Lampiran 1 Certificate of Analysis SoyPro 900ES

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Lampiran 5 Uji beda atau tidak dan hasil analisis uji rangking sederhana

(friedman test)

Panelis Beda/tidak Memed 1 Dila 1 Rub 1 Hans 1* Rhais 0 Mega 1 Ety 1 Kia 1 Wulan 1* tuko 1 Total sama 8

*tidak terlalu beda diangap sama

Label Susu ultra coklat (per 250 ml)

Lemak total 5 g

Protein 8 g

Karbohidrat total 28 g

Gula 17 g

Natrium 55 mg

Response Name

Soy Pro 900

_{ES (A)}

Profarm 974

_(B)

Arcon SJ (C)

Y1

Ivla

2 1 3

Y2

Belinda

2 1 3

Y3

WUlan

1 3 2

Y4

Rhais

2 1 3

Y5

Novia

2 1 3

Y6

Ratih

1 2 3

Y7

hesti

2 3 1

Y8

M. Luthfi

1 2 3

Y9

Hans

C

W

1 3 2

Y10

Ety

2 1 3

Y11

Aldilla S U

2

1

3 Y12

Kia

3 1 2

Y13

Fina

1 2 3

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Y14

Bima

2 1 3

Y15

Akhmad Arief S

1

3

2 Y16

Harits

2 3 1

Y17

Pratiwi

2 3 1

Y18

Sissy

2 1 3

Y19

eveline

1 2 3

Y20

Septi

3 1 2

35 36 49

Lampiran 5. (lanjutan)

NPar Test

Friedman Test

Test Statistics(a) N 20 Chi-Square 6.100 df ₂ Asymp. Sig. .047 a Friedman Test

Ket:

• N = Banyaknya Panelis

• Nilai Chi-square=Friedman’s T = 6.100

• dF= derajat bebas =2

• Asymp.Sig. = Signifikansi asimtotik = 0.047

LSD

rank

= t

α/2

√[p. n(n+1) : 6]

=1.96

√[20.3(4):6

=1.96

√40

=12.396

~12.4

LSD A-B = 36-35

= 1

LSD B-C =49-36

=

13 LSD A-C = 49-35

Ranks 1.75 1.80 2.45 Soypro Profarm Arcon Mean Rank

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=14

A---1----B

(Tidak berbeda nyata)

B----13----C

(berbeda

nyata)

A---14---C (berbeda nyata)

Lampiran 6 Analisis sidik ragam atribut warna

Response 1 warna

ANOVA for Mixture Cubic Model

*** Mixture Component Coding is L_Pseudo. ***

Analysis of variance table [Partial sum of squares - Type III]

Sum of Mean F p-value

Source Squares df Square Value Prob > F

Model 0.3316073 3 0.1105358 849.44714 < 0.0001 significant Linear Mixture 0.195003 1 0.195003 1498.5624 < 0.0001 AB 0.0210494 1 0.0210494 161.76078 < 0.0001 AB(A-B) 0.1155549 1 0.1155549 888.01821 < 0.0001 Residual 0.0014314 11 0.0001301 Lack of Fit 0.0014314 1 0.0014314 Pure Error 0 10 0 Cor Total 0.3330387 14

The Model F-value of 849.45 implies the model is significant. There is only a 0.01% chance that a "Model F-Value" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant.

In this case Linear Mixture Components, AB, AB(A-B) are significant model terms. Values greater than 0.1000 indicate the model terms are not significant.

model reduction may improve your model.

