Assessment of premium organic orange juices authenticity using HPLC-HR-MS and HS-SPME-GC-MS combining data fusion and chemometrics

A review of the emerging technologies and systems to mitigate food fraud in supply chains

Food fraud has serious consequences including reputational damage to businesses, health and safety risks and lack of consumer confidence. New technologies targeted at ensuring food authenticity has emerged and however, the penetration and diffusion of sophisticated analytical technologies are faced with challenges in the industry. This review is focused on investigating the emerging technologies and strategies for mitigating food fraud and exploring the key barriers to their application. The review discusses three key areas of focus for food fraud mitigation that include systematic approaches, analytical techniques and package-level anti-counterfeiting technologies. A notable gap exists in converting laboratory based sophisticated technologies and tools in high-paced, live industrial applications. New frontiers such as handheld laser-induced breakdown spectroscopy (LIBS) and smart-phone spectroscopy have emerged for rapid food authentication. Multifunctional devices with hyphenating sensing mechanisms together with deep learning strategies to compare food fingerprints can be a great leap forward in the industry. Combination of different technologies such as spectroscopy and separation techniques will also be superior where quantification of adulterants are preferred. With the advancement of automation these technologies will be able to be deployed as in-line scanning devices in industrial settings to detect food fraud across multiple points in food supply chains.

Integration of electronic nose, electronic tongue, and colorimeter in combination with chemometrics for monitoring the fermentation process of Tremella fuciformis

This study proposes an efficient method for monitoring the submerged fermentation process of Tremella fuciformis (T. fuciformis) by integrating electronic nose (e-nose), electronic tongue (e-tongue), and colorimeter sensors using a data fusion strategy. Chemometrics was employed to establish qualitative identification and quantitative prediction models. The Pearson correlation analysis was applied to extract features from the e-nose and tongue sensor arrays. The optimal sensor arrays for monitoring the submerged fermentation process of T. fuciformis were obtained, and four different data fusion methods were developed by incorporating the colorimeter data features. To achieve qualitative identification, the physicochemical data and principal component analysis (PCA) results were utilized to determine three stages of the fermentation process. The fusion signal based on full features proved to be the optimal data fusion method, exhibiting the highest accuracy across different models. Notably, random forest (RF) was shown to be the most accurate pattern recognition method in this paper. For quantitative prediction, partial least squares regression (PLSR) and support vector regression (SVR) were employed to predict the sugar content and dry cell weight during fermentation. The best respective predictive R2 values for reducing sugar, tremella polysaccharide and dry cell weight were found to be 0.965, 0.988, and 0.970. Furthermore, due to its ability to capture nonlinear data relationships, SVR had superior performance in prediction modeling than PLSR. The results demonstrated that the combination of electronic sensor fusion signals and chemometrics provided a promising method for effectively monitoring T. fuciformis fermentation.

Determination of adulteration, geographical origins, and species of food by mass spectrometry

Food adulteration, mislabeling, and fraud, are rising global issues. Therefore, a number of precise and reliable analytical instruments and approaches have been proposed to ensure the authenticity and accurate labeling of food and food products by confirming that the constituents of foodstuffs are of the kind and quality claimed by the seller and manufacturer. Traditional techniques (e.g., genomics-based methods) are still in use; however, emerging approaches like mass spectrometry (MS)-based technologies are being actively developed to supplement or supersede current methods for authentication of a variety of food commodities and products. This review provides a critical assessment of recent advances in food authentication, including MS-based metabolomics, proteomics and other approaches.

Utilization of Lemon Peel for the Production of Vinegar by a Combination of Alcoholic and Acetic Fermentations

Lemon peel is the major by-product of lemon juice processing and is currently underutilized. In this study, we explored the feasibility of using lemon peel as a raw material for making vinegar. Lemon peel was homogenized, treated with pectinase (30,000 U/g, 0.1%) at 50 °C for 4 h, and then filtered. The obtained lemon peel juice was first subjected to alcoholic fermentation by Saccharomyces cerevisiae var. FX10, and then acetic fermentation by an acid tolerant Acetobacter malorum, OQY-1, which was isolated from the lemon peels. The juice yield of the lemon peel was 62.5%. The alcoholic fermentation yielded a lemon peel wine with an alcoholic content of 5.16%, and the acetic acid fermentation produced a vinegar with a total acid content of 5.04 g/100 mL. A total of 36 volatile compounds were identified from the lemon vinegar, with some compounds such as esters and some alcohols that increased significantly during alcoholic fermentation while alcohols, terpenoids, and some esters decreased significantly during the fermentations. E-nose and E-tongue analyses coupled with principal component and discriminant factor analyses (PCA and DFA) were able to discriminate the samples at different fermentation stages. Overall, this work demonstrates the potential to transform lemon peel into a valuable product, thus reducing the waste of lemon processing and adding value to the industry.

