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Citation: Maffioli EM, Anyakora C (2025) A
comparative study between Near-Infrared (NIR)
spectrometer and High-Performance Liquid
Chromatography (HPLC) on the sensitivity and
specificity. PLoS ONE 20(3): e0319523. https://
doi.org/10.1371/journal.pone.0319523
Editor: Hope Onohuean, Kampala International
University - Western Campus, UGANDA
Received: November 27, 2024
Accepted: February 3, 2025
Published: March 25, 2025
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RESEARCH ARTICLE
A comparative study between Near-Infrared
(NIR) spectrometer and High-Performance
Liquid Chromatography (HPLC) on the
sensitivity and specificity
Elisa M. Maffioli 1*, Chimezie Anyakora2
1 Department of Health Management and Policy, University of Michigan, School of Public Health,
Washington Heights, Ann Arbor, Michigan, United States of America, 2 Bloom Public Health, No 4, Thabo
Mbeki Close, Off TY Danjuma Street, Asokoro, Abuja, Nigeria and School of Science and Technology, Pan
Atlantic University, Lagos, Nigeria
* elisamaf@umich.edu
Abstract
It is estimated that 10.5% of medicines in low- and middle-income countries are substan-
dard or falsied (SF), causing approximately 1 million deaths annually. Over the past two
decades, there have been signicant technological advancements in low-cost, portable
screening devices to detect poor-quality medicines, which could be especially benecial
in these countries. The pharmaceutical market in Nigeria is valued at USD 4.5 billion
and is growing at over 9% annually. However, SF medicines remain a major public health
concern. We compared a novel Near-Infrared (NIR) Spectrometer with high-performance
liquid chromatography (HPLC) by analyzing 246 drug samples purchased from retail
pharmacies across the six geopolitical regions of Nigeria. We measured the sensitivity
and specicity of a patented and Articial Intelligence (AI) - powered handheld NIR spec-
trometer, which uses a proprietary machine-learning algorithm as well as hardware and
software, across four categories of medicines: analgesics, antimalarials, antibiotics, and
antihypertensives. Our ndings reveal that the prevalence of SF medicines remains high,
with 25% of samples failing the HPLC test. When tested with the NIR spectrometer, only
a smaller subset of medicines—specically analgesics—failed the test. Sensitivity and
specicity for all medicines were 11% and 74%, respectively. For analgesics, the sensitivity
was 37%, and the specicity was 47%. While these devices hold great potential, regu-
lators should require more independent evaluations of various drug formulations before
implementing them in real-world settings. Improving the sensitivity of these devices should
be prioritized to ensure that no SF medicines reach patients.
Background
Substandard and falsified (SF) drugs remain a major public health concern. It is estimated
that between 10% and 14% of medicines in low- and middle-income countries (LMICs) are
SF, leading to an estimated 1 million deaths annually [1, 2]. The prevalence of SF medicines
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PLOS ONE Validation of Near-Infrared Spectrometer to detect Substandard and Falsied Medicines
varies by country and drug type, with Sub-Saharan Africa and Southeast Asia experiencing the
highest rates, especially for antimalarials and antibiotics [1,3,4].
SF medicines pose significant risks to patients, including adverse effects from incorrect
active pharmaceutical ingredients (API), treatment failure, prolonged illness, and preventable
deaths. They also contribute to antimicrobial resistance [5]. From a health systems perspec-
tive, SF medicines increase care costs, place added strain on providers and erode trust in the
health system. A higher disease burden can lead to income loss for patients, reduced produc-
tivity, and greater poverty.
SF medicines are difficult to detect. The illegal trafficking of poor-quality medicines
remains a highly profitable business—valued between $200 and $431 billion [6]. This issue
is especially prevalent in LMICs, where high prices limit access to authentic medicines, and
inadequate government oversight allows illegal suppliers to go unpunished [7–9]. Over the
past two decades, several low-cost, portable devices have been developed to help regulatory
authorities detect poor-quality medicines [10]. However, their accuracy remains limited.
Our study was conducted in Nigeria, where the pharmaceutical market is valued at USD
4.5 billion and is growing at 9% annually [11]. The country is highly import-dependent, sourc-
ing 70% of its finished products from abroad, and it relies almost entirely on other countries
for API for local manufacturing [12]. Analgesics account for the largest market share (25%),
followed by antibiotics (15%), multivitamins (15%), antimalarials (14%), and antihyperten-
sives (8%) [13].
