From Net Carb Math to Direct Measurement: Why HPAEC-PAD Should Anchor Keto Carbohydrate Claims

Abstract

The central problem in quantifying carbohydrates for ketogenic product claims is the continued dependence on legacy calculation approaches that conceal both analytical uncertainty and person-to-person variability in metabolic response. This review reevaluates the difference method used for net carbohydrate labeling and shows how its stepwise subtraction of macronutrients and non-digestible fractions introduces cumulative measurement error, erases structural detail about specific carbohydrate classes, and cannot account for individual metabolic diversity. In parallel, it examines the glycemic index (GI) as a tool routinely invoked to support "keto-friendly" positioning, despite evidence of extreme inter- and intra-individual variation in postprandial glycemia to the same foods that undermines GI's predictive value. Using tomato-containing foods as a case study, the review illustrates how a food labeled as "low GI" can nevertheless generate highly divergent glucose responses across people and even within a single person across different circumstances. High-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD) is presented as a contemporary analytical solution that directly measures the composition and quantity of individual carbohydrate species, thereby overcoming key weaknesses of calculation-based methods. Drawing on published studies of glycemic variability, the article argues that scientifically defensible ketogenic product claims should be anchored in standardized HPAEC-PAD testing and independent certification, rather than in inferred "net carb" values and GI-based assurances that ignore well-documented metabolic heterogeneity.

KEYWORDS

carbohydrate quantification; difference method; net carbohydrates; glycemic index; inter-individual variability; intra-individual variability; personalized nutrition; HPAEC-PAD; ketogenic diet; product labeling; precision nutrition; continuous glucose monitoring; postprandial glycemic response; keto claims; analytical chemistry; food composition; precision medicine

Introduction: Reconciling Analytical Precision with Biological Reality

Over more than a century, methods for characterizing food composition have become increasingly sophisticated, yet the tools most commonly used to support "net carbohydrate" and ketogenic claims have changed surprisingly little. In routine practice, the carbohydrate content that appears on a label is still often derived from an indirect difference calculation, in which measured protein, fat, moisture, ash, and selected non-digestible fractions are subtracted from 100% to yield "available" carbohydrate. This strategy—often referred to as the difference method—aggregates the uncertainties of each underlying assay and collapses chemically distinct carbohydrate structures into a single undifferentiated value that cannot distinguish rapidly digestible sugars from non-digestible polymers. At the same time, glycemic index values are frequently used to imply that particular products will "support ketosis" or are "keto-appropriate," even though GI was designed to rank foods by average postprandial glycemia and has been repeatedly shown to vary dramatically between individuals and across repeat tests in the same person. [1]

This review contends that pairing difference-based net carbohydrate calculations with GI-based reassurance is inadequate as a validation framework for ketogenic products. The review integrates evidence from four domains: (1) the methodological limitations of the difference method for carbohydrate calculation; (2) the profound inter- and intra-individual variability in glycemic responses to identical foods, illustrated through tomato as a concrete example; (3) the inadequacy of glycemic index as a tool for predicting individual metabolic responses or determining keto-appropriateness; and (4) modern analytical alternatives including HPAEC-PAD that provide direct measurement of carbohydrate composition. The ultimate conclusion is that valid keto product certification requires direct analytical measurement combined with transparent acknowledgment that metabolic individuality cannot be collapsed into any single label metric.

The Difference Method: Historical Context, Core Assumptions, and Accumulated Errors

Historical Origins and Underlying Logic

The difference method for estimating available carbohydrates emerged as a pragmatic solution in early twentieth-century food analysis when direct carbohydrate measurement was technically impractical. Rather than quantifying carbohydrates directly, analysts measured the other major constituents—protein, fat, moisture, and ash—and assigned whatever remained to the carbohydrate fraction. In US labeling, this approach is codified for total carbohydrate, which is calculated by subtracting the sum of crude protein, total fat, moisture, and ash from the total weight of the food (21 CFR 101.9(c)(6)):

TOTAL CARBOHYDRATE = 100 – (PROTEIN + FAT + MOISTURE + ASH)

—represents an indirect calculation that estimates carbohydrate content by subtracting all non-carbohydrate components from the total sample mass. "Net carbohydrate" values are then usually obtained by subtracting components such as dietary fiber and sugar alcohols from total carbohydrate. FDA regulations do not define "net carbs," so companies make these subtractions in different ways. For example, polydextrose is among the added fibers FDA allows in the dietary fiber declaration, so subtracting it again after subtracting fiber would count it twice. Each subtracted component requires its own analytical determination, and the propagated errors from each assay combine to produce a final estimate whose precision is inherently compromised.

