Spice adulteration goes back to ancient times and has been documented throughout history.  Spices are considered a high value target and methods have evolved over time from the simple addition of undesirable components to increase mass to deliberate and calculated adulteration meant to mask the presence of adulterants in quality control tests.  Some high-profile incidents in recent years have highlighted the need for fast and cost-effective monitoring of spice adulteration.  Examples include lead oxide in paprika and Sudan I dye in chili powder, which was used in contaminated Worcestershire sauce.  In the case of the withdrawn obesity medication sibutramine as an adulterant in herbal medicine, the sibutramine acts as an appetite suppressant but presents serious side effects and cardiovascular risks.  While there are effective analytical methods for monitoring for spice adulteration, they are often expensive, time-consuming, and difficult to implement as a large-scale quality control method.  One method that has been examined for spice adulteration monitoring is NIR spectroscopy and a summary of some research studies is presented below.   


Products: Paprika, Chili Powder, Chinese Shanyao, Onion Powder, Black Pepper, Cinnamon, Goldenseal, Herbal Medicines 

Adulterants: Sudan Dye, Tomato Skin, Brick Dust, Starch, Buckwheat, Millet, Papaya Seeds, Chili, Spent Material, Lower Grade Cinnamon, Exhaustively Extracted Material, Yellow Root, Yellow Dock, Oregon Grape, Coptis, Sibutramine 

Scientific References and Statistics  

Fast Detection of Paprika Adulteration Using FT-NIR Spectroscopy – Galaxy Scientific, Inc., Oct. 17, 2016 

Spice adulteration goes back thousands of years and can take on different forms which are always evolving.  Many forms of adulteration are relatively harmless in a health sense but have can have economic consequences, such as misrepresentation of the geographical origin of a product.  However, when adulteration involves contamination with toxic products, the consequences can be deadly.  One real-life incident was the discovery of the carcinogenic and banned food additive Sudan I dye in Worcestershire sauce, which was added by way of adulterated chili powder.  Food adulteration is at the top of the list when it comes to safety concerns and there is a need for fast, non-invasive testing for adulterants as current methods are time-consuming, expensive, and often require the use of wet chemistry.  One method that has been examined is NIR spectroscopy, offering the advantages of no sample preparation, high speed, and ease of use without chemicals. 

A study was conducted at Galaxy Scientific in Nashua, NH for the purpose of using FT-NIR spectroscopy to determine the presence of adulterants in paprika using tomato skin, red brick dust, and Sudan I dye.  Four different paprika samples were purchased: McCormick Paprika, McCormick Gourmet Hot Hungarian Paprika, Morton & Bassett Paprika, and Spice Chain Pride of Szeged Hungarian Style Paprika.  Sudan I with a dye content greater than 95% was purchased from Sigma Aldrich.  Red brick was obtained from Home Depot and ground into fine powder.  Tomato was purchased from a local market and the skin was peeled, dried, and ground into fine powder.  Samples were mixed in the following concentrations of each adulterant: 

Sudan I Dye0.110.620.884.5810.32 (% w/w) 
Tomato Skin0.110.521.235.5410.86 (% w/w) 
Brick Dust0.10.681.044.7714.12 (% w/w) 

Two different Galaxy Scientific QuasIR™ 3000 spectrometers were used to collect FT-NIR spectra of the adulterated samples as well as of each pure paprika and adulterant sample.    Samples were stored in 25 X 95 mm glass vials and placed on top of the 23 mm sample window of the integrating sphere for scanning.  FT-NIR spectra were collected from 10000 cm-1 to 4000 cm-1 using 4 cm-1 resolution and 200 scans per average.  Each sample was scanned twice using each spectrometer and the vial was shaken between each measurement.  Spectra were averaged for each sample before visual examination and chemometric analysis. 

Visual examination of the NIR spectra for all four paprika samples and all three adulterants showed distinct absorbance bands except in the case of brick dust, which was expected because it is an inorganic material.  The main absorbance band in brick dust is an O-H band due to moisture.  Sudan I dye has distinct absorbance bands around 4600 cm-1 and 6000 cm-1.  Because both are natural organic products, the spectral features of tomato skin are very similar to paprika.  The Advanced ID™ CLS algorithm was used to analyze the NIR spectra of the paprika and adulterant mixtures for chemometric analysis. 

