QCL Infrared Spectroscopy Combined with Machine Learning as a Useful Tool for Classifying Acetaminophen Tablets by Brand.

The development of new methods of identification of active pharmaceutical ingredients (API) is a subject of paramount importance for research centers, the pharmaceutical industry, and law enforcement agencies. Here, a system for identifying and classifying pharmaceutical tablets containing acetaminophen (AAP) by brand has been developed. In total, 15 tablets of 11 brands for a total of 165 samples were analyzed. Mid-infrared vibrational spectroscopy with multivariate analysis was employed. Quantum cascade lasers (QCLs) were used as mid-infrared sources. IR spectra in the spectral range 980-1600 cm-1 were recorded. Five different classification methods were used. First, a spectral search through correlation indices. Second, machine learning algorithms such as principal component analysis (PCA), support vector classification (SVC), decision tree classifier (DTC), and artificial neural network (ANN) were employed to classify tablets by brands. SNV and first derivative were used as preprocessing to improve the spectral information. Precision, recall, specificity, F1-score, and accuracy were used as criteria to evaluate the best SVC, DEE, and ANN classification models obtained. The IR spectra of the tablets show characteristic vibrational signals of AAP and other APIs present. Spectral classification by spectral search and PCA showed limitations in differentiating between brands, particularly for tablets containing AAP as the only API. Machine learning models, specifically SVC, achieved high accuracy in classifying AAP tablets according to their brand, even for brands containing only AAP.

Characterization and detailed mapping of C by spectral sensor for soils of the Western Plateau of São Paulo.

Soil mineralogy and texture are directly related to soil carbon due to the physical properties of the clay surface. Traditional techniques for quantifying carbon in soil are time-consuming and expensive, making large-scale quantification for mapping unfeasible. The alternative is the use of soil sensors, such as diffuse reflectance spectroscopy (DRS), an economical, fast, and accurate technique for predicting carbon stocks. In this sense, this study aimed to (a) investigate the relationship of C with different soil mineralogical, chemical, and physical attributes for different geological and geomorphological compartments; (b) understand which spectral bands are most important for estimating C content; (c) estimate C content from diffuse reflectance spectroscopy using different mathematical techniques and indicate which one is the best for tropical soil conditions; and (d) map C contents in detail. The study area was the Western Plateau of São Paulo (WPSP), which covers approximately 13 million hectares (~ 48% of the State of São Paulo, Brazil). A total of 265 samples were collected in this area. The attributes clay, silt, sand, crystalline and non-crystalline iron, base saturation, soil density, total pore volume, total C, C stock, kaolinite/(kaolinite + gibbsite) and hematite/(hematite + goethite), hematite and goethite contents, and spectral curves were evaluated. The spectra were recorded at 0.5-nm intervals, with an integration time of 2.43 nm s-1 over the 350 to 2500-nm range (350-800 nm-visible-VIS and 801-2500 nm-near-infrared-NIR). The data were subjected to descriptive statistics, Spearman correlation, stepwise analysis, and cluster grouping for characterization purposes; partial least squares regression (PLSR) and random forest (RF) for estimation purposes; and geostatistics analysis for creation of spatial maps. Our results indicate that the highest C contents are associated with more clayey soils, oxidic mineralogy, higher total pore volume, and lower soil density in highly dissected basalt compartments. The random forest algorithm associated with the Vis-NIR spectral range is more efficient for estimating and mapping C contents. This suggests that integrating diffuse reflectance spectroscopy with machine learning techniques holds promise for shaping public policies related to land use, mitigating CO2 emissions, and facilitating the implementation of carbon credit policies in a rapid and economically efficient manner.

Characterization of the Polyphenolic Profile in Tomato (Lycopersicon esculentum P. Mill) Peel and Seeds by LC-HRMS/MS.