Error Accumulation and Measurement Uncertainty

A critical weakness of the difference method lies in how measurement errors from individual component analyses accumulate in the final carbohydrate value. When protein is measured by nitrogen determination and converted using a factor like 6.25, when fat is extracted with variable efficiency depending on matrix composition, when moisture is measured under conditions that may not fully capture bound water, and when ash determination varies with combustion completeness—each step introduces its own uncertainty. Critically, these errors do not cancel; they compound in the final subtraction.

Studies examining commercial food products have revealed that products labeled with identical carbohydrate content can differ meaningfully in actual carbohydrate composition when subjected to direct analytical measurement. This variability arises not from product inconsistency but from the inherent limitations of the calculation method itself. For ketogenic dieters who carefully track carbohydrate intake, such variability can have meaningful consequences for maintaining ketosis.

Loss of Structural Information and Carbohydrate Heterogeneity

One of the most significant limitations of the difference method is its complete inability to distinguish between different carbohydrate types. Whether a product contains rapidly digestible glucose, slowly digestible resistant starch, non-digestible fiber, or metabolically distinct sugar alcohols—all are collapsed into a single "carbohydrate by difference" value. This erasure of structural information has profound implications for ketogenic product validation.

For ketogenic products specifically, this limitation becomes critical. A product calculated to have 10g "available carbohydrate" might actually contain predominantly non-digestible fiber that passes through the gut unabsorbed, or it might contain rapidly digestible maltodextrin that spikes blood glucose. The difference method cannot distinguish these scenarios, yet the metabolic consequences for ketosis maintenance could not be more different.

Glycemic Index: Theoretical Promise versus Practical Limitations

Conceptual Foundations and Standardized Protocols

The glycemic index (GI) represents an attempt to classify carbohydrate-containing foods based on their postprandial glycemic effects relative to a reference food, typically glucose or white bread. The standardized methodology requires measuring blood glucose response curves in groups of healthy subjects following consumption of fixed carbohydrate portions of test foods compared to reference foods. [2] The theoretical assumptions underlying GI classification include the premise that a food has a relatively consistent glycemic effect that can be meaningfully represented by a population average value.

However, research examining the reliability and reproducibility of glycemic index values has revealed profound variability both between and within individuals. In one study of white bread tested against glucose in healthy adults, the within-person coefficient of variation of GI values was 42.8%, compared with 17.8% between individuals. [1] This means that knowing a food's published GI value—typically derived from testing in perhaps 10-20 subjects—provides very limited predictive power for how any specific individual will respond to that food.

The Tomato as Exemplar of Glycemic Response Heterogeneity

Why Tomatoes Illuminate the Problem

The tomato is an ideal exemplar of the complexity underlying glycemic response heterogeneity because it represents a commonly consumed food with well-characterized carbohydrate composition yet remarkably variable metabolic effects across individuals. Tomatoes contain primarily fructose and glucose with modest amounts of sucrose, along with fiber and various organic acids that could theoretically modulate absorption rates. The published glycemic index for tomatoes is relatively low, suggesting it should produce modest postprandial glucose excursions.

When examining heterogeneity in postprandial glucose responses to different carbohydrate-rich foods, researchers found that while foods may be ranked by average glycemic response across a population, individuals differed in which foods raised their glucose the most. [3, 4] In other words, while certain foods produce the highest average glycemic response across the population, the person-to-person variation in responses means that population averages fail to predict individual experience.

Inter-Individual Variation in Tomato-Containing Meal Responses

Furthermore, when examining whether fat, fiber, or protein preloads could mitigate glycemic responses to carbohydrate-containing meals, researchers found substantial inter-individual heterogeneity in the effectiveness of these dietary strategies. [4] This means that the effect of adding fat, fiber, or protein to the same food (like tomato sauce with pasta) cannot be assumed to be the same for everyone.

Deep phenotyping of postprandial glycemic responses (PPGRs) in 55 participants given seven standard carbohydrate meals in replicate found considerable interindividual variability: individuals with the highest PPGR to potatoes were more insulin resistant and had lower beta cell function, whereas those with the highest PPGR to grapes were more insulin sensitive. [4] When applied to the tomato, this suggests that individuals with different insulin sensitivity profiles, gut microbiome compositions, and metabolic phenotypes may experience fundamentally different glycemic trajectories following consumption of tomato-containing meals.

Intra-Individual Variation: Same Person, Same Food, Different Outcomes

Beyond the profound differences between individuals, identical individuals consuming the same food show substantial variation in glycemic response across different occasions. This intra-individual variability further undermines the utility of population-based GI values for predicting personal metabolic responses.