Std. Dev. 0.0114073 R-Squared 0.995702 Mean 4.8155549 Squared 0.9945298 Adj R-C.V. % 0.2368846 Squared 0.9932301 Pred R-PRESS 0.0022546 Precision 63.42949 Adeq

Coefficient Standard 95% CI 95% CI

Component Estimate df Error Low High VIF

A-Isolat Protein

Kedelai 4.7338676 1 0.0057014 4.721319 4.7464162 1.4779757 B-Sweet whey 4.9672034 1 0.0057014 4.9546548 4.979752 1.4779757

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AB 0.3313955 1 0.0260561 - -0.3887446 0.2740463 1.8719807 -AB(A-B) -1.9499944 1 0.0654369 -2.0940199 -1.8059688 1.0833333 Final Equation in Terms of L_Pseudo Components:

warna = 4.7338676 * A +4.9672034 * B

-0.3313955 * A * B -1.9499944 * A * B * (A-B)

Lampiran 7 Analisis sidik ragam atribut aroma

Response 2 aroma

ANOVA for Mixture Quadratic Model *** Mixture Component Coding is L_Pseudo. ***

Model 0.2070463 2 0.1035231 23.792495 < 0.0001 significant Linear Mixture 0.1851282 1 0.1851282 42.547611 < 0.0001 AB 0.0219181 1 0.0219181 5.0373783 0.0444 Residual 0.052213 12 0.0043511 Lack of Fit 0.052213 2 0.0261065 Pure Error 0 10 0 Cor Total 0.2592593 14

The Model F-value of 23.79 implies the model is significant. There is only a 0.01% chance that a "Model F-Value" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case Linear Mixture Components, AB are significant model terms. Values greater than 0.1000 indicate the model terms are not significant.

If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve your model.

Std. Dev. 0.0659627 R-Squared 0.798607 Mean 4.5111111 Adj R-Squared 0.7650415 C.V. % 1.4622283 Pred R-Squared 0.7068989 PRESS 0.0759892 Adeq Precision 9.908925

The "Pred R-Squared" of 0.7069 is in reasonable agreement with the "Adj R-Squared" of 0.7650. "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your

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ratio of 9.909 indicates an adequate signal. This model can be used to navigate the design space. Coefficient Standard 95% CI 95% CI

A-Isolat Protein Kedelai 4.4006524 1 0.0323274 4.3302171 4.4710876 1.4210847 B-Sweet whey 4.6929601 1 0.0323274 4.6225248 4.7633953 1.4210847 AB -0.3381643 1 0.1506695 -0.6664449 -0.0098836 1.8719807 Final Equation in Terms of L_Pseudo Components:

Aroma = 4.4006524 * A + 4.6929601 * B -0.3381643 * A * B

Lampiran 8 Analisis sidik ragam atribut rasa

Response 3 rasa

Model 0.720348 3 0.240116009 439.08822 < 0.0001 significant Linear Mixture 0.6281997 1 0.628199744 1148.7577 < 0.0001 AB 0.0714648 1 0.071464805 130.68414 < 0.0001 AB(A-B) 0.0206835 1 0.020683479 37.82285 < 0.0001 Residual 0.0060154 11 0.000546851 Lack of Fit 0.0060154 1 0.006015365 Pure Error 0 10 0 Cor Total 0.7263634 14

Std. Dev. 0.0233849 R-Squared 0.9917185 Mean 5.0155562

Adj

R-Squared 0.9894599 C.V. % 0.4662465 Pred R-Squared 0.9869556

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PRESS 0.009475 Adeq Precision 46.925264

The "Pred R-Squared" of 0.9870 is in reasonable agreement with the "Adj R-Squared" of 0.9895. "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of 46.925 indicates an adequate signal. This model can be used to navigate the design space.

