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Detection Method and Research for the Quality of Edible Oil Based on Near Infrared Spectroscopy

Author: LiangDan
Tutor: LiXiaoZuo
School: Huazhong Agricultural University
Course: Agricultural Mechanization Engineering
Keywords: Vegetable oil Near Infrared Spectroscopy Partial least squares Systematic Clustering Principal Component Analysis BP artificial neural network method
CLC: TN219
Type: Master's thesis
Year: 2009
Downloads: 158
Quote: 1
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Abstract

The cooking oil is an important and indispensable part of people's diet structure have an important impact on human health, the quality of the pros and cons. Traditional edible oil quality testing chemical methods often require a variety of chemical instruments and reagents, samples require pretreatment operation tedious, time-consuming and laborious. This paper is based on the edible vegetable oil using near-infrared spectroscopy and chemometric analysis method to carry out the study of edible vegetable oil quality detection and identification methods for the study. The main results are as follows: 1. Comparative analysis of the impact of the 10 kinds of preprocessing methods 3 fatty acid content of vegetable oils in the quantitative analysis of the results of the calibration model, we can see a first derivative and multiplicative scatter correction is to establish a quantitative analysis model of oleic acid content in vegetable oils most excellent pretreatment methods; deal with the second derivative the optimal pretreatment methods to establish a quantitative analysis model of the linoleic acid content of vegetable oils; first derivative and vector normalization process is to establish a quantitative analysis model of the linolenic acid content of vegetable oils The optimal pretreatment methods. 2 were established near-infrared quantitative analysis model based on the detection of partial least squares PLS edible vegetable oil 3 fatty acids (oleic acid, linoleic acid, linolenic acid) content. Then the decision oleic acid, linoleic acid, linolenic acid quantitative correction model coefficient R ~ 2 respectively 0.9752,0.937,0.9853 internal cross-validation mean square error RMSECV 1.04%, 1.3%, 0.388%, three model calibration decision coefficient are higher; quantitative analysis model validation determination coefficient R ~ 2 were 0.9753,0.9655,0.9722; predicted standard deviation of 1.11% RMSEP, 1.55%, 0.487%, model predictive better. That the quantitative analysis model can achieve rapid non-destructive testing of oleic acid, linoleic acid, linolenic acid content of edible vegetable oil. Established near-infrared quantitative analysis model based on partial least squares PLS can simultaneously detect vegetable oil 3 fatty acids (oleic acid, linoleic acid, linolenic acid) content. Known the the correction model checking oleic decision coefficient R ~ 2 to 0.9693, and the internal cross-validation mean square error RMSECV 1.15%, detection of linoleic acid determination coefficient R ~ 2 0.9671, internal cross-validation mean square error RMSECV 1.46%, detection flax acid determination coefficient R ~ 2 0.9792, internal cross-validation mean square error RMSECV of 0.461%; oleic verification of the model coefficient of determination R ~~ 2 to 0.9693 RMSEP of predicted standard deviation of 1.3% linoleic acid verify decision coefficient R ~ 2 0.9606 RMSEP of predicted standard deviation of 1.66%, validation coefficient of determination of linolenic acid R ~ 2 0.9731 RMSEP of predicted standard deviation of 0.479%. Show that the proposed model can better detect oleic acid, linoleic acid, linolenic acid content of edible vegetable oil at the same time. 4. Qualitative analysis of the use of near-infrared spectroscopy method of identifying the four varieties of edible vegetable oil, sesame soy blend oil identification methods, methods of identification of six different brands of sesame oil. System cluster, principal component analysis and BP artificial neural network method Identification of four varieties of edible vegetable oil, the principal component analysis results show that the optimal combination of BP artificial neural network method to establish the the vegetable oil varieties identify qualitative analysis model identification of four varieties of vegetable oil was 100%; system cluster, principal component analysis, Identification of a sesame soy blend oil BP artificial neural network method, principal component analysis results show that the combination of BP artificial neural network established blended oils identify qualitative analysis model is optimal, sesame oil, soybean oil, blended oil identification rate was 96.15%; system cluster, principal component analysis, BP artificial neural network method 6 Identification of different brands of sesame oil, the principal component analysis results showed that the optimal combination of BP artificial neural network method to establish the different brands of sesame oil differential qualitative analysis model, identification of six different brands of sesame oil was 83.33%. The results show that near infrared spectroscopy can be used as edible vegetable oil quality rapid identification of a new method of detection.

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