Near-Infrared Spectroscopy for Multivariate Adulteration Detection
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Solution Overview
Problem
Current methods for detecting adulteration in edible oils are limited in identifying multiple types of cheaper oils added to expensive edible oils, as they often require extensive sample preparation and are not effective in distinguishing authentic from adulterated oils, especially when multiple cheaper oils are mixed in varying proportions.
Innovation Solution
A method using near-infrared spectroscopy combined with simplex linear programming and chemometric techniques such as principal component analysis and one class partial least squares to establish a multivariate adulteration detection model, selecting critical variables and preprocessing spectra to accurately identify authentic from adulterated edible oils.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional chemometric methods are used for authentication, then identification of known adulterants can be achieved, but the method cannot effectively detect multivariate adulteration with multiple cheap oils
Solution Approach 1:
Instead of building classification models for each type of adulteration, the patent inverts the approach by constructing a single reference model for authentic oil only. The system detects adulteration by measuring deviation from this authentic reference, enabling detection of any combination of adulterants without requiring prior knowledge of their types or proportions.
Solution Approach 2:
The patent creates a universal authentication model that can detect any type of adulteration (single or multiple cheap oils) using a single detection system. The simplex linear programming method provides a unified mathematical framework that handles diverse adulteration scenarios without requiring separate models for each case.
2Measurement precision
If extensive sample preparation and conventional analysis methods are used, then quality indicators can be measured, but the process is time-consuming and cannot rapidly identify adulteration
Solution Approach 1:
The patent replaces conventional mechanical/chemical analysis methods (refractive index measurement, iodine value determination, etc.) with near-infrared spectroscopy combined with chemometric analysis. This substitution enables rapid authentication without the time-consuming sample preparation and multiple measurement steps required by traditional methods.
Solution Approach 2:
The patent performs preliminary data preprocessing (standard normal transformation, mean centering, scaling) on spectral data to enhance the effectiveness of subsequent simplex linear programming analysis. This preliminary processing prepares the data for rapid and accurate adulteration detection, reducing the time needed for actual authentication.
3Ease of manufacture
If characteristic indicators from national standards are used for authentication, then quality grades can be determined, but adulterated oils cannot be effectively distinguished from authentic ones
Solution Approach 1:
The patent shifts from using traditional quality indicators (refractive index, iodine value, saponification value) to using near-infrared spectral parameters. These spectral parameters provide additional information about the oil's chemical composition and structure, enabling reliable distinction between authentic and adulterated oils while maintaining ease of analysis.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables rapid, non-destructive, and cost-effective detection of multivariate adulteration in edible oils, providing a powerful tool for ensuring authenticity and preventing fraudulent practices in the edible oil market.
Implementation Method 1
acquiring near-infrared spectra of the samples by using the near-infrared spectrometer
Data Source
AI summary
A method for multivariate adulteration detection on an edible oil includes (1) construction of a model: S1, acquiring near-infrared spectra of edible oils; S2, establishing a near-infrared spectral database of the edible oils; S3, establishing a multivariate adulteration detection model for a type of edible oil; and (2) application of the model: acquiring spectra of a sample to be tested according to the near-infrared spectral signal acquisition method in step S1, preprocessing the obtained near-infrared spectra by using the method in step S2 to obtain near-infrared spectral data of the sample, and determining the authenticity of the sample to be tested by using the multivariate adulteration detection model for the edible oil established in step S3. The method is simple and rapid in operation, can effectively and rapidly screen the authenticity of an edible vegetable oil, and has strong practicability.