Untargeted metabolomics-based approach using UHPLC-HRMS to authenticate carrots (Daucus carota L.) based on geographical origin and production mode

Carrots produced in different agricultural regions with organic or conventional mode were analyzed by untargeted UHPLC-HRMS using reversed-phase and HILIC modes. Data were first treated separately, and further combined to possibly improve results. An in-house data processing workflow was applied to identify relevant features after peak detection. Based on these features, discrimination models were built using chemometrics. A tentative annotation of chemical markers was performed using online databases and UHPLC-HRMS/MS analyses. An independent set of samples was analyzed to assess the discrimination potential of these markers. Carrots produced in the New Aquitaine region could be successfully discriminated from carrots originating from the Normandy region by an OLPS-DA model. Arginine and 6-methoxymellein could be identified as potential markers with the C18-silica column. Additional markers (N-acetylputrescine, l-carnitine) could be identified thanks to the polar column. Discrimination based on production mode was more challenging: some trend was observed but model metrics remained unsatisfactory.

LC-MS and GC-MS Data Fusion Metabolomics Profiling Coupled with Multivariate Analysis for the Discrimination of Different Parts of Faustrime Fruit and Evaluation of Their Antioxidant Activity

The comparative chemical composition of different part of Faustrime fruits (peels, pulp, albedo, and seeds) extracted with different solvents was determined by GC-MS and UHPLC-HRMS QTof. The obtained data were also combined for their in vitro antioxidant activity by multivariate analysis to define a complex fingerprint of the fruit. The principal component analysis model showed the significative occurrence of volatile organic compounds as α-bisabolol and α-trans-bergamotol in the pulp and albedo, hexanoic acid in the seeds, and several coumarins and phenolics in the peels. The higher radical scavenging activity of the pulp was related to the incidence of citric acid in partial least square regression.

Chemometrics-assisted excitation-emission matrix fluorescence spectroscopy for rapid identification of commercial reconstituted and sweetened grape juices

As a common fruit juice, grape juice is delicious and nutritious, making it very popular among consumers. However, some illegal manufacturers used shoddy products to lower costs and obtain high profits, which seriously threatens the health and interests of consumers. Hence, this paper proposed excitation-emission matrix (EEM) fluorescence spectroscopy combined with chemometric methods for the rapid identification and classification of commercial grape juices. Spectral characterization of different samples was achieved using the alternating trilinear decomposition (ATLD) algorithm, and chemically meaningful information was obtained and analyzed. Although both reconstituted and sweetened grape juices contain methyl anthranilate (MA) and 2'-aminoacetophenone (o-AAP), the content of MA in sweetened grape juice far exceeds that in reconstituted grape juice, and the MA in sweetened grape juice mainly comes from artificially added grape essence. Then two chemometric methods of hierarchical cluster analysis (HCA) and partial least squares discriminant analysis (PLS-DA) were used for the classification of reconstituted and sweetened grape juices. The results showed that the supervised classification model had a higher correct classification rate (CCR) than the unsupervised classification model, with PLS-DA obtaining 100% CCRs in both training and prediction sets. Therefore, the proposed strategy can be used as a powerful analytical method for the identification and classification of reconstituted and sweetened grape juices and provides a reliable scientific means for ensuring the authenticity and safety of the juice market.

