Discrimination of Robusta Amazônico coffee farmed by indigenous and non-indigenous people in Amazon: comparing benchtop and portable NIR using ComDim and duplex.

Robusta Amazônico is the name given to the Amazonian coffee that has been becoming popular and has recently been registered as a geographical indication in Brazil. It is produced by indigenous and non-indigenous coffee producers in regions that are geographically very close to one another. There is a need to authenticate whether coffee is truly produced by indigenous people and near-infrared (NIR) spectroscopy is an excellent technique for this. To meet the substantial trend towards NIR spectroscopy miniaturization, this work compared benchtop and portable NIR instruments to discriminate Robusta Amazônico samples using partial least squares discriminant analysis (PLS-DA). To ensure the results to be fairly comparable and, at the same time, to guarantee representative selection of both training and test set for the discriminant analysis, a sample selection strategy based on coupling ComDim multi-block analysis and the duplex algorithm was applied. Different pre-processing techniques were tested to create multiple matrices to be used in ComDim, as well as to build the discriminant models. The best PLS-DA model for benchtop NIR provided an accuracy of 96% for the test samples, while for the portable NIR the correct classification rate was 92%. It was demonstrated that portable NIR provides similar results to benchtop NIR for coffee origin classification by performing an unbiased sample selection strategy.

Effect of pollination on the composition of raw arabica coffee (Coffea arabica L.): antioxidant capacity, bioactive compounds, and volatiles precursors.

BACKGROUND: The importance of pollination is recognized worldwide. This study investigates the influence of biotic pollination on the chemical composition of coffee (Coffea arabica L.) cultivated in the municipalities of Araguari and Monte Carmelo, in Minas Gerais state, Brazil. Twenty samples were analyzed, of which 10 were biotically pollinated (P) and 10 non-biotically pollinated (NP). Analyses of bioactive compounds, antioxidant capacity and centesimal composition of coffees were performed. RESULTS: The results revealed chemical differences between the P and NP samples, with significantly higher levels of the bioactive compounds caffeine, trigonelline, and chlorogenic acids in the P samples. The findings indicated that pollination of the coffee plants assisted germination and made the beans less susceptible to attack by external agents. CONCLUSION: The results demonstrated that pollination services are important mechanisms affecting the chemical composition of coffee beans. Considering that the compounds evaluated are precursors of volatile compounds in coffee, this directly affects the quality of the beans. © 2023 Society of Chemical Industry.

Application of chemometric tools in the development and sensory evaluation of gluten-free cracknel biscuits with the addition of chia seeds and turmeric powder.

Gluten-free biscuits were developed with the addition of chia seeds (Salvia hispanica L.) and turmeric powder. An experimental design 22 were employed in the formulation optimization that promotes better sensory acceptance through acceptance test with a hedonic scale of 9 points. For characterization purposes, the centesimal composition from chia seeds and the turmeric powder were determined. The biscuits were produced from an optimized formulation with and without chia seeds and turmeric powder with microbiological results safe for consumption by evaluation Salmonella sp., positive coagulase Staphylococcus, and Coliforms to 45 °C. The Principal Components Analysis (PCA) was used in the investigation of sensory results (color, flavor, texture, smell, appearance, overall impression). It was also considered the habits of consuming food with/without gluten, purchase intentions, including age and gender. The results show that there is no distinction between the biscuits with the addition of chia seeds and turmeric powder. A statistical test using the confidence ellipse confirms that there no significant difference, at a 95% confidence level, among the sensory results for the biscuits with and without chia seeds and turmeric powder.

N-Way NIR Data Treatment through PARAFAC in the Evaluation of Protective Effect of Antioxidants in Soybean Oil.

