scholarly journals Nonlinear Least Square Based on Control Direction by Dual Method and Its Application

2016 ◽  
Vol 2016 ◽  
pp. 1-5
Author(s):  
Zhengqing Fu ◽  
Guolin Liu ◽  
Lanlan Guo ◽  
Weike Liu ◽  
Hua Guo
Keyword(s):  
Quantitative Analysis ◽  
Root Mean Square Error ◽  
Qualitative Analysis ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Least Square ◽  
Dual Method ◽  
Dual Model ◽  
Mean Square ◽  
Primal Dual

A direction controlled nonlinear least square (NLS) estimation algorithm using the primal-dual method is proposed. The least square model is transformed into the primal-dual model; then direction of iteration can be controlled by duality. The iterative algorithm is designed. The Hilbert morbid matrix is processed by the new model and the least square estimate and ridge estimate. The main research method is to combine qualitative analysis and quantitative analysis. The deviation between estimated values and the true value and the estimated residuals fluctuation of different methods are used for qualitative analysis. The root mean square error (RMSE) is used for quantitative analysis. The results of experiment show that the model has the smallest residual error and the minimum root mean square error. The new estimate model has effectiveness and high precision. The genuine data of Jining area in unwrapping experiments are used and the comparison with other classical unwrapping algorithms is made, so better results in precision aspects can be achieved through the proposed algorithm.

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%.

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 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 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 Development of calibration models for rapid determination of moisture content in rubber sheets using portable near-infrared spectrometers

2020 ◽  
Vol 13 (02) ◽  
pp. 2050009
Author(s):  
Amorndej Puttipipatkajorn ◽  
Amornrit Puttipipatkajorn
Keyword(s):  
Moisture Content ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared ◽  
Least Square ◽  
Mean Square ◽  
Ann Model ◽  
Rubber Sheet ◽  
Calibration Models

Rubber sheets are one of the primary products of natural rubber and are the main raw material in various rubber industries. The quality of a rubber sheet can be visually examined by holding it against clear light to inspect for any specks and impurities inside, but its moisture content is difficult to evaluate based on a visual inspection and this might lead to unfair trading. Herein, we developed a rapid, robust and nondestructive near-infrared spectroscopy (NIRS)-based method for moisture content determination in rubber sheets. A set of 300 rubber sheets were divided into a calibration (200 samples) and prediction groups (100 samples). The calibration set was used to develop NIRS calibration equation using different calibration models, Partial Least Square Regression (PLSR), Least Square Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN). Among the models investigated, the ANN model with the first derivative of spectral preprocessing presented the best prediction with a coefficient of determination ([Formula: see text] of 0.993, root mean square error of calibration (RMSEC) of 0.126% and root mean square error of prediction (RMSEP) of 0.179%. The results indicated that the proposed NIRS-ANN model will be able to reduce human error and provide a highly accurate estimate of the moisture content in a rubber sheet compared to traditional wet chemistry estimation methods according to AOAC standards.

Nondestructive Method for Analysis of the Soybean Quality

2012 ◽  
Vol 8 (4) ◽  
Author(s):  
Yan Dong ◽  
Shi You Qu
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Crude Protein ◽  
Near Infrared ◽  
Nondestructive Method ◽  
Partial Least Square ◽  
Least Square ◽  
Mean Square ◽  
Pre Treatment

Abstract Fourier transform near infrared (NIR) spectra combined with chemometric methods was proposed to the analysis of the crude protein and fat contents in whole-kernel soybean. The calibration models were established by partial least square. After optimizing spectral pre-treatment, the determination coefficient (R2) of the crude protein and fat were 0.971, 0.970, and root mean square error of calibration (RMSEC) were 0.610, 0.365，respectively. For the prediction samples of the crude protein and fat, root mean square error of prediction (RMSEP) were 0.766, 0.420, respectively. The analytical results showed that NIR spectra had significant potential as a rapid and nondestructive method for the crude protein and fat contents in soybean.

scholarly journals Chemometric Analysis of UV-Visible Spectral Fingerprints for the Discrimination and Quantification of Clinical Anthracycline Drug Preparation Used in Oncology

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Aimen El Orche ◽  
Casimir Adade Adade ◽  
Hafid Mefetah ◽  
Amine Cheikh ◽  
Khalid Karrouchi ◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Drug Preparation ◽  
Mean Square ◽  
Chemotherapeutic Drugs ◽  
Uv Visible

In clinical treatment, the analytical quality assessment of the delivery of chemotherapeutic preparations is required to guarantee the patient’s safety regarding the dose and most importantly the appropriate anticancer drug. On its own, the development of rapid analytical methods allowing both qualitative and quantitative control of the formulation of prepared solutions could significantly enhance the hospital’s workflow, reducing costs, and potentially providing optimal patient care. UV-visible spectroscopy is a nondestructive, fast, and economical technique for molecular characterization of samples. A discrimination and quantification study of three chemotherapeutic drugs doxorubicin, daunorubicin, and epirubicin was conducted, using clinically relevant concentration ranges prepared in 0.9% NaCl solutions. The application of the partial least square discriminant analysis PLS-DA method on the UV-visible spectral data shows a perfect discrimination of the three drugs with a sensitivity and specificity of 100%. The use of partial least square regression PLS shows high quantification performance of these molecules in solution represented by the low value of root mean square error of calibration (RMSEC) and root mean square error of cross validation (RMSCECV) on the one hand and the high value of R -square on the other hand. This study demonstrated the viability of UV-visible fingerprinting (routine approach) coupled with chemometric tools for the classification and quantification of chemotherapeutic drugs during clinical preparation.

