Authenticity assessment of ground black pepper by combining headspace gas-chromatography ion mobility spectrometry and machine learning.

Currently, the authentication of ground black pepper is a major concern, creating a need for a rapid, highly sensitive and specific detection tool to prevent the introduction of adulterated batches into the food chain. To this aim, head space gas-chromatography ion mobility spectrometry (HS-GC-IMS), combined with machine learning, is tested in this initial, proof-of-concept study. A broad variety of authentic samples originating from eight countries and three continents were collected and spiked with a range of adulterants, both endogenous sub-products and an assortment of exogenous materials. The method is characterized by no sample preparation and requires 20 min for chromatographic separation and ion mobility data acquisition. After an explorative analysis of the data, those were submitted to two different machine learning algorithms (partial least squared discriminant analysis-PLS-DA and support vector machine-SVM). While the PLS-DA model did not provide fully satisfactory performances, the combination of HS-GC-IMS and SVM successfully classified the samples as authentic, exogenously-adulterated or endogenously-adulterated with an overall accuracy of 90 % and 96 % on withheld test set 1 and withheld test set 2, respectively (at a 95 % confidence level). Some limitations, expected to be mitigated by further research, were encountered in the correct classification of endogenously adulterated ground black pepper. Correct categorization of the ground black pepper samples was not adversely affected by the operator or the time span of data collection (the method development and model challenge were carried out by two operators over 6 months of the study, using ground black pepper harvested between 2015 and 2019). Therefore, HS-GC-IMS, coupled to an intelligent tool, is proposed to: (i) aid in industrial decision-making before utilization of a new batch of ground black pepper in the production chain; (ii) reduce the use of time-consuming conventional analyses and; (iii) increase the number of ground black pepper samples analyzed within an industrial quality control frame.

Thermal desorption direct analysis in real-time high-resolution mass spectrometry and machine learning allow the rapid authentication of ground black pepper and dried oregano: A proof-of-concept study.

Thermal desorption direct analysis in real-time high-resolution mass spectrometry (TD-DART-HRMS) approaches have gained popularity for fast screening of a variety of samples. With rapid volatilization of the sample at increasing temperatures outside the mass spectrometer, this technique can provide a direct readout of the sample content with no sample preparation. In this study, TD-DART-HRMS's utility for establishing spice authenticity was examined. To this aim, we directly analyzed authentic (typical) and adulterated (atypical) samples of ground black pepper and dried oregano in positive and negative ion modes. We analyzed a set of authentic ground black pepper samples (n = 14) originating from Brazil, Sri Lanka, Madagascar, Ecuador, Vietnam, Costa Rica, Indonesia, Cambodia, and adulterated samples (n = 25) consisting of mixtures of ground black pepper with this spice's nonfunctional by-products (pinheads or spent) or with different exogenous materials (olive kernel, green lentils, black mustard seeds, red beans, gypsum plaster, garlic, papaya seeds, chili, green aniseed, or coriander seeds). TD-DART-HRMS facilitated the capture of informative fingerprinting of authentic dried oregano (n = 12) originating from Albania, Turkey, and Italy and those spiked (n = 12) with increasing percentages of olive leaves, sumac, strawberry tree leaves, myrtle, and rock rose. A predictive LASSO classifier was built, after merging by low-level data fusion, the positive and negative datasets for ground black pepper. Fusing multimodal data allowed retrieval of more comprehensive information from both datasets. The resultant classifier achieved on the withheld test set accuracy, sensitivity, and specificity of 100%, 75%, and 90%, respectively. On the contrary, the sole TD-(+)DART-HRMS spectra of the oregano samples allowed construction of a LASSO classifier that predicted the adulteration of the oregano with excellent statistical indicators. This classifier achieved, on the withheld test set, 100% each for accuracy, sensitivity, and specificity.

A Rapid Non-Targeted Method for Detecting the Adulteration of Black Pepper with a Broad Range of Endogenous and Exogenous Material at Economically Motivating Levels Using Micro-ATR-FT-MIR Imaging.

Infrared spectroscopy is often used as a simple, fast, and green method to screen for economically motivated adulteration in spices. However, conventional microscopy remains the reference method. In this research, the combination of microscopy and Fourier-transform infrared spectroscopy in mapping mode, namely, micro-FTIR imaging, along with Principle Component Analysis were used to develop a non-targeted method for detecting a broad range of organic and mineral bulking agents that could potentially be used to adulterate black pepper. This method, based on the spatial distribution of black pepper chemical composition, has been thoroughly validated as a one-class, non-targeted classification method. Results are categorized as Typical or Atypical, where an Atypical result indicates a high probability of adulteration. For an Atypical outcome, a multitool investigational approach is then used for the detection and identification of the potential adulterant.

Quantitation of chlorophylls and 22 of their colored degradation products in culinary aromatic herbs by HPLC-DAD-MS and correlation with color changes during the dehydration process.

Chlorophylls and their green and olive-brown derivatives were successfully separated from culinary herb extracts by HPLC with photodiode-array and mass spectrometry detection. The method involved a ternary gradient elution and reverse-phase separation conditions capable of resolving 24 different pigments (2 chlorophylls and 22 of their derivatives) of different polarities within 28 min. The method was applied to monitor color changes in 50 samples of culinary aromatic herbs subjected to five different drying treatments. Of the 24 pigments, 14 were key to understanding the differences between the primary degradation pathways of chlorophyll a and chlorophyll b in culinary herbs during drying processes. A color degradation ladder based on the total molar percentage of all the remaining green pigments was also proposed as a tool to measure the impact of drying treatments on aromatic herb visual aspects.

Determination of added sulfites in dried garlic with a modified version of the optimized Monier-Williams method.

The optimized Monier-Williams method is slightly modified so that it could be applied for determining sulfite content in dried garlic. Dried garlic sample is directly acidified in a reactor at a pH below 3. At this pH level, the alliinase enzyme activity is irreversibly blocked, and the sulfur-containing amino acids such as alliin (the most abundant) present in dried garlic cannot be transformed into corresponding thiosulfinates such as allicin, which is absent in dried garlic. This prevents allicin from reacting with added sulfites and being probably converted to S-allyl thiosulfate, which is not volatile and has no taste. It is found that at a pH below 2.4 and at boiling water temperature, allicin produces sulfur dioxide in adequate quantity to explain the false-positive results when utilizing the optimized Monier-Williams method with allicin suppression for unsulfited dried garlic samples. Finally, when garlic samples are stabilized in a phosphoric acid buffer at a final pH around 2.4, no sulfite is produced during the Monier-Williams distillation, which is further proof there are no naturally occurring sulfites in unsulfited dried garlic under these mild conditions.