An overview of multivariate qualitative methods for food fraud detection

Comprehensive two dimensional gas chromatography - time of flight mass spectrometry (GC×GC-TOFMS) for the investigation of botanical origin of raw spirits

Comprehensive two dimensional gas chromatography - time of flight mass spectrometry (GC × GC-TOFMS) with sample introduction using headspace solid phase microextraction (HS-SPME) was used for the botanical classification of raw spirits obtained from C3 (corn and sorghum) and C4 (rye, wheat and potato) plants. 45 spirit samples representing these raw materials (10 spirits produced from rye, corn, wheat and potato, and 5 from sorghum) were analyzed. Volatile compounds profiles were compared by PCA, and after removal of outliers samples were subjected to the classification model. OPLS-DA model was built (R2Y = 0.924 Q2Y = 0.895) that enabled clear separation of all tested spirits of different botanical origin. The model was validated with training and testing sets and 100 % correct assignment was achieved. GC × GC-TOFMS proved to be a method that not only can be used as a tool for botanic origin of raw spirits, but also provides detailed information of volatile fermentation by-products, characteristic for particular spirit.

Ultrafast and Ultrasensitive Bacterial Detection in Biofluids: Leveraging Resazurin as a Visible and Fluorescent Spectroscopic Marker

Here we report the application of chemometric analysis for modeling absorbance spectroscopy and fluorescence emission data from a resazurin-based assay targeting low-level bacterial detection in biofluids. Bacteria spiked samples were incubated with resazurin and absorbance and fluorescence data were collected at 30 min intervals. The absorbance data was subjected to Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) and compared with the univariate fluorescence spectroscopy approach. The analysis demonstrated the multidimensional nature of the absorbance data, highlighting the appearance of the resorufin peak at the 2 h time point with a low bacterial inoculum of 0.01 CFU mL-1 across all the samples tested-water, urine and serum. The PLSR models supported the PCA data and exhibited strong predictive capabilities for water (RC2 = 0.937, RCV2 = 0.934), urine (RC2 = 0.899, RCV2 = 0.880) and serum (RC2 = 0.985, RCV2 = 0.967). Conversely, fluorescence is contingent upon resorufin existence, necessitating a prolonged waiting period postincubation with resazurin to verify the presence of bacteria, especially when contamination levels are low. Given the substantial global impact of bacteria-related infections, this method detects bacteria at low concentrations precisely and rapidly, improving efficiency and adaptability for point-of-care settings, promising swift diagnosis of bacterial infections, environmental monitoring, or food-quality control.

Research advances in detection of food adulteration and application of MALDI-TOF MS: A review

Food adulteration and illegal supplementations have always been one of the major problems in the world. The threat of food adulteration to the health of consumers cannot be ignored. Food of questionable origin causes economic losses to consumers, but the potential health risks cannot be ignored. However, the traditional detection methods are time-consuming and complex. This review mainly discusses the types of adulteration and technologies used to detect adulteration. Matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is also emphasized in the detection of adulteration and authenticity of origin analysis of various types of food (milk, meat, edible oil, etc.), and the future application direction and feasibility of this technology are analyzed. On this basis, MALDI-TOF MS was compared with other detection methods, highlighting the advantages of this technology in the detection of food adulteration. The future development prospect and direction of this technology are also emphasized.

A Microbial Phenomics Approach to Determine Metabolic Signatures to Enhance Seabream Sparus aurata Traceability, Differentiating between Wild-Caught and Farmed

BACKGROUND

The need for efficient and simplified techniques for seafood traceability is growing. This study proposes the Biolog EcoPlate assay as an innovative method for assessing wild and farmed Sparus aurata traceability, offering advantages over other molecular techniques in terms of technical simplicity.

METHODS

The Biolog EcoPlate assay, known for its high-throughput capabilities in microbial ecology, was utilized to evaluate the functional diversity of microbial communities from various organs of S. aurata (seabream) from the Mediterranean area. Samples were taken from the anterior and posterior gut, cloaca swabs and gills to distinguish between farmed and wild-caught individuals. The analysis focused on color development in OmniLog Units for specific carbon sources at 48 h.

RESULTS

Gills provided the most accurate clusterization of sample origin. The assay monitored the development of color for carbon sources such as α-cyclodextrin, D-cellobiose, glycogen, α-D-lactose, L-threonine and L-phenylalanine. A mock experiment using principal component analysis (PCA) successfully identified the origin of a blind sample. Shannon and Simpson indexes were used to statistically assess the diversity, reflecting the clusterization of different organ samples; Conclusions: The Biolog EcoPlate assay proves to be a quick, cost-effective method for discriminate S. aurata traceability (wild vs. farmed), demonstrating reliable reproducibility and effective differentiation between farmed and wild-caught seabream.

