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REVIEW 4 major objections 5 minor 29 references

Analyzing Blood Glucose Levels with Near Infra-Red Spectroscopy and Chemometric Multivariate Methods

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Multiplying blood spectra by the first derivative of water's absorbance suppresses water interference and improves FTIR glucose prediction in this paper's experiments.

desk verdict The DMS/DMR/DMSR preprocessing trick is new and the authors are honest about ARA's failure, but the central water-removal claim is asserted rather than shown, and the reported gains are too weak and possibly artifact-driven to accept as-is. read the letter →

arxiv 2507.14557 v1 pith:IMVHUIWE submitted 2025-07-19 physics.med-ph physics.bio-ph

Hadi Barati , Arian Mousavi Madani , Soheil Moradi , Mehdi Fardmanesh This is my paper · ORCID
classification physics.med-phphysics.bio-ph
keywords bloodglucosenear-infraredspectroscopyFTIRprincipalcomponentregressionwaterinterferencesuppressionderivativemultiplicationmolarabsorptivityLambert-Beerlaw
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Near-infrared blood spectra are dominated by a strong water absorption band that obscures the glucose signal. This paper proposes multiplying each measured absorbance curve by the first derivative of the pure-water absorbance (DMS), by one minus the first derivative of the glucose absorbance (DMR), or by both (DMSR) before building a principal component regression (PCR) model for glucose. The authors report that the water peak near 5200 $cm^{-1}$ disappears, a glucose-related peak near 5100 $cm^{-1}$ becomes visible, and PCR errors drop slightly while the number of principal components falls from 12 to as few as 3. A separate Lambert-Beer regression using glucose molar absorptivity yields an RMSE of about 22.4 mg/dL. If correct, the method improves FTIR-based glucose estimation without any hardware change.

What carries the argument

The central objects are three preprocessing transforms built from reference derivative spectra. DMS multiplies every sample absorbance by the L2-normalized first derivative of the water absorbance with respect to wavenumber, dA_water/d-nu-tilde; DMR multiplies by the L2-normalized quantity (1 - dA_glucose/d-nu-tilde); DMSR applies both factors. Because the first derivative of a spectrum is zero at a local maximum, the water band at 5200 $cm^{-1}$ is suppressed, and the glucose band near 5100 $cm^{-1}$ is retained or emphasized. These weighted spectra replace net absorbances as inputs to principal component regression, and a separate linear calibration, absorptivity regression analysis (ARA), uses glucose molar absorptivity through the Lambert-Beer relation.

What would settle it

Measure glucose-in-water mixtures with known concentrations, apply DMS, DMR, and DMSR exactly as described, then run PCR with the same 90/10 training/test split; if the RMSE advantage over net absorbances disappears, or if the predictions track the derivative weight rather than the true concentration, the claimed water removal is not physical.

Watch

Extended reading notes

Core claim

The central claim is that water interference in blood-glucose NIR analysis can be suppressed by pointwise multiplication of the blood absorbance by the first derivative of the water absorbance spectrum, because a derivative vanishes at a peak maximum and therefore zeros out the water band. Adding a second multiplication by one minus the first derivative of the glucose absorbance reinforces the glucose signature. Compared with the standard net-absorbance approach, which subtracts a scaled water spectrum and depends on an unknown sample-dependent scaling factor gamma, the derivative-weighted spectra are claimed to preserve glucose information and to make principal component regression more stable. In experiments on 200 blood samples from twenty subjects, the derivative-based PCR models achieve slightly lower RMSE values (12.50 to 13.00 mg/dL versus about 13.1 to 13.6 mg/dL for net absorbances) and much smaller cross-validation-score standard deviations, while requiring fewer principal components.

Load-bearing premise

The load-bearing premise is that multiplying each blood absorbance by the first derivative of the water absorbance removes water's contribution at every wavenumber—asserted in the Results from the derivative vanishing at the water peak, but not derived or independently validated—so if that premise fails, the reported improvement is a reweighting artifact.

