Selection of Commercial Robots with Anticipated Cost and Design Specifications using Regression Models

The selection of robots used for industry purpose is a crucial practice where various parameters have to be considered during appropriate selection process. The decision strategy of robot selection has a potential research direction to justify the necessity of industrial needs. We have compared three different mathematical models and selected the best method for choosing the industrial robot to provide a complete selection framework to the present article. Principal Component Regression (PCR), Partial Least Square Regression (PLSR) and Linear Regression using Feed Forward Neural Network (FNN) are the three mathematical models used to correlate input with output parameters. During the testing procedure, eleven numbers of distinct parameters are considered to estimate the best possible rank selection. The strata or rank of the robot is approximated by utilizing the proposed algorithm. However, the most approved rank has met the desired genuinity for a targeted application. In addition to the mathematical methodologies applied here, the performance characteristics for selecting the robot is examined by assessment of statistical errors namely Mean Square Error (MSE), Root Mean Square Error (RMSE), and R-Squared Error (RSE).

Download Full-text

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

Indonesian Journal of Chemometrics and Pharmaceutical Analysis ◽

10.22146/ijcpa.487 ◽

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%.

Download Full-text

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)

10.31219/osf.io/6j2d5 ◽

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.

Download Full-text

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

BioMed Research International ◽

10.1155/2021/5580102 ◽

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.

Download Full-text

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

International Journal of Applied Pharmaceutics ◽

10.22159/ijap.2018v10i5.28372 ◽

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.

Download Full-text

Integration of Partial Least Squares Regression and Hyperspectral Data Processing for the Nondestructive Detection of the Scaling Rate of Carp (Cyprinus carpio)

Foods ◽

10.3390/foods9040500 ◽

2020 ◽

Vol 9 (4) ◽

pp. 500

Author(s):

Huihui Wang ◽

Kunlun Wang ◽

Xinyu Zhu ◽

Peng Zhang ◽

Jixin Yang ◽

...

Keyword(s):

Root Mean Square Error ◽

Mean Square Error ◽

Root Mean Square ◽

Hyperspectral Data ◽

Correlation Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Coefficient Of Determination ◽

Mean Square

The scaling rate of carp is one of the most important factors restricting the automation and intelligence level of carp processing. In order to solve the shortcomings of the commonly-used manual detection, this paper aimed to study the potential of hyperspectral technology (400–1024.7 nm) in detecting the scaling rate of carp. The whole fish body was divided into three regions (belly, back, and tail) for analysis because spectral responses are different for different regions. Different preprocessing methods, including Savitzky–Golay (SG), first derivative (FD), multivariate scattering correction (MSC), and standard normal variate (SNV) were applied for spectrum pretreatment. Then, the successive projections algorithm (SPA), regression coefficient (RC), and two-dimensional correlation spectroscopy (2D-COS) were applied for selecting characteristic wavelengths (CWs), respectively. The partial least square regression (PLSR) models for scaling rate detection using full wavelengths (FWs) and CWs were established. According to the modeling results, FD-RC-PLSR, SNV-SPA-PLSR, and SNV-RC-PLSR were determined to be the optimal models for predicting the scaling rate in the back (the coefficient of determination in calibration set (RC2) = 96.23%, the coefficient of determination in prediction set (RP2) = 95.55%, root mean square error by calibration (RMSEC) = 6.20%, the root mean square error by prediction (RMSEP)= 7.54%, and the relative percent deviation (RPD) = 3.98), belly (RC2 = 93.44%, RP2 = 90.81%, RMSEC = 8.05%, RMSEP = 9.13%, and RPD = 3.07) and tail (RC2 = 95.34%, RP2 = 93.71%, RMSEC = 6.66%, RMSEP = 8.37%, and RPD = 3.42) regions, respectively. It can be seen that PLSR integrated with specific pretreatment and dimension reduction methods had great potential for scaling rate detection in different carp regions. These results confirmed the possibility of using hyperspectral technology in nondestructive and convenient detection of the scaling rate of carp.

Download Full-text

A Novel Protection Algorithm for Phase to Phase Faults in Double Circuit Transmission Lines

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c1111.1083s219 ◽

2019 ◽

Vol 8 (3S2) ◽

pp. 486-488

Keyword(s):

Mean Square Error ◽

Transmission Lines ◽

Selection Process ◽

Research Direction ◽

Mean Square ◽

Decision Strategy ◽

Squared Error ◽

Industrial Needs ◽

Robot Selection ◽

Selection Of

Download Full-text

Feed forensic strategy: Incorporating multivariate data analysis with high-performance liquid chromatography refractive index detector and differential scanning calorimeter for authentication of fish feed sources

10.21203/rs.3.rs-1156557/v1 ◽

2021 ◽

Author(s):

Mohamed Haniff Hanafy Idris ◽

Muhamad Shirwan Abdullah Sani ◽

Amalia Mohd Hashim ◽

Nor Nadiha Mohd Zaki ◽

Yanty Noorzianna Abdul Manaf ◽

...

