Derivative-Based Mir Spectroscopy for Blood Glucose Estimation Using Pca-Driven Regression Models

Hadi Barati; Mehdi Fardmanesh; Saeed Mansourlakouraj

REVIEW 4 major objections 5 minor 28 references

Derivative-Based Mir Spectroscopy for Blood Glucose Estimation Using Pca-Driven Regression Models

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Selectively fusing FTIR absorbance with its first derivative raises the coefficient of determination of blood-glucose regression from 0.44 to 0.596 in ridge regression and from 0.47 to 0.60 in SVR.

desk verdict A plausible derivative-based preprocessing idea whose reported R2 gains are undermined by selection leakage in the shared LOOCV loop; worth testing but needs a rigorous validation redesign. read the letter →

arxiv 2412.07821 v1 pith:CTMV3NA3 submitted 2024-12-10 eess.IV cs.LGphysics.med-ph

Saeed Mansourlakouraj , Hadi Barati , Mehdi Fardmanesh This is my paper · ORCID

classification eess.IVcs.LGphysics.med-ph

keywords bloodglucoseestimationmid-infraredspectroscopyFouriertransforminfraredderivativethreshold-basedfeatureselectionsupportvectorregressionridgeprincipalcomponentanalysis

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

Blood glucose is hard to read from mid-infrared spectra because glucose absorbs weakly and the informative peaks sit in a noisy, sloping baseline. This paper proposes two preprocessing methods—Threshold-Based Derivative (TBD) and Adaptive Derivative Peak Detection (ADPD)—that merge the absorbance spectrum with its first derivative, keeping the original peak information while adding derivative contrast only where it helps. On 46 FTIR samples from five healthy subjects, the authors report that TBD raises the coefficient of determination $R^2$ from 0.44 to 0.596 for ridge regression and from 0.47 to 0.60 for support vector regression, with lower mean squared and mean absolute errors, and that ADPD produces smaller improvements. They also report that every prediction falls inside clinically acceptable zones of the Clarke and Parkes error grids. If those numbers survive an honest out-of-sample test, TBD is a cheap preprocessing step toward making MIR spectroscopy practical for non-invasive glucose monitoring.

What carries the argument

TBD is a per-wavenumber switch: for each point $i$, if $|100 \cdot A_i'| < \tau$ the output keeps the absorbance $A_i$, and otherwise it uses $100 \cdot A_i'$, with threshold $\tau$ tuned in $[0.02, 0.3]$. ADPD forms $y = x - \alpha\, z \cdot x$, where $z$ is the first derivative of $x$ with respect to wavenumber and $\alpha$ is tuned in $[0,70]$, so the raw signal is cut back most where the derivative is large. Both operations run after rubber-band baseline correction, min-max scaling, and Savitzky-Golay smoothing, and the resulting spectra are reduced with principal component analysis before entering ridge or SVR regression.

What would settle it

Re-run the whole procedure so that, for each held-out sample, the threshold, $\alpha$, PCA component count, and model hyperparameters are chosen using only the other samples, and the held-out sample is never used for tuning; if the $R^2$ gains over the base and derivative methods shrink to near zero, the reported improvements are artifacts of tuning on the scored samples.

Watch

Extended reading notes

Core claim

On its own terms, the paper's central claim is that derivative preprocessing works best when it is selective: instead of replacing the absorbance spectrum with its derivative everywhere, the methods keep the original absorbance where the scaled derivative is small and substitute the derivative where it is large (TBD), or downweight the absorbance by an adaptive multiple of the derivative (ADPD). In the reported leave-one-out cross-validation, TBD is the strongest variant: for ridge regression the MSE falls from 75.9 to 55.2 and $R^2$ rises from 0.44 to 0.596, and for SVR the MSE falls from 72.24 to 54.53 and $R^2$ rises from 0.47 to 0.60, corresponding to relative $R^2$ gains of roughly 36% and 27%. The authors interpret this as the methods balancing the molecular-composition information carried by absorption peaks against the resolving power of the first derivative.

Load-bearing premise

The load-bearing assumption is that the reported leave-one-out scores honestly measure how well the method will predict on new people, even though the same 46 blood samples were used to choose the method settings and then to produce those scores.

