ATR-FTIR spectroscopy non-destructively detects damage-induced sour rot infection in whole tomato fruit

Attenuated total reflection Fourier-transform infrared spectroscopy for the prediction of hormone concentrations in plants

Plant hormones are important in the control of physiological and developmental processes including seed germination, senescence, flowering, stomatal aperture, and ultimately the overall growth and yield of plants. Many currently available methods to quantify such growth regulators quickly and accurately require extensive sample purification using complex analytic techniques. Herein we used ultra-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) to create and validate the prediction of hormone concentrations made using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectral profiles of both freeze-dried ground leaf tissue and extracted xylem sap of Japanese knotweed (Reynoutria japonica) plants grown under different environmental conditions. In addition to these predictions made with partial least squares regression, further analysis of spectral data was performed using chemometric techniques, including principal component analysis, linear discriminant analysis, and support vector machines (SVM). Plants grown in different environments had sufficiently different biochemical profiles, including plant hormonal compounds, to allow successful differentiation by ATR-FTIR spectroscopy coupled with SVM. ATR-FTIR spectral biomarkers highlighted a range of biomolecules responsible for the differing spectral signatures between growth environments, such as triacylglycerol, proteins and amino acids, tannins, pectin, polysaccharides such as starch and cellulose, DNA and RNA. Using partial least squares regression, we show the potential for accurate prediction of plant hormone concentrations from ATR-FTIR spectral profiles, calibrated with hormonal data quantified by UHPLC-HRMS. The application of ATR-FTIR spectroscopy and chemometrics offers accurate prediction of hormone concentrations in plant samples, with advantages over existing approaches.

Biospectroscopic fingerprinting phytotoxicity towards environmental monitoring for food security and contaminated site remediation

Human activities have resulted in severe environmental pollution since the industrial revolution. Phytotoxicity-based environmental monitoring is well known due to its sedentary nature, abundance, and sensitivity to environmental changes, which are essential preconditions to avoiding potential environmental and ecological risks. However, conventional morphological and physiological methods for phytotoxicity assessment mainly focus on descriptive determination rather than mechanism analysis and face challenges of labour and time-consumption, lack of standardized protocol and difficulties in data interpretation. Molecular-based tests could reveal the toxicity mechanisms but fail in real-time and in-situ monitoring because of their endpoint manner and destructive operation in collecting cellular components. Herein, we systematically propose and lay out a biospectroscopic tool (e.g., infrared and Raman spectroscopy) coupled with multivariate data analysis as a relatively non-destructive and high-throughput approach to quantitatively measure phytotoxicity levels and qualitatively profile phytotoxicity mechanisms by classifying spectral fingerprints of biomolecules in plant tissues in response to environmental stresses. With established databases and multivariate analysis, this biospectroscopic fingerprinting approach allows ultrafast, in situ and on-site diagnosis of phytotoxicity. Overall, the proposed protocol and validation of biospectroscopic fingerprinting phytotoxicity can distinguish the representative biomarkers and interrogate the relevant mechanisms to quantify the stresses of interest, e.g., environmental pollutants. This state-of-the-art concept and design broaden the knowledge of phytotoxicity assessment, advance novel implementations of phytotoxicity assay, and offer vast potential for long-term field phytotoxicity monitoring trials in situ.

Determination of soluble solids content of multiple varieties of tomatoes by full transmission visible-near infrared spectroscopy

Introduction

Soluble solids content (SSC) is a pivotal parameter for assessing tomato quality. Traditional measurement methods are both destructive and time-consuming.

Methods

To enhance accuracy and efficiency in SSC assessment, this study employs full transmission visible and near-infrared (Vis-NIR) spectroscopy and multi-point spectral data collection techniques to quantitatively analyze SSC in two tomato varieties ('Provence' and 'Jingcai No.8' tomatoes). Preprocessing of the multi-point spectra is carried out using a weighted averaging approach, aimed at noise reduction, signal-to-noise ratio improvement, and overall data quality enhancement. Taking into account the potential influence of various detection orientations and preprocessing methods on model outcomes, we investigate the combination of partial least squares regression (PLSR) with two orientations (O1 and O2) and two preprocessing techniques (Savitzky-Golay smoothing (SG) and Standard Normal Variate transformation (SNV)) in the development of SSC prediction models.