Sudan I DyeR2 > 0.95 for 0.11%, 0.62%, 0.88%, 4.58%, and 10.32% samples 
Tomato SkinR2 > 0.93 for 0.52%, 1.23%, 5.54%, and 10.86% samples 
Brick DustR2 > 0.95 for 4.77% and 14.12% samples 

The Advanced ID™ algorithm extracts a spectrum from a mixture using the spectra of each individual component of the mixture.  Clear differences were observed in the spectra of the paprika mixed with different concentrations of the Sudan I dye, especially in the distinctive absorbance region for Sudan I dye between 6200 cm-1 and 5800 cm-1.  The high correlation coefficient shown by the algorithm indicates that Sudan I dye adulterant in paprika can be detected at a concentration as low as 0.1%. In the case of tomato skin, first derivative processing of the spectra was used because the absorbance spectra are so similar.  Good correlation was shown if the tomato skin concentration was above 0.5% and this is the sensitivity for detecting tomato skin adulterant in paprika.  The lack of distinctive absorbance features in NIR spectra of brick dust made it more difficult to detect low concentrations of adulteration, but results still showed good correlation for samples with 5% or higher amounts of brick dust adulterant.  In practice, any real adulteration contamination will be at a high concentration to make sense economically, so the FT-NIR spectra and Advanced ID™ algorithm can be used as a fast screening tool to detect adulteration in Paprika with no sample preparation or use of wet chemistry. 




Galaxy Scientific QuasIR™ and Other Products: 


The Feasibility of Using Near Infrared and Raman Spectroscopic Techniques to Detect Fraudulent Adulteration of Chili Powders with Sudan Dye – Haughey, Galvin-King, Ho, et al., Food Control 48 (2015) 75-83 

Chili powder is a valuable and globally traded commodity which has been found to be adulterated with Sudan I dye.  Sudan I dye is classified as a Class 3 carcinogen and banned as a food additive.  One known incident was the discovery of contaminated Worcestershire Sauce, which was made from chili powder adulterated with Sudan I Dye.  Methods of adulteration are continually evolving and thus there is a demand for new testing methods that are fast and require little or no sample preparation.  Both NIR and Raman spectroscopy were examined as screening tools for detecting the adulteration of chili powder with Sudan I dye.  Commercial chili powder samples were purchased from local markets for the study.  Different samples were spiked with Sudan I dye at the following w/w concentrations:  0.1%. 0.2%. 0.5%, 1%, 2.5%, and 5%.  Pure unadulterated samples were used as well and in total, one hundred and twelve samples were procured for the study.  NIR spectra were collected using a FT-NIR spectrometer operating in reflectance mode.  Samples were scanned from 12000 cm-1 to 4000 cm -1 at 8 cm-1 resolution.  Sixty-four scans were collected per reading and averaged into one spectrum.  This process was repeated three times for each sample.  In order to make the Raman spectrometer suitable for sample collection, a custom sample compartment was constructed that allowed a longer focal length collection lens to be fitted and a larger area of the sample to be scanned using a rotating sample holder.  Spectra were acquired with an integration time of 5 seconds from 2000 cm-1 to 200 cm-1 at 10 cm-1 resolution. Two different chemometric software packages were used for both qualitative and quantitative multivariate analysis of both sets of spectra.  Various pretreatments were applied to the NIR and Raman spectra before chemometric analysis. Principle Component Analysis (PCA) and Partial Least Squares Discriminate Analysis (PLS-DA) were used for qualitative analysis while Principal Components Regression (PCR) and Partial Least Squares (PLS) were used for quantitative analysis.  The results shown below are the best obtained from the different pre-treated spectral data. 

PCA (NIR and Raman) 

Clear scores plot separation between pure and adulterated samples 

PLS-DA (NIR)R2= 0.883 
PLS (NIR)R2= 0.993RMSEP= 0.141% 
PCR (Raman)R2= 0.971RMSEP= 0.592% 

The scores plot of a PCA shows grouping between data points and separation between two groups indicates the spectral data can be used to distinguish between the two groups.  Likewise, PLS-DA uses an arbitrary grouping number for both sets of data and quantitatively predicts the number to choose the proper grouping from the data.  PLS analysis of the NIR spectra showed the best results with Standard Normal Variate (SNV) and first derivative pre-processing, with high correlation and a very low RMSEP.  PCR analysis of the Raman spectra showed the best results with Standard Normal Variate (SNV) and second derivative pre-processing.  Correlation was still high but the RMSEP was over four times higher than the prediction error from the NIR spectra model.  Results for both sets of spectra are considered good enough to use these models for screening for Sudan I Dye adulterant in chili powder, but the NIR method would be better applied in a real-time setting because of the lower prediction error and no need for a modified sample presentation method, which was the case when using the Raman instrument. 


