Development of Dispersive and Fourier Transform Near Infrared Spectroscopy Methodology for Food and Edible Seed Analysis

Fast and economical measurements of food composition allow online quality control and chemical analysis in food production. Soybean breeding programs require rapid and accurate composition determinations for large populations through a suitable technique, such as Near Infrared (NIR). Novel calibrations for soy and other health foods were developed with Fourier Transform Near Infrared Reflectance Spectroscopy (FT-NIRS). Accurate calibrations were also developed for soy tofu and soymilk. All major food components protein, fat, moisture and carbohydrates were quantitated. These calibrations are characterized by low standard errors (<&sim;1.0% for protein, fat and moisture) and also by high degrees of correlation (&sim;99%). The first calibrations for soybean isoflavones reported here were also characterized by low standard errors (<0.02%) and high degrees of correlation (&sim;99%). Improved data processing procedures, novel calibration models and improved high quality calibrations were developed for both whole and ground soybeans with Dual Diode Array NIR Reflectance Spectroscopy. NIR calibrations for total small sugars in whole soybean seeds were developed for the first time. Black seed coat effects on soybean composition were eliminated by measuring ground soybean samples with FT-NIR. Data analysis of 17 different soybean groups indicates that there is a high level of inverse correlation between protein and oil mean contents of soybeans (-R > 0.90). The protein-oil inverse correlations are similarly high for soybean groups with a large number of lines developed over ten years (-R between 0.80 and 0.97), which can be another indicator for consistent and accurate measurements. Validation and evaluation of four new dispersive NIR instruments that are suitable for soybean composition analysis showed that, novel NIR methodology developed over last four years in this research establishes NIR as a secondary analytical method in agriculture and food industry.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Food Science and Human Nutrition

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2015

Author dc:creator

• Guo, Jun

Contributors dc:contributor

• Baianu, Ion C.

• Agriculture, Food Science and Technology

Language dc:language

eng

Identifier

(MiAaPQ)AAI3130929

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/83688