Nontargeted 1 H NMR fingerprinting and multivariate statistical analysis for traceability of Greek PDO Vostizza currants

In this study, non-targeted ¹ H NMR fingerprinting was used in combination with multivariate statistical analyses for the classification of Greek currants based on their geographical origins (Aeghion, Nemea, Kalamata, Zante, and Amaliada). As classification techniques, Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were carried out. To elucidate different components according to PDO (Protected Designation of Origin), products from Aeghion (Vostizza) were statistically compared with each one of the four other regions. PLS-DA plots ensure that currants from Kalamata, Nemea, Zante, and Amaliada are well classified with respect to the PDO currants, according to differences observed in metabolites. Results suggest that composition differences in carbohydrates, amino, and organic acids of currants are sufficient to discriminate them in correlation to their geographical origin. In conclusion, currants metabolites which mostly contribute to classification performance of such discriminant analysis model present a suitable alternative technique for currants traceability. The study results contribute information to the currants' metabolite fingerprinting by NMR spectroscopy and their geographical origin. PRACTICAL APPLICATION: This study presents an analytical approach for a high nutritional value Greek PDO product, Vostizza currant. A further research and implementation of this method in food industry, can be the key to food fraud incidents. Thus, application of this work opens up posibilities to "farm to table" mission.

Attenuated total reflectance Fourier transform (ATR-FTIR) spectroscopy and chemometrics for organic cinnamon evaluation

Organic food consumption has increased significantly over time. This contributes to the increased demand and price of this kind of food. Among the organic products, cinnamon stands out for its characteristic flavor and bioactive compounds. Thus, the work aimed to verify the potentials of attenuated total reflectance Fourier transform mid-infrared spectroscopy (ATR-FT-MIR) coupled with Parallel Factor Analysis (PARAFAC) for evaluation of cinnamon organic samples. As result, the proposal is feasible in the differentiation of organic cinnamon powder, in which ATR-FT-MIR coupled with PARAFAC showed the differentiation of organic from non-organic ones on the scores mode, the precision at repeatability level on one loading mode, and the spectral region, on the other loading mode, above 2600 cm^-1 was related to the differentiation of the organic and non-organic samples.

Potential of 1H NMR fingerprinting and a model system approach to study non-enzymatic browning in shelf-stable orange juice during storage

For the first time, a model system approach was combined with ¹H NMR fingerprinting in studying non-enzymatic browning (NEB) of pasteurized shelf-stable orange juice during storage. Various NEB precursors were used individually or in combinations to formulate simple or complex model systems, respectively, in citric acid buffer. Based on orange juice composition, ascorbic acid, sugars (sucrose, glucose and fructose) and amino acids (proline, arginine, asparagine, aspartic acid, serine and glutamic acid) were selected as the precursors for the model systems. After pasteurization and during subsequent accelerated storage (42 °C, 16 weeks) the model systems displayed a three-phase browning development. The initial browning phase was mainly the result of ascorbic acid degradation especially in the presence of amino acids and sugars. In the later phases, the contribution of reactions of sugars and amino acids to browning became apparent. The application of ¹H NMR fingerprinting on a simple model system containing ascorbic acid revealed that its degradation pathway to intermediates such as xylonic acid, acetic acid and erythrulose was responsible for the major changes during storage. When this model system was complexed by inclusion of sugars and amino acids, the hydrolysis of sucrose to glucose and fructose was identified as the main reaction leading to differences in the samples throughout storage. These three sugars dominated the NMR spectra of the samples, overshadowing several important compounds for NEB such as ascorbic acid and its degradation products. Other more advanced NMR experiments such as two-dimensional NMR analyses should be applied in future research to identify unknown compounds from NEB reactions.

Volatilomic Profiling of Citrus Juices by Dual-Detection HS-GC-MS-IMS and Machine Learning-An Alternative Authentication Approach

A prototype dual-detection headspace-gas chromatography-mass spectrometry-ion mobility spectrometry (HS-GC-MS-IMS) system was used for the analysis of the volatile profile of 47 Citrus juices including grapefruit, blood orange, and common sweet orange juices without requiring any sample pretreatment. Next to reduced measurement times, substance identification could be improved substantially in case of co-elution by considering the characteristic drift times and m/z ratios obtained by IMS and MS. To discriminate the volatile profiles of the different juice types, extensive data analysis was performed with both datasets, respectively. By principal component analysis (PCA), a distinct separation between grapefruit and orange juices was observed. While in the IMS data grapefruit juices not from fruit juice concentrate could be separated from grapefruit juices reconstituted from fruit juice concentrate, in the MS data, the blood orange juices could be differentiated from the orange juices. This observation leads to the assumption that the IMS and MS data contain different information about the composition of the volatile profile. Subsequently, linear discriminant analysis (LDA), support vector machines (SVM), and the k-nearest-neighbor (kNN) algorithm were applied to the PCA data as supervised classification methods. Best results were obtained by LDA after repeated cross-validation for both datasets, with an overall classification and prediction ability of 96.9 and 91.5% for the IMS data and 94.5 and 87.9% for the MS data, respectively, which confirms the results obtained by PCA. Additional data fusion could not generally improve the model prediction ability compared to the single data, but rather for certain juice classes. Consequently, depending on the juice class, the most suitable dataset should be considered for the prediction of the class membership. This volatilomic approach based on the dual detection by HS-GC-MS-IMS and machine learning tools represent a simple and promising alternative for future authenticity control of Citrus juices.

