scholarly journals Identification of European and Japanese Larch and Their Interspecific Hybrid with Morphological Markers: Application to Young Seedlings

2006 ◽  
Vol 55 (1-6) ◽  
pp. 123-134 ◽  
Author(s):  
L. E. Pâques ◽  
G. Philippe ◽  
D. Prat
Keyword(s):  
Molecular Markers ◽  
Parental Species ◽  
Correct Classification ◽  
Morphological Markers ◽  
Hybrid Features ◽  
Pure Species ◽  
Seed Orchards ◽  
Linear Discriminant ◽  
Non Destructive

Abstract Open-pollinated hybridisation seed orchards of European and Japanese larches produce mixed progenies combining a highly variable proportion of hybrids along with pure parental species. For several reasons, it is desirable to identify and to sort out hybrids from pure species at the seedling stage. Taxa identification of 1-2 yr-old seedlings was attempted using non-destructive assessment of several traits, including morphology, phenology, growth and architecture parameters. Two sets of progenies originating from 10 open-pollinated hybridisation seed orchards were used, relying in a first step on taxa identification of individual seedlings with diagnostic molecular markers. Based on 21 traits assessed, some clear trends in pure species and hybrid features were apparent but due to the large and overlapping ranges of taxa characteristics, no single parameter allowed unambiguous identification of taxa. Combination of traits through linear discriminant analysis made possible correct classification of 90.2% to 98.6% of individuals depending on the orchard although there were a few problematic orchards. Two traits appeared particularly pertinent for discriminating young plants taxa, namely 1st-yr leaf retention (marcescence) and the bark colour of 2nd-year shoot increments. Results were corroborated using progenies from several orchards and over two experimental periods.

scholarly journals Probabilistic approaches for classifying highly variable anti-SARS-CoV-2 antibody responses

2020 ◽  
Author(s):  
Xaquin C Dopico ◽  
Leo Hanke ◽  
Daniel J. Sheward ◽  
Sandra Muschiol ◽  
Soo Aleman ◽  
...  
Keyword(s):  
Negative Control ◽  
Antibody Responses ◽  
Training Data ◽  
Support Vector ◽  
Correct Classification ◽  
Probabilistic Algorithms ◽  
Past Infection ◽  
Linear Discriminant

AbstractAntibody responses vary widely between individuals1, complicating the correct classification of low-titer measurements using conventional assay cut-offs. We found all participants in a clinically diverse cohort of SARS-CoV-2 PCR+ individuals (n=105) – and n=33 PCR+ hospital staff – to have detectable IgG specific for pre-fusion-stabilized spike (S) glycoprotein trimers, while 98% of persons had IgG specific for the receptor-binding domain (RBD). However, anti-viral IgG levels differed by several orders of magnitude between individuals and were associated with disease severity, with critically ill patients displaying the highest anti-viral antibody titers and strongest in vitro neutralizing responses. Parallel analysis of random healthy blood donors and pregnant women (n=1,000) of unknown serostatus, further demonstrated highly variable IgG titers amongst seroconverters, although these were generally lower than in hospitalized patients and included several measurements that scored between the classical 3 and 6SD assay cut-offs. Since the correct classification of seropositivity is critical for individual- and population-level metrics, we compared different probabilistic algorithms for their ability to assign likelihood of past infection. To do this, we used tandem anti-S and -RBD IgG responses from our PCR+ individuals (n=138) and a large cohort of historical negative controls (n=595) as training data, and generated an equal-weighted learner from the output of support vector machines and linear discriminant analysis. Applied to test samples, this approach provided a more quantitative way to interpret anti-viral titers over a large continuum, scrutinizing measurements overlapping the negative control background more closely and offering a probability-based diagnosis with potential clinical utility. Especially as most SARS-CoV-2 infections result in asymptomatic or mild disease, these platform-independent approaches improve individual and epidemiological estimates of seropositivity, critical for effective management of the pandemic and monitoring the response to vaccination.

Electronic Nose: A Non-destructive Technology to Screen Tomato Fruit with Internal Bruising

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 489c-489
Author(s):  
Celso L. Moretti ◽  
Steven A. Sargent ◽  
Rolf Puschmann
Keyword(s):  
Electronic Nose ◽  
Mahalanobis Distance ◽  
Tomato Fruit ◽  
System Stability ◽  
Linear Discriminant ◽  
Polymer Sensors ◽  
Ripe Stage ◽  
Multivariate Discriminant ◽  
Non Destructive