The use of chemometric tools is progressing to scientific areas where analytical chemistry is present, such as food science. In analytical food evaluation, oils represent an important field, allowing the exploration of the antioxidant effects of herbs and seeds. However, traditional methodologies have some drawbacks which must be overcome, such as being time-consuming, requiring sample preparation, the use of solvents/reagents, and the generation of toxic waste. The objective of this study is to evaluate the protective effect provided by plant-based substances (directly, or as extracts), including pumpkin seeds, poppy seeds, dehydrated goji berry, and Provençal herbs, against the oxidation of antioxidant-free soybean oil. Synthetic antioxidants tert-butylhydroquinone and butylated hydroxytoluene were also considered. The evaluation was made through thermal degradation of soybean oil at different temperatures, and near-infrared spectroscopy was employed in an n-way mode, coupled with Parallel Factor Analysis (PARAFAC) to extract nontrivial information. The results for PARAFAC indicated that factor 1 shows oxidation product information, while factor 2 presents results regarding the antioxidant effect. The plant-based extract was more effective in improving the frying stability of soybean oil. It was also possible to observe that while the oxidation product concentration increased, the antioxidant concentration decreased as the temperature increased. The proposed method is shown to be a simple and fast way to obtain information on the protective effects of antioxidant additives in edible oils, and has an encouraging potential for use in other applications.

The Potential Use of Near Infrared Spectroscopy (NIRS) to Determine the Heavy Metals and the Percentage of Blends in Tea.

The following study analyzed the potential of Near Infrared Spectroscopy (NIRS) to predict the metal composition (Al, Pb, As, Hg and Cu) of tea and for establishing discriminant models for pure teas (green, red, and black) and their different blends. A total of 322 samples of pure black, red, and green teas and binary blends were analyzed. The results showed that pure red teas had the highest content of As and Pb, green teas were the only ones containing Hg, and black teas showed higher levels of Cu. NIRS allowed to predict the content of Al, Pb, As, Hg, and Cu with ratio performance deviation values > 3 for all of them. Additionally, it was possible to discriminate pure samples from their respective blends with an accuracy of 98.3% in calibration and 92.3% in validation. However, when the samples were discriminated according to the percentage of blending (>95%, 95-85%, 85-75%, or 75-50% of pure tea) 100% of the samples of 10 out of 12 groups were correctly classified in calibration, but only the groups with a level of pure tea of >95% showed 100% of the samples as being correctly classified as to validation.

A conventional and chemometric analytical approach to solving urea determination with accuracy and precision.

Urea is an essential molecule usually detected using spectroscopy, particularly ultraviolet and visible spectroscopy (UV-vis). However, its detection represents a not always fully acknowledged issue. Its concentration dependency has raised questions about the reliability of the UV-vis results. Derivatization reactions, common alternatives to achieve accuracy and precision with UV-vis measurements, still represent an additional step in the measurement process. Besides the problems mentioned earlier, urea forms complex mixtures in aqueous mediums. Therefore, this work proposes to investigate the accuracy and precision of urea determination by UV-vis spectroscopy in the pure form and derivatized with para-dimethylaminobenzaldehyde. The results show that UV-vis spectroscopy could not quantify urea in both forms with precision and accuracy. On the other hand, when applying multivariate curve resolution with alternating least squares (MCR-ALS) to the UV-vis data, the pure urea analytical signal is mathematically separated. Then, those parameters of merit were successfully achieved.

Authentication and discrimination of new Brazilian Canephora coffees with geographical indication using a miniaturized near-infrared spectrometer.

New Brazilian Canephora coffees (Conilon and Robusta) of high added value from specific origins have been protected by geographical indication to guarantee their origin and quality. Recently, benchtop near-infrared (NIR) spectroscopy combined with chemometrics has demonstrated its usefulness to discriminate them. It was the first study, however, and therefore the possibility exists to develop a new portable NIR method for this purpose. This work assessed a miniaturized NIR as a cheaper spectrometer to discriminate and authenticate new Brazilian Canephora coffees with certified geographical origins and to differentiate them from specialty Arabica. Discriminant chemometric and class modeling techniques have been applied and have obtained good predictive ability on external test sets. In addition, models with similar classification purpose were compared with those obtained in previous research carried out with benchtop NIR for the same samples, obtaining comparable results. In this context, the portable method was used as a laboratory technique and has the advantage of being cheaper than benchtop NIR spectrometer. Furthermore, it brings a high possibility to be implemented in small coffee cooperatives, industries or control agencies in the future that do not have high economic resources.