Several methods have been developed to determine the authenticity of SF medicines.
Traditional laboratory analysis is costly, labor-intensive, and requires sample transport,
preparation, and expert handling. In recent decades, portable testing devices and mobile mini
labs have allowed for testing in remote areas, though many are still expensive, complex, and
lack real-time data capabilities. Handheld spectrometers offer promise but are often costly
and heavy. Thus, lower-cost, portable screening tools are being developed for use by regula-
tors to detect poor-quality drugs at key points in the supply chain [14]. While the Nigerian
National Agency for Food and Drug Administration and Control (NAFDAC) has gained
international recognition in the use of cutting-edge technologies such as Raman Spectroscopy,
GPHF Minilab and Mobile Authentication Service, the number of deployable technologies
is insufficient for such a large population. In the fight against SF medicines, NAFDAC just
launched a “Green Book” to verify drugs in January 2024, i.e., “a database of 6432 registered
and approved drugs for sale and distribution” [15].
We present insights from a comparative study between a proprietary Near-Infrared (NIR)
Spectrometer and high-performance liquid chromatography (HPLC). To protect proprietary
information and sensitive business data that could potentially affect the company’s competi-
tive position, the company’s identity has been anonymized in this study.
Methods
We tested the validity of this NIR spectrometer against HPLC. The device is a patented
and Artificial Intelligence (AI) - powered handheld spectrometer, which uses a proprietary
machine-learning algorithm as well as hardware and software with a NIR-Dispersive range of
750 to 1500nm.
The spectrometer analyzes a drug’s spectral signature to detect poor-quality medicines by
comparing it to a cloud-based AI reference library of spectral signatures. The NIR is capable
of detecting both substandard and counterfeit drugs. The device captures the spectrum of the
entire drug (API and excipients) and stores the spectral signature of this medical product.
It also captures the intensity of the spectrum, which is proportional to that of the authen-
tic product from the manufacturer. The device can determine counterfeit drug samples by
Data availability statement: The datasets used
and/or analyzed during the current study are in
Supplementary Information.
Funding: The project was funded by USAID-
DIV (7200AA21FA00006) and internal funding
from the University of Michigan. The funders
had no role in study design, data collection and
analysis, decision to publish, or preparation of
the manuscript.
Competing interests: The authors have
declared that no competing interests exist.
Abbreviations: AI, Artificial Intelligence; API,
Active Pharmaceutical Ingredients; HPLC,
High-Performance Liquid Chromatography;
LMICs, Low- and Middle-Income Countries;
NAFDAC, National Agency for Food and Drug
Administration and Control; NIR, Near-Infrared
against; SF, Substandard and Falsified
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PLOS ONE Validation of Near-Infrared Spectrometer to detect Substandard and Falsied Medicines
matching the signature spectrum of the reference product with the drug sample collected
in the field. If the spectrum of the pill collected in the field differs from the spectrum of the
authentic sample, the spectrometer displays a “non-match” result. The device is also able to
detect substandard drug samples by matching the intensity of the reference product with the
drug sample collected in the field. Researchers were not given details on other parameters or
thresholds, which may vary by drug. The process takes about 20 seconds, with a quality report
sent to a smartphone app.
Customized chemometric models are necessary to develop the reference library, with
authentic samples sourced for a fee by the company. The company claimed at the time
that any product (in pill form) could be analyzed with their NIR device, provided the
reference library was updated. For this specific study, the company was responsible for
sourcing the exact branded drug samples and dosage forms (though not the lot numbers
tested in the field). More specifically, the company stated that 3 out of the 20 drugs in
our sample were already included in their library: May & Baker Para (Paracetamol) Tabs.
(x96), Emzor Paracetamol Tabs., 500mg, and Lonart-DS Artemether Lumefantrine Tabs.,
80mg/480mg. The remaining drug samples were sourced and added to the reference
library by the company. The company did not share the results of their training exercises.
Unlike HPLC, this portable device does not require sample destruction and enables real-
time analysis, making it accessible for regulators, law enforcement, customs, manufactur-
ers, and pharmacies.