Tomato (Lycopersicon esculentum Mill) fruit, cv. Solar Set, were harvested at the mature-green stage and gassed with 100 mg·kg–1 of ethylene at 20 °C. At the breaker stage, fruit were held by vacuum to avoid fruit rotation and dropped from a 40 cm height on a metallic, solid, smooth surface. Following impact, fruit were stored at 20 °C and 85% to 95% relative humidity until table-ripe stage. Bruised and unbruised fruit were then placed individually inside the electronic nose-sampling vessel and the 12 conducting polymer sensors were lowered into the vessel and exposed to the volatile given off by the fruit. Data were analyzed employing multivariate discriminant analysis (MVDA), which maximizes the variance between treatments. The degree of dissimilarity was defined using the Mahalanobis distance and posterior probabilities were calculated to accurate re-classification of cases. The differences found between bruised and unbruised fruit were highly significant (P < 0.0041). The Mahalanobis distance between groupings (28.19 units) was a dramatic indicative of the differences between the two treatments. The re-classification of bruised and unbruised fruit using a single linear discriminant function was highly accurate, being 1.0 for both bruised and unbruised fruit. The electronic nose proved to be a useful tool to nondestructively identify and classify tomato fruit exposed to harmful postharvest practices such as mechanical injuries. However, there are still some factors that must be investigated, including system stability and the development of specific sensors for specific commodities.

scholarly journals Distinguishing between juniper-flavoured spirit drinks from different producers

2017 ◽  
Vol 16 (2) ◽  
pp. 132-137
Author(s):  
Veronika Uríčková ◽  
Kinga Dobóová ◽  
Jana Sádecká
Keyword(s):  
Discriminant Analysis ◽  
Fluorescence Intensity ◽  
Fluorescence Spectra ◽  
Roc Analysis ◽  
Correct Classification ◽  
Simple Method ◽  
Linear Discriminant ◽  
Synchronous Fluorescence Spectra ◽  
Intensity Ratios

Abstract A simple method for classifying juniper-flavoured spirit drinks is proposed based on the ratio of fluorescence intensity values in synchronous fluorescence spectra. Receiver operating curves (ROC) and linear discriminant analysis (LDA) were used to compute the performance of the classification. Significant differences in the fluorescence intensity ratios (I316/I287 and I324/I287) observed in the spectra recorded using wavelength difference 10 nm were evaluated by ROC analysis to identify cutoff values that gave ideal AUCs equal to one, thus allowing for 100% correct classification of the samples according to producer criteria. LDA showed that drinks of different producers could be distinguished (100% correct classification) on the basis of their differences in the fluorescence intensity ratios (I323/I287, I324/I287, I316/I287 and I325/I287). These results show that complete synchronous spectra are not required to discriminate between producers. Instead of them, fluorescence intensity could be measured at selected wavelengths.

Classification of jaboticaba fruits at three maturity stages using NIRS and LDA

2016 ◽  
Vol 8 (11) ◽  
pp. 2533-2538 ◽  
Author(s):  
Rosangela Câmara Costa ◽  
Luis C. Cunha Junior ◽  
Thayara Bittencourt Morgenstern ◽  
Gustavo Henrique de Almeida Teixeira ◽  
Kássio Michell Gomes de Lima
Keyword(s):  
Near Infrared ◽  
Principal Component ◽  
Reflectance Spectroscopy ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance Spectroscopy ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Maturity Stages ◽  
Non Destructive

This study proposes a rapid and non-destructive method of jaboticaba [Myrciaria cauliflora (Mart.) O. Berg] fruit classification at three maturity stages using Near-Infrared Reflectance Spectroscopy (NIRS) combined with principal component analysis-linear discriminant analysis (PCA-LDA).

scholarly journals A hybrid CNN-LSTM model for pre-miRNA classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abdulkadir Tasdelen ◽  
Baha Sen
Keyword(s):  
Evaluation Criteria ◽  
Mature Mirnas ◽  
Structural Features ◽  
Linear Discriminant ◽  
Sequential Structure ◽  
Precursor Mirnas ◽  
Lstm Network ◽  
Future Work ◽  
Cumulative Evidence

