Dataset for electronic nose from various beef cuts

Citation Author(s):
Dedy Rahman
Wijaya
Submitted by:
Dedy Wijaya
Last updated:
Fri, 12/06/2019 - 05:37
DOI:
10.21227/596e-xn25
Data Format:
License:
0
0 ratings - Please login to submit your rating.

Abstract 

The development of electronic nose (e-nose) for a rapid, simple, and low-cost meat assessment system becomes the concern of researchers in recent years. Hence, we provide time-series datasets that were recorded from e-nose for beef quality monitoring experiment. This dataset is originated from 12 type of beef cuts including round (shank), top sirloin, tenderloin, flap meat (flank), striploin (shortloin), brisket, clod/chuck, skirt meat (plate), inside/outside, rib eye, shin, and fat. The process of beef spoilage is recorded using 11 Metal-Oxide Semiconductor (MOS) gas sensors during 2220 minutes. The availability of this dataset can enable further discussion about proper signal processing and robust machine learning algorithm. Furthermore, this dataset can also be useful as a comparison dataset for similar e-nose applications, such as air quality monitoring, smart packaging system, and food quality monitoring.

Instructions: 

This dataset is originated from 12 type of beef cuts including round (shank), top sirloin, tenderloin, flap meat (flank), striploin (shortloin), brisket, clod/chuck, skirt meat (plate), inside/outside, rib eye, shin, and fat. The process of beef spoilage is recorded using 11 Metal-Oxide Semiconductor (MOS) gas sensors during 2220 minutes. The dataset is formatted in "xlsx" file. Each sheet represents one beef cut which is contained columns as follows: Minute: time in minute TVC: continuous label in the total viable count Label: discrete label, 1,2,3,4 denote “excellent”,”good”,”acceptable”, and “spoiled”, respectively. MQ_: the resistant value of gas sensors (2018-10-27). The dataset is available as a reference for e-nose signal processing, notably for meat quality studies. The two main objectives of this dataset are multiclass beef classification and microbial population prediction by regression.