Food authenticity and the interactions with human health and climate change

Food authenticity and fraud, as well as the interest in food traceability have become a topic of increasing interest not only for consumers but also for the research community and the food manufacturing industry. Food authenticity and fraud are becoming prevalent in both the food supply and value chains since ancient times where different issues (e.g., food spoilage during shipment and storage, mixing decay foods with fresh products) has resulted in foods that influence consumers health. The effect of climate change on the quality of food ingredients and products could also have the potential to influence food authenticity. However, this issue has not been considered. This article focused on the interactions between consumer health and the potential effects of climate change on food authenticity and fraud. The role of technology and development of risk management tools to mitigate these issues are also discussed. Where applicable papers that underline the links between the interactions of climate change, human health and food fraud were referenced.

Chemometric analysis illuminates the relationship among browning, polyphenol degradation, Maillard reaction and flavor variation of 5 jujube fruits during air-impingement jet drying

This study was designed to reveal the relationship among browning, polyphenol degradation, Maillard reaction (MR) and flavor variation in jujube fruit (JF) during air-impingement jet drying (AIJD). Five kinds of JFs were dried by AIJD at 60 °C and vacuum freeze drying. Colorimeter and chemometric analysis found that AIJD induced color changes of JF pulp and peel. AIJD also reduced the total polyphenols content and total flavonoids levels in JF. The Fe3+ reducing capacity and 2,2'-Azinobis-(3-ethylbenzothiazoline-6-sulphonate) cationic radical scavenging capacity of JF were reduced by 31.6% and 8.2%, respectively. Seven polyphenols were identified in JF, and epicatechin was found related to change of JF pulp color by sparse partial least square (sPLS). sPLS revealed that 3-deoxy glucosone, N-ε-carboxymethyl-l-lysine and 5-hydroxymethylfurfural associated with JF color. sPLS found that MR generated 3-methyl-butanoic acid and cyclobutanone during AIJD of JF. Chemometrics is an effective tool to disclose mechanism of color changes in food.

Multiblock data applied in organic grape juice authentication by one-class classification OC-PLS

A new strategy has been developed to enhance the assessment of the authenticity of whole grape juice within the organic class. This approach is based on the analysis of data from different analytical sources. The novel method employs a multiblock regression technique, specifically the one-class partial least squares (OC-PLS) classifier, to establish a relationship between each predictor block and the response variable. Sequential calculations are performed after orthogonalization with respect to the preceding regression scores. The proposed method has demonstrated effectiveness in detecting targeted samples. The results achieved of the best models for the test set had rates of up to 100 % sensitivity, 89 % specificity, and 83 % accuracy. To compare with the multiblock models, the DD-SIMCA method was employed, but it yielded inferior results when applied to visible data. The multiblock approach proved to be efficient in evaluating from different datasets of varied sources to classification of organic grape juice.

Rapid discrimination of Panax ginseng powder adulterated with various root plants by FT-IR spectroscopy coupled with multivariate analysis

Panax ginseng powder adulterated with other root plants (arrowroot, bellflower, and lance asiabell) was discriminated using Fourier transform infrared (FT-IR) spectroscopy, combined with multivariate analysis. Principal component analysis visually diagnosed the adulteration by showing two distinct clusters based on presence of adulteration. Wavenumber regions (1000 cm-1 and 3300 cm-1) selected from the loading plot associated with the vibration of OH and CH bond in ginsenoside and aromatic compounds. A quantitative model for the content of ginsenosides and specific aromatic compounds as indicators of pure ginseng powder, was developed based on partial least square regression analysis. The performance of the prediction model preprocessed with the Savizky-Golay 1st derivative was improved to R2 of 0.9650, 0.9635, and 0.9591 for Rb1, Rc, and β-Panasinsene, respectively. Therefore, FT-IR technology makes it possible to rapidly authenticate pure ginseng product based on the ginsenoside contents and aroma compound.

1H NMR-based approach to determine the geographical origin and cultivation method of roasted coffee

A comprehensive study of 603 roasted arabica coffee samples using NMR fingerprinting and multivariate data analysis was performed to differentiate coffee samples according to their geographical origin and cultivation method. Both lipophilic and hydrophilic coffee metabolites were recorded using 1H NMR spectroscopy, and principal component analysis followed by linear discriminant analysis (PCA-LDA) was applied. Coffee samples were fist differentiated according to their continents of origin followed by discrimination of coffee samples from Brazil, Ethiopia, and Colombia from coffee samples originating from another continent. Discrimination of coffee samples according to their continent of origin and additional assignment to the countries Brazil and Ethiopia were successful. However, an unambiguous separation of Colombian coffee samples from coffee samples of another continent (other than South America) was not possible. Also, differentiation of organically and conventionally produced coffee samples by using 1H NMR and PCA-LDA was not achieved.

