ABASTRACT. Determination of nitrate content and ascorbic acid in intact pineapple by Vis-NIR spectroscopy Student. Thesis

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1 Thesis Determination of nitrate content and ascorbic acid in intact pineapple by Vis-NIR spectroscopy Student Mrs. Sasathorn Srivichien Student ID Degree Doctor of Philosophy Program Food Science Year 2015 Thesis Advisor Assoc. Prof. Dr. Sontisuk Teerachaichayut ABASTRACT Near-infrared spectroscopy (NIRs), a spectroscopic technique, was used for nondestructive inspection of pineapples quality. NIRs technique was used for prediction of components in pineapple based on the absorbance energy of near infrared radiation. Therefore NIRs was used to predict nitrate content, ascorbic acid (AA) and ratio of total soluble solid and titratable acid (TSS/TA ratio) of intact pineapple in this research. Compositions in pineapple are not homogeneous due to large fruit size. Hence, spectral measurements were done at several positions of the fruit. The averaged acquired spectra were used to construct calibration models for nitrate content, AA and TSS/TA ratio in intact pineapple. Relationships between absorbance spectra and nutrition were evaluated through the application of chemometric techniques based on partial least squares (PLS) regression and cross validation calibration method. The averaged spectra obtained better results compared with using individual spectra on the fruit. Correlation coefficients (R) for nitrate content, AA and TSS/TA ratio were 0.95, 0.99 and 0.95, respectively. Root mean square errors of prediction (RMSEP) for nitrate content, AA and TSS/TA ratio were 1.77 ppm, 4.75 ppm and 2.47 Brix/% citric acid, respectively. The results showed that NIR technology can be used to rough screen nitrate content, AA and TSS/TA ratio.

2 ACKNOWLEDGEMENTS I would like to express my special appreciation and thank to my advisor Associate Professor Dr. Sontisuk Teerachaichayut. He has been a tremendous mentor for me. I would like to say thanks for encouraging my research and for allowing me to grow as a research scientist and teacher. His advice on both research, as well as on my career, have been invaluable. I would like to thank my committee members, Associate Professor Dr. Anupun Terdwongworakul, Assistant Professor Dr. Aphacha Jindaprasert and Dr. Rachit Suwapanich for being committee members even at hardship. I also to would like to say thanks for their encouragement, brilliant comments and suggestions. I would especially like to thank Rajamangala University of Technology Thanyaburi for financial assistance of my Ph.D. study at King Mongkut's University of Technology Ladkrabang, without which it would have been impossible to undertake this curriculum. Special thanks for research funding from National Research Council of Thailand as well as for lending use needed equipment from Sithiphorn Assosiate Co., Ltd. and Mr. Shawn Mayes for proofreading this thesis. I would like to thank the technical staff in the Faculty of Agro-Industry at King Mongkut's University of Technology Ladkrabang and everyone in my life for help, encouragement and their wishes for success in the completion of this dissertation. Finally, a special thanks to my Mom and Dad for their confidence in me and for living to see my success. Thank you to my sister, Sojikarn Sataporn and my husband, Jongjaroen Tunchanapradit for supporting me in everything, I can t thank you enough for encouraging me throughout this experience. To my son Master Fonluang Inon, I would like to express my thanks for being a good boy and offer my apologies for any suffering during my study. Sasathorn Srivichien 2015 II

3 CONTENTS III Abstract. I Acknowledgements... II Contents. III List of Table.. VI List of Figure. VII List of Abbreviation.. VIII 1. Introduction Statement of the problems Objective Conceptual framework Scope of study Research methodology Spectrum acquisition Sample preparation Chemical acquisition Spectra analysis Literature review Pineapple Varity Maturity of pineapple Nitrate in pineapple Ascorbic acid in pineapple Principle of NIR spectroscopy Equipment Incident radiation Vis/NIR measurement Spectrum acquisitions Selection measurement method Data preprocessing to reduce or eliminate the noise 20

