Coagulation process of soymilk characterized by electrical impedance spectroscopy

Developing radio frequency (RF) heating protocol in packed tofu processing by computer simulation

Packed tofu was produced by reheating the mixture of preheated soymilk and coagulant in a sealed container. This study aimed to replace the conventional heating method with RF heating during the reheating of soymilk for packed tofu production. In this study, dielectric properties (DPs), thermal properties (TPs), and rheological properties of soymilk were determined. A mathematical model was developed to simulate the RF heating process of soymilk to determine the appropriate packaging geometry. Water holding capacity (WHC), texture analysis, color measurement, and microstructure observation were performed to evaluate the quality of RF-heated packed tofu. Results showed that soymilk added with Glucono-Delta-Lactone (GDL) coagulated at the temperature above 60 °C, and the loss factor (ε″) was slightly reduced when soymilk was converted to tofu at coagulation temperature. Based on the simulation results, the cylindrical vessel (φ50 mm × 100 mm) was chosen as the soymilk container for desired heating rate (5.9 °C/min) and uniformity (λ = 0.0065, 0.0069, 0.0016 for top, middle, and bottom layers). The texture analysis revealed that the hardness and chewiness of packed tofu prepared by RF heating were enhanced (maximum 1.36 times and 1.21 times) compared with commercial packed tofu, while the springiness were not significantly changed. Furthermore, the denser network structure was observed inside RF-heated packed tofu by SEM. These results indicated that packed tofu prepared by RF heating was of higher gel strength and sensory quality. RF heating has the potential to be applied in packed tofu production.

Rapid Sensing of Key Quality Components in Black Tea Fermentation Using Electrical Characteristics Coupled to Variables Selection Algorithms

Based on the electrical characteristic detection technology, the quantitative prediction models of sensory score and physical and chemical quality Index (theaflavins, thearubigins, and theabrownins) were established by using the fermented products of Congou black tea as the research object. The variation law of electrical parameters during the process of fermentation and the effects of different standardized pretreatment methods and variable optimization methods on the models were discussed. The results showed that the electrical parameters vary regularly with the test frequency and fermentation time, and the substances that hinder the charge transfer increase gradually during the fermentation process. The Zero-mean normalization (Zscore) preprocessing method had the best noise reduction effect, and the prediction set correlation coefficient (Rp) value of the original data could be increased from 0.172 to 0.842. The mixed variable optimization method (MCUVE-CARS) of Monte Carlo uninformed variable elimination (MC UVE) and competitive adaptive reweighted sampling (CARS) was proved that the characteristic electrical parameters were the loss factor (D) and reactance (X) of the low range. Based on the characteristic variables screened by MCUVE-CARS, the quantitative prediction models for each fermentation quality indicator were established. The Rp values of the sensory score, theaflavin, thearubigin and theabrownins of the predicted models were 0.924, 0.811, 0.85 and 0.938 respectively. The relative percent deviation (RPD) values of the sensory score, theaflavins, thearubigins and theabrownins of the predicted models were 2.593, 1.517, 1,851 and 2.920 respectively, and it showed that these models have good performance and could realize quantitative characterization of key fermentation quality indexes.

Classification of Soymilk and Tofu with Diffuse Reflection Light Using a Deep Learning Technique

Tofu is an ancient soybean product that is produced by heating soymilk containing a coagulation agent. Owing to its benefits to human health, it has become popular all over the world. An important index that determines the final product’s (tofu’s) quality is firmness. Coagulants such as CaSO4 and MgCl2 affect the firmness. With the increasing demand for tofu, a monitoring methodology that ensures high-quality tofu is needed. In our previous paper, an opportunity to monitor changes in the physical properties of soymilk by studying its optical properties during the coagulation process was implied. To ensure this possibility, whether soymilk and tofu can be discriminated via their optical properties should be examined. In this study, a He–Ne laser (Thorlabs Japan Inc., Tokyo, Japan, 2015) with a wavelength of 633 nm was emitted to soymilk and tofu. The images of the scattered light on their surfaces were discriminated using a type of deep learning technique. As a result, the images were classified with an accuracy of about 99%. We adjusted the network architecture and hyperparameters for the learning, and this contributed to the successful classification. The construction of a network that is specific to our task led to the successful classification result. In addition to this monitoring method of the tofu coagulation process, the classification methodology in this study is worth noting for possible use in many relevant agricultural fields.

Smart Sensing System for the Prognostic Monitoring of Bone Health

The objective of this paper is to report a novel non-invasive, real-time, and label-free smart assay technique for the prognostic detection of bone loss by electrochemical impedance spectroscopy (EIS). The proposed system incorporated an antibody-antigen-based sensor functionalization to induce selectivity for the C-terminal telopeptide type one collagen (CTx-I) molecules—a bone loss biomarker. Streptavidin agarose was immobilized on the sensing area of a silicon substrate-based planar sensor, patterned with gold interdigital electrodes, to capture the antibody-antigen complex. Calibration experiments were conducted with various known CTx-I concentrations in a buffer solution to obtain a reference curve that was used to quantify the concentration of an analyte in the unknown serum samples. Multivariate chemometric analyses were done to determine the performance viability of the developed system. The analyses suggested that a frequency of 710 Hz is the most discriminating regarding the system sensitivity. A detection limit of 0.147 ng/mL was achieved for the proposed sensor and the corresponding reference curve was linear in the range of 0.147 ng/mL to 2.669 ng/mL. Two sheep blood samples were tested by the developed technique and the results were validated using enzyme-linked immunosorbent assay (ELISA). The results from the proposed technique match those from the ELISA.

Water distribution in tofu and application of T2 relaxation measurements in determination of tofu's water-holding capacity

Low-field nuclear magnetic resonance (LF-NMR) was introduced for the elucidation of tofu in the present study. After multiexponential analysis of relaxation decays, three water fractions centered at about 1.5-2.6, 24-114, and 132-305 ms were detected and identified as T2b, T21, and T22, respectively. Principal component analysis (PCA) of the data revealed that sample aggregation was dependent on solubility of coagulants and contained anions. Stepwise centrifugation and microwave drying were employed as dehydration methods. Significant correlations were observed between T21 and T22 relaxation times and water-holding capacity (WHC) in both dehydration processes, which implied LF-NMR measurements could be an efficient method for determination and prediction of tofu's water-holding capacity. Ten linear equations that could be applied in prediction of WHC for tofu were reported. LF-NMR was suggested to be a powerful tool for the study of tofu.