Canola quality by handheld NIR spectrometer

2021 ◽  
Vol 32 (9) ◽  
pp. 22-27
Author(s):  
Véronique J. Barthet ◽  
◽  
Michael Petryk ◽  
Bert Siemens
Keyword(s):  
Cross Validation ◽  
Sampling Error ◽  
Quality Parameters ◽  
Small Sample ◽  
Model Stability ◽  
Chlorophyll Contents ◽  
Test Models ◽  
Multiple Measurements ◽  
Compact Design ◽  
Canola Quality

Handheld NIR spectrometers can be used to predict some of the most important compounds that define canola quality (high oil and protein contents, low glucosinolate and chlorophyll contents, and fatty acid composition). It is important to test models on true external verification sets instead of relying only on cross-validation for model stability. For handheld NIR spectrometers, the compact design necessarily has small sample windows, necessitating multiple measurements in intact seeds to reduce sampling error. Ground/milled seeds or flour samples have lesser sampling effect. It is important to understand how the limited wavelength range of some handheld NIR spectrometers makes NIR spectrometers unsuitable for the prediction of certain quality parameters.

scholarly journals Tillage systems and nitrogen timings effect on growth, stay green and grain quality in maize (Zea mays L.)

2019 ◽  
Vol 40 (6Supl2) ◽  
pp. 3007
Author(s):  
Allah Wasaya ◽  
Muhammad Tahir ◽  
Tauqeer Ahmad Yasir ◽  
Muhammad Mansoor Javed ◽  
Muhammad Ali Raza ◽  
...  
Keyword(s):  
Leaf Area ◽  
Grain Quality ◽  
Management Practices ◽  
Quality Parameters ◽  
Nitrogen Application ◽  
Tillage Systems ◽  
Stay Green ◽  
Chlorophyll Contents ◽  
Tillage Practices ◽  
Mouldboard Plough

Nitrogen (N), being mobile in soil is exposed to various losses owing to unwise use of nitrogen fertilizer, and conventional soil and crop management practices which can be minimized by temporal nitrogen application and different tillage practices. This study was conducted to elucidate the effect of different tillage systems and nitrogen timings on growth, stay green and grain quality in maize. Three tillage systems viz. T1: tillage with cultivator, T2: mouldboard plough + 2-cultivations, T3: chisel plough + 2-cultivations; and five nitrogen timings viz. N1: whole at sowing, N2: ½ at sowing+½ at V5 (5-leaf stage), N3: ½ at sowing+½ at tasseling, N4: ½ at V5+½ at tasseling, N5: 1/3 at sowing+1/3 at V5+1/3 at tasseling). Tillage systems and nitrogen application had significant effect on leaf area per plant, specific leaf area and leaf area ratio. Tillage systems had non-significant effect on stay green and grain quality parameters except for oil contents. However, nitrogen timings had significant effect on chlorophyll a, b and total contents as well as grain quality parameters. The higher a, b and total chlorophyll contents were noted with three splits i.e. 1/3 at sowing+1/3 at V5+1/3 at tasseling compared with other treatments. The results suggest to grow maize by preparing the field through chisel plough and applying N in three splits to improve its growth, chlorophyll contents and grain quality.

scholarly journals Consistency and overfitting of multi-omics methods on experimental data

2019 ◽  
Vol 21 (4) ◽  
pp. 1277-1284 ◽  
Author(s):  
Sean D McCabe ◽  
Dan-Yu Lin ◽  
Michael I Love
Keyword(s):  
Experimental Data ◽  
Factor Analysis ◽  
Canonical Correlation ◽  
Cross Validation ◽  
Small Sample Size ◽  
R Package ◽  
Small Sample ◽  
Ready Availability ◽  
Variation Explained ◽  
The Relationship

Abstract Knowledge on the relationship between different biological modalities (RNA, chromatin, etc.) can help further our understanding of the processes through which biological components interact. The ready availability of multi-omics datasets has led to the development of numerous methods for identifying sources of common variation across biological modalities. However, evaluation of the performance of these methods, in terms of consistency, has been difficult because most methods are unsupervised. We present a comparison of sparse multiple canonical correlation analysis (Sparse mCCA), angle-based joint and individual variation explained (AJIVE) and multi-omics factor analysis (MOFA) using a cross-validation approach to assess overfitting and consistency. Both large and small-sample datasets were used to evaluate performance, and a permuted null dataset was used to identify overfitting through the application of our framework and approach. In the large-sample setting, we found that all methods demonstrated consistency and lack of overfitting; however, in the small-sample size setting, AJIVE provided the most stable results. We provide an R package so that our framework and approach can be applied to evaluate other methods and datasets.