scholarly journals QUANTITATIVE ANALYSISOF STEVIOSIDE AND REBAUDIOSIDE A IN STEVIAREBAUDIANA LEAVES USING INFRARED SPECTROSCOPY AND MULTIVARIATE CALIBRATION

2019 ◽  
Vol 11 (1) ◽  
pp. 38 ◽  
Author(s):  
Yohannes Martono ◽  
Abdul Rohman
Keyword(s):  
Multivariate Analysis ◽  
Quantitative Analysis ◽  
Root Mean Square Error ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Rebaudioside A ◽  
Pls Regression ◽  
Mean Square ◽  
Wavenumber Region

Objective: The objective of this research was to develop Fourier transform infrared (FTIR) spectroscopy in combination with multivariate analysis of partial least square (PLS) regression for quantitative analysis of stevioside and rebaudioside A in S. rebaudiana leaves extract.Methods: Stevia rebaudiana leaves with various ages were obtained from several high hills in Central Java, Indonesia. The extract samples were scanned using FTIR spectrophotometer in wavenumbers region of 4000–650 cm-1. PLS calibration model was established by plotting the actual value of stevioside and rebaudioside A as determined by high-performance liquid chromatography (HPLC) and FTIR predicted value. The performance of PLS regression was evaluated using coefficient determination (R2), root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP).Results: PLS regression for stevioside determination was successfully established using the combined wavenumber region of 671–1450 and 3279-3301 cm-1. PLS regression revealed R2of 0.9952with RMSEC value of0.84%. Meanwhile, rebaudioside A was determined at wavenumber region of 921–1508 cm-1using normal spectra. PLS model revealed R2 and RMSEC of 0.9911 and 0.70%, respectively.Conclusion: FTIR spectroscopy in combination with multivariate analysis of PLS regression could be used as an alternative method for quantitative analysis ofstevioside and rebaudioside A in S. rebaudiana leaves.

scholarly journals Spektrofotometer Quali-Vis dan Kemometrika untuk Klasifikasi Kualitas Daun Wungu (Graptophyllum pictum)

2018 ◽  
Vol 3 (3) ◽  
pp. 89-101
Author(s):  
Silvi Agusri Putri ◽  
Rudi Heryanto ◽  
Eti Rohaeti
Keyword(s):  
Discriminant Analysis ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Principle Component Analysis ◽  
Component Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Mean Square ◽  
Principle Component

Spektrofotometer Quali-Vis digunakan untuk mengklasifikasikan 3 sampel daun wungu dengan usia panen berbeda. Penelitian ini bertujuan mengevaluasi model klasifikasi menggunakan spektrofotometer Quali-Vis secara kualitatif. Model dikembangkan dengan akuisisi data menggunakan principle component analysis (PCA) dan partial least square-discriminant analysis (PLS-DA). Karakteristik awal sampel daun wungu ditentukan dengan analisis sidik jari kromatografi lapis tipis (KLT) yang menampilkan profil yang berbeda pada masing-masing usia panen. Perbedaan ini menjadi dasar klasifikasi untuk model PLS-DA yang terdiri atas model usia 1 bulan, 2 bulan, dan 3 bulan, yang menghasilkan rerata R2pred = 0.7794 dan root mean square error predict (RMSEP) = 0.2180. Model divalidasi dengan menentukan nilai parameter sensitivitas, spesifitas, presisi, dan akurasi yang menghasilkan nilai rerata masing-masing 0.95, 0.99, 0.98, dan 0.98 kesesuaian prediksi. Nilai-nilai ini menunjukkan bahwa model yang dikembangkan memiliki kemampuan klasifikasi yang cukup baik.

scholarly journals APPLICATION OF FTIR SPECTRA COMBINED WITH CHEMOMETRICS FOR ANALYSIS OF CANDLENUT OIL ADULTERATION

2018 ◽  
Vol 10 (5) ◽  
pp. 54
Author(s):  
Fitri Yuliani ◽  
Sugeng Riyanto ◽  
Abdul Rohman
Keyword(s):  
Root Mean Square Error ◽  
Ftir Spectroscopy ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Corn Oil ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square

Objective: The aim of this study was to use FTIR spectroscopy in combination with chemometrics techniques for quantification and classification of candlenut oil (CnO) from oil adulterants, namely sunflower oil (SFO), soybean oil (SyO), and corn oil (CO).Methods: The spectra of all samples were scanned using Fourier Transform Infrared (FTIR) Spectrophotometer using attenuated total reflectance (ATR) as sampling technique at mid infrared region (4000-650 cm-1). Multivariate calibrations of principle component regression (PCR) and partial least square regression (PLSR) were used for quantitative models to predict the levels of CnO in the binary mixtures with SFO, SyO, and CO.Results: The results showed that CnO in SFO was best quantified using PCR at wavenumbers region of 3100-2800 cm-1. Quantitative analysis of CnO in SyO was carried out using PLSR with normal spectra mode using combined wavenumbers of 1765-1625 and 839-663 cm-1, while CnO in CO was analyzed quantitatively using normal spectra at wavenumbers of 970-857 cm-1. The coefficient of determination (R2) obtained were>0.99 with low values of root mean square error of calibration (RMSEC) and root mean square error of prediction (RMSEP). The results of discriminant analysis revealed that authentic CnO can be discriminated from CnO adulterated with SFO, SyO and CO using selected wavenumbers.Conclusion: FTIR spectroscopy combined with chemometrics could be used as rapid and reliable method for authentication of candlenut oil (CnO) adulterated with other oils.

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