Editorial extensions

If this is right

TBD on SVR yields $R^2 = 0.60$ with MSE 54.53 and MAE 5.63, the best reported numbers in the paper; TBD on ridge yields $R^2 = 0.596$ with MSE 55.2.
ADPD also outperforms both the raw absorbance and the pure derivative baselines for both models, but by a smaller margin than TBD.
In the Clarke error grid, TBD leaves both models with zero points outside zone A; in the Parkes type-1 grid it also reaches zero, while in the Parkes type-2 grid it ties the baseline.
Because the two methods act as a preprocessing stage before PCA and the regression models, the same features can be paired with other estimators without changing the claimed benefit.

Reading between the lines

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

If the gains hold under nested cross-validation, the same threshold-switch idea could be tried on near-infrared spectra or on urine and tissue FTIR, where glucose peaks are similarly weak.
The TBD switch is a hard binary feature selector; comparing it with a soft-threshold version would show whether the improvement comes from the binary switch or from the derivative scaling itself.
Because all settings were tuned on the same 46 samples that were then scored, the ranking of TBD over ADPD could shrink, widen, or reverse on a larger cohort with separate training, validation, and test partitions.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Colleague,

The short version: this paper proposes two derivative-based preprocessing rules, TBD and ADPD, for blood glucose estimation from MIR spectra, and reports that they lift R2 from about 0.44–0.47 to 0.6 on a 46-sample dataset. The idea is simple but not silly, and the gains are exactly the kind that would matter if real. My concern, which matches your stress-test, is that the numbers are likely inflated by selection leakage.

What's genuinely new: the specific combination of a thresholded derivative (TBD) and an adaptive derivative weighting (ADPD) is not something I've seen in the glucose-MIR literature. The authors also do several things right: they compare against raw absorbance and plain derivative baselines, they report both regression models, they provide violin plots and error grids, and they explicitly concede the dataset is small. The preprocessing pipeline is described clearly enough to reimplement, aside from the missing TBD equation—the mathematical expression is simply absent from Section 2.4.

The soft spot is load-bearing. Section 3 states that hyperparameters for every model and preprocessing method were identified using LOOCV, and the PCA component count was tuned from 1 to 20 'within the hyperparameter tuning process.' That means the same folds that produce the Table 2 metrics were used to pick τ, α, ridge penalty, SVR C/kernel/ε, and the number of components. With 46 points, no nested CV, and no held-out set, the R2 gains from TBD and ADPD are fitted values, not unbiased predictions. Missing code and data mean we can't check whether the improvement survives an honest protocol. The clinical claims also rest on one-point differences in the error grids, and the exclusion of four samples is vaguely justified.

None of this proves the preprocessing ideas are bad; it means the paper as written doesn't demonstrate they're good. The fix is straightforward: nested CV or a proper train/test split, release code and data, report confidence intervals or significance tests, and validate on an external cohort. If the TBD gain holds under those conditions, it's a useful calibration trick. If not, it joins a long list of preprocessing variants that look good on the training set.

Who should read it: anyone working on spectroscopic calibration who wants a concrete example of how easy it is to fool LOOCV when tuning and evaluation share the same loop. It's not a strong empirical contribution as it stands, but the method deserves a serious referee if resubmitted with rigorous validation.

My recommendation: I would engage with it enough to run the test on my own data, but I wouldn't cite the performance numbers until the leakage is resolved.

Referee Report

4 major / 5 minor

Summary. The paper proposes two preprocessing methods, Threshold-Based Derivative (TBD) and Adaptive Derivative Peak Detection (ADPD), that combine mid-infrared absorbance spectra with their first derivatives to improve blood glucose concentration estimation. The methods are evaluated with Ridge Regression and Support Vector Regression under leave-one-out cross-validation (LOOCV) on 46 FTIR spectra from five healthy subjects. The authors report that TBD and ADPD outperform raw absorbance and simple derivative baselines, with lower MSE/MAE, higher R2, and better Clarke and Parkes Error Grid zones, and they argue these gains are clinically meaningful.