EFFECT OF HARVEST DATE AND SWATHING ON MOISTURE CONTENT AND CHLOROPHYLL CONTENT OF CANOLA SEED

1989 ◽  
Vol 69 (3) ◽  
pp. 925-928 ◽  
Author(s):  
S. CENKOWSKI ◽  
S. SOKHANSANJ ◽  
F. W. SOSULSKI
Keyword(s):  
Moisture Content ◽  
Standing Crop ◽  
Brassica Campestris ◽  
Seed Color ◽  
Canola Seed ◽  
Seed Moisture Content ◽  
Chlorophyll Contents ◽  
Green Seed ◽  
Seed Moisture ◽  
Canola Quality

A standing crop of Tobin canola-quality rapeseed (Brassica campestris L.) was sampled and swathed at 4-d intervals between 52 and 10% (wb) seed moisture content. Percentages of distinctly green seeds and chlorophyll contents of seeds were assessed. Swathing hastened loss of green seed color by about 2 d as compared to seeds on standing plants.Key words: Canola, harvest stage, green seed, chlorophyll

scholarly journals RFU derived LRs for activity level assignments using Bayesian Networks

2021 ◽  
Author(s):  
Peter Gill ◽  
Øyvind Bleka ◽  
Ane Elida Fonneløp
Keyword(s):  
Bayesian Networks ◽  
Sampling Error ◽  
Small Sample ◽  
Activity Level ◽  
Likelihood Ratios ◽  
Dna Profile ◽  
Transfer Data ◽  
Secondary Transfer ◽  
Logistic Regressions ◽  
Small Sample Sizes

A comparative study has been carried out, comparing two different methods to calculate activity level likelihood ratios (LRa) using Bayesian Networks. The first method uses the sub-source likelihood ratio (log10LRφ) as a 'quality indicator'. However, this has been criticised as introducing potential bias from population differences in allelic proportions. An alternative method has been introduced that is based upon the total RFU of a DNA profile that is adjusted, by multiplication, of the mixture proportion (Mx) which is calculated from quantitative probabilistic genotyping software (EuroForMix). Bayesian logistic regressions of direct transfer data showed that the two methods were comparable. Differences were attributed to sampling error, and small sample sizes of secondary transfer data. The Bayesian approach facilitates comparative studies by taking account of sampling error; it can easily be extended to compare different methods.

PLS/OPLS models in metabolomics: the impact of permutation of dataset rows on the K-fold cross-validation quality parameters

2015 ◽  
Vol 11 (1) ◽  
pp. 13-19 ◽  
Author(s):  
Mohamed N. Triba ◽  
Laurence Le Moyec ◽  
Roland Amathieu ◽  
Corentine Goossens ◽  
Nadia Bouchemal ◽  
...  
Keyword(s):  
Cross Validation ◽  
Quality Parameters ◽  
Quality Parameter ◽  
Parameter Values ◽  
The Impact ◽  
Fold Cross Validation

In some cases, quality parameter values (the number of significant components,Q2, CV-ANOVAp-value,…) of PLS/OPLS models calculated with K-fold cross-validation can be strongly determined by the composition of the different validation subsets.

Modeling and Simulation of the GA_LSSVM-Based Soft Measurement for Torque

2010 ◽  
Vol 44-47 ◽  
pp. 733-737
Author(s):  
Zhen Chen ◽  
An Yi Huang
Keyword(s):  
Support Vector Machine ◽  
Genetic Algorithms ◽  
Modeling And Simulation ◽  
Traditional Method ◽  
Measurement Accuracy ◽  
Cross Validation ◽  
Measurement Methods ◽  
Small Sample ◽  
Support Vector ◽  
Soft Measurement

Given the traditional method of direct measurement which is of high cost, difficult installation and poor reliability,this paper is presented a new model of the torque soft measure method based on least squares support vector machine using genetic algorithms optimization:genetic algorithms replaces the previous cross-validation method for model parameter’s optimization, in order to avoid the blindness of the parameter choices.Verified by simulation, the model can effectively address the deficiencies of traditional measurement methods and obtain better measurement accuracy and speed , possessing benefits of an outstanding ability for small sample study and being easy to compute.

scholarly journals The Pure Expectations Theory and Quarterly Interest Rate Premiums

2017 ◽  
Vol 7 (1) ◽  
pp. 161
Author(s):  
Samih Antoine Azar
Keyword(s):  
Interest Rate ◽  
Interest Rates ◽  
Risk Premium ◽  
Sampling Error ◽  
Small Sample ◽  
Short Term ◽  
Risk Premiums ◽  
Sample Bias ◽  
Conditional Tests ◽  
The Difference

