To explore the relationship between the structural characteristics of white kidney bean proteins and the stability of white kidney bean milk, this study compared the protein structure, composition and physicochemical properties of eight cultivars of white kidney bean (WK1–WK8). After enzymatic hydrolysis, we investigated changes in protein solubility, emulsifying activity and secondary structure. Furthermore, we clarified the mechanism of action of proteins in emulsion systems. Results showed that WK1, WK2 and WK3 white kidney bean milks contained highly hydrophilic proteins and exhibited uniform microstructures, and proteins and lipids were well distributed in them. In contrast, the microstructure of WK4–WK8 exhibited large particle aggregates, the continuous phase was disrupted and the emulsion stability was poor. Protein hydrolysates from WK1–WK3 displayed significantly higher emulsifying activity and solubility compared to those from WK4–WK8. The elevated protein solubility was beneficial for improving both emulsion stability and sensory attributes. These findings not only provide a theoretical basis for selecting white kidney bean cultivars and optimizing the processing conditions for plant-based milk analogues but also offer insights for the application of other high-protein plant materials in emulsion systems.

The sensory characteristics of foods have become an important part of measuring the quality of foods, and the descriptive analysis of sensory characteristics is of great significance to the evaluation of the sensory characteristics of foods. Currently, the commonly used ‘static’ sensory descriptive methods include quantitative descriptive analysis (QDA) primarily based on expert panels, and the check-all-that-apply (CATA) and rate-all-that-apply (RATA) methods that are available for experts or consumers to profile the overall sensory characteristics of a product. However, the sensory characteristics of foods can change during consumption, so ‘static’ description methods are unable to fully and accurately represent the sensory characteristics of food products and their delicate changes. Therefore, dynamic sensory description methods such as temporal check-all-that-apply (TCATA), time intensity (TI), and temporal dominance of sensation (TDS), which more accurately and completely describe the sensory characteristics of food products, have attracted great attention. In this article, several common ‘static’ and dynamic sensory description methods and their applications are introduced in order to provide methodological guidance for research on the sensory characteristics of food products.

The changes in the volatile flavor composition of fresh milk fan during storage were analyzed by solid phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS), gas chromatography-olfactometry (GC-O) and descriptive sensory evaluation. A total of 70 volatile flavor compounds were detected, and 19 characteristic flavor compounds identified by GC-O were accurately quantified, and their odor activity values (OAV) were calculated. Sixteen of these compounds with OAV greater than 1 were identified as key aroma components. The sensory properties, key aroma components and storage time of milk fan were analyzed by partial least squares (PLS). The results showed that the storage period of milk fan could be divided into three stages. The first stage was the first day, and the sensory quality of milk fan was the highest at this stage, which may be related to the high content of ester components such as ethyl acetate and ethyl hexanoate. The second stage comprised the fourth and seventh days. At this stage, milk fan had mild sensory quality and the characteristics of wine aroma, which may be related to the content of ethyl butyrate. The third stage comprised the 11th and 15th days, where the sensory quality of milk fan was the worst, and the contents of all esters and lactones except for delta-decalactone decreased with storage time.

In order to evaluate the effect of spices on the storage quality of thermal reaction-derived flavorings, the volatiles of beef flavoring produced by thermal reaction with and without the addition of spices were analyzed by solid phase microextraction (SPME) or solvent-assisted flavor evaporation (SAFE) combined with gas chromatography-olfactometry-mass spectrometry (GC-O-MS), and the aroma quality was investigated by sensory evaluation. Results revealed that 217 and 163 volatile compounds were identified by SPME and SAFE, respectively, mainly including aldehydes, ketones, terpenes, phenols, sulphur-containing compounds and nitrogen-containing compounds. Nitrogen-containing or sulphur-containing compounds with low odor thresholds were the key volatile aroma active compounds (flavor dilution factor ≥ 27, and odor activity value ≥ 1). Correlation analysis indicated that eucalyptol, diallyl disulfide and furaneol were the key odorants that maintain the spicy and meaty notes of beef flavoring during storage. Therefore, the addition of spices not only affected the types of volatile compounds, but also slowed down the decrease in the intensity of meaty note and the increase in burnt and soybean paste-like notes. This study can provide a theoretical basis for the development of high-quality thermal reactionderived flavorings and a reference for the diversification of thermal reaction-derived flavorings.

In this study, the check-all-that-apply (CATA) method, headspace solid phase microextraction (HS-SPME)-Arrow combined with gas chromatography-mass spectrometry (GC-MS) and gas chromatography-mass spectrometry-olfactometry (GC-MS-O) were used to analyze volatile flavor substances that affect the sensory differences among milk from four pastures. The results of the CATA questionnaire showed that milky aroma, creamy aroma, fragrant and sweet aroma, milky aroma, plastic odor, cooked odor and metallic odor were significantly different among the four milk samples (P < 0.05), and milk from pasture Ⅰ were the most preferred by respondents, and the preference scores of milk from pastures Ⅲ and Ⅳ were lower. Totally 14 aroma-active substances with an aroma intensity greater than or equal to 1 were obtained from the four milk samples through olfactometry. Using partial least squares regression analysis (PLSR), correlation analysis among sensory data, aroma-active substances and milk samples was conducted. The results showed that milky and creamy aroma from pasture Ⅰ were prominent, which was strongly correlated with 2-heptanone, butanoic acid, decanoic acid, and hexanoic acid. Milk from pasture Ⅱ showed a fragrant and sweet aroma, which may be related to the concentration of limonene. Milk from pasture Ⅲ had prominent metallic, plastic, and milky odors, which may be related to 1-octene-3-alcohol. Milk from pasture Ⅳ had a cooked odor, which mainly came from hexanal and styrene.

Flavor substances affect the sensory properties of food and consumer choices, and flavor substance analysis is crucial for improving food quality and developing new products. However, the vast amount of flavor substance data and inappropriate statistical analysis greatly limit the development of this field. Therefore, it is crucial to use new chemometrics methods, such as artificial intelligence algorithms, correctly and reasonably to obtain effective information in this field. In recent years, chemometrics methods have been widely applied in food research. In addition to dimensionality reduction, classification and regression methods, various neural network methods have also emerged in the field of food research. However, a summary of their reasonable application is lacking. Therefore, this article summarizes the statistical analysis methods available to study food flavor, including principal component analysis, linear discriminant analysis, linear regression methods such as partial least squares regression, and nonlinear methods such as fuzzy logic and artificial neural networks, explains their principles and provides application examples. This article aims to provide effective methods and ideas for further research on chemometrics in the field of food flavor.

Five commercially available plant-based milk samples were selected to investigate their differences in aroma components using headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). Subsequently, quantitative descriptive analysis (QDA) was performed uing an expert sensory panel to determine the correlation characteristics between the odor and QDA attributes. Moreover, penalty analysis was conducted based on consumer responses to explore priority sensory attributes for improvement and improvement directions for each product. A total of 92 volatile components were identified in the five samples, with the most diverse and abundant aroma compounds being aldehydes, alcohols, and pyrazines. These compounds imparted various nutty flavor such as peanut, bitter apricot kernel, and walnut flavors to plant-based milk. The penalty analysis results showed that blended plant-based milk did not need to be improved, while pure plant-based milk needed further improvement.