Novel method based on ion mobility spectroscopy for the quantification of adulterants in honeys

Novel Method Based on Ion Mobility Spectrometry Combined with Machine Learning for the Discrimination of Fruit Juices

Fruit juices are one of the most widely consumed beverages worldwide, and their production is subject to strict regulations. Therefore, this study presents a methodology based on the use of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) in combination with machine-learning algorithms for the characterization juices of different raw material (orange, pineapple, or apple and grape). For this purpose, the ion mobility sum spectrum (IMSS) was used. First, an optimization of the most important conditions in generating the HS was carried out using a Box-Behnken design coupled with a response surface methodology. The following factors were studied: temperature, time, and sample volume. The optimum values were 46.3 °C, 5 min, and 750 µL, respectively. Once the conditions were optimized, 76 samples of the different types of juices were analyzed and the IMSS was combined with different machine-learning algorithms for its characterization. The exploratory analysis by hierarchical cluster analysis (HCA) and principal component analysis (PCA) revealed a clear tendency to group the samples according to the type of fruit juice and, to a lesser extent, the commercial brand. The combination of IMSS with supervised classification techniques reported an excellent result with 100% accuracy on the test set for support vector machines (SVM) and random forest (RF) models regarding the specific fruit used. Nevertheless, all the models have proven to be an effective alternative for characterizing and classifying the different types of juices.

Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning

Honey is one of the most adulterated foods, usually through the addition of sweeteners or low-cost honeys. This study presents a method based on visible near infrared spectroscopy (Vis-NIRs), in combination with machine learning (ML) algorithms, for the correct identification and quantification of adulterants in honey. Honey samples from two botanical origins (orange blossom and sunflower) were evaluated and adulterated with low-cost honey in different percentages (5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, and 50%). The results of the exploratory analysis showed a tendency to group the samples according to botanical origin, as well as the presence of adulteration. A supervised analysis was performed to detect the presence of adulterations. The best performance with 100% accuracy was achieved by support vector machines (SVM) and random forests (RF). A regression study was also carried out to quantify the percentage of adulteration. The best result was obtained by support vector regression (SVR) with a coefficient of determination (R²) of 0.991 and a root mean squared error (RMSE) of 1.894. These results demonstrate the potential of combining ML with spectroscopic data as a method for the automated quality control of honey.

A non-targeted metabolomic strategy for characterization of the botanical origin of honey samples using headspace gas chromatography-ion mobility spectrometry

In this work, characterization of the botanical origin of honey was carried out using headspace gas chromatography coupled to ion mobility spectrometry (HS-GC-IMS). The proposed methodology was applied for the analysis of 89 samples from ten different botanical origins. A total of 15 volatile compounds could be identified, namely, 3-methyl-1-butanol, heptanal, valeraldehyde, octanal, trans-2-hexenal, nonanal, hexanal, benzaldehyde, 2-heptanone, 2-butanone, 2-hexanone, 6-methyl-5-hepten-2-one, 2-pentanone, ethyl acetate and linalool. The analytical method was characterized in terms of limits of detection and quantification, and precision, in order to quantify the identified compounds. Compounds were quantified using the sum of the protonated monomer and proton-bound dimer and logarithmic regression (R² > 0.98), although the establishment of a concentration threshold that would allow creation of classification rules was not possible since there was variability within the group. Consequently, the establishment of chemometric models was necessary. A non-targeted strategy using 275 features is proposed. Orthogonal partial least squares-discriminant analysis (OPLS-DA) allowed the differentiation of five botanical origins: thousand flowers, rosemary, albaida, orange blossom, and "others" (rest of the investigated botanical origins, since a limited number of samples was available). A success validation rate of 100% allowed the classification of 14 honeys with unknown botanical origin.

Assessment of Volatile Compound Transference through Firefighter Turnout Gear

There is high concern about the exposure of firefighters to toxic products or carcinogens resulting from combustion during fire interventions. Firefighter turnout gear is designed to protect against immediate fire hazards but not against chemical agents. Additionally, the decontamination of firefighter personal protective equipment remains unresolved. This study evaluated the feasibility of a screening method based on headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) in combination with chemometrics (cluster analysis, principal component analysis, and linear discriminant analysis) for the assessment of the transference of volatile compounds through turnout gear. To achieve this, firefighter turnout gears exposed to two different fire scenes (with different combustion materials) were directly analyzed. We obtained a spectral fingerprint for turnout gears that were both exposed and non-exposed to fire scenes. The results showed that (i): the contamination of the turnout gears is different depending on the type of fire loading; and (ii) it is possible to determine if the turnout gear is free of volatile compounds. Based on the latest results, we concluded that HS-GC-IMS can be applied as a screening technique to assess the quality of turnout gear prior to a new fire intervention.