The expectations theory posits that the long interest rate is an average of expected short term interest rates with the possibility of the existence of a risk premium. This paper looks upon fourteen samples of investments for which the difference in maturity is three months. All yields are actual yields and are adjusted to have the same maturities as the short rate. The evidence is strong for the pure expectations theory which predicts that the risk premiums are zero. This should not be surprising because the premium that we are looking for is merely 4 basis points per quarter. The contribution of this paper, besides giving support to the pure expectations theory, is to lay out the fundamental and basic methodology that one should follow in order to study other investments similar to ours. Both unconditional and conditional tests are performed. Because of sampling error and small-sample bias the unconditional tests may be preferable. 

scholarly journals The Optimization of Nitrogen Fertilization Regulates Crop Performance and Quality of Processing Tomato (Solanum lycopersicum L. cv. Heinz 3402)

Agronomy ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 715 ◽  
Author(s):  
Spyridon A. Petropoulos ◽  
Ângela Fernandes ◽  
Eustratios Xyrafis ◽  
Nikolaos Polyzos ◽  
Vasileios Antoniadis ◽  
...  
Keyword(s):  
Linoleic Acid ◽  
Chemical Composition ◽  
Solanum Lycopersicum ◽  
Quality Parameters ◽  
Soluble Solids ◽  
Control Treatment ◽  
Chlorophyll Contents ◽  
Processing Tomato ◽  
Solanum Lycopersicum L ◽  
Varied Response

In this study, we evaluated the effect of various fertilization regimes on processing tomato (Solanum lycopersicum L. cv. Heinz 3402) yield and quality by applying the following treatments: (i) control (C), (ii) conventional fertilizer (21-0-0, N-P-K) (CF), (iii) slow release nitrogen fertilizer 46-0-0 (SR), (iv) conventional fertilizer (21-0-0, N-P-K) + Zeolite (CFZ), and v) composted sheep manure (M). The results of the study showed that the SR and CFZ treatments resulted in the highest fruit yield per hectare compared to the rest of the fertilizer and the control treatments. Fruit firmness was higher for the treatments C, M and SR, while color parameters (chroma and hue angle) were higher for the C and M treatments, respectively. Moreover, the total soluble solids content (TSS; °Brix) was higher when manure (M) was applied. In terms of chemical composition, the total and individual tocopherols and sugars were the highest for the M and C treatments, respectively, whereas the oxalic, malic and total organic acid contents were the highest for the CFZ treatment. Moreover, the tested treatments showed a varied response in different antioxidant assays, although the M treatment exhibited a high antioxidant capacity in most of the assays, except for the β-carotene/linoleate assay. The carotenoid and chlorophyll contents were the highest for the control treatment. The main detected fatty acid was linoleic acid, followed by palmitic, oleic and α-linolenic acid, while the CFZ treatment had the highest content of polyunsaturated fatty acids (PUFA) due to its high content of linoleic acid. In conclusion, although the application of fertilizers increased yield, the quality parameters and chemical composition showed a varied response to the fertilization regime, especially the TSS content and juice pH and electric conductivity (EC), which are significant for the marketability of the final product.

Error-rate estimation in discriminant analysis of non-linear longitudinal data: A comparison of resampling methods

2016 ◽  
Vol 27 (4) ◽  
pp. 1153-1167 ◽  
Author(s):  
Rolando de la Cruz ◽  
Claudio Fuentes ◽  
Cristian Meza ◽  
Vicente Núñez-Antón
Keyword(s):  
Sample Size ◽  
Error Rate ◽  
Cross Validation ◽  
Numerical Study ◽  
Small Sample Size ◽  
Parametric Bootstrap ◽  
Small Sample ◽  
Misclassification Error ◽  
Non Linear ◽  
Misclassification Error Rate

Consider longitudinal observations across different subjects such that the underlying distribution is determined by a non-linear mixed-effects model. In this context, we look at the misclassification error rate for allocating future subjects using cross-validation, bootstrap algorithms (parametric bootstrap, leave-one-out, .632 and [Formula: see text]), and bootstrap cross-validation (which combines the first two approaches), and conduct a numerical study to compare the performance of the different methods. The simulation and comparisons in this study are motivated by real observations from a pregnancy study in which one of the main objectives is to predict normal versus abnormal pregnancy outcomes based on information gathered at early stages. Since in this type of studies it is not uncommon to have insufficient data to simultaneously solve the classification problem and estimate the misclassification error rate, we put special attention to situations when only a small sample size is available. We discuss how the misclassification error rate estimates may be affected by the sample size in terms of variability and bias, and examine conditions under which the misclassification error rate estimates perform reasonably well.

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