Effect of Rootstock and Harvesting Period on the Bioactive Compounds and Antioxidant Activity of Two Orange Cultivars (‘Salustiana’ and ‘Sanguinelli’) Widely Used in Juice Industry

Oranges are a rich source of bioactive compounds with recognized benefits for human health. To guarantee high quality and production levels, citrus farms usually employ the combination of selected cultivars with well adapted rootstocks. This study analyzes the impact of four different citrus rootstocks (Forner-Alcaide no.5, ‘Cleopatra mandarin’, Citrus volkameriana and Carrizo citrange) on the bioactive compounds and antioxidant activity of two orange cultivars (‘Salustiana’ and ‘Sanguinelli’) widely used in the orange juice industry. For the hydrophilic fraction, the phenolic compound, anthocyanin, and organic acid profiles were determined by HPLC-DAD-HRMS, and the antioxidant activity by ABTS, DPPH, and ORAC assays. Besides, the total carotenoids and ABTS concentrations were calculated for the hydrophobic fraction. A set of three flavanones, one flavone, and eight anthocyanins were tentatively identified and quantified in the orange cultivars tested. The predominant phenolic compounds obtained in both orange cultivars were hesperidin and narirutin, while cyanidin-3-O-(6″-malonyl) glucoside followed by cyanidin-3-O-rutinoside and cyanidin-3-O-glucoside were the main anthocyanins found in the ‘Sanguinelli’ cultivar. Citric acid, followed by malic, oxalic, and ascorbic acids were the main organic acids. The higher amount of antioxidant compounds was found in fruit from the Forner-Alcaide no.5 rootstock. These results indicate that Forner-Alcaide n.5 affects positively the phenolic and organic acid composition and the antioxidant capacity of ‘Sanguinelli’ and ‘Salustiana’ cultivars, and is therefore a good option for the sector based on the healthy promoting properties.

Volatile Organic Compounds Profiles to Determine Authenticity of Sweet Orange Juice Using Head Space Gas Chromatography Coupled with Multivariate Analysis

An efficient and practical method for identifying mandarin juice over-blended into not from concentrate (NFC) orange juice was established. Juices were extracted from different cultivars of sweet orange and mandarin fruits. After being pasteurized, the volatile organic compounds (VOCs) in the juice samples were extracted using headspace solid-phase microextraction, and qualitatively and quantitatively analyzed using gas chromatography–mass spectrometry detection. Thirty-two VOCs contained in both the sweet orange juice and mandarin juice were used as variables, and the identification model for discriminating between the two varieties of juice was established by principal component analysis. Validation was applied by using common mandarin juices from Ponkan, Satsuma and Nanfengmiju cultivars blended at series of proportions into orange juices from Long-leaf, Olinda, and Hamlin cultivars. The model can visually identify a blending of mandarin juice at the volume fraction of 10% or above.

Microbial quality assessment of minimally processed pineapple using GCMS and FTIR in tandem with chemometrics

Microbial quality is the critical parameter determining the safety of refrigerated perishables. Traditional methods used for assessing microbial quality are time consuming and labour intensive. Thus rapid, non-destructive methods that can accurately predict microbial status is warranted. Models using partial least square regression (PLS-R) from chemical finger prints of minimally processed pineapple during storage obtained by Headspace Solid Phase Microextraction Gas Chromatography Mass Spectrometry (HS-SPME-GCMS), Fourier Transform Infrared (FTIR) spectroscopy and their data fusion are developed. Models built using FTIR data demonstrated good prediction for unknown samples kept under non-isothermal conditions. FTIR based models could predict 87 and 80% samples within ±1 log CFU/g for TVC and Y&M, respectively. Analysis of PLS-R results suggested the production of alcohols and esters with utilization of sugars due to microbial spoilage.

