Lampiran 1 Certificate of Analysis SoyPro 900ES

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1 LAMPIRAN

2 Lampiran 1 Certificate of Analysis SoyPro 900ES

3 Lampiran 2 Certificate of Analysis Profarm 974

4 Lampiran 3 Certificate of Analysis Arcon Sj

5 Lampiran 4 Certificate of Analysis Sweet whey Kraft

6 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 Protein Karbohidrat total Gula Natrium 5 g 8 g 28 g 17 g 55 mg Response Name Soy Pro 900 Profarm 974 ES (A) (B) Arcon SJ (C) Y1 Ivla Y2 Belinda Y3 WUlan Y4 Rhais Y5 Novia Y6 Ratih Y7 hesti Y8 M. Luthfi Y9 Hans C W Y10 Ety Y11 Aldilla S U Y12 Kia Y13 Fina 1 2 3

7 Y14 Bima Y15 Akhmad Arief S Y16 Harits Y17 Pratiwi Y18 Sissy Y19 eveline Y20 Septi Lampiran 5. (lanjutan) NPar Test Friedman Test Ranks Soypro Profarm Arcon Mean Rank Test Statistics(a) Ket: N 20 Chi-Square df 2 Asymp. Sig..047 a Friedman Test N = Banyaknya Panelis Nilai Chi-square=Friedman s T = df= derajat bebas =2 Asymp.Sig. = Signifikansi asimtotik = LSD rank = t α/2 [p. n(n+1) : 6] =1.96 [20.3(4):6 = = ~12.4 LSD A-B = = 1 LSD B-C =49-36 = 13 LSD A-C = 49-35

8 =14 A B (Tidak berbeda nyata) B C (berbeda nyata) A 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 < significant Linear Mixture < AB < AB(A-B) < Residual Lack of Fit Pure Error Cor Total The Model F-value of 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 indicate model terms are significant. In this case Linear Mixture Components, AB, AB(A-B) are significant model terms. Values greater than indicate the model terms are not significant. model reduction may improve your model. Std. Dev R-Squared Mean Adj R- Squared C.V. % Pred R- Squared PRESS Adeq Precision Coefficient Standard 95% CI 95% CI Component Estimate df Error Low High VIF A-Isolat Protein Kedelai B-Sweet whey

9 AB AB(A-B) Final Equation in Terms of L_Pseudo Components: warna = * A * B * A * B * 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. *** Analysis of variance table [Partial sum of squares - Type III] Sum of Mean F p-value Source Squares df Square Value Prob > F Model < significant Linear Mixture < AB Residual Lack of Fit Pure Error Cor Total The Model F-value of 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 indicate model terms are significant. In this case Linear Mixture Components, AB are significant model terms. Values greater than 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 R-Squared Mean Adj R- Squared C.V. % Pred R- Squared PRESS Adeq Precision The "Pred R-Squared" of is in reasonable agreement with the "Adj R-Squared" of "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your

10 ratio of indicates an adequate signal. This model can be used to navigate the design space. Coefficient Standard 95% CI 95% CI Component Estimate df Error Low High VIF A-Isolat Protein Kedelai B-Sweet whey AB Final Equation in Terms of L_Pseudo Components: Aroma = * A * B * A * B Lampiran 8 Analisis sidik ragam atribut rasa Response 3 rasa 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 < significant Linear Mixture < AB < AB(A-B) < Residual Lack of Fit Pure Error Cor Total The Model F-value of 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 indicate model terms are significant. In this case Linear Mixture Components, AB, AB(A-B) are significant model terms. Values greater than 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 R-Squared Mean Adj R- Squared C.V. % Pred R- Squared

11 PRESS Adeq Precision The "Pred R-Squared" of is in reasonable agreement with the "Adj R-Squared" of "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of indicates an adequate signal. This model can be used to navigate the design space. Coefficient Standard 95% CI 95% CI Component Estimate df Error Low High VIF A-Isolat Protein Kedelai B-Sweet whey AB AB(A-B) Final Equation in Terms of L_Pseudo Components: rasa = * A * B * A * B * A * B * (A-B) Lampiran 9 Analisis sidik ragam atribut tekstur Response 4 tekstur 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 < significant Linear Mixture < AB < AB(A-B) < Residual E-05 Lack of Fit Pure Error Cor Total The Model F-value of 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 indicate model terms are significant. In this case Linear Mixture Components, AB, AB(A-B) are significant model terms. Values greater than 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 R-Squared Mean Adj R- Squared C.V. % Pred R- Squared

