Consumers’ choices, infringements and market competition

The study fits the individuals?? characteristics of consumption into an analysis of the demands for both genuine goods and counterfeit goods. The consumers?? substitutability of genuine goods for counterfeits and attitudes toward a dispersed consumption of counterfeit varieties are the dimensions that affect the niche markets for counterfeits. We show that it is not necessary to increase the competition among counterfeits to reduce the demand for individual counterfeits if at the margin the variety of counterfeits enhances the value of consuming the good. The enforcements against counterfeiting deter the number of counterfeit firms, but encourage the output of individual counterfeits if the market includes a significant number of counterfeiters. The optimal private enforcement against counterfeiting is also fully discussed in the model.     

Cue utilization in the product authentication process: a framework and research agenda for product counterfeit prevention

Scholars suggest that consumers rely on heuristics to evaluate products. They employ both extrinsic and intrinsic cues to evaluate product quality and utilize the same cues to differentiate between counterfeit and genuine products. This has far-reaching public health, economic, product authentication, and even criminal justice policy implications due to the emerging ideas of criminalizing the purchase of counterfeit goods. In order to predict the viability and the possible effects of such a policy, a thorough understanding of consumer attitudes toward counterfeits, purchase motivation, and behavior, as well as of their ability to distinguish between genuine and counterfeit goods, is imperative. Building on cue utilization theory, this article develops a framework for understanding how consumers develop and utilize heuristics for product authentication in the context of intellectual property infringements and outlines a research agenda. Additionally, it helps to inform the development of prevention and enforcement efforts, in consumer education and enforcement agent training, as well as a basis for policy decisions.     

Near-infrared spectroscopy combined with chemometrics to classify cosmetic foundations from a crime scene

Cosmetic smears are a form of trace evidence that can link the crime scene, suspects, and victims. Foundation and lipstick are the most common sources of cosmetics that can easily smear, with most current research focused on the evidential analysis of lipsticks. This research aims to create a database of cosmetic foundations on different materials and to access the robustness of using Near-infrared with chemometrics as a non-destructive technique to identify unknown samples collected from a crime scene. Small amounts of six shades of three brands of foundations were smeared on clothing materials, which were then analysed with a combination of Near-infrared with chemometric analysis. Principle component analysis (PCA) was used to reduce data dimensionality and explore potential patterns in sample separation and Linear Discriminant Analysis (LDA) was utilised to assign unknown samples to one of the established classes. The selected techniques proved to be promising for database construction and as a preliminary method of analysis, with 93% of the spectra being correctly classified. Notably, darker foundation shades were less likely to be correctly classified (90% classified correctly) compared to lighter ones (96.7% classified correctly). This could not be improved with Standard Normal Variate (SNV) data pre-treatment or selecting specific NIR regions. This finding is of particular importance; according to the Crime Survey for England and Wales (year ending March 2020) police recorded sexual offences demonstrated that those in Mixed and Black or Black British ethnic groups were significantly more likely to be a victim of sexual assault compared to White, Asian or Other ethnic groups. It is, therefore, crucial to add a wide range of foundation shades, particularly of darker tones, to the future database.     

A market study of green spray paints by Fourier transform infrared (FTIR) and Raman spectroscopy.

A market study of 40 different green spray paints was carried out using infrared (FTIR) and Raman spectroscopy. The infrared technique distinguished between the 12 main groups based on their binder and extender composition. After visual comparison of the spectra 22 subgroups were observed. Raman spectroscopy was also carried out on the 40 reference paints in order to determine the pigment content. Analyses were undertaken using two different excitation sources: Argon ion (514.5 nm) and Helium-Neon (632.8 nm). The first generated strong fluorescence for most of the samples and created eight groups. Using the red laser, 15 classes were observed. Finally, using an analytical sequence starting with infrared spectroscopy followed by Raman Helium-Neon and then by Raman Argon laser, most of the paints were differentiated. In this study infrared and Raman spectroscopy complemented each other. FTIR supplied information about the binder and some extenders, and Raman provided information on the main organic pigments present.     