Application of Nontarget High-Resolution Mass Spectrometry Fingerprints for Qualitative and Quantitative Source Apportionment: A Real Case Study

High-resolution mass spectrometry (HRMS) provides extensive chemical data, facilitating the differentiation and quantification of contaminants of emerging concerns (CECs) in aquatic environments. This study utilizes liquid chromatography-HRMS for source apportionment in Chebei Stream, an urban water stream in Guangzhou, South China. Initially, 254 features were identified as potential CECs by the nontarget screening (NTS) method. We then established 1689, 1317, and 15,759 source-specific HRMS fingerprints for three distinct sources, the mainstream (C3), the tributary (T2), and the rain runoff (R1), qualitatively assessing the contribution from each source downstream. Subsequently, 32, 55, and 3142 quantitative fingerprints were isolated for sites C3, T2, and R1, respectively, employing dilution curve screening for source attribution. The final contribution estimates downstream from sites C3, T2, and R1 span 32-96, 12-23, and 8-23%, respectively. Cumulative contributions from these sources accurately mirrored actual conditions, fluctuating between 103 and 114% across C6 to C8 sites. Yet, with further tributary integration, the overall source contribution dipped to 52%. The findings from this research present a pioneering instance of applying HRMS fingerprints for qualitative and quantitative source tracking in real-world scenarios, which empowers the development of more effective strategies for environmental protection.

Recent trends of machine learning applied to multi-source data of medicinal plants

In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.

Application of spectroscopic techniques combined with chemometrics to the authenticity and quality attributes of rice

Rice is a staple food for two-thirds of the world's population and is grown in over a hundred countries around the world. Due to its large scale, it is vulnerable to adulteration. In addition, the quality attribute of rice is an important factor affecting the circulation and price, which is also paid more and more attention. The combination of spectroscopy and chemometrics enables rapid detection of authenticity and quality attributes in rice. This article described the application of seven spectroscopic techniques combined with chemometrics to the rice industry. For a long time, near-infrared spectroscopy and linear chemometric methods (e.g., PLSR and PLS-DA) have been widely used in the rice industry. Although some studies have achieved good accuracy, with models in many studies having greater than 90% accuracy. However, higher accuracy and stability were more likely to be obtained using multiple spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. Future research should develop larger rice databases to include more rice varieties and larger amounts of rice depending on the type of rice, and then combine various spectroscopic techniques, nonlinear chemometric methods, and key wavelength selection algorithms. This article provided a reference for a more efficient and accurate determination of rice quality and authenticity.

Metabolomics and genetics of reproductive bud development in Ficus carica var. sativa (edible fig) and in Ficus carica var. caprificus (caprifig): similarities and differences

In figs, reproductive biology comprises cultivars requiring or not pollination, with female trees (edible fig) and male trees (caprifig) bearing different types of fruits. Metabolomic and genetic studies may clarify bud differentiation mechanisms behind the different fruits. We used a targeted metabolomic analysis and genetic investigation through RNA sequence and candidate gene investigation to perform a deep analysis of buds of two fig cultivars, 'Petrelli' (San Pedro type) and 'Dottato' (Common type), and one caprifig. In this work, proton nuclear magnetic resonance (¹H NMR-based metabolomics) has been used to analyze and compare buds of the caprifig and the two fig cultivars collected at different times of the season. Metabolomic data of buds collected on the caprifig, 'Petrelli', and 'Dottato' were treated individually, building three separate orthogonal partial least squared (OPLS) models, using the "y" variable as the sampling time to allow the identification of the correlations among metabolomic profiles of buds. The sampling times revealed different patterns between caprifig and the two edible fig cultivars. A significant amount of glucose and fructose was found in 'Petrelli', differently from 'Dottato', in the buds in June, suggesting that these sugars not only are used by the ripening brebas of 'Petrelli' but also are directed toward the developing buds on the current year shoot for either a main crop (fruit in the current season) or a breba (fruit in the successive season). Genetic characterization through the RNA-seq of buds and comparison with the literature allowed the identification of 473 downregulated genes, with 22 only in profichi, and 391 upregulated genes, with 21 only in mammoni.

CRISPR/Cas-based Aptasensor as an Innovative Sensing Approaches for Food Safety Analysis: Recent Progresses and New Horizons

