Using near-infrared spectroscopy to determine moisture content, gel strength, and viscosity of gelatin

Effect of Ultrasonic Treatment on the Physicochemical Properties of Bovine Plasma Protein-Carboxymethyl Cellulose Composite Gel

In order to improve the stability of bovine plasma protein-carboxymethyl cellulose composite gels and to expand the utilization of animal by-product resources, this study investigated the impact of different ultrasound powers (300, 400, 500, 600, and 700 W) and ultrasound times (0, 10, 20, 30, and 40 min) on the functional properties, secondary structure and intermolecular forces of bovine plasma protein-carboxymethyl cellulose composite gel. The results showed that moderate ultrasonication resulted in the enhancement of gel strength, water holding capacity and thermal stability of the composite gels, the disruption of hydrogen bonding and hydrophobic interactions between gel molecules, the alteration and unfolding of the internal structure of the gels, and the stabilization of the dispersion state by electrostatic repulsive forces between the protein particles. The content of α-helices, β-turns, and β-sheets increased and the content of random curls decreased after sonication (p < 0.05). In summary, appropriate ultrasound power and time can significantly improve the functional and structural properties of composite gels. It was found that controlling the thermal aggregation behavior of composite gels by adjusting the ultrasonic power and time is an effective strategy to enable the optimization of composite gel texture and water retention properties.

Efficient and accurate multi-scale simulation for viscosity mechanism of konjac glucomannan colloids

The viscosity is a foundational parameter of biomacromolecule in the food industry. The viscosity of macroscopic colloids is closely related to the dynamical behaviors of mesoscopic biomacromolecule clusters, which are difficult to be investigated at molecular resolution by common methods. In this study, based on experimental data, multi-scale simulations combining microscopic molecular dynamics simulation, mesoscopic Brownian dynamics simulation, and macroscopic flow field construction were used to investigate the dynamical behaviors of mesoscopic clusters of konjac glucomannan (KGM) colloids (~500 nm) over a long time (~100 ms). Numerical statistical parameters of the mesoscopic simulation of macroscopic clusters were proposed and proved to represent the viscosity of colloids. Based on the intermolecular interaction and macromolecular conformation, the mechanism of the shear thinning effect was revealed as both the regular arrangement of macromolecules at low shear rates (<100 s^-1) and structural collapse of macromolecules at high shear rates (>500 s^-1). Then, the effect of molecular concentration, molecular weight, and temperature on the colloid viscosity and cluster structure of KGM colloids was investigated by experiments and simulations. This study provides a novel multi-scale numerical method and insight into the viscosity mechanism of biomacromolecule.

Near-Infrared Reflectance Spectroscopy for Quantitative Analysis of Fat and Fatty Acid Content in Living Tenebrio molitor Larvae to Detect the Influence of Substrate on Larval Composition

Several studies have shown that mealworms (Tenebrio molitor L.) could provide animals and humans with valuable nutrients. Tenebrio molitor larvae were studied to determine whether their rearing diets affected their fat and fatty acid content and to ascertain if it is possible to detect the changes in the larval fat composition using near-infrared reflectance spectroscopy (NIRS). For this reason, a standard control diet (100% wheat bran) and an experimental diet, consisting of wheat bran and the supplementation of a different substrate (coconut flour, flaxseed flour, pea protein flour, rose hip hulls, grape pomace, or hemp protein flour) were used. The results showed lesser weight gain and slower growth rates for larvae raised on diets with a high fat content. A total of eight fatty acids were identified and quantified, where palmitic, oleic, and linoleic acids were the most prevalent and showed a correlation between larval content and their content in the rearing diets. There was a high content of lauric acid (3.2-4.6%), myristic acid (11.4-12.9%), and α-linolenic acid 8.4-13.0%) in mealworm larvae as a result of the high dietary content of these fatty acids. NIR spectra were also influenced by the fat and fatty acid composition, as larval absorbance values differed greatly. The coefficient of the determination of prediction (R²_P) was over 0.97, with an RPD value of 8.3 for the fat content, which indicates the high predictive accuracy of the NIR model. Furthermore, it was possible to develop calibration models with great predictive efficiency (R²_P = 0.81-0.95, RPD = 2.6-5.6) for all fatty acids, except palmitoleic and stearic acids which had a low predictive power (R²_P < 0.5, RPD < 2.0). The detection of fat and fatty acids using NIRS can help insect producers to quickly and easily analyze the nutritional composition of mealworm larvae during the rearing process.

Ultrathin and handleable nanofibrous net as a novel biomimetic basement membrane material for endothelial barrier formation

Many strategies have been adopted to develop porous membranes to reconstitute basement membrane in vitro, which play a key role in the development of in vitro biomimetic models. However, the development of an artificial basement membrane combines cytocompatibility and nano-thickness is still challenging. Herein, a monolayer nanofibrous net patch was fabricated by combining microfabrication and electrospinning as a biomimetic basement membrane material, which was demonstrated for endothelial barrier formation. The nanofibrous net patches with different fiber densities were obtained by controlling electrospinning time. The net was with high porosity and ultrathin thickness approximate to the diameter of nanofibers, which is comparable to that of the native basement membrane. The morphology, proliferation and cell-cell/cell-substrate interactions of endothelial cells on the nanofibrous nets were studied and compared with track-etched polycarbonate membrane and traditional multilayer nanofibers membrane. In addition, the results of TEER measurement and permeability test demonstrated that the endothelial barrier formed on the nanofibrous net patch displayed stronger barrier integrity and function. Therefore, the proposed nanofibrous net patch shows great potential as a novel biomimetic basement membrane, which is promising to be applied for in vitro tissue mimetic applications.