A-Isolat Protein

AB 0.6106225 1 0.053414771 0.4930574 0.7281876 1.8719807 AB(A-B) 0.8249944 1 0.134144805 0.5297436 1.1202451 1.0833333 Final Equation in Terms of L_Pseudo Components:

rasa = 4.6677695 * A 5.2344337 * B 0.6106225 * A * B 0.8249944 * A * B * (A-B)

Lampiran 9 Analisis sidik ragam atribut tekstur

Response 4 tekstur

Model 1.4992657 3 0.4997552 18965.711 < 0.0001 significant Linear Mixture 1.3866667 1 1.3866667 52624 < 0.0001 AB 0.0837101 1 0.0837101 3176.8 < 0.0001 AB(A-B) 0.0288889 1 0.0288889 1096.3333 < 0.0001 Residual 0.0002899 11 2.635E-05 Lack of Fit 0.0002899 1 0.0002899 Pure Error 0 10 0 Cor Total 1.4995556 14

Std. Dev. 0.0051333 R-Squared 0.9998067

Mean 4.7133333 Adj R-Squared 0.999754

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PRESS 0.0004566 Adeq Precision 289.21986 The "Pred R-Squared" of 0.9997 is in reasonable agreement with the "Adj R-Squared" of 0.9998. "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of 289.220 indicates an adequate signal. This model can be used to navigate the design space.

A-Isolat Protein

AB -0.6608696 1 0.0117252 -0.6866766 -0.6350625 1.8719807 AB(A-B) -0.975 1 0.0294465 -1.0398113 -0.9101887 1.0833333 Final Equation in Terms of L_Pseudo Components:

tekstur = 4.3997585 * A 5.1664251 * B

-0.6608696 * A * B -0.975 * A * B * (A-B)

Lampiran 10 Data Hasil Pengolahan Overall

Response 5 overall

ANOVA for Mixture Quadratic Model *** Mixture Component Coding is L_Pseudo. ***

Model 0.5078663 2 0.2539331 135.40637 < 0.0001 significant Linear Mixture 0.4770513 1 0.4770513 254.38106 < 0.0001 AB 0.030815 1 0.030815 16.431673 0.0016 Residual 0.0225041 12 0.0018753 Lack of Fit 0.0225041 2 0.011252 Pure Error 0 10 0 Cor Total 0.5303704 14

The Model F-value of 135.41 implies the model is significant. There is only a 0.01% chance that a "Model F-Value" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case Linear Mixture Components, AB are significant model terms. Values greater than 0.1000 indicate the model terms are not significant.

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Mean 4.8888889 Adj R-Squared 0.9504973 C.V. % 0.8857884

Pred

R-Squared 0.9382063 PRESS 0.0327736 Adeq Precision 24.228768

The "Pred R-Squared" of 0.9382 is in reasonable agreement with the "Adj R-Squared" of 0.9505. "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of 24.229 indicates an adequate signal. This model can be used to navigate the design space.

A-Isolat Protein Kedelai 4.6965977 1 0.0212232 4.6503563 4.7428392 1.4210847 B-Sweet whey 5.1658285 1 0.0212232 5.119587 5.2120699 1.4210847 AB -0.4009662 1 0.0989161 -0.6164858 -0.1854466 1.8719807 Final Equation in Terms of L_Pseudo Components:

overall = 4.6965977 * A+ 5.1658285 * B -0.4009662 * A * B

Lampiran 11

Response 6 Harga

ANOVA for Mixture Linear Model

Model 7617187.5 1 7617187.5 63660000 < 0.0001 significant Linear Mixture 7617187.5 1 7617187.5 63660000 < 0.0001 Residual 0 13 0 Lack of Fit 0 3 0 Pure Error 0 10 0 Cor Total 7617187.5 14

The Model F-value of 63660000.00 implies the model is significant. There is only a 0.01% chance that a "Model F-Value" this large could occur due to noise. Values of "Prob > F" less than 0.0500 indicate model terms are significant. In this case Linear Mixture Components are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. model reduction may improve your model.

