Raman spectroscopy for the differentiation of Arabic coffee genotypes

Natural fermentation with delayed inoculation of the yeast Torulaspora delbrueckii: Impact on the chemical composition and sensory profile of natural coffee

All coffee production stages occur in a microbiome, which is generally composed of bacteria, yeasts, and filamentous fungi. The use of starter cultures in post-harvest processing stages is an interesting alternative, since they promote faster removal of mucilage and incorporation of compounds that improve sensory quality, which can result in diverse sensory attributes for the beverage. This study was therefore developed with the objective of evaluating the effect of the following processing procedures on the chemical and sensory characteristics of the coffee beverage: first, fermentation of coffee fruit of the yellow Catucaí variety of Coffea arabica with indigenous microorganisms, followed by inoculation of the starter culture Torulaspora delbrueckii CCMA 0684 during the drying stage. The fruit was divided into two lots, which were differentiated by a natural fermentation process before drying began. The starter culture was inoculated on the coffee at different times during the drying process: at 0 h, 24 h, 48 h, or 72 h after drying began. The sensory attributes, the volatile compound composition of the roasted beans, the organic acid profile, the bioactive compounds, and the fatty acid profile of the green coffee beans were analyzed. The fatty acid and bioactive compound content showed little variation among treatments. Analysis of volatile compounds and organic acids and evaluation of sensory attributes made it possible to distinguish the two treatments. We conclude that natural fermentation of coffee fruit improve the chemical and sensory quality of the coffee beverage. The effect of natural fermentation may be before inoculation of the starter cultures or even during drying.

Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches

In today's era of increased food consumption, consumers have become more demanding in terms of safety and the quality of products they consume. As a result, food authorities are closely monitoring the food industry to ensure that products meet the required standards of quality. The analysis of food properties encompasses various aspects, including chemical and physical descriptions, sensory assessments, authenticity, traceability, processing, crop production, storage conditions, and microbial and contaminant levels. Traditionally, the analysis of food properties has relied on conventional analytical techniques. However, these methods often involve destructive processes, which are laborious, time-consuming, expensive, and environmentally harmful. In contrast, advanced spectroscopic techniques offer a promising alternative. Spectroscopic methods such as hyperspectral and multispectral imaging, NMR, Raman, IR, UV, visible, fluorescence, and X-ray-based methods provide rapid, non-destructive, cost-effective, and environmentally friendly means of food analysis. Nevertheless, interpreting spectroscopy data, whether in the form of signals (fingerprints) or images, can be complex without the assistance of statistical and innovative chemometric approaches. These approaches involve various steps such as pre-processing, exploratory analysis, variable selection, regression, classification, and data integration. They are essential for extracting relevant information and effectively handling the complexity of spectroscopic data. This review aims to address, discuss, and examine recent studies on advanced spectroscopic techniques and chemometric tools in the context of food product applications and analysis trends. Furthermore, it focuses on the practical aspects of spectral data handling, model construction, data interpretation, and the general utilization of statistical and chemometric methods for both qualitative and quantitative analysis. By exploring the advancements in spectroscopic techniques and their integration with chemometric tools, this review provides valuable insights into the potential applications and future directions of these analytical approaches in the food industry. It emphasizes the importance of efficient data handling, model development, and practical implementation of statistical and chemometric methods in the field of food analysis.

Study of the first step of hydrogen release in ammonia borane using high-resolution Raman spectroscopy and different heating ramps

Ammonia borane, as a source of hydrogen, has attracted much attention due to its high hydrogen content, low molecular weight, and high stability in solution. However, the process and enhancement of hydrogen release must be done practically under ambient conditions. For this work, Raman spectroscopy, principal component analysis (PCA), and molecular simulation were applied to study the hydrogen release process of ammonia borane. Three stages of release were observed from room temperature to 1300 °C. The shift, the appearance, and the disappearance of the Raman bands were evident in the whole process. In-situ monitoring of Raman and PCA, with four different heating rates between 70 and 130 °C, was done; ammonia borane showed visible variations in its first release step, in which a fast rate helped reduce distortion in the release process. Finally, molecular simulation of ammonia borane using the Density Functional Theory (DFT) in calculations showed that dihedral rotation and stretching of the hydrogen bonds can occur before the first release step.

