The Potential of NIR Spectroscopy and Chemometrics to Discriminate Roast Degrees and Predict Volatiles in Coffee.

This study aimed to establish a rapid and practical method for monitoring and predicting volatile compounds during coffee roasting using near-infrared (NIR) spectroscopy coupled with chemometrics. Washed Arabica coffee beans from Ethiopia and Congo were roasted to industry-validated light, medium, and dark degrees. Concurrent analysis of the samples was performed using gas chromatography-mass spectrometry (GC-MS) and NIR spectroscopy, generating datasets for partial least squares (PLS) regression analysis. The results showed that NIR spectroscopy successfully differentiated the differently roasted samples, similar to the discrimination achieved by GC-MS. This finding highlights the potential of NIR spectroscopy as a rapid tool for monitoring and standardizing the degree of coffee roasting in the industry. A PLS regression model was developed using Ethiopian samples to explore the feasibility of NIR spectroscopy to indirectly measure the volatiles that are important in classifying the roast degree. For PLSR, the data underwent autoscaling as a preprocessing step, and the optimal number of latent variables (LVs) was determined through cross-validation, utilizing the root mean squared error (RMSE). The model was further validated using Congo samples and successfully predicted (with R2 values > 0.75 and low error) over 20 volatile compounds, including furans, ketones, phenols, and pyridines. Overall, this study demonstrates the potential of NIR spectroscopy as a practical and rapid method to complement current techniques for monitoring and predicting volatile compounds during the coffee roasting process.

Support vector regression for prediction of stable isotopes and trace elements using hyperspectral imaging on coffee for origin verification.

The potential of using rapid and non-destructive near-infrared - hyperspectral imaging (HSI-NIR) for the prediction of an integrated stable isotope and multi-element dataset was explored for the first time with the help of support vector regression. Speciality green coffee beans sourced from three continents, eight countries, and 22 regions were analysed using a push-broom HSI-NIR (700-1700 nm), together with five isotope ratios (δ13C, δ15N, δ18O, δ2H, and δ34S) and 41 trace elements. Support vector regression with the radial basis function kernel was conducted using X as the HSI-NIR data and Y as the geochemistry markers. Model performance was evaluated using root mean squared error, coefficient of determination, and mean absolute error. Three isotope ratios (δ18O, δ2H, and δ34S) and eight elements (Zn, Mn, Ni, Mo, Cs, Co, Cd, and La) had an R2predicted 0.70 - 0.99 across all origin scales (continent, country, region). All five isotope ratios were well predicted at the country and regional levels. The wavelength regions contributing the most towards each prediction model were highlighted, including a discussion of the correlations across all geochemical parameters. This study demonstrates the feasibility of using HSI-NIR as a rapid and non-destructive method to estimate traditional geochemistry parameters, some of which are origin-discriminating variables related to altitude, temperature, and rainfall differences across origins.

Near-infrared reflectance spectroscopy accurately predicted isotope and elemental compositions for origin traceability of coffee.

Stable isotope ratios and trace elements are well-established tools that act as signatures of the product's environmental conditions and agricultural processes; but they involve time, money, and environmentally destructive chemicals. In this study, we tested for the first time the potential of near-infrared reflectance spectroscopy (NIR) to estimate/predict isotope and elemental compositions for the origin verification of coffee. Green coffee samples from two continents, 4 countries, and 10 regions were analysed for five isotope ratios (δ13C, δ15N, δ18O, δ2H, and δ34S) and 41 trace elements. NIR (1100-2400 nm) calibrations were developed using pre-processing with extended multiplicative scatter correction (EMSC) and mean centering and partial-least squares regression (PLS-R). Five elements (Mn, Mo, Rb, B, La) and three isotope ratios (δ13C, δ18O, δ2H) were moderately to well predicted by NIR (R2: 0.69 to 0.93). NIR indirectly measured these parameters by association with organic compounds in coffee. These parameters were related to altitude, temperature and rainfall differences across countries and regions and were previously found to be origin discriminators for coffee.

Stable isotope and trace element analyses with non-linear machine-learning data analysis improved coffee origin classification and marker selection.

