Dairy Product Age Assessment Using PCA and PLS Calibration
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Solution Overview
Problem
Existing methods for determining the shelf life of dairy products are time-consuming, costly, and often provide only rough estimates, making it difficult to ensure product quality and techno-functionality within declared minimum shelf life.
Innovation Solution
A method using Principal Component Analysis (PCA) and Partial Least Squares (PLS) analysis to create a calibration model that quickly and reliably assesses the age and quality of dairy products by evaluating chemical and physical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard qualification tests (product storage tests in real time) are used to determine quality and minimum shelf life, then reliable quality assessment is achieved, but the test period becomes excessively long (several months)
Solution Approach 1:
The method performs preliminary actions by collecting quality and composition data at multiple time points during storage and using statistical evaluation (PCA and PLS) to predict future quality trends. This allows shelf life determination before the actual minimum shelf life expires, avoiding the need to wait several months for traditional tests to complete while maintaining assessment reliability through multivariate analysis of degradation patterns.
2Loss of time
If indirect methods (pressurized chamber with test gas) are used to estimate shelf life, then test time is reduced, but measurement precision becomes very rough and often unsuitable for qualitative statements
Solution Approach 1:
The method replaces mechanical/physical estimation systems (pressurized chambers with test gas permeation) with statistical evaluation systems. Instead of using physical models of gas permeation through packaging, the invention uses multivariate statistical analysis (PCA and PLS) of actual product quality data collected during storage to predict shelf life, achieving both speed and precision.
3Reliability
If sensor devices (electrochemical processors) are equipped on each individual package to indicate shelf life, then real-time quality monitoring is achieved, but device complexity and technical requirements increase significantly
Solution Approach 1:
Instead of placing physical sensors on each package, the method creates a statistical model (PLS calibration model) that copies the relationship between quality parameters and shelf life based on training data. This virtual model can then predict shelf life from routine quality measurements without requiring complex sensor hardware on individual packages.
Data Source
AI summary
A method is proposed to assess the stability and quality of dairy products, primary and main milk-based products including cheeses and plant-based milk alternatives over the storage period by aging dairy products, primary and main milk-based products including cheeses or plant-based milk alternatives under defined conditions, taking regular samples and analyzing them using different parameters. The data sets are analyzed using Principal Component Analysis (PCA) and the Partial Least Square (PLS) calibration method. The calculated ages are compared with the actual sample age and provide a calibration line that can be used to determine the age of unknown samples.