Dataset of near-infrared spectral data of illicit-drugs and forensic casework samples analyzed by five portable spectrometers operating in different wavelength ranges.

The increasing amount of globally seized controlled substances in combination with the more diverse drugs-of-abuse market encompassing many new psychoactive substances (NPS) provides challenges for rapid and reliable on-site presumptive drug testing. Long-established colorimetric spot tests tend to fail due to the unavailability of reliable tests for novel drugs and to false-positive reactions on commonly encountered substances. In addition, handling of samples and chemicals is required. Spectroscopic techniques do not have these disadvantages as spectra are compound-specific and non-invasive tests are possible. Near-infrared (NIR) spectroscopy is a promising technique for on-scene forensic drug detection. Numerous portable devices were introduced in the market in recent years. However, most handheld spectrometers operate in different and relatively confined wavelength ranges compared to the full 780 - 2500 nm NIR wavelength range. In addition, their spectral resolution is limited compared to benchtop instruments. This dataset presents the NIR spectra of 430 forensic samples, including regularly encountered illicit-drugs, NPS, commonly used adulterants, bulking-agents and excipients, and seized casework materials (powders and tablets). Data is available from 5 different NIR spectrometers; including a benchmark high-resolution, full range 350-2500 nm laboratory grade instrument and 4 portable spectrometers operating in the ranges of 1300-2600 nm, 1550-1950 nm, 950-1650 nm and 740-1070 nm. Via this dataset, spectra of illicit-drugs become available to institutes that typically do not have access to controlled substances. This data can be used to develop chemometric detection and classification models for illicit-drugs and provide insight in diagnostic spectral features that need to be recorded for reliable detection models. Additionally, the high-resolution, full range VIS-NIR spectra of the benchmark ASD instrument can be used for in-silica predictions of spectra in a certain wavelength range to provide insight in the optimal resolution and wavelength range of a prospective portable device.

Predicting the performance of handheld near-infrared photonic sensors from a master benchtop device.

Many industries see a shifting focus towards performing on-site analysis using handheld spectroscopic devices. A determining factor for decision-making on the commissioning of these devices is available information on the potential performance of the device for specific applications. By now, myriad handheld solutions with very different specifications and pricing are available on the market. Although specifications are generally available for new devices, this does not directly quantify or predict how available devices will perform for targeted cases. We present a novel chemometric method to estimate the prediction performance of handheld NIR hardware and apply it to estimate the performance of two commercially available handheld NIR technologies in predicting protein content (ranging 120-180 g kg-1) in pig feed from existing data of a benchtop device. Adjusting benchtop data to the wavelength range and resolution of the handheld device lead to over-optimistic estimates of the handheld performances. Our method additionally utilizes information on the error structure of the handheld devices for the estimation. It yielded performance estimates differing less than 1 g kg-1 from the experimentally determined handheld performances and similar model parameters. Our method was effective for linear and nonlinear calibration algorithms, also when estimating performance after averaging multiple scans. Replicate spectra of twenty samples recorded using the handheld were required for replication error estimation to obtain an accurate performance estimation. The error structure could be reported by manufacturers in the future for this approach to be universally employed for predictive quantitative technology assessment. Overall, our method provides estimates of the performance of a handheld device for a specific task with minimal testing required and can thus be used as a device or application screening tool before committing to develop calibrations.

Comparison of Portable and Benchtop Near-Infrared Spectrometers for the Detection of Citric Acid-adulterated Lime Juice: A Chemometrics Approach.

