scholarly journals Prediction of Chromatographic Elution Order of Analytical Mixtures Based on Quantitative Structure-Retention Relationships and Multi-Objective Optimization

Molecules ◽  
2020 ◽  
Vol 25 (13) ◽  
pp. 3085
Author(s):  
Petar Žuvela ◽  
J. Jay Liu ◽  
Ming Wah Wong ◽  
Tomasz Bączek
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Retention Time ◽  
Elution Order ◽  
Mean Square ◽  
Quantitative Structure ◽  
Multi Objective Optimization ◽  
Structure Retention ◽  
Relative Root ◽  
Quantitative Structure Retention Relationships

Prediction of the retention time from the molecular structure using quantitative structure-retention relationships is a powerful tool for the development of methods in reversed-phase HPLC. However, its fundamental limitation lies in the fact that low error in the prediction of the retention time does not necessarily guarantee a prediction of the elution order. Here, we propose a new method for the prediction of the elution order from quantitative structure-retention relationships using multi-objective optimization. Two case studies were evaluated: (i) separation of organic molecules in a Supelcosil LC-18 column, and (ii) separation of peptides in seven columns under varying conditions. Results have shown that, when compared to predictions based on the conventional model, the relative root mean square error of the elution order decreases by 48.84%, while the relative root mean square error of the retention time increases by 4.22% on average across both case studies. The predictive ability in terms of both retention time and elution order and the corresponding applicability domains were defined. The models were deemed stable and robust with few to no structural outliers.

scholarly journals Quality of wood in the stands of poplar clones

2008 ◽  
Vol 54 (No. 1) ◽  
pp. 9-16
Author(s):  
R. Petráš ◽  
J. Mecko ◽  
V. Nociar
Keyword(s):  
Root Mean Square Error ◽  
Systematic Error ◽  
Mean Square Error ◽  
Statistical Tests ◽  
Mean Square ◽  
Poplar Clones ◽  
Mean Diameter ◽  
Empirical Material ◽  
Relative Root

The results obtained in research on the quality of raw timber by means of the structure of assortments for the stands of poplar clones Robusta and I-214 are presented in the paper. Models for an estimation of the structure of basic assortments of poplar stands were constructed separately for each clone in dependence on mean diameter, quality of stems, and damage to stems in the stand. The clone Robusta has higher proportions of higher-quality assortments than the clone I-214. The accuracy of models was determined on empirical material. It was confirmed by statistical tests that the models did not have a systematic error. The relative root mean-square error for main assortments of the clone I-214 is 15–27% and Robusta 13–24%.

scholarly journals BISAKAH MEMPEROLEH STATISTIK INDEKS HARGA KONSUMEN TINGKAT PROVINSI DI INDONESIA DENGAN KETELITIAN YANG LEBIH BAIK?

2020 ◽  
Vol 2019 (1) ◽  
pp. 297-306
Author(s):  
Andi Okta Fengki ◽  
Khairil Anwar Notodiputro ◽  
Kusman Sadik
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Small Area ◽  
Price Index ◽  
Small Area Estimation ◽  
Consumer Price Index ◽  
Mean Square ◽  
Area Estimation ◽  
Relative Root

Statistik indeks harga konsumen (IHK) atau consumer price index (CPI) juga dibutuhkan pada tingkat provinsi di era desentralisasi saat ini. Ketika IHK ingin diduga pada tingkat provinsi, permasalahan ukuran contoh kecil (small area) muncul karena survei untuk menghasilkan IHK ini di Indonesia dirancang untuk tingkat nasional. Akan tetapi, informasi dari statistik IHK 82 kota dapat membantu untuk menduga IHK provinsi. Metode pendugaan area kecil atau small area estimation (SAE) dapat diterapkan sebagai solusi untuk meningkatkan ketelitian hasil pendugaan langsung. Pada penelitian ini IHK provinsi diduga menggunakan model Fay-Herriot (FH). Hasilnya menunjukan bahwa model FH dapat menghasilkan statistik IHK provinsi dengan ketelitian yang lebih baik dari pendugaan langsung. Hal ini ditunjukan dengan nilai average relative root mean square error (ARRMSE) penduga FH IHK provinsi yang lebih kecil dari penduga langsungnya.

scholarly journals Quantitative Structure–Retention Relationships with Non-Linear Programming for Prediction of Chromatographic Elution Order

2019 ◽  
Vol 20 (14) ◽  
pp. 3443 ◽  
Author(s):  
Liu ◽  
Alipuly ◽  
Bączek ◽  
Wong ◽  
Žuvela
Keyword(s):  
Linear Programming ◽  
Case Studies ◽  
Retention Time ◽  
Reversed Phase ◽  
Elution Order ◽  
Quantitative Structure ◽  
Reversed Phase Liquid Chromatography ◽  
Non Linear Programming ◽  
Phase Liquid ◽  
Structure Retention

