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. 2020 Aug 10;9(8):1090.
doi: 10.3390/foods9081090.

Rapid Analysis of Milk Using Low-Cost Pocket-Size NIR Spectrometers and Multivariate Analysis

Jordi Riu  1 , Giulia Gorla  2 , Dib Chakif  1 , Ricard Boqué  1 , Barbara Giussani  2
Affiliations

Rapid Analysis of Milk Using Low-Cost Pocket-Size NIR Spectrometers and Multivariate Analysis

Jordi Riu et al. Foods. .

Abstract

The miniaturisation of analytical devices, reduction of analytical data acquisition time, or the reduction of waste generation throughout the analytical process are important requirements of modern analytical chemistry, and in particular of green analytical chemistry. Green analytical chemistry has fostered the development of a new generation of miniaturized near-infrared spectroscopy (NIR) spectrometric systems. However, one of the drawbacks of these systems is the need for a compromise between the performance parameters (accuracy and sensitivity) and the aforementioned requirements of green analytical chemistry. In this paper, we evaluated the capabilities of two recently developed portable NIR instruments (SCiO and NeoSpectra) to achieve a rapid, simple and low-cost quantitative determination of commercial milk macronutrients. Commercial milk samples from Italy, Switzerland and Spain were chosen, covering the maximum range of variability in protein, carbohydrate and fat content, and multivariate calibration was used to correlate the recorded spectra with the macronutrient content of milk. Both SCiO and NeoSpectra can provide a fast and reliable analysis of fats in commercial milk, and they are able to correctly classify milk according to fat level. SCiO can also provide predictions of protein content and classification according to presence or absence of lactose.

Keywords: NIR; classification; green analytical chemistry; milk; multivariate analysis; portable instrumentation; spectroscopy.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Milk spectra recorded using (a) SCiO and (b) NeoSpectra.
Figure 2
Figure 2
Score plots for the principal component analysis (PCA) models for SCiO (a) and NeoSpectra (b).
Figure 3
Figure 3
(a) Dendrogram for the SCiO fat data and (b) the dendrogram for the NeoSpectra fat data.
Figure 4
Figure 4
Regression line between the measured and predicted fat content for SCiO (a) and NeoSpectra (b).
Figure 5
Figure 5
Loadings and regression coefficients of the PLS models. SCiO: (a) first (solid line) and second (dashed line) loadings; (b) regression coefficients. NeoSpectra: (c) first (solid line) and second (dashed line) loadings; and (d) regression coefficients.
Figure 6
Figure 6
Joint confidence interval for the intercept and the slope of the comparison between the SCiO and NeoSpectra PLS best predictions.
Figure 7
Figure 7
Regression line between the measured and predicted protein contents for SCiO.
Figure 8
Figure 8
Joint confidence interval for the intercept and the slope of the comparison between the declared and predicted % of proteins.
Figure 9
Figure 9
Results of the low-(a) and mid-(b) level data fusion models for the prediction of fats.
See this image and copyright information in PMC

References

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