An analytical representation of the geomagnetic field in Canada for 1975. Part I: The main field

1977 ◽  
Vol 14 (3) ◽  
pp. 477-487 ◽  
Author(s):  
E. Dawson ◽  
L. R. Newitt
Keyword(s):  
Root Mean Square ◽  
Geomagnetic Field ◽  
Input Data ◽  
Least Square Method ◽  
Magnetic Activity ◽  
Auroral Zone ◽  
Least Square ◽  
Mean Square ◽  
Main Field ◽  
Laborious Method

Approximately 51 000 observations made between 1955 and 1973 were used to produce the magnetic charts of Canada for 1975. A least-square method was used to derive sixth degree polynominals for the rectangular components X(north), Y(east), and Z(vertical). Cubic time terms were included to eliminate the customary laborious method of updating the data to the desired epoch.To reflect desired wavelengths of approximately 1000 km it was necessary to divide the map area of 31 × 106 km2 into quadrants with a unifying overlap of 10%. For consistency, X and Y were analysed together using Maxwell's curl-free relation (curl F)Z = 0. All data were weighted according to type and age.From the derived polynomials, values of D (magnetic declination), H (horizontal intensity), and Z were computed at 2° geographic grid intervals. These values were used to derive the final charts using a standard contouring package.The overall root mean square (rms) fit of the model to the input data is 174 nT. In the auroral zone, an area of high magnetic activity, the fit is generally poor. The fit to over 8500 supplementary observations is 170 nT, confirming the reliability of the model.

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

scholarly journals Authentication of Patin Fish Oil (Pangasius micronemus) using FTIR Spectroscopy Combined with Chemometrics

2020 ◽  
pp. 23
Author(s):  
Anggita Rosiana Putri ◽  
Abdul Rohman ◽  
Sugeng Riyanto ◽  
Widiastuti Setyaningsih
Keyword(s):  
Root Mean Square Error ◽  
Fish Oil ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square

Authentication of Patin fish oil (MIP) is essential to prevent adulteration practice, to ensure quality, nutritional value, and product safety. The purpose of this study is to apply the FTIR spectroscopy combined with chemometrics for MIP authentication. The chemometrics method consists of principal component regression (PCR) and partial least square regression (PLSR). PCR and PLSR were used for multivariate calibration, while for grouping the samples using discriminant analysis (DA) method. In this study, corn oil (MJ) was used as an adulterate. Twenty-one mixed samples of MIP and MJ were prepared with the adulterate concentration range of 0-50%. The best authentication model was obtained using the PLSR technique using the first derivative of FTIR spectra at a wavelength of 650-3432 cm-1. The coefficient of determination (R2) for calibration and validation was obtained 0.9995 and 1.0000, respectively. The value of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP) were 0.397 and 0.189. This study found that the DA method can group the samples with an accuracy of 99.92%.

scholarly journals The Accuracy of Real-Time hmF2 Estimation from Ionosondes

2020 ◽  
Vol 12 (17) ◽  
pp. 2671
Author(s):  
Carlo Scotto ◽  
Dario Sabbagh
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Root Mean Square ◽  
Magnetic Activity ◽  
Mean Square ◽  
Incoherent Scatter Radar ◽  
Incoherent Scatter ◽  
Scatter Radar ◽  
Mean Square Errors

A total of 4991 ionograms recorded from April 1997 to December 2017 by the Millstone Hill Digisonde (42.6°N, 288.5°E) were considered, with simultaneous Ne(h)[ISR] profiles recorded by the co-located Incoherent Scatter Radar (ISR). The entire ionogram dataset was scaled with both the Autoscala and ARTIST programs. The reliability of the hmF2 values obtained by ARTIST and Autoscala was assessed using the corresponding ISR values as a reference. Average errors Δ and the root mean square errors RMSE were computed for the whole dataset. Data analysis shows that both the Autoscala and ARTIST systems tend to underestimate hmF2 values with |Δ| in all cases less than 10 km. For high magnetic activity ARTIST offers better accuracy than Autoscala, as evidenced by RMSE[ARTIST] < RMSE[Autoscala], under both daytime and nighttime conditions, and considering all hours of the day. Conversely, under low and medium magnetic activity Autoscala tends to estimate hmF2 more accurately than the ARTIST system for both daytime and nighttime conditions, when RMSE[Autoscala] < RMSE[ARTIST]. However, RMSE[Autoscala] slightly exceeds RMSE[ARTIST] for the day as a whole. RMSE values are generally substantial (RMSE > 16 km in all cases), which places a limit on the results obtainable with real-time models that ingest ionosonde data.