Background: Since the incidence of food adulteration is rising, finding a rapid, accurate, precise, low-cost, user-friendly, high-throughput, ruggedized, and ideally portable method is valuable to combat food fraud. Near-infrared spectroscopy (NIRS), in combination with a chemometrics-based approach, allows potentially rapid, frequent, and in situ measurements in supply chains. Methods: This study focused on the feasibility of a benchtop Fourier-transformation-NIRS apparatus (FT-NIRS, 1000 - 2500 nm) and a portable short wave NIRS device (SW-NIRS, 740 - 1070 nm) for the discrimination of genuine and citric acid-adulterated lime juice samples in a cost-effective manner following chemometrics study. Results: Principal component analysis (PCA) of the spectral data resulted in a noticeable distinction between genuine and adulterated samples. Wavelengths between 1100 - 1400 nm and 1550 - 1900 nm were found to be more important for the discrimination of samples for the benchtop FT-NIRS data, while variables between 950 - 1050 nm contributed significantly to the discrimination of samples based on the portable SW-NIRS data. Following partial least squares discriminant analysis (PLS-DA) as a discriminant model, standard normal variate (SNV) or multiplicative scatter correction (MSC) transformation of benchtop FT-NIRS data and SNV in combination with the second derivative transformation of portable SW-NIRS data on the training set delivered equal accuracy (94%) in the prediction of the test set. In the soft independent modeling of class analogy (SIMCA) as a class-modeling approach, the overall performances of generated models on the auto-scaled data were 98% and 94.5% for benchtop FT-NIRS and portable SW-NIRS, respectively. Conclusions: As a proof of concept, NIRS technology coupled with appropriate multivariate classification models enables fast detection of citric acid-adulterated lime juices. In addition, the promising results of portable SW-NIRS combined with SIMCA indicated its use as a screening tool for on-site analysis of lime juices at various stages of the food supply chain.

Detecting Food Fraud in Extra Virgin Olive Oil Using a Prototype Portable Hyphenated Photonics Sensor.

BACKGROUND: Current developments in portable photonic devices for fast authentication of extra virgin olive oil (EVOO) or EVOO with non-EVOO additions steer towards hyphenation of different optic technologies. The multiple spectra or so-called "fingerprints" of samples are then analyzed with multivariate statistics. For EVOO authentication, one-class classification (OCC) to identify "out-of-class" EVOO samples in combination with data-fusion is applicable. OBJECTIVE: Prospecting the application of a prototype photonic device ("PhasmaFood") which hyphenates visible, fluorescence, and near-infrared spectroscopy in combination with OCC modelling to classify EVOOs and discriminate them from other edible oils and adulterated EVOOs. METHOD: EVOOs were adulterated by mixing in 10-50% (v/v) of refined and virgin olive oils, olive-pomace olive oils, and other common edible oils. Samples were analyzed by the hyphenated sensor. OCC, data-fusion, and decision thresholds were applied and optimized for two different scenarios. RESULTS: By high-level data-fusion of the classification results from the three spectral databases and several multivariate model vectors, a 100% correct classification of all pure edible oils using OCC in the first scenario was found. Reducing samples being falsely classified as EVOOs in a second scenario, 97% of EVOOs adulterated with non-EVOO olive oils were correctly identified and ones with other edible oils correctly classified at score of 91%. CONCLUSIONS: Photonic sensor hyphenation in combination with high-level data fusion, OCC, and tuned decision thresholds delivers significantly better screening results for EVOO compared to individual sensor results. HIGHLIGHTS: Hyphenated photonics and its data handling solutions applied to extra virgin olive oil authenticity testing was found to be promising.

Performance evaluation of handheld Raman spectroscopy for cocaine detection in forensic case samples.

Handheld Raman spectroscopy is an emerging technique for rapid on-site detection of drugs of abuse. Most devices are developed for on-scene operation with a user interface that only shows whether cocaine has been detected. Extensive validation studies are unavailable, and so are typically the insight in raw spectral data and the identification criteria. This work evaluates the performance of a commercial handheld Raman spectrometer for cocaine detection based on (i) its performance on 0-100 wt% binary cocaine mixtures, (ii) retrospective comparison of 3,168 case samples from 2015 to 2020 analyzed by both gas chromatography-mass spectrometry (GC-MS) and Raman, (iii) assessment of spectral selectivity, and (iv) comparison of the instrument's on-screen results with combined partial least square regression (PLS-R) and discriminant analysis (PLS-DA) models. The limit of detection was dependent on sample composition and varied between 10 wt% and 40 wt% cocaine. Because the average cocaine content in street samples is well above this limit, a 97.5% true positive rate was observed in case samples. No cocaine false positives were reported, although 12.5% of the negative samples were initially reported as inconclusive by the built-in software. The spectral assessment showed high selectivity for Raman peaks at 1,712 (cocaine base) and 1,716 cm-1 (cocaine HCl). Combined PLS-R and PLS-DA models using these features confirmed and further improved instrument performance. This study scientifically assessed the performance of a commercial Raman spectrometer, providing useful insight on its applicability for both presumptive detection and legally valid evidence of cocaine presence for law enforcement.

