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.

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.

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.

Effects of Hydrothermal Processing on Volatile and Fatty Acids Profile of Cowpeas (Vigna unguiculata), Chickpeas (Cicer arietinum) and Kidney Beans (Phaseolus vulgaris).

Legumes are an economical source of protein, starch, dietary fibre, fatty acids, vitamins and minerals. However, they are not as fully utilised, due to volatile compounds contributing to their undesirable odour. The purpose of this work was to understand the processing time's effect on the legumes' volatile profile. Hence, this study investigated the effects of hydrothermal processing times on the volatile and fatty acids profiles of cowpeas, chickpeas and kidney beans. All legumes were pre-soaked (16 h) and then hydrothermally processed at 95 °C for 15 to 120 min, using an open system to approximate standard household cooking practices and a closed system to represent industrial processing. Alcohol, aldehyde, acid and ester volatile compounds showed decreasing trends during processing, which can be associated with enzyme inactivation and process-induced degradation. This work showed that processing at 95 °C for 30 min significantly reduced the number of compounds commonly associated with undesirable odour, but showed no significant change in the fatty acid profile. Other volatiles, such as furanic compounds, pyrans and sulphur compounds, showed an increasing trend during processing, which can be related to the Maillard reactions. This observation contributes to the growing knowledge of legume processing and its impact on volatile flavour. It can advise consumers and the industry on selecting processing intensity to maximise legume utilisation.

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.

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.

Insight into non-enzymatic browning of shelf-stable orange juice during storage: A fractionation and kinetic approach.

BACKGROUND: Non-enzymatic browning (NEB) is the main quality defect in shelf-stable orange juice and other fruit juices during storage. Previous studies on NEB focused solely on the soluble fraction of orange juice, regardless of the fact that both soluble and insoluble fractions turn brown during extended storage. Clear evidence of the relative contribution of both fractions to NEB is currently lacking in the literature. This study investigated the contribution of the soluble and insoluble fractions of orange juice, which were obtained by centrifugation and ethanol precipitation, to NEB during storage. Changes in different NEB-related attributes, such as ascorbic acid (AA) degradation, and the browning index (BI), were quantified and kinetically modeled. RESULTS: Evaluation of color during storage showed that the orange juice and the soluble compound-containing fractions turned brown whereas the insoluble fractions did not. The soluble compound-containing fractions showed exactly the same browning behavior with storage as the plain orange juice. Based on the kinetic parameters obtained, the degradation of AA, the hydrolysis of sucrose, the increase in the glucose and fructose content, and the formation of furfural and 5-hydroxymethylfurfural during storage were similar for the plain orange juice and the soluble compound-containing fractions. CONCLUSION: This work provided evidence that the soluble fraction of orange juice plays the major role in NEB, unlike the insoluble fraction, which seems to make no contribution. Results from this work also demonstrate the potential use of the soluble fraction as an orange-juice-based model system of reduced complexity that can be used for the further investigation of NEB processes. © 2020 Society of Chemical Industry.

Comparison of Four Extraction Techniques for the Evaluation of Volatile Compounds in Spray-Dried New Zealand Sheep Milk.

Recent growth and diversification of sheep milk products means more sophisticated methods are required to ensure their flavour quality. The objective of this study was to compare four extraction techniques for the analysis of volatile compounds in sheep milk by gas chromatography-mass spectrometry (GC-MS). Solvent Assisted Flavour Evaporation (SAFE), Solid Phase Microextraction (SPME), Headspace Sorptive Extraction (HSSE) and Stir Bar Sorptive Extraction (SBSE) were evaluated for their sensitivity, selectivity, reproducibility, and overall efficiency. A total of 48 volatile compounds from nine compound classes were identified in the spray-dried sheep milk. Alcohols, aldehydes, alkanes, carboxylic acids, ketones, lactones, sulphur compounds, nitrogen compounds, and terpenes were all present, but the differences between the methods were most apparent for lactones. SBSE extracted eight lactones, SAFE extracted four lactones and HSSE and SPME only detected trace levels of two lactones. Six of the lactones-δ-hexa-lactone, δ-octalactone, γ-decalactone, γ-dodecalactone, δ-tetradecalactone, and δ-hexadeca-lactone-were identified for the first time in spray-dried sheep milk. The present work demonstrated that SBSE is an effective tool for the extraction and analysis of volatiles, especially lactones, in sheep milk and dairy products in general. A discussion of the benefits and limitations of each method is included.

