Chemometrics-driven monitoring of cheese ripening: a multimodal spectroscopic and scanning electron microscopy investigation.

The integration of spectroscopic techniques with chemometrics offers a means to monitor quality changes in dairy products throughout processing and storage. This study employed Attenuated Total Reflectance-Mid-Infrared Spectroscopy (ATR-MIR) coupled with Independent Components Analysis (ICA), and 3D Front-Face Fluorescence Spectroscopy (FFFS) paired with Common Components and Specific Weight Analysis (CCSWA). The research focused on Cheddar cheeses aged for 1, 2, 3, and 5 years, alongside Comté cheeses aged for 6, 9, and 12 months. The adopted approach offered valuable insights into the intricate cheese aging process within the food matrix. The ICA proportions and CCSWA scores highlighted the significant impact of biochemical transformations during maturation on the aging process. The extracted independent components (ICs) revealed variations in the vibration modes of amides, lipids, amino acids, and organic acids, facilitating the distinction between different cheese age categories. Additionally, CCSWA outcomes identified age-related differences through shifts in tryptophan fluorescence characteristics as the cheeses aged. These results were consistent with the observed alterations in the microstructure of cheese samples over time, corroborated by Scanning Electron Microscopy (SEM) imagery. The introduced multimodal methodology serves as a significant asset for determining the ripening stage of various types of cheese, offering a detailed perspective of cheese maturation beneficial to the dairy industry and researchers.

Recent developments of e-sensing devices coupled to data processing techniques in food quality evaluation: a critical review.

A greater demand for high-quality food is being driven by the growth of economic and technological advancements. In this context, consumers are currently paying special attention to organoleptic characteristics such as smell, taste, and appearance. Motivated to mimic human senses, scientists developed electronic devices such as e-noses, e-tongues, and e-eyes, to spot signals relative to different chemical substances prevalent in food systems. To interpret the information provided by the sensors' responses, multiple chemometric approaches are used depending on the aim of the study. This review based on the Web of Science database, endeavored to scrutinize three e-sensing systems coupled to chemometric approaches for food quality evaluation. A total of 122 eligible articles pertaining to the e-nose, e-tongue and e-eye devices were selected to conduct this review. Most of the performed studies used exploratory analysis based on linear factorial methods, while classification and regression techniques came in the second position. Although their applications have been less common in food science, it is to be noted that nonlinear approaches based on artificial intelligence and machine learning deployed in a big-data context have generally yielded better results for classification and regression purposes, providing new perspectives for future studies.

Consumption of Boiled, but Not Grilled, Roasted, or Barbecued Beef Modifies the Urinary Metabolite Profiles in Rats.

SCOPE: The consumption of processed meat is associated with increased risk of chronic diseases, but determining how the exposure to specific cooking processes alters the metabolome is an analytical challenge. This study aims to evaluate the impact of four typical cooking methods for beef (boiling, barbecuing, grilling, and roasting) on the urinary metabolite profiles in rats, using a non-targeted approach. METHODS AND RESULTS: Male Wistar rats (n  =  48) are fed for 3 weeks with experimental diets containing either raw or cooked (boiled, barbecued, grilled, and roasted) beef. A control group is fed with milk proteins. The 24 h-urines are analyzed using LC-MS. The consumption of boiled meat leads to the specific excretion of di- and tri-peptides (aspartyl-leucine, glycyl-aspartate, and aspartyl-prolyl-threonine) and a cyclo-prolyl-proline (p < 0.001). No singular metabolite specifically associated with the groups "grilled," "roasted," and "barbecued" meat is observed. CONCLUSION: Urinary metabolite profiles of rats fed boiled beef are clearly distinct from those of rats fed with raw, grilled, roasted, or barbecued beef. The specific metabolites include the products of non-digested proteins and may be useful as potential intake biomarkers of this meat cooking method.

Screening of metallic pollution in complex environmental samples through a transcriptomic fingerprint method.