Peel and seeds are the main byproducts from tomato (Lycopersicon esculentum P. Mill) processing with high concentrations of polyphenols that have been underexploited. Herein, polyphenolic profiles in tomato peel and seeds were elucidated by untargeted liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) with an LTQ Orbitrap analyzer. Samples from two Spanish regions─"Murcia" and "Almería"─were analyzed to obtain complementary results. 57 compounds were found, mainly phenolic acids and flavonoids, of which eight were identified for the first time in tomato. Polyphenols were more abundant in byproducts from "Murcia" samples than in those from"Almería" samples, where the abundance of compounds like coutaric, caffeic, neochlorogenic, dicaffeoylquinic and ferulic acids, vanillic acid hexoside, catechin, naringenin, prunin, apigenin-O-hexoside, rutin, and rutin-O-pentoside was even much higher in byproducts than that in whole fruits. These results reveal the wide range of polyphenols found in tomato byproducts, with potential applications in pharmaceutical research, food preservation, and cosmetic development, among others.

Application of a microfluidic electronic tongue based on impedance spectroscopy for coconut water analysis.

The food industry has grown with the demands for new products and their authentication, which has not been accompanied by the area of analysis and quality control, thus requiring novel process analytical technologies for food processes. An electronic tongue (e-tongue) is a multisensor system that can characterize complex liquids in a fast and simple way. Here, we tested the efficacy of an impedimetric microfluidic e-tongue setup - comprised by four interdigitated electrodes (IDE) on a printed circuit board (PCB), with four pairs of digits each, being one bare sensor and three coated with different ultrathin nanostructured films with different electrical properties - in the analysis of fresh and industrialized coconut water. Principal Component Analysis (PCA) was applied to observe sample differences, and Partial Least Squares Regression (PLSR) was used to predict sample physicochemical parameters. Linear Discriminant Analysis (LDA) and Partial Least Square - Discriminant Analysis (PLS-DA) were compared to classify samples based on data from the e-tongue device. Results indicate the potential application of the microfluidic e-tongue in the identification of coconut water composition and determination of physicochemical attributes, allowing for classification of samples according to soluble solid content (SSC) and total titratable acidity (TTA) with over 90% accuracy. It was also demonstrated that the microfluidic setup has potential application in the food industry for quality assessment of complex liquid samples.

Alkaloids from Siparuna (Siparunaceae) are predicted as the inhibitors of proteolysis and plasma coagulation caused by snake venom and potentially counteract phospholipase A2 activity of Bothrops jararaca.

ETHNOPHARMACOLOGICAL RELEVANCE: Snakebite envenomation (SBE) is the world's most lethal neglected tropical disease. Bothrops jararaca is the species that causes the greatest number of SBEs in the South and Southeastern of Brazil. The main symptoms are local (inflammation, edema, hemorrhage, and myonecrosis) and systemic (hemorrhage, hemostatic alterations with consumptive coagulopathy, and death) effects. Species of the genus Siparuna, Siparunaceae, are used in folk and traditional medicine to treat SBE. However, limited information is available concerning Brazilian Siparuna species against SBE. AIM OF THE STUDY: To investigate the correlation between the compounds present in the extracts of five Siparuna species as potential agents against proteolytic activity, plasma coagulation, and phospholipase A2 (PLA2) activity caused by B. jararaca venom, using data obtained by UHPLC-MS/MS, biological activity, and multivariate statistics. MATERIALS AND METHODS: The ethanol extracts from leaves of S. ficoides, S. decipiens, S. glycycarpa, S. reginae, and S. cymosa were fractionated by liquid-liquid extraction using different solvents of increasing polarity (hexane, dichloromethane, ethyl acetate, and n-butanol), affording their respective extracts, totaling 25 samples that were assayed through in vitro plasma coagulation and proteolytic activity assays. Moreover, the extracts were analyzed by UHPLC-MS/MS, using electrospray ionization (ESI) and atmospheric-pressure chemical ionization (APCI) in negative and positive ionization modes. The data was processed in MZmine v. 2.53 and evaluated by multivariate statistical tests (PLS) using the software UnscramblerX v. 10.4. These data were also used to build molecular networks (GNPS), and some ions of interest could be annotated using the library of molecules on the GNPS platform. RESULTS: A total of 19 extracts inhibited B. jararaca-induced plasma coagulation, with emphasis on S. cymosa and S. reginae (800 s). The inhibition of the proteolytic activity was also promising, ranging from 16% (S. glycycarpa) to 99% (S. cymosa, S. decipiens, and S. reginae). In addition, most extracts from S. cymosa and S. reginae inhibited 70-90% of PLA2 activity. Based on data from positive mode APCI analyses, it was possible to obtain a statistic model with reliable predictive capacity which exhibited an average R2 of 0.95 and a Q2 of 0.88, indicating a robust fit. This process revealed five ions, identified as the alkaloids: coclaurine (1), stepholidine (2) O-methylisopiline (3), nornantenine (4) and laurolitsine (5). This is the first study to evidence the potential antivenom of alkaloids from Siparuna species. CONCLUSIONS: Altogether, our results give support to the popular use of Siparuna extracts in SBE accidents, suggesting their potential as an alternative or complementary strategy against envenoming by B. jararaca venom. The predicted ions in the chemometric analysis for the assayed activities can also be correlated with the blocking activity and encourage the continuation of this study for possible isolation and testing of individual compounds on the used models.

