scholarly journals Analysis of pork adulteration in the corned products using FTIR associated with chemometrics analysis

10.5219/1412 ◽  
2020 ◽  
Vol 14 ◽  
pp. 1042-1046
Author(s):  
Any Guntarti ◽  
Mustofa Ahda ◽  
Aprilia Kusbandari ◽  
Faradita Natalie
Keyword(s):  
Principal Component Analysis ◽  
Fourier Transform ◽  
Quantitative Analysis ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Soxhlet Apparatus ◽  
Infrared Spectral ◽  
Ftir Spectrophotometry ◽  
Reference Samples

Meat-based foods such as beef corned became one of the targets of counterfeiting with pork because relatively cheaper. This becomes a serious problem for Muslims, especially in Indonesia. One method that can be used to detect fat was Fourier transform infrared (FTIR) spectrophotometry. The purpose of this study was to quantitatively analyze and a group of corned beef and corned pork using FTIR spectrophotometry combined with chemometrics. Reference samples corned pork-beef made of 7 various concentration (0%, 25%, 35%, 50%, 65%, 75%, 100%) and 6 product samples purchased in the Umbulharjo, Yogyakarta. Extraction was carried out by the soxhlet apparatus using n-hexane technical solvent for 4 – 5 hours at 69 – 70 °C. Fat analyzed using FTIR spectrophotometry for generating infrared spectral data then processed with Partial least square (PLS) chemometrics for quantitative analysis and Principal component analysis (PCA) for grouping. Results of quantitative analysis chemometrics PLS, selected areas fingerprints for analysis corned pork-beef was 1180 – 730 cm-1 with R2 0.9833; RMSEC 2.06%; RMSEP 1.65% and RMSECV 2.22%. The results of PCA showed groupings in different quadrants between corned pork 100% and corned beef 100%. Results showed that FTIR spectrophotometry combined with chemometrics can be used for quantitative analysis and grouping of pork corned and beef corned on the market but it can not identify pork in corned after choking process.

scholarly journals Application of FTIR Spectroscopy and HPLC Combined with Multivariate Calibration for Analysis of Xanthones in Mangosteen Extracts

2020 ◽  
Vol 88 (3) ◽  
pp. 35
Author(s):  
Endjang Prebawa Tejamukti ◽  
Widiastuti Setyaningsih ◽  
Irnawati ◽  
Budiman Yasir ◽  
Gemini Alam ◽  
...  
Keyword(s):  
Quantitative Analysis ◽  
Multivariate Calibration ◽  
Principal Component Regression ◽  
Principal Component ◽  
Ethanolic Extract ◽  
Ftir Spectra ◽  
Partial Least Square ◽  
Least Square ◽  
Coefficient Of Determination ◽  
Garcinia Mangostana

Mangosteen, or Garcinia mangostana L., has merged as an emerging fruit to be investigated due to its active compounds, especially xanthone derivatives such as α -mangostin (AM), γ-mangostin (GM), and gartanin (GT). These compounds had been reported to exert some pharmacological activities, such as antioxidant and anti-inflammatory, therefore, the development of an analytical method capable of quantifying these compounds should be investigated. The aim of this study was to determine the correlation between FTIR spectra and HPLC chromatogram, combined with chemometrics for quantitative analysis of ethanolic extract of mangosteen. The ethanolic extract of mangosteen pericarp was prepared using the maceration technique, and the obtained extract was subjected to measurement using instruments of FTIR spectrophotometer at wavenumbers of 4000–650 cm−1 and HPLC, using a PDA detector at 281 nm. The data acquired were subjected to chemometrics analysis of partial least square (PLS) and principal component regression (PCR). The result showed that the wavenumber regions of 3700–2700 cm−1 offered a reliable method for quantitative analysis of GM with coefficient of determination (R2) 0.9573 in calibration and 0.8134 in validation models, along with RMSEC value of 0.0487% and RMSEP value 0.120%. FTIR spectra using the second derivatives at wavenumber 3700–663 cm−1 with coefficient of determination (R2) >0.99 in calibration and validation models, along with the lowest RMSEC value and RMSEP value, were used for quantitative analysis of GT and AM, respectively. It can be concluded that FTIR spectra combined with multivariate are accurate and precise for the analysis of xanthones.

scholarly journals Analisis Partial Least Square Menghasilkan Output Alternatif Antarmuka Tambahan Dari Kansei Engineering

2020 ◽  
Vol 17 (2) ◽  
pp. 67
Author(s):  
Arief Ginanjar ◽  
Awan Setiawan
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Correlation Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Type I ◽  
Kansei Engineering ◽  
Coefficient Corrélation

Ketika menggunakan Kansei Engineering dalam mencari kandidat terbaik untuk menentukan model perancangan antarmuka website, peneliti menggunakan metode analisis Partial Least Square (PLS) yang dilakukan secara berulang hingga ditemukan elemen terbaik yang dapat diimplementasikan. PLS sebagai alat bantu untuk menentukan nilai terbaik antara elemen website. Output perbandingan yang dihasilkan akan dikelompokkan berdasarkan Kansei Word sebagaimana yang telah ditentukan dalam rencana awal implementasi Kansei Engineering, output perbandingan PLS iterasi pertama mempunyai kemungkinan mendapatkan nilai usulan terbaik jika digabung dengan melakukan iterasi kedua terhadap asimilasi dua atau tiga elemen yang mempunyai nilai tertinggi. Metodologi yang digunakan mengacu kepada Kansei Engineering Type I dengan melalui pengolahan data menggunakan Cronbach’s Alpha untuk menguji kelayakan responden, kemudian untuk mengetahui hubungan Kansei Words dapat menggunakan Coefficient Correlation Analysis (CCA), sedangkan hubungan antara Kansei Words dengan spesimen dapat menggunakan Principal Component Analysis (PCA), sedangkan mencari pengaruh Kansei Words paling kuat dapat menggunakan Factor Analysis (FA) dan analisis Partial Least Square (PLS) namun harus dilakukan iterasi proses PLS hingga variabel rekomendasi model perancangan antarmuka yang dihasilkan menjadi lebih bervariatif.