Shelf-life dating of shelf-stable strawberry juice based on survival analysis of consumer acceptance information.

BACKGROUND: Accurate shelf-life dating of food products is crucial for consumers and industries. Therefore, in this study we applied a science-based approach for shelf-life assessment, including accelerated shelf-life testing (ASLT), acceptability testing and the screening of analytical attributes for fast shelf-life predictions. Shelf-stable strawberry juice was selected as a case study. RESULTS: Ambient storage (20 °C) had no effect on the aroma-based acceptance of strawberry juice. The colour-based acceptability decreased during storage under ambient and accelerated (28-42 °C) conditions. The application of survival analysis showed that the colour-based shelf-life was reached in the early stages of storage (≤11 weeks) and that the shelf-life was shortened at higher temperatures. None of the selected attributes (a* and ΔE* value, anthocyanin and ascorbic acid content) is an ideal analytical marker for shelf-life predictions in the investigated temperature range (20-42 °C). Nevertheless, an overall analytical cut-off value over the whole temperature range can be selected. CONCLUSIONS: Colour changes of strawberry juice during storage are shelf-life limiting. Combining ASLT with acceptability testing allowed to gain faster insight into the change in colour-based acceptability and to perform shelf-life predictions relying on scientific data. An analytical marker is a convenient tool for shelf-life predictions in the context of ASLT. © 2017 Society of Chemical Industry.

New Implications of Metabolites and Free Fatty Acids in Quality Control of Crossbred Wagyu Beef during Wet Aging Cold Storage.

Efficient cold-chain delivery is essential for maintaining a sustainable global food supply. This study used metabolomic analysis to examine meat quality changes during the "wet aging" of crossbred Wagyu beef during cold storage. The longissimus thoracic (Loin) and adductor muscles (Round) of hybrid Wagyu beef, a cross between the Japanese Black and Holstein-Friesian breeds, were packaged in vacuum film and refrigerated for up to 40 days. Sensory evaluation indicated an increase in the umami and kokumi taste owing to wet aging. Comprehensive analysis using gas chromatography-mass spectrometry identified metabolite changes during wet aging. In the Loin, 94 metabolites increased, and 24 decreased; in the Round, 91 increased and 18 decreased. Metabolites contributing to the umami taste of the meat showed different profiles during wet aging. Glutamic acid increased in a cold storage-dependent manner, whereas creatinine and inosinic acid degraded rapidly even during cold storage. In terms of lipids, wet aging led to an increase in free fatty acids. In particular, linoleic acid, a polyunsaturated fatty acid, increased significantly among the free fatty acids. These results provide new insight into the effects of wet aging on Wagyu-type beef, emphasizing the role of free amino acids, organic acids, and free fatty acids generated during cold storage.

Understanding the volatile flavour changes during accelerated shelf-life testing of oats using chemometrics and kinetic modelling.

This study investigated volatile flavour changes during accelerated shelf-life testing (ASLT) of oats using chemometrics and kinetic modelling. Oat samples were stored at 15, 25, 35 and 45 °C for up to 42 weeks. Headspace fingerprinting enabled the detection of a wide range of volatiles. Chemometrics was applied to explore the volatile changes and related reaction pathways and identify discriminant compounds. Most volatiles significantly increased as a function of time and could be linked to lipid oxidation and the Maillard reaction. Volatiles, such as hexanal, could be selected as potential ASLT markers for processed oat products. The volatile changes were adequately modelled using an empirical logistic model to estimate reaction rate constant and temperature dependency. Based on the estimated kinetic parameters, the selective ASLT markers could be used as a reference to track volatile changes, control off-flavour generation and improve oat product storage stability.

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.

Metabolomics: A suitable foodomics approach to the geographical origin traceability of Ethiopian Arabica specialty coffees.