BACKGROUND: This study investigated the geographical origin classification of green coffee beans from continental to country and regional levels. An innovative approach combined stable isotope and trace element analyses with non-linear machine learning data analysis to improve coffee origin classification and marker selection. Specialty green coffee beans sourced from three continents, eight countries, and 22 regions were analyzed by measuring five isotope ratios (δ13 C, δ15 N, δ18 O, δ2 H, and δ34 S) and 41 trace elements. Partial least squares discriminant analysis (PLS-DA) was applied to the integrated dataset for origin classification. RESULTS: Origins were predicted well at the country level and showed promise at the regional level, with discriminating marker selection at all levels. However, PLS-DA predicted origin poorly at the continental and Central American regional levels. Non-linear machine learning techniques improved predictions and enabled the identification of a higher number of origin markers, and those that were identified were more relevant. The best predictive accuracy was found using ensemble decision trees, random forest and extreme gradient boost, with accuracies of up to 0.94 and 0.89 for continental and Central American regional models, respectively. CONCLUSION: The potential for advanced machine learning models to improve origin classification and the identification of relevant origin markers was demonstrated. The decision-tree-based models were superior with their embedded variable identification features and visual interpretation. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

A review of conventional and rapid analytical techniques coupled with multivariate analysis for origin traceability of soybean.

Soybean has developed a reputation as a superfood due to its nutrient profile, health benefits, and versatility. Since 1960, its demand has increased dramatically, going from a mere 17 MMT to almost 358 MMT in the production year 2021/22. These extremely high production rates have led to lower-than-expected product quality, adulteration, illegal trade, deforestation, and other concerns. This necessitates the development of an effective technology to confirm soybean's provenance. This is the first review that investigates current analytical techniques coupled with multivariate analysis for origin traceability of soybeans. The fundamentals of several analytical techniques are presented, assessed, compared, and discussed in terms of their operating specifics, advantages, and shortcomings. Additionally, significance of multivariate analysis in analyzing complex data has also been discussed.

Reply to Horacek, M. The Need to Consider Geochemistry When Interpreting Sr-Isotopes. Comment on "Gregorcic et al. The Provenance of Slovenian Milk Using 87Sr/86Sr Isotope Ratios. Foods 2021, 10, 1729".

We appreciate Dr Horacek's interest in our paper and his feedback [...].

Stable Isotope and Element Profiling for Determining the Agroclimatic Origin of Cow Milk within a Tropical Country.

Information on the geographic origin of milk is important in determining quality attributes and for economic gain through building brand value associated with origin. Stable isotope signatures and trace element concentrations have been increasingly used in authentication of milk, though information on the power of such technology in verifying agroclimatic origin in small continents with diverse climatic, environmental conditions, and animal management practice is scarce. Therefore, the main objective of this study was to investigate the possibility of using a stable isotope composition of C, N, O, and H and element fingerprints to determine the agroclimatic origin of milk produced in different agroclimatic zones of Sri Lanka. Stable isotopes ratios of C, N, H, and O, and elemental fingerprints of milk samples were determined by IRMS and ICP-MS, respectively. Significant variations were observed in stable isotope ratios, especially δ18O and the mean content of Li, Al, Cr, Mn, and Sr in the bulk milk samples obtained from different agroclimatic zones. A linear discriminant analysis differentiated cow milk produced from four agroclimatic zones based on stable isotope ratios, and the inclusion of elemental ratios enhanced the discriminating ability.

The Provenance of Slovenian Milk Using 87Sr/86Sr Isotope Ratios.

This work presents the first use of Sr isotope ratios for determining the provenance of bovine milk from different regions of Slovenia. The analytical protocol for the determination of 87Sr/86Sr isotope ratio was optimised and applied to authentic milk samples. Considerable variability of 87Sr/86Sr ratios found in Slovenian milk reflects the substantial heterogeneity of the geological background of its origin. The results, although promising, cannot discount possible inter-annual or annual variation of the Sr isotopic composition of milk. The 87Sr/86Sr ratios of groundwater and surface waters are in good correlation with milk, indicating that the Sr isotopic fingerprint in milk is reflective of cow drinking water. The 87Sr/86Sr ratio has the potential to distinguish between different milk production areas as long as these areas are characterised by geo-lithology. Discriminant analysis (DA) incorporating the elemental composition and stable isotopes of light elements showed that 87Sr/86Sr ratio together with δ13Ccas and δ15Ncas values have the main discrimination power to distinguish the Quaternary group (group 6) from the others. Group 1 (Cretaceous: Carbonate Rocks and Flysch) is associated with Br content, 1/Sr and δ18Ow values. The overall prediction ability was found to be 63.5%. Pairwise comparisons using OPLS-DA confirmed that diet and geologic parameters are important for the separation.

Comprehensive Assessment of Essential and Potentially Toxic Trace Elements in Bovine Milk and Their Feeds in Different Agro-climatic Zones of Sri Lanka.

