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O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

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[Partial least squares regression variable screening studies on apple soluble solids NIR spectral detection].

Ai-Guo Ouyang¹, Xiao-Qiang Xie, Yan-Rui Zhou

¹Institute of Optical and Electrical Machinery Technology and Application, East China Jiaotong University, Nanchang 330013, China. ouyang1968711@163.com

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu

|January 5, 2013

Summary

This summary is machine-generated.

A genetic algorithm improved near-infrared (NIR) spectroscopy models for predicting apple soluble solid content (SSC). This method enhanced prediction accuracy, making it more robust for quality assessment.

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Area of Science:

Agricultural Science
Spectroscopy
Chemometrics

Context:

Accurate prediction of soluble solid content (SSC) in apples is crucial for quality assessment and market value.
Near-infrared (NIR) spectroscopy offers a rapid, non-destructive method for analyzing fruit quality.
Developing robust predictive models for SSC using NIR data presents challenges due to spectral complexity.

Purpose:

To enhance the predictive ability and robustness of near-infrared (NIR) spectroscopy models for apple soluble solid content (SSC).
To compare the effectiveness of different variable selection methods, including genetic algorithms (GA), reverse interval partial least squares (RIPLS), and continuous projection (CP).
To establish a partial least squares regression (PLSR) model optimized with the best variable selection technique.

Summary:

Variable selection was performed on 141 spectral variables using GA, RIPLS, and CP methods to build a PLSR model for apple SSC.
The genetic algorithm demonstrated superior performance in screening spectral variables, leading to the most effective predictive model.
The optimized model achieved a correlation coefficient of 0.96 and a root mean square error of 0.23 degrees Brix, outperforming the full spectrum model (0.93 and 0.30 degrees Brix, respectively).

Impact:

The study demonstrates that combining genetic algorithms with partial least squares regression significantly improves the precision of NIR-based detection for apple SSC.
This optimized approach offers a more accurate and reliable method for non-destructive quality assessment of apples.
The findings contribute to advancements in chemometric applications for agricultural product analysis.