Raman spectroscopy of blue gel pen inks

Raman spectroscopy is becoming a tool of major importance in forensic science. It is a non-invasive, non-destructive analytical method allowing samples to be examined without any preparation. This paper demonstrates the use of the technique as a general tool for gel pen inks analysis. For this purpose, 55 blue gel pen inks, of different brands and models representative of gel pen available on the world market at the time of this study, were collected. A preliminary solubility test in methanol allowed separating them into two classes: 19 dye-based and 36 pigment-based gel inks. The latter were analysed by Raman spectroscopy (RS) using two wavelength laser sources (514.5 and 830 nm). Two main pigments were identified, pigment blue 15 and pigment violet 23.     

The Discrimination of Colored Acrylic,Cotton, and Wool Textile Fibers Using Micro‐Raman Spectroscopy. Part 1: In situ Detection and Characterization of Dyes

Raman spectroscopy has been applied to characterize fiber dyes and determine the discriminating ability of the method. Black, blue, and red acrylic, cotton, and wool samples were analyzed. Four excitation sources were used to obtain complementary responses in the case of fluorescent samples. Fibers that did not provide informative spectra using a given laser were usually detected using another wavelength. For any colored acrylic, the 633‐nm laser did not provide Raman information. The 514‐nm laser provided the highest discrimination for blue and black cotton, but half of the blue cottons produced noninformative spectra. The 830‐nm laser exhibited the highest discrimination for red cotton. Both visible lasers provided the highest discrimination for black and blue wool, and NIR lasers produced remarkable separation for red and black wool. This study shows that the discriminating ability of Raman spectroscopy depends on the fiber type, color, and the laser wavelength.     

Automated detection and classification of counterfeit banknotes using quantitative features captured by spectral-domain optical coherence tomography

Counterfeiting of banknotes is still a severe crime problem in many countries. One of the most significant issue for solving the crime is to classify the counterfeit types and identify the sources. Most of the current methods to classify counterfeit banknotes rely on manual examination that is time-consuming and labor-intensive. Moreover, these methods only detect surface features which can be easily imitated through advanced printing technology. In this study, an automated method based on optical coherence tomography (OCT) and machine-learning algorithms was proposed to classify different types of banknotes based on the internal features. A spectral-domain OCT (SD-OCT) system was employed for sub-surface imaging and quantitative assessment of banknotes. A total of 29 Chinese 100-Yuan banknotes were collected, in which 4 of them were real and 25 of them were counterfeiting by three different printing processes. Each banknote was imaged 10 times in 3 distinct regions, which resulted in a dataset of 290 samples. Each sample was characterized by extracting 2 A-scan (OCT signal intensity along depth) based features and 14B-scan (cross-sectional OCT images) based features. Several machine-learning models, including logistic regression (LR), support vector machines (SVM), K-nearest neighbor (KNN) and random forest (RF), were built and optimized as the classifiers that were trained using 203 samples and applied to predict 87 testing samples. The best performance was achieved by SVM classifier in which the sensitivity of 96.55% and specificity of 98.85% were obtained in discriminating between authentic and counterfeit banknotes, and the sensitivity of 94.67% and specificity of 98.22% were obtained in predicting the types of counterfeit banknotes. These classifiers were also evaluated using the receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first study where A-scan and B-scan derived features from OCT images have been used for the detection and classification of different types of counterfeit banknotes.     

Identification of Different Forms of Cocaine and Substances Used in Adulteration Using Near‐infrared Raman Spectroscopy and Infrared Absorption Spectroscopy

Identification of cocaine and subsequent quantification immediately after seizure are problems for the police in developing countries such as Brazil. This work proposes a comparison between the Raman and FT‐IR techniques as methods to identify cocaine, the adulterants used to increase volume, and possible degradation products in samples seized by the police. Near‐infrared Raman spectra (785 nm excitation, 10 sec exposure time) and FT‐IR‐ATR spectra were obtained from different samples of street cocaine and some substances commonly used as adulterants. Freebase powder, hydrochloride powder, and crack rock can be distinguished by both Raman and FT‐IR spectroscopies, revealing differences in their chemical structure. Most of the samples showed characteristic peaks of degradation products such as benzoylecgonine and benzoic acid, and some presented evidence of adulteration with aluminum sulfate and sodium carbonate. Raman spectroscopy is better than FT‐IR for identifying benzoic acid and inorganic adulterants in cocaine.     