Coffee arabica, originated in Ethiopia, is considered a quality bean for its high sensory qualities, and has a special price in the world coffee market. The country is a pool of genetic diversity for Arabica coffee, and coffee from different regions has a distinct flavor profile. Their exceptional quality is attributed to their genetic diversity, favorable environmental conditions, and agroforestry-based production system. However, the country still needs to benefit from its single-origin product due to a lack of appropriate traceability information to register for its geographical indication. Certification of certain plants or plant-derived products emerged to inform consumers about their exceptional qualities due to their geographical origin and protect the product from fraud. The recently emerging foodomics approaches, namely proteomics, genomics, and metabolomics, are reported as suitable means of regional agri-food product authentication and traceability. Particularly, the metabolomics approach provides truthful information on product traceability. Despite efforts by some researchers to trace the geographical origin of Ethiopian Arabica coffees through stable isotope and phenolic compound profiling and elemental analysis, foodomics approaches are not used to trace the geographical origin of Arabica specialty coffees from various parts of the country. A metabolomics-based traceability system that demonstrates the connection between the exceptional attributes of Ethiopian Arabica specialty coffees and their geographic origin is recommended to maximize the benefit of single-origin coffees.

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.

Effect of industrial processing on the volatiles, enzymes and lipids of wholegrain and rolled oats.

The aim of this research was to study the effect of industrial processing stages (kilning, cutting, steaming, rolling and packaging) on the enzymes (lipase and peroxidase), lipids and volatiles of wholegrain and rolled oats. Chemometric data analysis was used to assess the evolution of flavor-related compounds, investigate the relationships between the attributes and select discriminant marker compounds. Oat groats (dehulled oat grain) had significantly (p < 0.05) higher lipase and peroxidase activities, and possessed higher amounts of short-chain volatile fatty acids than the processed oats. The combined effect of kilning and subsequent steaming significantly (p < 0.05) reduced the activity of these enzymes. The use of high temperatures during kilning and steaming triggered the Maillard reaction and Strecker degradation reactions, leading to the formation of odor-active volatile compounds, such as pyrazines, furans and Strecker aldehydes. These compounds are commonly associated with the desirable nutty and toasted aroma of oat-based products. Overall, this study successfully identified key volatile markers and their associated reaction pathways, which can be used to control and optimize the industrial oat processing steps.

Changes in Starch In Vitro Digestibility and Properties of Cassava Flour Due to Pulsed Electric Field Processing.

The research aimed to investigate the effect of pulsed electric field (PEF) treatment on cassava flour at mild intensities (1, 2, and 4 kV/cm) combined with elevated levels of specific energy input (250−500 kJ/kg). Influences on starch digestibility, morphological characteristics, birefringence, short-range order and thermal properties were evaluated. Application of PEF at energy input no greater than 250 kJ/kg had negligible influence on the different starch digestion fractions of cassava flour but raised the rapidly digestible starch fraction at a combined electric field strength >1 kV/cm and energy input >350 kJ/kg. Morphological evaluation revealed that at this PEF combination, cassava starch's external structure was consistently altered with swelling and disintegration, albeit some granules remained intact. Consequently, this led to disruption in the internal crystalline structure, supported by progressive loss of birefringence and significantly lower absorbance ratio at 1047/1022 cm−1. These physical and microstructural changes of the inherent starch promoted the shift in gelatinization temperatures to a higher temperature and reduced the gelatinization enthalpy. The study demonstrated that PEF can be utilized to change the starch fraction of cassava flour, which is driven by electric field strength and specific energy input, causing changes in the starch-related properties leading to increased digestibility.

Application of Mass Spectrometry for Determining the Geographic Production Area of Wagyu Beef.

Japanese Black cattle (Japanese Wagyu) beef is attracting attention for its aroma and marbling, and its handling is increasing worldwide. Here, we focused on the origin discrimination of Wagyu beef and analyzed the nutritional components of Japanese Wagyu (produced in multiple prefectures of Japan), Hybrid Wagyu (a cross between Angus and Wagyu cattle born in Australia and transported to Japan), and Australian Wagyu beef using mass spectrometry (MS). Triple-quadrupole liquid chromatography-MS was used to clarify the molecular species of lipids in Wagyu beef. Fourteen classes of lipids were separated, and 128 different triacylglycerides (TGs) were detected. A simple comparative analysis of these TGs using high-performance liquid chromatography revealed significantly higher levels of triolein (C18:1/C18:1/C18:1; abbreviated OOO) and C18:1/C18:1/C16:1 (OOPo) in Japanese Wagyu. Wagyu elements beef were comprehensively analyzed using inductively coupled plasma (ICP)-MS and ICP-optical emission spectrometry. We found significant differences in the rubidium, cesium, and lithium levels of Japanese and Australian Wagyu beef. On comparing metabolites using gas chromatography-MS, we identified significant differences in the levels of amino acids and other components of the Japanese and Australian Wagyu beef. These results suggest the possibility of determining the origin of Wagyu cattle breeds using MS and genetic discrimination.