Significance. If validated, the two preprocessing ideas are simple and potentially useful additions to the chemometric toolbox for infrared-based glucose sensing. The study includes a straightforward comparison of four feature representations and two regression models, and the use of Clarke and Parkes Error Grids is appropriate for clinical relevance. However, the current evidence is not conclusive: the study uses 46 samples with no independent test set, the hyperparameters are selected with the same LOOCV folds used for evaluation, and the central TBD equation is missing. The reported gains may therefore be artifacts of selection leakage. The paper does not supply code, data, or machine-checked proofs, so reproducibility currently rests entirely on the textual description, which is incomplete.

major comments (4)

[Section 3, Tables 1-2] The reported LOOCV metrics are not unbiased estimates of generalization because model selection and evaluation share the same folds. Section 3 states "Using LLOC, I identified the hyperparameters for each model," and the text says the PCA component count was adjusted from 1 to 20 "within the hyperparameter tuning process." Thus the TBD threshold τ, ADPD α, PCA dimension, Ridge α, and SVR C/kernel/ε are all chosen using the same 46 samples whose held-out predictions generate Table 2. This selection leakage can inflate R2 by fitting sample-specific idiosyncrasies. A nested cross-validation or a fixed, independent test set is required to support the claimed 24–36% improvements.
[Section 2.4] The TBD transformation equation is missing. After the sentence "Mathematically, this can be represented as follows:" the equation is absent, leaving only the variable definitions. Without the explicit formula for the threshold-based combination of A and dA/dν, the method cannot be reproduced or independently verified. Please provide the full equation, including the exact condition and output value for both branches.
[Section 2.1] The sample-exclusion rule is under-specified. The text says four samples were eliminated "since they did not fit the requirements for inclusion," but no criteria are given. With only 50 samples originally and 46 used in all analyses, the exclusion rule can materially affect the conclusions. The authors should state the inclusion/exclusion criteria and, ideally, show that the main results are robust to the exclusion decision.
[Section 3, Table 3] The clinical-accuracy claim is overstated relative to the evidence. The differences in Table 3 are at most one point in any error-grid zone (and some cells show the baseline method performing equally or better), yet the text repeatedly says TBD and ADPD "significantly outperform" conventional methods. No significance test or confidence interval is provided for these counts. The authors should either tone down the language or supply a statistical analysis (e.g., a bootstrap or McNemar-style test on zone membership).

minor comments (5)

[Section 2.6] The text says the models were evaluated using "Root Mean Square Error (MSE)" and then lists MSE and MAE. The acronym RMSЕ is not defined, and it is unclear whether Table 2 reports MSE or RMSE, since the equations and captions are inconsistent.
[Section 3] There are several typos and inconsistencies: "LLOC" should be "LOOCV", "EMSE" should be "MSE", "Absolye" should be "Absolute", and "Tabel" should be "Table". These should be corrected throughout.
[Section 2.5] The ADPD formula y = x − αz ∙ x is ambiguous because of operator precedence. Please write it as y = x − α · (z · x) or y = x · (1 − αz), with a clear statement of which form is intended.
[Abstract] The phrase "these Ridge Regression values were between 36% and 24%" is unclear. It would be better to report the actual R2 values and the absolute or relative improvements with a clear notation, e.g., "increasing R2 from 0.44 to 0.60 (a 36% relative increase)".
[Section 2.6] The statement "This ensures that no information from the valid data leaked into the training process" appears to contain a typo: "valid" should be "validation" or "test" data.

Circularity Check

1 steps flagged · score 6.0 of 10

Model selection and evaluation share the same LOOCV loop, so the reported TBD/ADPD gains are partly fitted values rather than independent predictions.

fitted input called prediction [Section 3 'Results and discussion', Table 1 and Table 2; see also Sections 2.4-2.5 for TBD/ADPD parameters]
"Using LLOC, I identified the hyperparameters for each model, with their ranges displayed in Table 1. Additionally, I have included the ranges for both TBD and ADPD in Table."