Editorial extensions

If this is right

  • If the derivative-weighting claim holds, the same FTIR spectra yield glucose predictions with lower RMSE and MAE than the net-absorbance baseline in all tested wavenumber windows.
  • The derivative-based models become less sensitive to wavenumber range selection, since their cross-validation-score standard deviations are far smaller than those of the net-absorbance models.
  • DMR needs only three principal components to reach an RMSE of 12.7 mg/dL in the full 4000-8000 cm^-1 range, which would simplify the calibration model.
  • The ARA Lambert-Beer regression gives an RMSE of about 22.4 mg/dL with most predictions falling in Clarke error grid region A, indicating clinically plausible accuracy for a simple linear model.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the derivative weight is fixed by pure water and pure glucose spectra, the same preprocessing could be tested on other instruments or on matrices with a dominant known interferent, provided the weight spectra are measured on the same setup.
  • If the improvement proves robust, the method could be combined with partial least squares or machine-learning regressors, which the paper does not test.
  • The mechanism is not fully settled by the experiments: the multiplication may be acting as a band-limiting weight rather than physically removing water absorption, so a synthetic-mixture study with known glucose concentrations would separate those explanations.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The manuscript reports FTIR transmittance measurements of blood samples from twenty subjects over five days (200 samples, 4000–8000 cm^-1) and compares principal component regression (PCR) glucose predictions based on net absorbances (obtained by subtracting a scaled water spectrum) with three preprocessing variants: multiplication by the normalized first derivative of the water absorbance (DMS), multiplication by (1 minus the first derivative of the glucose absorbance) (DMR), and their combination (DMSR). The authors report modest RMSE improvements over net absorbances (e.g., 13.14 to 12.50 mg/dL for DMS in range I), fewer principal components for the derivative-based methods, and an additional linear-regression approach called Absorptivity Regression Analysis (ARA) that yields an RMSE of 22.42 mg/dL after setting an error-compensation vector R to 105. The central claim is that multiplying spectra by the first derivative of water absorbance 'effectively removes' water interference and makes glucose peaks more distinguishable.

Significance. If the central claim were established, the proposed preprocessing would be a simple and inexpensive addition to NIR-based glucose chemometrics. The paper's strengths include an explicit acknowledgment of the scaling-factor ambiguity in water subtraction, a comparison across three wavenumber windows, and the use of Clarke error grids. However, the core mechanism is not demonstrated mathematically, the reported gains are small and unquantified, and the ARA result is obtained by fitting an error term on the same data used for evaluation. No code, data, or subject-independent validation is provided, so the claims cannot be independently checked or reproduced from the manuscript. The paper's central assertion therefore remains unsupported, and the evidence presented is not sufficient to justify its conclusions.

