Use of portable devices and confocal Raman spectrometers at different wavelength to obtain the spectral information of the main organic components in tomato (Solanum lycopersicum) fruits

Research Progress of Rapid Non-Destructive Detection Technology in the Field of Apple Mold Heart Disease

Apples are rich in vitamins and dietary fiber and are one of the essential fruits in people's daily diet. China has always been a big apple consumer, and with the improvement of people's life quality, nutrition, and health requirements, the demand for high-quality apples has increased year by year. Apple mold heart disease is one of the main diseases affecting apple quality. However, this disease cannot be easily detected from the surface, so it is difficult to detect mold heart disease. Therefore, this paper focuses on the analysis of seven non-destructive detection technologies, including near infrared spectroscopy technology, hyperspectral technology, Raman spectroscopy technology, electronic nose technology, acoustic technology, electrical technology, and magnetic technology, summarizes their application status in the detection of apple mold heart disease, and then analyzes their advantages and disadvantages. Combined with the current rapid development of artificial intelligence (AI) technology, this paper proposes the future development trends of using non-destructive technologies to detect apple mold heart disease. It is expected to provide basic theory and application references for the intelligent detection of apple mold heart disease.

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.

Correlation Study between the Organic Compounds and Ripening Stages of Oil Palm Fruitlets Based on the Raman Spectra

The degree of maturity of oil palm fresh fruit bunches (FFB) at the time of harvest heavily affects oil production, which is expressed in the oil extraction rate (OER). Oil palm harvests must be harvested at their optimum maturity to maximize oil yield if a rapid, non-intrusive, and accurate method is available to determine their level of maturity. This study demonstrates the potential of implementing Raman spectroscopy for determining the maturity of oil palm fruitlets. A ripeness classification algorithm has been developed utilizing machine learning by classifying the components of organic compounds such as β-carotene, amino acid, etc. as parameters to distinguish the ripeness of fruits. In this study, 47 oil palm fruitlets spectra from three different ripeness levels-under ripe, ripe, and over ripe-were examined. To classify the oil palm fruitlets into three maturity categories, the extracted features were put to the test using 31 machine learning models. It was discovered that the Medium, Weighted KNN, and Trilayered Neural Network classifier has a maximum overall accuracy of 90.9% by using four significant features extracted from the peaks as the predictors. To conclude, the Raman spectroscopy method may offer a precise and efficient means to evaluate the maturity level of oil palm fruitlets.

Oil Palm Fruits Ripeness Classification Based on the Characteristics of Protein, Lipid, Carotene, and Guanine/Cytosine from the Raman Spectra

The capacity of palm oil production is directly affected by the ripeness of the fresh fruit bunches (FFB) upon harvesting. Conventional harvesting standards rely on rigid harvesting scheduling as well as the number of fruitlets that have loosened from the bunch. Harvesting is usually done every 10 to 14 days, and an FFB is deemed ready to be harvested if there are around 5 to 10 empty sockets on the fruit bunch. Technology aided by imaging techniques relies heavily on the color of the fruit bunch, which is highly dependent on the surrounding light intensities. In this study, Raman spectroscopy is used for ripeness classification of oil palm fruits, based on the molecular assignments extracted from the Raman bands between 1240 cm^-1 and 1360 cm^-1. The Raman spectra of 52 oil palm fruit samples which contain the fingerprints of different organic compounds were collected. Signal processing was applied to perform baseline correction and to reduce background noises. Characteristic data of the organic compounds were extracted through deconvolution and curve fitting processes. Subsequently, a correlation study between organic compounds was developed and eight hidden Raman peaks including protein, beta carotene, carotene, lipid, guanine/cytosine, chlorophyll-a, and tryptophan were successfully located. Through ANOVA statistical analysis, a total of six peak intensities from proteins through Amide III (β-sheet), beta-carotene, carotene, lipid, guanine/cytosine, and carotene and one peak location from lipid were found to be significant. An automated oil palm fruit ripeness classification system deployed with artificial neural network (ANN) using the seven signification features showed an overall performance of 97.9% accuracy. An efficient and accurate ripeness classification model which uses seven significant Raman peak features from the correlation analysis between organic compounds was successfully developed.

