Variable Selection, Outlier Detection, and Figures of Merit Estimation in a Partial Least-Squares Regression Multivariate Calibration Model. A Case Study for the Determination of Quality Parameters in the Alcohol Industry by Near-Infrared Spectroscopy

• Published in: Journal of agricultural and food chemistry Vol. 55; no. 21; pp. 8331 - 8338
• Main Authors: Valderrama, Patrícia, Braga, Jez Willian B., Poppi, Ronei Jesus
• Format: Journal Article
• Language: English
• Published: Washington, DC American Chemical Society 17.10.2007
• Subjects: alcohol industry; Analysis of Variance; Analytical Methods; biofuels; Biological and medical sciences; brix; Calibration; Chemical Industry; estimation; Ethanol - chemical synthesis; ethanol industry; ethanol production; Food industries; Fundamental and applied biological sciences. Psychology; General aspects; least squares; Least-Squares Analysis; Methods of analysis, processing and quality control, regulation, standards; Models, Statistical; multivariate analysis; near-infrared spectroscopy; outliers; partial least squares regression; PLSR; polarizable sugars; Quality Control; reducing sugars; regression analysis; Saccharum - chemistry; Sensitivity and Specificity; soluble solids; Spectroscopy, Near-Infrared - statistics & numerical data; sugarcane juice; sugars; Validation; Validation; near-infrared spectroscopy; outliers; alcohol industry; PLSR; Variable; Estimation; Selection; Alcohol; Calibration; Case study; Industry; PLS regression; Analysis method; Quality; Models; Control method; Detection
• ISSN: 0021-8561; 1520-5118
• DOI: 10.1021/jf071538s

Practical implementation of multivariate calibration models has been limited in several areas due to the requirement of appropriate development and validation to prove their performance to standardization agencies. Herein, a detailed description of the application of multivariate calibration based on partial least-squares regression models (PLSR) for the determination of soluble solids (BRIX), polarizable sugars (POL), and reducing sugars (RS) in sugar cane juice, based on near infrared spectroscopy (NIR), for the alcohol industries is presented. The development of the models, including variable selection and outlier elimination, and their validation by determination of figures of merit, such as accuracy, precision, sensitivity, analytical sensitivity, prediction intervals, and limits of detection and quantification, are described for a representative data set of 1381 sugar cane samples. Values estimated by PLSR are compared with appropriate reference methods, where the results indicated that the PLSR models can be used in the alcohol industry as an alternative to refractometry and lead clarification before polarization measurements (standard methods for BRIX and POL, respectively). For RS, the results of a titration reference method were compared with the PLSR estimates and also with an estimate based on BRIX and POL values, as actually used in the alcohol industry. The PLSR method presented a better agreement with the titration method. However, the results indicated that the RS estimates from both PLSR and those based on the BRIX and POL values, actually used, should be improved to a safe determination of RS.