Rapid Authentication of Starch Adulterations in Ultrafine Granular Powder of Shanyao by Near-Infrared Spectroscopy Coupled with Chemometric Methods – Ma, Wang, Chen, et al., Food Chemistry 215 (2017) 108-115 

Shanyao is a Chinese yam (not to be confused with the American and Canadian misnomer for sweet potato) rich in many nutritional properties.  In herb form, it is used as a dietary supplement as well as a traditional Chinese medicine.  Consumption of the ultrafine granular powder of Shanyao (UGPSY) is extensive and has high market demand, subjecting it to adulteration with cheaper materials such as cornstarch and wheat starch.  Such adulteration degrades nutritional, physiochemical, and medicinal properties as well as creating economic loss and potential health hazards.  Current methods for detecting adulteration in UGPSY include microscopic identification, HPLC, mass spectrometry, and DNA-based marker systems.  These methods are often laborious, expensive, time-consuming, destructive, and difficult to implement on a large scale.  NIR spectroscopy was examined as a method for determining the presence of cornstarch and wheat starch adulterants in UGPSY.  Two batches of forty samples of UGPSY and locally purchased cornstarch and wheat starch were procured for the study.  All samples were dried in an oven overnight and adulterated samples were prepared after drying.  Adulterated samples were prepared using both types of starch and UGPSY at w/w% ranging from 10% to 95% adulterant.  Pure samples were also set aside and all samples were sieved through a mill to make them homogenous.  In total, one hundred and ninety-two samples were prepared.  NIR spectra were collected in reflectance mode from 1100 nm to 2300 nm at 2 nm intervals.  Thirty-two scans were collected for each reading and averaged into one spectrum.  Various pre-processing treatments were applied to the NIR spectra before chemometric analysis.  Discriminant analysis was performed using Principle Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA).  Quantitative analysis of starch adulterant was performed using Partial Least Squares (PLS), Interval PLS (iPLS), and Synergy Interval PLS (siPLS).  All types of samples were separated into a training set and test set in order to validate the models.   

siPLS (Cornstarch)R2= 0.998RMSEP= 1.634% 
siPLS (Wheat Starch)R2= 0.997RMSEP= 2.045% 

PCA was unable to distinguish between pure samples and those adulterated with both cornstarch and wheat starch, especially at lower concentration.  OPLS-DA helps interpret discriminant variation by selective wavelength analysis that removes wavelength ranges that are not correlated to class separation.  The model showed that not only pure and adulterated UGSPY samples could be separated using the NIR spectra, but the cornstarch and wheat starch adulterated groups can be separated as well.  Analysis of the test set samples showed that 100% correct classification was achieved from the OPLS-DA model.  Both iPLS and siPLS use interval and selective wavelength analysis to improve results from a normal PLS model.  The best results were shown using siPLS from the wavelengths that were chosen from the OPLS-DA model.  Good correlation and low prediction error were shown for both adulterant models and the validity of the models was proven from prediction results using the test set samples.  Overall, the results of this study prove the feasibility of detecting both cornstarch and wheat starch adulterants in UGSPY using NIR spectra and calibration models.  These models show potential to replace the current expensive and time-consuming methods used for adulterant detection. 


Detection of Starch Adulteration in Onion Powder by FT-NIR and FT-IR Spectroscopy – Lohumi, Lee, Kim, et al., Journal of Agricultural and Food Chemistry, 2014, 62, 9246-9251 