Nondestructive 3D imaging and quantification of hydrated biofilm matrix by confocal Raman microscopy coupled with non-negative matrix factorization

Biofilms are ubiquitous in natural and engineered environments and of great importance in drinking water distribution and biological wastewater treatment systems. Simultaneously acquiring the chemical and structural information of the hydrated biofilm matrix is essential for the cognition and regulation of biofilms in the environmental field. However, the complexity of samples and the limited approaches prevent a holistic understanding of the biofilm matrix. In this work, an approach based on the confocal Raman mapping technique integrated with non-negative matrix factorization (NMF) analysis was developed to probe the hydrated biofilm matrix in situ. The flexibility of the NMF analysis was utilized to subtract the undesired water background signal and resolve the meaningful biological components from Raman spectra of the hydrated biofilms. Diverse chemical components such as proteins, bacterial cells, glycolipids and polyhydroxyalkanoates (PHA) were unraveled within the distinct Pseudomonas spp. biofilm matrices, and the corresponding 3-dimensional spatial organization was visualized and quantified. Of these components, glycolipids and PHA were unique to the P. aeruginosa and P. putida biofilm matrix, respectively. Furthermore, their high abundances in the lower region of the biofilm matrix were found to be related to the specific physiological functions and surrounding microenvironments. Overall, the results demonstrate that our NMF Raman mapping method could serve as a powerful tool complementary to the conventional approaches for identifying and visualizing the chemical components in the biofilm matrix. This work may facilitate the online characterization of the biofilm matrix widely present in the environment and advance the fundamental understanding of biofilm.

Confocal Raman spectroscopy at different laser wavelengths in analyzing stratum corneum and skin penetration properties of mixed PEGylated emulsifier systems

Emulsifier mixtures are widely used in cosmetics and pharmaceutics and thus, brought extensive studies for their performances on skin applications. PEG-20cetyl ether (C20) is recently proposed to induce skin irritation and is of interest to study its skin interactions when mixed with other emulsifiers. PEG-2oleyl ether (O2) and PEG-20stearyl ether (S20) are selected and in specific, 50 mM of C20, O2, S20 as well as Mix1 (50 mM C20 mixed with 50 mM O2) and Mix2 (50 mM C20 mixed with 50 mM S20) solutions were applied on skin samples. Confocal Raman spectroscopy (CRS) analyses of stratum corneum (SC) thickness and SC lipid content were performed after 4 h skin treatments. In parallel, skin penetration properties were also evaluated via CRS by applying procaine solutions with/without emulsifiers on skin samples for 24 h. In terms of the CRS measurements, two excitation wavelengths of 532 nm and 785 nm are both utilized in this study and we secondly aimed to compare their results and suitability in SC and skin analyses. Based on the experimental observations, comparable results are obtained by using both excitation wavelengths of 532 nm and 785 nm demonstrating their suitability in analyzing SC and skin samples. Thereinto, 785 nm laser wavelength shows the advantage of deeper skin penetration and allows the measurements of fluorescent skin samples; 532 nm laser wavelength enables simple measurement performance without substrate and coverslip interference. With regards to the results of emulsifier mixtures, the addition of S20 and O2 reduced the skin interactions and penetration enhancing ability of C20, giving us the hint to build milder systems with emulsifier mixtures. Besides, the CRS results of stronger skin interruption were also correlated with the higher critical micelle concentration (CMC) values of emulsifiers and their mixtures, which may provide evidence in explaining the interactions between emulsifiers and skin.