Analysis of glycemic responses to duplicate meals in people with type 1 and type 2 diabetes identified time of day and the menstrual cycle as sources of within-person variability. [5] In the white bread study above, within-person variation in GI values (CV 42.8%) exceeded between-person variation. [1] These findings mean that the same woman consuming a tomato-based salad might show different glycemic responses on Monday versus Friday, even under seemingly identical conditions.

Because within-person variation in GI values can be this large, a food classified as "low GI" based on initial testing might produce "medium" or even "high" GI responses on subsequent consumption in that same individual. [1] Applied to tomatoes, this means that even if you had a "personal tomato glycemic index" calculated from your own testing, that value would provide only rough guidance for predicting your response to tomatoes on any given day.

Mechanistic Underpinnings of Individual Variation: Microbiota and Metabolic Phenotypes

Understanding why individuals respond so differently to the same tomato requires examining the mechanistic factors that drive glycemic response heterogeneity. Research has associated inter-individual differences in postprandial glycemic responses with insulin resistance, beta cell function, and gut microbiome pathways. [4]

Furthermore, research demonstrating that "Mitigators were less effective in reducing PPGRs in insulin-resistant as compared to insulin-sensitive participants" [4] indicates that the same person's response to adding fat, fiber, or protein to a tomato-based meal would depend on their underlying metabolic status—a factor that changes over time and in response to diet, exercise, sleep, and stress.

The "No Two Individuals" Principle in Predicting Tomato Responses

Perhaps the most damning finding for the utility of glycemic index in predicting individual responses comes from research in 67 adults with type 2 diabetes in which "no two individuals shared the same dietary and temporal predictors of PPG excursions." [6] Applied to tomatoes, this suggests there may be no "typical" tomato response, because each person's glucose response depends on individual factors.

The Misapplication of Glycemic Index for Validating Ketogenic Products

Category Error: Conflating GI with Keto-Appropriateness

The application of glycemic index testing to validate whether a product meets ketogenic diet requirements represents a fundamental category error that conflates glycemic response with ketogenic metabolic states. GI measures the rate and magnitude of blood glucose rise following carbohydrate consumption; ketosis is a metabolic state characterized by elevated blood ketone levels resulting from fatty acid oxidation when carbohydrate availability is restricted. These are related but distinct phenomena.

In a small randomized crossover trial in eight adults with type 1 diabetes, postprandial glucose curves were virtually identical after high-GI and low-GI bread eaten with peanut butter (19 g protein, 30 g fat). [7] That study was designed to inform insulin dosing in type 1 diabetes and cannot be applied directly to ketogenic products, but it suggests that in high-fat, high-protein meals the glycemic index of the carbohydrate component may be less informative than expected.

Individual Metabolic Heterogeneity as the Central Problem

The fundamental problem is that whether a product "supports ketosis" or is "appropriate for ketogenic diets" depends entirely on individual metabolic responses that cannot be predicted from population-average GI values. Research has shown that the dietary and temporal predictors of glucose excursions differ between individuals. [6] In other words, the factors that matter for your glucose control might be completely irrelevant for someone else's, and vice versa.

In the ketogenic diet context, the critical question is whether a product's carbohydrate content will interrupt an individual's ketosis—and this depends on individual carbohydrate tolerance, which differs between people. A product that "supports ketosis" for someone with high carbohydrate tolerance might knock a more sensitive individual out of ketosis entirely.

Metabolic Response Heterogeneity and Genetic Factors

Recent research examining genetic predisposition for macronutrient preference associations with postprandial metabolic responses has reported associations between genetic factors and postprandial metabolic responses to foods. [8] This suggests that genetic factors may influence how individuals respond metabolically to foods, and these genetic factors cannot be captured in any product label regardless of how accurately the carbohydrate content is measured.

Advanced Analytical Methods: HPAEC-PAD as a Direct Measurement Solution

Technical Principles and Analytical Advantages

High-performance anion-exchange chromatography coupled with pulsed amperometric detection (HPAEC-PAD) represents a mature analytical technology that can directly measure individual carbohydrate species in complex food matrices. Unlike the difference method, which calculates carbohydrates by subtraction, HPAEC-PAD separates carbohydrates based on their chromatographic behavior on specialized columns and detects them through electrochemical oxidation at gold electrodes. This direct measurement approach provides several critical advantages for ketogenic product validation.

First, HPAEC-PAD identifies and quantifies specific carbohydrate species rather than lumping all carbohydrates together. This means a certified keto product could report not just "5g net carbs" but the actual composition: "2.1g glucose, 1.8g fructose, 0.6g maltose, 0.5g other sugars." This level of detail allows individuals who have identified their personal responses to specific sugars to make informed choices.