In November 2022, we purchased medicine samples from randomly selected pharmacies
in rural and urban areas of the six largest cities in Nigeria’s geopolitical zones: Abuja, Kano,
Lagos, Onitsha, Port Harcourt, and Yola. Twelve enumerators, acting as mystery shoppers,
began at recorded locations and conducted random walks to locate pharmacies and were
instructed to purchase a randomly selected branded drug from a list of 20. All drugs were
tested using the NIR spectrometer, and a sub-sample (N = 246) was selected as a weighted
average by drug category, reflecting the proportions found by mystery shoppers in pharma-
cies, and sent to a laboratory for HPLC compositional quality analysis between December
2022 and February 2023 (Table 1). This sub-sample excluded multivitamins, which were less
common in the pharmacies sampled (Table 2).
We conducted HPLC analysis at Hydrochrom Analytical Services Limited, located in
Gowon Estate, Lagos. Drug samples were initially collected at our partner’s office in Abuja,
categorized, and then shipped to the laboratory. The HPLC analysis was performed on
an Agilent 1100 HPLC system equipped with an online degasser, variable UV detector,
quaternary pump, autoliquid sampler, and a thermostated column compartment. Chro-
matographic data were processed using Chemstation Rev. B.04.03-SP1 software. A validated
method was employed for each molecule, depending on its specific requirements. Prior to
each analysis, system suitability was confirmed using a reference standard for each analyte.
S1 Table presents the analytical parameters for linearity, correlation and detection limits of
compounds by HPLC. S2 Table describes the sample preparation and analytical conditions
for each analyte.
We compared the results from the NIR spectrometer and HPLC to measure sensitivity and
specificity by medicine category. Sensitivity is the proportion of medicines detected as poor
quality by the NIR spectrometer out of all those identified as poor quality by HPLC (true
positives/[true positives+false negatives]). Specificity is defined as the proportion of medicines
identified as authentic by the NIR spectrometer out of all those determined to be good quality
by HPLC (true negatives/[true negatives+false positives] [16]. Additionally, we calculated the
positive predictive value (true positives/ [true positives + false positives]) and negative predic-
tive value (true negatives/ [true negatives + false negatives]).
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PLOS ONE Validation of Near-Infrared Spectrometer to detect Substandard and Falsied Medicines
Ethics Approval
This study was approved by the University of Michigan (HUM00214684) and the National
Health Research Ethics Committee (NHREC) in Nigeria (NHREC/01/01/2007). Oral or writ-
ten consent was not required as this study does not involve human subjects and falls outside
the scope of human subjects’ research.
Results
Enumerators visited 1,296 pharmacies and successfully purchased one drug from the ran-
dom list in 93.7% (N = 1,214) of cases. All medicines were tested with the NIR spectrometer
(N = 1,142), and a sub-sample (N = 246) was also tested with HPLC. The NIR spectrometer
Table 1. Description of medicines purchased.
Dosage Ingredients N samples tested
Analgesics
Panadol Extra 500mg/65mg Paracetamol/Caffeine 28
May & Baker Paracetamol 500mg Paracetamol 17
Emzor Paracetamol 500mg Paracetamol 51
Tuyil Cenpain Night 500mg/25mg Paracetamol/Diphenhydramine HCL 14
Antibiotics
Swipha Tiniflox Tinidazole + Ofloxacine 600/200mg Tinidazole/Ofloxacin 4
Sanofi Avensis Flagyl Metronidazole 400mg Metronidazole 7
Fidson Ciprotab (Ciprofloxacin) 500mg Ciprofloxacin 14
Neimeth Pyrantrin Pyrantel Pamoate 125mg Pyrantel Pamoate 13
Antihypertensives
Normoretic 5mg/ 50mg Amiloride HCL/Hydrochlorothiazide 12
Bonduretic 5mg/ 50mg Amiloride HCL/Hydrochlorothiazide 9
Nifedin Nifedipine Dexcel 20mg Nifedipine 8
Bondomet Methyldopa 250mg Methyldopa 2
Antimalarials
Swipha Swidar Sulphadoxine + Pyrimethamine 500/25mg Sulphadoxine/Pyrimethamine 16
Lonart-DS Artemether Lumefantrine 80mg/480mg Artemether/Lumefantrine 23
Coartem Artemether Lumefantrine 80mg/480mg Artemether/Lumefantrine 14
Artequick Artemisinin Piperaquine 62.5mg/375mg Artemisinin/Piperaquine 14
Multivitamins
Kunimed Ascomed Vitamin Coloured Vitamin C 100mg Vitamin C N/A
Vitamin C (Chemo-Pharma) White 100mg Vitamin C N/A
Chemo-Pharma Vitamin C Colored 100mg Vitamin C N/A
sMeyer B Complex 100mg Vitamin B N/A
https://doi.org/10.1371/journal.pone.0319523.t001
Table 2. Sample of medicines tested, by category.