Current methods that are used to authenticate spices are based on morphological, microscopic, chemical, or DNA-based characteristics.  These methods are often inconvenient for routine sample analysis and can be time-consuming, expensive, and require a degree of expertise to perform the analysis.  Chemical standards that are used for such analysis can be difficult to obtain or lack identifiable markers. Both FT-NIR and FT Mid-IR spectroscopy were examined to determine the feasibility of detecting starch adulteration in onion powder.  Pure onion powder and 95% cornstarch were procured for the study.  Before mixing, samples were dried in an oven for two hours to equilibrate the moisture since spectroscopic measurement is highly sensitive to differences in moisture.  Pure samples of onion powder were set aside and adulterated mixtures were prepared by w/w% ranging from 1% to 35% starch adulterant.  Ten samples of each of the pure and adulterated samples were prepared, making a total of one hundred and eighty samples.  NIR spectra were collected in reflectance mode from 10000 cm-1 to 4000 cm-1 at 4 cm-1 intervals.  Thirty-two scans were collected for each reading and averaged into one spectrum.  Mid-IR spectra were collected using an ATR background from 4000 cm-1 to 650 cm-1 at 4 cm-1 intervals.  Thirty-two scans were collected for each reading and averaged into one spectrum.  Various pre-processing treatments were applied to both sets of spectra.  Principle Component Analysis (PCA) was applied to determine wavelengths of interest and Partial Least Squares (PLS) analysis was performed to correlate the spectral data to the amount of starch adulterant in onion powder.  Before chemometric analysis, seven types of each sample were separated into a calibration set and the remaining three types of each sample were set aside as a training set for validation analysis. 

PLS (FT-NIR)R2= 0.98RMSEP= 1.18% 
PLS (FT-IR)R2= 0.90RMSEP= 3.12% 

Examination of PCA proved that the basis for visual differences in both sets of spectra were related to wavelengths known to absorb in chemical regions that make up the composition of starch.  Likewise, the relevant wavelengths for the PLS regression models were contained in the same regions.  Predictive analysis of the training set sample proved the validity of the models.  The model from NIR spectra showed a much better predictive ability than the model from the Mid-IR spectra as well as much higher correlation and a lower RMSEP.  The likely reasons for this is the ability of NIR light to penetrate far deeper into a sample than light in the Mid-IR region and a larger sampling area when using NIR.   NIR light can penetrate several millimeters into a sample while Mid-IR penetration is typically measured in micrometers.  The results here prove the feasibility of using spectroscopic data to monitor starch adulteration in onion powder, with NIR spectroscopy proving to be more accurate than Mid-IR spectroscopy.  Similar models could be created for other types of spices using the same process to create calibrations, as it was shown that changes in starch clearly have an effect on both NIR and Mid-IR absorption that can be used to monitor for starch adulteration. 


Near Infrared and Mid-Infrared Spectroscopy for the Quantification of Adulterants in Ground Black Pepper – McGoverin, September, Geladi, and Manley, Journal of Near Infrared Spectroscopy, 20, 521-528 (2012) 

The development of reliable methods to detect adulterants in food has received considerable interest in recent years.  Spices in particular are considered economically worthwhile targets for adulteration due to both a high value per unit mass and specific desired flavor attributes. Vibrational spectroscopic methods are considered attractive techniques for detecting adulteration as they are information rich, fast, require little sample preparation, and are environmentally friendly.  Both NIR and Mid-IR spectroscopy were examined as methods for determining the adulteration level of both buckwheat and millet in powdered black pepper.  Four different batches of whole black pepper kernels were obtained from four different spice companies for the study.  Whole kernels of buckwheat and millet were obtained as well.  Before mixing, all samples were milled and oven dried.  Each separate batch of black pepper was mixed with nineteen amounts of buckwheat and nineteen amounts of millet at 5% w/w intervals, resulting in samples from 5% to 95% of each type of adulterant.  Pure amounts of each sample type were set aside as well.  NIR hyperspectral data were collected using an imaging system.  Images were acquired from 1000 nm to 2500 nm at 6.3 nm intervals and a spatial resolution of 300 µm x 300 µm.  The images of all samples were examined to determine the heterogeneity.  The spectra from each sample were averaged, resulting in a data set of one hundred and sixty-four samples with one spectrum per sample.  Mid-IR spectra were collected using a diamond crystal background from 3999 cm-1 to 550 cm-1 using a spectral resolution of 4 cm-1 and a sampling interval of 1.42 cm-1.  Thirty-two scans were collected per reading and averaged into one spectrum.  Two duplicate spectra were collected per sample and averaged into a single spectrum.  Various pre-processing treatments and selective wavelength ranges were used for both the NIR and Mid-IR spectra during chemometric analysis.  Partial Least Squares (PLS) regression models were created correlating the spectral data to the percentage of adulterant in black pepper.  All samples using three of the types of black pepper were used as a training set to create the calibration models and the fourth set was kept out of the model for a test set that was used to validate the models. 