In situ extraction/encapsulation of olive leaves antioxidants in zein for improved oxidative stability of edible oils.

This study presents a sustainable and cost-effective method for preserving the bioactivity of phenolic compounds in olive leaves (OLE) during their application. The extraction and nanoencapsulation of OLE were performed in a single-step process using a rotor-stator system with zein as the encapsulating agent. The nanoprecipitation step was carried out using an aqueous sodium caseinate solution, resulting in spherical particles with an average diameter of about 640 nm, as confirmed by Transmission Electron Microscopy. Thermal characterization showed that the produced nanoparticles were more thermally stable than free OLE until 250 °C, and FTIR spectra indicated effective interaction between the phenolic compounds and zein. Antioxidant activity was evaluated using TBARS, DPPH, ABTS, and FRAP assays, with results showing that encapsulated OLE had lower antioxidant activity than free OLE. The best antioxidant capacity results were determined by TBARS assay, with IC50 results equal to 43 and 103 µgOLE/mL for free and encapsulated OLE, respectively. No anti-inflammatory potential was detected for both samples using the RAW 264.7 model, and only free OLE showed cytotoxic activity against lung cancer and gastric carcinoma. Encapsulated and free OLE were used as antioxidants in soy, palm, and palm kernel oils and compared to BHT using Rancimat. The Schaal Oven Test was also performed, and the PARAFAC chemometric method analyzed the UV-Vis spectra, which revealed high stability of the oil when 300 mg or the nanoparticles were added per kg oil. Results suggested that zein-encapsulated olive leaf antioxidants can improve the oxidative stability of edible oils.

A semi-quantitative model through PLS-DA in the evaluation of carbendazim in grape juices.

Carbendazim (CBZ) is a fungicide employed in grape crop disease controls, and its maximum residue limit in food is regulated by specialized agencies. This study aimed to determine the CBZ content in the grape juices in a semi-quantitative classification model based on portable Ultraviolet-Visible spectroscopy and partial least squares with discriminant analysis. The sensitivity and specificity of the obtained model ranged from 83 to 100%, with the external validation set. These results are therefore promising for industrial application, and the model presents robustness for the evaluation of grape juices produced from a different grape variety. The VIP scores allowed identifying important variables involved in class modeling. This study suggests a methodology that is fast and demands minimal sample preparation (only dilution), besides being less expensive compared to the traditional methods, free of reagent/solvent, contributing to quality control in the juice industry.

Analysis of HMF and furfural in hydrolyzed lignocellulosic biomass by HPLC-DAD-based method using FDCA as internal standard.

HMF (hydroxymethylfurfural), a compound that occurs naturally in food, is derived from the dehydration of monosaccharides (glucose and fructose) in products subjected to high-temperature treatments or to aging processes. HMF can be obtained by acid-catalyzed dehydration of lignocellulosic biomasses such as sugarcane bagasse and other agricultural residues. In this work, analytical quantification of HMF and furfural (the main co-product) was performed using high-performance liquid chromatography coupled with diode array detection (HPLC-DAD). The official method employs isocratic elution with a mobile phase composed of water and acetonitrile at a ratio of 80:20 (v/v). The analytical method proposed here was developed using 2,5-furandicarboxylic acid (FDCA) as internal standard, for the first time, with 0.01 mol L-1 trisodium citrate and ultrapure water as the mobile phase, adjusted to pH 2.5. The acidity of the mobile phase was required to avoid FDCA deprotonation. Good peak resolution and selectivity were obtained, without differences in the retention times of the analytes present in the standard solutions used to obtain the analytical curve and in the aqueous and organic phases from the synthesis of HMF using lignocellulosic biomass hydrolysis. The method complies with the current recommendations of AOAC regarding validation parameters.•The proposed HPLC method improves peak selectivity and resolution.•The method is suitable for acid sample media, such as aqueous and organic hydrolysis phases.•2,5-Furandicarboxylic acid (FDCA) was used as internal standard.