Categories % N
Analgesics 44.72 110
Antibiotics 15.45 38
Antihypertensives 12.60 31
Antimalarials 27.24 67
Total 100 246
https://doi.org/10.1371/journal.pone.0319523.t002
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PLOS ONE Validation of Near-Infrared Spectrometer to detect Substandard and Falsied Medicines
identified 4.8% (N = 55) of the samples as failing the test, all of which were analgesics. Among
the 246 tested, 22% (N = 55) failed the NIR spectrometer (S3 Table). When tested with HPLC,
25% (N = 62) failed due to API falling outside the 90–110% range, indicating a high preva-
lence of poor-quality medicines. Of those failing the HPLC test, 35% (N = 22) were antihyper-
tensives, 31% (N = 19) were analgesics, 19% (N = 12) were antibiotics, and 15% (N = 9) were
antimalarials [17].
Table 3 compares the passing and failing rates between the two tests. The overall sensitiv-
ity of the NIR spectrometer was 11%, with a specificity of 74%. The positive predictive value
was 13%, and the negative predictive value was 71%. These metrics varied by drug type (S4
Table): for analgesics, sensitivity was 37% and specificity was 47%, while the NIR spectrometer
performed poorly for other drug categories, with sensitivity at 0% and specificity at 100%. A
highly sensitive test effectively detects SF medicines by minimizing false negatives, ensuring
that SF medicines are not missed and mistakenly classified as genuine. Conversely, a highly
specific test reduces false positives, accurately identifying genuine medicines without incor-
rectly labeling them as SF. Although low specificity can lead to additional costs and work in
reference laboratory assays, prioritizing higher sensitivity is essential to prevent SF medicines
from reaching patients.
Discussion
In this study, we build on existing evidence that reviews field detection devices for screening
medicine quality, including evaluations of both laboratory and field devices. Previous research
has underscored the potential and limitations of different devices in detecting SF medicines
under controlled and field conditions [14,18–22]. Our study contributes to this body of work
by testing a novel, low-cost technology designed to offer real-time data, which could represent
a significant advancement if proven viable. This technology, if effective, holds promise for
reducing dependency on traditional laboratory testing by allowing immediate screening of
pharmaceutical products in the field, potentially strengthening quality assurance processes in
remote or resource-limited settings. However, our findings indicate that this technology is not
yet suitable for real-world deployment.
Our comparative exercise, combined with expert discussions, highlighted key consider-
ations for further investment in these technologies and their use by regulatory authorities.
First, the low sensitivity of the NIR spectrometer in our study may align with findings from
laboratory evaluations of 12 portable devices, which showed high sensitivities for detecting
medicines with no or incorrect APIs but variable sensitivities (0% to 100%) for samples with
Table 3. Sensitivity and Specificity of NIR spectrometer.
HLPC Lab
Fail Pass
NIR spectrometer Fail True positives (TP) False positives (FP)
N = 7 N = 48 N = 55
Pass False negatives (FN) True negatives (TN)
N = 55 N = 136 N = 191
Total N = 62 N = 184 N = 246
Sensitivity TP/(TP+FN) 11%
Specificity TN/(TN+FP) 74%
Notes: N represents the number of drug samples in each cell.
https://doi.org/10.1371/journal.pone.0319523.t003
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PLOS ONE Validation of Near-Infrared Spectrometer to detect Substandard and Falsied Medicines
50% to 80% API [18]. Our results suggest the NIR spectrometer may require further techno-
logical improvements, especially for detecting substandard formulations.
Second, adjusting the pass/fail threshold for the test may be necessary. Increasing the
threshold could improve sensitivity but reduce specificity [20]. Public health concerns suggest
that enhancing sensitivity for detecting SF medicines is crucial, even at the expense of specific-
ity. This is particularly important when considering the potential harm that SF medicines can
cause to patients, including adverse reactions or treatment failures. Therefore, policymakers
and regulatory bodies must carefully weigh the trade-off between sensitivity and specificity,
taking into account the broader implications for patient safety and public trust in the health-
care system.