Food safety is one of the greatest public problems occurring around the world. Chemical, physical, and microbiological hazards could lead to food safety problems, which might occur at all stages of the supply chain. To tackle food safety problems and protect consumer health, specific, accurate, and rapid diagnosis techniques meeting various requirements are the imperative measures to ensure food safety. CRISPR-Cas system, a novel emerging technology, is effectively repurposed in (bio)sensing and has shown a tremendous capability to develop on-site and portable diagnostic methods with high specificity and sensitivity. Among numerous existing CRISPR/Cas systems, CRISPR/Cas13a and CRISPR/Cas12a are extensively employed in the design of biosensors, owing to their ability to cleave both non-target and target sequences. However, the specificity limitation in CRISPR/Cas has hindered its progress. Nowadays, nucleic acid aptamers recognized for their specificity and high-affinity characteristics for their analytes are incorporated into CRISPR/Cas systems. With the benefits of reproducibility, high durability, portability, facile operation, and cost-effectiveness, CRISPR/Cas-based aptasensing approaches are an ideal choice for fabricating highly specific point-of-need analytical tools with enhanced response signals. In the current study, we explore some of the most recent progress in the CRISPR/Cas-mediated aptasensors for detecting food risk factors including veterinary drugs, pesticide residues, pathogens, mycotoxins, heavy metals, illegal additives, food additives, and other contaminants. The nanomaterial engineering support with CRISPR/Cas aptasensors is also signified to achieve a hopeful perspective to provide new straightforward test kits toward trace amounts of different contaminants encountered in food samples.

One-class model with two decision thresholds for the rapid detection of cashew nuts adulteration by other nuts

A green screening method to determine cashew nut adulteration with Brazilian nut, pecan nut, macadamia nut and peanut was proposed. The method was based on the development of a one-class soft independent modelling of class analogy (SIMCA) model for non-adulterated cashew nuts using near-infrared (NIR) spectra obtained with portable equipment. Once the model is established, the assignment of unknown samples depends on the threshold established for the authentic class, which is a key aspect in any screening approach. The authors propose innovatively to define two thresholds: lower model distance limit and upper model distance limit. Samples with distances below the lower threshold are assigned as non-adulterated with a 100% probability; samples with distance values greater than the upper threshold are assigned as adulterated with a 100% probability; and samples with distances within these two thresholds will be considered uncertain and should be submitted to a confirmatory analysis. Thus, the possibility of error in the sample assignment significantly decreases. In the present study, when just one threshold was defined, values greater than 95% for the optimized threshold were obtained for both selectivity and specificity. When two class thresholds were defined, the percentage of samples with uncertain assignment changes according to the adulterant considered, highlighting the case of peanuts, in which 0% of uncertain samples was obtained. Considering all adulterants, the number of samples that were submitted to a confirmatory analysis was quite low, 5 of 224 adulterated samples and 3 of 56 non-adulterated samples.

Nontargeted detection and recognition of adulterants in milk powder using Raman imaging and neural networks

Raman imaging technology combined with targeted chemometrics can play a vital role in the rapid detection of milk powder adulteration, which threatens the lives of infants and other people. However, these methods always suffer from a narrow detection range. Nontargeted methods show a broader detection range but cannot recognize adulterants. Here, a novel nontargeted chemometric method, named as the adversarial discrimination neural network (ADNN), is proposed to detect and recognize adulterants simultaneously. The method comprises building a tight boundary in the feature space of Raman images to discriminate milk powder samples from the majority of adulterated cases. Then a first-order partial derivative of the ADNN is calculated to recognize different adulterants through a local approximation strategy. A validation set containing samples adulterated with various adulterants at concentrations ranging from 0.3% to 1.5% w/w was provided to challenge the proposed method. The validated detection accuracy of the proposed method for authentic and adulterated samples was 99.9% and 99.7% and the adulterants were recognized correctly. The ADNN-Raman represents a novel nontargeted and end-to-end tool for detecting and recognizing adulterants in milk powder simultaneously, providing new insights into nontargeted chemometric analysis.

Combining e-nose and e-tongue for improved recognition of instant starch noodles seasonings

Seasonings play a key role in determining sensory attributes of instant starch noodles. Controlling and improving the quality of seasoning is becoming important. In this study, five different brands along with fifteen instant starch noodles seasonings (seasoning powder, seasoning mixture sauce and the mixture of powder and sauce) were characterized by electronic nose (e-nose) and electronic tongue (e-tongue). Feature-level fusion for the integration of the signals was introduced to integrate the e-nose and e-tongue signals, aiming at improving the performances of identification and prediction models. Principal component analysis (PCA) explained over 85.00% of the total variance in e-nose data and e-tongue data, discriminated all samples. Multilayer perceptron neural networks analysis (MLPN) modeling demonstrated that the identification rate of the combined data was basically 100%. PCA, cluster analysis (CA), and MLPN proved that the classification results acquired from the combined e-nose and e-tongue data were better than individual e-nose and e-tongue result. This work demonstrated that in combination e-nose and e-tongue provided more comprehensive information about the seasonings compared to each individual e-nose and e-tongue. E-nose and e-tongue technologies hold great potential in the production, quality control, and flavor detection of instant starch noodles seasonings.