Multivariate analysis of three chemometric algorithms on rapid prediction of some important quality parameters of crude shea butter using Fourier transform-near infrared spectroscopy

2019 ◽  
Vol 27 (3) ◽  
pp. 220-231
Author(s):  
Emmanuel Amomba Seweh ◽  
Zou Xiaobo ◽  
Feng Tao ◽  
Shi Jiachen ◽  
Haroon Elrasheid Tahir ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Cross Validation ◽  
Partial Least Square ◽  
Least Square ◽  
Mean Square ◽  
Saponification Value ◽  
Peroxide Values

A comparative study of three chemometric algorithms combined with NIR spectroscopy with the aim of determining the best performing algorithm for quantitative prediction of iodine value, saponification value, free fatty acids content, and peroxide values of unrefined shea butter. Multivariate calibrations were developed for each parameter using supervised partial least squares, interval partial least squares, and genetic-algorithm partial least square regression methods to establish a linear relationship between standard reference and the Fourier transformed-near infrared predicted. Results showed that genetic-algorithm partial least square models were superior in predicting iodine value and saponification value while partial least squares was excellent in predicting free fatty acids content and peroxide values. The nine-factor genetic-algorithm partial least square iodine value calibration model for predicting iodine value yielded excellent ( R2 cal = 0.97), ( R2 val = 0.97), low (root mean square error of cross-validation = 0.26), low (root mean square error of Prediction = 0.23), and (ratio of performance to deviation = 6.41); for saponification value, the nine-factor genetic-algorithm partial least square saponification value calibration model had excellent R2 cal (0.97), R2 val (0.99); low root mean square error of cross-validation (0.73), low root mean square error of Prediction (0.53), and (ratio of performance to deviation = 8.27); while for free fatty acids, the 11-factor partial least square free fatty acids produced very high R2 cal (0.97) and R2 val (0.97) with very low root mean square error of cross-validation (0.03), low root mean square error of Prediction (0.04) and (ratio of performance to deviation = 5.30) and finally for peroxide values, the 11-factor partial least square peroxide values calibration model obtained excellent R2 cal (0.96) and R2val (0.98) with low root mean square error of cross-validation (0.05), low root mean square error of Prediction (0.04), and (ratio of performance to deviation = 5.86). The built models were accurate and robust and can be reliably applied in developing a handheld quality detection device for screening, quality control checks, and prediction of shea butter quality on-site.

scholarly journals Magnetic local time dependence of geomagnetic disturbances contributing to the AU and AL indices

2011 ◽  
Vol 29 (4) ◽  
pp. 673-678 ◽  
Author(s):  
S. Tomita ◽  
M. Nosé ◽  
T. Iyemori ◽  
H. Toh ◽  
M. Takeda ◽  
...  
Keyword(s):  
Local Time ◽  
Geomagnetic Field ◽  
Field Data ◽  
Geomagnetic Latitude ◽  
Magnetic Activity ◽  
Auroral Zone ◽  
Magnetic Local Time ◽  
Geomagnetic Disturbances ◽  
Current Systems ◽  
Local Time Dependence

Abstract. The Auroral Electrojet (AE) indices, which are composed of four indices (AU, AL, AE, and AO), are calculated from the geomagnetic field data obtained at 12 geomagnetic observatories that are located in geomagnetic latitude (GMLAT) of 61.7°–70°. The indices have been widely used to study magnetic activity in the auroral zone. In the present study, we examine magnetic local time (MLT) dependence of geomagnetic field variations contributing to the AU and AL indices. We use 1-min geomagnetic field data obtained in 2003. It is found that both AU and AL indices have two ranges of MLT (AU: 15:00–22:00 MLT, ~06:00 MLT; and AL: ~02:00 MLT, 09:00–12:00 MLT) contributing to the index during quiet periods and one MLT range (AU: 15:00–20:00 MLT, and AL: 00:00–06:00 MLT) during disturbed periods. These results are interpreted in terms of various ionospheric current systems, such as, Sqp, Sq, and DP2.

