scholarly journals Determination of Total Steroid Saponins in Different Species of Paris Using FTIR Combined with Chemometrics

2018 ◽  
Vol 101 (3) ◽  
pp. 732-738 ◽  
Author(s):  
Yuangui Yang ◽  
Hang Jin ◽  
Jinyu Zhang ◽  
Yuanzhong Wang
Keyword(s):  
Biological Properties ◽  
Support Vector ◽  
Least Squares Regression ◽  
Standard Normal Variate ◽  
Saponin Content ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
Standard Normal ◽  
Validation Set

Abstract The saponins of Paris spp. have antimicrobial, immune-stimulating, and antitumor biological properties. In this investigation, FTIR and ultra-HPLC (UHPLC) were used for the determination of total steroid saponins in different species of Paris from Yunnan Province, China. A 52-sample calibration set and a 26-sample validation set for partial least-squares regression (PLSR) and support vector machine regression (SVMR) combined with FTIR and UHPLC were investigated. The optimal parameters C and γ were screened by a grid search with a sevenfold cross-validation. The results indicate that pretreatment with the combination of standard normal variate, second derivative, and orthogonal signal correction had the best performance. When comparing the SVMR and PLSR models, linear PLSR had better performance than nonlinear SVMR for the determination of total steroid saponins in different species of Paris. The highest total saponin content was found in P. axialis from Baoshan City (40.92 ± 9.06 mg/g). These results demonstrate that this approach would provide a fast and robust strategy for the QC of Paris in further analyses.

scholarly journals PREDIKSI TOTAL PADATAN TERLARUT BUAH MELON GOLDEN (Cucumis melo L.) MENGGUNAKAN VIS-SWNIRS DAN ANALISIS MULTIVARIAT

2020 ◽  
Vol 25 (2) ◽  
Author(s):  
Yuda Hadiwijaya ◽  
Kusumiyati Kusumiyati ◽  
Agus Arip Munawar
Keyword(s):  
Cucumis Melo ◽  
Partial Least Squares Regression ◽  
Scatter Correction ◽  
Least Squares Regression ◽  
Orthogonal Signal ◽  
Standard Normal Variate ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
Standard Normal ◽  
Cucumis Melo L

Penelitian ini bertujuan memprediksi total padatan terlarut buah melon golden menggunakan Vis-SWNIRS dan analisis multivariat. 82 sampel buah melon golden dipanen untuk dianalisis di Laboratorium Hortikultura, Fakultas Pertanian, Universitas Padjadjaran. Nirvana AG410 spectrometer dengan rentang panjang gelombang 300 sampai 1050 nm digunakan untuk pengambilan data spektra pada sampel buah melon utuh. Metode koreksi spektra yang digunakan yaitu standard normal variate (SNV), multiplicative scatter correction (MSC), dan orthogonal signal correction (OSC). Pemodelan kalibrasi dilakukan menggunakan partial least squares regression (PLSR). Hasil penelitian menunjukkan bahwa penggunaan metode koreksi spektra OSC menampikan model kalibrasi terbaik dibandingkan spektra original dan 2 spektra lainnya yang telah dikoreksi. Koefisien determinasi pada spektra OSC memperlihatkan nilai R2 tertinggi yaitu 0.99, disamping itu nilai ratio performance to deviation (RPD) yang diperoleh sebesar 3.40. Hal ini membuktikan bahwa total padatan terlarut buah melon golden dapat diprediksi dengan akurasi yang tinggi menggunakan Vis-SWNIRS dan analisis multivariat.

scholarly journals Determination of Total Flavonoids for Paris Polyphylla Var. Yunnanensis in Different Geographical Origins Using UV and FT-IR Spectroscopy

2019 ◽  
Vol 102 (2) ◽  
pp. 457-464
Author(s):  
Yuangui Yang ◽  
Yanli Zhao ◽  
Zhitian Zuo ◽  
Yuanzhong Wang
Keyword(s):  
Uv Spectrophotometry ◽  
Total Flavonoids ◽  
Least Squares Regression ◽  
Standard Normal Variate ◽  
Paris Polyphylla ◽  
Orthogonal Signal Correction ◽  
Relevant Variables ◽  
Standard Normal ◽  
Ft Ir

Abstract Background: Paris polyphylla var. Yunnanensis (PPY) is used in the clinical treatment of tumors, hemorrhages, and anthelmintic. Objective: The aim of this study is to determine total flavonoids of PPY in the Yunnan and Guizhou Provinces, China. Methods: In this study, total flavonoids were determined by UV spectrophotometry at first. Then, Fourier transform mid-infrared (FT-IR) based on various pretreatments include standard normal variate (SNV), first derivative (FD), second derivative (SD), Savitzky-Golay (SG), and orthogonal signal correction (OSC) were investigated. In addition, several relevant variables were screened by competitive adaptive reweighted sampling (CARS). The contentof total flavonoids and selected variables of FT-IRwere used to establish a partial least squares regression for PPY in different regions. Results: The results indicated that CARS was an effective method for decreasing the variable of thedatabase and improving the prediction of the model.FT-IR with pretreatment SNV + OSC + FD + SG had thebest performance, with R2 > 0.9 and residual predictive deviation = 3.3515, which could be used forthe predictive model of total flavonoids. Conclusions: Those results would provide a fast and robust strategy for the determination of total flavonoids of PPY in different geographical origin. Highlights: Various pretreatments, including SNV, FD, SD, SG, and OSC, were compared; several relevant variables were selected by CARS; and the content of total flavonoids and selected variable were used to establish a partial leastsquares regression for PPY in different regions.