Novel Application of Near-infrared Spectroscopy and Chemometrics Approach for Detection of Lime Juice Adulteration.

The aim of this study is to investigate the novel application of a handheld near infra-red spectrophotometer coupled with classification methodologies as a screening approach in detection of adulterated lime juices. For this purpose, a miniaturized near infra-red spectrophotometer (Tellspec®) in the spectral range of 900-1700 nm was used. Three diffuse reflectance spectra of 31 pure lime juices were collected from Jahrom, Iran and 25 adulterated juices were acquired. Principal component analysis was almost able to generate two clusters. Partial least square discriminant analysis and k-nearest neighbors algorithms with different spectral preprocessing techniques were applied as predictive models. In the partial least squares discriminant analysis, the most accurate prediction was obtained with SNV transforming. The generated model was able to classify juices with an accuracy of 88% and the Matthew's correlation coefficient value of 0.75 in the external validation set. In the k-NN model, the highest accuracy and Matthew's correlation coefficient in the test set (88% and 0.76, respectively) was obtained with multiplicative signal correction followed by 2nd-order derivative and 5th nearest neighbor. The results of this preliminary study provided promising evidence of the potential of the handheld near infra-red spectrometer and machine learning methods for rapid detection of lime juice adulteration. Since a limited number of the samples were used in the current study, more lime juice samples from a wider range of variability need to be analyzed in order to increase the robustness of the generated models and to confirm the promising results achieved in this study.

Rapid and robust on-scene detection of cocaine in street samples using a handheld near-infrared spectrometer and machine learning algorithms.

On-scene drug detection is an increasingly significant challenge due to the fast-changing drug market as well as the risk of exposure to potent drug substances. Conventional colorimetric cocaine tests involve handling of the unknown material and are prone to false-positive reactions on common pharmaceuticals used as cutting agents. This study demonstrates the novel application of 740-1070 nm small-wavelength-range near-infrared (NIR) spectroscopy to confidently detect cocaine in case samples. Multistage machine learning algorithms are used to exploit the limited spectral features and predict not only the presence of cocaine but also the concentration and sample composition. A model based on more than 10,000 spectra from case samples yielded 97% true-positive and 98% true-negative results. The practical applicability is shown in more than 100 case samples not included in the model design. One of the most exciting aspects of this on-scene approach is that the model can almost instantly adapt to changes in the illicit-drug market by updating metadata with results from subsequent confirmatory laboratory analyses. These results demonstrate that advanced machine learning strategies applied on limited-range NIR spectra from economic handheld sensors can be a valuable procedure for rapid on-site detection of illicit substances by investigating officers. In addition to forensics, this interesting approach could be beneficial for screening and classification applications in the pharmaceutical, food-safety, and environmental domains.

From Extra Virgin Olive Oil to Refined Products: Intensity and Balance Shifts of the Volatile Compounds versus Odor.