Characterizing waste ecotoxicity is laborious because of both the undefined nature of environmental samples and the diversity of contaminants that can be present. With regard to these limitations, traditional approaches do not provide information about the nature of the pollution encountered. To improve such assessments, a fluorescent library of 1870 transcriptomic reporters from Escherichia coli K12 MG1655 was used to report the ecotoxic status of environmental samples. The reliability of the approach was evaluated with 6 metallic pollutants (As, Cu, Cd, Hg, Pb, Zn) used alone and in mixture in pure and complex matrices. A total of 18 synthetic samples were used to characterize the specificity of the resulting metallic contamination fingerprints. Metallic contamination impacted 4.5 to 10.2% of the whole transcriptomic fingerprint of E. coli. The analysis revealed that a subset of 175 transcriptomic reporters is sufficient to characterize metallic contamination, regardless of the nature of the sample. A statistical model distinguished patterns due to metallic contamination and provided information about the level of toxicity with 93 to 98% confidence. The use of the transcriptomic assessment was validated for 17 complex matrices with various toxicities and metal contaminants, such as activated sludge, wastewater effluent, soil, wood and river water. The presence of metals and their associated toxicity, which seems linked to their bioavailabilities, were thereby determined. This method constitutes a possible tool to screen unknown complex samples for their metallic status and identify those for which a deeper characterization must be achieved by the use of traditional biosensors and analytical methods.

Does Variability Affect the Performance of Front-Face Fluorescence Spectroscopy? A Study Case on Commercial Lebanese Olive Oil.

The potential of front-face fluorescence spectroscopy coupled with chemometric techniques, namely multiple linear regression (MLR) applied on parallel factor (PARAFAC) scores and partial least squares (PLS), was tested on Lebanese olive oil samples possessing natural variability within their chemical parameters. Ninety-six olive oil samples have been harvested at different dates and from two seasons, processed using different extraction methods, collected from different altitudes and other factors that can increase the variability of the samples' chemical composition. Fluorescence excitation-emission matrices (EEM) of the collected samples were measured, and the relationship between them and the chemical parameters was examined. Twenty-two MLR regression models based on PARAFAC scores were generated, the majority of which showed a good correlation coefficient (R > 0.7 for ten predicted variables). A second model using PLS on the unfolded EEM was also conducted to improve the regression and to assess if it can handle the variability in hand. However, similar results, with a slight improvement over the MLR model, were obtained. In a non-experimental design, such variability may hinder the potentials of front-face fluorescence; however average to good MLR and PLS models were obtained, predicting the Lebanese olive oil deterioration quality parameters and fatty acid content.

Subpixel detection of peanut in wheat flour using a matched subspace detector algorithm and near-infrared hyperspectral imaging.

The detection of adulterations in food powder products represents a high interest especially when it concerns the health of the consumers. The food industry is concerned by peanut adulteration since it is a major food allergen often used in transformed food products. Near-infrared hyperspectral imaging is an emerging technology for food inspection. It was used in this work to detect peanut flour adulteration in wheat flour. The detection of peanut particles was challenging for two reasons: the particle size is smaller than the pixel size leading to impure spectral profiles; peanut and wheat flour exhibit similar spectral signatures and variability. A Matched Subspace Detector (MSD) algorithm was designed to take these difficulties into account and detect peanut adulteration at the pixel scale using the associated spectrum. A set of simulated data was generated to overcome the lack of reference values at the pixel scale and to design appropriate MSD algorithms. The best designs were compared by estimating the detection sensitivity. Defatted peanut flour and wheat flour were mixed in eight different proportions (from 0.02% to 20%) to test the detection performances of the algorithm on real hyperspectral measurements. The number and positions of the detected pixels were investigated to show the relevancy of the results and validate the design of the MSD algorithm. The presented work proved that the use of hyperspectral imaging and a fine-tuned MSD algorithm enables to detect a global adulteration of 0.2% of peanut in wheat flour.

Assessment of the microbial interplay during anaerobic co-digestion of wastewater sludge using common components analysis.