Results

The model achieved the best results in the O2 orientation and SNV pretreatment as follows: 'Provence' tomato (Rp = 0.81, RMSEP = 0.69°Brix) and 'Jingcai No.8' tomatoes (Rp = 0.84, RMSEP = 0.64°Brix). To further optimize the model, characteristic wavelength selection is introduced through Least Angle Regression (LARS) with L1 and L2 regularization. Notably, when λ=0.004, LARS-L1 produces superior results ('Provence' tomato: Rp = 0.95, RMSEP = 0.35°Brix; 'Jingcai No.8' tomato: Rp = 0.96, RMSEP = 0.33°Brix).

Discussion

This study underscores the effectiveness of full transmission Vis-NIR spectroscopy in predicting SSC in different tomato varieties, offering a viable method for accurate and swift SSC assessment in tomatoes.

Approach strategies and application of metabolomics to biotechnology in plants

Metabolomics refers to the technology for the comprehensive analysis of metabolites and low-molecular-weight compounds in a biological system, such as cells or tissues. Metabolites play an important role in biological phenomena through their direct involvement in the regulation of physiological mechanisms, such as maintaining cell homeostasis or signal transmission through protein-protein interactions. The current review aims provide a framework for how the integrated analysis of metabolites, their functional actions and inherent biological information can be used to understand biological phenomena related to the regulation of metabolites and how this information can be applied to safety assessments of crops created using biotechnology. Advancement in technology and analytical instrumentation have led new ways to examine the convergence between biology and chemistry, which has yielded a deeper understanding of complex biological phenomena. Metabolomics can be utilized and applied to safety assessments of biotechnology products through a systematic approach using metabolite-level data processing algorithms, statistical techniques, and database development. The integration of metabolomics data with sequencing data is a key step towards improving additional phenotypical evidence to elucidate the degree of environmental affects for variants found in genome associated with metabolic processes. Moreover, information analysis technology such as big data, machine learning, and IT investment must be introduced to establish a system for data extraction, selection, and metabolomic data analysis for the interpretation of biological implications of biotechnology innovations. This review outlines the integrity of metabolomics assessments in determining the consequences of genetic engineering and biotechnology in plants.

Cuticles and postharvest life of tomato fruit: A rigid cover for aerial epidermis or a multifaceted guard of freshness?

Fruit cuticle is a specialized cell wall hydrophobic architecture covering the aerial surfaces of fruit, which forms the interface between the fruit and its environment. As a specialized seed-bearing organ, fruit utilize cuticles as physical barriers, water permeation regulator and resistance to pathogens, thus appealing extensive research interests for its potential values in developing postharvest freshness-keeping strategies. Here, we provide an overview for the composition and functions of fruit cuticles, mainly focusing on its functions in mechanical support, water permeability barrier and protection over pathogens, further introduce key mechanisms implicated in fruit cuticle biosynthesis. Moreover, currently available state-of-art techniques for examining compositional diversity and architecture of fruit are also compared.

Fourier transform infrared spectroscopy as a non-destructive method for analysing herbarium specimens

Dried plant specimens stored in herbaria are an untapped treasure chest of information on environmental conditions, plant evolution and change over many hundreds of years. Owing to their delicate nature and irreplaceability, there is limited access for analysis to these sensitive samples, particularly where chemical data are obtained using destructive techniques. Fourier transform infrared (FTIR) spectroscopy is a chemical analysis technique which can be applied non-destructively to understand chemical bonding information and, therefore, functional groups within the sample. This provides the potential for understanding geographical, spatial and species-specific variation in plant biochemistry. Here, we demonstrate the use of mid-FTIR microspectroscopy for the chemical analysis of Drosera rotundifolia herbarium specimens, which were collected 100 years apart from different locations. Principal component and hierarchical clustering analysis enabled differentiation between three main regions on the plant (lamina, tentacle stalk and tentacle head), and between the different specimens. Lipids and protein spectral regions were particularly sensitive differentiators of plant tissues. Differences between the different sets of specimens were smaller. This study demonstrates that relevant information can be extracted from herbarium specimens using FTIR, with little impact on the specimens. FTIR, therefore, has the potential to be a powerful tool to unlock historic information within herbaria.