Raman Method in Identification of Species and Varieties, Assessment of Plant Maturity and Crop Quality—A Review

The present review covers reports discussing potential applications of the specificity of Raman techniques in the advancement of digital farming, in line with an assumption of yield maximisation with minimum environmental impact of agriculture. Raman is an optical spectroscopy method which can be used to perform immediate, label-free detection and quantification of key compounds without destroying the sample. The authors particularly focused on the reports discussing the use of Raman spectroscopy in monitoring the physiological status of plants, assessing crop maturity and quality, plant pathology and ripening, and identifying plant species and their varieties. In recent years, research reports have presented evidence confirming the effectiveness of Raman spectroscopy in identifying biotic and abiotic stresses in plants as well as in phenotyping and digital selection of plants in farming. Raman techniques used in precision agriculture can significantly improve capacities for farming management, crop quality assessment, as well as biological and chemical contaminant detection, thereby contributing to food safety as well as the productivity and profitability of agriculture. This review aims to increase the awareness of the growing potential of Raman spectroscopy in agriculture among plant breeders, geneticists, farmers and engineers.

Integration of surface-enhanced Raman spectroscopy (SERS) and machine learning tools for coffee beverage classification

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for molecule identification. However, profiling complex samples remains a challenge because SERS peaks are likely to overlap, confounding features when multiple analytes are present in a single sample. In addition, SERS often suffers from high variability in signal enhancement due to nonuniform SERS substrate. The machine learning classification techniques widely used for facial recognition are excellent tools to overcome the complexity of SERS data interpretation. Herein, we reported a sensor for classifying coffee beverages by integrating SERS, feature extractions, and machine learning classifiers. A versatile and low-cost SERS substrate, called nanopaper, was used to enhance Raman signals of dilute compounds in coffee beverages. Two classic multivariate analysis techniques, Principal Component Analysis (PCA) and Discriminant Analysis of Principal Components (DAPC), were used to extract the significant spectral features, and the performance of various machine learning classifiers was evaluated. The combination of DAPC with Support Vector Machine (SVM) or K-Nearest Neighbor (KNN) shows the best performance for classifying coffee beverages. This user-friendly and versatile sensor has the potential to be a practical quality-control tool for the food industry.

Origin geographical classification of green coffee beans (Coffea Arabica L.) produced in different regions of the Minas Gerais state by FT-MIR and chemometric

The present work was proposal the potential evaluation of Fourier-Transform Mid-Infrared (FT-MIR) associated with chemometric approach in green beans, in order to discriminate the origin of special Arabica coffees in a single state that has heterogeneous environments. Partial Least Squares Discriminant Analysis (PLS-DA) model presented as result: 3 latent variables, R2X (cum) = 0.892, R2Y (cum) = 0.659; Q2Y (cum) = 0.494, RMSEP = 0.182387, p-value CV-Anova = 0.009, 100% of both sensitivity and specificity and the prediction classification obtained was: 100, 83.33, 100, 83.33% for class 1, class 2, class 3 and class 4, respectively. These results can be considered adequate for the proposed hypothesis. The obtained results that the regions have markers such as trigonelline, chlorogenic and fatty acids, sensitive to absorption in the mid-infrared and that are able to determine the origin of green coffee beans of Arabica. Thus, the FT-MIR associated with chemometrics has the potential to employ speed, modernity and cost reduction in the certification of origin of coffees.

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The origin of special arabica coffee beans in the same state was discriminated using MIR.

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The study identified green coffee beans of the same species from neighboring regions.

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Trigonelline, chlorogenic and fatty acid absorption bands are good origin markers.

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The coffee cultivation environment interferes decisively in the final composition.

