ArticleFrontiers in nutrition2023
Rapid detection of micronutrient components in infant formula milk powder using near-infrared spectroscopy.
Article in Frontiers in nutrition, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 18 citations in OpenAlex.
- Hyperspectral imaging with machine learning for non-destructive prediction of quality attributes in Lanzhou frozen pears.Food chemistry: X · 2026Article
- Article
- Effects of different milk powders on the growth and intestinal flora in weaned rats: Comparison of special formula milk powder with ordinary milk powder.Food science & nutrition · 2024Article
- Quality Assessment of Reconstructed Cow, Camel and Mare Milk Powders by Near-Infrared Spectroscopy and Chemometrics.Molecules (Basel, Switzerland) · 2024Article
- Comparison of the Lipid Composition of Milk Fat Globules in Goat (Foods (Basel, Switzerland) · 2024Article
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Authors and funding
13 authors at 5 institutions in 4 countries.
Funding
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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.
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