Proof-of-concept on the effect of human milk storage time: Lipid degradation and spectroscopic characterization using portable near-infrared spectrometer and chemometrics.

Human milk (HM) modifications over time represent an important issue. This work proposed to evaluate the changes in HM during one-year storage through total lipids (TL) degradation and portable near-infrared (NIR) spectrometer combined with chemometrics. Colostrum, transition, and mature stages were obtained from donors and considered in the raw and pasteurized forms. Principal component analysis in TL content showed changes in the mature stages for both forms after 75 days. Multivariate curve resolution with alternating least squares in NIR spectral data reveals a decrease in protein and triacylglycerol contents while an increase in free fatty acids (palmitic acid) contents were observed through the storage after around 5-6 months. Therefore, more than 5-6 months of storage suggest possible biochemical changes in the HM nutritional composition. Moreover, the chemometrics investigation was crucial in extracting information, bringing coherent results, and helping to understand the chemical changes in human milk during storage.

Kurtosis-based projection pursuit analysis to evaluate South American rapadura.

Rapadura is an artisanal candy obtained from concentrated sugarcane juice. In this study, a differentiation between South American rapadura producers has been tried using a Kurtosis-based projection pursuit analysis (kPPA) concerning essential minerals, acrylamide, moisture contents, pH, and color. These parameters revealed significant inter- and intra-country differences. Based on the employed measurements, a multivariate exploration with kPPA extracted information from rapadura even though it is a very artisanal product and was effective in separating classes, especially Brazilian and Ecuadorian rapadura, where principal component analysis failed. Moreover, ellipse confidence regions showed significant differences between non-organic and organic rapadura from Colombia and Peru in granulated form. From a chemometric point of view, the application of kPPA can be used in cases when other metrics (as based on the variance) fail and can be useful in the exploratory analysis of complex multivariate chemical data.

Multivariate optimization approach applied to natural polymers from Ceratonia siliqua L. and Moringa oleifera Lam as coagulating/flocculating agents.

In this study, a multivariate 23 experimental design was applied to optimize the operational conditions (seed mass, salt concentration, and pH) to employ Ceratonia siliqua L. (carob) and Moringa oleifera Lam (moringa) as coagulating/flocculating agents for water treatment. Currently, the coagulation stage in water treatment uses aluminium compounds, due to the characteristic reaction to natural alkalinity in raw water, and for its low market value. Considering that aluminium effects on human health are not sufficiently studied to acknowledge its toxicity, and its significant environmental impacts, it is suitable for the studies to search for alternatives to be employed in the water treatment that will be distributed to human consumption. This study was carried out with raw water of high turbidity level, 83.7 NTU. The raw water collected was also characterized according to pH, colour, Total Organic Carbon (TOC), Dissolved Organic Carbon (DOC), and Dissolved Organic Matter (DOM), with values of 6.7, 178 NTU, 6.80, 2.45 and 138.58 mg/L, respectively. The optimized results showed that with 2 g of seed, 0.5 mol L-1 of NaCl, and pH 11.0 In these conditions, moringa coagulant reached 90%, 86%, 6%, 67%, and 81% for turbidity, colour, DOC, TOC, and DOM removal, respectively, whereas the carob coagulant achieved 85%, 76%, 5%, 55.6%, 66.7%, respectively for the same parameters' removal. Both coagulants presented lower sludge formation, 1.1 mL L-1 for moringa coagulant, and 1.1 mL L-1 for carob coagulant. The results could be considered promises, and natural polymers carob and moringa can be suggested as alternatives agents in coagulation/flocculation stages for water treatment.