Toward the Non-Targeted Detection of Adulterated Virgin Olive Oil with Edible Oils via FTIR Spectroscopy & Chemometrics: Research Methodology Trends, Gaps and Future Perspectives

Fourier-Transform mid-infrared (FTIR) spectroscopy offers a strong candidate screening tool for rapid, non-destructive and early detection of unauthorized virgin olive oil blends with other edible oils. Potential applications to the official anti-fraud control are supported by dozens of research articles with a "proof-of-concept" study approach through different chemometric workflows for comprehensive spectral analysis. It may also assist non-targeted authenticity testing, an emerging goal for modern food fraud inspection systems. Hence, FTIR-based methods need to be standardized and validated to be accepted by the olive industry and official regulators. Thus far, several literature reviews evaluated the competence of FTIR standalone or compared with other vibrational techniques only in view of the chemometric methodology, regardless of the inherent characteristics of the product spectra or the application scope. Regarding authenticity testing, every step of the methodology workflow, and not only the post-acquisition steps, need thorough validation. In this context, the present review investigates the progress in the research methodology on FTIR-based detection of virgin olive oil adulteration over a period of more than 25 years with the aim to capture the trends, identify gaps or misuses in the existing literature and highlight intriguing topics for future studies. An extensive search in Scopus, Web of Science and Google Scholar, combined with bibliometric analysis, helped to extract qualitative and quantitative information from publication sources. Our findings verified that intercomparison of literature results is often impossible; sampling design, FTIR spectral acquisition and performance evaluation are critical methodological issues that need more specific guidance and criteria for application to product authenticity testing.

An Overview on the Application of Chemometrics Tools in Food Authenticity and Traceability

The use of advanced chemometrics tools in food authenticity research is crucial for managing the huge amount of data that is generated by applying state-of-the-art analytical methods such as chromatographic, spectroscopic, and non-targeted fingerprinting approaches. Thus, this review article provides description, classification, and comparison of the most important statistical techniques that are commonly employed in food authentication and traceability, including methods for exploratory data analysis, discrimination, and classification, as well as for regression and prediction. This literature revision is not intended to be exhaustive, but rather to provide a general overview to non-expert readers in the use of chemometrics in food science. Overall, the available literature suggests that the selection of the most appropriate statistical technique is dependent on the characteristics of the data matrix, but combining complementary tools is usually needed for properly handling data complexity. In that way, chemometrics has become a powerful ally in facilitating the detection of frauds and ensuring the authenticity and traceability of foods.

Differentiation of meat species of raw and processed meat based on polar metabolites using 1H NMR spectroscopy combined with multivariate data analysis

Meat species of raw meat and processed meat products were investigated by 1H NMR spectroscopy with subsequent multivariate data analysis. Sample preparation was based on aqueous extraction combined with ultrafiltration in order to reduce macromolecular components in the extracts. 1H NMR data was analyzed by using a non-targeted approach followed by principal component analysis (PCA), linear discrimination analysis (LDA), and cross-validation (CV) embedded in a Monte Carlo (MC) resampling approach. A total of 379 raw meat samples (pork, beef, poultry, and lamb) and 81 processed meat samples (pork, beef, poultry) were collected between the years 2018 and 2021. A 99% correct prediction rate was achieved if the raw meat samples were classified according to meat species. Predicting processed meat products was slightly less successful (93 %) with this approach. Furthermore, identification of spectral regions that are relevant for the classification via polar chemical markers was performed. Finally, data on polar metabolites were fused with previously published 1H NMR data on non-polar metabolites in order to build a broader classification model and to improve prediction accuracy.

Front-face synchronous fluorescence spectroscopy: a rapid and non-destructive authentication method for Arabica coffee adulterated with maize and soybean flours

This article describes a novel front-face synchronous fluorescence spectroscopy (FFSFS) method for the fast and non-invasive authentication of ground roasted Arabica coffee adulterated with roasted maize and soybean flours. The detection was based on the different composition of fluorescent Maillard reaction products and caffeine in roasted coffee and cereal flours. For each roasted maize or soybean adulterant flour (5-40 wt%), principal component analysis coupled with linear discriminant analysis (PCA-LDA) was used for qualitative discrimination. Quantitative prediction models were constructed based on the combination of unfolded total synchronous fluorescence spectra and partial least square regression (PLSR), followed by fivefold cross-validation and external validation. The PLSR models produced suitable results, with the determination coefficient of prediction (R p 2) > 0.9, root mean square error of prediction (RMSEP) < 5%, relative error of prediction (REP) < 25% and residual predictive deviation (RPD) > 3. The limits of detection (LOD) were both 10% for roasted maize and soybean flours. Most relative errors for the prediction of simulated blind samples were between -30% and + 30%. The benefits of this strategy are simplicity, rapidity, and non-destructive detection. However, owing to the high similarity between roasted coffee and roasted cereal flours and the influence of the roasting degree on fluorescent Maillard reaction products, its application is limited to the preliminary screening of roasted coffee with the same roasting degree, adulterated with relatively large amounts of roasted cereal flours which are roasted to analogous color to the coffee.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00003-022-01396-8.