Comparison of hyperspectral imaging and spectrometers for prediction of cheeses composition.

Artisanal cheeses are part of the heritage and identity of different countries or regions. In this work, we investigated the spectral variability of a wide range of traditional Brazilian cheeses and compared the performance of different spectrometers to discriminate cheese types and predict compositional parameters. Spectra in the visible (vis) and near infrared (NIR) region were collected, using imaging (vis/NIR-HSI and NIR-HSI) and conventional (NIRS) spectrometers, and it was determined the chemical composition of seven types of cheeses produced in Brazil. Principal component analysis (PCA) showed that spectral variability in the vis/NIR spectrum is related to differences in color (yellowness index) and fat content, while in NIR there is a greater influence of productive steps and fat content. Partial least squares discriminant analysis (PLSDA) models based on spectral information showed greater accuracy than the model based on chemical composition to discriminate types of traditional Brazilian cheeses. Partial least squares (PLS) regression models based on vis/NIR-HSI, NIRS, NIR-HSI data and HSI spectroscopic data fusion (vis/NIR + NIR) demonstrated excellent performance to predict moisture content (RPD > 2.5), good ability to predict fat content (2.0 < RPD < 2.5) and can be used to discriminate between high and low protein values (∼1.5 < RPD < 2.0). The results obtained for imaging and conventional equipment are comparable and sufficiently accurate, so that both can be adapted to predict the chemical composition of the Brazilian traditional cheeses used in this study according to the needs of the industry.

1H-NMR Metabolomic Study of the Mushroom Pleurotus djamor for the Identification of Nematocidal Compounds.

Due to the increasing populations of anthelmintic-resistant gastrointestinal nematodes and as a consequence of the adverse effects of synthetic drugs, this study focuses on the search for secondary metabolites with nematocidal activity from the edible mushroom Pleurotus djamor using The proton nuclear magnetic resonance (1H-NMR) metabolomics. The highest activity was shown by the ethyl acetate fractions of mycelium (EC50 290.8 µg/mL) and basidiomes (EC50 282.7 µg/mL). Principal component analysis (PCA) and hierarchical data analysis (HCA) of the 1H-NMR metabolic profiles data showed that the ethanolic extracts, the ethyl acetate, butanol, and water fractions from mycelium have different metabolic profiles than those from basidiomes, while low polarity (hexane) fractions from both stages of fungal development show similar profiles. Orthogonal partial least squares discriminant analysis (OPLS-DA) allowed the identification of signals in the 1H-NMR metabolic profile associated with nematocidal activity. The signals yielded via OPLS-DA and bidimensional NMR analysis allowed the identification of uracil as a component in the ethyl acetate fraction from basidiomes, with an EC50 of 237.7 µg/mL. The results obtained showed that chemometric analyses of the 1H-NMR metabolic profiles represent a viable strategy for the identification of bioactive compounds from samples with complex chemical profiles.

Revealing the 1H-NMR Profiling of Six Edible Mushrooms Consumed in the Northeastern Highlands of Puebla, Mexico.

The 1H-NMR metabolomics profiling of six edible mushrooms consumed in the northeastern highlands of Puebla, Mexico is presented. These fungi were morpho- and molecularly identified as Infundibulicybe squamulosa, Amanita jacksonii, Lepista nuda, Russula delica, Russula brevipes, and Lactarius indigo. The chemical profiling confirmed the presence of eight essential amino acids and their derivatives, six organic acids, six nucleosides, low amounts of reducing sugars, and valuable nutraceuticals such as betaine, carnitine, glycero-3-phosphocholine and O-acetylcarnitine which were differentially determined and quantified in the six mushrooms by qNMR. Principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) generated four different groups. Two of these groups were constituted by fungal species with phylogenic relationships whereas non-phylogenetic related species were separated from each other. The potential use of 1H-NMR metabolomics and chemometrics to group macromycetes and determine the nutritional and nutraceutical potential of these local foods is demonstrated.