scholarly journals Attenuated Total Reflectance-FTIR Spectra Combined with Multivariate Calibration and Discrimination Analysis for Analysis of Patchouli Oil Adulteration

2019 ◽  
Vol 20 (1) ◽  
pp. 1
Author(s):  
Zaki Fahmi ◽  
Mudasir Mudasir ◽  
Abdul Rohman
Keyword(s):  
Root Mean Square Error ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Multivariate Calibration ◽  
Ftir Spectra ◽  
Partial Least Square ◽  
Least Square ◽  
Calibration Model ◽  
Mean Square

The adulteration of high priced oils such as patchouli oil with lower price ones is motivated to gain the economical profits. The aim of this study was to use FTIR spectroscopy combined with chemometrics for the authentication of patchouli oil (PaO) in the mixtures with Castor Oil (CO) and Palm Oil (PO). The FTIR spectra of PaO and various vegetable oils were scanned at mid infrared region (4000–650 cm–1), and were subjected to principal component analysis (PCA). Quantitative analysis of PaO adulterated with CO and PO were carried out with multivariate calibration of Partial Least Square (PLS) regression. Based on PCA, PaO has the close similarity to CO and PO. From the optimization results, FTIR normal spectra in the combined wavenumbers of 1200–1000 and 3100–2900 cm–1 were chosen to quantify PaO in PO with coefficient of determination (R2) value of 0.9856 and root mean square error of calibration (RMSEC) of 4.57% in calibration model. In addition, R2 and root mean square error of prediction (RMSEP) values of 0.9984 and 1.79% were obtained during validation, respectively. The normal spectra in the wavenumbers region of 1200–1000 cm–1 were preferred to quantify PaO in CO with R2 value of 0.9816 and RMSEC of 6.89% in calibration, while in validation model, the R2 value of 0.9974 and RMSEP of 2.57% were obtained. Discriminant analysis was also successfully used for classification of PaO and PaO adulterated with PO and CO without misclassification observed. The combination of FTIR spectroscopy and chemometrics provided an appropriate model for authentication study of PaO adulterated with PO and CO.

scholarly journals Penerapan Generalized Regression Neural Networks untuk Memprediksi Produksi Padi Terhadap Perubahan Iklim

2017 ◽  
Vol 2 (2) ◽  
pp. 117 ◽  
Author(s):  
Muhammad Alkaff ◽  
Yuslena Sari
Keyword(s):  
Neural Networks ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Input Data ◽  
Mean Square ◽  
Generalized Regression Neural Networks ◽  
Data Output ◽  
Generalized Regression

Padi sebagai bahan makanan pokok utama bagi masyarakat Indonesia merupakan tanaman pangan yang rentan terhadap perubahan iklim. Pendataan dan perhitungan ramalan hasil produksi padi sangat diperlukan untuk mendukung kebijakan yang berkaitan dengan ketahanan pangan. Penelitian ini bertujuan untuk melakukan peramalan terhadap produksi padi di Kabupaten Barito Kuala sebagai kabupaten penghasil padi terbesar di Kalimantan Selatan dengan menggunakan data iklim sebagai input. Data iklim yang digunakan berasal dari Stasiun Meteorologi Syamsudin Noor, sedangkan sebagai data output adalah data produksi padi dari Badan Pusat Statistika (BPS) Provinsi Kalimantan Selatan. Metode yang digunakan untuk melakukan peramalan produksi padi adalah Generalized Regression Neural Networks (GRNN). Dari hasil pengujian didapatkan nilai Root Mean Square Error (RMSE) sebesar 0,296 dengan menggunakan parameter smoothness bernilai 1.Kata kunci: padi, iklim, Barito Kuala, GRNN, RMSE

scholarly journals Prediction Is The Determination Of The Levels Of Total Flavonoid Extract Of 70% Ethanol On The Leaves Of Rose Guava (Syzigium Jambos (L.) Alston) By Using A Combination Of FTIR And Chemometrics Plsr (Partial Least Square Regression)