Multi-Laboratory Validation and International Standards

HPAEC-PAD is an established technique for sugar and oligosaccharide analysis, and consistent testing across laboratories depends on standardized, validated protocols for each application.

The method's capacity to distinguish between different carbohydrate types and to identify components that might be missed or mischaracterized by the difference method makes it particularly valuable for ketogenic product claims, where the distinction between digestible and non-digestible carbohydrates has direct implications for maintaining ketosis.

Applications to Ketogenic Product Testing

HPAEC-PAD has particular value for ketogenic product analysis because it can simultaneously quantify simple sugars (glucose, fructose, sucrose), oligosaccharides, and sugar alcohols (erythritol, xylitol, maltitol). It does not by itself measure "dietary fiber" as defined for US labeling, which depends on whether a non-digestible carbohydrate is intrinsic and intact in plants or has been determined by FDA to have beneficial physiological effects (21 CFR 101.9(c)(6)(i)). This comprehensive carbohydrate profiling provides the detailed compositional information necessary for meaningful keto product certification.

For example, a product labeled "5g net carbs" via the difference method could be revealed through HPAEC-PAD to contain 8g total carbohydrates with 3g erythritol—a sugar alcohol with essentially no glycemic impact—or alternatively, 5g maltodextrin, which despite being technically a "complex carbohydrate" is rapidly digested to glucose. This level of resolution transforms the information available for keto product validation.

The Imperative for HPAEC-PAD Testing and Formal Keto Certification

Current Regulatory Gaps and Market Vulnerabilities

The current regulatory landscape for "keto claims" on food packaging lacks standardized analytical verification requirements. Manufacturers can claim products are "keto-friendly" or contain specific net carbohydrate amounts based entirely on difference method calculations without any requirement for direct analytical verification. This creates substantial opportunity for both intentional and unintentional mislabeling.

This gap creates substantial opportunity for mislabeling—either intentional or unintentional. A product might be reformulated with different ingredients while maintaining the same calculated net carb value, yet the actual carbohydrate composition and metabolic effects could change substantially. Without direct analytical verification, neither consumers nor regulators can identify such discrepancies.

Framework for Evidence-Based Keto Certification Systems

The development of formal keto product certification systems based on HPAEC-PAD testing or equivalent direct analytical methods would provide several benefits to the ketogenic diet community. First, it would establish a science-based standard for carbohydrate claims that goes beyond the limitations of calculation methods. Second, it would create accountability mechanisms through third-party testing and certification.

Third, formal certification could establish standardized terminology for net carbohydrate calculation that explicitly accounts for different sugar alcohols, fiber types, and other carbohydrate fractions that have varying metabolic effects. Currently, products use "net carb" calculations with different assumptions about which components to subtract, creating confusion and incomparability across products.

Addressing Individual Variability While Improving Analytical Accuracy

While HPAEC-PAD testing and formal certification would substantially improve the accuracy of product carbohydrate information, it cannot resolve the fundamental challenge of inter-individual metabolic variability. Even perfectly accurate carbohydrate composition data cannot predict how any specific individual will respond to a product.

However, accurate labeling of actual carbohydrate composition through HPAEC-PAD testing provides consumers with the detailed information necessary to learn their own patterns. A person who discovers through personal testing that they tolerate fructose better than glucose can use detailed carbohydrate composition data to make informed product choices—choices that would be impossible with difference-method-derived "net carb" values that collapse all sugars into a single number.

Discussion: Toward Analytical Precision and Acknowledgment of Biological Reality

The Irreconcilable Tension Between Population Averages and Individual Reality

The research reviewed in this literature review reveals a fundamental tension between the approach of food labeling—which necessarily provides single values intended to be useful across the entire population—and the reality of metabolic responses—which vary so dramatically between individuals that population averages have minimal predictive value for any specific person.

When examining individuals consuming the same standardized meals, research finds that different individuals have their highest glycemic responses to different foods. [4] For keto product validation, this means that determining whether a specific product "supports ketosis" is inherently an individual-level question that cannot be answered by any product testing methodology, no matter how sophisticated.

The Necessity of Moving Beyond the Difference Method

The difference method for calculating available carbohydrates has served industry and consumers reasonably well for products where precise carbohydrate quantification is not critical. However, for ketogenic products where small differences in carbohydrate content can determine whether an individual maintains or exits ketosis, the method's inherent limitations become untenable.

Modern HPAEC-PAD and related technologies provide direct chemical measurement of carbohydrate composition that avoids the error accumulation, structural information loss, and classification ambiguities of the difference method.