Nonparametric estimation of measurement uncertainty arising from sampling.

BACKGROUND: Increasingly, measurement uncertainty has been used by pure and applied analytical chemistry to ensure decision-making in commercial transactions and technical-scientific applications. Until recently, it was considered that measurement uncertainty boiled down to analytical uncertainty; however, over the last two decades, uncertainty arising from sampling has also been considered. However, the second version of the EURACHEM guide, published in 2019, assumes that the frequency distribution is approximately normal or can be normalized through logarithmic transformations, without treating data that deviate from the normality. RESULTS: Here, six examples (four from Eurachem guide) were treated by classical ANOVA and submitted to an innovative nonparametric approach for estimating the uncertainty contribution arising from sampling. Based on bootstrapping method, confidence intervals were used to guarantee metrological compatibility between the uncertainty ratios arising from the results of the traditional parametric tests and the unprecedented proposed nonparametric methodology. SIGNIFICANCE AND NOVELTY: The present study proposed an innovative methodology for covering this gap in the literature based on nonparametric statistics (NONPANOVA) using the median absolute deviation concepts. Supplementary material based on Excel spreadsheets was developed, assisting users in the statistical treatment of their real examples.

Infrared spectroscopy combined with chemometrics in transflectance mode: An alternative approach in the photodiagnosis of COVID-19 using saliva.

The Coronavirus Disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has required the search for sensitive, rapid, specific, and lower-cost diagnostic methods to meet the high demand. The gold standard method of laboratory diagnosis is real-time reverse transcription polymerase chain reaction (RT-PCR). However, this method is costly and results can take time. In the literature, several studies have already described the potential of Fourier transform infrared spectroscopy (FTIR) as a tool in the biomedical field, including the diagnosis of viral infections, while being fast and inexpensive. In view of this, the objective of this study was to develop an FTIR model for the diagnosis of COVID-19. For this analysis, all private clients who had performed a face-to-face collection at the Univates Clinical Analysis Laboratory (LAC Univates) within a period of six months were invited to participate. Data from clients who agreed to participate in the study were collected, as well as nasopharyngeal secretions and a saliva sample. For the development of models, the RT-PCR result of nasopharyngeal secretions was used as a reference method. Absorptions with high discrimination (p < 0.001) between GI (28 patients, RT-PCR test positive to SARS-CoV-2 virus) and GII (173 patients who did not have the virus detected in the test) were most relevant at 3512 cm-1, 3385 cm-1 and 1321 cm-1 after 2nd derivative data transformation. To carry out the diagnostic modeling, chemometrics via FTIR and Discriminant Analysis of Orthogonal Partial Least Squares (OPLS-DA) by salivary transflectance mode with one latent variable and one orthogonal signal correction component were used. The model generated predictions with 100 % sensitivity, specificity and accuracy. With the proposed model, in a single application of an individual's saliva in the FTIR equipment, results related to the detection of SARS-CoV-2 can be obtained in a few minutes of spectral evaluation.

Role of the molecular structure of humified organic matter in rice plant response to environmental lead pollution.

Humified organic matter has been shown to decrease Pb toxicity in plants. However, there are still gaps in our understanding of the mechanism by which this phenomenon occurs. In this study, we aimed to assess the ability of humic substances (HSs), humic acids (HAs), and fulvic acids (FAs) to enhance defense mechanisms in rice plants under lead (Pb)-stressed conditions. HS fractions were isolated from vermicompost using the chemical fractionation methodology established by the International Humic Substances Society. These fractions were characterized by solid-state NMR and FTIR. Chemometric analysis was used to compare humic structures and correlate them with bioactivity. Three treatments were tested to evaluate the protective effect of humic fractions on rice plants. The first experiment involved the application of humic fractions along with Pb. The second comprised pretreatment with humic fractions followed by subsequent exposure to Pb stress. The third experiment involved Pb stress and subsequent treatment with humic fractions. The root morphology and components of the antioxidative defense system were evaluated and quantified. The results showed that HS + Pb, HA + Pb, and FA + Pb treatment preserved root growth and reduced the levels of O2- and malondialdehyde (MDA) in the roots by up to 5% and 2%, respectively. Pretreatment of the plants with humic fractions promoted the maintenance of root growth and reduced the contents of O2-, H2O2, and MDA by up to 48%, 22%, and 20%, respectively. Combined application of humic fractions and Pb reduced the Pb content in plant tissues by up to 60%, while pretreatment reduced it by up to 80%. The protective capacity of humic fractions is related to the presence of peptides, lignin, and carbohydrate fragments in their molecular structures. These results suggest that products could be developed that can mitigate the adverse effects of heavy metals on agricultural crops.