NIR R2= 0.99RMSEP= 2.70% 
Mid-IRR2= 0.96RMSEP= 7.25% 

The results shown above were the best obtained for each set of spectra and show that NIR spectroscopy is a much better suited technique to detect adulteration in black pepper than Mid-IR.  The NIR PLS model used the wavelength range from 1100 nm to 2500 nm with Standard Normal Variate (SNV) and first derivative pre-processing.  The Mid-IR model used the wavenumber ranges from 3050 cm-1 to 2800 cm-1 and 1770 cm-1 to 550 cm-1 with Multivariate Scatter Correction (MSC) pre-processing. The poorer correlation and prediction error in the Mid-IR model are a direct result of low light penetration, sample heterogeneity, and insufficient sampling area.  The NIR modeling results are good enough to be used for process control purposes.  NIR spectroscopy and calibration models can be used as a real-time, fast, and accurate tool for detecting both buckwheat and millet adulteration in ground black pepper requiring no sample preparation.  This method has the potential to replace the time-consuming and expensive methods that are currently used for such analysis.   


The Feasibility of Applying NIR and FT-IR Fingerprinting to Detect Adulteration in Black Pepper – Wilde, Haughey, Galvin-King, and Elliott, Food Control 100 (2019) 1-7 

Black pepper is the most widely used spice in the world.  It is cultivated in approximately twenty-six tropical countries, with India, Vietnam, and Indonesia being the main producers.  It is estimated that in 2016, 430,000 tons were consumed worldwide with about 63,000 tons of that exported to Europe.  The high value and complex supply chain of black pepper makes it especially vulnerable to adulteration.  Two possible black pepper adulterants are papaya seeds and chili.  Papaya seeds are cheap, widely available, and structurally resemble black pepper.   Chili was discovered in market samples of black pepper during one study.  Another form of adulteration that has been pointed out by the FDA in their guidance on authenticity of herbs and spices is extraneous matter from the same plant, referred to as spent, defatted, and depleted material.  Current testing methods for black pepper adulterants are not only expensive and time-consuming, they lack the scope to adequately detect multiple forms of adulteration in the large global black pepper market.  Spectroscopic methods have been shown to be low-cost, rapid, and comprehensive compared to traditional methods.  The feasibility of using NIR and Mid-IR spectroscopy to detect economically motivated adulteration of black pepper was examined.  After research and discussion with industry insiders, papaya seeds, chili, black pepper husk, and black pepper spent material were chosen as the adulterants for the study.  A total of one-hundred and fifteen black pepper samples from six different countries were procured for the study.  At least two different types of each adulterant were acquired.  All samples were ground to a homogeneous powder before mixing.  Three separate pepper pools were prepared by mixing different combinations of the black pepper samples.  For each pool, pure samples were set aside and the remaining samples were spiked with each of the four adulterants from 10 g/100 g to 40 g/100 g to make the spiked samples.  For both methods, spectra were collected of the pure black pepper samples, pure adulterant samples, and spiked samples. NIR spectra were collected from 12000 cm-1 to 4000 cm-1 at 8 cm-1 resolution. Thirty-two scans were collected per reading and averaged into one spectrum.  Three replicates of each sample were scanned and averaged before data pre-processing.  Mid-IR spectra were collected from 4000 cm-1 to 400 cm-1 using an ATR accessory.  Thirty-two scans were collected per average at 4 cm-1 resolution.  Different pre-processing methods were applied to both sets of data before chemometric analysis.  Principle Component Analysis (PCA) was first conducted to analyze the differences in spectra of pure black pepper with different geographical origins as well the differences in pure black pepper vs. adulterated samples.  The pure black pepper and pure adulterant spectra were used to create a binary classification Orthogonal Projections to Least Structures Discriminant Analysis (OPLS-DA) model. Forty-eight spectra of the spiked samples and thirty spectra of the pure black pepper samples were used as a test set to validate the model. 