Effects of Hydrothermal Processing Duration on the Texture, Starch and Protein In Vitro Digestibility of Cowpeas, Chickpeas and Kidney Beans.

Legumes are a vital candidate in the fight for food security as a sustainable and nutritious food source. The current study systematically investigated the effects of hydrothermal processing of varying durations (15-120 min) on the texture, starch and protein digestibility of cowpeas (Vigna unguiculata), chickpeas (Cicer arietinum) and kidney beans (Phaseolus vulgaris). Texture analysis and in vitro oral-gastro-intestinal digestion of each legume was combined with kinetic modelling to explore the rate and extent of their changes observed during hydrothermal processing. All three legumes showed rapid initial texture decay in the first 30 min of processing. Chickpeas showed the fastest rate of texture degradation with processing duration, whereas texture degradation of kidney bean was slower but reached the lowest hardness value among all beans when processed up to 120 min. The rate of starch and protein digestion increased with prolonged processing duration, whilst showing an inverse relationship with texture values. The extent of starch digestion continually increased with processing duration for all three legumes, whereas the extent of protein digestion decreased after 60 min in cowpeas. This study systematically demonstrated how choosing different processing times can modulate the rate of texture degradation, starch and protein digestion in legumes. The findings of this study can aid consumers and manufacturers on optimal processing to achieve the desired texture or modulate starch and protein digestibility.

Characterization of blue cheese volatiles using fingerprinting, self-organizing maps, and entropy-based feature selection.

Understanding which volatile compounds discriminate between products can be useful for quality, innovation or product authenticity purposes. As dataset size and dimensionality increase, linear chemometric techniques like partial least squares discriminant analysis and variable identification (PLS-DA-VID) may not identify the most discriminant compounds. This research compared the performance of self-organizing maps and entropy-based feature selection (SOM-EFS) and PLS-DA-VID to identify discriminant compounds in 17 blue cheese varieties. A total of 172 volatiles were detected using headspace solid phase microextraction, gas chromatography and mass spectrometry, including 1-nonene and 2,6-dimethylpyridine, which were newly identified in blue cheese. Despite SOM-EFS selecting only 14 volatiles compared to 78 for PLS-DA-VID, SOM-EFS proved more effectively discriminant and improved the median five-fold cross-validated prediction accuracy of the model to 0.94 compared to 0.82 for PLS-DA-VID. These findings introduce SOM-EFS as a powerful non-linear exploratory data analysis approach in the field of volatile analytical chemistry.

Differences in New Zealand Hop Cultivars Based on Their Unique Volatile Compounds: An Integrated Fingerprinting and Chemometrics Approach.

Hop aroma characteristics originate from hop essential oils, which have complex chemical profiles that remain poorly understood, particularly for New Zealand hops. The aim of this study was to determine volatile compounds that distinguish New Zealand hop cultivars. Untargeted fingerprinting methods based on headspace gas chromatography mass spectrometry (GC-MS) were used to analyse nine hop cultivars. A total of 61 volatile compounds were identified as compounds that differentiated the commercial hop varieties using advanced chemometrics and feature selection techniques. Similarities in volatile composition were found between Wakatu, Wai-iti™ and Kohatu®, which are rich in alcohols. Another grouping was found between Waimea™ and Nelson Sauvin™, where ketones and esters were commonly found. Rakau™ was distinct from the other eight cultivars, distinguished by 2-methylbutyl 3-methylbutanoate and methanethiol hexanoate. Riwaka™ contained the greatest number of discriminating volatile compounds when compared to other cultivars, which was dominated by terpenoids, such as geranyl 2-methylbutanoate, perillene and D-limonene. The chemical fingerprinting approach successfully identified volatile compounds that had not been previously found in New Zealand hop cultivars and that discriminated the commercial cultivars. The data obtained in the present study further extend the knowledge of New Zealand hops and will help facilitate targeted breeding.