Feasibility of using colorimetric devices for whole and ground coffee roasting degrees prediction.

BACKGROUND: Coffee roasting is one of the crucial steps in obtaining a high-quality product as it forms the product's color and flavor characteristics. Roast control is made by visual inspection or traditional instruments such as the Agtron spectrophotometer, which can have high implementation costs. Therefore, the present study evaluated colorimetric approaches (a bench colorimeter, smartphone digital images, and a colorimetric sensor) to predict the Agtron roasting degrees of whole and ground coffee. Two calibration approaches were assessed, that is, multiple linear regression and least-squares support vector machine. For that, 70 samples of whole and ground roasted coffees comprising the Agtron roasting range were prepared. RESULTS: The results showed that all three colorimetric acquisition types were efficient for the model building, but the bench colorimeter and the smartphone digital images generally performed with good determination coefficients and low errors as measured by external validation. For the whole bean coffee, the best model presented a determination coefficient (R2) of 0.99 and a root-mean-squared error (RMSE) of 1.91%, while R2 of 0.99 and RMSE of 0.87% was obtained for ground coffee, both using the colorimeter. CONCLUSION: The obtained models presented good prediction capability, as assessed by external validation and randomization tests. The obtained findings point to an alternative for coffee roasting monitoring that can lead to higher digitalization and local control of the process, even for smaller producers, due to its lower costs. © 2024 Society of Chemical Industry.

Feasibility of Using a Cheap Colour Sensor to Detect Blends of Vegetable Oils in Avocado Oil.

This proof-of-concept study explored the use of an RGB colour sensor to identify different blends of vegetable oils in avocado oil. The main aim of this work was to distinguish avocado oil from its blends with canola, sunflower, corn, olive, and soybean oils. The study involved RGB measurements conducted using two different light sources: UV (395 nm) and white light. Classification methods, such as Linear Discriminant Analysis (LDA) and Least Squares Support Vector Machine (LS-SVM), were employed for detecting the blends. The LS-SVM model exhibited superior classification performance under white light, with an accuracy exceeding 90%, thus demonstrating a robust prediction capability without evidence of random adjustments. A quantitative approach was followed as well, employing Multiple Linear Regression (MLR) and LS-SVM, for the quantification of each vegetable oil in the blends. The LS-SVM model consistently achieved good performance (R2 > 0.9) in all examined cases, both for internal and external validation. Additionally, under white light, LS-SVM models yielded root mean square errors (RMSE) between 1.17-3.07%, indicating a high accuracy in blend prediction. The method proved to be rapid and cost-effective, without the necessity of any sample pretreatment. These findings highlight the feasibility of a cost-effective colour sensor in identifying avocado oil blended with other oils, such as canola, sunflower, corn, olive, and soybean oils, suggesting its potential as a low-cost and efficient alternative for on-site oil analysis.

The terroir of Brazilian Coffea canephora: Characterization of the chemical composition.

FTIR spectroscopy and multivariate analysis were used in the chemical study of the terroirs of Coffea canephora. Conilon coffees from Espírito Santo and Amazon robusta from Matas of Rondônia, were separated by PCA, with lipids and caffeine being the markers responsible for the separation. Coffees from Bahia, Minas Gerais, and São Paulo did not exhibit separation, indicating that the botanical variety had a greater effect on the terroir than geographic origin. Thus, the genetic factor was investigated considering the conilon and robusta botanical varieties. This last group was composed of hybrid robusta and apoatã. The DD-SIMCA favored the identification of the genetic predominance of the samples. PLS-DA had a high classification performance regarding the conilon, hybrid robusta, and apoatã genetic nature. Lipids, caffeine, chlorogenic acids, quinic acid, trigonelline, proteins, amino acids, and carbohydrates were identified as chemical markers that discriminated the genetic groups.

