Evolution of volatile compounds of baked dried tofu during catalytic infrared baking process and their correlation with relevant physicochemical properties

BACKGROUND

Energy-saving and low-carbon baking processes, as well as the need to determine the flavor-forming mechanisms of baked dried tofu, are becoming increasingly necessary. The application of emerging catalytic infrared radiation (CIR) technology in baking of dried tofu is considered of high interest due to the low energy consumption and high baking efficiency compared to traditional baking methods. Hence, this study aimed to investigate the evolution of aroma compounds in baked dried tofu during the CIR baking process and reveal relevant relationships between physical qualities, potential flavor precursors and key volatile compounds.

RESULTS

The results showed that the surface color of dried tofu gradually turned an appetizing golden yellow color during the rapid heating process, caused by the uniform infrared radiation from the radiant emitters. Meanwhile, the moisture of dried tofu experienced minimal reduction and the hardness of dried tofu gradually increased with the formation of crust on the surface. In addition, 49 volatile compounds were identified by headspace solid-phase microextraction-gas chromatography-mass spectrometry and 13 substances - 1-hexanol, 1-octen-3-ol, 1-pentanol, heptanal, nonanal, hexanal, (E,E)-2,4-decadienal, (E,Z)-2,4-decadienal, octanal, (E)-2-octenal, (E)-2-nonenal, 2-heptanone and 2-pentylfuran - were confirmed as key aroma compounds. Moreover, the amino acids aspartic acid, glutamic acid, isoleucine, lysine and arginine, and the fatty acids butyric, caprylic, capric, tridecanoic, stearic, oleic and linolenic were responsible for the unique flavor of CIR-baked dried tofu.

CONCLUSION

Consequently, the findings can provide a scientific basis for manufacturers to achieve precise quality control and large-scale production of CIR-baked dried tofu products. © 2024 Society of Chemical Industry.

Exploration of porous imine-based covalent organic framework for solid-phase extraction of five trace sulfonamides in food samples

In this article, a highly crystalline porous imine-based covalent organic framework was synthesized at room temperature and used as solid-phase extraction (SPE) adsorbent for the purification and enrichment of trace sulfonamides (SAs) from food samples. The structure of the obtained material was characterized and studied in detail. The extraction process was optimized and the final elution was determined by the ultra-high-performance liquid chromatography-quadrupole time of flight mass spectrometry method. Low limits of detection (0.02-0.19 μg/kg) were obtained under optimal conditions, with the recoveries ranging from 70.5% to 105.3% when spiked at different levels. The adsorption process of the material for SAs was fitted by the Langmuir and Freundlich adsorption isotherm model, and the extraction capacity for Nitrofuran metabolites from food samples was also investigated for comparison. The results demonstrated that the framework was a good candidate SPE adsorbent that can be used for the enrichment of drug residues in complex matrix, and the work may provide a systematic study method for the development of porous adsorbents.

Application of Hyperspectral Imaging as a Nondestructive Technology for Identifying Tomato Maturity and Quantitatively Predicting Lycopene Content

Maturity is a crucial indicator in assessing the quality of tomatoes, and it is closely related to lycopene content. Using hyperspectral imaging, this study aimed to monitor tomato maturity and predict its lycopene content at different maturity stages. Standard normal variable (SNV) transformation was applied to preprocess the hyperspectral data. Then, using competitive adaptive reweighted sampling (CARS), the characteristic wavelengths were selected to simplify the calibration models. Based on the full and characteristic wavelengths, a support vector classifier (SVC) model was developed to determine tomato maturity qualitatively. The results demonstrated that the classification accuracy using the characteristic wavelength led to the obtention of better results with an accuracy of 95.83%. In addition, the support vector regression (SVR) and partial least squares regression (PLSR) models were utilized to predict lycopene content. With a coefficient of determination for prediction (R2P) of 0.9652 and a root mean square error for prediction (RMSEP) of 0.0166 mg/kg, the SVR model exhibited the best quantitative prediction capacity based on the characteristic wavelengths. Following this, a visual distribution map was created to evaluate the lycopene content in tomato fruit intuitively. The results demonstrated the viability of hyperspectral imaging for detecting tomato maturity and quantitatively predicting the lycopene content during storage.