Authentication of forage-based milk by mid-level data fusion of (+/−) DART-HRMS signatures

Vibrational spectroscopy data fusion for enhanced classification of different milk types

The aim of this study is to classify seven types of Irish milk (butter, fresh, heart active, lactose free, light, protein, and slimline), supplied by a specific company, using vibrational spectroscopy methods: Near infrared (NIR), mid infrared (MIR), and Raman spectroscopy. In this regard, chemometric methods were used, and the impact of spectral data fusion on prediction accuracy was evaluated. A total of 105 samples were tested, with 21 used in the test set. The study assessed principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and sequential and orthogonalized partial least squares linear discriminant analysis (SO-PLS-LDA) for classifying different milk types. The prediction accuracy, when applying PLS-DA on individual blocks of data and low-level fused data, did not exceed 85.71 %. However, implementing the SO-PLS-LDA strategy significantly improved the accuracy to 95 %, suggesting a promising method for the development of classification models for milk using data fusion strategies.

Recent advances in the application of direct analysis in real time-mass spectrometry (DART-MS) in food analysis

Direct analysis in real time-mass spectrometry (DART-MS) has evolved as an effective analytical technique for the rapid and accurate analysis of food samples. The current advancements of DART-MS in food analysis are described in this paper. We discussed the DART principles, which include devices, ionization mechanisms, and parameter settings. Numerous applications of DART-MS in the fields of food and food products analysis published during 2018-2023 were reviewed, including contamination detection, food authentication and traceability, and specific analyte analysis in the food matrix. Furthermore, the challenges and limitations of DART-MS, such as matrix effect, isobaric component analysis, cost considerations and accessibility, and compound selectivity and identification, were discussed as well.

A Snapshot, Using a Multi-Omic Approach, of the Metabolic Cross-Talk and the Dynamics of the Resident Microbiota in Ripening Cheese Inoculated with Listeria innocua

Raw milk cheeses harbor complex microbial communities. Some of these microorganisms are technologically essential, but undesirable microorganisms can also be present. While most of the microbial dynamics and cross-talking studies involving interaction between food-derived bacteria have been carried out on agar plates in laboratory-controlled conditions, the present study evaluated the modulation of the resident microbiota and the changes of metabolite production directly in ripening raw milk cheese inoculated with Listeria innocua strains. Using a proxy of the pathogenic Listeria monocytogenes, we aimed to establish the key microbiota players and chemical signals that characterize Latteria raw milk cheese over 60 days of ripening time. The microbiota of both the control and Listeria-inoculated cheeses was analyzed using 16S rRNA targeted amplicon sequencing, while direct analysis in real time mass spectrometry (DART-HRMS) was applied to investigate the differences in the metabolic profiles of the cheeses. The diversity analysis showed the same microbial diversity trend in both the control cheese and the inoculated cheese, while the taxonomic analysis highlighted the most representative genera of bacteria in both the control and inoculated cheese: Lactobacillus and Streptococcus. On the other hand, the metabolic fingerprints revealed that the complex interactions between resident microbiota and L. innocua were governed by continuously changing chemical signals. Changes in the amounts of small organic acids, hydroxyl fatty acids, and antimicrobial compounds, including pyroglutamic acid, hydroxy-isocaproic acid, malic acid, phenyllactic acid, and lactic acid, were observed over time in the L. innocua-inoculated cheese. In cheese that was inoculated with L. innocua, Streptococcus was significantly correlated with the volatile compounds carboxylbenzaldheyde and cyclohexanecarboxylic acid, while Lactobacillus was positively correlated with some volatile and flavor compounds (cyclohexanecarboxylic acid, pyroxidal acid, aminobenzoic acid, and vanillic acid). Therefore, we determined the metabolic markers that characterize a raw milk cheese inoculated with L. innocua, the changes in these markers with the ripening time, and the positive correlation of flavor and volatile compounds with the resident microbiota. This multi-omics approach could suggest innovative food safety strategies based on the enhanced management of undesirable microorganisms by means of strain selection in raw matrices and the addition of specific antimicrobial metabolites to prevent the growth of undesirable microorganisms.

Rapid analysis technologies with chemometrics for food authenticity field: A review

In recent years, the problem of food adulteration has become increasingly rampant, seriously hindering the development of food production, consumption, and management. The common analytical methods used to determine food authenticity present challenges, such as complicated analysis processes and time-consuming procedures, necessitating the development of rapid, efficient analysis technology for food authentication. Spectroscopic techniques, ambient ionization mass spectrometry (AIMS), electronic sensors, and DNA-based technology have gradually been applied for food authentication due to advantages such as rapid analysis and simple operation. This paper summarizes the current research on rapid food authenticity analysis technology from three perspectives, including breeds or species determination, quality fraud detection, and geographical origin identification, and introduces chemometrics method adapted to rapid analysis techniques. It aims to promote the development of rapid analysis technology in the food authenticity field.