The Problem of Personalization Within Standardized Product Labeling

One critical challenge in developing keto product certification systems is acknowledging that no standardized label can adequately convey the information each individual needs to predict their personal metabolic response. Even with perfect analytical accuracy, research showing that individuals differ in the predictors of their glucose responses [6] means that product labels can provide compositional information but not metabolic predictions.

This raises a deeper question: Can product labeling ever adequately convey the information individuals need to manage their ketogenic diets? The answer is probably no—but accurate compositional information provides a foundation for individual learning that calculation-based methods cannot support.

Integration with Continuous Glucose Monitoring for Personalized Assessment

The future of evidence-based keto product validation likely involves integrating detailed analytical composition data from HPAEC-PAD testing with individual metabolic response monitoring through continuous glucose monitors (CGMs) and ketone meters. In adults with type 2 diabetes, personalized models trained on CGM data predicted postprandial glucose excursions with an average F1-score of about 76%. [6]

As CGM technology becomes more accessible and affordable, individuals following ketogenic diets could develop personal databases of how they respond to certified products with known carbohydrate compositions. This combination—accurate product data plus personal response tracking—represents the most promising approach to evidence-based ketogenic diet management.

This approach would acknowledge both the analytical reality (we can measure actual carbohydrate composition with precision) and the biological reality (individual responses to identical compositions vary dramatically), creating a framework that serves consumers better than current approaches based on calculated values and population-average GI estimates.

Implications for Public Policy and Regulatory Frameworks

The evidence reviewed in this literature review suggests that current regulatory frameworks allowing "keto" and "net carb" claims based solely on difference method calculations may not adequately protect consumers who rely on these claims to manage their metabolic health. Regulatory bodies should consider requiring direct analytical verification for products making specific carbohydrate claims, particularly those marketed to ketogenic diet followers.

Such regulatory evolution would place keto product claims on a firmer scientific foundation while supporting consumer choice through accurate information rather than calculated approximations.

The Tomato as Continuing Example: From Science to Practice

The tomato, with its low published glycemic index yet individually variable metabolic effects, serves as a paradigmatic example of how scientifically validated product certification could operate in practice. A comprehensive approach would:

Analytically verify the actual sugar content of the product using HPAEC-PAD, distinguishing glucose from fructose from sucrose rather than reporting a single "sugar" value.

Label transparently with detailed carbohydrate fractions: "3.2g total carbs: 1.1g glucose, 0.8g fructose, 0.4g sucrose, 0.9g fiber."

Acknowledge variability with clear language: "Individual metabolic responses to this product vary substantially. The carbohydrate composition represents analytical measurement; your personal response depends on individual factors that cannot be predicted from product composition alone."

Support individual learning by providing access to detailed composition information that allows individuals who have characterized their personal responses to specific carbohydrate types to make informed decisions.

This approach transforms the tomato from a product classified by an abstract GI value into a specifically characterized food that individuals can evaluate against their own metabolic experience.

Conclusion

The quantification of carbohydrates in food products and the validation of ketogenic product claims currently rely on methodologies whose limitations have become increasingly apparent as research reveals the profound heterogeneity of individual metabolic responses. The difference method for calculating net carbohydrates accumulates errors from multiple component analyses and erases the structural information that determines metabolic effects.

The tomato exemplifies this complexity: classified as a "low glycemic index" food, it nevertheless produces highly variable glucose responses across individuals, and the same individual responds differently to tomatoes on different occasions. The finding that no two individuals shared the same dietary and temporal predictors of glucose excursions [6] suggests that the factors determining whether a specific product "supports ketosis" are inherently individual and cannot be captured in any population-derived metric.

The path forward requires multiple complementary changes: (1) adoption of direct analytical methods like HPAEC-PAD for carbohydrate quantification in certified keto products; (2) development of standardized certification systems that require third-party analytical verification; (3) transparent labeling of detailed carbohydrate fractions rather than calculated "net carb" values; and (4) acknowledgment in labeling and marketing that individual metabolic responses vary substantially and cannot be predicted from product composition alone.

Until such changes are implemented, the scientific credibility of carbohydrate claims on food product labels—and particularly claims about ketogenic appropriateness—will remain fundamentally limited by methodologies that neither accurately measure what they claim to measure nor account for the biological reality that metabolic responses are irreducibly individual.

References

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[8] J. E. Gervis, S. J. Cromer, M. Udler, and J. Merino, "2129-LB: Genetic Predisposition for Macronutrient Preference Associates with Postprandial Glycemic Responses to Foods—A Recall-by-Genotype Study," Diabetes, 2025, doi: 10.2337/db25-2129-lb.