2. 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

3. 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

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

5. 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

6. Resolved Normalized Profiles of Tartrazine Three-way by PARAFAC4

7. Three-way of Sunset Yellow Photo-degradation5

8. Three-way of Sunset Yellow Photo-degradation6

9. Three-way of Sunset Yellow Photo-degradation7

10. Resolved Normalized Profiles of Sunset Yellow Three-way by PARAFAC8

11. Mixture of Tartrazine and Sunset at different Samples at pH=12
SY 4 ppm
TA 10 ppm
SY ppm
TA ppm
SY ppm
TA ppm 9

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

13. The Resolved Profiles of SY and TA in Mixtures by PARAFAC analysis : Three-way data at pH=1211

14. 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

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

16. The Resolved Profiles of SY and TA in Real Sample: Three-way data at pH=1214

17. 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

18. PARALIND Analysis of TA and SY Three-way at pH=1216

19. 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

20. 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

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

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

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

24. Time-wavelength of Sunset Yellow 4 ppm, Tartrazine 10 ppm at different pH22

25. Time-wavelength of Sunset Yellow 16 ppm, Tartrazine 30 ppm at different pH23

26. Time-wavelength of Sunset Yellow 8 ppm, Tartrazine 15 ppm at different pH24

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

28. Time-wavelength of Sunset Yellow 12 ppm, Tartrazine 15 ppm at different pH26

29. Time-wavelength of Sunset Yellow 12 ppm, Tartrazine 25 ppm at different pH27

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

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

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

33. 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
KIT-6] NPs
KIT-6] NPs
CUR + KIT-6] NPs 31

34. 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

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

36. 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

37. 3-D Responses Plots of Releasing Factors of Curcumin35

38. 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

39. 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

40. 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

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

42. 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

43. The 3-D Response SurfacesB)
C )
0.014 g of the catalyst
104 0C
66 min 41

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

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

46. Experimental
Response Surface Plot 44

47. 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

48. Desirability Function
Experimental
Desirability Function
Salt % 46

49. 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

50. 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

51. 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

52. 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

53. 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

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

55. Obtained Resolved Profiles by MCR-ALS53