DART-HRMS allows the detection of toxic alkaloids in animal autopsy specimens and guides the selection of confirmatory methods in accidental plant poisoning

BACKGROUND

In cases of suspected animal poisonings or intoxications, there is the need for high-throughput, rapid and accurate analytical tools capable of giving rapid answers and, thus, speeding up the early stages of investigations. Conventional analyses are very precise, but do not meet the need for rapid answers capable of orienting the decisions and the choice of appropriate countermeasures. In this context, the use of ambient mass spectrometry (AMS) screening methods in toxicology laboratories could satisfy the requests of forensic toxicology veterinarians in a timely manner.

RESULTS

As a proof of principle, direct analysis in real time high resolution mass spectrometry (DART-HRMS) was applied to a veterinary forensic case in which 12 of a group of 27 sheep and goats died with an acute neurological onset. Because of evidence in the rumen contents, the veterinarians hypothesized an accidental intoxication after ingestion of vegetable materials. The DART-HRMS results showed abundant signals of the alkaloids calycanthine, folicanthidine and calycanthidine, both in the rumen content and at the liver level. The DART-HRMS phytochemical fingerprinting of detached Chimonanthus praecox seeds was also compared with those acquired from the autopsy specimens. Liver, rumen content and seed extracts were then subjected to LC-HRMS/MS analysis to gather additional insights and confirm the putative assignment of calycanthine anticipated by DART-HRMS. HPLC-HRMS/MS confirmed the presence of calycanthine in both rumen contents and liver specimens and allowed its quantification, ranging from 21.3 to 46.9 mg kg^-1 in the latter. This is the first report detailing the quantification of calycanthine in liver after a deadly intoxication event.

SIGNIFICANCE AND NOVELTY

Our study illustrates the potential of DART-HRMS to offer a rapid and complementary alternative to guide the selection of confirmatory chromatography-MSⁿ strategies in the analysis of autopsy specimens from animals with suspected alkaloid intoxication. This method offers the consequent saving of time and resources over those needed for other methods.

Seasonal Variation in Raw Milk VOC Profile within Intensive Feeding Systems

The study aimed to assess the seasonal variation in raw milk volatile organic compounds (VOCs) from three indoor feeding systems based on maize silage (n = 31), silages/hay (n = 19) or hay (n = 16). After headspace solid-phase microextraction (HS-SPME), VOC profiles were determined by gas chromatography (GC). Chemical and VOC (log₁₀ transformations of the peak areas) data were submitted to a two-way ANOVA to assess the feeding system (FS) and season (S) effects; an interactive principal component analysis (iPCA) was also performed. The interaction FS × S was never significant. The FS showed the highest (p < 0.05) protein and casein content for hay-milk samples, while it did not affect any VOCs. Winter milk had higher (p < 0.05) proportions of protein, casein, fat and some carboxylic acids, while summer milk was higher (p < 0.05) in urea and 2-pentanol and methyl aldehydes. The iPCA confirmed a seasonal spatial separation. Carboxylic acids might generate from incomplete esterification in the mammary gland and/or milk lipolytic activity, while aldehydes seemed to be correlated with endogenous lipid or amino acid oxidation and/or feed transfer. The outcomes suggested that VOCs could be an operative support to trace raw milk for further mild processing.

Spectroscopic technologies and data fusion: Applications for the dairy industry

Increasing consumer awareness, scale of manufacture, and demand to ensure safety, quality and sustainability have accelerated the need for rapid, reliable, and accurate analytical techniques for food products. Spectroscopy, coupled with Artificial Intelligence-enabled sensors and chemometric techniques, has led to the fusion of data sources for dairy analytical applications. This article provides an overview of the current spectroscopic technologies used in the dairy industry, with an introduction to data fusion and the associated methodologies used in spectroscopy-based data fusion. The relevance of data fusion in the dairy industry is considered, focusing on its potential to improve predictions for processing traits by chemometric techniques, such as principal component analysis (PCA), partial least squares regression (PLS), and other machine learning algorithms.

