Merete Halkjær Olesen

Optimal Sample Size for Predicting Viability of Cabbage and Radish Seeds Based on near Infrared Spectra of Single Seeds

The effects of the number of seeds in a training sample set on the ability to predict the viability of cabbage or radish seeds are presented and discussed. The supervised classification method extended canonical variates analysis (ECVA) was used to develop a classification model. Calibration sub-sets of different sizes were chosen randomly with several iterations and using the spectral-based sample selection algorithms DUPLEX and CADEX. An independent test set was used to validate the developed classification models. The results showed that 200 seeds were optimal in a calibration set for both cabbage and radish data. The misclassification rates at optimal sample size were 8%, 6% and 7% for cabbage and 3%, 3% and 2% for radish respectively for random method (averaged for 10 iterations), DUPLEX and CADEX algorithms. This was similar to the misclassification rate of 6% and 2% for cabbage and radish obtained using all 600 seeds in the calibration set. Thus, the number of seeds in the calibration set can be reduced by up to 67% without significant loss of classification accuracy, which will effectively enhance the cost-effectiveness of NIR spectral analysis. Wavelength regions important for the discrimination between viable and non-viable seeds were identified using interval ECVA (iECVA) models, ECVA weight plots and the mean difference spectrum for viable and non-viable seeds.

Viability Prediction of Ricinus cummunis L. Seeds Using Multispectral Imaging

The purpose of this study was to highlight the use of multispectral imaging in seed quality testing of castor seeds. Visually, 120 seeds were divided into three classes: yellow, grey and black seeds. Thereafter, images at 19 different wavelengths ranging from 375–970 nm were captured of all the seeds. Mean intensity for each single seed was extracted from the images, and a significant difference between the three colour classes was observed, with the best separation in the near-infrared wavelengths. A specified feature (RegionMSI mean) based on normalized canonical discriminant analysis, were employed and viable seeds were distinguished from dead seeds with 92% accuracy. The same model was tested on a validation set of seeds. These seeds were divided into two groups depending on germination ability, 241 were predicted as viable and expected to germinate and 59 were predicted as dead or non-germinated seeds. This validation of the model resulted in 96% correct classification of the seeds. The results illustrate how multispectral imaging technology can be employed for prediction of viable castor seeds, based on seed coat colour.

Use of Multispectral Imaging in Varietal Identification of Tomato

Multispectral imaging is an emerging non-destructive technology. In this work its potential for varietal discrimination and identification of tomato cultivars of Nepal was investigated. Two sample sets were used for the study, one with two parents and their crosses and other with eleven cultivars to study parents and offspring relationship and varietal identification respectively. Normalized canonical discriminant analysis (nCDA) and principal component analysis (PCA) were used to analyze and compare the results for parents and offspring study. Both the results showed clear discrimination of parents and offspring. nCDA was also used for pairwise discrimination of the eleven cultivars, which correctly discriminated upto 100% and only few pairs below 85%. Partial least square discriminant analysis (PLS-DA) was further used to classify all the cultivars. The model displayed an overall classification accuracy of 82%, which was further improved to 96% and 86% with stepwise PLS-DA models on high (seven) and poor (four) sensitivity cultivars, respectively. The stepwise PLS-DA models had satisfactory classification errors for cross-validation and prediction 7% and 7%, respectively. The results obtained provide an opportunity of using multispectral imaging technology as a primary tool in a scientific community for identification/discrimination of plant varieties in regard to genetic purity and plant variety protection/registration.

Use of partial least squares discriminant analysis on visible-near infrared multispectral image data to examine germination ability and germ length in spinach seeds

Because of the difficulties in obtaining homogenous germination of spinach seeds for baby leaf production, the possibility of using partial least squares discriminant analysis (PLS‐DA) on features extracted from multispectral images of spinach seeds was investigated. The objective has been to discriminate between different seed sizes, as well as to predict germination ability and germ length. Images of 300 seeds including small, medium, and large seeds were taken, and the seeds were examined for germination ability and germ length. PLS‐DA loadings plots were used to reduce the multidimensional image features to a few important features. The PLS‐DA prediction resulted in an independent test set not only providing discrimination of seed size but also demonstrating how germination ability and germ length vary according to seed size. The result indicated that larger seeds had both a significantly higher germination potential and germ length compared with smaller seeds. The variable importance for projection method showed that the near infrared (NIR) wavelength region is important for germination predictability. However, the PLS‐DA model did not improve when only the NIR region was used. Copyright

Classification of Viable and Non-Viable Spinach (Spinacia Oleracea L.) Seeds by Single Seed near Infrared Spectroscopy and Extended Canonical Variates Analysis

Near-infrared (NIR) reflectance spectroscopy is a common non-destructive method for predicting seed quality parameters, such as moisture, oil, carbohydrates and protein content. Furthermore, variations in absorbance between germinating and non-germinating seeds have been shown in single seed studies. Spinach (Spinacia oleracea L.) is the major crop in vegetable seed production in Denmark and two seed lots with viability percentages of 90% and 97% were chosen for examination by single seed NIR spectroscopy. Lipids play a major role in both ageing and germination. During accelerated ageing, lipid peroxidation leads to deterioration of cell membranes and contributes in that way to reducing seed viability of the seed sample. These biochemical changes may be the reason for a clear grouping between aged and non-aged seeds when performing the extended canonical variates analysis (ECVA). Assigning the difference of scatter corrected absorbance spectra from aged and non-aged seeds also lead to CH2, CH3 and HC=CH structures, which are some of the functional groups in lipids. In the ECVA plot, there was a clear difference between seeds with and without a pericarp. Evaluating the spectra, the pattern of peaks was almost similar, but the intensity was different in the absorption band at 1350 nm. The number of misclassified seeds ranged from 1.7% to 10.5% and it was lowest in seeds with a pericarp. This indicates the influence of the pericarp during germination, which is in accordance with earlier studies of spinach seeds. Single seed NIR and ECVA classification are potential methods for the prediction of seed viability.