Spectral characterization and severity assessment of rice blast disease using univariate and multivariate models

Rice is the staple food of more than half of the population of the world and India as well. One of the major constraints in rice production is frequent occurrence of pests and diseases and one of them is rice blast which often causes yield loss varying from 10 to 30%. Conventional approaches for disease assessment are time-consuming, expensive, and not real-time; alternately, sensor-based approach is rapid, non-invasive and can be scaled up in large areas with minimum time and effort.  In the present study, hyperspectral remote sensing for the characterization and severity assessment of rice blast disease was exploited. Field experiments were conducted with 20 genotypes of rice having sensitive and resistant cultivars grown under upland and lowland conditions at Almora, Uttarakhand, India. The severity of the rice blast was graded from 0 to 9 in accordance to International Rice Research Institute (IRRI).  Spectral observations in field were taken using a hand-held portable spectroradiometer in range of 350-2500 nm followed by spectral discrimination of different disease severity levels using Jeffires-Matusita (J-M) distance. Then, evaluation of 26 existing spectral indices (r≥0.8) was done corresponding to blast severity levels and linear regression prediction models were also developed. Further, the proposed ratio blast index (RBI) and normalized difference blast index (NDBI) were developed using all possible combinations of their correlations with severity level followed by their quantification to identify the best indices. Thereafter, multivariate models like support vector machine regression (SVM), partial least squares (PLS), random forest (RF), and multivariate adaptive regression spline (MARS) were also used to estimate blast severity. Jeffires-Matusita distance was separating almost all severity levels having values >1.92 except levels 4 and 5. The 26 prediction models were effective at predicting blast severity with R² values from 0.48 to 0.85. The best developed spectral indices for rice blast were RBI (R1148, R1301) and NDBI (R1148, R1301) with R² of 0.85 and 0.86, respectively. Among multivariate models, SVM was the best model with calibration R²=0.99; validation R²=0.94, RMSE=0.7, and RPD=4.10. The methodology developed paves way for early detection and large-scale monitoring and mapping using satellite remote sensors at farmers' fields for developing better disease management options.

Recent advancements in NIR spectroscopy for assessing the quality and safety of horticultural products: A comprehensive review

The qualitative and quantitative evaluation of agricultural products has often been carried out using traditional, i.e., destructive, techniques. Due to their inherent disadvantages, non-destructive methods that use near-infrared spectroscopy (NIRS) coupled with chemometrics could be useful for evaluating various agricultural products. Advancements in computational power, machine learning, regression models, artificial neural networks (ANN), and other predictive tools have made their way into NIRS, improving its potential to be a feasible alternative to destructive measurements. Moreover, the incorporation of suitable preprocessing techniques and wavelength selection methods has arguably proven its practical feasibility. This review focuses on the various computation methods used for processing the spectral data collected and discusses the potential applications of NIRS for evaluating the quality and safety of agricultural products. The challenges associated with this technology are also discussed, as well as potential future perspectives. We conclude that NIRS is a potentially useful tool for the rapid assessment of the quality and safety of agricultural products.

Fourier transform infrared spectrum pre-processing technique selection for detecting PYLCV-infected chilli plants

Pre-processing is a crucial step in analyzing spectra from Fourier transform infrared (FTIR) spectroscopy because it can reduce unwanted noise and enhance system performance. Here, we present the results of pre-processing technique optimization to facilitate the detection of pepper yellow leaf curl virus (PYLCV)-infected chilli plants using FTIR spectroscopy. Optimization of a range of pre-processing techniques was undertaken, namely baseline correction, normalization (standard normal variate, vector, and min-max), and de-noising (Savitzky-Golay (SG) smoothing, 1st and 2 derivatives). The pre-processing was applied to the mid-infrared spectral range (4000 - 400 cm^-1) and the biofingerprint region (1800 - 900 cm^-1) then the discrete wavelet transform (DWT) was used for dimension reduction. The pre-processed data were then used as an input for classification using a multilayer perceptron neural network, a support vector machine, and linear discriminant analysis. The pre-processing method with the highest classification model accuracy was selected for the further use in the processing. It was seen that only the SG 1st derivative method applied to both wavenumber ranges could produce 100% accuracy. This result was supported by principal component analysis clustering. Thus, we have demonstrated that by using the right pre-processing technique, classification success can be increased, and the process simplified by optimization and minimization of the technique used.

