Differentiation of Organically and Conventionally Grown Tomatoes by Chemometric Analysis of Combined Data from Proton Nuclear Magnetic Resonance and Mid-infrared Spectroscopy and Stable Isotope Analysis

Classification of Organic and Conventional Vegetables Using Machine Learning: A Case Study of Brinjal, Chili and Tomato

Growing organic food is becoming a challenging task with increasing demand. Food fraud activity has increased considerably with the increase in population growth. Consumers cannot visually distinguish between conventional and organically grown food products. Spectroscopic methodologies are presented to identify chemicals in food, thereby identifying organic and conventional food. Such spectroscopic techniques are laboratory-based, take more time to produce an outcome, and are costlier. Thus, this research designed a portable, low-cost multispectral sensor system to discriminate between organic and conventional vegetables. The designed multispectral sensor system uses a wavelength range (410 nm–940 nm) that includes three bands, namely visible (VIS), ultraviolet (UV) and near-infrared (NIR) spectra, to enhance the accuracy of detection. Tomato, brinjal and green chili samples are employed for the experiment. The organic and conventional discrimination problem is formulated as a classification problem and solved through random forest (RF) and neural network (NN) models, which achieve 92% and 89% accuracy, respectively. A two-stage enhancement mechanism is proposed to improve accuracy. In the first stage, the fuzzy logic mechanism generates additional feature sets. Ant colony optimization (ACO) algorithm-based parameter tuning and feature selection are employed in the second stage to enhance accuracy further. This two-stage improvement mechanism results in 100% accuracy in discriminating between organic and conventional vegetable samples. The detected adulterant is displayed on a web page through an IoT-developed application module to be accessed from anywhere.

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.

High-resolution 1H NMR profiling of triacylglycerols as a tool for authentication of food from animal origin: Application to hen egg matrix

Metabolomics of complex biological matrices conducted by means of ¹H NMR leads to spectra suffering from severe signal overlapping. Previously, we have developed a high-resolution spectral treatment method to help solving this issue in ¹H NMR of triacylglycerols. In this work, we tested the potential of the developed method in the characterization and authentication of food products from animal origin using egg yolk as a model matrix. The approach consisted in a spectral deconvolution guided by the precision obtained on the deconvoluted peaks after reference lineshape adjustment of spectra. Thus, 135 peaks were quantitated and successfully used as biomarkers of origin, of hens breed, and of farming system. This required multivariate statistical analyses for classification. The same pool of variables allowed construction of multivariate quantitation models for individual fatty acids. Furthermore, minute amounts of conjugated fatty acids were quantitated and used as fingerprints of samples from backyard and free-range farming.

Chemometric Discrimination of the Varietal Origin of Extra Virgin Olive Oils: Usefulness of 13C Distortionless Enhancement by Polarization Transfer Pulse Sequence and 1H Nuclear Magnetic Resonance Data and Effectiveness of Fusion with Mid-Infrared Spectroscopy Data

The label authentication of monovarietal extra virgin olives is of great relevance from a socio-economical point of view. This work aims to gain insights into the prediction of the varietal origin of extra virgin olive oil (EVOO) samples obtained from single olive cultivars, French cultivars Olivière, Salonenque, and Tanche and Portuguese cultivars Blanqueta, Carrasquenha, and Galega Vulgar, collected in 2016-2017 and 2017-2018 harvest seasons. To pursue this study, spectroscopic approaches based on one-dimensional nuclear magnetic resonance (1D NMR) spectroscopy, namely, ¹H and ¹³C NMR distortionless enhancement by polarization transfer (DEPT) 45 pulse sequence, and Fourier transform mid-infrared spectroscopy (FT-MIR) are used in combination with partial least square discriminant analysis (PLS1-DA). The results obtained by PLS1-DA models using ¹H and ¹³C NMR DEPT 45 data are compared to those of PLS1-DA models using MIR data. The application of a control chart method allows for the optimization of the interpretation of the PLS1-DA results, and an efficient two-step strategy is proposed to improve the discrimination of the six studied cultivars. Then, NMR and MIR data are combined by either a mid- or high-level data fusion approach to further improve the discrimination. The models are also tested on samples from other cultivars to check their ability to reject varieties that were not considered in the calibration process.

Tracing the origin of paracetamol tablets by near-infrared, mid-infrared, and nuclear magnetic resonance spectroscopy using principal component analysis and linear discriminant analysis

Most drugs are no longer produced in their own countries by the pharmaceutical companies, but by contract manufacturers or at manufacturing sites in countries that can produce more cheaply. This not only makes it difficult to trace them back but also leaves room for criminal organizations to fake them unnoticed. For these reasons, it is becoming increasingly difficult to determine the exact origin of drugs. The goal of this work was to investigate how exactly this is possible by using different spectroscopic methods like nuclear magnetic resonance and near- and mid-infrared spectroscopy in combination with multivariate data analysis. As an example, 56 out of 64 different paracetamol preparations, collected from 19 countries around the world, were chosen to investigate whether it is possible to determine the pharmaceutical company, manufacturing site, or country of origin. By means of suitable pre-processing of the spectra and the different information contained in each method, principal component analysis was able to evaluate manufacturing relationships between individual companies and to differentiate between production sites or formulations. Linear discriminant analysis showed different results depending on the spectral method and purpose. For all spectroscopic methods, it was found that the classification of the preparations to their manufacturer achieves better results than the classification to their pharmaceutical company. The best results were obtained with nuclear magnetic resonance and near-infrared data, with 94.6%/99.6% and 98.7/100% of the spectra of the preparations correctly assigned to their pharmaceutical company or manufacturer.