Comprehensive examination and comparison of machine learning techniques for the quantitative determination of adulterants in honey using Fourier infrared spectroscopy with attenuated total reflectance accessory

Facile, robust, and accurate analyses of honey adulterants are required in the honey industry to assess its purity for commercialization purposes. A stacked regression ensemble approach using Fourier transform infrared spectroscopic method was developed for the quantitative determination of corn, cane, beet, and rice syrup adulterants in honey. A training set (n=81) was used to predict the percent adulterant composition of the aforementioned constituents in an independent test set (n=32). A comprehensive comparison of the performance of various machine learning techniques including support vector regression using linear function, least absolute shrinkage and selection operator, ride regression, elastic net, partial least squares, random forests, recursive partitioning and regression trees, gradient boosting, and gaussian process regression was assessed. The predictive performance of the aforementioned machine learning approaches was then compared with stacked regression, an ensemble learning technique which collates the performance of the various abovementioned techniques. Results show that stacked regression did not primarily outperform other techniques across all four syrup adulterant constituents in the testing set data. Further, elastic net generalized linear model generated the optimum results (Rootmeansquareerrorofprediction(RMSEP)_average=0.0107,R_average²=0.809) across all four honey adulterant constituents. Elastic net coupled with Fourier transform infrared spectroscopy may offer a novel, direct, and accurate method of simultaneously quantifying corn, cane, beet, and rice syrup adulterants in honey.

Nontargeted Analysis of Lipid Extracts Using 1H NMR Spectroscopy Combined with Multivariate Statistical Analysis to Discriminate between the Animal Species of Raw and Processed Meat

The animal species of raw meat and processed meat products was determined by ¹H NMR spectroscopy with subsequent multivariate data analysis. Sample preparation was based on comprehensive lipid extraction to capture nonpolar and polar (amphiphilic) fat components of meat. A nontargeted approach was used to analyze the ¹H NMR data, followed by a principal component analysis, linear discrimination analysis, and cross-validation embedded in a Monte Carlo re-sampling approach. A total of 437 raw meat samples (pork, beef, poultry, and lamb) and 81 processed meat samples (pork, beef, and poultry) were collected to build and/or test the classification model. On average, 98% of the analyzed raw meat samples and 97% of the processed meat products were correctly classified with respect to meat species. Furthermore, relevant spectral regions to identify potential chemical markers such as linoleic acids, trans-fatty acids, and cholesterol for the meat species classification were described.

Metabolomic Study of Dactylis glomerata Growing on Aeolian Archipelago (Italy)

The Aeolian Islands (Italy) are a volcanic archipelago in the Tyrrhenian Sea comprising seven main islands, among which are two active volcanoes. The peculiar geological features and the wide variety of environments and soils have an important impact on native plants, and in particular, the Aeolian populations of Dactylis glomerata (a perennial cool-season bunchgrass) exhibit remarkable phenotypic variability. Considering that environmental drivers also strongly affect the production of plant metabolites, this work aimed at comparing the metabolomic profiles of D. glomerata (leaves) harvested at different altitudes on four islands of the Aeolian archipelago, namely: Lipari, Vulcano, Stromboli and Panarea. Samples were analyzed by ¹H NMR profiling, and data were treated by PCA. Samples collected on Stromboli were very different from each other and from the samples collected in the other islands. Through an Orthogonal Partial Least Squares (OPLS) model, using altitude as the y variable, it emerged that the concentration of proline, glycine betaine, sucrose, glucose and chlorogenic acid of D. glomerata growing on Stromboli decreased at increasing altitude. Conversely, increasing altitude was associated with an increment in valine, asparagine, fumaric acid and phenylalanine.

Machine Learning Applications for Chemical Fingerprinting and Environmental Source Tracking Using Non-target Chemical Data

A frequent goal of chemical forensic analyses is to select a panel of diagnostic chemical features─colloquially termed a chemical fingerprint─that can predict the presence of a source in a novel sample. However, most of the developed chemical fingerprinting workflows are qualitative in nature. Herein, we report on a quantitative machine learning workflow. Grab samples (n = 51) were collected from five chemical sources, including agricultural runoff, headwaters, livestock manure, (sub)urban runoff, and municipal wastewater. Support vector classification was used to select the top 10, 25, 50, and 100 chemical features that best discriminate each source from all others. The cross-validation balanced accuracy was 92-100% for all sources (n = 1,000 iterations). When screening for diagnostic features from each source in samples collected from four local creeks, presence probabilities were low for all sources, except for wastewater at two downstream locations in a single creek. Upon closer investigation, a wastewater treatment facility was located ∼3 km upstream of the nearest sample location. In addition, using simulated in silico mixtures, the workflow can distinguish presence and absence of some sources at 10,000-fold dilutions. These results strongly suggest that this workflow can select diagnostic subsets of chemical features that can be used to quantitatively predict the presence/absence of various sources at trace levels in the environment.