Std. Dev. 0 R-Squared 1

Mean 38437.5 Adj R-Squared 1

C.V. % 0 Pred R-Squared 1

PRESS 0 Adeq Precision

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A-Isolat Protein Kedelai 37500 1 1.0771368

B-Sweet whey 39375 1 1.0771368

Base Point in Terms of Pseudo Components: 0.5 0.5

Constraint Region Bounded Component Effects for Piepel Direction

Gradient Component Gradient Approx t for H0 Component in Reals Effect df Std Error Gradient=0 Prob > |t| A-Isolat Protein Kedelai -7500 -1875 1 0 -7978.7217 < 0.0001 B-Sweet whey 7500 1875 1 0 7978.7217 < 0.0001 Base Point in Terms of Real Components:

0.875 0.125

Constraint Region Bounded Component Effects for Cox Direction

Gradient Component Gradient Approx t for H0 Component in Reals Effect df Std Error Gradient=0 Prob > |t| A-Isolat Protein Kedelai -7500 -1875 1 0 -7978.7217 < 0.0001 B-Sweet whey 7500 1875 1 0 7978.7217 < 0.0001 Final Equation in Terms of L_Pseudo Components:

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Lampiran 12 Form Uji Organoleptik

UJI HEDONIK

Nama: Tanggal:

Minuman berprotein tinggi berbasiskan kedelai

Atribut : Warna Instruksi :

1. Berikan tanda (√) pada pernyataan yang sesuai dengan pilihan Anda untuk setiap kode sampel. Jangan membandingkan antarsampel !

Penilaian Kode sampel

Sangat suka Suka Agak suka Netral

Agak tidak suka Tidak suka Sangat tidak suka

Atribut : Aroma Instruksi :

Atribut : Rasa Instruksi :

1. Netralkan lidah Anda dengan air putih yang disediakan (sebelum memulai dan antarsampel)

2. Cicipilah sampel (diamkan selama 10 detik) dan berikan penilaian. Berikan tanda (√) pada pernyataan yang sesuai dengan pilihan Anda untuk setiap kode sampel. Jangan

membandingkan antarsampel !

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Atribut : Tekstur Instruksi :

1. Netralkan lidah Anda dengan air putih yang disediakan (sebelum memulai dan menilai antarsampel)

2. Cicipilah sampel (diamkan selama 10 detik) dan berikan penilaian. Berikan tanda (√) pada pernyataan yang sesuai dengan pilihan Anda untuk setiap kode sampel. Jangan

membandingkan antarsampel !

Atribut : Overall Instruksi :

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Lampiran 13Input data dalam worksheet DX-7 (respon overall)

Perlakuan

Parameter

Warna Aroma Rasa Tekstur

Overall Harga (Rp) R1 4.97 4.70 5.23 5.17 5.17 39375.00 R2 4.97 4.47 5.13 4.83 4.90 38750.00 R3 4.97 4.70 5.23 5.17 5.17 39375.00 R4 4.60 4.40 5.07 4.43 4.73 38125.00 R5 4.97 4.47 5.13 4.83 4.90 38750.00 R6 4.73 4.67 5.03 4.63 4.97 38437.50 R7 4.97 4.47 5.13 4.83 4.90 38750.00 R8 4.73 4.40 4.67 4.40 4.70 37500.00 R9 4.97 4.70 5.23 5.17 5.17 39375.00 R10 4.73 4.40 4.67 4.40 4.70 37500.00 R11 4.73 4.40 4.67 4.40 4.70 37500.00 R12 4.60 4.40 5.07 4.43 4.73 38125.00 R13 4.97 4.70 5.23 5.17 5.17 39375.00 R14 4.60 4.40 5.07 4.43 4.73 38125.00 R15 4.73 4.40 4.67 4.40 4.70 37500.00

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Lampiran 15 Prediksi Titik Formulasi Optimum (Design Expert 7.1)

Name Goal Lower _Limit Upper _Limit _WeightLower _WeightUpper Importance Formula _terpilih Isolat Protein Kedelai is in range 75.00 100.00 1.00 1.00 3.00 77.283 Sweet whey is in range 0.00 25.00 1.00 1.00 3.00 22.717 warna maximize 4.60 4.97 1.00 1.00 3.00 5.05 aroma maximize 4.40 4.70 1.00 1.00 3.00 4.64 rasa maximize 4.67 5.23 1.00 1.00 3.00 5.18 tekstur maximize 4.40 5.17 1.00 1.00 5.00 5.11 overall maximize 4.70 5.17 1.00 1.00 3.00 5.09 Harga minimize 37500.00 39375.00 1.00 1.00 2.00 39203.8 Desirability 0.702