Species discrimination and total polyphenol prediction of porcini mushrooms by fourier transform mid-infrared (FT-MIR) spectrometry combined with multivariate statistical analysis

The plateau specialty agricultural products, wild porcini mushrooms, have great value both as a superb cuisine and as a potential medication. Due to quality different between species added with the fraud behavior in sales process, make poor quality or poisonous sample inflow into the market, which pose a health risk for consumers, but also disrupted the mushroom market. Traditional analysis way is time-consuming and laborious. Therefore, the aim of this study is to develop a way using fourier transform mid-infrared (FT-MIR) spectrometry and data fusion strategies for the fast and accurate species discrimination and predict amount of total polyphenol in four porcini mushrooms. The t-distributed stochastic neighbor embedding based on mid-level data fusion showed two species of Boletus edulis and B. umbriniporus have been identified. The order of correct rate of PLS-DA models was mid-level data fusionq (100%) > mid-level data fusione (97.06%) = mid-level data fusionv (97.06%) = stipes (97.06%) > low-level data fusion (94.12%) > caps (91.18%). The order of correct rate of grid-search support vector machine models was low-level data fusion (100%) > caps (94.12%) > stipes (91.18%), and the order of particle swarm optimization support vector machine was low-level data fusion (100%) > caps (97.06%) > stipes (88.24%). The mid-level data fusionq and low-level data fusion had best discrimination accuracy (100%) allowing each mushroom classed into its real species, which could be used for accurate discrimination of samples. B. edulis mushrooms had highest total polyphenol, with 14.76 mg/g dw and 17.33 in caps and stipes mg/g dw, respectively. The phenols were easier to accumulate in the caps in Leccinum rugosiceps (1.03) and B. tomentipes (1.19), and the opposite phenomenon is observed in B. edulis (0.85) and B. umbriniporus (0.95). The correlation coefficient and residual predictive deviation of best prediction model were 86.76% and 2.40%, respectively, indicating that that there is good relevance between FT-MIR and total polyphenol content, which could be used to predict roughly polyphenols content in mushrooms.

Quality and Authenticity Control of Fruit Juices-A Review

Food fraud, being the act of intentional adulteration of food for financial advantage, has vexed the consumers and the food industry throughout history. According to the European Committee on the Environment, Public Health and Food Safety, fruit juices are included in the top 10 food products that are most at risk of food fraud. Therefore, reliable, efficient, sensitive and cost-effective analytical methodologies need to be developed continuously to guarantee fruit juice quality and safety. This review covers the latest advances in the past ten years concerning the targeted and non-targeted methodologies that have been developed to assure fruit juice authenticity and to preclude adulteration. Emphasis is placed on the use of hyphenated techniques and on the constantly-growing role of MS-based metabolomics in fruit juice quality control area.

Food fingerprints - A valuable tool to monitor food authenticity and safety

In recent years, food frauds and adulterations have increased significantly. This practice is motivated by fast economical gains and has an enormous impact on public health, representing an important issue in food science. In this context, this review has been designed to be a useful guide of potential biomarkers of food authenticity and safety. In terms of food authenticity, we focused our attention on biomarkers reported to specify different botanical or geographical origins, genetic diversity or production systems, while at the food safety level, molecular evidences of food adulteration or spoilage will be highlighted. This report is the first to combine results from recent studies in a format that allows a ready overview of metabolites (<1200 Da) and potentially molecular routes to monitor food authentication and safety. This review has therefore the potential to unveil important aspects in food adulteration and safety, contributing to improve the current regulatory frameworks.

Metabolomics in chronic kidney disease: Strategies for extended metabolome coverage