Monitoring the ripening attributes of Turkish white cheese using miniaturized vibrational spectrometers

Monitoring the ripening process by prevalent analytic methods is laborious, expensive, and time consuming. Our objective was to develop a rapid and simple method based on vibrational spectroscopic techniques to understand the biochemical changes occurring during the ripening process of Turkish white cheese and to generate predictive algorithms for the determination of the content of key cheese quality and ripening indicator compounds. Turkish white cheese samples were produced in a pilot plant scale and ripened for 100 d, and samples were analyzed at 20 d intervals during storage. The collected spectra (Fourier-transform infrared, Raman, and near-infrared) correlated with major composition characteristics (fat, protein, and moisture) and primary products of the ripening process and analyzed by pattern recognition to generate prediction (partial least squares regression) and classification (soft independent analysis of class analogy) models. The soft independent analysis of class analogy models classified cheese samples based on the unique biochemical changes taking place during the ripening process. partial least squares regression models showed good correlation (R_Pre = 0.87 to 0.98) between the predicted values by vibrational spectroscopy and the reference values, giving low standard errors of prediction (0.01 to 0.57). Portable and handheld vibrational spectroscopy units can be used as a rapid, simple, and in situ technique for monitoring the quality of cheese during aging and provide real-time tools for addressing deviations in manufacturing.

Classification of oil palm fresh fruit maturity based on carotene content from Raman spectra

The oil yield, measured in oil extraction rate per hectare in the palm oil industry, is directly affected by the ripening levels of the oil palm fresh fruit bunches at the point of harvesting. A rapid, non-invasive and reliable method in assessing the maturity level of oil palm harvests will enable harvesting at an optimum time to increase oil yield. This study shows the potential of using Raman spectroscopy to assess the ripeness level of oil palm fruitlets. By characterizing the carotene components as useful ripeness features, an automated ripeness classification model has been created using machine learning. A total of 46 oil palm fruit spectra consisting of 3 ripeness categories; under ripe, ripe, and over ripe, were analyzed in this work. The extracted features were tested with 19 classification techniques to classify the oil palm fruits into the three ripeness categories. The Raman peak averaging at 1515 cm⁻¹ is shown to be a significant molecular fingerprint for carotene levels, which can serve as a ripeness indicator in oil palm fruits. Further signal analysis on the Raman peak reveals 4 significant sub bands found to be lycopene (ν1a), β-carotene (ν1b), lutein (ν1c) and neoxanthin (ν1d) which originate from the C=C stretching vibration of carotenoid molecules found in the peel of the oil palm fruit. The fine KNN classifier is found to provide the highest overall accuracy of 100%. The classifier employs 6 features: peak intensities of bands ν1a to ν1d and peak positions of bands ν1c and ν1d as predictors. In conclusion, the Raman spectroscopy method has the potential to provide an accurate and effective way in determining the ripeness of oil palm fresh fruits.

Raman Spectroscopy and Machine-Learning for Early Detection of Bacterial Canker of Tomato: The Asymptomatic Disease Condition

Bacterial canker of tomato is caused by Clavibacter michiganensis subsp. michiganensis (Cmm). The disease is highly destructive, because it produces latent asymptomatic infections that favor contagion rates. The present research aims consisted on the implementation of Raman spectroscopy (RS) and machine-learning spectral analysis as a method for the early disease detection. Raman spectra were obtained from infected asymptomatic tomato plants (BCTo) and healthy controls (HTo) with 785 nm excitation laser micro-Raman spectrometer. Spectral data were normalized and processed by principal component analysis (PCA), then the classifiers algorithms multilayer perceptron (PCA + MLP) and linear discriminant analysis (PCA + LDA) were implemented. Bacterial isolation and identification (16S rRNA gene sequencing) were realized of each plant studied. The Raman spectra obtained from tomato leaf samples of HTo and BCTo exhibited peaks associated to cellular components, and the most prominent vibrational bands were assigned to carbohydrates, carotenoids, chlorophyll, and phenolic compounds. Biochemical changes were also detectable in the Raman spectral patterns. Raman bands associated with triterpenoids and flavonoids compounds can be considered as indicators of Cmm infection during the asymptomatic stage. RS is an efficient, fast and reliable technology to differentiate the tomato health condition (BCTo or HTo). The analytical method showed high performance values of sensitivity, specificity and accuracy, among others.