Fresh milk is an important source of essential mineral supplement for humans. However, the levels of trace elements in milk are an important component of its safety and quality. Trace elements also act as a bio-indicator of agricultural pollution. Samples of raw cow milk (n = 68), animal feed [forage (n = 36) and concentrates (n = 14)], and water (n = 35) were collected from different agro-climatic regions of Sri Lanka. The concentrations of 15 trace elements including toxic heavy metals such as Cd, Pb, and As were quantified using inductively coupled plasma mass spectrometry after microwave-assisted digestion. Among the studied trace elements, the mean elemental concentrations of Se, Cd, As, and Cu in cow milk were 18.1, 1.45, 7.35, and 71.7 µg L-1, respectively. The mean concentrations of these elements in forage were 0.216, 0.066, 0.046, and 9.21 mg kg-1, and in concentrate feed were 0.329, 0.202, 0.229, and 2.28 mg kg-1, respectively. The As content of the studied milk was well below the maximum permissible level while 15% of the samples had Pb exceeding the permissible limit of 20 µg L-1. However, As, Cd, and Pb levels in drinking water provided to animals were well below the WHO permissible limits. The results of this study revealed that the trace elements in cow milk depend mainly on the feed, and hence, levels of these contaminants in feed materials should be monitored. Although due to the consumption of milk, the populations have a minor exposure to trace elements and also minimum health risks, a detailed study with a large number of samples is highly recommended.

Optimization of nuclear magnetic resonance and gas chromatography-mass spectrometry-based fingerprinting methods to characterize goat milk powder.

This study is the first to provide a comprehensive characterization of the liquid and volatile fractions of whole goat milk powder (GMP). Robust nuclear magnetic resonance (NMR)- and gas chromatography-mass spectrometry (GC-MS)-based chemical fingerprinting methods were optimized and implemented. The untargeted 1H-NMR analysis resolved 44 metabolites in the liquid fractions of GMP. The NMR fingerprinting technique effectively identified metabolites coming from the aliphatic, sugar, and aromatic regions that can be important in defining the technological properties and quality of the GMP. The untargeted headspace gas chromatography-mass spectrometry fingerprinting was able to detect a total of 50 volatiles including alkanes, ketones, alcohols, aromatics, alkenes, aldehydes, esters, acid, and sulfur compounds. The GMP was dominated by volatiles in the alkane group, while only a few esters were detected. Goat milk is a premium product and vulnerable to fraudulent activities such as adulteration or counterfeit. Therefore, proper characterization and identification is a crucial first step to verify its authenticity and quality.

Feasibility of Casein to Record Stable Isotopic Variation of Cow Milk in New Zealand.

Dairy products occupy a special place among foods in contributing to a major part of our nutritional requirements, while also being prone to fraud. Hence, the verification of the authenticity of dairy products is of prime importance. Multiple stable isotopic studies have been undertaken that demonstrate the efficacy of this approach for the authentication of foodstuffs. However, the authentication of dairy products for geographic origin has been a challenge due to the complex interactions of geological and climatic drivers. This study applies stable isotope measurements of d2H, d18O, d13C and d15N values from casein to investigate the inherent geo-climatic variation across dairy farms from the South and North Islands of New Zealand. The stable isotopic ratios were measured for casein samples which had been separated from freeze-dried whole milk samples. As uniform feeding and fertilizer practices were applied throughout the sampling period, the subtropical (North Island) and temperate (South Island) climates were reflected in the variation of d13C and d15N. However, highly correlated d2H and d18O (r = 0.62, p = 6.64 × 10-10, a = 0.05) values did not differentiate climatic variation between Islands, but rather topographical locations. The highlight was the strong influence of d15N towards explaining climatic variability, which could be important for further discussion.

Effects of the vat pasteurization process and refrigerated storage on the bovine milk metabolome.

This study is the first to investigate the evolution of cow milk metabolites throughout the vat pasteurization process and storage using untargeted metabolomics based on a multiplatform approach. Nuclear magnetic resonance and ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry were used for fingerprinting water-soluble nutritional compounds, and headspace gas chromatography-mass spectrometry was used to fingerprint the volatile organic compounds. This study demonstrated that vat pasteurization was an efficient and mild means of milk preservation resulting in only minor changes to the metabolites. The pasteurized milk samples exhibited a stable metabolome during the first 8 d of refrigerated storage. However, at the latter stage of storage, the concentrations of pantothenic acid and butyrylcarnitine decreased, whereas some fatty acids, organic acids, α-AA, peptides, and ketones increased. These selected metabolites that changed during milk storage could be used as potential biomarkers to track the storage conditions of pasteurized cow milk.