Chronic kidney disease (CKD) is becoming a major public health issue as prevalence is increasing worldwide. It also represents a major challenge for the identification of new early biomarkers, understanding of biochemical mechanisms, patient monitoring and prognosis. Each metabolite contained in a biofluid or tissue may play a role as a signal or as a driver in the development or progression of the pathology. Therefore, metabolomics is a highly valuable approach in this clinical context. It aims to provide a representative picture of a biological system, making exhaustive metabolite coverage crucial. Two aspects can be considered: analytical and biological coverage. From an analytical point of view, monitoring all metabolites within one run is currently impossible. Multiple analytical techniques providing orthogonal information should be carried out in parallel for coverage improvement. The biological aspect of metabolome coverage can be enhanced by using multiple biofluids or tissues for in-depth biological investigation, as the analysis of a single sample type is generally insufficient for whole organism extrapolation. Hence, recording of signals from multiple sample types and different analytical platforms generates massive and complex datasets so that chemometric tools, including data fusion approaches and multi-block analysis, are key tools for extracting biological information and for discovery of relevant biomarkers. This review presents the recent developments in the field of metabolomic analysis, from sampling and analytical strategies to chemometric tools, dedicated to the generation and handling of multiple complementary metabolomic datasets enabling extended metabolite coverage to improve our biological knowledge of CKD.

Novel analytical method for detection of orange juice adulteration based on ultra-fast gas chromatography

ABSTRACT

The food authenticity assessment is an increasingly important issue in food quality and safety. The application of an electronic nose based on ultra-fast gas chromatography technique enables rapid analysis of the volatile compounds from food samples. Due to the fact that this technique provides chemical profiling of natural products, it can be a powerful tool for authentication in combination with chemometrics. In this article, a methodology for classification of Not From Concentrate (NFC) juices was presented. During research samples of 100% orange juice, 100% apple juice, as well as mixtures of these juices with known percentage of base juices were tested. Classification of juice samples was carried out using unsupervised and supervised statistical methods. As chemometric methods, Hierarchical Cluster Analysis, Classification Tree, Naïve Bayes, Neural Network, and Random Forest classifiers were used. The ultra-fast GC technique coupled with supervised statistical methods allowed to distinguish juice samples containing only 1.0% of impurities. The developed methodology is a promising analytical tool to ensure the authenticity and good quality of juices.

GRAPHICAL ABSTRACT

Rapid qualitative and quantitative analyses of eighteen phenolic compounds from Lycium ruthenicum Murray by UPLC-Q-Orbitrap MS and their antioxidant activity

Lycium ruthenicum Murray (LR) is a functional food, and it has long been used in traditional folk medicine. However, detailed qualitative and quantitative analyses related to its phenolic compounds remains scarce. This work reports, for the first time, the establishment of a rapid method for simultaneous identification and quantification of 25 phenolic compounds by UPLC-quadrupole-Orbitrap mass spectrometry (UPLC-Q-Orbitrap MS). This method was validated by LODs, LOQs, precision, repeatability, stability, mean recovery, recovery range and RSD. The confirmed method was applied to the analysis of phenolic compounds in LR. Finally, 18 phenolic compounds in LR were qualitatively and quantitatively analyzed. Among them, 11 constituents were detected for the first time, which included two flavonoids (catechin and naringenin) and seven phenolic acids (gallic acid, vanillic acid, 2,4-dihydroxybenzoic acid, veratronic acid, benzoic acid, ellagic acid and salicylic acid). Moreover, Phloretin and protocatechuate, belonging to the dihydrochalcone flavonoid and protocatechuic acid respectively, were also identified and quantified. The total phenolics content (20.17 ± 2.82 mg/g) and the total anthocyanin content (147.43 ± 1.81 mg/g) were determined. In addition, the antioxidant activities of the LR extract were evaluated through 2,2-azinobis (3-ethylbenzthiazoline-6-sulphonic acid) (ABTS) radical scavenging activity, ferric reducing antioxidant power (FRAP) and total antioxidant activity (T-AOC) assays.

Chemical profiling and multivariate data fusion methods for the identification of the botanical origin of honey

The characterization of 72 Italian honey samples from 8 botanical varieties was carried out by a comprehensive approach exploiting data fusion of IR, NIR and Raman spectroscopies, Proton Transfer Reaction - Time of Flight - Mass Spectrometry (PTR-MS) and electronic nose. High-, mid- and low-level data fusion approaches were tested to verify if the combination of several analytical sources can improve the classification ability of honeys from different botanical origins. Classification was performed on the fused data by Partial Least Squares - Discriminant Analysis; a strict validation protocol was used to estimate the predictive performances of the models. The best results were obtained with high-level data fusion combining Raman and NIR spectroscopy and PTR-MS, with classification performances better than those obtained on single analytical sources (accuracy of 99% and 100% on test and training samples respectively). The combination of just three analytical sources assures a limited time of analysis.