Component Name Level Low Level Level Std. High Dev. Coding

A

Isolat Protein

Kedelai 77.28325 75 100 0 Actual

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whey

Total = 100

Response Prediction SE Mean 95% CI low 95% CI high SE Pred 95% PI low 95% PI high

warna 5.05 0.01 5.04 5.06 0.01 5.02 5.08 aroma 4.64 0.03 4.58 4.69 0.07 4.48 4.79 rasa 5.18 0.01 5.15 5.20 0.03 5.12 5.23 tekstur 5.11 0.00 5.10 5.11 0.01 5.10 5.12 overall 5.09 0.02 5.05 5.13 0.05 4.99 5.19 Harga 39203.76 0.00 39203.76 39203.76 0.00 39203.76 39203.76

CI= Confidence interval =interval selang kepercayaan

PI= prediction interval = ( titik prediksi + 5%) selang nilai perkiraan SEmean = nilai tengah prediksi

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Lampiran 16 Perhitungan kadar air

ulangan 1

ulangan 2

Berat Cawan

5.22919

5.16009

Beratcawan + Contoh

10.26579

10.24809

Berat Contoh

5.03669

5.088 Berat Cawan + Contoh Kering

9.9083

9.8928

Berat Contoh Kering

4.6792

4.7328

Kehilangan Berat

0.35749

0.3552

Kadar Air Basis Basah

7.0961

6.9811

Kadar Air Basis Basah

Rata-Rata 7.04

Contoh perhitungan:

Kadar air basis basah ulangan 1

%

–

=

. _. – .

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Lampiran 17 Perhitungan kadar abu

Kadar abu FT

Ulangan 1

Ulangan 2

berat cawan

19.0922

23.4781

beratcawan + contoh

21.1636

25.4944

berat contoh

2.0714

2.0163

berat cawan + abu

19.1879

23.5678

berat abu

0.0957

0.0897

4.6201 4.4487

4.53 Kadar abu ISP

Ulangan 1

Ulangan 2

berat cawan

20.8822

17.9982

beratcawan + contoh

22.9473

20.098 berat contoh

2.0651

2.0998

berat cawan + abu

20.9682

18.0884

berat abu

0.086 0.0902

4.6445 4.2956

4.47 Kadar abu SW

Ulangan 1

Ulangan 2

berat cawan

17.3211

21.8301

beratcawan + contoh

19.3323

23.8905

berat contoh

2.0112

2.0604

berat cawan + abu

17.4171

21.9264

berat abu

0.096 0.0963

4.7733

4.6739

4.72 Contoh perhitungan:

Ulangan 1 Formula terpilih (FT)

%

.

%

(25)

Lampiran 18 Perhitungan kadar protein metode mikro kjeldahl

Ulangan 1 ulangan 2

Kadar protein FT

normalitas HCl 0.0235 0.0235

vol Hcl titrasi blanko 0 0

vol HCl titrasi sampel 38.3 37.6

berat contoh 0.1324 0.1239

59.5119 62.4232

hasil rata-rata 60.97

Ulangan 1 ulangan 2

Kadar protein ISP

vol HCl titrasi sampel 38 38.5

75.8996 75.7945

Kadar protein Whey Ulangan 1 ulangan 2

vol HCl titrasi sampel 7.5 7.6

11.6538 11.4797

Contoh perhitungan:

Kadar protein FT ulangan 1

%

,

.

,

.

%

.

%

.