To explore relationships between the volatile organic compounds (VOCs) of different grades of olive oils (OOs) (extra virgin olive oil (EVOO), refined olive oil (ROO), and pomace olive oil (POO)) and odor quality, VOCs were measured in the headspace of the oils by proton transfer reaction quadrupole ion guide time-of-flight mass spectrometry. The concentrations of most VOCs differed significantly between the grades (EVOO>ROO>POO), whereas the abundance of m/z 47.012 (formic acid), m/z 49.016 (fragments), m/z 49.027 (fragments), and m/z 115.111 (heptanal/heptanone) increased in that order. Although the refined oils had considerably lower VOC abundance, the extent of the decline varied with the VOCs. This results in differences in VOCs proportions. The high VOC abundance in the EVOO headspace in comparison to ROO and POO results in a richer and more complex odor. The identified C5-C6 compounds are expected to contribute mainly to the green odor notes, while the identified C1-C4 and C7-C15 are mainly responsible for odor defects of OOs. Current results reveal that processing strongly affects both the quantitative and relative abundance of the VOCs and, therefore, the odor quality of the various grades of OOs.

Towards harmonization of test methods for in vitro hepatic clearance studies.

Non-animal methods for toxicokinetics, such as in vitro hepatic metabolic clearance studies, play an important role in chemical risk evaluations. To gain regulatory acceptance of such clearance data, the development of a test guideline for performing in vitro hepatic clearance studies is crucial. The aim of the present study was to obtain insight in the experimental conditions of clearance studies that influence obtained intrinsic clearance (CLint) values. To that end, in vitro hepatic CLint data obtained with rat or human hepatocytes and methodological aspects of the experiments, were collected from 42 different suitable studies published between 1995 and 2018. The CLint values for the majority of chemicals differed by more than one order of magnitude. We estimated the systematic effect of different experimental setups on the CLint values using a random forest regression analysis, revealing that 'hepatocyte concentration', 'species' (rat or human hepatocytes) and 'culture medium' have the largest impact. Calculating unbound CLint (CLint,u) values slightly reduced the variation for most chemicals. Given that in vivo clearance is in general underpredicted based on in vitro clearance data, a harmonized protocol is preferably based on a protocol that provides relatively high in vitro CLint values.

Importance of harmonised sample preparation for moisture and protein content determinations in official food control laboratories: A poultry meat case study.

Commission Regulation (EC) 543/2008 limits moisture and protein contents in poultry meat. However, this regulation leaves room for interpretation regarding sample homogenisation, potentially affecting comparability of laboratory results. Therefore, a proficiency test and sample homogenisation study were organised amongst 19 European National Reference Laboratories (NRL). In the proficiency test, three different pre-homogenised chicken samples (fillets, drumsticks and carcasses) were analysed. Only one NRL produced unsatisfactory results. In the homogenisation study, NRLs were supplied with uniform fillet, drumstick and carcass materials. Homogenisation was performed according to the NRLs in-house methods. Five NRLs did not return satisfactory results. As these NRLs produced satisfactory results in the proficiency test, their increase in z-scores was related to their homogenisation practices. Overall, scattering of individual results was higher for drumsticks compared to fillets and carcasses. Homogenisation practices for poultry meat introduced significant differences in moisture and protein results and standardisation is therefore advisable.

Which cocoa bean traits persist when eating chocolate? Real-time nosespace analysis by PTR-QiToF-MS.

More consumers have become aware of the existence of different cocoa genotypes and their origins, which resulted in a growing market of premium chocolates with single-origin beans. The question is whether traits of cocoa botanical and geographical origins still persist in the end product, especially when it is consumed. By analysing the concentrations of volatile organic compounds (VOCs) in the nose of subjects over time while they are eating, new insights about aroma release can be gained. In the current study, in vivo release of VOCs during consumption of dark chocolates with different botanical and geographical origins was examined. Proton Transfer Reaction-Quadrupole interface Time of Flight- Mass Spectrometry (PTR-Qi ToF- MS) was applied to analyse nosespace VOC profiles of 10 subjects while they were eating 10 different chocolates manufactured with beans of different botanical origins (Criollo-Forastero-Trinitario) and geographical origins (Africa-South America-Asia). The headspace of the chocolates were also analysed for comparison. Cocoa botanical information appeared to affect the nosespace profiles more than geographical information. The subjects varied considerable in their VOC release, and inter-individual differences were larger than cocoa beans differences. Nevertheless, the botanical origin was consistently reflected in the nosespace profile during eating. It was clearly possible to distinguish Criollo chocolates from the nosespace profiles despite inter-individual differences.