Amazonian cacao-clone nibs discrimination using NIR spectroscopy coupled to naïve Bayes classifier and a new waveband selection approach

Near-Infrared Spectroscopy (NIRS) has shown to be helpful in the study of rice, tea, cocoa, and other foods due to its versatility and reduced sample treatment. However, the high complexity of the data produced by NIR sensors makes necessary pre-treatments such as feature selection techniques that produce compact profiles. Supervised and unsupervised techniques have been tested, creating different subsets of features for classification, which affect the performance of the classifiers based on such compact profiles. In this sense, we propose and test a new covering array feature selection (CAFS) algorithm coupled to the naïve Bayes classifier (NBC) to discriminate among Amazonian cacao nibs from six cacao clones. The CAFS wrapper approach looks for the wavebands that maximize the F₁-score, and then, are more relevant for classification. For this purpose, cacao pods of six varieties were collected, and their grains were extracted and processed (fermented, dried, roasted, and milled) to obtain cacao nibs. Then from each clone NIR spectral profiles in the range of 1100-2500 nm were extracted, and relevant wavebands were selected using the proposed CAFS algorithm. For comparison, two standard feature selection techniques were implemented the multi-cluster feature selection MCFS and the eigenvector centrality feature selection ECFS. Then, based on the different selected variables, three NBCs were built and compared among them through statistical metrics. The results showed that using the wavebands selected by CAFS, the NBC performed an average accuracy of 99.63%; being this superior to the 94.92% and 95.79% for ECFS and MCFS respectively. These results showed that the wavebands selected by the proposed CAFS algorithm allowed obtaining a better fit concerning other feature selection methods reported in the literature.

Data Fusion Approaches for the Characterization of Musts and Wines Based on Biogenic Amine and Elemental Composition

Samples from various winemaking stages of the production of sparkling wines using different grape varieties were characterized based on the profile of biogenic amines (BAs) and the elemental composition. Liquid chromatography with fluorescence detection (HPLC-FLD) combined with precolumn derivatization with dansyl chloride was used to quantify BAs, while inductively coupled plasma (ICP) techniques were applied to determine a wide range of elements. Musts, base wines, and sparkling wines were analyzed accordingly, and the resulting data were subjected to further chemometric studies to try to extract information on oenological practices, product quality, and varieties. Although good descriptive models were obtained when considering each type of data separately, the performance of data fusion approaches was assessed as well. In this regard, low-level and mid-level approaches were evaluated, and from the results, it was concluded that more comprehensive models can be obtained when joining data of different natures.

Strategic Priorities of the Scientific Plan of the European Research Infrastructure METROFOOD-RI for Promoting Metrology in Food and Nutrition

The pan-European distributed Research Infrastructure for Promoting Metrology in Food and Nutrition (METROFOOD-RI) has evolved in the frame of the European Strategy Forum on Research Infrastructures (ESFRI) to promote high-quality metrology services across the food chain. The METROFOOD-RI comprises physical facilities and electronic facilities. The former includes Reference Material plants and analytical laboratories (the ‘Metro’ side) and also experimental fields/farms, processing/storage plants and kitchen-labs (the ‘Food’ side). The RI is currently prepared to apply for receiving the European Research Infrastructure Consortium (ERIC) legal status and is organised to fulfil the requirements for operation at the national, European Union (EU) and international level. In this view, the METROFOOD-RI partners have recently reviewed the scientific plan and elaborated strategic priorities on key thematic areas of research in the food and nutrition domain to which they have expertise to contribute to meet global societal challenges and face unexpected emergencies. The present review summarises the methodology and main outcomes of the research study that helped to identify the key thematic areas from a metrological standpoint, to articulate critical and emerging issues and demands and to structure how the integrated facilities of the RI can operate in the first five years of operation as ERIC.

Uses of FT-MIR Spectroscopy and Multivariate Analysis in Quality Control of Coffee, Cocoa, and Commercially Important Spices

Nowadays, coffee, cocoa, and spices have broad applications in the food and pharmaceutical industries due to their organoleptic and nutraceutical properties, which have turned them into products of great commercial demand. Consequently, these products are susceptible to fraud and adulteration, especially those sold at high prices, such as saffron, vanilla, and turmeric. This situation represents a major problem for industries and consumers’ health. Implementing analytical techniques, i.e., Fourier transform mid-infrared (FT-MIR) spectroscopy coupled with multivariate analysis, can ensure the authenticity and quality of these products since these provide unique information on food matrices. The present review addresses FT-MIR spectroscopy and multivariate analysis application on coffee, cocoa, and spices authentication and quality control, revealing their potential use and elucidating areas of opportunity for future research.