Natal origin of the invasive biosecurity pest, brown marmorated stink bug (Halyomorpha halys: Penatomidae), determined by dual-element stable isotope-ratio mass spectrometry.

BACKGROUND: Post-border detection of a single brown marmorated stink bug (BMSB) in New Zealand warranted a biosecurity response, the nature of which would be influenced by its status as part of an established population or as a new arrival. Stable isotope analysis has the potential to determine natal origins, but is difficult to achieve for samples as small as a single insect. Here an analytical modification to measure small samples was successfully trialled as a means to supply evidence as to the local or exotic natal origin of the intercepted BMSB specimen. RESULTS: Sufficient analytical sensitivity was achieved using a modified isotope ratio mass spectrometry method, involving thermolysis and carbon monoxide cryofocusing, to enable the simultaneous analysis of δ2 H and δ18 O from wings of the post-border BMSB sample. The values were much lower than those of the New Zealand green vegetable bug, used as a local reference. However, they fell within the range of those for BMSB of Northern Hemisphere origin intercepted at the New Zealand border over the same time period, specifically overlapping with the USA and Italy, but not China. CONCLUSION: The isotope signature of the post-border detected BMSB suggested a significantly cooler climate than the North Island of New Zealand, indicating that it was a new arrival and did not represent an established population. © 2019 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

A δ2H Isoscape of blackberry as an example application for determining the geographic origins of plant materials in New Zealand.

In this investigation, two previously reported precipitation δ2H isoscapes for New Zealand were used to develop a δ2H isoscape for blackberry (Rubus sp.) leaf. These isoscapes were calibrated using the measured δ2H values of 120 authentic blackberry leaf samples collected from across the country. A regression model based on environmental variables available for New Zealand was also determined to predict δ2H values measured from blackberry leaves without initially modelling the precipitation δ2H values. The three models were compared for their accuracy and precision when assigning 10 samples of blackberry leaves for their geographic location based on their measured δ2H values. One of the models based on a precipitation isoscape was similar in accuracy and precision of assignment to the model determined from the environmental variables and provides an approach for determining valid isoscapes for future plant materials.

Extending the Limit of Measurement for Dual H and O Isotope Ratios Using Thermolysis.

Hydrogen and oxygen isotope ratios are of use to determine the origin of matter. Thermolysis is used to convert matter to H2 and CO gases, which are the respective substrates for measurement of these two isotope ratios, using isotope ratio mass spectrometry (IRMS). This work was done in response to the need for analysis of small invasive insects, requiring a decrease in the limit of measurement for isotope ratiometry of hydrogen and oxygen, while determining both isotope ratios on the same sample. Miniaturization of a thermolysis reactor using commercially available components is presented that results in improvement in the limit of measurement for both hydrogen and oxygen isotope ratios. δ2H was determined on 0.4 µg of H and δ18O determined on 5 µg of O with precisions of 3 mUr and 0.7 mUr, respectively. To extend the usable sample size range or increase the resolution of sampling gives obvious advantages in forensic and environmental sciences. The technique has been applied to determining the natural origin of Tephritidae fruit flies for which only the wing is suitable for analysis and provides just 60 µg of material for analysis.

31P NMR-Based Phospholipid Fingerprinting of Powdered Infant Formula.

Infant formula (IF), regarded as the optimal substitute for human breast milk, is very important for infant growth and development. Phospholipids (PLs) are ubiquitous components of infant formula as they have good emulsifier properties in addition to their nutritional and biological functions. In this study, the PL contents in four different commercial IF brands (indicated as A, M, O, and W) were characterized and quantified using optimized 31P NMR spectroscopy. PLs (nine) were identified and quantified, and among these, phosphatidylethanolamine and sphingomyelin occurred at lower concentrations (5.72 and 8.89 mg/100 g, respectively) in IFs from brand O, whereas phosphatidic acid was higher (2.83 mg/100 g) in IFs from brand W. In summary, 31P NMR spectroscopy, combined with the multivariate data analysis, proved to be an effective analytical toolbox for evaluating the PL contents in IF and the comparative differences between IF brands.

Stable isotope measurements to differentiate sources of monofluoroacetate in a blackmail case.