S-Adenosyl-L-Methionine and Cu(II) Impact Green Plant Regeneration Efficiency

The biological improvement of triticale, a cereal of increasing importance in agriculture, may be accelerated via the production of doubled haploid lines using in vitro culture. Among the relevant factors affecting the culture efficiency are Cu(II) or Ag(I) acting, e.g., as cofactors of enzymes. The copper ions are known to positively affect green plant regeneration efficiency. However, the biochemical basis, mainly its role in the generation of in vitro-induced genetic and epigenetic variation and green plant regeneration efficiency, is not well understood. Here, we employed structural equation modeling to evaluate the relationship between de novo DNA methylation affecting the asymmetric context of CHH sequences, the methylation-sensitive Amplified Fragment Length Polymorphism related sequence variation, and the concentration of Cu(II) and Ag(I) ions in induction media, as well as their effect on S-adenosyl-L-methionine perturbations, observed using FTIR spectroscopy, and the green plant regeneration efficiency. Our results allowed the construction of a theory-based model reflecting the biological phenomena associated with green plant regeneration efficiency. Furthermore, it is shown that Cu(II) ions in induction media affect plant regeneration, and by manipulating their concentration, the regeneration efficiency can be altered. Additionally, S-adenosyl-L-methionine is involved in the efficiency of green plant regeneration through methylation of the asymmetric CHH sequence related to de novo methylation. This shows that the Yang cycle may impact the production of green regenerants.

Know your enemy: Application of ATR-FTIR spectroscopy to invasive species control

Extreme weather and globalisation leave our climate vulnerable to invasion by alien species, which have negative impacts on the economy, biodiversity, and ecosystem services. Rapid and accurate identification is key to the control of invasive alien species. However, visually similar species hinder conservation efforts, for example hybrids within the Japanese Knotweed complex.We applied the novel method of ATR-FTIR spectroscopy combined with chemometrics (mathematics applied to chemical data) to historic herbarium samples, taking 1580 spectra in total. Samples included five species from within the interbreeding Japanese Knotweed complex (including three varieties of Japanese Knotweed), six hybrids and five species from the wider Polygonaceae family. Spectral data from herbarium specimens were analysed with several chemometric techniques: support vector machines (SVM) for differentiation between plant types, supported by ploidy levels; principal component analysis loadings and spectral biomarkers to explore differences between the highly invasive Reynoutria japonica var. japonica and its non-invasive counterpart Reynoutria japonica var. compacta; hierarchical cluster analysis (HCA) to investigate the relationship between plants within the Polygonaceae family, of the Fallopia, Reynoutria, Rumex and Fagopyrum genera.ATR-FTIR spectroscopy coupled with SVM successfully differentiated between plant type, leaf surface and geographical location, even in herbarium samples of varying age. Differences between Reynoutria japonica var. japonica and Reynoutria japonica var. compacta included the presence of two polysaccharides, glucomannan and xyloglucan, at higher concentrations in Reynoutria japonica var. japonica than Reynoutria japonica var. compacta. HCA analysis indicated that potential genetic linkages are sometimes masked by environmental factors; an effect that can either be reduced or encouraged by altering the input parameters. Entering the absorbance values for key wavenumbers, previously highlighted by principal component analysis loadings, favours linkages in the resultant HCA dendrogram corresponding to expected genetic relationships, whilst environmental associations are encouraged using the spectral fingerprint region.The ability to distinguish between closely related interbreeding species and hybrids, based on their spectral signature, raises the possibility of using this approach for determining the origin of Japanese knotweed infestations in legal cases where the clonal nature of plants currently makes this difficult and for the targeted control of species and hybrids. These techniques also provide a new method for supporting biogeographical studies.