NIRR2= 0.93 
Adulteration 0%Correct Classification: 90% 
All Adulterants 10-40%Correct Classification: 100% 
Spent/Husk 10-20%Correct Classification 100% 
Spent/Husk 20-30%Correct Classification 100%
Papaya/Chili 10-20%Correct Classification 100%
Papaya/Chili 20-30%Correct Classification 100%


Mid-IRR2= 0.83 
Adulteration 0%Correct Classification: 93%
All Adulterants 10-40%Correct Classification: 98% 
Spent/Husk 10-20%Correct Classification 100% 
Spent/Husk 20-30%Correct Classification 100% 
Papaya/Chili 10-20%Correct Classification 92% 
Papaya/Chili 10-30%Correct Classification 100% 

Classification results for the test set data were excellent for the NIR model, with a lower correlation and higher prediction error for the Mid-IR model but still good.  The analysis above shows the threshold of sensitivity that the predictivity of the model could reasonably be expected to meet.  In practice, an adulteration level less than 10% would provide little economic benefit while a level higher than 40% would be high enough to risk the discovery of the adulterant by visualization alone.  The most important conclusion is that the classification will work simply from a model created from pure black pepper samples and pure adulterants.  In practice, many more types of black pepper and adulterants would need to be added to make a universal model robust enough to accurately detect and classify any adulterants.  The research here shows great potential for finding adulterants in black pepper using spectroscopic methods. 


Rapid Authentication and Quality Evaluation of Cinnamomum Verum Powder Using Near-Infrared Spectroscopy and Multivariate Analyses – Shawky, Selim, Planta Med 2018: 84: 1380-1387 

Ceylon Cinnamon is the inner bark of the tree of Cinnamomum Verum and is often referred to as “true cinnamon”.  As well as being one of the tree species used for manufacture of high-quality cinnamon spice, it is widely incorporated in the medicinal and cosmetic industry.  The bioactive content of cinnamon-based products has been of great interest to both manufacturers and consumers in recent years.  Because Ceylon cinnamon is such a valuable product, it is often subjected to adulteration.  Cinnamomum Cassia, known as Chinese Cassia, is closely related to Ceylon cinnamon but has a rougher, thicker, and less aromatic bark, making it a cheaper and lower quality product.  Another method of adulteration is to add exhaustively extracted Ceylon cinnamon to genuine Ceylon cinnamon.  In the case of Chinese Cassia, adulteration can be dangerous for human consumption because it contains high amounts of coumarin, a blood-thinning agent that can be toxic if consumed in excessive amounts.  The only known methods that can distinguish between Ceylon cinnamon and Chinese Cassia are expensive and time-consuming, such as HPLC-MS.  NIR spectroscopy was examined as a method for detecting both Chinese Cassia and exhaustively extracted Ceylon cinnamon adulterants in pure Ceylon cinnamon.  Twenty-seven authenticated samples each of Ceylon cinnamon and Chinese Cassia were procured for the study.  Samples were dried, powdered, and passed through a sieve.  Exhaustively extracted samples were prepared by hydrodistillation from cinnamon barks and were subsequently dried and powdered.  For both types of adulterant, samples were prepared by mixing with the pure samples from 5% to 95% at 5% intervals.  Triplicate samples at each value were prepared in this fashion.  NIR spectra were collected using an FT-NIR spectrometer from 8000 cm-1 to 4000 cm-1 at 16 cm-1 resolution.  Sixty-four scans were collected per reading and averaged into one spectrum.  This process was repeated three times for each sample and the three spectra were averaged into one spectrum before chemometric analysis.  Standard Normal Variate (SNV) and first derivative pre-processing methods were used to eliminate baseline drift and enhance differences between the spectra.  Principle Component Analysis (PCA) was used to analyze differences in the spectra between groups.  A SIMCA classification model was created to determine the feasibility of separating pure Ceylon cinnamon, both adulterants, and the Ceylon cinnamon samples mixed with both adulterants.  Partial Least Squares (PLS) regression models were created to correlate the NIR spectra to the amount of adulterant in the Ceylon cinnamon. 


100% correct classification of all samples 


Chinese CasciaR2= 0.988RMSEP= 0.041 %/g 
Exhaustively ExtractedR2= 0.9871RMSEP= 0.048 %/g 

The results for the SIMCA model and both PLS models were excellent and proved the feasibility of using NIR spectra to identify and quantify both types of adulterants.  Validation of the SIMCA model correctly classified all samples at a 95% confidence level.  Cross validation of the PLS models provided independent predictions for all samples and confirmed the validity of the models.  Most predictions had an error lower than 1% and no prediction had an error greater than 2%.  NIR spectroscopy shows great potential for use as a screening tool to replace the current expensive and time-consuming methods for determining the presence of adulterant in the valuable Ceylon cinnamon. 