Advances in Protein-Based Nanocarriers of Bioactive Compounds: From Microscopic Molecular Principles to Macroscopical Structural and Functional Attributes

Many proteins can be used to fabricate nanocarriers for encapsulation, protection, and controlled release of nutraceuticals. This review examined the protein-based nanocarriers from microscopic molecular characteristics to the macroscopical structural and functional attributes. Structural, physical, and chemical properties of protein-based nanocarriers were introduced in detail. The spatial size, shape, water dispersibility, colloidal stability, etc. of protein-based nanocarriers were largely determined by the molecular physicochemical principles of protein. Different preparative techniques, including antisolvent precipitation, pH-driven, electrospray, and gelation methods, among others, can be used to fabricate different protein-based nanocarriers. Various modifications based on physical, chemical, and enzymatic approaches can be used to improve the functional performance of these nanocarriers. Protein is a natural resource with a wide range of sources, including plant, animal, and microbial, which are usually used to fabricate the nanocarriers. Protein-based nanocarriers have many advantages in aid of the application of bioactive ingredients to the medical, food, and cosmetic industries.

Study on the identification of resistance of rice blast based on near infrared spectroscopy

Rice Blast is the most devastating rice disease which poses a serious threat to the safe production of rice. The most effective way to prevent rice blast is to cultivate the rice varieties that have resistance to the disease, however, traditional resistance testing requires professional personnel, a tedious process, long determination time and high cost. In order to quickly identify different resistant rice seeds which are difficult to distinguish with the naked eye, a rapid non-destructive identification method based on Near-Infrared Spectroscopy (NIRS) was proposed. Four different types of resistant rice seeds (high resistance, high susceptibility, susceptibility and resistance) came from in HeiLongjiang province of China were selected as the research objects. A total of 240 spectral data (60 from each variety) were scanned by the NIR spectrometer. The BP neural network (BP), Support Vector Machines (SVM), Probabilistic Neural Network (PNN) models were established based on the original spectral data in the full-spectrum (11520-4000 cm^-1). Among all, Raw-BP has the best identification accuracy which reaches 100% with an iteration time of 869 s. After extracting the feature wavelengths by successive projections algorithm (SPA) on the spectral data, Raw-SPA-BP, Raw-SPA-SVM and Raw-SPA-PNN models were established. The accuracy of these three models didn't improve. But the iteration time of the SPA-BP model was shortened to 791 s. Another group of BP, SVM, and PNN models were established after using different spectral pretreatment methods and the SPA feature extraction. After Multivariate Scatter Correction (MSC), the accuracy of the MSC-SPA-BP model was still 100% and the iteration time was shortened to 840 s, which is 1/30 of the time at which the original data model was formed. The accuracy of the MSC-SPA-PNN model increased from 60% to 90% and the accuracy of the MSC-SPA-SVM model increased from 60% to 85%. Based on the comparison analysis of the models mentioned above, a best neural network identification model of the MSC-SPA-BP with 513 inputs, 8 hidden layers and 4 outputs was established. Its classification accuracy reached 100% with an iteration time of 29 s, indicating that the MSC-SPA-BP model can completely achieve identification of four different resistant rice seeds. Therefore, the proposed method of the BP neural network identification model based on NIRS can be fully applied to the non-destructive rapid identification of rice seeds. Meanwhile, it provides a reference for the rapid identification of other crop seeds.

Effect of ultrasound assisted konjac glucomannan treatment on properties of chicken plasma protein gelation

The effect of ultrasound assisted konjac glucomannan treatment on the properties of chicken plasma protein gelation was investigated in this study. There were four gelation groups as follows: untreated plasma protein gelation (Control), gelation added konjac glucomannan (KGG), gelation by ultrasound treatment alone (UG) and gelation added konjac glucomannan combined with ultrasound treatment (KGUG). The data showed that the gelation strength and water-holding capacity of the treated groups were significantly increased compared with those of Control. The strongest bonding water was present in KGUG, followed by KGG and UG in low-field nuclear magnetic resonance. The storage energy (G') and loss energy modulus (G″) of KGUG showed the largest rheological properties, and the G' value was higher than that of G″. Furthermore, the elastic and gelatinous properties of UG, KGG and KGUG played a dominant role in viscoelasticity. After konjac glucomannan addition, the particle size of KGG increased significantly. Compared with that of the Control and KGG, the average particle size of UG and KGUG decreased significantly after ultrasound treatment. The hydrophobicity and disulfide bonds mainly affected the formation of heat-induced gelation in these four groups. Furthermore, KGUG with the highest hydrophobicity and disulfide bonds revealed the best stability. Therefore, the gelation of chicken plasma protein by ultrasound assisted konjac glucomannan treatment had excellent gelling properties.