scholarly journals PENERAPAN KEMOMETRIK PADA METODE CITRA DIGITAL UNTUK ANALISIS ION MERKURI (II) DENGAN INDIKATOR NANOPARTIKEL PERAK

Alotrop ◽  
2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Angga Aprian Dinata ◽  
M. Lutfi Firdaus ◽  
Rina Elvia
Keyword(s):  
Quantitative Analysis ◽  
Digital Image ◽  
Limit Of Detection ◽  
Principal Component Regression ◽  
Image Data ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Image Method ◽  
Rgb Values

Digital image method in quantitative analysis usually uses one of the RGB primary color components (Red, Green, Blue), so that not all digital image data can be extracted. Then needed a method that can render the whole RGB values as variables in quantitative analysis are known as chemometric. This research aims to know the influence of the application of chemometric against the sensitivity of the digital image. Chemometry method used is the Principal Component Regression (PCR) and Partial Least Square (PLS) using Unscramber X software from Camo software, USA.. This method is applied for the quantitative analysis of Mercury (II) ion with silver nanoparticles (NPP) immobilization on filter paper indicator. The research results showed that chemometric has a good influence against the level of the Limit of Detection (LOD) of the digital image, where the level of LOD with chemometric application of the Principal Component Regression (PCR) is 0.4311 ppb, and Partial Least Square (PLS) is  0.4310 ppb smaller than without the application of chemometric Single Linear Regression (SLR) at 0.837 ppb. 

Compression of Infrared Libraries by Eigenvector Projection

1987 ◽  
Vol 41 (3) ◽  
pp. 449-453 ◽  
Author(s):  
P. B. Harrington ◽  
T. L. Isenhour
Keyword(s):  
Principal Component Analysis ◽  
Fourier Transform ◽  
Principal Component ◽  
Fourier Transform Infrared ◽  
Data Preprocessing ◽  
Component Analysis ◽  
Search Performance ◽  
Infrared Spectral ◽  
Spectral Libraries

Different methods of data preprocessing were evaluated for the compression of Fourier transform-infrared spectral libraries by principal component analysis (PCA). The effect of noise on compressed library searches was examined. A PCA compression of an infrared library achieved an 81% reduction in size without any loss in search performance.

scholarly journals Fault identification for chiller sensor based on partial least square method

2021 ◽  
Vol 233 ◽  
pp. 03057
Author(s):  
Bang Wu ◽  
Yunpeng Hu ◽  
Chuanhui Zhou ◽  
Guaiguai Chen ◽  
Guannan Li
Keyword(s):  
Principal Component Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Analysis ◽  
Partial Least Square ◽  
Least Square ◽  
Fault Identification ◽  
Least Squares Regression

Sensor failures can lead to an imbalance in heating, ventilation and air conditioning (HVAC) control systems and increase energy consumption. The partial least squares algorithm is a multivariate statistical method, compared with the principal component analysis, its compression factor score contains more original data characteristic information, therefore, partial least squares have greater potential for fault diagnosis than the principal component analysis. However, there are few studies based on partial least squares in the field of HVAC. In order to introduce partial least squares into the field, based on the partial least squares fault detection theory, a fault analysis method suitable for this field is proposed, and the RP1403 data published by ASHARE was used to verify this method. The results show that on the basis of selecting the appropriate number of principal components, partial least squares have the ability to diagnose the fault of the chiller sensor. With the known fault source, partial least squares regression, a method with better data reconstruction accuracy than principal component analysis, is used to repair the fault. Finally, the purpose of fault identification can be achieved.

scholarly journals Analysis of Adulterated Pangasius Hypopthalmus Oil by ATR-FTIR Spectroscopy and Chemometric

2019 ◽  
pp. 4
Author(s):  
Lisa Andina ◽  
Revita Saputri ◽  
Aristha Novyra Putri ◽  
Endang Lukitaningsih ◽  
Abdul Rohman
Keyword(s):  
Quantitative Analysis ◽  
Ftir Spectroscopy ◽  
Palm Oil ◽  
Coconut Oil ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
First Derivative ◽  
Derivative Spectra

In this recent study, ATR-FTIR spectroscopy and chemometrics have been successfully used for theclassification and quantitative analysis of adulterated Pangasius Hypopthalmus (P. hypopthalmus) oil. The aimof this research was to evaluate the ability of ATR-FTIR spectroscopy and chemometric to perform theclassification and quantitative analysis of adulterated P. hypopthalmus oil in a binary mixture with palm oil(PO) and coconut oil (CO). In the development of FTIR spectroscopy combined with chemometrics for theclassification and quantitative analysis of P. hypopthalmus oil, P. hypopthalmus oil (MP and LFP) mixed withother oils such as coconut oil (CO) and palm oil (PO) at concentrations of 1-99% v/v. Classification of P.hypopthalmus oil, PO and CO were performed by the principal component analysis (PCA) and thequantification analysis was carried out by partial least square (PLS). Based on the optimization process, thebest classification results were obtained using the first derivative spectra at wave numbers of 1400-1100 cm-1.The prediction of percentage adulterated oil by PLS method also showed very good values of R2 greater than0.9999 and low standard error values in the range of 0.0176-0.703. The prediction was also perform at1400-1100 cm-1 wavenumbers using the first derivative spectra.

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