The headspace analysis was performed with a commercial PEN3.5 E-nose (Airsense Analytics, GmBH, Schwerin, Germany). The system contained 10 metal oxide sensors (namely, W1C, W5S, W3C, W6S, W5C, W1S, W1W, W2S, W2W and W3S). Prior to detection, each sample (2 g of scale samples) was placed in an airtight glass vial and closely capped with a PTFE–silicone stopper. Then, the samples were kept at 25 ± 1 °C for approximately 40 min (headspace generation time). The detection time of the sample was 120 s, the cleaning time of the sensor was 300 s and the adjustment time of automatic zero was 5 s. All samples were run with three repetitions.
The values of 101~110 s for each measurement using an E-nose were imported into WinMuster software and repeated 3 times to generate a principal component analysis (PCA) figure. PCA employs the idea of dimensionality reduction to simplify problems. A plurality of number indexes interconnected to each other were translated into several comprehensive and unrelated indicators, which are the principal components of the original multiple indexes. The between-group linkage method with a metric of Euclidean distance was performed to apply hierarchical cluster analysis (HCA) in this study. The merged data presented as a dendrogram, where the horizontal axis represented the Euclidean distance amongst groups and the vertical axis indicated the lily scale flavor similarity. The data obtained in Winmuster were averaged in excel to calculate the response values of the ten electronic metal sensors for the control and H2 fumigation during the storage period, and radar plots were generated using the data analysis tool.
The values of 101~110 s for each measurement using an E-nose were imported into WinMuster software and repeated 3 times to generate a principal component analysis (PCA) figure. PCA employs the idea of dimensionality reduction to simplify problems. A plurality of number indexes interconnected to each other were translated into several comprehensive and unrelated indicators, which are the principal components of the original multiple indexes. The between-group linkage method with a metric of Euclidean distance was performed to apply hierarchical cluster analysis (HCA) in this study. The merged data presented as a dendrogram, where the horizontal axis represented the Euclidean distance amongst groups and the vertical axis indicated the lily scale flavor similarity. The data obtained in Winmuster were averaged in excel to calculate the response values of the ten electronic metal sensors for the control and H2 fumigation during the storage period, and radar plots were generated using the data analysis tool.
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