Applications of nuclear magnetic resonance spectroscopy to the evaluation of complex food constituents

Due to a number of unparalleled advantages such as fastness, accuracy, intactness, nuclear magnetic resonance spectroscopy (NMR) has fulfilled a significant role in determining structures and dynamics of various physical, chemical and biological systems in the field of food analysis. This study introduced the principle of NMR, key NMR techniques such as ¹H NMR, DOSY, NOESY, HSQC, etc., and the knowledge of NMR applications on the evaluation of complex food system, especially the interactions of food components. The reviewed research work provides sufficient evidence that NMR spectroscopy has been an invaluable tool and will play an increasingly important role in specific technical support for food assessment. In addition, NMR combined with various other technologies could give a complete picture of the mechanism of the performance of functional food compounds, which are vital for human health and influence the intrinsic food properties during processing, storage and transportation at the molecular level.

NMR Metabolomics Applied on the Discrimination of Variables Influencing Tomato (Solanum lycopersicum)

Tomato composition and nutritional value are attracting increasing attention and interest from both consumers and producers. The interest in enhancing fruits' quality with respect to beneficious nutrients and flavor/aroma components is based not only in their economic added value but also in their implications involving organoleptic and healthy properties and has generated considerable research interest among nutraceutical and horticultural industries. The present article reviews up to March 2020 some of the most relevant studies based on the application of NMR coupled to multivariate statistical analysis that have addressed the investigation on tomato (Solanum lycopersicum). Specifically, the NMR untargeted technique in the agri-food sector can generate comprehensive data on metabolic networks and is paving the way towards the understanding of variables affecting tomato crops and composition such as origin, variety, salt-water irrigation, cultivation techniques, stage of development, among many others. Such knowledge is helpful to improve fruit quality through cultural practices that divert the metabolism towards the desired pathways and, probably more importantly, drives further efforts towards the differentiation of those crops developed under controlled and desired agronomical conditions.

Traceability of fruits and vegetables

Food safety and traceability are nowadays a constant concern for consumers, and indeed for all actors in the food chain, including those involved in the fruit and vegetable sector. For the EU, the principles and legal requirements of traceability are set out in Regulation 178/2002. Currently however the regulation does not describe any analytical traceability tools. Furthermore, traceability systems for fruits and vegetables face increasing competition due to market globalization. The current challenge for actors in this sector is therefore to be sufficiently competitive in terms of price, traceability, quality and safety to avoid scandal and fraud. For all these reasons, new, flexible, cheap and efficient traceability tools, as isotopic analysis, DNA fingerprinting and metabolomic profiling coupled with chemometrics are needed.

Fusion of near-infrared and fluorescence spectroscopy for untargeted fraud detection of Chinese tea seed oil using chemometric methods

BACKGROUND

This paper investigated the feasibility of data fusion of near-infrared (NIR) and fluorescence spectroscopy for rapid analysis of cheap vegetable oils in Chinese Camellia oleifera Abel. (COA) oil. Because practical frauds usually involve adulterations of multiple known and unknown cheap oils, traditional analytical methods aimed at detecting one or more known adulterants are insufficient to identify adulterated COA oil. Therefore, untargeted analysis was performed by developing class models of pure COA oil using robust one-class partial least squares (OCPLS).

RESULTS

The most accurate OCPLS model was obtained with fusion of standard normal variate (SNV)-NIR and SNV-fluorescence spectra with sensitivity of 0.954 and specificity of 0.91. Robust OCPLS could detect adulterations with 2% (w/w) or more cheap oils, including rapeseed oil, sunflower seed oil, corn oil and peanut oil.

CONCLUSION

Fusion of NIR and fluorescence data and chemometrics provided enhanced capacity for rapid and untargeted analysis of multiple adulterations in Chinese COA oils. © 2018 Society of Chemical Industry.

Data fusion approaches in spectroscopic characterization and classification of PDO wine vinegars

Spain is one of the major producers of high-quality wine vinegars having three protected designations of origin (a.k.a. PDOs): "Vinagre de Jerez", "Vinagre de Condado de Huelva" and "Vinagre de Montilla-Moriles". Their high prices due to their high quality and their high production costs explain the need for developing an adequate quality control technique and the interest in extensive characterization in order to capture the identity of each denomination. In this framework, methodologies based on non-targeted techniques, such as spectroscopies, are becoming popular in food authentication. Thus, for improving vinegar quality assessment, fusion of data blocks obtained from the same samples but different analytical techniques could be a good strategy, since the quantity and quality of sample knowledge could be enhanced providing new insights into the differentiation of vinegars. Therefore, the aim of this manuscript is the development of a multi-platform methodology and a model able to classify the Spanish wine vinegar PDOs. Sixty-five PDO wine vinegars were analyzed by four spectroscopic techniques: Fourier-transform mid-infrared spectroscopy (MIR), near infrared spectroscopy (NIR), multidimensional fluorescence spectroscopy (EEM) and proton nuclear magnetic resonance (¹H-NMR). Two different data fusion strategies were evaluated: Mid-level data fusion with different preprocessing, and Common Component and Specific Weights analysis multiblock method. Exploratory and classification analysis on the data from individual techniques were also performed and compared with data fusion models. The data fusion models improved the classification, providing a more efficient differentiation, than the models based on single methods, and supporting the approach to combine these methods to achieve synergies for an optimized PDO differentiation.