Proton-transfer reaction mass spectrometry (PTR-MS) for the authentication of regionally unique South African lamb.

The volatile fingerprints of South African lamb meat and fat were measured by proton-transfer mass spectrometry (PTR-MS) to evaluate it as an authentication tool. Meat and fat of the Longissimus lumborum (LL) of lambs from six different regions were assessed. Analysis showed that the volatile fingerprints were affected by the origin of the meat. The classification of the origin of the lamb was achieved by examining the calculated and recorded fingerprints in combination with chemometrics. Four different partial least squares discriminant analysis (PLS-DA) models were fitted to the data to classify lamb meat and fat samples into "region of origin" (six different regions) and "origin" (Karoo vs. Non-Karoo). The estimation models classified samples 100% correctly. Validation of the first two models gave 42% (fat) and 58% (meat) correct classification of region, while the second two models performed better with 92% (fat) and 83% (meat) correct classification of origin.

New approaches towards discrimination of fresh/chilled and frozen/thawed chicken breasts by HADH activity determination: Customized slope fitting and chemometrics.

Fresh/chilled chicken breasts retail at a higher price than their frozen/thawed counterparts. Verification of the fresh/thawed status of chicken meat is determined by measuring ß-hydroxyacyl-Coenzyme A-hydrogenase (HADH) activity present in meat intra-cellular liquids spectrophotometrically. However, considerable numbers of reference samples are required for the current arithmetic method, adding to laboratory costs. Therefore, two alternative mathematical approaches which do not require such reference samples were developed and evaluated: curve fitting and multivariate classification. The approaches were developed using 55 fresh/thawed fillet samples. The performance of the methods was examined by an independent validation set which consisted of 16 samples. Finally, the approach was tested in practice in a market study. With the exception of two minor false classifications, both newly proposed methods performed equally well as the classical method. All three methods were able to identify two apparent fraudulent cases in the market study. Therefore, the experiments showed that the costs of HADH measurements can be reduced by adapting alternative mathematics.

Verification of Egg Farming Systems from The Netherlands and New Zealand Using Stable Isotopes.

Stable isotopes were used to develop authentication criteria of eggs laid under cage, barn, free range, and organic farming regimens from The Netherlands and New Zealand. A training set of commercial poultry feeds and egg albumen from 49 poultry farms across The Netherlands was used to determine the isotopic variability of organic and conventional feeds and to assess trophic effects of these corresponding feeds and barn, free range, and organic farming regimens on corresponding egg albumen. A further 52 brands of New Zealand eggs were sampled from supermarket shelves in 2008 (18), 2010 (30), and 2014 (4) to characterize and monitor changes in caged, barn, free range, and organic egg farming regimens. Stable carbon (δ(13)C) and nitrogen (δ(15)N) isotopes of 49 commercial poultry feeds and their corresponding egg albumens reveals that Dutch poultry are fed exclusively on a plant-based feed and that it is possible to discriminate between conventional and organic egg farming regimens in The Netherlands. Similarly, it is possible to discriminate between New Zealand organic and conventional egg farming regimens, although in the initial screening in 2008, results showed that some organic eggs had isotope values similar to those of conventional eggs, suggesting hens were not exclusively receiving an organic diet. Dutch and New Zealand egg regimens were shown to have a low isotopic correlation between both countries, because of different poultry feed compositions. In New Zealand, both conventional and organic egg whites have higher δ(15)N values than corresponding Dutch egg whites, due to the use of fishmeal or meat and bone meal (MBM), which is banned in European countries. This study suggests that stable isotopes (specifically nitrogen) show particular promise as a screening and authentication tool for organically farmed eggs. Criteria to assess truthfulness in labeling of organic eggs were developed, and we propose that Dutch organic egg whites should have a minimum δ(15)N value of 4.8‰ to account for an organic plant derived diet. Monitoring of New Zealand egg isotopes over the past 7 years suggests that organic eggs should have a minimum δ(15)N value of 6.0‰, and eggs falling below this value should be investigated further by certification authorities.

