electronic nose characteristics of apples infected with different fungi

This dataset mainly contains data collected from apples infected with different fungi and fresh apples using an electronic nose, The electronic nose contains 8 sensors, and sensor No. 1 and sensor No. 5 use the same sensor to eliminate outliers in the data，If the difference between the 150-300s of the two sensors was greater than 1.2mg/L, the specimen will then be considered anomalous and removed, and the data for removing outlier samples using this method is stored in 'Data/sensors_eliminate' file. The "Fuji" apples selected in this dataset came from apple plantations in Gansu Province, China. 160 ripe apples were selected and randomly divided into 4 groups, 40 apples in each group, namely Group A, Group B, Group C and Group D; The fungi inoculated in the middle apples were Aspergillus niger, Penicillium expansum and Penicillium crustosum. The apple samples were pretreated with 75% alcohol on a sterile bench and dried at room temperature. Then, four holes were punched in four directions of each apple of the three groups A, B, and C with the inoculator. Sample apples were inoculated with 7-day-old molds through drilled loops, and the holes were covered with sterile film. The mold-inoculated apples were then placed in a 1000ml beaker, sealed with plastic wrap, and then placed in a 25°C constant temperature incubator for 5 days. Before the test, the apple samples were taken out of the incubator and placed for 30 minutes. To eliminate the influence of residual gas on the experimental results, electronic nose was cleaned with inert gas before using. Setting electronic nose parameters: cleaning time 500s, collection time 350s, sampling interval 1s, injection flow 150ml/min, the raw data store in 'Data/raw_data' file. Then Matlab is used to preprocess the raw data，The first is to smooth and filter the data, and use 3-point smoothing, 5-point smoothing, 7-point smoothing, 9-point smoothing and 11-point smoothing to smooth and filter the data after removing abnormal samples. The smoothed filtered data is stored in ‘Data/smoothed_data’ file. The second is feature extraction, we take the integral value, variance value, average differential value, maximum gradient value, relatively stable average value and energy value of the response curve of each sensor for 30-300s as the characteristic information of electronic nose. However, the value of 7NE/H2S-1000 and VOC-300 sensors is always 0 during the whole acquisition process, so this dataset only store the data measured by 6 sensors except 7NE/H2S-1000 and VOC-300, which stores in 'Data/feature_parameters_data' file, The second is to use Mahalanobis distance to propose abnormal samples in the data again, and the eliminated data is stored in ‘Data/Data/eliminate_anomalous_data‘ file. Finally, principal component analysis, factor analysis and linear discriminant analysis are used to reduce the dimension of the above data, and the data after dimension reduction is stored in ‘Data/dimensionality_reduction_data' file.