2019 ◽  
Author(s):  
Nur Tsalits Fahman Mughni
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Partial Least Square ◽  
Least Square ◽  
Total Flavonoid ◽  
Partial Least Square Regression ◽  
Mean Square ◽  
Syzygium Jambos ◽  
Least Square Regression

Rose Guava (Syzygium jambos (L.) Alston) is known to have flavonoid compounds. Where flavonoids are natural product compounds that have uses as a treatment. An alternative method used to determine the prediction of total flavonoid levels is a combination of FTIR and Chemometrics (Partial least square regression) through the parameter RMSEC value (Root mean square error of calibration), RMSECV (Root mean square error of validation), PRESS (Predicted residual error sum of squares) and R2. The results of the combination of FTIR and CEMOMETRICS (Partial least square regression) on the prediction of total flavonoid levels can provide a good model with calibration obtained R2 value0.9999, RMSEC 0.0000637 and the results of vaid obtained PRESS value0.19225, R2 0.978 and RMSECV 0.041 . Based on the literature, the model can be said to be good if the RMSEC and RMSECV values are smaller than R2.

scholarly journals MODEL SIMULASI PRAKIRAAN CH BULANAN PADA WILAYAH RIAU DENGAN MENGGUNAKAN INPUT DATA SOI, SST, NINO 3.4, DAN IOD

2013 ◽  
Vol 14 (2) ◽  
pp. 95
Author(s):  
Aristya Ardhitama ◽  
Rias Sholihah
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Input Data ◽  
Model Output ◽  
Mean Square

INTISARI  Saat ini, kondisi cuaca di Pekanbaru dewasa ini begitu cepat perubahannya sehingga sulit diprediksi. Fenomena ini menuntut  prakiraan untuk meningkatkan kualitas hasil prakiraan sehingga lebih cepat, tepat, dan akurat untuk hasil yang diinginkan tersebut. Simulasi prakiraan jumlah curah hujan dengan menggunakan input data prediktor SOI, SST, Nino 3.4 dan IOD dengan parameter cuaca di Kota Pekanbaru telah  dilakukan menggunakan model persamaan regresi linear berganda. Prediktor tersebut digunakan untuk memprediksi curah hujan (CH) tahun 2011 dan 2012.Selain itu berfungsi untuk mengecek kebenaran hasil prakiraan jumlah curah hujan dengan model persamaan regresi linear berganda menggunakan rumus Root Mean Square Error (RMSE) dan Standar Deviasi (SD).Serta kajian penelitian ini berfungsi untuk membuktikan faktor prediktor (SOI, SST, Nina 3.4 dan IOD) yang paling mempengaruhi kondisi curah hujan di Pekanbaru.Data yang digunakan dalam kajian ini adalah data curah hujan sebaran normal dari tahun 1981-2010 pada stasiun wilayah Pekanbaru-Provinsi Riau. Data jumlah curah hujan tahun 2011 dan 2012 hasil observasi dianggap sebagai pembanding untuk verifikasi dan validasi nilai curah hujan (CH) hasil model output simulasi.Berdasarkan penelitian yang telah dilakukan maka dapat disimpulkan bahwa data dari SOI, SST, Nino 3.4 dan IOD memiliki pengaruh terhadap curah hujan di wilayah Pekanbaru Provinsi Riau.Kondisi cuaca terutama curah hujan untuk wilayah Pekanbaru dipengaruhi oleh factor global, regional dan lokal.Dari hasil penelitian terlihat hubungan yang memiliki tingkat korelasi yang tinggi terhadap curah hujan (CH) adalah prediktor SOI.Selain itu, dengan menggunakan RMSE membuktikan bahwa nilai kebenaran pada tahun 2011 lebih baik dibandingkan pada tahun 2012.  

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