Milk metabolome reveals pyrimidine and its degradation products as the discriminant markers of different corn silage-based nutritional strategies

The purpose of this study was to evaluate the effect of 6 different feeding systems (based on corn silage as the main ingredient) on the chemical composition of milk and to highlight the potential of untargeted metabolomics to find discriminant marker compounds of different nutritional strategies. Interestingly, the multivariate statistical analysis discriminated milk samples mainly according to the high-moisture ear corn (HMC) included in the diet formulation. Overall, the most discriminant compounds, identified as a function of the HMC, belonged to AA (10 compounds), peptides (71 compounds), pyrimidines (38 compounds), purines (15 compounds), and pyridines (14 compounds). The discriminant milk metabolites were found to significantly explain the metabolic pathways of pyrimidines and vitamin B₆. Interestingly, pathway analyses revealed that the inclusion of HMC in the diet formulation strongly affected the pyrimidine metabolism in milk, determining a significant up-accumulation of pyrimidine degradation products, such as 3-ureidopropionic acid, 3-ureidoisobutyric acid, and 3-aminoisobutyric acid. Also, some pyrimidine intermediates (such as l-aspartic acid, N-carbamoyl-l-aspartic acid, and orotic acid) were found to possess a high discrimination degree. Additionally, our findings suggested that the inclusion of alfalfa silage in the diet formulation was potentially correlated with the vitamin B₆ metabolism in milk, being 4-pyridoxic acid (a pyridoxal phosphate degradation product) the most significant and up-accumulated compound. Taken together, the accumulation trends of different marker compounds revealed that both pyrimidine intermediates and degradation products are potential marker compounds of HMC-based diets, likely involving a complex metabolism of microbial nitrogen based on total splanchnic fluxes from the rumen to mammary gland in dairy cows. Also, our findings highlight the potential of untargeted metabolomics in both foodomics and foodomics-based studies involving dairy products.

Rapid, novel screening of toxicants in poison baits, and autopsy specimens by ambient mass spectrometry

Animal poisoning and dissemination of baits in the environment have public health and ethological implications, which can be followed by criminal sanctions for those responsible. The reference methods for the analysis of suspect baits and autopsy specimens are founded on chromatographic-based techniques. They are extremely robust and sensitive, but also very expensive and laborious. For this reason, we developed an ambient mass spectrometry (AMS) method able to screen for 40 toxicants including carbamates, organophosphate and chlorinated pesticides, coumarins, metaldehyde, and strychnine. Spiked samples were firstly purified and extracted by dispersive solid phase extraction (QuEChERS) and then analyzed by direct analysis in real time high-resolution mass spectrometry (DART-HRMS). To verify the performance of this new approach, 115 authentic baits (n = 59) and necropsy specimens (gastrointestinal content and liver, n = 56) were assessed by the official reference methods and combined QuEChERS-DART-HRMS. The agreement between the results allowed evaluation of the performances of the new screening method for a variety of analytes and calculation of the resultant statistical indicators (the new method had overall accuracy 89.57%, sensitivity of 88.24%, and a specificity of 91.49%). Taking into account only the baits, 96.61% of overall accuracy was achieved with 57/59 samples correctly identified (statistical sensitivity 97.50%, statistical specificity 94.74%). Successful identification of the bitter compound, denatonium benzoate, in all the samples that contained rodenticides (28/28) was also achieved. We believe initial screening of suspect poison baits could guide the choice of reference confirmatory methods, reduce the load in official laboratories, and help the early stages of investigations into cases of animal poisoning.

Ambient mass spectrometry for rapid authentication of milk from Alpine or lowland forage

Metabolomics approaches, such as direct analysis in real time-high resolution mass spectrometry (DART-HRMS), allow characterising many polar and non-polar compounds useful as authentication biomarkers of dairy chains. By using both a partial least squares discriminant analysis (PLS-DA) and a linear discriminant analysis (LDA), this study aimed to assess the capability of DART-HRMS, coupled with a low-level data fusion, discriminate among milk samples from lowland (silages vs. hay) and Alpine (grazing; APS) systems and identify the most informative biomarkers associated with the main dietary forage. As confirmed also by the LDA performed against the test set, DART-HRMS analysis provided an accurate discrimination of Alpine samples; meanwhile, there was a limited capacity to correctly recognise silage- vs. hay-milks. Supervised multivariate statistics followed by metabolomics hierarchical cluster analysis allowed extrapolating the most significant metabolites. Lowland milk was characterised by a pool of energetic compounds, ketoacid derivates, amines and organic acids. Seven informative DART-HRMS molecular features, mainly monoacylglycerols, could strongly explain the metabolomic variation of Alpine grazing milk and contributed to its classification. The misclassification between the two lowland groups confirmed that the intensive dairy systems would be characterised by a small variation in milk composition.

