New insights into how freeze-drying and cryo-milling affects the fine structure and digestibility of gelatinized starch.

Freeze-drying (FD) and cryo-milling (CM) are common methods for preparing powder gelatinized starch samples. This study investigates the structural characterization of raw/gelatinized maize starches and digestibility after FD/CM processes to elucidate their effect on starch digestibility determination. Results showed that FD slightly increased digestibility, while higher initial glucose content in CM samples, especially for gelatinized samples. Only FD retained the granular morphology and relative crystallinity (RC), while gelatinized-FD decreased RC by 75%. CM decreased RC by 12%, while gelatinized-CM decreased it by 97%. Combined with short-range and chain structural results, FD tended to disrupt internal connected chains through volume stress, while CM cleaved glycosidic bonds in external chain. Stretched chains in gelatinized starch promoted the breakage of chains during shearing and their efficient binding with digestive enzymes. These findings would provide a basis for pre-treatment of powder samples and processes of starch- rich foods.

Effects of frozen storage on the quality characteristics of frozen whole buckwheat extruded noodles.

The effects of frozen storage (-18 °C, 180 days) on the quality of frozen whole buckwheat extruded noodles (FWBEN) were investigated. The water content of FWBEN decreased, while the reheating time, water absorption, and dry consumption rate increased with prolonged storage time. Cooking loss increased from 3.20% to 4.31%. Texture analysis indicated that the hardness initially increased, then decreased. Microstructure results showed that the starch gel structure was damaged to a certain extent after storage for a longer period of time, whereas the porous structure became non-uniform with the appearance of cracks. The relative crystallinity gradually increased, and the freezable water content decreased with prolonged storage. These results demonstrated that FWBEN quality was affected by starch retrogradation and ice recrystallization. In general, FWBEN quality was relatively stable during 180 days of frozen storage at -18 °C.

Intelligent detection of flavor changes in ginger during microwave vacuum drying based on LF-NMR.

Low-field nuclear magnetic resonance (LF-NMR) and electronic nose combined with Gas chromatography mass spectrometry (GC-MS) were used to collect the data of moisture state and volatile substances to predict the flavor change of ginger during drying. An back propagation artificial neural network (BP-ANN) model was established with the input values of LF-NMR parameters and the output values of sensors for different flavor substances obtained from electronic nose. The results showed that fresh ginger contained three water components: bound water (T21), immobilized water (T22) and free water (T23), with the corresponding peak areas of A21, A22 and A23, respectively. During drying, the changes of A21 and A22 were not significant, while A23 and ATotal decreased significantly (p < .05). Linear discriminant analysis (LDA) of electronic nose data showed that samples with different drying time can be well distinguished. Hierarchical clustering analysis (HCA) confirmed that the electronic nose characteristic sensor data S4, S5, S8 and S13 corresponded with the data measured by GC-MS. The correlation analysis between LF-NMR parameters and characteristic sensors showed that A23 and ATotal were significantly correlated with the volatile components (p < .05). The results of the BP-ANN prediction showed that the model fitted well and had strong approximation ability (R > 0.95 and error < 3.65%) and stability, which indicated that the ANN model can accurately predict the flavor change during ginger drying based on LF-NMR parameters.

Variable Temperature Nuclear Magnetic Resonance and Magnetic Resonance Imaging System as a Novel Technique for In Situ Monitoring of Food Phase Transition.

A nuclear magnetic resonance (NMR) and magnetic resonance imaging (MRI) system with a 45 mm variable temperature (VT) sample probe (VT-NMR-MRI) was developed as an innovative technique for in situ monitoring of food phase transition. The system was designed to allow for dual deployment in either a freezing (-37 °C) or high temperature (150 °C) environment. The major breakthrough of the developed VT-NMR-MRI system is that it is able to measure the water states simultaneously in situ during food processing. The performance of the VT-NMR-MRI system was evaluated by measuring the phase transition for salmon flesh and hen egg samples. The NMR relaxometry results demonstrated that the freezing point of salmon flesh was -8.08 °C, and the salmon flesh denaturation temperature was 42.16 °C. The protein denaturation of egg was 70.61 °C, and the protein denaturation occurred at 24.12 min. Meanwhile, the use of MRI in phase transition of food was also investigated to gain internal structural information. All these results showed that the VT-NMR-MRI system provided an effective means for in situ monitoring of phase transition in food processing.

Fully-Automated High-Throughput NMR System for Screening of Haploid Kernels of Maize (Corn) by Measurement of Oil Content.

One of the modern crop breeding techniques uses doubled haploid plants that contain an identical pair of chromosomes in order to accelerate the breeding process. Rapid haploid identification method is critical for large-scale selections of double haploids. The conventional methods based on the color of the endosperm and embryo seeds are slow, manual and prone to error. On the other hand, there exists a significant difference between diploid and haploid seeds generated by high oil inducer, which makes it possible to use oil content to identify the haploid. This paper describes a fully-automated high-throughput NMR screening system for maize haploid kernel identification. The system is comprised of a sampler unit to select a single kernel to feed for measurement of NMR and weight, and a kernel sorter to distribute the kernel according to the measurement result. Tests of the system show a consistent accuracy of 94% with an average screening time of 4 seconds per kernel. Field test result is described and the directions for future improvement are discussed.

[Study of the algorithm for inversion of low field nuclear magnetic resonance relaxation distribution].

It is difficult to reflect the properties of samples from the signal directly collected by the low field nuclear magnetic resonance (NMR) analyzer. People must obtain the relationship between the relaxation time and the original signal amplitude of every relaxation component by inversion algorithm. Consequently, the technology of T2 spectrum inversion is crucial to the application of NMR data. This study optimized the regularization factor selection method and presented the regularization algorithm for inversion of low field NMR relaxation distribution, which is based on the regularization theory of ill-posed inverse problem. The results of numerical simulation experiments by Matlab7.0 showed that this method could effectively analyze and process the NMR relaxation data.