12 PRESS Adeq Precision The "Pred R-Squared" of is in reasonable agreement with the "Adj R-Squared" of "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of indicates an adequate signal. This model can be used to navigate the design space. Coefficient Standard 95% CI 95% CI Component Estimate df Error Low High VIF A-Isolat Protein Kedelai B-Sweet whey AB AB(A-B) Final Equation in Terms of L_Pseudo Components: tekstur = * A * B * A * B * A * B * (A-B) Lampiran 10 Data Hasil Pengolahan Overall Response 5 overall ANOVA for Mixture Quadratic 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 < significant Linear Mixture < AB Residual Lack of Fit Pure Error Cor Total The Model F-value of 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 indicate model terms are significant. In this case Linear Mixture Components, AB are significant model terms. Values greater than 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 R-Squared

13 Mean C.V. % PRESS Adj R- Squared Pred R- Squared Adeq Precision The "Pred R-Squared" of is in reasonable agreement with the "Adj R-Squared" of "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. Your ratio of indicates an adequate signal. This model can be used to navigate the design space. Coefficient Standard 95% CI 95% CI Component Estimate df Error Low High VIF A-Isolat Protein Kedelai B-Sweet whey AB Final Equation in Terms of L_Pseudo Components: overall = * A * B * A * B Lampiran 11 Response 6 Harga ANOVA for Mixture Linear 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 < significant Linear Mixture < Residual Lack of Fit Pure Error Cor Total The Model F-value of 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 indicate model terms are significant. In this case Linear Mixture Components are significant model terms. Values greater than indicate the model terms are not significant. model reduction may improve your model. Std. Dev. 0 R-Squared 1 Mean Adj R-Squared 1 C.V. % 0 Pred R-Squared 1 PRESS 0 Adeq Precision Coefficient Standard 95% CI 95% CI Component Estimate df Error Low High VIF

14 A-Isolat Protein Kedelai B-Sweet whey Base Point in Terms of Pseudo Components: 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 < B-Sweet whey < Base Point in Terms of Real Components: 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 < B-Sweet whey < Final Equation in Terms of L_Pseudo Components: Harga = * A * B

15 Lampiran 12 Form Uji Organoleptik UJI HEDONIK Nama: Minuman berprotein tinggi berbasiskan kedelai Atribut : Warna Tanggal: Instruksi : 1. Berikan tanda ( ) pada pernyataan yang sesuai dengan pilihan Anda untuk setiap kode sampel. Jangan membandingkan antarsampel! Penilaian Sangat suka Suka Agak suka Netral Agak tidak suka Tidak suka Sangat tidak suka Kode sampel Atribut : Aroma Instruksi : 1. Berikan tanda ( ) pada pernyataan yang sesuai dengan pilihan Anda untuk setiap kode sampel. Jangan membandingkan antarsampel! Kode sampel Penilaian Sangat suka Suka Agak suka Netral Agak tidak suka Tidak suka Sangat tidak suka 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! Penilaian Sangat suka Suka Agak suka Netral Agak tidak suka Tidak suka Sangat tidak suka Kode sampel

16 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! Penilaian Sangat suka Suka Agak suka Netral Agak tidak suka Tidak suka Sangat tidak suka Kode sampel Atribut : Overall Instruksi : 1. Berikan tanda ( ) pada pernyataan yang sesuai dengan pilihan Anda untuk setiap kode sampel. Jangan membandingkan antarsampel! Kode sampel Penilaian Sangat suka Suka Agak suka Netral Agak tidak suka Tidak suka Sangat tidak suka

17 Lampiran 13Input data dalam worksheet DX-7 (respon overall) Parameter Perlakuan Overall Harga Warna Aroma Rasa Tekstur (Rp) R R R R R R R R R R R R R R R

18 Lampiran 14 Kurva ideal respon

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21 Lampiran 15 Prediksi Titik Formulasi Optimum (Design Expert 7.1) Name Isolat Protein Kedelai Sweet whey Goal is in range is in range Lower Limit Upper Limit Lower Weight Upper Weight Importance Formula terpilih warna maximize aroma maximize rasa maximize tekstur maximize overall maximize Harga minimize Desirability Component Name Level Low Level High Level Std. Dev. Coding A Isolat Protein Kedelai Actual B Sweet Actual