A non-targeted metabolomic approach to identify food markers to support discrimination between organic and conventional tomato crops

In the last decade, the consumption trend of organic food has increased dramatically worldwide. However, the lack of reliable chemical markers to discriminate between organic and conventional products makes this market susceptible to food fraud in products labeled as "organic". Metabolomic fingerprinting approach has been demonstrated as the best option for a full characterization of metabolome occurring in plants, since their pattern may reflect the impact of both endogenous and exogenous factors. In the present study, advanced technologies based on high performance liquid chromatography-high-resolution accurate mass spectrometry (HPLC-HRAMS) has been used for marker search in organic and conventional tomatoes grown in greenhouse under controlled agronomic conditions. The screening of unknown compounds comprised the retrospective analysis of all tomato samples throughout the studied period and data processing using databases (mzCloud, ChemSpider and PubChem). In addition, stable nitrogen isotope analysis (δ¹⁵N) was assessed as a possible indicator to support discrimination between both production systems using crop/fertilizer correlations. Pesticide residue analyses were also applied as a well-established way to evaluate the organic production. Finally, the evaluation by combined chemometric analysis of high-resolution accurate mass spectrometry (HRAMS) and δ¹⁵N data provided a robust classification model in accordance with the agricultural practices. Principal component analysis (PCA) showed a sample clustering according to farming systems and significant differences in the sample profile was observed for six bioactive components (L-tyrosyl-L-isoleucyl-L-threonyl-L-threonine, trilobatin, phloridzin, tomatine, phloretin and echinenone).

Fourier Transform Infrared (FT-IR) and Laser Ablation Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) Imaging of Cerebral Ischemia: Combined Analysis of Rat Brain Thin Cuts Toward Improved Tissue Classification

Microspectroscopic techniques are widely used to complement histological studies. Due to recent developments in the field of chemical imaging, combined chemical analysis has become attractive. This technique facilitates a deepened analysis compared to single techniques or side-by-side analysis. In this study, rat brains harvested one week after induction of photothrombotic stroke were investigated. Adjacent thin cuts from rats' brains were imaged using Fourier transform infrared (FT-IR) microspectroscopy and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The LA-ICP-MS data were normalized using an internal standard (a thin gold layer). The acquired hyperspectral data cubes were fused and subjected to multivariate analysis. Brain regions affected by stroke as well as unaffected gray and white matter were identified and classified using a model based on either partial least squares discriminant analysis (PLS-DA) or random decision forest (RDF) algorithms. The RDF algorithm demonstrated the best results for classification. Improved classification was observed in the case of fused data in comparison to individual data sets (either FT-IR or LA-ICP-MS). Variable importance analysis demonstrated that both molecular and elemental content contribute to the improved RDF classification. Univariate spectral analysis identified biochemical properties of the assigned tissue types. Classification of multisensor hyperspectral data sets using an RDF algorithm allows access to a novel and in-depth understanding of biochemical processes and solid chemical allocation of different brain regions.

Isotope ratio mass spectrometry in combination with chemometrics for characterization of geographical origin and agronomic practices of table grape

BACKGROUND

Although table grape is one of the most cultivated and consumed fruits worldwide, no study has been reported on its geographical origin or agronomic practice based on stable isotope ratios. This study aimed to evaluate the usefulness of isotopic ratios (i.e. ² H/¹ H, ¹³ C/¹² C, ¹⁵ N/¹⁴ N and ¹⁸ O/¹⁶ O) as possible markers to discriminate the agronomic practice (conventional versus organic farming) and provenance of table grape.

RESULTS

In order to quantitatively evaluate which of the isotopic variables were more discriminating, a t test was carried out, in light of which only δ¹³ C and δ¹⁸ O provided statistically significant differences (P ≤ 0.05) for the discrimination of geographical origin and farming method. Principal component analysis (PCA) showed no good separation of samples differing in geographical area and agronomic practice; thus, for classification purposes, supervised approaches were carried out. In particular, general discriminant analysis (GDA) was used, resulting in prediction abilities of 75.0 and 92.2% for the discrimination of farming method and origin respectively.

CONCLUSION

The present findings suggest that stable isotopes (i.e. δ¹⁸ O, δ² H and δ¹³ C) combined with chemometrics can be successfully applied to discriminate the provenance of table grape. However, the use of bulk nitrogen isotopes was not effective for farming method discrimination. © 2016 Society of Chemical Industry.