The LOOCV loop that produces the Table 2 metrics is the same loop used to select the TBD threshold τ, the ADPD α, the PCA component count (1–20), the Ridge α, and the SVR C/kernel/ε (Table 1). Tuning these parameters by minimizing MSE on the same 46 samples and then reporting LOOCV MSE/MAE/R2 of the chosen configuration turns the reported 'predictions' into optimized fits rather than out-of-sample predictions. Configurations that exploit sample-specific idiosyncrasies will look better in this LOOCV than on new spectra, so the claimed R2 gains (36% Ridge, 27% SVR) are at least partly selection artifacts. No nested CV or independent test set is provided to separate tuning from evaluation.

full rationale

The paper's central derivation—TBD/ADPD preprocessing plus PCA plus Ridge/SVR—is self-contained and does not rest on self-citations or on a prior theorem. The TBD and ADPD formulas are new combinations of absorbance and derivative, and the reported improvements are not definitional identities. The circularity is statistical: the claim 'TBD and ADPD significantly outperform conventional methods' is supported by LOOCV metrics, but the LOOCV loop was also used to choose the preprocessing thresholds, PCA dimensionality, and regression hyperparameters. The paper explicitly states 'Using LLOC, I identified the hyperparameters for each model' and that PCA component count was adjusted 'within the hyperparameter tuning process' to optimize MSE. Because the same 46-sample folds are used for both selection and evaluation, the selected configuration is optimized on the evaluation criterion, so Table 2's metrics are not unbiased estimates of generalization. This is fitted-input-called-prediction rather than full definitional circularity, and the authors' own limitation note only mentions small dataset size and MIR focus, not the selection-evaluation overlap. Score 6 reflects partial circularity: the reported gains are partly fitted values, but the method itself has independent algorithmic content.

Assumptions & free parameters 7 free parameters · 6 assumptions · 0 invented entities

The central claim depends on several hyperparameters and preprocessing constants that are fitted or hand-chosen on the same small dataset. The paper also implicitly assumes that glucometer labels, healthy-subject samples, and LOOCV with same-data selection provide unbiased validation. No new physical entities are introduced.

free parameters (7)

TBD threshold tau = not reported, tuning range 0.02-0.3
Central parameter in the TBD method, selected via LOOCV to minimize prediction error.
ADPD scaling alpha = not reported, tuning range 0-70
Controls the balance between absorbance and derivative in ADPD, selected via LOOCV.
Savitzky-Golay window size = 100
Hand-chosen smoothing window in Section 2.3; affects derivative quality and all subsequent features.
Derivative scaling factor = 100
Hand-chosen in Section 2.4 to raise derivative amplitude before thresholding; changes all TBD features.
PCA component count = not reported, range 1-20
Selected by minimizing MSE within the LOOCV tuning process in Section 3.
Ridge regularization alpha = not reported, tuning range 10-100
Tuned during LOOCV to control shrinkage.
SVR hyperparameters C, epsilon, kernel = C 0-2, epsilon 0.1-0.5, kernel linear/RBF/poly
Selected via LOOCV in Section 3; final values not reported.

assumptions (6)

domain assumption Absorbance conversion A = -log10(T) and rubber-band, min-max, and Savitzky-Golay preprocessing preserve glucose-related signal while removing baseline and noise.
Invoked in Section 2.3 without validation that these choices do not remove glucose information or introduce artifacts.
domain assumption Reference blood glucose values from a standard glucometer are accurate enough to serve as training labels and clinical ground truth.
Section 2.1 uses glucometer readings as labels; no independent laboratory reference is described.
domain assumption The 46 retained samples from five healthy subjects aged 21-28 with glucose 72-125 mg/dL are representative enough to support clinical accuracy claims.
Section 2.1 and the conclusion generalize beyond this narrow healthy population without evidence.
domain assumption LOOCV with hyperparameter selection on the same 46 samples gives an unbiased estimate of generalization performance.
Section 3 identifies hyperparameters using LOOCV and then reports metrics; no nested CV or independent test set is described.
domain assumption PCA dimensionality reduction from 1 to 20 components can be chosen by minimizing MSE within the same LOOCV without leaking test information.
Section 3 states PCA components were adjusted from 1 to 20 to optimize MSE; if selection uses held-out folds it is sound, but the paper does not specify.
standard math Standard mathematical machinery of Ridge regression, SVR, and Clarke/Parkes error grids is used as published.
Sections 2.7 and 3 rely on off-the-shelf methods and error grid definitions.