%

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Lampiran 19 Perhitungan kadar lemak metode soklet

Ulangan 1 ulangan 2

Berat Labu 107.1469 107.093

Berat Contoh 5.0083 5.0122

Berat Labu + Lemak 107.1739 107.1191

Berat Lemak 0.027 0.02619 Kadar Lemak 0.5391 0.5207 Hasil Rata-Rata 0.5299

Contoh perhitungan:

%

.

%

.

%

(27)

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Lampiran 20 Data pengukuran pH, a

w

, Derajat Putih dan Viskositas

Data pengukuran ph larutan formula terpilih

pH

ulangan 1

ulangan 2

sampel FT

6.52

6.54 Derajat putih

ulangan 1

ulangan 2

Susu bubuk “dancow”

74.8

74.8 Susu kedelai instant

39.3

39.9 sampel FT

76.2

74.52 Standard 100

Data pengukuran aktifitas air (a

w

)

Suhu

a

w

29.2 0.48

29.5 0.436

Data pengukuran viskositas (Centipoise)

Viskositas

sampel

benchmark "susu Ultra"

Ulangan 1

5.6

24 Ulangan 2

6

22

(29)

Lampiran 21 Perhitungan daya cerna protein

N HCl = 0.03188226N

Blanko =0.1ml

Ulangan Berat Sampel Titrasi HCL % N Protein Tidak Tercerna

Daya Cerna (%)

1 0.2580 3.5 3.68 93.94

2 0.2787 3.95 3.85 93.66

Contoh perhitungan ulangan 1

% , . . . , . % % . . = 3.68 %

%

.

x 100 %

.

%

(30)

Lampiran 22 Pengukuran total fenol

Kurva standar asam galat

konsentrasi (mg/l)

absorbansi

50 0.222

100 0.451

150 0.720

200 1.000

250 1.285

Ulangan berat

(mg)

Duplo absorbansi Kurva standar

(mg/L)

Total fenol

(mg/100g sampel)

1 55

1 0.008 13.7963

_{2 0.009 14.0370}

125.4209

_127.6094

2 55

1 0.013 14.7778

_{2 0.010 14.2963}

134.3434

_129.9663

Rata-rata

129.335 Contoh perhitungan:

Ulangan 1

Total fenol/100 g sampel

=

.

= 125.42 mg /100 g

Kurva standar asam galat

y = 0.005x - 0.066 R² = 0.998 0.000 0.200 0.400 0.600 0.800 1.000 1.200 1.400 0 50 100 150 200 250 300 Absorbansi

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Lampiran 23 Pengukuran aktifitas antioksidan

Berat sampel = 1.0009 g

Volume pelarut = 10 ml

Konsentrasi Standar Asam Askorbat (ppm)

Absorbansi

A ktrl (-) - A

0 0,762

0,038

50 0,76

0,04

100 0,71

0,09

200 0,594

0,206

500 0,354

0,446

Kontrol negatif (rataan)

0,8

Sample

Absorbansi (A)

rata-rata

A

A kontrl (-)

- A sampel

AEAC

sampel

Kapasitas

Antioksidan

(%)

ulangan

1 ulangan

2 Formula

terpilih 0.754

0.774 0,764

0.036

21.11

4.5 Kontrol

negatif 0,77

0,83

0,8

Persamaan linier standar Asam askorbat: y = 0.0009x + 0.0179

y = {A ktrl (-) – A sampel}

x = konsentrasi (mg/100ml)

0.036 = 0.0009x + 0.0179

0.036 -0.0179 = 0.0009(x)

0.0181

= 0.0009(x)

x = 20.11

= 20.11 AEAC

Kapasitas Antioksidan= A kntrl negatif- A Sampel x 100%

A kntrl negatif

= 0.8 – 0.764 x 100%

0.8 = 4.5 %

Kurva standar absorbansi asam askorbat (vit C)

y = 0.0009x + 0.0179 R² = 0.989 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0 50 100 150 200 250 300 350 400 450 500 550 600 ppm