An empirical examination of product counterfeiting crime impacting the U.S. military

Counterfeit parts in the U.S. Department of Defense (DOD) supply chain threaten national security by compromising critical military operations and placing the lives of military service members at risk. With the goal of illustrating the nature of the risk as it relates to types of counterfeit parts, how they entered the supply chain, and were identified and processed through the criminal justice system, we assemble and analyze open-source information on criminal schemes involving counterfeits in the DOD supply chain. We utilize the Product Counterfeiting Database (PCD) to capture every indicted scheme linked to the DOD and describe characteristics of the schemes, offenders, and victims. These data provide empirical insights into the counterfeiting of parts and equipment in the DOD supply chain. We conclude with an overview of key issues to consider when weighing opportunities for product counterfeiting as well as implications for policy and practice.     

Evaluation of Raman spectroscopy for the analysis of colored fibers: a collaborative study

A collaborative study on Raman spectroscopy was carried out by members of the ENFSI (European Network of Forensic Science Institutes) European Fibres Group (EFG) on three dyed fibers: two red acrylics and one red wool. Raman instruments from six different manufacturers were tested as well as nine different laser wavelengths ranging from blue (lambda = 458 nm) to near infrared-NIR (lambda = 1064 nm). This represents the largest comparison study of Raman analytical parameters carried out on identical fiber samples. For the chosen fiber and dye samples, red lasers (lambda = 633 and 685 nm) gave the poorest spectral quality whereas blue (458 nm), green (514 nm) and near infrared lasers (785, 830 and 1064 nm) provided average results. Blue (488 nm) and green lasers (532 nm) globally gave the best quality spectra. Fluorescence problems were often encountered with some of the excitation wavelengths and therefore a flexible Raman instrument equipped with different lasers can be recommended to measure forensic fiber samples. The instrument should also be equipped with a Raman microscope in order to be able to focus on a single fiber. This study shows that Raman spectroscopy usually enables the identification of the main dye present in a colored fiber; however, minor dye components are much more difficult to detect. SERRS (Surface Enhanced Resonance Raman Scattering) techniques give an improvement of the dye's spectral intensity but no spectral improvement was observed for the two red acrylic and red wool fibers tested.     

Analyzing Brazilian vehicle documents for authenticity by easy ambient sonic-spray ionization mass spectrometry

Using desorption/ionization techniques such as easy ambient sonic-spray ionization mass spectrometry (EASI-MS), it is possible to analyze documents of Brazilian vehicles for authenticity, providing a chemical profile directly from the surface of each document. A method for the detection of counterfeit documents is described, and the falsification procedure is elucidated. Forty authentic and counterfeit documents were analyzed by both positive and negative ion modes, EASI(±)-MS. EASI(+)-MS results identified the presence of (bis(2-ethylhexyl)phthalate plasticizer and of dihexadecyldimethylammonium biocide in both types of documents. For EASI(-)-MS results, the 4-octyloxybenzoic acid additive ([M + H](+): m/z 249) is present only in counterfeit documents. It was also found that counterfeit vehicle documents are produced via Laserjet printers. Desorption/ionization techniques, such as EASI-MS, offer therefore, an intelligent way to characterize the counterfeiting method.     

Evaluation and classification of fentanyl-related compounds using EC-SERS and machine learning

Multiple analytical techniques for the screening of fentanyl-related compounds exist. High discriminatory methods such as GC–MS and LC–MS are expensive, time-consuming, and less amenable to onsite analysis. Raman spectroscopy provides a rapid, inexpensive alternative. Raman variants such as electrochemical surface-enhanced Raman scattering (EC-SERS) can provide signal enhancements with 10¹⁰ magnitudes, allowing for the detection of low-concentration analytes, otherwise undetected using conventional Raman. Library search algorithms embedded in instruments utilizing SERS may suffer from accuracy when multicomponent mixtures involving fentanyl derivatives are analyzed. The complexing of machine learning techniques to Raman spectra demonstrates an improvement in the discrimination of drugs even when present in multicomponent mixtures of various ratios. Additionally, these algorithms are capable of identifying spectral features difficult to detect by manual comparisons. Therefore, the goal of this study was to evaluate fentanyl-related compounds and other drugs of abuse using EC-SERS and to process the acquired data using machine learning—convolutional neural networks (CNN). The CNN was created using Keras v 2.4.0 with Tensorflow v 2.9.1 backend. In-house binary mixtures and authentic adjudicated case samples were used to evaluate the created machine-learning models. The overall accuracy of the model was 98.4 ± 0.1% after 10-fold cross-validation. The correct identification for the in-house binary mixtures was 92%, while the authentic case samples were 85%. The high accuracies achieved in this study demonstrate the advantage of using machine learning to process spectral data when screening seized drug materials comprised of multiple components.     

