Potential biomonitoring of atmospheric carbon dioxide in Coffea arabica leaves using near-infrared spectroscopy and partial least squares discriminant analysis

Vibrational Spectroscopy as a Tool for Bioanalytical and Biomonitoring Studies

The review briefly describes various types of infrared (IR) and Raman spectroscopy methods. At the beginning of the review, the basic concepts of biological methods of environmental monitoring, namely bioanalytical and biomonitoring methods, are briefly considered. The main part of the review describes the basic principles and concepts of vibration spectroscopy and microspectrophotometry, in particular IR spectroscopy, mid- and near-IR spectroscopy, IR microspectroscopy, Raman spectroscopy, resonance Raman spectroscopy, Surface-enhanced Raman spectroscopy, and Raman microscopy. Examples of the use of various methods of vibration spectroscopy for the study of biological samples, especially in the context of environmental monitoring, are given. Based on the described results, the authors conclude that the near-IR spectroscopy-based methods are the most convenient for environmental studies, and the relevance of the use of IR and Raman spectroscopy in environmental monitoring will increase with time.

Authentication of carioca common bean cultivars (Phaseolus vulgaris L.) using digital image processing and chemometric tools

Bean authentication can result in higher quality products for commerce. Partial least squares discriminant analysis (PLS-DA) was applied to digital images in order to develop a methodology that allows the non-destructive discrimination of three Phaseolus vulgaris L. cultivars (Agro ANfc9, IPR-Andorinha, and IPR-Sabiá) having different technological characteristics. Principal component analysis resulted in a separation of these cultivars, but with a certain amount of overlap. Supervised analysis showed that three PLS1-DA models, each for two cultivars, was moderately better than the simultaneous treatment of all three cultivars (PLS2-DA). Permutation test evaluated statistical significance of PLS-DA models. The classification models were more accurate for Agro ANfc9 and IPR-Sabiá cultivars than for IPR-Andorinha. The Agro ANfc9-IPR-Sabiá model correctly classified 100% of the two bean classes in both training and test sets. This analytical strategy is fast, inexpensive, environmentally friendly, and can be applied for bean quality control helping cultivar authenticity for commerce.

Ecometabolic mixture design-fingerprints from exploratory multi-block data analysis in Coffea arabica beans from climate changes: Elevated carbon dioxide and reduced soil water availability

Ecometabolic mixture design-fingerprinting in coffee cultivated under climate change was chemically explored using ComDim. Multi-blocks were formed using UV, NIRS, ¹H NMR, SWV, and FT-IR data. ComDim investigated all these different fingerprints according to the extractor solvent and in virtue of atmospheric CO₂ increase. Ethanol and ethanol-dichloromethane showed the best separations due to CO₂ environment. ¹H NMR loading indicate increases of fatty acids, caffeine, trigonelline, and glucose in beans under current CO₂ levels, whereas quinic acid/chlorogenic acids, malic acid, and kahweol/cafestol increased in beans under elevated CO₂ conditions. SWV indicated quercetin and chlorogenic acid as important compounds in coffee beans cultivated under current and elevated CO₂, respectively. Based on the ethanol and ethanol-dichloromethane fingerprints, k-NN correctly classified the beans cultivated under different carbon dioxide environments and water availabilities, confirming the existence of metabolic changes due to climate changes. SWV proved to be promising compared with widely used spectrometric methods.

The main effects of elevated CO2 and soil-water deficiency on 1H NMR-based metabolic fingerprints of Coffea arabica beans by factorial and mixture design

The metabolic response of Coffea arabica trees in the face of the rising atmospheric concentration of carbon dioxide (CO₂) combined with the reduction in soil-water availability is complex due to the various (bio)chemical feedbacks. Modern analytical tools and the experimental advance of agronomic science tend to advance in the understanding of the metabolic complexity of plants. In this work, Coffea arabica trees were grown in a Free-Air Carbon Dioxide Enrichment dispositive under factorial design (2²) conditions considering two CO₂ levels and two soil-water availabilities. The ¹H NMR mixture design-fingerprinting effects of CO₂ and soil-water levels on beans were strategically investigated using the principal component analysis (PCA), analysis of variance (ANOVA) - simultaneous component analysis (ASCA) and partial least squares-discriminant analysis (PLS-DA). From the ASCA, the CO₂ factor had a significant effect on changing the ¹H NMR profile of fingerprints. The soil-water factor and interaction (CO₂ × soil-water) were not significant. ¹H NMR fingerprints with PCA, ASCA and PLS-DA analysis determined spectral profiles for fatty acids, caffeine, trigonelline and glucose increases in beans from current CO₂, while quinic acid/chlorogenic acids, malic acid and kahweol/cafestol increased in coffee beans from elevated CO₂. PLS-DA results revealed a good classification performance between the significant effect of the atmospheric CO₂ levels on the fingerprints, regardless of the soil-water availabilities. Finally, the PLS-DA model showed good prediction ability, successfully classifying validation data-set of coffee beans collected over the vertical profile of the plants and included several fingerprints of different extracting solvents. The results of this investigation suggest that the association of experimental design, mixture design, PCA, ASCA and PLS-DA can provide accurate information on a series of metabolic changes provoked by climate changes in products of commercial importance, in addition to minimizing the extra work necessary in classic analytical approaches, encouraging the development of similar strategies.

Modeling CO2 exchange and meteorological factors of an apple orchard using partial least square regression

The eddy covariance (EC) technique was used to measure variations of orchard-atmosphere CO₂ exchange, as a function of meteorological variables in an apple orchard in 2016-2017. The annual average CO₂ exchange rate was 2.295 kg m^-2. Excavations and biomass assessments demonstrated that the orchard stored close to 20.6 tC ha^-1 as plant C over a 15-year period. Seasonally, high rates of CO₂ uptake and low CO₂ emissions occurred between May and August and December and March, respectively. The maximum rates of monthly CO₂ exchange were 144.44 and 153.61 gC m^-2 month^-1 in August 2016 and June 2017, respectively. Partial least squares (PLS) regressions were used to analyze the influence of meteorological factors to on CO₂ exchange rates. Temperature and photosynthetic active radiation (PAR) were observed to exert the largest influence on driving variation in CO₂ exchange. The main meteorological factors affecting CO₂ exchange on daily and monthly time scales were soil temperature (T_soil), air temperature (T_a), PAR, below canopy CO₂ concentration (BCC), vapor pressure deficit (VPD), and soil water content at 50 cm (SWC_50cm). The regression model equation describing CO₂ exchange included T_a, VPD, precipitation (PPT), and sunshine duration (SD), as significant variables. This model curve fitting explains over 80% of the variation in CO₂ exchange. This study provides CO₂ exchange characteristics and a model equation capable of predicting CO₂ exchange of an apple orchard. Graphical Abstract.