In this work, we employed a non-linear programming (NLP) approach via quantitative structure–retention relationships (QSRRs) modelling for prediction of elution order in reversed phase-liquid chromatography. With our rapid and efficient approach, error in prediction of retention time is sacrificed in favor of decreasing the error in elution order. Two case studies were evaluated: (i) analysis of 62 organic molecules on the Supelcosil LC-18 column; and (ii) analysis of 98 synthetic peptides on seven reversed phase-liquid chromatography (RP-LC) columns with varied gradients and column temperatures. On average across all the columns, all the chromatographic conditions and all the case studies, percentage root mean square error (%RMSE) of retention time exhibited a relative increase of 29.13%, while the %RMSE of elution order a relative decrease of 37.29%. Therefore, sacrificing %RMSE(tR) led to a considerable increase in the elution order predictive ability of the QSRR models across all the case studies. Results of our preliminary study show that the real value of the developed NLP-based method lies in its ability to easily obtain better-performing QSRR models that can accurately predict both retention time and elution order, even for complex mixtures, such as proteomics and metabolomics mixtures.

scholarly journals Estimation of coarse dead wood stocks in intact and degraded forests in the Brazilian Amazon using airborne lidar

2019 ◽  
Vol 16 (17) ◽  
pp. 3457-3474 ◽  
Author(s):  
Marcos A. S. Scaranello ◽  
Michael Keller ◽  
Marcos Longo ◽  
Maiza N. dos-Santos ◽  
Veronika Leitold ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Dead Wood ◽  
Model Performance ◽  
Forest Carbon ◽  
Airborne Lidar ◽  
Mean Square ◽  
Improved Model ◽  
Relative Root

Abstract. Coarse dead wood is an important component of forest carbon stocks, but it is rarely measured in Amazon forests and is typically excluded from regional forest carbon budgets. Our study is based on line intercept sampling for fallen coarse dead wood conducted along 103 transects with a total length of 48 km matched with forest inventory plots where standing coarse dead wood was measured in the footprints of larger areas of airborne lidar acquisitions. We developed models to relate lidar metrics and Landsat time series variables to coarse dead wood stocks for intact, logged, burned, or logged and burned forests. Canopy characteristics such as gap area produced significant individual relations for logged forests. For total fallen plus standing coarse dead wood (hereafter defined as total coarse dead wood), the relative root mean square error for models with only lidar metrics ranged from 33 % in logged forest to up to 36 % in burned forests. The addition of historical information improved model performance slightly for intact forests (31 % against 35 % relative root mean square error), not justifying the use of a number of disturbance events from historical satellite images (Landsat) with airborne lidar data. Lidar-derived estimates of total coarse dead wood compared favorably with independent ground-based sampling for areas up to several hundred hectares. The relations found between total coarse dead wood and variables quantifying forest structure derived from airborne lidar highlight the opportunity to quantify this important but rarely measured component of forest carbon over large areas in tropical forests.

scholarly journals Estimation of Coarse Woody Debris Stocks in Intact and Degraded Forests in the Brazilian Amazon Using Airborne Lidar

2019 ◽  
Author(s):  
Marcos A. S. Scaranello ◽  
Michael Keller ◽  
Marcos Longo ◽  
Maiza N. dos-Santos ◽  
Veronika Leitold ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Dead Wood ◽  
Woody Debris ◽  
Model Performance ◽  
Forest Carbon ◽  
Airborne Lidar ◽  
Mean Square ◽  
Relative Root

Abstract. Coarse dead wood is an important component of forest carbon stocks, but it is rarely measured in Amazon forests and is typically excluded from regional forest carbon budgets. Our study is based on line intercept sampling for fallen coarse dead wood conducted along 103 transects with a total length of 48 km matched with forest inventory plots where standing coarse dead wood was measured in the footprints of larger areas of airborne lidar acquisitions. We developed models to relate lidar metrics and Landsat time series variables to coarse dead wood stocks for intact, logged, and burned or logged and burned forests. Canopy characteristics such as gap area produced significant individual relations for logged forests. For total fallen plus standing coarse dead wood (hereafter defined as total coarse dead wood), the relative root mean square error for models with only lidar metrics ranged from 33 % in logged forest to up to 36 % in burned forests. The addition of historical information improved model performance slightly for intact forests (31 % against 35 % relative root mean square error), not justifying the use of number of disturbances events from historical satellite images (Landsat) with airborne lidar data. Lidar-derived estimates of total coarse dead wood compared favorably to independent ground-based sampling for areas up to several hundred hectares. The relations found between total coarse dead wood and structural variables derived from airborne lidar highlight the opportunity to quantify this important, but rarely measured component of forest carbon over large areas in tropical forests.