The Use of Image-Spectroscopy Technology as a Diagnostic Method for Seed Health Testing and Variety Identification.

Application of rapid and time-efficient health diagnostic and identification technology in the seed industry chain could accelerate required analysis, characteristic description and also ultimately availability of new desired varieties. The aim of the study was to evaluate the potential of multispectral imaging and single kernel near-infrared spectroscopy (SKNIR) for determination of seed health and variety separation of winter wheat (Triticum aestivum L.) and winter triticale (Triticosecale Wittm. & Camus). The analysis, carried out in autumn 2013 at AU-Flakkebjerg, Denmark, included nine winter triticale varieties and 27 wheat varieties provided by the Faculty of Agriculture and Life Sciences Maribor, Slovenia. Fusarium sp. and black point disease-infected parts of the seed surface could successfully be distinguished from uninfected parts with use of a multispectral imaging device (405–970 nm wavelengths). SKNIR was applied in this research to differentiate all 36 involved varieties based on spectral differences due to variation in the chemical composition. The study produced an interesting result of successful distinguishing between the infected and uninfected parts of the seed surface. Furthermore, the study was able to distinguish between varieties. Together these components could be used in further studies for the development of a sorting model by combining data from multispectral imaging and SKNIR for identifying disease(s) and varieties.

Effect of Verticillium dahliae Soil Inoculum Levels on Spinach Seed Infection

Verticillium dahliae is a soilborne pathogen and a threat to spinach seed production. The aim of this study was to understand the relation between V. dahliae soil inoculum and infection in harvested seed. Quantitative polymerase chain reaction was used for quantification of the pathogen. Semifield experiments in which spinach was grown in soils with different inoculum levels enabled us to determine a threshold level for V. dahliae DNA of 0.003 ng/g of soil for seed infection to occur. Soils from production fields were sampled in 2013 and 2014 during and before planting, as well as the harvested seed. Seed from plants grown in infested soils were infected with V. dahliae in samples from both the semifield and open-field experiments. Lower levels of pathogen were found in seed from spinach grown in soils with a scattered distribution of V. dahliae (one or two positive of three soil subsamples) than in soils with a uniform distribution of the pathogen (three of three positive soil subsamples). Our results showed that infection of V. dahliae in harvested seed strongly depended on the presence of pathogen inoculum in the soil.

Single Seed NIR as a Fast Method to Predict Germination Ability in Pak Choi

Introduction I n order to balance the food–population equation on a sustainable basis, it is imperative that food production must be increased at a faster rate than the rate of population growth. To achieve this goal, it is necessary to use better inputs in the production system. There are potential benefits from the distribution and use of good quality vegetable seed. Enhanced productivity, higher harvest index and higher incomes are some of the direct benefits potentially accrued to farmers and producers worldwide. Thus, “seed quality” is a broad and complex term. To a farmer or producer, quality means the suitability for sowing in a particular field in order to achieve a satisfactory yield. This suitability is determined by some components of seed quality. Seed quality is used in different ways for different seed characteristics such as: size, viability, vigour, seedling performance, seed health, genetic purity and freedom from other crops and weeds. Unlike many other seed attributes, seed quality is not a single quantifiable constituent but it encompasses many individual components, each of which can be separately defined and assessed, and thus jointly results in a general indication of the value and usefulness of a given seed lot. Each of these components can influence crop yield individually or in association with other quality components. In most vegetable crops, specified plant populations are recommended for maximising both yield and/or quality. If seedling emergence is inadequate, crop yield will be reduced and, in most situations, no amount of effort and expense later in crop development can compensate for this effect. In many leafy vegetable productions, the time from sowing to harvest is relatively short which therefore emphasises a focus on uniformity in the field from seedling emergence perhaps even more. Thus, the initial step is selecting the right quality seeds of the most robust varieties to also withstand further process manipulations. Knowing the seed composition and measuring chemical constituents might be one future key for obtaining optimum uniform seedling emergence and subsequently stand establishment leading to improved product quality of vegetables. Future complementary sorting might thus be based on non-destructive technologies such as near infrared (NIR) reflectance spectroscopy. The NIR reflectance spectroscopy approach is now commercially available for qualitative and quantitative analysis in chemical, pharmaceutical and the agro–food industries. Single seed NIR has further been tested to determine the applicability for prediction of seed viability in radish (Raphanus sativus L.) seeds and spinach (Spinacia oleracea L.) seeds. The studies show the possibility of using NIR spectroscopy in a seed separating process in the future, provided that appropriate sorting devices are developed. In cereals, the NIR approach has been examined as a tool for various quality traits and it is foreseen as a potentially powerful tool for seed sorting according to complex functionality traits, thus increasing overall quality, applicability and value of the sorted crop. A future interesting feature regarding seed quality is to combine traditional cleaning and sorting technologies with non-destructive technologies, based on a range of wavelengths calibrated for various species and cultivars. Such complementary sorting of lots can in the future be based on NIR spectroscopy. If non-destructive upgrading of seed quality can be improved, an additional step to a further improvement of uniform standard establishment in the vegetable chain can be achieved. As an example, we have provided data on Pak Choi (Brassica rapa L. ssp. chinensis) seeds analysed by single seed NIR spectroscopy. The work on the use of single seed NIR to determine germination ability in Pak Choi is continuing and the present study is only part of this work.