Combinatorial projection pursuit analysis for exploring multivariate chemical data.

Kurtosis-based projection pursuit analysis (kPPA) has demonstrated the ability to visualize multivariate data in a way that complements other exploratory data analysis tools, such as principal components analysis (PCA). It is especially useful for partitioning binary data sets (2k classes) with a balanced design. Since kPPA is not a variance-based method, it can often provide unsupervised class separation where other methods fail. However, when multiple classifications are possible (e.g. by gender, age, disease state, etc.), the projection provided by kPPA (corresponding to the global minimum kurtosis) will not necessarily be the one of greatest interest to the researcher. Fortunately, the optimization algorithm for kPPA allows for interrogation of projections obtained from numerous local minima. This strategy provides the basis of a new method described here, referred to as combinatorial projection pursuit analysis (CombPPA) because it presents alternative combinations of class separation. The method is truly exploratory in that it allows the landscape of interesting projections to be more fully probed. The approach uses Procrustes rotation to map local minima among the kPPA solutions, whereupon the researcher can visualize different projections. To demonstrate the new method, the clustering of grape juice samples using visible spectroscopy is presented as a model problem. This problem is well-suited to this type of study because there are eight classes of samples symmetrically partitioned into two classes by type (organic/non-organic) or four classes by brand. Results presented show the different combinations of projections that can be obtained, including the desired partitions. In addition, this work describes new enhancements to the kPPA algorithm that improve the orthogonality of solutions obtained.

Multivariate classification for the direct determination of cup profile in coffee blends via handheld near-infrared spectroscopy.

Professional cupping is a reliable methodology for the coffee industry and its professionals. However, it faces barriers for its implementation on an industrial scale. To date, no study has determined a coffee cup profile using a handheld near-infrared (NIR) spectrometer. Therefore, the aim of this study was to evaluate directly cup profiles in roasted and ground coffee blends via handheld NIR spectroscopy combined with partial least squares with discriminant analysis (PLS-DA), in an industrial case study. The sensitivity and specificity of the model obtained ranged from 91 to 100%, 84-100%, and 73-95% in the training, prediction, and internal cross-validation sets, respectively. These results are therefore promising for the industrial reality and the methodology could assist coffee professionals in their decisions during cup evaluation in further tests. The proposed method is viable for the direct determination of cup profile at industrial scale since it is portable, fast, simple, robust, and less expensive compared to the benchtops equipment.

Integrated 1H NMR fingerprint with NIR spectroscopy, sensory properties, and quality parameters in a multi-block data analysis using ComDim to evaluate coffee blends.

Coffee quality is determined by several factors and, in the chemometric domain, the multi-block data analysis methods are valuable to study multiple information describing the same samples. In this industrial study, the Common Dimension (ComDim) multi-block method was applied to evaluate metabolite fingerprints, near-infrared spectra, sensory properties, and quality parameters of coffee blends of different cup and roasting profiles and to search relationships between these multiple data blocks. Data fusion-based Principal Component Analysis was not effective in exploiting multiple data blocks like ComDim. However, when a multi-block was applied to explore the data sets, it was possible to demonstrate relationships between the methods and techniques investigated and the importance of each block or criterion involved in the industrial quality control of coffee. Coffee blends were distinguished based on their qualities and metabolite composition. Blends with high cup quality and lower roasting degrees were generally differentiated from those with opposite characteristics.

Near-infrared spectroscopy and multivariate calibration as an alternative to the Agtron to predict roasting degrees in coffee beans and ground coffees.