Anaerobic digestion (AD) is used to minimize solid waste while producing biogas by the action of microorganisms. To give an insight into the underlying microbial dynamics in anaerobic digesters, we investigated two different AD systems (wastewater sludge mixed with either fish or grass waste). The microbial activity was characterized by 16S RNA sequencing. 16S data is sparse and dispersed, and existent data analysis methods do not take into account this complexity nor the potential microbial interactions. In this line, we proposed a data pre-processing pipeline addressing these issues while not restricting only to the most abundant microorganisms. The data were analyzed by Common Components Analysis (CCA) to decipher the effect of substrate composition on the microorganisms. CCA results hinted the relationships between the microorganisms responding similarly to the AD physicochemical parameters. Thus, in overall, CCA allowed a better understanding of the inter-species interactions within microbial communities.

Assessment of substrate biodegradability improvement in anaerobic Co-digestion using a chemometrics-based metabolomic approach.

Anaerobic co-digestion (AcoD) can increase methane production of anaerobic digesters in plants treating wastewater sludge by improving the nutrient balance needed for the microorganisms to grow in the digesters, resulting in a faster process stabilization. Substrate mixture proportions are usually optimized in terms of biogas production, while the metabolic biodegradability of the whole mixture is neglected in this optimisation. In this aim, we developed a strategy to assess AcoD using metabolomics data. This strategy was explored in two different systems. Specifically, we investigated the co-digestion of wastewater sludge with different proportions of either grass or fish waste using untargeted High Performance Liquid Chromatography coupled to Mass Spectrometry (HPLC-MS) metabolomics and chemometrics methods. The analysis of these data revealed that adding grass waste did not improve the metabolic biodegradability of wastewater sludge. Conversely, a synergistic effect in the metabolic biodegradability was observed when fish waste was used, this effect being the highest for 25% of fish waste. In conclusion, metabolomics can be regarded as a promising tool both for characterizing the biochemical processes occurring during anaerobic digestion, and for providing a better understanding of the anaerobic digestion processes.

A 3D-Fluorescence Fingerprinting Approach to Detect Physiological Modifications Induced by Pesticide Poisoning in Apis mellifera: A Preliminary Study.

The combined use of 3D-fluorescence spectroscopy and independent component analysis using a differential fingerprinting approach has been applied with success to detect physiological effects of dimethoate in honeybees. Biochemical determinations combined with the identification of fluorescence zones that may correspond to proteins, NADH or neurotransmitters/neurohormones (octopamine, dopamine and serotonin) related to the physiological stress caused by the pesticide enabled phenomenological modeling of the physiological response in the honeybee using a simple and rapid method. The signals associated with the fluorophores involved in the response to stress were extracted from the fluorescence spectra using an unsupervised algorithm such as independent component analysis. The signals of different neurotransmitters were isolated on separated factorial components, thus facilitating their biochemical interpretation.

Exploratory study on the possibility to link gasoline samples sharing a common source after alteration by evaporation or combustion.

The source inference of ignitable liquids in forensic science is still a challenging and ongoing research area. In real case applications, specimens of different natures, which may have been exposed to fire or not, may have to be compared. These comparisons are difficult since specimens may have been altered by evaporation, combustion or both. Plus, the extent of the alteration is often difficult to evaluate. Most studies concerning source inference of ignitable liquids worked on neat samples or samples altered by evaporation. However, there is a lack of studies comparing the influence of evaporation and combustion within a source inference framework. In this study, the same collection of gasoline samples was altered by both evaporation under a nitrogen stream and combustion of the gasoline adsorbed on a matrix. The possibility to link gasoline samples sharing a common source was then explored using an adaptive untargeted chemometrics workflow from feature detection to feature selection. This data treatment approach was successfully applied to the data and it was shown that the possibility to link samples with a common source was not compromised despite evaporation or combustion for degrees of alteration from 0% to 99%.

Evaluation of an untargeted chemometric approach for the source inference of ignitable liquids in forensic science.