A Four-Level Maturity Index for Hot Peppers (Capsicum annum) Using Non-Invasive Automated Mobile Raman Spectroscopy for On-Site Testing

A handheld Raman spectrometer was used to determine the ripeness of peppers. Raman spectra were recorded non-invasively on the fruit surface. The spectroscopic data were transformed into a classification scheme referred to as the maturity index which allowed for attribution of the fruit stadium to four levels from immature to fully mature. Hot pepper and tomato ripening includes pectic polysaccharide depolymerization, chlorophyll degradation and carotenoid formation, among others. The latter were followed non-invasively by Raman spectroscopy. Two portable systems and one benchtop system were compared for their applicability and robustness to establish a suitable maturity index. Spectral acquisition, data treatment and multivariate data analysis were automated using a Matlab script on a laptop computer. The automated workflow provided a graphic visualization of the relevant parameters and results on-site in real time. In terms of reliability and applicability, the chemometric model to determine the maturity of fruits was compared to a univariate procedure based on the average intensity and ratio of three characteristic signals. Portable Raman spectrometers in combination with the maturity index or a chemometric model should be suitable to assess the stage of maturing for carotenoid-containing fruits and thus to determine ripeness on-site or during a sorting process in an automated manner.

Raman Imaging of Plant Cell Walls

Raman imaging is a microspectroscopic approach revealing the chemistry and structure of plant cell walls in situ on the micro- and nanoscale. The method is based on the Raman effect (inelastic scattering) that takes place when monochromatic laser light interacts with matter. The scattered light conveys a change in energy that is inherent of the involved molecule vibrations. The Raman spectra are thus characteristic for the chemical structure of the molecules and can be recorded spatially ordered with a lateral resolution of about 300 nm. Based on thousands of acquired Raman spectra, images can be assessed using univariate as well as multivariate data analysis approaches. One advantage compared to staining or labeling techniques is that not only one image is obtained as a result but different components and characteristics can be displayed in several images. Furthermore, as every pixel corresponds to a Raman spectrum, which is a kind of "molecular fingerprint," the imaging results should always be evaluated and further details revealed by analysis (e.g., band assignment) of extracted spectra. In this chapter, the basic theoretical background of the technique and instrumentation are described together with sample preparation requirements and tips for high-quality plant tissue sections and successful Raman measurements. Typical Raman spectra of the different plant cell wall components are shown as well as an exemplified analysis of Raman data acquired on the model plant Arabidopsis. Important preprocessing methods of the spectra are included as well as single component image generation (univariate) and spectral unmixing by means of multivariate approaches (e.g., vertex component analysis).

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.

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

ATR-FTIR spectroscopy with subsequent multivariate analysis non-destructively identifies plant-pathogen interactions during disease progression, both directly and indirectly, through alterations in the spectral fingerprint. Plant-environment interactions are essential to understanding crop biology, optimizing crop use, and minimizing loss to ensure food security. Damage-induced pathogen infection of delicate fruit crops such as tomato (Solanum lycopersicum) are therefore important processes related to crop biology and modern horticulture. Fruit epidermis as a first barrier at the plant-environment interface, is specifically involved in environmental interactions and often shows substantial structural and functional changes in response to unfavourable conditions. Methods available to investigate such systems in their native form, however, are limited by often required and destructive sample preparation, or scarce amounts of molecular level information. To explore biochemical changes and evaluate diagnostic potential for damage-induced pathogen infection of cherry tomato (cv. Piccolo) both directly and indirectly, mid-infrared (MIR) spectroscopy was applied in combination with exploratory multivariate analysis. ATR-FTIR fingerprint spectra (1800-900 cm^-1) of healthy, damaged or sour rot-infected tomato fruit were acquired and distinguished using principal component analysis and linear discriminant analysis (PCA-LDA). Main biochemical constituents of healthy tomato fruit epidermis are characterized while multivariate analysis discriminated subtle biochemical changes distinguishing healthy tomato from damaged, early or late sour rot-infected tomato indirectly based solely on changes in the fruit epidermis. Sour rot causing agent Geotrichum candidum was detected directly in vivo and characterized based on spectral features distinct from tomato fruit. Diagnostic potential for indirect pathogen detection based on tomato fruit skin was evaluated using the linear discriminant classifier (PCA-LDC). Exploratory and diagnostic analysis of ATR-FTIR spectra offers biological insights and detection potential for intact plant-pathogen systems as they are found in horticultural industries.