Counterfeiting and an Optimal Monitoring Policy

This paper studies the impact of adopting a monitoring system on counterfeiting, in which a criterion to minimize welfare losses due to counterfeiting is provided. If the degree to which the genuine product is imitated (hence the imitation rate) is not high, then counterfeit products might be allowed to exist in such a market. For the case of a high imitation rate, the use of a counterfeit monitoring regime with an optimal counterfeit-monitoring rate is the best policy. If the duration of intellectual property protection is set as infinite, then a higher monitoring rate corresponds to a narrower protection scope. Finally, the enforcement of the laws of intellectual property rights should be flexible according to a products inherent attributes as pertaining to the difficulties at imitating the original products.JEL Classification: D42, K42, L43     

Identifying Counterfeit Cigarettes Using Environmental Pollen Analysis: An Improved Procedure*

Traditional pollen preparation techniques provide clear residues for pollen identification; however, such methods are time-consuming, requiring repeated centrifugation, heating, and digestion with high-concentration hazardous chemicals. Tobacco leaves can effectively trap environmental pollen due to hairy surface and terpene-rich exudates. A new tobacco sample processing method was developed by using different extraction chemistry with surfactant. Marlboro Gold cigarettes were employed as model samples for method development. Parameters critical for pollen extraction, which include number of cigarette sticks used, extraction solution, and extraction temperature, were optimized. By using 1% dishwashing detergent to treat three cigarettes at room temperature, the improved method was able to recover sufficient pollen for microscopic analysis in three repeated centrifuge-washing steps and omit hazardous chemicals involved in traditional methods. We focused on the pollen of common ragweed (Ambrosia artemisiifolia), a plant native to North America, as an indicator to differentiate genuine and counterfeit U.S. brand cigarettes. Results from analyzing randomly purchased genuine (authenticated by forensic examination) and known counterfeit Marlboro Gold provided by law enforcement revealed that a significant amount (39%) of Ambrosia were consistently present in all genuine samples, while counterfeit contained none or only trace count. Similar results were found in other counterfeit U.S. brand cigarettes (all seized in the U.S.) involved in this study as well. Lack of Ambrosia in cigarette strongly indicates the product was not originated in the United States.     

Near Infrared Spectroscopy Detection and Quantification of Herbal Medicines Adulterated with Sibutramine

There is an increasing demand for herbal medicines in weight loss treatment. Some synthetic chemicals, such as sibutramine (SB), have been detected as adulterants in herbal formulations. In this study, two strategies using near infrared (NIR) spectroscopy have been developed to evaluate potential adulteration of herbal medicines with SB: a qualitative screening approach and a quantitative methodology based on multivariate calibration. Samples were composed by products commercialized as herbal medicines, as well as by laboratory adulterated samples. Spectra were obtained in the range of 14,000–4000 per cm. Using PLS‐DA, a correct classification of 100% was achieved for the external validation set. In the quantitative approach, the root mean squares error of prediction (RMSEP), for both PLS and MLR models, was 0.2%w/w. The results prove the potential of NIR spectroscopy and multivariate calibration in quantifying sibutramine in adulterated herbal medicines samples.     