Regional differences in clonal Japanese knotweed revealed by chemometrics-linked attenuated total reflection Fourier-transform infrared spectroscopy

BACKGROUND

Japanese knotweed (R. japonica var japonica) is one of the world's 100 worst invasive species, causing crop losses, damage to infrastructure, and erosion of ecosystem services. In the UK, this species is an all-female clone, which spreads by vegetative reproduction. Despite this genetic continuity, Japanese knotweed can colonise a wide variety of environmental habitats. However, little is known about the phenotypic plasticity responsible for the ability of Japanese knotweed to invade and thrive in such diverse habitats. We have used attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy, in which the spectral fingerprint generated allows subtle differences in composition to be clearly visualized, to examine regional differences in clonal Japanese knotweed.

RESULTS

We have shown distinct differences in the spectral fingerprint region (1800-900 cm^- 1) of Japanese knotweed from three different regions in the UK that were sufficient to successfully identify plants from different geographical regions with high accuracy using support vector machine (SVM) chemometrics.

CONCLUSIONS

These differences were not correlated with environmental variations between regions, raising the possibility that epigenetic modifications may contribute to the phenotypic plasticity responsible for the ability of R. japonica to invade and thrive in such diverse habitats.

Remote Sensing of Diseases

Detection, identification, and quantification of plant diseases by sensor techniques are expected to enable a more precise disease control, as sensors are sensitive, objective, and highly available for disease assessment. Recent progress in sensor technology and data processing is very promising; nevertheless, technical constraints and issues inherent to variability in host-pathogen interactions currently limit the use of sensors in various fields of application. The information from spectral [e.g., RGB (red, green, blue)], multispectral, and hyperspectral sensors that measure reflectance, fluorescence, and emission of radiation or from electronic noses that detect volatile organic compounds released from plants or pathogens, as well as the potential of sensors to characterize the health status of crops, is evaluated based on the recent literature. Phytopathological aspects of remote sensing of plant diseases across different scales and for various purposes are discussed, including spatial disease patterns, epidemic spread of pathogens, crop characteristics, and links to disease control. Future challenges in sensor use are identified.

Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods

Rice diseases are major threats to rice yield and quality. Rapid and accurate detection of rice diseases is of great importance for precise disease prevention and treatment. Various spectroscopic techniques have been used to detect plant diseases. To rapidly and accurately detect three different rice diseases [leaf blight (Xanthomonas oryzae pv. Oryzae), rice blast (Pyricularia oryzae), and rice sheath blight (Rhizoctonia solani)], three spectroscopic techniques were applied, including visible/near-infrared hyperspectral imaging (HSI) spectra, mid-infrared spectroscopy (MIR), and laser-induced breakdown spectroscopy (LIBS). Three different levels of data fusion (raw data fusion, feature fusion, and decision fusion) fusing three different types of spectral features were adopted to categorize the diseases of rice. Principal component analysis (PCA) and autoencoder (AE) were used to extract features. Identification models based on each technique and different fusion levels were built using support vector machine (SVM), logistic regression (LR), and convolution neural network (CNN) models. Models based on HSI performed better than those based on MIR and LIBS, with the accuracy over 93% for the test set based on PCA features of HSI spectra. The performance of rice disease identification varied with different levels of fusion. The results showed that feature fusion and decision fusion could enhance identification performance. The overall results illustrated that the three techniques could be used to identify rice diseases, and data fusion strategies have great potential to be used for rice disease detection.

Effects of silencing TT8 and HB12 on in vitro nutrients degradation and VFA production in relation to molecular structures of alfalfa (Medicago sativa)

BACKGROUND

Transparent Testa8 (TT8) and Homeobox12 (HB12) are two transcriptional factors in plant phenylpropanoid pathways and were reported to be positively related to lignin content. Alfalfa with silenced TT8 (TT8i) and HB12 (HB12i) was therefore generated using the RNA interference (RNAi) technique. Although lignin was found to be high in HB12i, such gene-silencing of alfalfa resulted in nutrient profiles that might be suitable for grazing. To extend the nutritional evaluation of transformed alfalfa, ground samples of 11 HB12i, 5 TT8i and 4 wild type (WT) were incubated in rumen fluid : buffer solution for 0, 2, 4, 8, 12, 24 and 48 h at 39 °C. Dry matter (DM) and neutral detergent fiber (NDF) degradations at each time point, and production of volatile fatty acids (VFA) at 4, 12, 24 and 48 h were analyzed, as well as degradation and production kinetics. The correlations and regressions between nutritive profiles and attenuated total reflection Fourier transform infrared (ATR-FTIR) spectral parameters were determined.