Third, building and maintaining an up to date “spectral reference library” with quality-
assured genuine samples is both challenging and costly. Authentic materials are often difficult
to obtain and may not be suitable for screening finished products, as spectra can be influenced
by both APIs and excipients and can vary between brands [14]. In our study, the company
was responsible for building a customized reference library using authentic samples procured
directly from the manufactures. The researchers did not have control over this process, and
the company did not share the results of their procedure. As a result, we cannot be certain that
the agreed-upon process was followed, although we have no reason to believe otherwise. Our
findings suggested that the NIR spectrometer’s AI algorithm was better trained on analgesics,
highlighting the need for a comprehensive and shareable “spectral reference library” to facili-
tate effective device training and comparison.
Our study is not without limitations. First, our comparative study is limited to 246 drug
samples purchased in Nigeria, which may not generalize to other branded or non-branded
medicines or to different geographical contexts. Second, due to the proprietary nature of the
machine-learning algorithm and the hardware and software, and to protect proprietary infor-
mation and sensitive business data, we are unable to disclose the company’s identity. This con-
straint limits the researchers’ ability to fully share technical details and methodologies related
to the device’s design, calibration, and performance metrics. Future research should seek to
test this and similar devices across a broader range of medicines, regions, and conditions to
validate their efficacy and robustness in diverse real-world settings.
Conclusion
This study addresses the critical issue of ensuring the reliability of devices, such as Near-
Infrared (NIR) spectrometers, for screening substandard and falsified (SF) medicines. We
compared a novel NIR spectrometer to high-performance liquid chromatography (HPLC)
by analyzing 246 drug samples purchased from retail pharmacies across the six geopolitical
regions of Nigeria. We measured the device’s specificity and sensitivity.
Our findings indicate that this device is not yet ready for field deployment in the con-
text studied, as it demonstrated particularly low sensitivity, limiting its ability to prevent
SF medicines from reaching patients. The results align with existing evidence, where no
current device has demonstrated the capability to assess the quality of active pharmaceutical
ingredients (APIs) across the diverse range of formulations and conditions encountered in
real-world settings [14]. This comparative study highlights significant gaps in the regulatory
framework and a lack of standardization for evaluating such devices, as observed in this
specific setting.
First, we noted limited transparency from the manufacturer regarding data analytics
methods and device validation processes. We recommend that manufacturers provide detailed
information on data processing methods and validation procedures to enable independent
evaluation of device reliability. Additionally, regulatory authorities should establish clear,
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PLOS ONE Validation of Near-Infrared Spectrometer to detect Substandard and Falsied Medicines
standardized performance criteria for screening devices, including field-testing protocols
tailored to the study region.
Second, the study revealed that manufacturers rely on limited laboratory datasets without
sufficient field testing. We suggest that devices with low sensitivity or inadequate validation
undergo additional laboratory and field evaluations in similar settings before deployment to
ensure they can effectively screen SF medicines. Collaboration among regulators, researchers,
and manufacturers may support the development and refinement of screening technologies
suited to specific contexts.
By addressing these gaps and implementing these interventions, stakeholders can improve
the reliability of NIR spectrometers, thereby enhancing patient safety and the integrity of
pharmaceutical supply chains. Our findings underscore the importance of further research
and rigorous validation to ensure these devices meet their intended purpose.
Supporting Information
S1 Table. Analytical Parameters for Linearity, Correlation, and Detection Limits of Com-
pounds by High-Performance Liquid Chromatography.
(DOCX)
S2 Table. Sample Preparation and HPLC Parameters for Compound Analysis.
(DOCX)
S3 Table. Passing and failing rates by test and category of medicines.
(DOCX)
S4 Table. Sensitivity and Specificity of NIR spectrometer, by category of medicines.
(DOCX)
Acknowledgements
We thank the team at Bloom Public Health who collected the drug samples and gathered the
data.
Author contributions
Conceptualization: Elisa M Maffioli, Chimezie Anyakora.
Data curation: Elisa M Maffioli.
Formal analysis: Elisa M Maffioli.
Funding acquisition: Elisa M Maffioli.
Methodology: Elisa M Maffioli, Chimezie Anyakora.
Project administration: Chimezie Anyakora.
Supervision: Elisa M Maffioli, Chimezie Anyakora.
Validation: Elisa M Maffioli, Chimezie Anyakora.
Writing – original draft: Elisa M Maffioli.
Writing – review & editing: Chimezie Anyakora.
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