Chemical profile of counterfeit buprenorphine vials seized in Tehran, Iran

Buprenorphine, commonly known by the trademark Temgesic, is one of the most popular drugs of abuse among the opioid-addicted young individuals in Iran. Temgesic, Bungesic, etc. are the most popular and important illicit opioid drugs in Tehran's illicit drugs black market, and are now among the most widely abused by opioid addicts. Because of this, counterfeiting of this drug has increased in Tehran. In this study, the qualitative analysis of counterfeit buprenorphine by gas chromatography-mass spectrometry (GC-MS) and high performance liquid chromatography (HPLC) demonstrates the presence of diacetylmorphine, acetylcodeine and pheniramine, as well as the absence of buprenorphine. In conclusion, due to the absence of quality control and difficulties in differentiating counterfeit buprenorphine from genuine products, the use of counterfeit buprenorphine leads the opioid abusers to health risks.     

How a luxury monopolist might benefit from the aspirational utility effect of counterfeiting?

Thanks to an intertemporal analytical model, we incorporate aspirational consumers in Veblen markets for luxury fashion items. We show how a luxury monopolist can increase its profits thanks to the presence of counterfeit products. The genuine producer profit is shaped by two opposite effects: (1) a positive aspirational effect resulting from a sales increase due to the aspirational consumers who seek to imitate the lifestyle of snob consumers (2) a negative snob effect, resulting from a sales decrease due to the reduction of consumption by some snob consumers. We identify the conditions under which the overall effect generated by counterfeiting can increase the genuine firm profit. These conditions imply the existence of large aspirational effects and high additional utility gain associated with buying an original product instead of obtaining a counterfeit product.     

Gold-plating of Mylar lift films to capitalize on surface enhanced Raman spectroscopy for chemical extraction of drug residues

The method of residue extraction through electrostatic lifting provides a distinctive mode of performing ultra-trace analysis. These lifts provide a medium for analyte extraction via nanomanipulation-coupled to nanospray ionization-mass spectrometry (NSI-MS). This method of extraction can be coupled to Raman spectroscopy for supplemental verification of analytes using surface enhanced Raman scattering (SERS). The gold surface used for SERS provides an enhanced effect on peak signal intensity allowing ultra-trace amounts to be detected more effectively. The aim of this research is to utilize gold-coated films with electrostatic lifting in order to collect latent materials and analyze chemicals of interest contained in them via SERS.     

Latent evidence detection using a combination of near infrared and high dynamic range photography: an example using bloodstains

In this paper, we use bloodstains to illustrate an approach for identifying latent evidence on dark cloth using near infrared (NIR) photography combined with high dynamic range (HDR) photography techniques. NIR photography alone has been used to capture latent evidence that cannot be seen in normal ambient light. HDR techniques combine multiple bracketed photographs of the same image to increase the dynamic range of the photograph which can provide greater contrast. Using NIR photography alone, we were able to detect a bloodstain up to a 1/16 dilution, an improvement over previous studies. Combining NIR photography with the HDR process resulted in a noticeable increase in visibility up to 1/16 dilution when compared to NIR photographs alone. At 1/32 dilution, we were able to detect bloodstains that were not visible using NIR alone. NIR is a useful tool for imaging latent evidence, and combining NIR with HDR consistently provides better results over NIR alone.     

Selecting relevant Fourier transform infrared spectroscopy wavenumbers for clustering authentic and counterfeit drug samples

This paper proposes a novel method for selecting subsets of wavenumbers provided by attenuated total reflectance by Fourier transform infrared (ATR-FTIR) spectroscopy able to improve the clustering of medicine samples into two groups; i.e., authentic or fraudulent. For that matter, we apply principal components analysis (PCA) to ATR-FTIR data, and derive two variable importance indices from the PCA parameters. Next, an iterative variable (i.e. wavenumbers) elimination procedure and sample clustering through k-means and Fuzzy C-means techniques are carried out; clustering performance is assessed by the Silhouette Index (SI). The performance of the proposed method is compared with a greedy variable selection method, the “leave one variable out at a time” approach, in terms of clustering quality, percent of retained variables, and computational time. When applied to Viagra ATR-FTIR data, our propositions increased the average SI from 0.5307 to 0.8603 using 0.61% of the original 661 wavenumbers; as for Cialis ATR-FTIR data, clustering quality increased from 0.7548 to 0.8681 when 1.21% of the original wavenumbers were retained in the procedure. The retained wavenumbers, located in the 1091–1046 cm^− 1 region, comprise the lactose typically hailed as key substance to discriminate between authentic and counterfeit samples.