Evaluation of processing mechanism in Astragali Radix by low-field nuclear magnetic resonance and magnetic resonance imaging

Rapid discrimination of different primary processing Arabica coffee beans using FT-IR and machine learning

In this study, fourier transform infrared spectroscopy (FT-IR) analysis was combined with machine learning, while various analytical techniques such as colorimetry, low-field nuclear magnetic resonance spectroscopy, scanning electron microscope, two-dimensional correlation spectroscopy (2D-COS), and multivariate statistical analysis were employed to rapidly distinguish and compare three different primary processed Arabica coffee beans. The results showed that the sun-exposed processed beans (SPB) exhibited the highest total color difference value and the largest pore size. Meanwhile, the wet-processed beans (WPB) retained the most bound and immobilized water in green and roast coffee beans. The FT-IR data analysis results indicated that the functional group composition was similar across the three different primary processed coffee beans, while significant differences in structural characteristics were observed in 2D-COS. The multivariate statistical analysis demonstrated that the orthogonal partial least squares-discriminant analysis model could effectively distinguish the different types of coffee beans. The machine learning results indicated that the six models could rapidly identify different samples of primary processed coffee beans. Notably, the SNV-Voting model demonstrated superior predictive performance, with an average precision, recall, and F1-score of 88.67%, 88.67%, and 0.88 for three primary processing coffee beans, respectively.

Investigation of the soybean infiltration process utilizing low-field nuclear magnetic resonance technology

This paper employs low-field nuclear magnetic resonance (LF-NMR) technology to meticulously analyze and explore the intricate soybean infiltration process. The methodology involves immersing soybeans in distilled water, with periodic implementation of Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence experiments conducted at intervals of 20 to 30 minutes to determine the relaxation time T2. Currently, magnetic resonance imaging (MRI) is conducted every 30 minutes. The analysis uncovers the existence of three distinct water phases during the soybean infiltration process: bound water denoted as T21, sub-bound water represented by T22, and free water indicated as T23. The evolution of these phases unfolds as follows: bound water T21 displays a steady oscillation within the timeframe of 0 to 400 minutes; sub-bound water T22 and free water T23 exhibit a progressive pattern characterized by a rise-stable-rise trajectory. Upon scrutinizing the magnetic resonance images, it is discerned that the soybean infiltration commences at a gradual pace from the seed umbilicus. The employment of LF-NMR technology contributes significantly by affording an expeditious, non-destructive, and dynamic vantage point to observe the intricate motion of water migration during soybean infiltration. This dynamic insight into the movement of water elucidates the intricate mass transfer pathway within the soybean-water system, thus furnishing a robust scientific foundation for the optimization of processing techniques.