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Cite this review

Pith. "Pith review of Derivative-Based Mir Spectroscopy for Blood Glucose Estimation Using Pca-Driven Regression Models." pith.science (2026). https://pith.science/paper/CTMV3NA3

@misc{pith2026241207821,
  author       = {Pith},
  title        = {Pith review of: Derivative-Based Mir Spectroscopy for Blood Glucose Estimation Using Pca-Driven Regression Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/CTMV3NA3}},
  note         = {Machine review of arXiv:2412.07821}
}

read the original abstract

In this study, we presented two innovative methods, which are Threshold-Based Derivative (TBD) and Adaptive Derivative Peak Detection(ADPD), that enhance the accuracy of Learning models for blood glucose estimation using Mid-Infrared (MIR) spectroscopy. In these presented methods, we have enhanced the model's accuracy by integrating absorbance data and its differentiation with critical points. Blood samples were characterized with Fourier Transform Infrared (FTIR) spectroscopy and advanced preprocessing steps. The learning models were Ridge Regression and Support Vector Regression(SVR) using Leave-One-out Cross-Validation. Results exhibited that TBD and ADPD significantly outperform basic used methods. For SVR, the TBD increased the r2 score by around 27%, and ADPD increased it by around 10%. these Ridge Regression values were between 36% and 24%. In addition, Results demonstrate that TBD and ADPD significantly outperform conventional methods, achieving lower error rates and improved clinical accuracy, validated through Clarke and Parkes Error Grid Analysis.

Figures

Figures reproduced from arXiv: 2412.07821 by the authors.

**Figure 1.** Illustration of the processing steps: a: The original absorbance spectrum. b: The first derivative of the absorbance spectrum, multiplied by 100 to increase its amplitude for improved analysis. c: The digitized threshold result, where a value of 11 indicates retention of the original absorbance value and a value of 1 indicates use of the derivative. d: The final processed result combining absorbance and derivative d… view at source ↗

**Figure 2.** Illustration of the processing steps: a: The original absorbance spectrum. b: The first derivative of the absorbance spectrum, multiplied by 100 to increase its amplitude for improved analysis. c: Multiples of the first derivative and the absorbance signal d: The final processed result, which is used for learning 2.6 Model Training and Evaluation To predict blood glucose levels from the feature vectors extracted fro… view at source ↗

**Figure 3.** demonstrates the results for both models and the two innovative approaches. As shown, the performance of both methods is approximately superior for the Support Vector Regression model, which is to be expected given its higher complexity. Additionally, Hyperparameters used for both models are detailed in [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Absolute Error (AE) distribution for Ridge Regression and Support Vector Regression (SVR) using different [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]

**Figure 5.** Figure 5: For TBD and ADPD, the results improved an [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]

**Figure 6.** Figure 6: Completion of Base and TBD methods for SVR using Parkes and Clarke Error Grids, emphasizing the [PITH_FULL_IMAGE:figures/full_fig_p010_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

Mid-infrared photonic chip for label-free glucose sensing

Pao Lin. Mid-infrared photonic chip for label-free glucose sensing. page JW3A.11, 01 2018

work page 2018

[2] [2]

Mid -infrared spectroscopy with an effective variable selection method based on mpa for glucose detection

Jiming Sa, Yuyan Song, Hanwen Gu, and Zhushanying Zhang. Mid -infrared spectroscopy with an effective variable selection method based on mpa for glucose detection. Chemometrics and Intelligent Laboratory Systems, 233:104731, 2023

work page 2023

[3] [3]

Noninvasive glucose level determination using diffuse reflectance near infrared spectroscopy and chemometrics analysis based on in vitro sample and human skin

Noor Nazurah Mohd Yatim, Z ainiharyati Mohd Zain, Mohd Zuli Jaafar, Zalhan Md Yusof, Abdur Rehman Laili, Muhammad Hafiz Laili, and Mohd Hafizulfika Hisham. Noninvasive glucose level determination using diffuse reflectance near infrared spectroscopy and chemometrics analysis based on in vitro sample and human skin. pages 30–35, 2014

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Prashant Bhandare, Yitzhak Mendelson, Robert A. Peura, Günther Janatsch, Jürgen D. Kruse -Jarres, Ralf Marbach, and H. Michael Heise. Multivariate determination of glucose in whole blood using partial leas t- squares and artificial neural networks based on mid -infrared spectroscopy. Appl. Spectrosc., 47(8):1214–1221, Aug 1993

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