The Influence of Pasture and Non-pasture-Based Feeding Systems on the Aroma of Raw Bovine Milk

Aroma-active compounds in raw bovine milk produced from cows fed perennial ryegrass (GRS) or total mixed ration (TMR) consisting of grass silage, maize silage, and concentrates were identified by direct immersion sorptive extraction (DI Hi-Sorb), coupled with gas-chromatography-mass spectrometry and olfactometry using odour intensity (OI) and aroma extraction dilution analysis (AEDA). Ninety-nine volatile organic compounds (VOC) were identified in these raw GRS and TMR milk samples; 33 of which were also present in the feed and rumen samples from these diets. Only the abundance of 13 VOC varied significantly based on diet. However, the odours of both raw milks were quite distinct as aroma perception is not influenced by abundance alone but also by the odour activity of each VOC. Approximately, 30% of the VOC influenced the aroma perception of these raw milks. This study clearly highlighted the significant impact of VOC transferring from the diet that influenced the aroma perception of both raw GRS and TMR milk. The aroma of the raw TMR milk was more complex than that of the raw GRS milk, and many of the key dietary-derived-odour-active VOC likely arose during the production of the TMR feed as most were either derived from Maillard reactions or impacted by heat. Seventeen of the 44 odour activities detected differed between both sample types. This study has clearly demonstrated the impact of diet on the aroma perception of raw bovine milk.

Multivariate modelling of milk fatty acid profile to discriminate the forages in dairy cows' ration

Although there are many studies on the importance of fatty acids (FA) in our diet and on the influence of dairy diets on FA metabolism, only a few investigate their predictive capacity to discriminate the type, amount and conservation method of farm forages. This research quantifies differences in milk FA concentrations and, using a supervised factorial discriminant analysis, assesses potential biomarkers when replacing maize with other silages, grass/lucerne hays or fresh grass. The statistical modelling identified three main clusters of milk FA profiles associated with silages, hays and fresh grass as dominant roughages. The main implication of a dairy cow feeding system based on poliphytic forages from permanent meadows is enhancing milk’s nutritional quality due to an increase in beneficial omega-3 polyunsaturated FA, conjugated linoleic acids and odd chain FA, compared to feeding maize silage. The study also identified a small but powerful and reliable pool of milk FA that can act as biomarkers to authenticate feeding systems: C16:1 c-9, C17:0, C18:0, C18:3 c-9, c-12, c-15, C18:1 c-9, C18:1 t-11 and C20:0.

Serum Metabolomic Profiles of Paratuberculosis Infected and Infectious Dairy Cattle by Ambient Mass Spectrometry

Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of paratuberculosis [Johne's disease (JD)], a chronic disease that causes substantial economic losses in the dairy cattle industry. The long incubation period means clinical signs are visible in animals only after years, and some cases remain undetected because of the subclinical manifestation of the disease. Considering the complexity of JD pathogenesis, animals can be classified as infected, infectious, or affected. The major limitation of currently available diagnostic tests is their failure in detecting infected non-infectious animals. The present study aimed to identify metabolic markers associated with infected and infectious stages of JD. Direct analysis in real time coupled with high resolution mass spectrometry (DART-HRMS) was, hence, applied in a prospective study where cohorts of heifers and cows were followed up annually for 2–4 years. The animals' infectious status was assigned based on a positive result of both serum ELISA and fecal PCR, or culture. The same animals were retrospectively assigned to the status of infected at the previous sampling for which all JD tests were negative. Stored sera from 10 infected animals and 17 infectious animals were compared with sera from 20 negative animals from the same herds. Two extraction protocols and two (-/+) ionization modes were tested. The three most informative datasets out of the four were merged by a mid-level data fusion approach and submitted to partial least squares discriminant analysis (PLS-DA). Compared to the MAP negative subjects, metabolomic analysis revealed the m/z signals of isobutyrate, dimethylethanolamine, palmitic acid, and rhamnitol were more intense in infected animals. Both infected and infectious animals showed higher relative intensities of tryptamine and creatine/creatinine as well as lower relative abundances of urea, glutamic acid and/or pyroglutamic acid. These metabolic differences could indicate altered fat metabolism and reduced energy intake in both infected and infectious cattle. In conclusion, DART-HRMS coupled to a mid-level data fusion approach allowed the molecular features that identified preclinical stages of JD to be teased out.