Agtron method is widely used in the industry to determine roasting degrees in whole and ground coffee but it suffers from some inconveniences associated with unavailability of equipment, high cost, and lack of reproductive results. This study investigates the feasibility to determine roasting degrees in coffee beans and ground specialty coffees using near-infrared (NIR) spectroscopy combined with multivariate calibration based on partial least squares (PLS) regression. Representative data sets were considered to cover all Agtron roasting profiles for whole and ground coffees. Proper development of models with outlier evaluation and complete validation using parameters of merit such as accuracy, adjust, residual prediction deviation, linearity, analytical sensitivity, and limits of detection and quantification are presented to prove their performance. The results indicated that predictive chemometric models, for intact coffee beans and ground coffee, could be used in the coffee industry as an alternative to Agtron, thus digitalizing the roasting quality control.

Attenuated total reflectance Fourier transform (ATR-FTIR) spectroscopy and chemometrics for organic cinnamon evaluation.

Organic food consumption has increased significantly over time. This contributes to the increased demand and price of this kind of food. Among the organic products, cinnamon stands out for its characteristic flavor and bioactive compounds. Thus, the work aimed to verify the potentials of attenuated total reflectance Fourier transform mid-infrared spectroscopy (ATR-FT-MIR) coupled with Parallel Factor Analysis (PARAFAC) for evaluation of cinnamon organic samples. As result, the proposal is feasible in the differentiation of organic cinnamon powder, in which ATR-FT-MIR coupled with PARAFAC showed the differentiation of organic from non-organic ones on the scores mode, the precision at repeatability level on one loading mode, and the spectral region, on the other loading mode, above 2600 cm-1 was related to the differentiation of the organic and non-organic samples.

Brazilian Coffee Blends: A Simple and Fast Method by Near-Infrared Spectroscopy for the Determination of the Sensory Attributes Elicited in Professional Coffee Cupping.

The diversity of compounds and variations in the aroma and flavor of ground and roasted coffee make the sensory evaluation by the "cupping test" a complex task to be performed. A total of 217 commercial coffee samples classified as different beverage type and with different roast degrees were evaluated by official cuppers in the "cupping test" and the responses for sensory attributes were used to verify the correlation to the near-infrared (NIR) spectra. Chemometric models based on partial least squares (PLS) were built for the powder fragrance, drink aroma, acidity, bitterness, flavor, body, astringency, residual flavor, and overall quality. The parameters of merit such as accuracy, fit, linearity, residual prediction deviation, sensitivity, analytical sensitivity, limits of detection, and quantification were evaluated. All sensory attributes were predicted with adequate values according to the parameters of merit. The proposed method, when compared to the "cupping test," is an alternative to the determination of the coffee sensory attributes. The results demonstrated that the use of NIR associated with chemometrics is efficient and recommended for the prediction of sensorial attributes of coffee by means of the direct analysis of roasted and ground samples, and without any additional preparation, it is a promising tool for the coffee industry. PRACTICAL APPLICATION: This study has shown potential use of near-infrared (NIR) spectroscopy coupled with a chemometric tool for the prediction of sensory attributes of commercial coffees. Prediction models for powder fragrance, drink aroma, acidity, bitterness, flavor, body, astringency, residual flavor, and overall quality were built and showed good predictive capacity. The use of NIR allows rapid analysis (1 min or less per sample), and it was possible to evaluate all sensory attributes directly in roasted and ground coffee, without beverage preparation.

Thermal rice oil degradation evaluated by UV-Vis-NIR and PARAFAC.

Thermal rice oil degradation was evaluated by UV-Vis and NIR in three-way arrays assembled with samples, different temperatures, and the absorbance at different wavelengths by applying PARAFAC chemometric method. The loadings matrix of the mode corresponding to the samples (scores) contains the information related to the samples. The loadings on the temperature mode resemble kinetics profiles. These profiles change with the nature of the component responsible for the factor and evolve with the heating temperature. The loadings on the spectral mode reveal the antioxidants γ-oryzanol and tocopherol, and oxidation products are the components responsible for the two factors. The results achieved showed that the antioxidants concentration decreases starting at 70 °C while oxidation products start to increase more pronounced after 90 °C. The proposed method is shown to be a simple and fast way to obtain information about the oxidative stability of rice oils.