In this paper, a portable electronic nose, that was independently developed, was employed to detect and classify a fish meal of different qualities. SPME-GC-MS (solid phase microextraction gas chromatography mass spectrometry) analysis of fish meal was presented. Due to the large amount of data of the original features detected by the electronic nose, a reasonable selection of the original features was necessary before processing, so as to reduce the dimension. The integral value, wavelet energy value, maximum gradient value, average differential value, relation steady-state response average value and variance value were selected as six different characteristic parameters, to study fish meal samples with different storage time grades. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), and five recognition modes, which included the multilayer perceptron neural network classification method, random forest classification method, k nearest neighbor algorithm, support vector machine algorithm, and Bayesian classification method, were employed for the classification. The result showed that the RF classification method had the highest accuracy rate for the classification algorithm. The highest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the integral value, stable value, and average differential value. The lowest accuracy rate for distinguishing fish meal samples with different qualities was achieved using the maximum gradient value. This finding shows that the electronic nose can identify fish meal samples with different storage times.

Clarifying the maize kernel movement during the crushing process is critical for improving the design and optimization of the impact mill. Rather than through experiments, maize kernel movement can be quantitatively analyzed through the discrete element method (DEM), and this could contribute more to the study of the crushing mechanism and equipment optimization. However, having an accurate particle model and contact parameters are prerequisites to ensure the accuracy of the DEM simulation. In this study, we proposed a maize kernel model construction method for the Rocky DEM simulation and a calibration method to calibrate contact parameters. The three-axis size, volume, and shape information of real maize kernels were obtained by 3D scanning, and then the maize kernel model was constructed by the section method. The particle–low-carbon-plate (p–w) and particle–particle (p–p) restitution coefficients were calibrated by using the improved inclined surface drop method. In addition, the angle of repose (AoR) and discharging time were considered together to calibrate the dynamical friction coefficients of p–w and p–p through the funnel method. Additionally, the maize kernel model and calibrated parameter values were used in a DEM simulation of the inclined surface drop test and the funnel test. The maximum relative errors between the simulation values and the measured values of the inclined surface drop test and the funnel test were 4.38% and 6.98%, respectively, which further verified that the proposed maize kernel model construction and contact parameter calibration methods are feasible and accurate. The research method used in this study is a novel idea that can be applied for the construction of the particle model and calibration of the contact parameters of granular materials with complex geometric structures, as well as the maize kernel model, and shows that calibrated contact parameters can provide a reference for the maize kernel crushing simulation to optimize the impact mill.

The release characteristics of polycyclic aromatic hydrocarbons during the pyrolysis of biomass remain poorly understood. This study investigated the effects of pyrolysis temperature on the polycyclic aromatic hydrocarbon release characteristics by analyzing both the transient vapor products and the physicochemical properties of corresponding biochar from the pyrolysis of corn stover pellets. The results revealed that the transient volatile compounds mainly included phenols, ketones, acids, furans, aldehydes, substances containing benzene ring, polycyclic aromatic compounds, and gaseous products. A range of 2 to 4 ring polycyclic aromatic hydrocarbons were generated at 400 to 700 °C with the peak at 560 °C, and the sum of relative content of polycyclic aromatic hydrocarbons ranged from 0.23% to 40.36%. For the biochar, the carbonization stage (400 to 700 °C) of corn stover pellets was further divided into three evolutionary stages, including the preliminary carbonization stage (380 to 480 °C), amorphous carbon structure stage (480 to 600 °C), and the stage of dehydrogenation and growth of aromatic rings (600 to 700 °C).The relationship between polycyclic aromatic hydrocarbon release in volatile compounds and H/C ratio of the biochar could be described by a power function.

Headspace solid-phase microextraction, combined with gas chromatography–mass spectrometry and partial least squares discriminant analysis, was adopted to study the rule of change in volatile organic compounds (VOCs) for domestic and imported fishmeal during storage with different freshness grades. The results showed that 318 kinds of VOCs were detected in domestic fishmeal, while 194 VOCs were detected in imported fishmeal. The total relative content of VOCs increased with storage time, among which acids and nitrogen-containing compounds increased significantly, esters and ketones increased slightly, and phenolic and ether compounds were detected only in domestic fishmeal. Regarding the volatile base nitrogen, acid value, pH value, and mold counts as freshness indexes, the freshness indexes were significantly correlated with nine kinds of VOCs (p < 0.05) through the correlation analysis. Among them, volatile base nitrogen had a significant correlation with VOCs containing nitrogen, acid value with VOCs containing carboxyl group and hydrocarbons, pH value with acids which could be used to adjust pH value, and mold counts with part of acids adjusting pH value and VOCs containing nitrogen. Due to the fact that the value of all freshness indexes increased with freshness degradation during storage, based on volatile base nitrogen and acid value, the fishmeal was divided into three freshness grades, superior freshness, corrupting, and completely corrupted. By using partial least squares discriminant analysis, this study revealed the differences in flavor of the domestic and imported fishmeal during storage with different freshness grades, and it identified four common characteristic VOCs, namely ethoxyquinoline, 6,7,8,9-tetrahydro-3H-benzo[e]indole-1,2-dione, hexadecanoic acid, and heptadecane, produced by the fishmeal samples during storage, as well as the characteristic VOCs of fishmeal at each freshness grade.