Physicochemical and chemosensory properties of pomegranate (Punica granatum L.) seeds under various oven-roasting conditions

This study investigated the effects of oven-roasting temperature (160, 180, and 200 ℃) and time (5, 10, 15, and 20 min) on pomegranate seeds. Physicochemical properties, such as color (L*, a*, and b* values), browning index (BI), total phenolic and flavonoid contents, 2,2-diphenyl-1-picrylhydrazyl radical scavenging capacity, and chemosensory properties, including taste and volatile compounds, were analyzed. The L* and a* values, and level of sourness, umami, sweetness, and terpenes decreased, whereas the b* value, BI, and level of saltiness, bitterness, furan derivatives, pyrazines, and sulfur-containing compounds, increased with roasting time. The findings of this study showed that the positive roasting conditions for pomegranate seeds were 10-20 min at 160 ℃ and, 5-10 min at 180 ℃. This study is expected to be used as a primary reference for selecting the optimal oven-roasting conditions in which positive effects appear and for developing products utilizing pomegranate seeds.

Rapid analysis technologies with chemometrics for food authenticity field: A review

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

Sensing of Acetaminophen Drug Using Zn-Doped Boron Nitride Nanocones: a DFT Inspection

During environmental testing, scientists face the problem of developing and designing a new type of sensor electrode with distinguished stability, high activity, and cost-effectiveness to detect acetaminophen (ACE). Density functional theory (DFT) calculations were used to investigate the interaction and electrical response of Zn-doped and pristine boron nitride nanocones (BNNCs) with and to ACE with the disclination angle of 240°. The adsorption energy for ACE in the Zn-doped was - 56.94 kJ.mol^-1. This value for BNNCs was approximately - 26.11 kJ.mol^-1. Furthermore, after the adsorption of ACE, the value of band gap (E_g) for Zn-doped BNNCs decreased significantly (from 4.01 to 3.10 eV), thereby increasing the electrical conductivity. However, E_g value of the pristine BNNCs decreased marginally after the adsorption of ACE. Compared with the pristine BNNCs, the Zn-doped BNNCs could be considered promising materials for the detection of ACE and could be employed in electronic sensors. In the Zn-doped BNNCs, the molecular and electrostatic interactions and the creation of Zn-O bond played key roles in the adsorption of ACE. The Zn-doped BNNCs had other merits such as slight recovery time which was approximately 7.09 ms for the desorption of ACE at ambient temperature.

Data visualization of Salmonella Typhimurium contamination in packaged fresh alfalfa sprouts using a Kohonen network

Class visualization of multi-dimensional data from analysis of volatile metabolic compounds monitored using an electronic nose based on metal oxide sensor array was attained using a Kohonen network. An array of 12 metal oxide based chemical sensors was used to monitor changes in the volatile compositions from the headspace of packaged fresh sprouts with and without Salmonella Typhimurium contamination. Kohonen׳s self-organizing map (SOM) was then created for learning different patterns of volatile metabolites. The Kohonen network comprising 225 nodes arranged into a two-dimensional hexagonal map was used to locate the samples on the map to facilitate sample classification. Graphical maps including the unified matrix, component planes, and hit histograms were described to characterize the relation between samples. The clustering of samples with different levels of S. Typhimurium contamination could be visually distinguishable on the SOM. The Kohonen network proved to be advantageous in visualization of multi-dimensional nonlinear data and provided a clearer separation of different sample groups than a conventional linear principal component analysis (PCA) approach. The sensor array integrated with the Kohonen network could be used as a rapid and nondestructive method to distinguish samples with different levels of S. Typhimurium contamination. Although the analyses were performed on samples with natural background microbiota of about 7 Log(CFU/g), this microbiota did not affect the S. Typhimurium detection. The proposed method has potential to rapidly detect a target foodborne pathogen in real-life food samples instantaneously without subsequently culturing stages.