scholarly journals Model Prediksi Kadar Air Buah Cabai Rawit Domba (Capsicum frutescens L.) Menggunakan Spektroskopi Ultraviolet Visible Near Infrared

2021 ◽  
Vol 4 (1) ◽  
pp. 15-22
Author(s):  
Kusumiyati Kusumiyati ◽  
Ine Elisa Putri ◽  
Agus Arip Munawar
Keyword(s):  
Partial Least Squares Regression ◽  
Near Infrared ◽  
Scatter Correction ◽  
Capsicum Frutescens ◽  
Least Squares Regression ◽  
Orthogonal Signal ◽  
Standard Normal Variate ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
Standard Normal

Penelitian ini bertujuan untuk menduga kadar air buah cabai rawit domba (Capsicum frutescens L.) menggunakan spektroskopi UV-Vis-NIR. Total sampel yang digunakan yaitu 45 buah. Analisis dilakukan di Laboratorium Hortikultura, Fakultas Pertanian, Universitas Padjadjaran. Akuisisi data spektra dengan rentang panjang gelombang 300 – 1050 nm (Nirvana AG410). Spektra diperbaiki dengan metode multiplicative scatter correction (MSC), standard normal variate transformation (SNV), orthogonal signal correction (OSC), first derivative (dg1) dan second derivative (dg2). Analisis data dilakukan dengan menggunakan partial least squares regression (PLSR). Berdasarkan penelitian ini menunjukkan bahwa metode koreksi OSC menghasilkan model kalibrasi tertinggi dengan Rkal, RMSEC, Rval, RMSECV, RPD dan faktornya masing-masing yaitu 0.99, 0.31, 0.98, 0.68, 6.62 dan 4. Hal ini menunjukkan bahwa spektroskopi UV-Vis-NIR dapat digunakan untuk memprediksi kadar air pada buah cabai rawit domba.

scholarly journals Ultrasound-assisted emulsification–microextraction and spectrophotometric determination of cobalt, nickel and copper after optimization based on Box-Behnken design and chemometrics methods

2018 ◽  
Vol 20 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Zohreh Doroudi ◽  
Ali Niazi
Keyword(s):  
Least Squares ◽  
Partial Least Squares Regression ◽  
Least Squares Regression ◽  
Orthogonal Signal ◽  
Box Behnken Design ◽  
Cobalt Nickel ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
Emulsification Microextraction

Abstract A fast, simple, and economical method for extraction, preconcentration and determination of cobalt, nickel and copper as their 1-(2-pyridilazo) 2-naphthol (PAN) complexes based on ultrasound-assisted emulsification–microextraction (USAEME) and multivariate calibration of spectrophotometric data is presented. Various parameters affecting the extraction efficiency were optimized both with univariate and Box–Behnken design. The resolution of ternary mixtures of these metallic ions was accomplished by using partial least-squares regression (PLS), orthogonal signal correction-partial least-squares regression (OSC-PLS), and orthogonal signal correction-genetic algorithmspartial least-squares regression (OSC-GA-PLS). Under the optimum conditions, the calibration graphs were linear in the range of 2.0–150.0, 2.0–120.0 and 2.0–150.0 ng mL−1 for Co2+, Ni2+, and Cu2+, respectively, with a limit of detection of 0.14 (Co2+), 0.13 (Ni2+) and 0.14 ng mL−1 (Cu2+) and the relative standard deviation was <2.5%. The method was successfully applied to the simultaneous determination of these cations in different samples.

scholarly journals Combination of NIR spectroscopy and machine learning for monitoring chili sauce adulterated with ripened papaya

2020 ◽  
Vol 187 ◽  
pp. 04001
Author(s):  
Ravipat Lapcharoensuk ◽  
Kitticheat Danupattanin ◽  
Chaowarin Kanjanapornprapa ◽  
Tawin Inkawee
Keyword(s):  
Machine Learning ◽  
Food Industry ◽  
Partial Least Squares Regression ◽  
Nir Spectroscopy ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
Least Squares Regression ◽  
Validation Set ◽  
Global Food

This research aimed to study the combination of NIR spectroscopy and machine learning for monitoring chilli sauce adulterated with papaya smoothie. The chilli sauce was produced by the famous community enterprise of chilli sauce processing in Thailand. The ingredients of the chilli sauce consisted of 45% chilli, 25% sugar, 20% garlic, 5% vinegar, and 5% salt. The chilli sauce sample was mixed with ripened papaya (Khaek Dam variety) smoothie with 9 levels from 10 to 90 %w/w. The NIR spectra of pure chilli sauce, papaya smoothie and 9 adulterated chilli sauce samples were recorded using FT-NIR spectrometer in the wavenumber range of 12500 and 4000 cm-1. Three machine learning algorithms were applied to develop a model for monitoring adulterated chilli sauce, including partial least squares regression (PLS), support vector machine (SVM), and backpropagation neural network (BPNN). All model presented performance of prediction in the validation set with R2al = 0.99 while RMSEP of PLS, SVM and BPNN were 1.71, 2.18 and 3.27% w/w respectively. This finding indicated that NIR spectroscopy coupled with machine learning approaches were shown to be an alternative technique to monitor papaya smoothie adulterated in chilli sauce in the global food industry.

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