Portable Raman spectroscopy for an in-situ monitoring the ripening of tomato (Solanum lycopersicum) fruits

Ripening is one of the most important transformations that fruits and vegetables suffer, from an unripe to a ripe stage. In this study, it was followed up and analyzed the variations in the composition of tomato fruits at different ripening stages (green or unripe, orange or middle ripe, red or ripe and brown or overripe). The results obtained from the Raman measurements carried out showed a change in the composition of tomato fruits in the transit from green to brown. The analysis confirmed an increase of carotenoids from an unripe to a ripe stage of these fruits, being lycopene the characteristic carotenoid of the optimum ripe stage. The presence of chlorophyll and cuticular waxes decrease from the unripe to the ripe stage. Moreover, the relative intensity of phytofluene, a transition compound in the carotenoid biosynthetic pathway, is higher in the orange or middle ripening stage. The results obtained in-situ, without cutting and handling the tomato fruits, by means of a portable Raman spectrometer offered the same information that can be achieved using a more expensive and sophisticated confocal Raman microscope.

Modeling Tomato Ripening Based on Carotenoid Raman Spectroscopy: Experimental Versus Kinetic Model

This work reports on a combined experimental and theoretical investigation on the carotenoid Raman signal in several tomato fruits during their postharvest time evolution and ripening. Both resonant (180°) backscattering and polarized (90°) Raman scattering were used to monitor the most prominent bands of carotenoid (lycopene and β-carotene) evolution in different tomato varieties. Relevant findings of the present investigations were that while the depolarization ratio of the ν₁ band hardly changed with time, the Raman ν₁ band intensity did change showing a similar pattern for all tomatoes investigated. Indeed, all cases investigated revealed a rise of the carotenoid signal coincident with the onset of the turning stage of the fruit ripening, a pronounced maximum of the Raman signal followed by a post-maximum decline at the red ripening stage. A kinetic model has been developed to describe the time evolution of the observed Raman signatures based on the rate coefficient of the carotenoid synthesis and the time evolution of the scattering coefficient of the fruit. The model describes satisfactorily the tomato evolution through the distinct ripening stages providing new insight on the assessment of the postharvest fruit control and quality.

Fourier transform infra-red spectroscopy using an attenuated total reflection probe to distinguish between Japanese larch, pine and citrus plants in healthy and diseased states

FTIR spectroscopy coupled with an Attenuated Total Reflection (ATR) sampling probe has been demonstrated as a technique for detecting disease in plants. Spectral differences were detected in Japanese Larch (Larix kaempferi) infected with Phytophthora ramorum at 3403cm(-1) and 1730cm(-1), from pine (Pinus spp.) infected with Bursaphelenchus xylophilus at 1070cm(-1), 1425cm(-)1, 1621cm(-1) and 3403cm(-1) and from citrus (Citrus spp.) infected with 'Candidatus liberibacter' at 960cm(-1), 1087cm(-1), 1109cm(-1), 1154cm(-1), 1225cm(-1), 1385cm(-1), 1462cm(-1), 1707cm(-1), 2882cm(-1), 2982cm(-1) and 3650cm(-1). A spectral marker in healthy citrus has been identified as Pentanone but is absent from the diseased sample spectra. This agrees with recent work by Aksenov, 2014. Additionally, the spectral signature of Cutin was identified in the spectra of Pinus spp. and Citrus spp. and is consistent with work by Dubis, 1999 and Heredia-Guerrero, 2014.