FT-NIR Characterization with Chemometric Analyses to Differentiate Goldenseal from Common Adulterants – Liu, Finley, Betz, Brown, Fitoterapia 127 (2018) 81-88 

Goldenseal is a native North American herb that has been popular since the 1970s and is mostly used for the antimicrobial properties of the roots and rhizomes.  It is found in a wide range of products including eardrops, feminine cleansing products, cold and flu remedies, allergy relievers, laxatives, and digestion aids.  In 2016, goldenseal had global sales over $31 million in the Nutrition Industry sector.  It exists in both wild and cultivated form.  The demand has led to a sharp reduction in wild goldenseal, even placing it on the endangered species list in some states, while cultivated goldenseal is both resource intensive and more expensive for both the producer and consumer.  International trade requires a CITES (Convention for International Trade in Endangered Species of Wild Fauna and Flora) permit to ensure the plant is four years old and was legally obtained.  Adulteration with visually similar but cheaper and more accessible plants has become prevalent.  One test of market samples discovered that one in three goldenseal materials was mixed with the following plants: Oregon grape, yellow dock, yellow root, and coptis, all similar in morphology and phytochemistry to goldenseal.  Visual inspection is often insufficient for discovering these adulterants, especially when the materials in trade have already been processed as biomass or extracts.  Tests for some alkaloids that are unique to goldenseal are effective but expensive and time-consuming while requiring skilled technicians to implement them.  NIR spectroscopy was examined as a method for detecting the presence of common adulterants in goldenseal.   Plant materials of goldenseal, coptis, Oregon grape, yellow dock, and yellow root were purchased from the American Herbal Pharmacopeia and Chromadex.  All varieties were authenticated using at least two methods outlined by authoritative references and verified by a Professor of Horticulture.  All samples were ground into powder and 20 grams of each material was homogenized for the test.  1.5 grams were removed from each pool, sent for FT-NIR analysis, and returned to the pool.  This process was replicated five times for each type of plant.  NIR spectra were collected using an FT-NIR spectrometer from 8998 cm-1 to 4200 cm-1 using 8 cm-1 resolution and thirty-two scans per average.  A theoretical adulteration was created using the goldenseal spectra and spectra of each adulterant.  The 100% goldenseal spectrum was used with the 100% adulterant spectrum to create desired ratios.  For example, 2% adulteration was calculated by the sum of 0.98 of the goldenseal spectrum and 0.02 of the adulterant spectrum.  For each adulterant, this process created five spectra each at the following adulterant levels: 2%, 5%, 10%, 15%, 20%, 25%, 50%, 75%, and 95%.  Various pre-processing algorithms were performed on the spectra before chemometric analysis.  Three types of chemometric models were created for the purpose of differentiating pure goldenseal from the pure four adulterants and detecting the presence of an adulterant in goldenseal: Partial Least Squares (PLS), Soft Independent Modeling of Class Analogy (SIMCA), and Moving Window Principle Component Analysis (MW-PCA). 

All Three Models: Successful separation of pure goldenseal and an adulterant amongst all adulterants 
PLS: Unable to accurately predict whether or not the tested materials were 100% target material or accurately quantify percentages of mixed materials 
SIMCA: Best results for detection of yellow root and Oregon grape – can detect both at 10% or higher 
MW-PCA Best results for detection of yellow dock and coptis – can detect yellow dock at 15% or higher and coptis at 5% or higher 

Analysis of all three models showed that they could successfully discriminate between the spectra of pure goldenseal and each adulterant.  However, the results varied based on model type when attempting to classify an adulterated sample of goldenseal.  PLS proved to be poor for quantifying the percentage of goldenseal and adulterant in an adulterated sample. Independent predictions of pure goldenseal using PLS ranged from 66% to 104%.  Similar poor results occurred for predictions of pure adulterants and in the case of yellow root, the amount of goldenseal predicted was almost 20% despite no goldenseal being present in the sample.  It is possible that the results will improve with more goldenseal samples in the calibration set and with actual mixing of the samples rather than calculating ratios of adulterant using the pure spectra of goldenseal and the adulterant.  The study also did not attempt to create individual PLS models for each adulterant and golden seal.  This analysis could potentially improve the results as well.  Better results were obtained using the classification algorithms SIMCA and MW-PCA.  Both types of models were analyzed for all four adulterants and as shown above, SIMCA gave better results for yellow root and Oregon grape while MW-PCA gave better results for yellow dock and coptis.  The potential was demonstrated in this study to use NIR spectroscopy and chemometric modeling to detect adulteration in goldenseal but the results warrant careful analysis.  As demonstrated by the varying results from different algorithms, the specific approach for creating models must be carefully evaluated and selected on a case-by-case basis if the desired results are to be achieved.  Such models must be validated carefully before implementing them for real-time analysis. 


