Multivariate analysis of three chemometric algorithms on rapid prediction of some important quality parameters of crude shea butter using Fourier transform-near infrared spectroscopy

Abstract

A comparative study of three chemometric algorithms combined with NIR spectroscopy with the aim of determining the best performing algorithm for quantitative prediction of iodine value, saponification value, free fatty acids content, and peroxide values of unrefined shea butter. Multivariate calibrations were developed for each parameter using supervised partial least squares, interval partial least squares, and genetic-algorithm partial least square regression methods to establish a linear relationship between standard reference and the Fourier transformed-near infrared predicted. Results showed that geneticalgorithm partial least square models were superior in predicting iodine value and saponification value while partial least squares was excellent in predicting free fatty acids content and peroxide values. The nine-factor genetic-algorithm partial least square iodine value calibration model for predicting iodine value yielded excellent (R 2 cal ¼ 0.97), (R 2 val ¼ 0.97), low (root mean square error of cross-validation ¼ 0.26), low (root mean square error of Prediction ¼ 0.23), and (ratio of performance to deviation ¼ 6.41); for saponification value, the nine-factor genetic-algorithm partial least square saponification value calibration model had excellent R 2 cal (0.97), R 2 val (0.99); low root mean square error of cross-validation (0.73), low root mean square error of Prediction (0.53), and (ratio of performance to deviation ¼ 8.27); while for free fatty acids, the 11factor partial least square free fatty acids produced very high R 2 cal (0.97) and R 2 val (0.97) with very low root mean square error of cross-validation (0.03), low root mean square error of Prediction (0.04) and (ratio of performance to deviation ¼ 5.30) and finally for peroxide values, the 11-factor partial least square peroxide values calibration model obtained excellent R 2 cal (0.96) and R 2 val (0.98) with low root mean square error of cross-validation (0.05), low root mean square error of Prediction (0.04), and (ratio of performance to deviation ¼ 5.86). The built models were accurate and robust and can be reliably applied in developing a handheld quality detection device for screening, quality control checks, and prediction of shea butter quality on-site.

Rapid Iodine Value Estimation Using a Handheld Raman Spectrometer for On-Site, Reagent-Free Authentication of Edible Oils

“… 23 − 29 At the same time, others have applied advanced spectroanalytical techniques such as multiple linear regression, genetic algorithm, principal component analysis, and partial least-squares regression for discriminating genuine samples from those adulterated and also for quantifying the extent of fraudulence. 13 , 16 , 30 − 32 In particular, Raman spectroscopy is a noninvasive technique that allows rapid and nondestructive data acquisition from samples in their native state. The recent technological advancement has realized portable and handheld versions of Raman spectrometers, facilitating their on-site and industrial applications.…”

“…Adulteration changes the composition of edible oils and will disrupt lipid metabolism and signaling networks in human bodies. This can be a potential source of cancer, cardiovascular, neurodegenerative, and metabolic disorders. ,− The rampant adulteration instances have necessitated an in-depth chemical analysis of foods and beverages. ,− The essential analytical parameters used for edible oil quality estimation are the iodine value (IV), saponification value (SV), peroxide value (PV), acid value (AV), hydroxyl value, moisture content, and Polenske value. − The SV and AV, respectively, provide information about the average molecular weight and the number of carboxylic acid groups of fatty acids present in the edible oil, while the IV measures the degree of unsaturation and the PV indicates its oxidative deterioration. The accurate estimation of IV is often used as an authentication tool by both the consumers and food industries and is a crucial parameter for lubricants and fuel industries.…”

“…Since the intensity of the vibrational band is directly proportional to the number of responsible molecules and functional groups, they are suitable for the composition examination of materials. Several researchers have illustrated the potential of infrared (IR) and Raman spectroscopies for the rapid characterization of edible oils and quantitative estimation of cis–trans composition and unsaturation of fatty acids present. − At the same time, others have applied advanced spectroanalytical techniques such as multiple linear regression, genetic algorithm, principal component analysis, and partial least-squares regression for discriminating genuine samples from those adulterated and also for quantifying the extent of fraudulence. ,,− In particular, Raman spectroscopy is a noninvasive technique that allows rapid and nondestructive data acquisition from samples in their native state. The recent technological advancement has realized portable and handheld versions of Raman spectrometers, facilitating their on-site and industrial applications. − …”