AbstractmiRNAs (or microRNAs) are small, endogenous, and noncoding RNAs construct of about 22 nucleotides. Cumulative evidence from biological experiments shows that miRNAs play a fundamental and important role in various biological processes. Therefore, the classification of miRNA is a critical problem in computational biology. Due to the short length of mature miRNAs, many researchers are working on precursor miRNAs (pre-miRNAs) with longer sequences and more structural features. Pre-miRNAs can be divided into two groups as mirtrons and canonical miRNAs in terms of biogenesis differences. Compared to mirtrons, canonical miRNAs are more conserved and easier to be identified. Many existing pre-miRNA classification methods rely on manual feature extraction. Moreover, these methods focus on either sequential structure or spatial structure of pre-miRNAs. To overcome the limitations of previous models, we propose a nucleotide-level hybrid deep learning method based on a CNN and LSTM network together. The prediction resulted in 0.943 (%95 CI ± 0.014) accuracy, 0.935 (%95 CI ± 0.016) sensitivity, 0.948 (%95 CI ± 0.029) specificity, 0.925 (%95 CI ± 0.016) F1 Score and 0.880 (%95 CI ± 0.028) Matthews Correlation Coefficient. When compared to the closest results, our proposed method revealed the best results for Acc., F1 Score, MCC. These were 2.51%, 1.00%, and 2.43% higher than the closest ones, respectively. The mean of sensitivity ranked first like Linear Discriminant Analysis. The results indicate that the hybrid CNN and LSTM networks can be employed to achieve better performance for pre-miRNA classification. In future work, we study on investigation of new classification models that deliver better performance in terms of all the evaluation criteria.

scholarly journals Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 196
Author(s):  
Araz Soltani Nazarloo ◽  
Vali Rasooli Sharabiani ◽  
Yousef Abbaspour Gilandeh ◽  
Ebrahim Taghinezhad ◽  
Mariusz Szymanek ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Low Cost ◽  
Moving Average ◽  
Nir Spectroscopy ◽  
Control Treatment ◽  
Residue Detection ◽  
Non Destructive ◽  
Reflective Spectroscopy

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

scholarly journals Classification of Tea Aromas Using Multi-Nanoparticle Based Chemiresistor Arrays

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2547 ◽  
Author(s):  
Tuo Gao ◽  
Yongchen Wang ◽  
Chengwu Zhang ◽  
Zachariah A. Pittman ◽  
Alexandra M. Oliveira ◽  
...  
Keyword(s):  
Chemical Sensor ◽  
Sensor Arrays ◽  
Machine Learning Algorithms ◽  
Single Type ◽  
Multivariate Techniques ◽  
Integrated Sensor ◽  
Resistance Change ◽  
Linear Discriminant ◽  
Lift Off

Nanoparticle based chemical sensor arrays with four types of organo-functionalized gold nanoparticles (AuNPs) were introduced to classify 35 different teas, including black teas, green teas, and herbal teas. Integrated sensor arrays were made using microfabrication methods including photolithography and lift-off processing. Different types of nanoparticle solutions were drop-cast on separate active regions of each sensor chip. Sensor responses, expressed as the ratio of resistance change to baseline resistance (ΔR/R0), were used as input data to discriminate different aromas by statistical analysis using multivariate techniques and machine learning algorithms. With five-fold cross validation, linear discriminant analysis (LDA) gave 99% accuracy for classification of all 35 teas, and 98% and 100% accuracy for separate datasets of herbal teas, and black and green teas, respectively. We find that classification accuracy improves significantly by using multiple types of nanoparticles compared to single type nanoparticle arrays. The results suggest a promising approach to monitor the freshness and quality of tea products.

Classification of EEG Mental Patterns by Using Two Scalp Electrodes and Mahalanobis Distance-Based Classifiers

2002 ◽  
Vol 41 (04) ◽  
pp. 337-341 ◽  
Author(s):  
F. Cincotti ◽  
D. Mattia ◽  
C. Babiloni ◽  
F. Carducci ◽  
L. Bianchi ◽  
...  
Keyword(s):  
Mahalanobis Distance ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Correct Classification ◽  
Interface Area ◽  
System A ◽  
The Brain ◽  
Full Covariance Matrix ◽  
Recording Electrodes

Summary Objectives: In this paper, we explored the use of quadratic classifiers based on Mahalanobis distance to detect mental EEG patterns from a reduced set of scalp recording electrodes. Methods: Electrodes are placed in scalp centro-parietal zones (C3, P3, C4 and P4 positions of the international 10-20 system). A Mahalanobis distance classifier based on the use of full covariance matrix was used. Results: The quadratic classifier was able to detect EEG activity related to imagination of movement with an affordable accuracy (97% correct classification, on average) by using only C3 and C4 electrodes. Conclusions: Such a result is interesting for the use of Mahalanobis-based classifiers in the brain computer interface area.

scholarly journals Species Boundaries and Molecular Markers for the Classification of 16SrI Phytoplasmas Inferred by Genome Analysis

2020 ◽  
Vol 11 ◽  
Author(s):  
Shu-Ting Cho ◽  
Hung-Jui Kung ◽  
Weijie Huang ◽  
Saskia A. Hogenhout ◽  
Chih-Horng Kuo
Keyword(s):  
Molecular Markers ◽  
Genome Analysis ◽  
Species Boundaries
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