Compositional Signatures of Conventional, Free Range, and Organic Pork Meat Using Fingerprint Techniques.

Consumers' interest in the way meat is produced is increasing in Europe. The resulting free range and organic meat products retail at a higher price, but are difficult to differentiate from their counterparts. To ascertain authenticity and prevent fraud, relevant markers need to be identified and new analytical methodology developed. The objective of this pilot study was to characterize pork belly meats of different animal welfare classes by their fatty acid (Fatty Acid Methyl Ester-FAME), non-volatile compound (electrospray ionization-tandem mass spectrometry-ESI-MS/MS), and volatile compound (proton-transfer-reaction mass spectrometry-PTR-MS) fingerprints. Well-defined pork belly meat samples (13 conventional, 15 free range, and 13 organic) originating from the Netherlands were subjected to analysis. Fingerprints appeared to be specific for the three categories, and resulted in 100%, 95.3%, and 95.3% correct identity predictions of training set samples for FAME, ESI-MS/MS, and PTR-MS respectively and slightly lower scores for the validation set. Organic meat was also well discriminated from the other two categories with 100% success rates for the training set for all three analytical approaches. Ten out of 25 FAs showed significant differences in abundance between organic meat and the other categories, free range meat differed significantly for 6 out of the 25 FAs. Overall, FAME fingerprinting presented highest discrimination power.

Typicality and geographical origin markers of protected origin cheese from The Netherlands revealed by PTR-MS.

Volatile fingerprints of 30 cumin cheese samples of artisanal farmers' cheese of Leiden with EU Protected Designation of Origin (PDO) and 29 cumin cheese samples of varying commercial Dutch brands without PDO protection were used to develop authentication models. The headspace concentrations of the volatiles, as measured with high sensitivity proton-transfer mass spectrometry, were subsequently subjected to partial least-squares discriminant analysis (PLS-DA). Farmers' cheese of Leiden showed a distinct volatile profile with 27 and 9 out of the 60 predominant ions showing respectively significantly higher and lower concentrations in the headspace of the cheese in comparison to the other cumin cheeses. The PLS-DA prediction models developed classified in cross-validation 96% of the samples of PDO protected, artisanal farmers' cheese of Leiden correctly, against 100% of commercial cumin cheese samples. The characteristic volatile compounds were tentatively identified by PTR-time-of-flight-MS. A consumer test indicated differences in appreciation, overall flavor intensity, creaminess, and firmness between the two cheese groups. The consumers' appreciation of the cumin cheese tested was not influenced by the presence of a name label or PDO trademark.

Chemical conversion of alpha-keto acids in relation to flavor formation in fermented foods.

Formation of flavor compounds from branched-chain alpha-keto acids in fermented foods such as cheese is believed to be mainly an enzymatic process, while the conversion of phenyl pyruvic acid, which is derived from phenylalanine, also proceeds chemically. In this research, the chemical conversion of alpha-keto acids to aldehydes with strong flavor characteristics was studied, with the main focus on the conversion of alpha-ketoisocaproic acid to the aldehyde 2-methylpropanal, and a manganese-catalyzed reaction mechanism is proposed for this conversion. The mechanism involves keto-enol tautomerism, enabling molecular oxygen to react with the beta-carbon atom of the alpha-keto acid, resulting in a peroxide. This peroxide can react in several ways, leading to unstable dioxylactone or noncyclic intermediates. These intermediates will break down into an aldehyde and oxalate or carbon oxides (CO and CO(2)). All the alpha-keto acids tested were converted at pH 5.5 and in the presence of manganese, although their conversion rates were rather diverse. This chemical reaction might provide new ways for controlling cheese flavor formation with the aim of acceleration of the ripening process or diversification of the flavor characteristics.