major comments (4)
  1. [§Results and discussion (DMS/DMR definitions)] The claim that multiplying by dA_water/dν̃ 'effectively removes' water interference is not supported. For a Beer-Lambert model A = c_w ε_w + c_g ε_g + ..., the DMS transform multiplies the entire spectrum by w(ν̃) = (dA_water/dν̃)/||dA_water/dν̃||; the water term becomes c_w ε_w(ν̃) w(ν̃), which vanishes only at isolated wavenumbers, while the glucose term c_g ε_g(ν̃) is rescaled by the same factor. This is a data-dependent reweighting, not a subtraction or an orthogonalization, and it destroys the linear relation between A and c_g. The disappearance of the 5200 cm^-1 peak in Figure 3 is a trivial consequence of the derivative being zero at that local maximum and does not demonstrate removal of water at other wavenumbers. A derivation, a simulated two-component experiment, or subject-independent validation is required before this operation can be said to preserve glucose information or cancel water absorption.
  2. [§Mathematical method (ARA, Eq. (1))] The ARA result is not an independent prediction in this manuscript. Equation (1) defines δ_i arbitrarily as ν̃_i/η_i, and the authors state that the original ARA 'did not provide accurate estimation' on this dataset. The error-compensation vector R is introduced and then set to 105 in the Results section specifically to minimize error on the same measured data (Figure 8). The resulting RMSE of 22.42 mg/dL is therefore a fitted quantity, and no held-out or cross-validated assessment of ARA is reported. The conclusion that ARA provides 'acceptable accuracy' is not established by the analysis as presented.
  3. [§Results and discussion (PCR validation, Table 1)] The PCR comparison rests on a single 90/10 partition of 200 samples drawn from only twenty subjects with repeated daily measurements. Because samples from the same subject appear in both training and test sets, the model can memorize subject-specific baselines, and the reported RMSE differences (Table 1: 13.14 vs 12.50, 12.70, and 12.77 in range I) are within plausible sampling noise. No significance tests, confidence intervals, or subject-independent cross-validation are provided, and the 'fewer PCs' claim (e.g., 3 PCs for DMR versus 12 for NET) is also based on one split. The authors should report repeated nested cross-validation that separates subjects between training and test sets and include uncertainty intervals on the metrics.
  4. [§Results and discussion and Table 1] The statement that DMSR provides 'the least prediction error among the other techniques' is contradicted by Table 1. In range I the lowest RMSE is DMS (12.50), in range II it is DMR (12.77), and in range III it is again DMS (12.80); DMSR is not the minimum in any range. Additionally, DMR achieves comparable or lower RMSE with only 3 principal components, versus 8 for DMSR. The ranking of the methods and the conclusion that DMSR is the most accurate should be revised to match the reported metrics, and any claim that one method is best needs statistical support rather than inspection of a single split.
minor comments (5)
  1. [§Results and discussion (wavenumber ranges)] The manuscript states 'III: 4000-5000 cm^-1' in the text, while Table 1 defines range III as '4000-5500 cm^-1'; these definitions should be reconciled, and Figure 11 should be checked against the intended range.
  2. [§Results and discussion (Figure 9 paragraph)] There are typographical inconsistencies in the method names: the text reads 'for the net, DMS, DMS, and DMSR cases' (DMR is omitted) and later refers to 'DMRS' instead of DMSR; these should be corrected.
  3. [§Data accessibility] The data availability statements are contradictory: the paper says 'This article has no additional data' while also claiming 'The data that support the findings of this study are available within the article,' yet no spectra or code are included or deposited. This prevents reproducibility of the reported RMSE values.
  4. [§Results and discussion (Figure 7)] The claim that the standardized DMS spectra are 'less affected' by standardization than the net spectra is qualitative and not quantified; the authors should provide a numerical measure or remove this assertion.
  5. [References] Reference 22 is cited as 'Appl. Spectrosc. 2024. 0(0)' without a volume, article number, or DOI; the entry is incomplete and should be updated.

Circularity Check

2 steps flagged · score 6.0 of 10

ARA prediction is an in-sample fit (error vector R chosen to minimize error on the same data), and the DMS water-peak removal is definitional; the PCR preprocessing comparison itself retains independent content.

  1. fitted input called prediction [Mathematical method: Absorptivity Regression Analysis; Results and discussion, Figure 8]
    "As a result, we suggest incorporating a term denoted by 𝐑n×1, for error compensation for the regression equation, namely 𝐜n×1 = 𝐀n×m × δm×1 + 𝐑n×1 . 𝐑n×1 is the error vector and can be defined in such a way that the errors introduced to the predictive model due to errors in the FTIR measurements, become the least. ... In figure 8, the measured and the ARA estimated blood glucose levels have been plotted for 𝐑n×1 = 105. With this value, the least error or the best accuracy is achievable for the glucose level prediction of our measured data."

    The error vector R is not derived from physics or calibrated on an independent set; it is selected by optimizing against the same measured glucose concentrations that the model is then said to predict. The reported ARA RMSE of 22.42 mg/dL is therefore an in-sample optimum, not an out-of-sample prediction: the equation c = Aδ + R is adjusted to make c close to the target c by construction. The paper also admits the original ARA failed on this dataset, so the claimed 'acceptable accuracy' rests on the fitted offset R=105 rather than on a predictive derivation.

