University of Nebraska - Lincoln DigitalCommons@University of Nebraska - Lincoln Dissertations, Theses, & Student Research in Food Food Science and Technology Department Science and Technology Spring 5-2015 Corn Characterization and Development of a Convenient Laboratory Scale Alkaline Cooking Process Shreya N. Sahasrabudhe University of Nebraska-Lincoln, [email protected] Follow this and additional works at:http://digitalcommons.unl.edu/foodscidiss Part of theFood Chemistry Commons, and theFood Processing Commons Sahasrabudhe, Shreya N., "Corn Characterization and Development of a Convenient Laboratory Scale Alkaline Cooking Process" (2015).Dissertations, Theses, & Student Research in Food Science and Technology. 57. http://digitalcommons.unl.edu/foodscidiss/57 This Article is brought to you for free and open access by the Food Science and Technology Department at DigitalCommons@University of Nebraska - Lincoln. It has been accepted for inclusion in Dissertations, Theses, & Student Research in Food Science and Technology by an authorized administrator of DigitalCommons@University of Nebraska - Lincoln. CORN CHARACTERIZATION AND DEVELOPMENT OF A CONVENIENT LABORATORY SCALE ALKALINE COOKING PROCESS By Shreya Narayan Sahasrabudhe A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science Major: Food Science and Technology Under the Supervision of Professor David S. Jackson May, 2015 Lincoln, Nebraska CORN CHARACTERIZATION AND DEVELOPMENT OF A CONVENIENT LABORATORY SCALE ALKALINE COOKING PROCESS Shreya N. Sahasrabudhe, M.S University of Nebraska, 2015 Advisor: David S. Jackson Nixtamalized (alkaline cooked) corn (Zea mays L.) products are increasing in popularity due to their affordable cost, ease of production, and the diversity of products that can be made using similar unit operations. The nixtamal produced after alkaline cooking depends on the processing parameters used during cooking and steeping, as well as the physicochemical properties of the corn hybrids used. Processors incur high costs in narrowing down hybrids suitable for a given process, or they must be able to adjust cooking conditions to obtain the desired end-product characteristics. Improper processing generates large quantities of waste. Researchers have developed small scale cooking procedures that can mimic industrial nixtamalization process. Many of these methods, however, still require significant quantities of grain, such that the screening processes are expensive. The primary aim of this study was to develop a small scale bench top method with 100 g corn, using simple apparatus that can be used to analyze multiple samples at a time. The method was compared to a previously established 500 g method using range of commercially used cook times, steep times and cook temperatures. Previous studies on relating physico-chemical parameters and nixtamal characteristics have concluded that it is essential to cook corn, at least in small quantities, to understand how corn will process when nixtamalized. The results indicate that the 100 g method can replicate industrial cooking process at a range of processing conditions as the intercept and slopes for response surface models were not significantly different (p < 0.05). The second aim of this study was to understand the effect of physicochemical properties of nine different hybrids, grown in the same season on nixtamal characteristics using the 100 g cooking method. The study found regressions for dry matter loss with thousand kernel weight and kernel calcium content (r2 = 0.98) and for nixtamal moisture with test weight (r2 = 0.52) when corn was cooked for 25 min and steeped for 12 h. Consistent with previous studies, this study found that no one grain parameter can predict all nixtamalized corn properties, confirming the need to cook corn to best understand its potential alkaline cooking performance. i ACKNOWLEDGEMENTS I wish to extend my deepest gratitude to my advisor, Dr. David S. Jackson for his constant support and encouragement throughout my graduate program. His guidance helped me develop skills and gain knowledge not only about Food science, but also about management and communication. I would like to especially thank Dr. Wajira Ratnayake for choosing me to join UNL Food Processing Center as a Master’s student in his lab group. His patience and support helped me in completing my graduate program. Drs. Rolando Flores and Randy Wehling are gratefully acknowledged for serving as my graduate advisory committee and for guiding me throughout my program at UNL. Special thanks to Drs. Devin Rose and Randy Wehling for letting me use their lab space to conduct some of my experiments. I would also like to thank my project external guide Mr. Sathya Kalambur, and all the people who provided me with the facilities required to complete this work. Dr. Anne Parkhurst assisted in the development of experimental design used for the 100 g method. I would also like to especially acknowledge help from Drs. George Cavender and Kathy Hanford with the statistical analysis of data, Paridhi Gulati for assistance in using the pycnometer, Franklin Sumargo for assistance with MVA analysis and Hui Wang for assistance in using DSC