The Use of SPME-GC-MS IR and Raman Techniques for Botanical and Geographical Authentication and Detection of Adulteration of Honey

The aim of this review is to describe the chromatographic, spectrometric, and spectroscopic techniques applied to honey for the determination of botanical and geographical origin and detection of adulteration. Based on the volatile profile of honey and using Solid Phase microextraction-Gas chromatography-Mass spectrometry (SPME-GC-MS) analytical technique, botanical and geographical characterization of honey can be successfully determined. In addition, the use of vibrational spectroscopic techniques, in particular, infrared (IR) and Raman spectroscopy, are discussed as a tool for the detection of honey adulteration and verification of its botanical and geographical origin. Manipulation of the obtained data regarding all the above-mentioned techniques was performed using chemometric analysis. This article reviews the literature between 2007 and 2020.

A Novel Method Based on Headspace-Ion Mobility Spectrometry for the Detection and Discrimination of Different Petroleum Derived Products in Seawater

The objective of the present study is to develop an optimized method where headspace-ion mobility spectrometry is applied for the detection and discrimination between four petroleum-derived products (PDPs) in water. A Box–Behnken design with a response surface methodology was used, and five variables (incubation temperature, incubation time, agitation, sample volume, and injection volume) with influences on the ion mobility spectrometry (IMS) response were optimized. An IMS detector was used as a multiple sensor device, in which, each drift time acts as a specific sensor. In this way, the total intensity at each drift time is equivalent to multiple sensor signals. According to our results, 2.5 mL of sample incubated for 5 min at 31 °C, agitated at 750 rpm, and with an injection volume of 0.91 mL were the optimal conditions for successful detection and discrimination of the PDPs. The developed method has exhibited good intermediate precision and repeatability with a coefficient of variation lower than 5%, (RSD (Relative Standard Deviation): 2.35% and 3.09%, respectively). Subsequently, the method was applied in the context of the detection and discrimination of petroleum-derived products added to water samples at low concentration levels (2 µL·L⁻¹). Finally, the new method was applied to determine the presence of petroleum-derived products in seawater samples.

Food safety and quality assessment: comprehensive review and recent trends in the applications of ion mobility spectrometry (IMS)

Ion mobility spectrometry (IMS) is an analytical separation and diagnostic technique that is simple and sensitive and a rapid response and low-priced technique for detecting trace levels of chemical compounds in different matrices. Chemical agents and environmental contaminants are successfully detected by IMS and have been recently considered to employ in food safety. In addition, IMS uses stand-alone or coupled analytical diagnostic tools with chromatographic and spectroscopic methods. Scientific publications show that IMS has been applied 21% in the pharmaceutical industry, 9% in environmental studies and 13% in quality control and food safety. Nevertheless, applications of IMS in food safety and quality analysis have not been adequately explored. This review presents the IMS-related analysis and focuses on the application of IMS in food safety and quality. This review presents the important topics including detection of traces of chemicals, rate of food spoilage and freshness, food adulteration and authenticity as well as natural toxins, pesticides, herbicides, fungicides, veterinary, and growth promoter drug residues. Further, persistent organic pollutants (POPs), acrylamide, polycyclic aromatic hydrocarbon (PAH), biogenic amines, nitrosamine, furfural, phenolic compounds, heavy metals, food packaging materials, melamine, and food additives were also examined for the first time. Therefore, it is logical to predict that the application of the IMS technique in food safety, food quality, and contaminant analysis will be impressively increased in the future. HighlightsCurrent status of IMS for residues and contaminant detection in food safety.To assess all the detected contaminants in food safety, for the first time.Identified IMS-related parameters and chemical compounds in food safety control.

Characterization of Arabica and Robusta Coffees by Ion Mobility Sum Spectrum

Aroma is one of the main characteristics of coffee specimens. Different mixtures of Arabica and Robusta coffees are usually found in the market to offer specific aroma or flavor profiles to consumers. However, the mixed samples or their proportions are not always identified in the product labels. Since the price of Arabica is much higher than that of Robusta, this lack of information is not only an economical issue but a possible fraud to consumers, besides the potential allergic reaction that these mixtures may trigger in some individuals. In this paper, two sample preparation techniques were compared before the analysis of the total volatile organic compounds (VOCs) found in Robusta, Arabica, and in the mixture from both coffee types. The comparison of the signals obtained from the analyses showed that the VOCs concentration levels obtained from the headspace (HS) analyses were clearly higher than those obtained from the pre-concentration step where an adsorbent, an active charcoal strip (ACS + HS), was used. In the second part of this study, the possibility of using the headspace gas-chromatography ion mobility spectrometry (HS-GC-IMS) for the discrimination between Arabica, Robusta, and mixed coffee samples (n = 30) was evaluated. The ion mobility sum spectrum (IMSS) obtained from the analysis of the HS was used in combination with pattern recognition techniques, namely linear discrimination analysis (LDA), as an electronic nose. The identification of individual compounds was not carried out since chromatographic information was not used. This novel approach allowed the correct discrimination (100%) of all of the samples. A characteristic fingerprint for each type of coffee for a fast and easy identification was also developed. In addition, the developed method is ecofriendly, so it is a good alternative to traditional approaches.