Recent research efforts in the domain of fire debris analysis have been mainly oriented towards the development of innovative analytical procedures and chemometric approaches for the detection and classification of ignitable liquids in fire specimens according to the ASTM E1618. However, less attention has been brought to the question of the source inference of ignitable liquids. Infer the identity of source of ignitable liquids recovered from arson sites is still a challenging and ongoing research area. In this study, the objective is to link neat gasoline samples sharing a common source through the use of an untargeted chemometric approach applied to data acquired by automated thermodesorption (ATD)-GC-MS following passive headspace extraction onto Tenax TA tubes. To that end, 190 unique gasoline samples from 19 gas stations collected over a year were used. A general and automated chemometric methodology for data treatment involving the following main steps is proposed: feature detection, normalization by exhaustive calculation of ratios between areas of pairs of features and selection of most discriminant ratios. The ratio selection procedure used here is based on the calculation of similarity measurements between pairs of samples sharing a common source or not. The algorithm maximizes the separation of the distributions of similarity measurements for related and unrelated samples by selecting a subset of ratios maximizing the area under the Receiver Operating Characteristics curve. The approach presented here was successfully applied to neat gasoline samples in order to assess if two gasoline samples share a common source or not.

Synergetic Use of Principal Component Analysis Applied to Normed Physicochemical Measurements and GC × GC-MS to Reveal the Stabilization Effect of Selected Essential Oils on Heated Rapeseed Oil.

Lipid oxidation leads to the formation of volatile compounds and very often to off-flavors. In the case of the heating of rapeseed oil, unpleasant odors, characterized as a fishy odor, are emitted. In this study, 2 different essential oils (coriander and nutmeg essential oils) were added to refined rapeseed oil as odor masking agents. The aim of this work was to determine a potential antioxidant effect of these essential oils on the thermal stability of rapeseed oil subject to heating cycles between room temperature and 180 °C. For this purpose, normed determinations of different parameters (peroxide value, anisidine value, and the content of total polar compounds, free fatty acids and tocopherols) were carried out to examine the differences between pure and degraded oil. No significant difference was observed between pure rapeseed oil and rapeseed oil with essential oils for each parameter separately. However, a stabilizing effect of the essential oils, with a higher effect for the nutmeg essential oil was highlighted by principal component analysis applied on physicochemical dataset. Moreover, the analysis of the volatile compounds performed by GC × GC showed a substantial loss of the volatile compounds of the essential oils from the first heating cycle.

Investigation of fragrance stability used in the formulation of cosmetic and hygienic products using headspace solid-phase microextraction by nanostructured materials followed by gas chromatography with mass spectrometry.

A new composite coating of polypyrrole and sodium lauryl ether sulfate was electrochemically prepared on a stainless-steel wire using cyclic voltammetry. The application and performance of the fiber was evaluated for the headspace solid-phase microextraction of a fragrance in aqueous bleach samples followed by gas chromatography combined with mass spectrometry to assess the fragrance stability in this kind of household cleaning product. To obtain a stable and efficient composite coating, parameters related to the coating process such as scan rate and numbers of cycles were optimized using a central composite design. In addition, the effects of various parameters on the extraction efficiency of the headspace solid-phase microextraction process such as extraction temperature and time, ionic strength, sample volume, and stirring rate were investigated by experimental design methods using Plackett-Burman and Doehlert designs. The optimum values of 53°C and 28 min for sample temperature and time, respectively, were found through response surface methodology. Results show that the combination of polypyrrole and sodium lauryl ether sulfate in a composite form presents desirable opportunities to produce new materials to study fragrance stability by headspace solid-phase microextraction.

Validation of a headspace trap gas chromatography and mass spectrometry method for the quantitative analysis of volatile compounds from degraded rapeseed oil.

Due to lipid oxidation, off-flavors, characterized by a fishy odor, are emitted during the heating of rapeseed oil in a fryer and affect the flavor of rapeseed oil even at low concentrations. Thus, there is a need for analytical methods to identify and quantify these products. To study the headspace composition of degraded rapeseed oil, and more specifically the compounds responsible for the fishy odor, a headspace trap gas chromatography with mass spectrometry method was developed and validated. Six volatile compounds formed during the degradation of rapeseed oil were quantified: 1-penten-3-one, (Z)-4-heptenal, hexanal, nonanal, (E,E)-heptadienal, and (E)-2-heptenal. Validation using accuracy profiles allowed us to determine the valid ranges of concentrations for each compound, with acceptance limits of 40% and tolerance limits of 80%. This method was then successfully applied to real samples of degraded oils.