RATIONALE: Stable isotope ratios can provide a 'fingerprint' to enable differentiation of sources of monofluoroacetate (MFA), hence providing a means to eliminate potential sources of MFA in a blackmail case involving the contamination of milk. METHODS: The stable isotopic compositions (δ2 H, δ13 C and δ18 O values) of a library of 43 samples of MFA were determined and multivariate models constructed to differentiate samples of different composition. The data from the MFA library were compared with those obtained from MFA extracted from contaminated milk powder (the case samples). The isotopic composition of the extracted samples was measured on dichloroaniline derivatives. RESULTS: A wide range of values was found for δ2 H, δ13 C and δ18 O of the MFA samples, much greater than the analytical repeatability between subsamples. Stable isotope data, therefore, provide a means of distinguishing samples of MFA. Of the 43 MFA samples tested, all but 6 could be eliminated as potential sources of the contamination, i.e. they had a distinctly different isotopic composition such that they must have had different histories and/or origins. CONCLUSIONS: Stable isotope measurements of bulk and derivatized MFA provide an effective means of discriminating MFA samples. Three of the library samples that could not be differentiated from the case samples were directly connected to the suspect, and this evidence contributed to the suspect's admission of guilt.

Lipidomics profiling of goat milk, soymilk and bovine milk by UPLC-Q-Exactive Orbitrap Mass Spectrometry.

Lipids are very important for human health and milk is a rich dietary source of lipids. In this study, the lipid content in three types of milk (goat, soy and bovine) were determined by using UPLC-Q-Exactive Orbitrap Mass Spectrometry. A total of 13 classes of lipids (including Cer, SM, LPC, PC, PE, DG, TG, PA, PG, PI, PS, LPE, FA) were measured. Moreover, lipid profiles differed significantly between the different milk types. Soymilk is rich in phospholipids including PC, PE, PS, PG, while goat milk is rich in medium chain triglycerides (MCT), USFA, ω-6 FA and ω-3 FA, especially EPA and DHA. Furthermore, a PLS model was established for differentiation of milk types based on the lipid profiles. A total of 14 lipids were identified as biomarkers for differentiation of milk types, thus providing a basis for milk authentication and detection of adulteration.

The application of NMR-based milk metabolite analysis in milk authenticity identification.

BACKGROUND: Milk is an important food component in the human diet and is a target for fraud, including many unsafe practices. For example, the unscrupulous adulteration of soymilk into bovine and goat milk or of bovine milk into goat milk in order to gain profit without declaration is a health risk, as the adulterant source and sanitary history are unknown. A robust and fit-for-purpose technique is required to enforce market surveillance and hence protect consumer health. Nuclear magnetic resonance (NMR) is a powerful technique for characterization of food products based on measuring the profile of metabolites. In this study, 1D NMR in conjunction with multivariate chemometrics as well as 2D NMR was applied to differentiate milk types and to identify milk adulteration. RESULTS: Ten metabolites were found which differed among milk types, hence providing characteristic markers for identifying the milk. These metabolites were used to establish mathematical models for milk type differentiation. The limit of quantification (LOQ) of adulteration was 2% (v/v) for soymilk in bovine milk, 2% (v/v) for soymilk in goat milk and 5% (v/v) for bovine milk in goat milk, with relative standard deviation (RSD) less than 10%, which can meet the needs of daily inspection. CONCLUSION: The NMR method described here is effective for milk authenticity identification, and the study demonstrates that the NMR-based milk metabolite analysis approach provides a means of detecting adulteration at expected levels and can be used for dairy quality monitoring. © 2016 Society of Chemical Industry.

Precipitation isoscapes for New Zealand: enhanced temporal detail using precipitation-weighted daily climatology.

Predictive understanding of precipitation δ(2)H and δ(18)O in New Zealand faces unique challenges, including high spatial variability in precipitation amounts, alternation between subtropical and sub-Antarctic precipitation sources, and a compressed latitudinal range of 34 to 47 °S. To map the precipitation isotope ratios across New Zealand, three years of integrated monthly precipitation samples were acquired from >50 stations. Conventional mean-annual precipitation δ(2)H and δ(18)O maps were produced by regressions using geographic and annual climate variables. Incomplete data and short-term variation in climate and precipitation sources limited the utility of this approach. We overcome these difficulties by calculating precipitation-weighted monthly climate parameters using national 5-km-gridded daily climate data. This data plus geographic variables were regressed to predict δ(2)H, δ(18)O, and d-excess at all sites. The procedure yields statistically-valid predictions of the isotope composition of precipitation (long-term average root mean square error (RMSE) for δ(18)O = 0.6 ‰; δ(2)H = 5.5 ‰); and monthly RMSE δ(18)O = 1.9 ‰, δ(2)H = 16 ‰. This approach has substantial benefits for studies that require the isotope composition of precipitation during specific time intervals, and may be further improved by comparison to daily and event-based precipitation samples as well as the use of back-trajectory calculations.