Waterproofing in Arabidopsis: Following Phenolics and Lipids In situ by Confocal Raman Microscopy

Waterproofing of the aerial organs of plants imposed a big evolutionary step during the colonization of the terrestrial environment. The main plant polymers responsible of water repelling are lipids and lignin, which play also important roles in the protection against biotic/abiotic stresses, regulation of flux of gases and solutes, and mechanical stability against negative pressure, among others. While the lipids, non-polymerized cuticular waxes together with the polymerized cutin, protect the outer surface, lignin is confined to the secondary cell wall within mechanical important tissues. In the present work a micro cross-section of the stem of Arabidopsis thaliana was used to track in situ the distribution of these non-carbohydrate polymers by Confocal Raman Microscopy. Raman hyperspectral imaging gives a molecular fingerprint of the native waterproofing tissues and cells with diffraction limited spatial resolution (~300 nm) at relatively high speed and without any tedious sample preparation. Lipids and lignified tissues as well as their effect on water content was directly visualized by integrating the 1299, 1600, and 3400 cm(-1) band, respectively. For detailed insights into compositional changes of these polymers vertex component analysis was performed on selected sample positions. Changes have been elucidated in the composition of lignin within the lignified tissues and between interfascicular fibers and xylem vessels. Hydrophobizing changes were revealed from the epidermal layer to the cuticle as well as a change in the aromatic composition within the cuticle of trichomes. To verify Raman signatures of different waterproofing polymers additionally Raman spectra of the cuticle and cutin monomer from tomato (Solanum lycopersicum) as well as aromatic model polymers (milled wood lignin and dehydrogenation polymer of coniferyl alcohol) and phenolic acids were acquired.

Toward food analytics: fast estimation of lycopene and β-carotene content in tomatoes based on surface enhanced Raman spectroscopy (SERS)

The application of SERS toward the differentiation of two carotenoid molecules (namely, lycopene and β-carotene) in tomato samples is introduced.

HD DVD substrates for surface enhanced Raman spectroscopy analysis: fabrication, theoretical predictions and practical performance

Commercially available HD-DVD templates have been used to theoretically predict the occurrence of surface plasmons supermodes which improve the detection of surface enhanced Raman signals.

Dispersive and FT-Raman spectroscopic methods in food analysis

Raman spectroscopy is a powerful technique for molecular analysis of food samples.

In situ evaluation of Paenibacillus alvei in reducing carriage of Salmonella enterica serovar Newport on whole tomato plants

Recently, tomatoes have been implicated as a primary vehicle in food-borne outbreaks of Salmonella enterica serovar Newport and other Salmonella serovars. Long-term intervention measures to reduce Salmonella prevalence on tomatoes remain elusive for growing and postharvest environments. A naturally occurring bacterium identified by 16S rRNA gene sequencing as Paenibacillus alvei was isolated epiphytically from plants native to the Virginia Eastern Shore tomato-growing region. After initial antimicrobial activity screening against Salmonella and 10 other bacterial pathogens associated with the human food supply, strain TS-15 was further used to challenge an attenuated strain of S. Newport on inoculated fruits, leaves, and blossoms of tomato plants in an insect-screened high tunnel with a split-plot design. Survival of Salmonella after inoculation was measured for groups with and those without the antagonist at days 0, 1, 2, and 3 and either day 5 for blossoms or day 6 for fruits and leaves. Strain TS-15 exhibited broad-range antimicrobial activity against both major food-borne pathogens and major bacterial phytopathogens of tomato. After P. alvei strain TS-15 was applied onto the fruits, leaves, and blossoms of tomato plants, the concentration of S. Newport declined significantly (P ≤ 0.05) compared with controls. Astonishingly, >90% of the plants had no detectable levels of Salmonella by day 5 for blossoms. The naturally occurring antagonist strain TS-15 is highly effective in reducing the carriage of Salmonella Newport on whole tomato plants. The application of P. alvei strain TS-15 is a promising approach for reducing the risk of Salmonella contamination during tomato production.

Infrared and Raman spectroscopic features of plant cuticles: a review

The cuticle is one of the most important plant barriers. It is an external and continuous lipid membrane that covers the surface of epidermal cells and whose main function is to prevent the massive loss of water. The spectroscopic characterization of the plant cuticle and its components (cutin, cutan, waxes, polysaccharides and phenolics) by infrared and Raman spectroscopies has provided significant advances in the knowledge of the functional groups present in the cuticular matrix and on their structural role, interaction and macromolecular arrangement. Additionally, these spectroscopies have been used in the study of cuticle interaction with exogenous molecules, degradation, distribution of components within the cuticle matrix, changes during growth and development and characterization of fossil plants.