3D-front-face fluorescence spectroscopy and independent components analysis: A new way to monitor bread dough development.

Following bread dough development can be a hard task as no reliable method exists to give the optimal mixing time. Dough development is linked to the evolution of gluten proteins, carbohydrates and lipids which can result in modifications in the spectral properties of the various fluorophores naturally present in the system. In this paper, we propose to use 3-D-front-face-fluorescence (3D-FFF) spectroscopy in the 250-550nm domain to follow the dough development as influenced by formulation (addition or not of glucose, glucose oxidase and ferulic acid in the dough recipe) and mixing time (2, 4, 6 and 8min). In all the 32 dough samples as well as in flour, three regions of maximum fluorescence intensities have been observed at 320nm after excitation at 295nm (Region 1), at 420nm after excitation at 360nm (Region 2) and 450nm after excitation at 390nm (Region 3). The principal components analysis (PCA) of the evolution of these maxima shows that the formulations with and without ferulic acid are clearly separated since the presence of ferulic acid induces a decrease of fluorescence in Region 1 and an increase in Regions 2 and 3. In addition, a kinetic effect of the mixing time can be observed (decrease of fluorescence in the Regions 1 and 2) mainly in the absence of ferulic acid. The analysis of variance (ANOVA) on these maximum values statistically confirms these observations. Independent components analysis (ICA) is also applied to the complete 3-D-FFF spectra in order to extract interpretable signals from spectral data which reflect the complex contribution of several fluorophores as influenced by their environment. In all cases, 3 signals can be clearly separated matching the 3 regions of maximal fluorescence. The signals corresponding to regions 1 and 2 can be ascribed to proteins and ferulic acid respectively, whereas the fluorophores associated with the 3rd signal (corresponding to region 3) remain unidentified. Good correlations are obtained between the IC score values of the 3 signals and the fluorescence intensities in Region 1, Region 2 and Region 3. Ferulic acid addition increases fluorescence in Region 2 and decreases fluorescence in Region 1, probably via a reabsorption of the protein fluorescence by ferulic acid. These phenomena are less pronounced when glucose oxidase is present. The enzymatic oxidation of ferulic acid by the glucose oxidase-peroxidase association could explain some of these effects.

Optimizing separation conditions of 19 polycyclic aromatic hydrocarbons by cyclodextrin-modified capillary electrophoresis and applications to edible oils.

For the first time, the separation of 19 polycyclic aromatic hydrocarbons (PAHs) listed as priority pollutants in environmental and food samples by the United States Environmental Protection Agency (US-EPA) and the European Food Safety Authority was developed in cyclodextrin (CD)-modified capillary zone electrophoresis with laser-induced fluorescence detection (excitation wavelength: 325 nm). The use of a dual CD system, involving a mixture of one neutral CD and one anionic CD, enabled to reach unique selectivity. As solutes were separated based on their differential partitioning between the two CDs, the CD relative concentrations were investigated to optimize selectivity. Separation of 19 PAHs with enhanced resolutions as compared with previous studies on the 16 US-EPA PAHs and efficiencies superior to 1.5 × 10(5) were achieved in 15 min using 10mM sulfobutyl ether-ß-CD and 20mM methyl-ß-CD. The use of an internal standard (umbelliferone) with appropriate electrolyte and sample compositions, rinse sequences and sample vial material resulted in a significant improvement in method repeatability. Typical RSD variations for 6 successive experiments were between 0.8% and 1.7% for peak migration times and between 1.2% and 4.9% for normalized corrected peak areas. LOQs in the low µg/L range were obtained. For the first time in capillary electrophoresis, applications to real vegetable oil extracts were successfully carried out using the separation method developed here.

Comparison of the amounts of volatile compounds in French protected designation of origin virgin olive oils.