The Potential of Spectroscopic Techniques in Coffee Analysis—A Review

This review provides an overview of recent studies on the potential of spectroscopy techniques (mid-infrared, near infrared, Raman, and fluorescence spectroscopy) used in coffee analysis. It specifically covers their applications in coffee roasting supervision, adulterants and defective beans detection, prediction of specialty coffee quality and coffees’ sensory attributes, discrimination of coffee based on variety, species, and geographical origin, and prediction of coffees chemical composition. These are important aspects that significantly affect the overall quality of coffee and consequently its market price and finally quality of the brew. From the reviewed literature, spectroscopic methods could be used to evaluate coffee for different parameters along the production process as evidenced by reported robust prediction models. Nevertheless, some techniques have received little attention including Raman and fluorescence spectroscopy, which should be further studied considering their great potential in providing important information. There is more focus on the use of near infrared spectroscopy; however, few multivariate analysis techniques have been explored. With the growing demand for fast, robust, and accurate analytical methods for coffee quality assessment and its authentication, there are other areas to be studied and the field of coffee spectroscopy provides a vast opportunity for scientific investigation.

Raman spectroscopy enables phenotyping and assessment of nutrition values of plants: a review

Our civilization has to enhance food production to feed world's expected population of 9.7 billion by 2050. These food demands can be met by implementation of innovative technologies in agriculture. This transformative agricultural concept, also known as digital farming, aims to maximize the crop yield without an increase in the field footprint while simultaneously minimizing environmental impact of farming. There is a growing body of evidence that Raman spectroscopy, a non-invasive, non-destructive, and laser-based analytical approach, can be used to: (i) detect plant diseases, (ii) abiotic stresses, and (iii) enable label-free phenotyping and digital selection of plants in breeding programs. In this review, we critically discuss the most recent reports on the use of Raman spectroscopy for confirmatory identification of plant species and their varieties, as well as Raman-based analysis of the nutrition value of seeds. We show that high selectivity and specificity of Raman makes this technique ideal for optical surveillance of fields, which can be used to improve agriculture around the world. We also discuss potential advances in synergetic use of RS and already established imaging and molecular techniques. This combinatorial approach can be used to reduce associated time and cost, as well as enhance the accuracy of diagnostics of biotic and abiotic stresses.

Ammonia borane structural study by temperature through high-resolution Raman spectroscopy and principal component analysis

The structural change by temperature of the ammonia borane has been studied through Raman spectroscopy and principal components analysis (PCA). Its phase transition, from tetragonal to orthorhombic, was observed at 212 K. In addition, between 99 and 83 K, a zone of structural stability was observed. These behaviors could be observed thanks to the application of the PCA technique to the Raman data. Here, we can find a characteristic Raman signal which can also be associated with the orthorhombic structure. Also using the PCA technique, we have observed two Raman bands in the low frequency region, below 100 cm^-1, which could be associated with the lattice vibrational contribution.

Coffee growing altitude influences the microbiota, chemical compounds and the quality of fermented coffees

The objective of this work was to evaluate the influence of different altitudes on the epiphytic microbiota of coffee beans and on sensorial and chemical quality of coffees grown at 800, 1000, 1200, and 1400 m in Serra do Caparaó, Espírito Santo, Brazil. For microbiological analysis, the population counts of mesophilic bacteria, lactic acid bacteria (LAB), and yeasts were performed from the surface plating. The isolates were grouped and identified from the Matrix Assisted Laser Desorption Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) and sequencing of the ribosomal region was used. The chemical composition of the green grains was evaluated by Raman spectroscopy, and the sensory analysis of the roasted grains was performed using temporal dominance of sensations (TDS). During fermentation, there was a decrease in the LAB in pulped coffee from 800 and 1000 m altitude, while an increase was observed at 1200 and 1400 m. In natural coffee, there was an increase of LAB population at all altitudes. The highest diversity of mesophilic bacteria and yeast were identified in natural 1400 m and 1000 m, respectively. However pulped coffee treatments it was at 1200 m and 800 m. The chlorogenic acid and fatty acids in the green bean changed with altitude variation and processing. The floral attribute was detected only at altitude 1400 m. Caramel, chocolate and almond attributes were most frequently detected in coffees at different altitudes and processing. Therefore, pulped coffee processing was most suitable at low altitude while at high altitudes, both processes can be conducted to obtain a beverage with unusual sensory profile.