15th Iranian Workshops on Chemometrics, IASBS, Zanjan, May 2017 Aiming at two distinct researches: Analysis of four-way and five-way chemical data with rank deficiency problems in three-modes; discerning the monitoring problems in drugs co-delivery By : S. Maryam Sajjadi 15th Iranian Workshops on Chemometrics, IASBS, Zanjan, May 2017

λ × time × pH× T× sample Produced Photo-degradation Multi-way Data Analytes with Acid-base Property Kinetic processes of analytes photo-degradation Spectrophotometric monitoring of the processes Samples with different concentrations of analytes Dependency of kinetic processes on temperature λ × time × pH× T× sample 1

Three-way data (λ × time × pH) Tartrazine Photo-degradation pH=8 pH=10 pH=11 pH=12 2

Parallel Factor Analysis Soft modeling parallel factor analysis method attempts to decompose a three-way data into the product of three significantly smaller matrices. λ1 λ2 λ3 t1 t2 t3 time λ pH = + B D C E A P λ pH time 3

Resolved Normalized Profiles of Tartrazine Three-way by PARAFAC 4

Three-way of Sunset Yellow Photo-degradation 5

Three-way of Sunset Yellow Photo-degradation 6

Three-way of Sunset Yellow Photo-degradation 7

Resolved Normalized Profiles of Sunset Yellow Three-way by PARAFAC 8

Mixture of Tartrazine and Sunset at different Samples at pH=12 SY 4 ppm TA 10 ppm SY 16 ppm TA 30 ppm SY 8 ppm TA 15 ppm 9

Mixture of Tartrazine and Sunset at Different Samples at pH=12 SY 8 ppm TA 25 ppm SY 12 ppm TA 15 ppm SY 12 ppm TA 25 ppm 10

The Resolved Profiles of SY and TA in Mixtures by PARAFAC analysis : Three-way data at pH=12 11

Figures of Merit of SY and TA by PARAFAC Analysis : Three-way data at pH=12 0.9977 0.9988 R2 0.5627 0.8083 RMSEC 0.6514 1.0753 RMSECV 12

Quantification of SY and TA in Saffron Samples by PARAFAC at pH=12 Time-wavelength data of saffron at pH=12 13

The Resolved Profiles of SY and TA in Real Sample: Three-way data at pH=12 14

Quantification of SY and TA in saffron samples by PARAFAC at pH=12 SY recovery TA recovery SY predicted TA predicted SY added TA added sample - 0.6 5.4 1 125 100.6 3.1 20.5 2 15 98.7 104 8.5 15.8 8 10 3 15

PARALIND Analysis of TA and SY Three-way at pH=12 16

Quantification of SY and TA in Saffron Samples by PARALIND at pH=12 Figures of Merit 0.9861 0.9905 R2 0.4954 0.7539 RMSEC 0.5640 0.9839 RMSECV SY recovery TA recovery SY predicted TA predicted SY added TA added Sample - 0.48 5 1 106 101.3 2.6 20.2 2 15 94 100 8.0 15.0 8 10 3 17

Mixture of Tartrazine and Sunset at different Samples at pH=8 SY 4 ppm TA 10 ppm SY 16 ppm TA 30 ppm SY 8 ppm TA 15 ppm 18

Mixture of Tartrazine and Sunset at different Samples at pH=8 SY 12 ppm TA 25 ppm SY 8 ppm TA 25 ppm SY12 ppm and TA 15 ppm 19

PARAFAC and PARALIND Analysis of TA and SY Three-way at pH=8 Was not Possible Because of High Complexity 20

Four-way data of TA and SY pH sample 1 λ time time λ pH sample2 4-way Data time λ pH sample 3 21

Time-wavelength of Sunset Yellow 4 ppm, Tartrazine 10 ppm at different pH 22

Time-wavelength of Sunset Yellow 16 ppm, Tartrazine 30 ppm at different pH 23

Time-wavelength of Sunset Yellow 8 ppm, Tartrazine 15 ppm at different pH 24

Time-wavelength of Sunset Yellow 8 ppm, Tartrazine 25 ppm at different pH

Time-wavelength of Sunset Yellow 12 ppm, Tartrazine 15 ppm at different pH 26

Time-wavelength of Sunset Yellow 12 ppm, Tartrazine 25 ppm at different pH 27

Combination of PARALIND and PARAFAC to Analyze Four-way Data 28

Access to Pure Profiles of Three-way Sensitive Sunset Yellow Data by PARAFAC 29

Access to Pure Profiles of Three-way Sensitive Tartrazin Data by PARAFAC 30

Optimization of Curcumin Loading and Release Processes in drug delivery systems by Response Surface Methodology Releasing Factors Loading Time CUR/ nano-carrier Loading Factors Max. Loading Efficiency Releasing Time pH Experimental design Desirable Release [β-CD@PEGylated KIT-6] NPs CUR@[β-CD@PEGylated KIT-6] NPs CUR + [β-CD@PEGylated KIT-6] NPs 31