and determining amylose-amylopectin ratios. I would also like to thank my lab members Hui Wang and Liya Mo and for their suggestions and assistance. I gratefully acknowledge Yiwei Liu, Jiayi Wang and Abby Burrows who assisted me in the lab. ii I express my deepest gratitude to my parents, Narayan A. Sahasrabudhe and Amita N. Sahasrabudhe for their unconditional love, trust and encouragement at every step in my career. I wish to thank all my friends: Sameer Kulkarni, Kanak Paradkar, Mayuri Ukidwe, Krutika Invally, Paridhi Gulati, Sandrayee Brahma and Anamika Bagchi who made my past two years at UNL enjoyable and memorable. Shreya N. Sahasrabudhe April 21, 2015 Lincoln, NE iii TABLE OF CONTENTS List of Tables v List of Figures vi Abbreviations vii INTRODUCTION 1 CHAPTER 1- LITERATURE REVIEW 1.1. Background 3 1.2. Nixtamalization 5 1.3. Traditional Nixtamalization Unit Operations 8 1.4. Effect of Processing Parameters on Nixtamal Quality 10 1.5. Effect of Grain Quality Parameters on Nixtamal Quality 11 1.6. Physico-chemical Changes during Nixtamalization 16 1.7. Nixtamal Moisture 22 1.8. Dry Matter Loss 23 1.9. Nixtamalization Studies 24 1.10. References 29 CHAPTER 2- ASSESSMENT OF CORN QUALITY FOR NIXTAMALIZATION: DEVELOPMENT OF A BENCH-TOP COOKING METHOD Abstract 32 2.1. Introduction 33 2.2. Materials and Methods 36 2.2.1. Corn Characterization 37 2.2.2. Five-hundred Gram Nixtamalization Process 38 2.2.3. One-hundred Gram Nixtamalization Process 39 2.2.4. Nixtamalization Time-temperature Profile 40 2.2.5. Experimental Design 40 2.2.6. Composition Analysis 41 2.2.7. Process Waste Analysis 41 2.2.8. Pasting Properties 42 2.2.9. Gelatinization Properties 43 2.2.10. Statistical Analysis 43 2.3. Results and Discussion 44 2.3.1. Corn characterization 44 2.3.2. Pericarp removal, pH and Total starch 44 2.3.3. Nixtamal Moisture 46 2.3.4. Dry-matter Loss 47 2.3.5. Nixtamalization Time-temperature Profile 50 2.2.6. MVA Analysis 50 2.2.7. DSC Enthalpy 52 2.4. Study Limitations 54 2.5. Conclusion 54 2.6. Acknowledgments 55 2.7. References 56 iv CHAPTER 3- EFFECT OF CORN PHYSICOCHEMICAL PROPERTIES ON NIXTAMAL MOISTURE AND DRY MATTER LOSS USING A BENCH-TOP NIXTAMALIZATION METHOD Abstract 68 3.1. Introduction 69 3.2. Materials and Methods 73 3.2.1. Corn Samples 73 3.2.2. Corn Characterization 73 3.2.3. Prototype Laboratory Nixtamalization 76 3.2.4. Nixtamal Moisture 79 3.2.5. Dry-matter Loss 79 3.2.6. Experimental Design and Statistical Analysis 79 3.3 Results and Discussion 80 3.3.1. Corn Characterization 80 3.3.2. Nixtamal Moisture 83 3.3.3. Nixtamalization DML 84 3.4. Conclusion 87 3.5. Acknowledgements 88 3.6. References 89 OVERALL SUMMARY 95 APPENDIX 97 Appendix A: Cooking assembly for 500 g nixtamalization method 98 Appendix B: Cooking assembly for 100 g nixtamalization method 99 Appendix C: Equipment and variable costs for running the 100 g and 500 g 100 methods Appendix D: Apparatus designed at UNL for washing step after alkaline cooking 101 Appendix E: Pericarp staining images of samples cooked for 3 min and 25 min 102 Appendix F: Correlations matrix for corn kernel physical and chemical 103 properties alkaline cook quality parameters v List of Tables Table 2.1: Levels of factors used in Response surface central composite design for corn. Table 2.2: Proximate composition and physico-chemical characteristics of yellow corn. Table 2.3: Mean and standard deviation values for non-significant parameters (pericarp removal, nejayote pH, nixtamal total starch, MVA properties). Table 2.4: Bonferroni confidence intervals (90 %) for nixtamal moisture, dry matter loss and DSC enthalpy for corn cooked using the 100 g and 500 g methods. Table 3.1: Summary of studies on correlation between kernel physico-chemical properties and cook quality Table 3.2: Corn composition characteristics of nine (unprocessed corn hybrids) Table 3.3: Physical characteristics of nine (unprocessed) corn hybrids vi List of Figures Fig 2.1: Bonferroni confidence intervals (90 %) for A) nixtamal moisture, B) dry matter loss and C) DSC enthalpy for corn cooked using the 100 g and 500 g methods. Fig 2.2: 100 g (A) and 500 g (B) predicted values for nixtamal moisture (%) as a function of cook time (min) and cook temperature (⁰ C), holding steeping time constant at 7 h. Fig 2.3: 100g method (A) and 500 g method (B) predicted values for dry matter loss as a function of cook time (min) and cook temperature (⁰ C), holding steep time constant at 7 h. Fig 2.4: Representative time-temperature profile for 100 g method and 500 G nixtamalization method at center point (steep time-7 h, cook time-21.5 min and cook temperature- 87.5⁰ C) as measured with a data logger at every 30 s interval. Fig 2.5: 100 g method (A) and 500 g method (B) predicted values for DSC enthalpy as a function of cook time (min) and steep time (min), holding cook temperature constant at 87.5⁰ C. Fig 2.6: 100 g method (A) and 500 g method (B) predicted values for DSC enthalpy as a function of steep time (min) and cook temperature (⁰ C), holding cook time constant at 21.5 min. Fig 3.1: Comparison between commercial scale alkaline cooking data and bench-top 100 g method data for nixtamal moisture for 3 min cook time.