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Example of PCR, interpretation of calibration equations

This study presents NIR analysis results of Casein, Lactate, and Glucose, with scatter correction to remove unwanted effects. Using PCA, principal components PC1 and PC2 explain 99.6% of the variability, allowing accurate regression predictions. Weighted regression coefficients for Glucose and Lactate show strong relationships with spectral variability. The linear Beer’s law model effectively models the NIR data matrix, enabling precise constituent predictions. The study identifies affected wavelengths, validates results with spectral patterns, and confirms accuracy through outlier-free data. Interpretation of scores and loadings align well, enhancing result reliability.

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Example of PCR, interpretation of calibration equations

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  1. Example of PCR, interpretation of calibration equations • NIR analysis of 3 constituents • Casein • Lactate • Glucose • Scatter corrected (eliminates unwanted effects)

  2. Exp. design PC1=76.5% PC2=23.1% SUM=99.6% All objects were measured by NIR (many hundred var. in X) The NIR spectra were submitted to PCA Number of variables much larger than the number of objects

  3. Principal components in regression Loadings ”Weighted” regression coefficients Glucose: 2.10 and -0.31 Lactate: -0.65 and 1.65 Two components explain > 99% of y Cross-validation Mean centred glucose spectrum Lactate-casein spectrum

  4. Conclusions • The NIR data matrix can be adequately modelled by a linear Beer’s law model (linear function of constituents) • Good predictions are obtained • Glucose is closely related to the first component and then responsible for more spectral variability than the other two constituents • Which wavelengths are affected by the different constituents. Verified by comparing with spectral patterns of the constituents • No outliers, strengthening the confidence of the results. • The interpretation of scores, X-loadings and y-loadings fit well together

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