Near Infrared Spectroscopy Detection and Quantification of Herbal Medicines Adulterated with Sibutramine – da Silva, Honorato, Pimental, et al., Journal of Forensic Sciences, September 2015, Vol. 60, No. 5 

There is an increasing demand for herbal medicines to treat obesity.  The impression on consumers is that such medicines are safer than synthetic drugs for weight loss and have minimal side effects.  However, a number of incidents have shown the detection of synthetic chemicals as an adulterant in herbal medicines.  One such substance is sibutramine, an inhibitor of the neurotransmitters serotonin and norepinephrine which decrease appetite.  Side effects include an increase in blood pressure and pulse rate as well as elevated risk of heart attack and stroke.  Sibutramine has a difficult history including reports of health issues, studies showing the cardiovascular risks, and US Senate hearings, eventually resulting in the withdrawal of the product in the US market by its manufacturer Abbott Laboratories in 2010.  In 2008 and 2009, the FDA both issued alerts and recalled products marketed as “herbal supplements” and “dietary supplements” for containing sibutramine.  Similar incidents occurred in other countries.  Despite the withdrawal of the product in the US market, sibutramine has continued to be found in dietary supplements as recently as 2018.  Analytical methods have been developed and proven to be effective in detecting sibutramine adulteration.  These include liquid chromatography with mass spectrometry (LC-MS), capillary electrophoresis (CE), and nuclear magnetic resonance (NMR).  While effective, these methods are expensive, time-consuming, require the use of chemical reagents, destroy the samples tested, and are ill-suited to implement on a large scale for routine analysis.  NIR spectroscopy was examined as a method for detecting sibutramine adulterant in herbal medicines which are intended for use as a weight loss supplement.  Thirteen different commercial herbal medicine samples and pure sibutramine were procured for the study.  A standard liquid chromatography method was employed to test the herbal medicines and sibutramine was found in four of them at the following concentrations (w/w): 0.018%, 0.033%, 0.358%, and 0.44%.  For the nine pure samples, a portion was set aside of each and the rest were spiked with sibutramine at concentrations ranging from 1% to 6%.  Both the originally adulterated and spiked samples were ground and homogenized.  In total, fifty-five samples were used: nine pure herbal medicines, the four commercial samples found to be adulterated, and forty-two spiked samples.  A FT-NIR spectrometer was used to collect NIR reflectance spectra from 14000 cm-1 to 4000 cm-1 at 2 cm-1 nominal resolution.  One hundred twenty-eight scans were collected per reading and averaged into one spectrum.  Each sample was collected in triplicate and the vial was removed and placed back in the sample holder before collecting a new spectrum.  Various pre-processing methods were applied to the NIR spectra before chemometric analysis.  Three separate modeling algorithms were applied.  Partial Least Squares Discriminant Analysis (PLS-DA) was used to classify samples based on being pure or having an adulterant present.  Partial Least Squares (PLS) and Multiple Linear Regression (MLR) were used to quantify the amount of sibutramine present in a sample. 

PLS-DA 100% classification of validation set samples 
PLS R2= 0.996RMSEP= 0.22% 
MLR R2= 0.996RMSEP= 0.18% 

All modeling results were excellent and proved the feasibility of using NIR spectroscopy and calibration models to detect sibutramine adulteration.  PLS-DA assigns an arbitrary number to two groups and uses those numbers to classify the groups from NIR spectra.  Ten adulterated and three non-adulterated samples were pulled from the model and their spectra were used as a validation set. All samples were classified correctly.  PLS correlates a reference value to the NIR spectra and likewise, independent prediction results proved the validity of the model.  In the case of MLR, selective wavelengths are chosen for the regression and the selected algorithm chose four individual wavelengths.  Results were very similar to the PLS model and the difference can be considered statistically insignificant.  The potential was shown for using NIR spectroscopy and calibration models as a real-time tool for the detection of sibutramine adulteration in herbal medicines.  This method offers an alternative to traditional methods that are time-consuming, expensive, require the use of chemical reagents and skilled labor, and are unsuitable for implementing for large-scale testing.