NMR and multivariate methods: Identification of chemical markers in extracts of pedra-ume-caá and their antiglycation, antioxidant, and enzymatic inhibition activities.

INTRODUCTION: In Brazil, the plant group popularly known as "pedra-ume-caá" is used in folk medicine for the treatment of diabetes, and its raw material is commonly sold. OBJECTIVE: The aim of the study was to apply a method for chemical identification of extracts of dry pedra-ume-caá leaves using HPLC-high-resolution mass spectrometry (HRMS) and NMR and develop a multivariate model with NMR data to authenticate commercial samples. In addition, to evaluate the biological activities of the extracts. MATERIALS AND METHODS: Dry extracts of Myrcia multiflora, Myrcia amazonica, Myrcia guianensis, Myrcia sylvatica, Eugenia punicifolia leaves, and 15 commercial samples (sold in Manaus and Belém, Brazil) were prepared by infusion. All the extracts were analysed using HPLC-high-resolution mass spectrometry (HRMS), NMR, principal component analysis (PCA), and hierarchical cluster analysis (HCA). The antidiabetic effect of extracts was evaluated according to enzymatic inhibition. Their content of total phenols, cell viability, and antioxidant and antiglycation activities were also determined. RESULTS: HPLC-HRMS and NMR analysis of these extracts permitted the identification of 17 compounds. 1H NMR data combined with multivariate analyses allowed us to conclude that catechin, myricitrin, quercitrin, and gallic and quinic acids are the main chemical markers of pedra-ume-caá species. These markers were identified in 15 commercial samples of pedra-ume-caá. Additionally, only the extracts of M. multiflora and E. punicifolia inhibited α-glucosidase. All the extracts inhibited the formation of advanced glycation end products (AGEs) and showed free-radical-scavenging activity. These extracts did not present cytotoxicity. CONCLUSION: This study revealed the chemical markers of matrices, and it was possible to differentiate the materials marketed as pedra-ume-caá. Moreover, this study corroborates the potential of these species for treating diabetes.

Prediction of some physicochemical properties in Colombian crude oils upgraded by catalytic aquathermolysis using UV-VIS spectroscopy associated with chemometric methods.

The simulated distillation curve (ASTM/D-7169) is a quantitative method to determine fractions of crude oils by boiling point temperature ranges (36-720 °C). In this work, 45 samples of typical Colombian crudes were selected, and the samples were produced under conventional process. Also 8 upgraded crude oils under catalytic aquathermolysis conditions at laboratory scale were added. The tests were developed at 270 °C and 800psi (@25 °C) during 66 h of reaction. In addition, 30 samples were selected for density tests, according to the pycnometer method. Subsequently, the crude oil samples under study were diluted in chloroform and analyzed by UV-VIS Spectroscopy. The UV-VIS spectra were correlated with selected properties by using PCA-MLR and PLS models. The distillation curves of the crude oils were modelled using the Riazi probability function. The prediction models of parameters To, A, and B from the Riazi probability function exhibited R2 correlation coefficients, higher than 0.94. The correlation model for the crude oil density showed a much better coefficient, higher than 0.99 and Root-Mean-Squared-Error (RMSE) close to 0.004. Additionally, even more important is the contribution of the use of UV-VIS spectroscopy as a useful tool to quickly evaluate the quality of crude oil.

Fast and low-cost method for direct and simultaneous determination of nitrogen and carbon in soybean leaves using benchtop and portable near-infrared devices.