  2. self definitional [Results and discussion: Derivative Multiplication Suppression (DMS), Figure 3]
    "In the first technique, it is suggested that each original sample absorbance be multiplied by the first derivative of the water absorbance with respect to wavenumber υ̃. By using this approach, it is expected to effectively suppress the absorbance peaks of water due to derivative vanishment at the peaks, leading to more distinguishable glucose peaks in the obtained absorbance spectrum. This method ... can be formulated as 𝐀DMS = 𝐀 ∙ 𝑑𝐀water/𝑑υ̃ ∙ ‖𝑑𝐀water/𝑑υ̃‖−𝟏. ... As can be seen, the peak due to water at 5200 cm−1 has completely disappeared in 𝐀DMS."

    At any local maximum of the water absorbance spectrum, dA_water/dν̃ = 0 by calculus, so the DMS product is exactly zero at that wavenumber regardless of the blood spectrum. The disappearance of the 5200 cm⁻¹ water peak is therefore a necessary consequence of the transform's definition, not independent evidence that water interference is removed at other wavenumbers. The paper uses precisely this by-construction zero as its validation of 'effective removal', while the same data-dependent weight multiplies the glucose signal everywhere else, so the claimed removal is a restatement of the definition rather than a demonstrated cancellation.

full rationale

The paper's main PCR comparison is not circular: NET, DMS, DMR, and DMSR are fixed preprocessing operations, and the PCR models are trained and tested on a data split with reported RMSE and cross-validation scores. That portion has independent empirical content. However, two load-bearing pieces reduce to their own construction. First, the ARA method is presented as a predictive model, but its error vector R is explicitly chosen to minimize error on the same measured data (R=105), making the reported RMSE of 22.42 mg/dL an in-sample fit rather than a prediction; the paper's own text calls the original ARA result a 'by chance' match and states the original form failed here. Second, the central DMS claim that water peaks are 'effectively removed' is supported by the disappearance of the 5200 cm⁻¹ water peak, which is a mathematical consequence of multiplying by the first derivative of water absorbance—zero at a peak maximum—and hence is built into the transform and cannot validate removal at other wavenumbers. The self-citation to reference [22] for the ARA prescription does not add independent support because the current text characterizes the earlier success as chance and the original method as failing on this dataset. Overall, because at least one advertised prediction (ARA) reduces to a fitted input and the DMS validation is definitional, while the PCR preprocessing comparison retains independent content, a score of 6 is appropriate.

Assumptions & free parameters 3 free parameters · 7 assumptions · 0 invented entities

The central claims rest on unverified assumptions: Lambert-Beer linearity in whole blood, water as the only significant interferent, ad hoc derivative multiplications, an arbitrary coefficient definition in ARA, and a tuned error vector R. The DMS/DMR/DMSR operations are not derived from a physical model, and the ARA prediction is partly fitted via R. No new physical entities are proposed; the main ad hoc constructs are mathematical weightings and the R correction term.