Exhaustive Investigation of Drug Delivery Systems to Achieve Optimal Condition of Drugs Release Using Nonlinear Generalized Artificial Neural Network Method: feedback from the loading step of drug 32

Exhaustive Investigation of Drug Delivery Systems …… . Releasing Factors Desirable Release Loaded drug Releasing Time pH CUR@[β-CD@PEGylated KIT-6] NPs [β-CD@PEGylated KIT-6] NPs + CUR Loading Factors: time, CUR Feedback 33

Design Matrices in the loading and Release Processes The design matrix for loading process Run Loading time (h) Ratio of drug / nano-carrier loaded drug 1 43 1.22 1.08 2 17 2.28 1.25 3 0.62 4 30 2.5 2.15 5 0.81 6 1.75 1.33 7 1.09 The design matrix for release process  Run Amount of the Loaded CUR   pH  Release time (h) Relative CUR Release 1 1.08 1.2 62.5 1.04 2 4.4 120 19.94 3 5.0 3.04 … 34 2.15 6.8 106 21.1 34

3-D Responses Plots of Releasing Factors of Curcumin 35

Optimization of Loading Steps by ANN to Find the Optimal Condition The design matrix for loading process Run Loading time (h) Ratio of drug / nano-carrier loaded drug 1 43 1.22 1.08 2 17 2.28 1.25 3 0.62 4 30 2.5 2.15 5 0.81 6 1.75 1.33 7 1.09 36

Thanks to Whom I have Cooperation with Dr. Asadpour Zeynali University of Tabriz Dr. Salehi Semnan University Dr. Behzad Semnan University Dr. Zavvar- Mousavi Semnan University Dr. Rajabi Semnan University 37

Thanks to Whom I have Cooperation with Dr. Amoozadeh Semnan University Dr. Nemati Semnan University Dr. Bagheri Semnan University  Dr. Maleki Zanjan University of Medical sciences Dr. Nabizadeh Semnan University 38

Catalytic Reaction: Biginelli Reaction Nano-catalyst A B Factors: The amount of the catalyst, Time, Temperature 39

Levels Factors and Levels Applied in the CCD Design Factors - ɑ -1 1 ɑ   - ɑ -1 1 ɑ catalyst (g) 0.005 0.014 0.034 0.041 0.050 Temp. (˚C) 40 56 75 104 120 Time (min) 10 24 43 65 80 40

The 3-D Response Surfaces B) C ) 0.014 g of the catalyst 104 0C 66 min 41

Microextraction of Pregabalin and Gpabapentin in Urine Sample Followed by Gas Chromatography Pregabalin (PRG) Gabapentin (GBP) 42

Air assisted liquid-liquid microextraction (AALLME) Number of extraction cycle (suction) pH Salt% 43

Experimental Response Surface Plot 44

Multiple Response Surface methodology (MRS) Reducing multiple responses to a single aggregated measure and solves the equation as a single objective optimization: DF=[ df 1 v1 × df 2 v2 ×⋯× df n vn ] 1 n , 0≤vi≤1 (i=1,2,⋯,n) 45

Desirability Function Experimental Desirability Function Salt % 46

Simultaneous Removal of Rhodamine B (RB) and Auramine O (AO) Dyes from Aqueous Solutions Using Carboxyl-functionalized Microporous Activated Carbon (COOH-AC). AO RB Abs. Wavelength (nm) 47

Factors: The amount of sorbent, Analytes Concentration, Time, pH Coupling Experimental Design and Multivariate Calibration Methods Factors: The amount of sorbent, Analytes Concentration, Time, pH 30 experimental condition Calibration model: PLS Prediction of concentration after removal by PLS 48

30 experimental condition Coupling of Experimental Design and Net Analyte Signal to Determine Hg in Trace Level 30 experimental condition Calibration model: NAS Prediction of concentration after removal by NAS Response surface Methodology for finding optimal condition 49

Coupling of experimental design and artificial neural network to determine Hg in trace level in the presence of unknown interference 30 experimental condition Calibration model: ANN Response surface Methodology for finding optimal condition Prediction of concentration after removal by ANN-RBL 50

Analysis of variation matrix array by bilinear least squares–residual bilinearization (BLLS–RBL) for resolving and quantifying of foodstuff dyes in a candy sample 51

Determination of Acetaminophen in Novafen Samples by Spectroelectrochemistry and MCR-ALS Abs. Abs. Wavelength (nm) Wavelength (nm) 52

Obtained Resolved Profiles by MCR-ALS 53