Keyword(s):

Mean Square Error ◽

High Performance ◽

Principal Component ◽

Factor Loading ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

High Loading ◽

Fish Feed ◽

Mean Square

Abstract This study authenticated fish feed sources and determined lard adulteration using dataset pre-processing, principal component analysis (PCA), discriminant analysis (DA) and partial least square regression (PLSR) on 19 triacylglycerols (TAGs) and 16 thermal properties (TPs). At cumulative variability (90.625%) and Keiser-Meyer Olkin (KMO) value (0.811), the PCA identified strong factor loading variables, i.e., OLL, PLL, OOL, POL, PPL, POO, PPO, PSO, ICT and FHT in PC1 and LLLn, OOO and CT2 in PC2. These variables were significantly (p < 0.05) contributing to lard-palm-oil (L-PO) clusters: (1) POO, PPO and PPL (high loading) and OLL, PLL, OOL, ICT, POL, PSO and FHT (low loading) in 0:100 and 25:75 L-PO clusters; (2) CT2, OOO and LLLn (high loading) in 50:50 L-PO cluster; and (3) OLL, PLL, OOL, ICT, POL, PSO and FHT (high loading) and POO, PPO and PPL (low loading) in 72:25 and 100:0 L-PO clusters. Training, validation and testing datasets had 100%, 84.44% and 100% correct-classification, respectively at p < 0.0001 of Wilks' lambda and p < 0.0001 Fisher distance. The DA selected PLL, OOL, POL, PPL, PSO, ICT and FHT as the significantly authenticating biomarkers (p < 0.05). With determination coefficient (R²) (0.9693), mean square error (MSE) (38.382) and root mean square error (RMSE) (6.195), the PLSR's variable importance in the projection (VIP) identified the most influential biomarkers, i.e., PPL, POL, PPO, OOL, ICT, PLL, FHT, POO and OLL. The Z-test result (p > 0.05) indicated that the PLSR could determine the lard adulteration percentage in fish feed.

Download Full-text

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

Foods ◽

10.3390/foods10040885 ◽

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.

Download Full-text

THE INFLUENCE OF RECRUITMENT AND SELECTION PROCESS ON TECHNICAL EMPLOYEE PERFORMANCE IN PT. TELKOM AKSES NORTH SURABAYA

Ekspektra Jurnal Bisnis dan Manajemen ◽

10.25139/ekt.v4i1.2588 ◽

2020 ◽

Vol 4 (1) ◽

pp. 84-93

Author(s):

Agustiawan Djoko Baruno ◽

Leny Novita Permatasari

Keyword(s):

Selection Process ◽

Employee Performance ◽

Selection Effect ◽

Analytical Techniques ◽

Partial Least Square ◽

Least Square ◽

Primary Data ◽

P Values ◽

Recruitment And Selection ◽

Selection Of

This research aims to analyze the influence of the process of recruitment and selection simultaneously as well as partially against employee performance support. In this study using a type of associative methods and quantitative data primary data sources by using the instrument of the questionnaire. The population in this research is the employee technician PT. Telkom Akses Surabaya Utara as many as 56 respondents.Sampling technique used was saturated samples or often called as well with a sample of the total. Analytical techniques used in this research is the Partial Least Square (PLS) includes test convergen validity, discriminan validity, composite realibility, cronch alpha, R- square, simulan test (test F) and partial test (test T). The results of the analysis explains that the process of recruitment and selection effect simultaneously against the performance of the employee, this is indicated by the value of the count of 40.991 F is greater than F table 4.02 and significance value (S ig) 0.000 smaller than 0.05 and the process of recruitment of influential partially against the performance of the employee, this is shown by the value T calculate of 3.024 is greater than 1.96 table T and value its significance (P values) 0.003 smaller than 0.05. As well as a selection of influential partially against the performance of the employee, this is shown by the value T calculate of 2.856 is greater than 1.96 table T and value its significance (P values) 0.004 smaller than 0.05

Download Full-text

Measurement of the Irganox Content in Polypropylene Polymers during Extrusion

Journal of Near Infrared Spectroscopy ◽

10.1255/jnirs.467 ◽

2005 ◽

Vol 13 (3) ◽

pp. 147-154 ◽

Cited By ~ 6

Author(s):

Wolfgang Becker ◽

Norbert Eisenreich

Keyword(s):

Near Infrared ◽

Prediction Models ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Process Conditions ◽

Transmission Spectra ◽

Mean Square ◽

Score Plot ◽

Least Square Regression

Near infrared spectroscopy was used as an in-line control system for the measurement of polypropylene filled with different amounts of Irganox additives. For this purpose transmission probes were installed in an extruder. The probes can withstand temperatures up to 300°C and pressures up to 60 MPa. Transmission spectra of polypropylene mixed with an Irganox additive were recorded. PCA score plot was carried out revealing the influence of varying conditions for the mixing of the sample preparation. Prediction models were generated with partial least square regression which resulted in a model which estimated Irganox with a coefficient of detremination of 0.984 and a root mean square error of prediction of 0.098%. Furthermore the possibilities for controlling process conditions by measuring transmission at a specific wavelength were shown.

Download Full-text