RESULTS

Both transformed genotypes had lower DM degradation and HB12i had lower VFA production compared with WT. Structural carbohydrate (STC) parameters were found to be negatively correlated with DM degradation and VFA production. The kinetics of DM degradation and VFA production were predicted from spectral parameters with good estimation power.

CONCLUSION

Silencing of HB12 and TT8 affected fermentation characteristics of alfalfa and some fermentation characteristics were predictable from spectral parameters using ATR-FTIR spectroscopy. © 2019 Society of Chemical Industry.

Identification of unhealthy Panax notoginseng from different geographical origins by means of multi-label classification

Root-knot nematode is a common plant-parasitic pest with a highly destructive that infects more than 2000 plant species. Panax notoginseng (P. notoginseng) is one of the most susceptible traditional medicine. More importantly, it is difficult to distinguish the powders of P. notoginseng infected with root-knot nematode from those of healthy P. notoginseng due to the color and shape are same after being ground into powder. In this paper, Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) was used to identify P. notoginseng samples. Multiplicative scatter correction (MSC) was applied to preprocess the spectral data. Competitive adaptive reweighted sampling (CARS) and successive projection algorithm (SPA) were employed to select feature variables. Density-based spatial clustering of application with noise (DBSCAN) was adopted to discover groups within the data. Also, we found that the geographical origin is a pivotal factor to consider when identifying unhealthy P. notoginseng. Therefore, we introduced a novel multi-label classification (MLC) method to identify healthy and unhealthy P. notoginseng powders from three different geographical origins. In addition, binary relevance method (BR), classifier chain (CC), ensembles of classifier chains (ECC), and multilayer perceptron classifier (MLPC) were applied to create classification models, ECC exhibits superior performance in particular.

Fourier transform infrared and Raman-based biochemical profiling of different grades of pure foetal-type hepatoblastoma

The biomolecular events resulting from the progression of hepatoblastoma remain to be elucidated. Fourier-transform infrared (FTIR) and Raman spectroscopies are capable of noninvasively and accurately capturing the biochemical properties of biological tissue from its pathological status. Our aim was to probe critial biomolecular changes of liver accompanying the progression of pure foetal hepatoblastoma (PFH) by FTIR and Raman spectroscopies. Herein, biochemical alterations were both evident in the FTIR spectra (regions of 3100-2800 cm^-1 and 1800-900 cm^-1 ) and the Raman spectra (region of 1800-400 cm^-1 ) among normal, borderline and malignant liver tissues. Compared with normal tissues, the ratios of protein-to-lipid, α-helix-to-β-sheet, RNA-to-DNA, CH3 methyl-to-CH2 methylene, glucose-to-phospholipids, and unsaturated-to-saturated lipids intensities were significantly higher in malignant tissues, while the ratios of RNA-to-Amide II, DNA-to-Amide II, glycogen-to-cholesterol and Amide I-to-Amide II intensities were remarkably lower. These biochemical alterations in the transition from normal to malignant have profound implications not only for cyto-pathological classification but also for molecular understanding of PFH progression. The successive changes of the spectral characteristics have been shown to be consistent with the development of PFH, indicating that FTIR and Raman spectroscopies are excellent tools to interrogate the biochemical features of different grades of PFH.

Probing the Molecular Structure and Orientation of the Leaf Surface of Brassica oleracea L. by Polarization Modulation-Infrared Reflection-Absorption Spectroscopy

The surface of most aerial plant organs is covered with the cuticle, a membrane consisting of a variety of organic compounds, including waxes, cutin (a polyester) and polysaccharides. The cuticle serves as the multifunctional interface between the plant and the environment, and plays a major role in protecting plants against various environmental stress factors. Characterization of the molecular arrangements in the intact cuticle is critical for the fundamental understanding of its physicochemical properties; however, this analysis remains technically challenging. Here, we describe the nondestructive characterization of the intact cuticle of Brassica oleracea L. leaves using polarization modulation-infrared (IR) reflection-absorption spectroscopy (PM-IRRAS). PM-IRRAS has a probing depth of less than several hundreds of nanometers, and reveals the crystalline structure of the wax covering the cuticle surface (epicuticular wax) and the nonhydrogen-bonding character of cutin. Combined analysis using attenuated total reflection-IR spectra suggested that hemicelluloses xylan and xyloglucan are present in the outer cuticle region close to the epicuticular wax, whereas pectins are dominant in the inner cuticle region (depth of ≤2 μm). PM-IRRAS can also determine the average orientation of the cuticular molecules, as indicated by the positive and negative spectral peaks. This unique advantage reveals the orientational order in the intact cuticle; the hydrocarbon chains of the epicuticular wax and cutin and the backbones of hemicelluloses are oriented perpendicular to the leaf surface. PM-IRRAS is a versatile, informative and easy-to-use technique for studying plant cuticles because it is nondestructive and does not require sample pretreatment and background measurements.