4 CONTENTS (continuous) Development of a calibration model Model validation and prediction Application of NIR spectroscopy to quality control of agricultural product NIR spectroscopy for measuring other fruit and vegetable NIR spectroscopy for measuring pineapple NIR spectroscopy for measuring nitrate and nitrogen NIR spectroscopy for measuring ascorbic acid (AA) Methodology Material Chemical Equipment Working Place Methodology Measuring spectrum Chemical Analysis Construction of calibration models Result and Discussion Nitrate in pineapple Ascorbic acid in pineapple Ratio of total soluble solids and titratable acid in pineapple Conclusion and Suggestion Conclusion Nitrate in pineapple Ascorbic acid in pineapple TSS/TA ratio in pineapple Suggestion. 57 References 58 Appendix IV

5 CONTENTS (continuous) Manuscript-Quantitative prediction of nitrate level in intact pineapple using Vis-NIRS 80 Manuscript-Quality classification of pineapple based on nitrate level by Vis-NIRS 87 Manuscript-Comparison of nitrate content in Smooth Cayenne pineapple flesh related to its different cut section, maturity and crop season. 90 Author biography.. 94 V

6 Table LIST OF TABLE 2.1 Commercially sold pineapple varieties Physico-chemical attributes of Smooth Cayenne pineapple fruits at different stage of maturity Guideline for the interpretation of the RDP The level of nitrate in the calibration and prediction sets of both the IS and AS groups Results of AS cross validation of PLS-calibration models in different wavelength ranges Results of cross validation of PLS-calibration model using different spectral pretreatments in the wavelength range of nm for IS and AS Results of calibration and prediction of the PLS model for nitrate in wavelengths of nm The characteristics of calibration and prediction groups of AA Results of cross validation by PLS-calibration models for AA in different wavelength ranges using original spectra of AS Results of cross validation of PLS-calibration models for AA using different spectral pretreatments Summary of the PLS model performance for AA assessment in pineapple The characteristics of calibration and prediction groups for TSS/TA ratio Results of PLS-calibration models in different wavelength ranges for TSS/TA ratio using AS original spectra Results of different spectral pretreatments for PLS-calibration models in wavelength range nm for TSS/TA ratio Summary of the PLS model performance for TSS/TA ratio in pineapple. 55 VI

7 Figure LIST OF FIGURES 1.1 Sample presentations for spectral acquisition Structure of AA Schematic diagram of measurements: (1) Reflectance (2) Transmittance (3) Interactance Reflection-type: specular reflectance, diffraction and scattering The hydrogen bond form between AA molecule Sample of pineapple with different color 25,50 and 75% yellow of skin color Schematic diagram of the experimental setup for NIR testing of pineapple fruit Preparation of pineapple fruit for chemical analysis Average original spectra of samples with different levels of nitrate a = low, b = middle and c = high Regression coefficients of the calibration model for pure nitrate prediction Absorbance spectrum of Potassium nitrate The Scatter plots of measured nitrate versus predicted nitrate of pineapple flesh in calibration set of AS (a) and IS (b) and in the prediction set of AS (c) and IS (d) Average near infrared (NIR) spectra of fruit samples with different AA: a = 3.7 ppm, b = 29.8 ppm and c = ppm The regression coefficients of the PLS calibration model for pure AA Near infrared (NIR) spectra of fruit samples with different TSS/TA ratio The regression coefficients of the PLS calibration model for TSS/TA ratio. 55 VII

8 AA Abs ANN AS AWNIR DHA HPLC InGaAS IS LS-SVM MLR MSC N NIR nm PbS PC PCR PLS ppm R R 2 RPD RMSEC RMSECV RMSEP SD SEC SECV LIST OF ABBREVIATION Ascorbic acid absorbance units nonlinear like alternative to neural networks Average spectrum short-wave Near Infrared dehydroascorbic acid High-performance liquid chromatography indium gallium arsenide Individual spectra least square support vector machines multiple linear regression multiplicative scatter correlation Number of sample Near Infrared nanometer lead sulfide Principle component principle component regression partial least squares part per million Correlation coefficient Coefficient of determination Ratio of Performance to Deviation root mean square error for calibration root mean square error of cross validation root mean square error of prediction standard deviation standard error of calibration standard error of cross validation VIII

9 LIST OF ABBREVIATION (continuous) SEP standard error of prediction SEP standard error of prediction Si silicon SMLR stepwise multilinear regression SNR signal-to-noise ratio SNV standard normal variate TAC Total Acid Content TSC Total Sugar Content TSS Total Soluble Solids TSS/TA ratio Ratio of total soluble solids and titratable acid Vis-NIR Visible Near Infrared IX

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