF-MS, HS-GC-IMS, Electronic-nose, and Electronic-tongue systems were applied in a feasibility study of the flavor characterization of five commercially available Chinese grilled lamb shashliks. A total of 198 volatile organic compounds (VOCs) were identified (∼71% by GC × GC-TOF-MS). Using data fusion strategies, five predictive models were applied to the composition of VOCs and brand identification of the lamb shashliks. Compared with partial least squares regression, support vector machine, deep neural network, and RegBoost modeling, a momentum deep belief network model performed best in predicting VOCs content and identifying shashlik brands (R2 above 0.96, and RMSE below 0.1). Intelligent sensory technology combined with chemometrics is a promising approach to the flavor characterization of shashliks and other food matrices.

Flavor Characterization of Rhei Radix et Rhizoma Processed with Nine-time Repeating Steaming and Sun-drying Based on Intelligent Sensory Technology / 中国实验方剂学杂志

ObjectiveTo analyze the flavor substances and change rules of Rhei Radix et Rhizoma during the process of nine-time repeating steaming and sun-drying. MethodThe flavor response values of Rhei Radix et Rhizoma samples were obtained by using PEN3 electronic nose system. The data were processed and analyzed by principal component analysis （PCA）， linear discriminant analysis （LDA） and Loadings analysis. ResultRhei Radix et Rhizoma processed with nine-time repeating steaming and sun-drying could be effectively distinguished into two categories as the sixth sample was the turning point. The samples steamed and dried for one to five times could be grouped into one category， the other four samples were obviously distinguished from them. The main flavor components reached the maximum response in the sample processed with six-time repeating steaming and sun-drying， and its response value of inorganic sulfur compounds was about 2.7 times that of the sample processed with one-time repeating steaming and sun-drying. In addition， compared with the raw products， the flavors of Rhei Radix et Rhizoma processed with nine-time repeating steaming and sun-drying and wine stewing changed significantly， and the response value of inorganic sulfur compounds in sample processed with nine-time repeating steaming and sun-drying was about 2.2 times that of raw products. From the perspective of flavor analysis， the response values of inorganic sulfur compounds and nitrogen-oxygen compounds in sample processed with nine-time repeating steaming and sun-drying were higher than those of wine-stewed products， and the two were not completely equivalent. ConclusionElectronic nose technology preliminarily clarifies the dynamic change rules of the flavor of Rhei Radix et Rhizoma during the process of nine-time repeating steaming and sun-drying from the flavor characteristics， and clarifies the difference between products processed with nine-time repeating steaming and sun-drying and wine-stewed products from the odor characteristics， which lays a foundation for revealing the processing principle of Rhei Radix et Rhizoma processed with nine-time repeating steaming and sun-drying.