Evaluation of Kluyveromyces marxianus endo-polygalacturonase activity through ATR-FTIR

The endo-polygalacturonase enzyme (endoPG: EC 3.2.1.15) plays an important role in the fruit juice and wine industries, so the development of new tools for the quantitative and qualitative analysis of its enzymatic action is necessary. In this work, we report the development of a simple, fast and practical method that did not use any chemical reagent to identify and evaluate the action of the endoPG enzyme, produced by the yeast Kluyveromyces marxianus CCT3172, using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy combined with principal component analysis-linear discriminant analysis (PCA-LDA). This method evaluated the action of the endoPG enzyme on the polygalacturonic acid (PGA) substrate at 5 different times (0, 10, 15, 20 and 30 minutes), and at each time interval the samples were analyzed by ATR-FTIR. It was demonstrated that there was clear segregation between the samples that were and that were not subjected to the action of the endoPG enzyme, and it was also possible to distinguish the samples that were subjected to different incubation times with the enzyme. Through PCA-LDA it was possible to obtain wavelengths that are biomarkers for this enzymatic reaction and the observed changes as a function of hydrolysis duration were found to be in agreement with the breakdown of the glycosidic chain (1011 cm^-1-CH-O- CH stretching) of PGA and release of oligosaccharides (1078 cm^-1 C-OH elongation). The activity of the endoPG enzyme and the release of galacturonic acid were verified by the dinitrosalicylic acid (DNS) method in all samples. The efficacy of an automatic classifier using a principal component analysis-linear discriminant classifier (PCA-LDC) was evaluated to diagnose the action of the endoPG enzyme. The results showed an accuracy of 100% for the identification of the endoPG enzyme action and from 91.67% to 100% for classification according to the hydrolysis duration in which PGA was exposed to endoPG. The present study indicates that this methodology may be a new approach for the qualitative evaluation of the endoPG enzyme with the potential to be used in laboratories and industries.

Determination of developmental and ripening stages of whole tomato fruit using portable infrared spectroscopy and Chemometrics

BACKGROUND

Development and ripening of tomato (Solanum lycopersicum) fruit are important processes for the study of crop biology related to industrial horticulture. Versatile uses of tomato fruit lead to its harvest at various points of development from early maturity through to red ripe, traditionally indicated by parameters such as size, weight, colour, and internal composition, according to defined visual 'grading' schemes. Visual grading schemes however are subjective and thus objective classification of tomato fruit development and ripening are needed for 'high-tech' horticulture. To characterize the development and ripening processes in whole tomato fruit (cv. Moneymaker), a biospectroscopy approach is employed using compact portable ATR-FTIR spectroscopy coupled with chemometrics.

RESULTS

The developmental and ripening processes showed unique spectral profiles, which were acquired from the cuticle-cell wall complex of tomato fruit epidermis in vivo. Various components of the cuticle including Cutin, waxes, and phenolic compounds, among others, as well as from the underlying cell wall such as celluloses, pectin and lignin like compounds among others. Epidermal surface structures including cuticle and cell wall were significantly altered during the developmental process from immature green to mature green, as well as during the ripening process. Changes in the spectral fingerprint region (1800-900 cm^- 1) were sufficient to identify nine developmental and six ripening stages with high accuracy using support vector machine (SVM) chemometrics.

CONCLUSIONS

The non-destructive spectroscopic approach may therefore be especially useful for investigating in vivo biochemical changes occurring in fruit epidermis related to grades of tomato during development and ripening, for autonomous food production/supply chain applications.