scholarly journals Implementasi Empirical Best Linear Unbiased Prediction Fay-Herriot dalam Menduga Rata-Rata Pengeluaran per Kapita Level Kecamatan di Provinsi Jawa Timur dengan Tambahan Informasi Cluster

2021 ◽  
Vol 2021 (1) ◽  
pp. 70-79
Author(s):  
Mochamad Wildan Maulana ◽  
Ika Yuni Wulansari
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Small Area ◽  
Best Linear Unbiased Prediction ◽  
Mean Square ◽  
Linear Unbiased Prediction ◽  
Area Estimation ◽  
Best Linear Unbiased ◽  
Relative Root ◽  
Unbiased Prediction

Salah satu indikator ekonomi yang dapat mengukur tingkat kesejahteraan adalah kemiskinan. Penduduk tergolong miskin apabila rata-rata pengeluaran per kapita setiap bulannya dibawah garis kemiskinan. Provinsi Jawa Timur terpilih sebagai lokus penelitian dikarenakan memiliki jumlah penduduk miskin tertinggi di Indonesia selama satu dekade terakhir. Data yang digunakan berasal dari Susenas Maret 2019 dan Podes 2018 dengan 666 observasi level kecamatan. Upaya pengentasan kemiskinan memerlukan data yang akurat dan menjangkau hingga wilayah terkecil. Akan tetapi tidak semua wilayah memiliki sampel yang cukup atau bahkan tidak memiliki sampel sama sekali. Hal ini tidak memungkinkan untuk melakukan estimasi langsung. Oleh karena itu dibutuhkan metode statistik untuk dapat mengestimasi area kecil dengan baik. Metode yang dapat digunakan untuk menduga area kecil adalah Small Area Estimation (SAE). Penelitian ini menggunakan metode SAE dengan Model Empirical Best Linear Unbiased Prediction Fay-Herriot. Hasil yang diperoleh bahwa metode SAE dapat memberikan pendugaan yang lebih baik dibanding estimasi langsung yang ditunjukan dengan nilai Relative Root Mean Square Error (RRMSE) lebih kecil dibanding estimasi langsung. Estimasi pada non-sample area dilakukan dengan memanfaatkan informasi cluster.

scholarly journals Projeção do diâmetro de árvores de Hevea brasiliensis por meio de modelos mistos não lineares

2020 ◽  
Vol 12 (1) ◽  
pp. 31-41
Author(s):  
Sandro Da Silva Barros ◽  
Jeferson Pereira Martins Silva ◽  
Evandro Ferreira da Silva ◽  
Jeangelis Silva Santos ◽  
Adriano Ribeiro de Mendonça ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Hevea Brasiliensis ◽  
Mean Square ◽  
Espírito Santo ◽  
Espirito Santo ◽  
Relative Root

O estudo teve como objetivo avaliar a acurácia de modelos mistos não lineares na projeção do crescimento em diâmetro de árvores individuais de Hevea brasiliensis. A área de estudo está localizada no município de Linhares, Espírito Santo e possui área total de 784 m². As árvores estão plantadas no espaçamento de 2,0 x 2,0 m. As medições do diâmetro a 1,3 m do solo das árvores foram realizadas anualmente dos dois aos 14 anos de idade. Foram ajustados três modelos não lineares considerando efeitos fixos e efeitos aleatórios, sendo estes os modelos de Pienaar e Schiver, Mitscherlich e Chapman-Richards. A avaliação das estimativas geradas pelos modelos mistos e fixos foi realizada, tanto para o ajuste como para a projeção, com base no coeficiente de correlação (r), viés [V (%)], relative root mean square error [RMSE(%)]. O desempenho dos modelos de regressão quando considerado também efeitos aleatórios foi superior aos modelos de efeito fixo, sendo capaz de modelar a heterocedasticidade e a autocorrelação observada na análise gráfica dos ajustes dos modelos com efeito fixo.  O RMSE mais baixo dos modelos de efeito fixo foi 4,53% e para o efeito misto foi 3,71%. Quando comparado o valor de RMSE da projeção, o menor valor obtido com o modelo de efeito fixo foi de 22% e com efeito misto de 4,38%. A utilização de modelos de efeitos fixos e aleatórios resultou em ganhos significativos de acurácia, boa aplicação em dados agrupados e permitiu modelar a heterocedasticidade e a autocorrelação dos dados.

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