Carbohydrate Analysis by NIRS-Chemometrics

Near-infrared spectroscopy (NIRS) is a high-throughput, low-cost, solvent-free, and nondestructive analytical tool. Chemometrics is the science that employs statistical and mathematical methods to explain near-infrared spectra; it has been proven that when they are coupled, their effectiveness highly improved in-depth carbohydrate characterization. This chapter focuses on the fundamentals of near-infrared spectroscopy in the study of carbohydrates, as well as the application of partial least squares regression (PLSR) and principal component analysis (PCA), as the most useful chemometric techniques involved in carbohydrate analysis. The theoretical aspects and practical applications starting from simple to complex carbohydrates mixtures are covered. Indeed, the contributions from different fields extend the implementation of near-infrared spectroscopy from industrial quality control to scientific research

Phenotyping of <em>Cercospora beticola</em> resistance of sugar beet genotypes by hyperspectral imaging

Cultivation of disease resistant crops is an important strategy in the integrated pest management which is a guiding principle for good agricultural practice. Therefore, high yielding cultivars with resistance to important plant diseases are needed. The integration of resistance sources such as wild relatives or compatible subspecies might help to enhance the resistance of crops and thus to reduce the need for chemical crop protection measures. For the selection of plants with a specific trait, such as resistance to a plant pathogen, precise determination of the genotype and a reliable characterization of the phenotype are necessary. The rather rapid development of molecular methods and knowledge about genes enhanced the genotyping in plant breeding greatly. The phenotyping, however, is still the bottleneck in resistance breeding. As the phenotype is the result of the interaction of the genotype and the environment phenotyping must be reliable, reproducible and non-invasive. The implementation of sensors in phenotyping systems provides many advantages. Hyperspectral imaging sensors are well suited to characterize different plant traits. Cercospora leaf spot (CLS) is the most important foliar disease of sugar beets and is mainly controlled by fungicide applications. The aim of this study was to characterize the resistance of sugar beet genotypes against Cercospora beticola and the development of a hyperspectral imaging system for phenotyping this disease resistance. A hyperspectral microscope that measures reflection in the visible and near-infrared range from 400 to 1000 nm with a magnification of up to 7.3x was established to determine spectral changes on the plant tissue level. Disease development on five genotypes infected with CLS was evaluated and compared under controlled conditions. Two additional genotypes were used to validate the results of the hyperspectral measurement of CLS dynamics. Resistant genotypes had a lower percentage of diseased leaf area, a reduced number of lesions, lesion size and growth rate and a decreased spore production. Apart from the quantitative difference between highly susceptible and more resistant sugar beets, the lesion phenotype varied in size and spatial composition depending on the host genotype. Using the hyperspectral microscope, lesions could be differentiated into subareas based on their spectral characteristics. Sugar beet genotypes with lower disease severity typically had lesions with smaller centers and produced fewer spores in comparison to highly susceptible genotypes. The differences in number of spores per lesion were closely associated to the spectral difference calculated as area between spectral signatures before and after sporulation. The CLS development, analyzed by hyperspectral imaging over ten days, differed depending on the host genotype and the resistance source. For example, lesion development on a resistant genotype carrying two quantitative trait loci (QTL) was characterized by a fast and abrupt change in spectral reflectance, whereas it was slower and ultimately more severe on the closely related genotype lacking the QTL. The analysis of reflectance and transmittance images by calculating spectral vegetation indices and extracting spectral signatures revealed a potential benefit of transmission measurements. Depending on the topic and analysis method, effects were sometimes stronger pronounced in the transmittance data. The resistance against C. beticola was not complete, instead, it can be described as quantitative and rate-reducing. Some resistance parameters such as a decreased sporulation matter particularly with regards to disease epidemics in the field. Based on the hyperspectral images, a detailed analysis of the lesions was possible. The presented method allowed a reliable differentiation of CLS dynamics and the characterization of even subtle differences in resistance. Hyperspectral imaging is a promising tool with the potential to improve the screening process in breeding for CLS resistance

Sensors and biosensors for pathogen and pest detection in agricultural systems : recent trends and oportunities