free parameters (3)
  • Water subtraction scale gamma = 0.5, 0.83, 1.0 in Fig. 2
    Used to define NET absorbances as A minus gamma times A_water; the paper says the water content per sample is unknown and the results depend on gamma. The NET baseline for PCR is therefore tied to an arbitrary gamma choice, though it is not used for DMS/DMR/DMSR.
  • ARA error compensation vector R = R_n x 1 = 10^5, written as 105 in the text
    Added to the ARA regression so that errors become least; tuned on the same dataset, making the reported RMSE of 22.42 mg/dL a fitted result.
  • Number of principal components = NET 12/11/11, DMS 10/7/5, DMR 3/3/3, DMSR 8/8/6 for ranges I/II/III
    Selected by cross-validation and preliminary investigation for each method and wavenumber range; the PC count is a model hyperparameter chosen from the data and changes the reported RMSE.
assumptions (7)
  • domain assumption Lambert-Beer law A = c*eta*l holds for whole blood in the 4000 to 8000 cm^-1 range.
    Invoked in the Mathematical method for ARA and implicitly for PCR. Whole blood is turbid and scattering, so deviations from linearity are expected; no scattering correction is applied.
  • domain assumption Water is the only significant interferent in blood NIR spectra; protein and lipid contributions are negligible.
    Stated in Results: only the water contribution is taken into account because other interferents have much less effect. This assumption justifies focusing on water removal.
  • ad hoc to paper Multiplying a spectrum by the first derivative of the water absorbance suppresses water peaks because the derivative vanishes at peak maxima.
    DMS formulation in Results. The operation only nulls the spectrum where the derivative is zero; it does not subtract water's contribution at other wavenumbers, and no proof is given that glucose information is preserved.
  • ad hoc to paper Multiplying by (1 - dA_glucose/d-tilde-nu) reinforces glucose peaks.
    DMR formulation in Results. No derivation or independent validation shows this weighting selectively amplifies glucose peaks rather than other spectral features.
  • ad hoc to paper The arbitrary coefficient definition delta_i = nu_tilde_i / eta_i is an acceptable solution to the sum constraint in Eq. (1).
    The paper states the constraint sum(eta_i delta_i) = pathlength^-1 has infinitely many solutions and that this is one arbitrary choice; nothing forces this particular solution.
  • ad hoc to paper An additive error vector R can compensate FTIR measurement errors without corrupting the glucose estimate.
    Introduced in the ARA discussion; R is tuned to minimize error on the same data, making the assumption unfalsifiable within the paper.
  • domain assumption A random 90/10 split of the 200 samples is a valid test of predictive performance.
    The paper does not state that the split is subject-level; with 20 subjects and 10 samples each, random splits can put the same subject in training and test sets, inflating the apparent accuracy.

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Pith. "Pith review of Analyzing Blood Glucose Levels with Near Infra-Red Spectroscopy and Chemometric Multivariate Methods." pith.science (2026). https://pith.science/paper/IMVHUIWE

@misc{pith2026250714557,
  author       = {Pith},
  title        = {Pith review of: Analyzing Blood Glucose Levels with Near Infra-Red Spectroscopy and Chemometric Multivariate Methods},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IMVHUIWE}},
  note         = {Machine review of arXiv:2507.14557}
}
read the original abstract

In this work, the blood NIR absorbances are recorded using the FT-IR method. It is shown that when the absorbance curves are multiplied by the first derivative of the water absorbance spectrum as well as by the first derivative of the glucose absorbance, the peaks related to the water interferent in the blood are effectively removed from the blood absorbance spectra, allowing for better distinction of the peaks of the blood glucose. The PCR prediction using this method shows smaller errors compared to the PCR employing the net absorbances, while the number of derived principal components is smaller in the PCR method based on the derivatives than the one based on the net absorbances. Additionally, the prediction of blood glucose levels using a linear regression model based on the molar absorptivity of glucose also demonstrates acceptable accuracy.

Figures

Figures reproduced from arXiv: 2507.14557 by the authors.

Figure 1
Figure 1. Measured transmittances. The glucose level of each sample has been shown in [mg/dL]. The water transmittance has also been drawn. As previously devised,28 a scale of the water absorbance is subtracted from the original absorbances, i.e. 𝐀net = 𝐀 − γ𝐀water in such a way that the water effect is canceled as much as possible. However, determining this scaling factor, i.e. γ, is a challenge because the amount of water i… view at source ↗
Figure 2
Figure 2. The effect of various values of the scaling factor on the net absorbances (𝐀net). The glucose level of each sample has been shown in [mg/dL] [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. the pure water absorbance (left), its first derivative (middle) and the obtained absorbances after applying derivative multiplication suppression (𝐀DMS) (right). The first derivative of the glucose absorbance with respect to the wavenumber is also depicted in [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.