Quality Analysis Of Edible Oil By Chemometrics Methods And FT-IR Spectra

Several chemometrics methods were introduced in this paper to identify edibleoil types and analysis oil adulteration. Aiming at the limitations of traditionalalgorithms themself, some methods were put forward to improve the efficiency of thetraditional algorithms. The proposed algorithms were applied for the analysis ofedible oil.1. The significance of edible oil quality analysis was introduced, variousmethods to analysis edible oil were presented, including chemical methods andchemometrics algorithm. The technology of Fourier transform infraredSpectroscopy(FT-IR) was also illustrated simply.2. This paper presented an efficient algorithm that optimized K-means clusteringby a hybrid particle swarm algorithm. The modified discrete algorithm was used toselect variables and was continuously applied to update cluster centers simultaneously.The nearest center classification was then employed to classify the test samples. Theproposed algorithm was applied to discriminate various edible oil varieties byemploying Fourier transform infrared spectroscopy. As comparison, the commonK-means clustering, principal component analysis, and partial least squarestechniques were also applied to classify these edible oil samples. Resultsdemonstrated that the proposed method was an accurate and rapid strategy foridentifying edible oils.3. The suitability score was used for variable selection to improve GMM andGMR. The improved GMM was used to discriminate the adulteration of peanut oilwith palm oil using Fourier transform infrared spectroscopy. The improved GMR wasapplied to quantify the level of palm oil adulterant present. As comparison, the GMMand GMR with principal component analysis for feature extraction, support vectormachine and linear discriminant analysis, back-propagation artificial neural network,nearest centroid classification and partial least squares analysis were also used toclassify and quantify these peanut oil samples. It had been demonstrated that theproposed method was better than these comparing algorithms. 4. The estimate of the GMM parameters is commonly obtained from theexpectation-maximization (EM) algorithm. To the limitations of the EM itself,artificial colony algorithm (ABC) was used in the gaussian mixture model andgaussian mixture regression to find the optimal parameters. To improve the optimizedperformance and reduce computational effort of ABC algorithm, the informationsharing mechanism among the global best food sources was introduced in ABC. Theimproved GMM and GMR by artificial bee colony algorithm (GMMRABC) was usedto discriminate and quantify the adulteration of olive oil with rapeseed oil usingFT-IR spectroscopy. It had been demonstrated that the proposed method was anaccurate, rapid, stable strategy for identifying and quantifying the olive oil.