Due to numerous edible oil safety problems in
China, an automatic oil quality detection technique is urgently
needed. In this study, rough set theory and Fourier transform spectrum
are combined for proposing a digital identification method for edible
oil. First, the Fourier transform spectra of three different types of
edible oil samples, including colza oil, waste oil, and peanut oil,
are measured. After the input spectra are differentially and smoothly
processed, the characteristic wavelength bands are selected with
neighborhood rough set attribution reduction (NRSAR). Moreover, the
classification models are established based on random forest (RF) and
extreme learning machine (ELM) algorithms. Finally, confusion matrix,
classification accuracy, sensitivity, specificity, and the
distribution of judgment are calculated for evaluating the
classification performances of different models and determining the
optimal oil identification model. The results show that by using the
third-order difference pre-processing method, 193 wavelength bands in
the visible range can be reduced to 10 characteristic wavelengths,
with a compression ratio of over 88.61%. Using the established NRS-RF
and NRS-ELM models, the total identification accuracies are 91.67% and
93.33%, respectively. In particular, the identification accuracy of
peanut oil using the NRS-ELM model reaches up to 100%, whereas the
identification accuracies obtained using the principal component
analysis (PCA)-based models that are commonly used in information
processing (PCA-RF and PCA-ELM) are 81.67% and 90.00%, respectively.
As compared with feature extraction methods, the proposed NRSAR shows
directive advantages in terms of precision, sensitivity, specificity,
and the distribution of judgment. In addition, the execution time is
also reduced by approximately 1/3. Conclusively, the NRSAR method and
NRS-ELM the model in the spectral identification of edible oil show
favorable performance. They are expected to bring forth insightful oil
identification techniques.