Headspace solid-phase microextraction (HS-SPME) -gas chromatography using flame ionization detection and multivariate analysis were applied to the study of the specificity of protected designation of origin (PDO) virgin olive oils produced in a southern French region (Alpes-Maritimes) based on their volatile compounds. A total of 35 PDO olive oils from Nice, 6 commercial oils, and 12 other French PDO olive oils were analyzed. Recorded data were subjected to principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA). The method developed here was able to perfectly distinguish different qualities of olive oils. Representative samples from each class obtained by chemometric treatment were analyzed by HS-SPME and GC-MS. PCA and SIMCA of chromatographic data were related to sensory analysis and led to a better understanding of the chemical features and observed sensory effects of olive oils.

A simple method for automated pretreatment of usable chromatographic profiles in pattern-recognition procedures: application to HPAEC-PAD chromatograms of honeys.

In this article a simple method for automated pretreatment of chromatograms is presented. The resulting data matrix can be used as input for multivariate statistical analysis. Application of this method to high-performance anion-exchange chromatography-pulsed amperometric detection (HPAEC-PAD) chromatograms of honeys before canonical discriminant analysis results in very good performance in the data reduction with regard to the preservation of the original information content of the data. This pretreatment of the chromatogram allows for the use of all the peaks corresponding to the sugars present in the sample. This results in a high-quality discrimination between honeys of various types. A versatile program has been developed to apply this method. This serves as a starting point for software suited for food characterization and adulteration detection by semi-automatic pattern recognition applied to chromatographic analysis.

Honey characterization and adulteration detection by pattern recognition applied on HPAEC-PAD profiles. 1. Honey floral species characterization.

An improved COFRAC (COmité FRançais d'ACréditation) method for the analysis and evaluation of the quality of honey by high-performance anion-exchange chromatography of sugar profiles is proposed. With this method, both minor and major sugars are simultaneously analyzed and the technique is integrated in a new chemometric approach, which uses the entire chromatographic sugars profile of each analyzed sample to characterize honey floral species. Sixty-eight authentic honey samples (6 varieties) were analyzed by high-performance anion-exchange chromatography-pulsed amperometric detection. A new algorithm was developed to create automatically the corresponding normalized data matrix, ready-to-use in various chemometric procedures. This algorithm transforms the analytical profiles to produce the corresponding calibrated table of the surfaces or intensities according to retention times of peaks. The possibility of taking into account unknown peaks (those for which no standards are available) allows the maximum chemical information provided by the chromatograms to be retained. The parallel application of principal component analysis (PCA)/linear discriminant analysis (LDA) and artificial neural networks (ANN) shows a high capability in the classification of the analyzed samples (LDA, 93%; ANN, 100%) and a very good discrimination of honey groups. This work is the starting point of the elaboration of a new system designed for the automatic pattern recognition of food samples (first application on honey samples) from chromatographic analyses for food characterization and adulteration detection.

Recent developments in food characterization and adulteration detection: technique-oriented perspectives.

This review covers mainly publications that appeared in Analytical Abstracts (Royal Society of Chemistry) from January 1990 to February 2001. The number of publications on this topic continues to grow, and during the past three years (1998-2000) about 150 reviews and/or overviews have been published in the area of food. Numerous techniques and food matrices or chemical components are presented and discussed in these reviews. The present review is intentionally limited to eight techniques or classes of techniques and intends to be a "technique by technique" presentation of "what was used" or "what is used" to characterize food products and to detect their possible adulteration. The present review focuses on the following techniques: microscopic analysis; HPLC; GC, GC-(MS, FTIR); UV-visible spectrophotometry; AAS/AES, ICP-(AES, MS); IRMS, GC-IRMS, GC-C-IRMS; DSC; IR, mid-IR, and NMR (202 references). Emphasis is placed as much as possible on chemometrical treatment of analytical data, which are commonly used to achieve the final objective, either food characterization or adulteration detection. Finally, a brief description is given of the new generation of analytical systems that combine powerful analytical techniques and powerful computer software for a best extraction of the information from analytical data.