BACKGROUND: The current techniques for determining carbon and nitrogen content to provide information about the nutritional status of plants are time-consuming and expensive. For this reason, the objective of this study was to develop an analytical method for the direct and simultaneous determination of nitrogen and carbon elemental content in soybean leaves using near-infrared spectroscopy and compare the performance of conventional (1100-2500 nm spectral range) and portable equipment (1100-1700 nm spectral range). Partial least-squares regression models were developed using 27 soybean leaf samples collected during the 2021 harvest and applied for the simultaneous determination of carbon and nitrogen in 13 samples collected during the 2022 harvest. RESULTS: The root-mean-square error of prediction values for nitrogen and carbon were low (2.42 g kg-1 and 4.37 g kg-1 respectively) for the benchtop method yielded low but higher for the portable method (3.82 g kg-1 and 10.7 g kg-1 respectively). The benchtop method did not show significant differences when compared with the reference method for determining nitrogen and carbon. In contrast, the portable methodology showed potential as a screening method for determining nitrogen levels, particularly in fieldwork. CONCLUSION: The methodologies evaluated in this study were implemented and evaluated under real crop monitoring conditions, using independent sets of calibration and prediction samples. Their utilization enables the acquisition of cost-effective, safe analytical data aligning with the principles of green analytical chemistry. © 2023 Society of Chemical Industry.

Direct Determination of Ca, K, and Mg in Soy Leaf Samples Using Laser-Induced Breakdown Spectroscopy.

This study was dedicated to developing analytical methods for determining macronutrients (Ca, K, and Mg) in soy leaf samples with and without petioles. The study's primary purpose was to present Laser-induced breakdown spectroscopy (LIBS) as a viable alternative for directly analyzing leaf samples using chemometric tools to interpret the data obtained. The instrumental condition chosen for LIBS was 70 mJ of laser pulse energy, 1.0 µs of delay time, and 100 µm of spot size, which was applied to 896 samples: 305 of soy without petioles and 591 of soy with petioles. The reference values of the analytes for the proposition of calibration models were obtained using inductively coupled plasma optical emission spectroscopy (ICP-OES) technique. Twelve normalization modes and two calibration strategies were tested to minimize signal variations and sample matrix microheterogeneity. The following were studied: multivariate calibration using partial least squares and univariate calibration using the area and height of several selected emission lines. The notable normalization mode for most models was the Euclidean norm. No analyte showed promising results for univariate calibrations. Micronutrients, P and S, were also tested, and no multivariate models presented satisfactory results. The models obtained for Ca, K, and Mg showed good results. The standard error of calibration ranged from 2.3 g/kg for Ca in soy leaves without petioles with two latent variables to 5.0 g/kg for K in soy leaves with petioles with two latent variables.

Evaluation of the effects of canning variables on the mineral composition of canned cowpeas (Vigna unguiculata l. Walp) using multi-response analysis.

This study aims to investigate the effects of canning variables (cooking time, storage time, volume of vinegar, salt and sugar) on the mineral composition of canned cowpea (Vigna unguiculata (L.) Walp) and which conditions provide optimised preservation of the mineral content of the grains. Different formulations of canned cowpeas were produced following two levels factorial experimental design using five variables. A set of 11 different formulations were evaluated using the desirability function with essential minerals (Ca, Cu, Fe, Mn, Mg, P and Zn) as the response. The optimal multi-response conditions for higher mineral retention were: 360 days of storage at 30 ± 5 °C (ST2), 30 ml of vinegar, 9.0 g of NaCl, 18 min of cooking time, and 9.0 g, 19.5 g or 30 g of sugar (the effect of the sugar content at the evaluated range was not significant at 95% confidence level).

Impact of temperature and humidity conditions as abiotic stressors on the phytochemical fingerprint of oat (Avena sativa L.) sprouts.

This study aimed to evaluate the effect of temperature (20, 25, and 30 °C) and relative humidity (RH, 50, 55, and 60 %) as abiotic stressors during oat (Avena sativa L.) germination using a 2-level factorial design with central point. UPLC-QToF-MSE identified eighty polyphenols, nine avenanthramides, twelve lignans, and five phytosterols Notably, 100 % germination was achieved at 25 °C/60 % RH from day 3, yielding the longest radicle size. The highest content of most phenolic acids, avenanthramides, and lignans occurred at 30 °C/65 % RH, where 100 % germination was attained by day 5, but with a shorter radicle size. The best flavonoid and phytosterol profle was obtained at 20 °C/55 % RH, achieving only a 67 % germination rate. Therefore, while these conditions enhance the bioactive compound profile, the associate decrease in germination metrics suggests potential distress effects. Consideration of both photochemical outcomes and germination yield is crucial for comprehensive assessments in future applications.