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Results of the partial least squares regression (PLSR) calibration model for quantification of human milk protein content. a Plots of RMSEC, RMSECV and RMSEP against the number of PLS factors. The optimum number of PLS factors was 16. b Scatter plots for the training and test data sets with Pearson correlation coefficients (r) of 0.98 and 0.97, respectively. c Bland-Altman plot for the test data set. The average of the differences between the reference Kjeldahl and transmission infrared spectroscopy (TIR)-based method was −0.01 g/100 mL
Source publication
Objective:
To develop partial least squares regression (PLSR) calibration models in combination with transmission infrared (TIR) spectroscopy for rapid and optimal quantification of human milk macronutrient concentrations.
Study design:
Human milk samples (n = 306) were characterized simultaneously by reference chemical analytical methods and TI...
Citations
... These methods are based on the interaction between specific infrared irradiation wavelengths with various chemical groups of HM components (García-Lara et al., 2012). The technology employed by these devices allows for rapid HM analysis, using only a small amount of HM (Elsohaby et al., 2018). However, measurements of the total carbohydrate content have been reported to be less accurate compared to other nutrients, among the available devices (Perrin et al., 2019). ...
Holder pasteurization is the current recommended method for donor human milk treatment. This method effectively eliminates most life-threatening contaminants in donor milk, but it also greatly reduces some of its biological properties. Consequently, there is a growing interest for developing novel processing methods that can ensure both microbial inactivation and a higher retention of the functional components of donor milk. Our aim was to offer a comprehensive overview of the analytical techniques available for the evaluation of such methods. To suggest an efficient workflow for the analysis of processed donor milk, a safety analytical panel as well as a nutritional value and functionality analytical panel are discussed, together with the principles, benefits, and drawbacks of the available techniques. Concluding on the suitability of a novel method requires a multifactorial approach which can be achieved by a combination of analytical targets and by using complementary assays to cross-validate the obtained results.
Given that protein peptide powders (PPPs) from different biological sources were inherited with diverse healthcare functions, which aroused adulteration of PPPs. A high-throughput and rapid methodology, united multi-molecular infrared (MM-IR) spectroscopy with data fusion, could determine the types and component content of PPPs from seven sources as examples. The chemical fingerprints of PPPs were thoroughly interpreted by tri-step infrared (IR) spectroscopy, and the defined spectral fingerprint region of protein peptide, total sugar, and fat was 3600-950 cm-1, which constituted MIR finger-print region. Moreover, the mid-level data fusion model was of great applicability in qualitative analysis, in which the F1-score reached 1 and the total accuracy was 100%, and a robust quantitative model was established with excellent predictive capacity (Rp: 0.9935, RMSEP: 1.288, and RPD: 7.97). MM-IR coordinated data fusion strategies to achieve high-throughput, multi-dimensional analysis of PPPs with better accuracy and robustness which meant a significant potential for the comprehensive analysis of other powders in food as well.
In this article, after reviewing the different mathematical methods used in quantification by IR, the main advantages and drawbacks and applications with or without previous sample treatment are presented. The bases of the use of quantitative IR in food analysis such as lipids (measurement of unsaturation degrees and lipids determination), carbohydrates, and proteins (secondary structures and quantitative analysis); original examples of the use of IR spectroscopy combined with enzymes; and modern applications in the clinical field are presented in detail, followed by examples of Fourier transform infrared (FTIR) analysis coupled with other analytical methods in different fields such as pharmaceuticals, petroleum, paints, and other industrial products and in the fields of health, environment, and trace compounds analysis in order to think about the limitations and perspectives of IR spectroscopy for quantitative analysis. It appears that the most significant developments in the field of quantitative analysis will probably come through the progress in chemometrics and instrumentation. More recently, approaches combining infrared spectroscopy and deep learning (DL) have shown great potential.
Nisin is a class I bacteriocin, which is produced by lactic acid bacteria (LAB) belonging to Lactococcus and Streptococcus genera. Nisin is approved by the FDA (Food and Drug Administration) as a natural biopreservative agent under reference E234. Besides food application, nisin has also a potential for therapeutic application to treat a range of infectious diseases. Nisin, which is commercially available, is mainly produced by liquid fermentation. The production of commercial high-quality nisin is hampered by the high cost of downstream processing, which often involves subsequent steps of salt precipitation, centrifugation, and chromatography. Membrane processes offer a set of advantages. Indeed, these processes are simple and effective technologies for protein concentration and separation; One of the biggest limitations is the decrease in permeate flux. In this study, the performance of three UF membranes (10, 50, and 100 kDa) for the separation and concentration of nisin obtained from Lactococcus lactis (L. lactis) culture supernatants was evaluated. Permeate flux decline, fouling resistances, fouling index, nisin recovery in the permeate and retentate streams were evaluated. The 10 kDa membrane showed the best nisin recovery properties with the highest recovery yield and purification factor, achieving 100% and 4 in the retentate stream, respectively.
Objectives:
To compare a large panel of plasma protein inflammatory biomarkers and mid-infrared (MIR) spectral patterns between patients with confirmed fracture related infections (FRIs) and controls without infection.
Design:
Prospective case-control.
Setting:
Academic, level 1 trauma center.
Patients:
Thirteen patients meeting confirmatory FRI criteria were matched to 13 controls based on age, time after surgery, and fracture region.
Intervention:
Plasma levels of 49 proteins were measured using enzyme-linked immunosorbent assay (ELISA) techniques. Fourier transform infrared (FTIR) spectroscopy of dried films was used to obtain MIR spectra of plasma samples.
Main outcome measurements:
Plasma protein levels and MIR spectra of samples.
Results:
Multivariate analysis-based predictive model developed utilizing ELISA-based biomarkers had sensitivity, specificity, and accuracy of 69.2±0.0%, 99.9±1.0%, and 84.5±0.6%, respectively, with PDGF-AB/BB, CRP, and MIG selected as the minimum number of variables explaining group differences (P<0.05). Sensitivity, specificity, and accuracy of the predictive model based on MIR spectra were 69.9±6.2%, 71.9±5.9%, and 70.9±4.8%, respectively, with six wavenumbers as explanatory variables (P<0.05).
Conclusions:
This pilot study demonstrates the feasibility of using a select panel of plasma proteins and FTIR spectroscopy to diagnose FRI. The preliminary data suggest that measurement of these select proteins and MIR spectra may be potential clinical tools to detect FRI. Further investigation of these biomarkers in a larger cohort of patients is warranted.
Level of evidence:
Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.