Assessing Reliability of Non-targeted High-Resolution Mass Spectrometry Fingerprints for Quantitative Source Apportionment in Complex Matrices

Effective management of contaminated sites requires differentiating and deconvoluting contaminant source impacts in complex environmental systems. The existing source apportionment approaches that use targeted analyses of preselected indicator chemicals are limited whenever target analytes are below the detection limits or derived from multiple sources. However, non-targeted analyses that leverage high-resolution mass spectrometry (HRMS) yield rich datasets that deeply characterize sample-specific chemical compositions, providing additional potential end-members for source differentiation and apportionment. Previous work demonstrated that HRMS fingerprints can define sample uniqueness and support accurate, quantitative source concentration estimates. Here, using two aqueous film-forming foams as representative complex sources, we assessed the qualitative fidelity and quantitative accuracy of HRMS source fingerprints in increasingly complex background matrices. Across all matrices, HRMS-derived source concentration estimates were 0.81 ± 0.11-fold and 0.64 ± 0.24-fold of actual in samples impacted solely by analytical matrix effects (MEs) or by sample processing recovery and analytical MEs, respectively. Isotopic internal standards were not easily paired to individual unidentified non-target features, but bulk internal standard-based abundance corrections improved apportionment accuracy in higher matrix samples (to 0.90 ± 0.12-fold of actual) and/or informed concentration estimate relative errors. HRMS fingerprint mining could identify, based on the dilution behavior, effective individual chemical end-members across 16 homologous series. Although method development is needed, the results further demonstrate the potential applications of non-targeted HRMS data for source apportionment and other quantitative outcomes.

Comparison of Various Signal Processing Techniques and Spectral Regions for the Direct Determination of Syrup Adulterants in Honey Using Fourier Transform Infrared Spectroscopy and Chemometrics

Honey consumption has become increasingly popular worldwide. However, the increase in demand for honey has also caused an increase in its adulteration, a deliberate fraud which involves adding of other substances to pure honey for economic purposes. This process not only lowers the quality of honey, but also has potential health risks, including high blood sugar, increased risk of diabetes, and weight gain. Herein, we develop an easy-to-use and direct method of quantifying corn, cane, beet, and rice syrup adulterants in honey using Fourier transform infrared spectroscopy and chemometrics. Various signal processing techniques, including derivatives, moving average, binning, Savitzky–Golay, and standard normal variate using the entire spectral region (3996–650 cm−1) and specific spectral region (1501–799 cm−1), were compared. Optimum results were obtained using first derivative signal processing for both the entire and specific spectral regions. The first derivative signal processing technique garnered the most optimum results using the specific spectral range (1501–799 cm−1) (RMSECVaverage = 0.021, RMSEPaverage = 0.014, R2average = 0.859) across all syrup adulterants. An exploratory analysis to assess the utility of this specific spectral region in pattern recognition of samples based on their adulterant content show that this region is effective in discriminating samples according to the presence or absence of honey syrup adulterants.

An Overview of the Successful Application of Vibrational Spectroscopy Techniques to Quantify Nutraceuticals in Fruits and Plants

Vibrational spectroscopy techniques are the most used techniques in the routine analysis of foods. This technique is widely utilised to measure and monitor the proximate chemical composition (e.g., protein, dry matter, fat and fibre) in an array of agricultural commodities, food ingredients and products. Developments in optics, instrumentation and hardware concomitantly with data analytics, have allowed for the progress in novel applications of these technologies in the field of nutraceutical and bio compound analysis. In recent years, several studies have demonstrated the capability of vibrational spectroscopy to evaluate and/or measure these nutraceuticals in a broad selection of fruit and plants as alternative to classical analytical approaches. This article highlights, as well as discusses, the challenges and opportunities that define the successful application of vibrational spectroscopy techniques, and the advantages that these techniques have to offer to evaluate and quantify nutraceuticals in fruits and plants.

Characterization of Musts, Wines, and Sparkling Wines Based on Their Elemental Composition Determined by ICP-OES and ICP-MS

Samples from the different processing stages in the elaboration of sparkling wine (cava)—including must, base wine, and sparkling wine—of Pinot Noir and Xarel·lo grape varieties from different vineyard qualities (A, B, C, D) have been analyzed by inductively coupled plasma (ICP) techniques to determine their elemental composition. The resulting data has been used to characterize these products according to oenological features and product qualities. For this purpose, box plot diagrams, bar charts, and principal components analysis (PCA) have been used. The study of the behavior of each given species has pointed out the relevance of some elements as markers or descriptors of winemaking processes. Among others, Cu and K are abundant in musts and their concentrations progressively decrease through the cava production process. S levels suddenly increase at the base wine step (and further decay) due to the addition of sulfites as preserving agents. Finally, concentrations of Na, Ca, Fe, and Mg increase from the first fermentation due to the addition of clarifying agents such as bentonite. PCA has been applied to try to extract solid and global conclusions on trends and chemical markers within the groups of samples more easily and efficiently than more conventional approaches.