Pathogen and pest-linked diseases across agriculture and ecosystems are a major issue towards enhancing current thresholds in terms of farming yields and food security. Recent developments in nanotechnology allowed the designing of new generation sensors and biosensors in order to detect and mitigate these biological hazards. However, there are still important challenges concerning its respective applications in agricultural systems, typically related to point-of-care testing, cost reduction and real-time analysis. Thus, an important question arises: what are the current state-of-the-art trends and relationships among sensors and biosensors for pathogen and pest detection in agricultural systems? Targeted to meet this gap, a comparative study is performed by a literature review of the past decade and further data mining analysis. With the majority of the results coming from recent studies, leading trends towards new technologies were reviewed and identified, along with its respective agricultural application and target pathogens, such as bacteria, viruses, fungi, as well as pests like insects and parasites. Results have indicated lateral flow assay, lab-on-a-chip technologies and infrared thermography (both fixed and aerial) as the most promising categories related to sensors and biosensors driven to the detection of several different pathogenic varieties. The main existing interrelations between the results are especially associated to cereals, fruits and nuts, meat and dairy along with vegetables and legumes, mostly caused by bacterial and fungal infections. Additional results also presented and discussed, providing a fertile groundwork for decision-making and further developments in modern smart farming and IoT-based agriculture

Quality Evaluation of Plant-Derived Foods

This new MDPI book compiles several manuscript highlighting the complexity of “Quality Evaluation of Plant-Derived Foods”. It should be of interest for students, researchers, and professors, as important data and methodologies are presented. Results available include not only fruit and plant characteristics, but also by-products valorization and pre-harvest application of coumpounds for fruit and plant quality

Recent developments in fast spectroscopy for plant mineral analysis

Ideal fertilizer management to optimize plant productivity and quality is more relevant than ever, as global food demands increase along with the rapidly growing world population. At the same time, sub-optimal or excessive use of fertilizers leads to severe environmental damage in areas of intensive crop production. The approaches of soil and plant mineral analysis are briefly compared and discussed here, and the new techniques using fast spectroscopy that offer cheap, rapid, and easy-to-use analysis of plant nutritional status are reviewed. The majority of these methods use vibrational spectroscopy, such as visual-near infrared and to a lesser extent ultraviolet and mid-infrared spectroscopy. Advantages of and problems with application of these techniques are thoroughly discussed. Spectroscopic techniques considered having major potential for plant mineral analysis, such as chlorophyll a fluorescence, X-ray fluorescence, and laser-induced breakdown spectroscopy are also described

Finding spectral features for the early identification of biotic stress in plants