22 whey Total = 100 Response Prediction SE Mean 95% CI low 95% CI high SE Pred 95% PI low 95% PI high warna aroma rasa tekstur overall Harga CI= Confidence interval =interval selang kepercayaan PI= prediction interval = ( titik prediksi + 5%) selang nilai perkiraan SE mean = nilai tengah prediksi

23 Lampiran 16 Perhitungan kadar air ulangan 1 ulangan 2 Berat Cawan Beratcawan + Contoh Berat Contoh Berat Cawan + Contoh Kering Berat Contoh Kering Kehilangan Berat Kadar Air Basis Basah Kadar Air Basis Basah Rata-Rata 7.04 Contoh perhitungan: Kadar air basis basah ulangan 1 % =... = %

24 Lampiran 17 Perhitungan kadar abu Kadar abu FT Ulangan 1 Ulangan 2 berat cawan beratcawan + contoh berat contoh berat cawan + abu berat abu Kadar abu ISP Ulangan 1 Ulangan 2 berat cawan beratcawan + contoh berat contoh berat cawan + abu berat abu Kadar abu SW Ulangan 1 Ulangan 2 berat cawan beratcawan + contoh berat contoh berat cawan + abu berat abu 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 vol Hcl titrasi blanko 0 0 vol HCl titrasi sampel berat contoh hasil rata-rata Ulangan 1 ulangan 2 Kadar protein ISP normalitas HCl vol Hcl titrasi blanko 0 0 vol HCl titrasi sampel berat contoh hasil rata-rata Kadar protein Whey Ulangan 1 ulangan 2 normalitas HCl vol Hcl titrasi blanko 0 0 vol HCl titrasi sampel berat contoh hasil rata-rata Contoh perhitungan: Kadar protein FT ulangan 1 %,..,.. % % %.. %. %

26 Lampiran 19 Perhitungan kadar lemak metode soklet Ulangan 1 ulangan 2 Berat Labu Berat Contoh Berat Labu + Lemak Berat Lemak Kadar Lemak Hasil Rata-Rata Contoh perhitungan: % %. %.. %

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28 Lampiran 20 Data pengukuran ph, a w, Derajat Putih dan Viskositas Data pengukuran ph larutan formula terpilih ph ulangan 1 ulangan 2 sampel FT Derajat putih ulangan 1 ulangan 2 Susu bubuk dancow Susu kedelai instant sampel FT Standard 100 Data pengukuran aktifitas air (a w ) Suhu a w Data pengukuran viskositas (Centipoise) Viskositas sampel benchmark "susu Ultra" Ulangan Ulangan Rata- Rata

29 Lampiran 21 Perhitungan daya cerna protein N HCl = N Blanko =0.1ml Ulangan Berat Sampel Titrasi HCL % N Protein Tidak Tercerna Daya Cerna (%) Contoh perhitungan ulangan 1 %,...,. % %.. = 3.68 % %.. x 100 %.. % %

30 Lampiran 22 Pengukuran total fenol Kurva standar asam galat konsentrasi (mg/l) absorbansi Ulangan berat (mg) Duplo absorbansi Kurva standar (mg/l) Total fenol (mg/100g sampel) Rata-rata Contoh perhitungan: Ulangan 1 Total fenol/100 g sampel = =. = mg /100 g y = 0.005x R² = Absorbansi Konsentrasi asam galat (mg/l) Kurva standar asam galat

31 Lampiran 23 Berat sampel = g Volume pelarut = 10 ml Pengukuran aktifitas antioksidan Konsentrasi Standar Asam Askorbat (ppm) Absorbansi A ktrl (-) - A 0 0,762 0, ,76 0, ,71 0, ,594 0, ,354 0,446 Kontrol negatif (rataan) 0,8 Sample Absorbansi (A) ulangan ulangan 1 2 ratarata A A kontrl (-) - A sampel AEAC sampel Kapasitas Antioksidan (%) Formula terpilih , Kontrol negatif 0,77 0,83 0,8 Persamaan linier standar Asam askorbat: y = x y = {A ktrl (-) A sampel} x = konsentrasi (mg/100ml) = x = (x) = (x) x = = AEAC Kapasitas Antioksidan= A kntrl negatif- A Sampel x 100% A kntrl negatif = x 100% 0.8 = 4.5 % y = x R² = ppm Kurva standar absorbansi asam askorbat (vit C)