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Rapid detection of micronutrient components in infant formula milk powder using near-infrared spectroscopy

Authors :
Shaoli Liu
Ting Lei
Guipu Li
Shuming Liu
Xiaojun Chu
Donghai Hao
Gongnian Xiao
Ayaz Ali Khan
Taqweem Ul Haq
Manal Y. Sameeh
Tariq Aziz
Manal Tashkandi
Guanghua He
Source :
Frontiers in Nutrition, Vol 10 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

In order to achieve rapid detection of galactooligosaccharides (GOS), fructooligosaccharides (FOS), calcium (Ca), and vitamin C (Vc), four micronutrient components in infant formula milk powder, this study employed four methods, namely Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), Normalization (Nor), and Savitzky–Golay Smoothing (SG), to preprocess the acquired original spectra of the milk powder. Then, the Competitive Adaptive Reweighted Sampling (CARS) algorithm and Random Frog (RF) algorithm were used to extract representative characteristic wavelengths. Furthermore, Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) models were established to predict the contents of GOS, FOS, Ca, and Vc in infant formula milk powder. The results indicated that after SNV preprocessing, the original spectra of GOS and FOS could effectively extract feature wavelengths using the CARS algorithm, leading to favorable predictive results through the CARS-SVR model. Similarly, after MSC preprocessing, the original spectra of Ca and Vc could efficiently extract feature wavelengths using the CARS algorithm, resulting in optimal predictive outcomes via the CARS-SVR model. This study provides insights for the realization of online nutritional component detection and optimization control in the production process of infant formula.

Details

Language :
English
ISSN :
2296861X
Volume :
10
Database :
Directory of Open Access Journals
Journal :
Frontiers in Nutrition
Publication Type :
Academic Journal
Accession number :
edsdoj.ffe6348073214882af277eeaab3fd129
Document Type :
article
Full Text :
https://doi.org/10.3389/fnut.2023.1273374