Early detection of biotic stress in plants is vital for precision crop protection, but hard to achieve. Prediction of plant diseases or weeds at an early stage has significant influence on the extent and effectiveness of crop protection measures. The precise measure depends on specific weeds and plant diseases and their economic thresholds. Weeds and plant diseases at an early stage, however, are difficult to identify. Non-invasive optical sensors with high resolution are promising for early detection of biotic stress. The data of these sensors, e.g. hyperspectral or fluorescence signatures, contain relevant information about the occurrence of pathogens. Shape parameters, derived from bispectral images, have enormous potential for an early identification of weeds in crops. The analysis of this high dimensional data for an identification of weeds and pathogens as early as possible is demanding as the sensor signal is affected by many influencing factors. Nevertheless, advanced methods of machine learning facilitate the interpretation of these signals. Whereas traditional statistics estimate the posterior probability of the class by probability distribution, machine learning methods provide algorithms for optimising prediction accuracy by the discriminant function. Machine learning methods with robust training algorithms play a key role in handling non-linear classification problems. This thesis presents an approach which integrates modern sensor techniques and advanced machine learning methods for an early detection and differentiation of plant diseases and weeds. Support vector machines (SVMs) equipped with non-linear kernels prove as effective and robust classifiers. Furthermore, it is shown that even a presymptomatic identification based on the combination of spectral vegetation indices is realised. Using well-established data analysis methods of this scientific field, this has not achieved so far. Identifying disease specific features from the underlying original high dimensional sensor data selection is conducted. The high dimensionality of data affords a careful selection of relevant and non-redundant features depending on classification problem and feature properties. In the case of fluorescence signatures an extraction of new features is necessary. In this context modelling of signal noise by an analytical description of the spectral signature improves the accuracy of classification substantially. In the case of weed discrimination accuracy is improved by exploiting the hierarchy of weed species. This thesis outlines the potential of SVMs, feature construction and feature selection for precision crop protection. A problem-specific extraction and selection of relevant features, in combination with task-oriented classification methods, is essential for robust identification of pathogens and weeds as early as possible.Früherkennung von biotischem Pflanzenstress ist für den Präzisionspflanzenschutz wesentlich, aber schwierig zu erreichen. Die Vorhersage von Pflanzenkrankheiten und Unkräutern in einem frühen Entwicklungsstadium hat signifikanten Einfluss auf das Ausmaß und die Effektivität einer Pflanzenschutzmaßnahme. Aufgrund der Abhängigkeit einer Maßnahme von der Art der Pflanzenkrankheit oder des Unkrauts und ihrer ökonomischer Schadschwelle ist eine präzise Identifizierung der Schadursache essentiell, aber gerade im Frühstadium durch die Ähnlichkeit der Schadbilder problematisch. Nicht-invasive optische Sensoren mit hoher Auflösung sind vielversprechend für eine Früherkennung von biotischem Pflanzenstress. Daten dieser Sensoren, beispielsweise Hyperspektral- oder Fluoreszenzspektren, enthalten relevante Informationen über das Auftreten von Pathogenen; Formparameter, abgeleitet aus bispektralen Bildern, zeigen großes Potential für die Früherkennung von Unkräutern in Kulturpflanzen. Die Analyse dieser hochdimensionalen Sensordaten unter Berücksichtigung vielfältiger Faktoren ist eine anspruchsvolle Herausforderung. Moderne Methoden des maschinellen Lernens bieten hier zielführende Möglichkeiten. Während die traditionelle Statistik die a-posteriori Wahrscheinlichkeit der Klasse basierend auf Wahrscheinlichkeitsverteilungen schätzt, verwenden maschinelle Lernverfahren Algorithmen für eine Optimierung der Vorhersagegenauigkeit auf Basis diskriminierender Funktionen. Grundlage zur Bearbeitung dieser nicht-linearen Klassi kationsprobleme sind robuste maschinelle Lernverfahren. Die vorliegende Dissertationsschrift zeigt, dass die Integration moderner Sensortechnik mit fortgeschrittenen Methoden des maschinellen Lernens eine Erkennung und Differenzierung von Pflanzenkrankheiten und Unkräutern ermöglicht. Einen wesentlichen Beitrag für eine effektive und robuste Klassifikation leisten Support Vektor Maschinen (SVMs) mit nicht-linearen Kernels. Weiterhin wird gezeigt, dass SVMs auf Basis spektraler Vegetationsindizes die Detektion von Pflanzenkrankheiten noch vor Auftreten visuell wahrnehmbarer Symptome ermöglichen. Dies wurde mit bekannten Verfahren noch nicht erreicht. Zur Identifikation krankheitsspezifischer Merkmale aus den zugrunde liegenden originären hochdimensionalen Sensordaten wurden Merkmale konstruiert und selektiert. Die Selektion ist sowohl vom Klassifikationsproblem als auch von den Eigenschaften der Merkmale abhängig. Im Fall von Fluoreszenzspektren war eine Extraktion von neuen Merkmalen notwendig. In diesem Zusammenhang trägt die Modellierung des Signalrauschens durch eine analytische Beschreibung der spektralen Signatur zur deutlichen Verbesserung der Klassifikationsgenauigkeit bei. Im Fall der Differenzierung von unterschiedlichen Unkräutern erhöht die Ausnutzung der Hierarchie der Unkrautarten die Genauigkeit signifikant. Diese Arbeit zeigt das Potential von Support Vektor Maschinen, Merkmalskonstruktion und Selektion für den Präzisionspflanzenschutz. Eine problemspezifische Extraktion und Selektion relevanter Merkmale in Verbindung mit sachbezogenen Klassifikationsmethoden ermöglichen eine robuste Identifikation von Pathogenen und Unkräutern zu einem sehr frühen Zeitpunkt