The National Science Foundation's (NSF) Tokyo Office periodically receives and disseminates reports on research developments in Japan that are related to the Foundation's mission. NSF-sponsored researchers currently working in Japan prepare many of these reports. These reports present information for use by NSF program managers and policy makers; they are not statements of NSF policy.
Ms. Andrea M. Ivers, a graduate student in the Department of Agricultural Engineering at Texas A&M University, prepared the following report. Ms. Ivers is a participant in the 1999 Summer Institute sponsored in the United States by NSF/NIH/USDA and the Science and Technology Agency and Japan Science and Technology Corporation in Japan. Dr. Takaaki Satake of Food Process Engineering Lab at University of Tsukuba hosted Ms. Ivers. Ms. Ivers can be reached via email at: andrea.ivers@usa.net
OBJECTIVES
ARTIFICIAL NEURAL NETWORK (ANN)
A Neural Network is an interconnected group of nodes that learns from a set of training patterns. This processing ability is stored as a set of weights. These nodes are arranged in a layered structure where generally the input nodes fed into the hidden nodes and then feed into output nodes. This is called a feedforward system and is one of many available. The question becomes what can neural networks be used for? Most often they are used for statistical analysis and data modeling. The main idea behind neural networks is to input a set of variables or characteristics that can be used to give a desired output. For example, one paper used Artificial Neural Network (ANN) to estimate the inner quality of apples. Such characteristics as weight, color, oxygen, and carbon dioxide levels were inputted. The output was total soluble solids (TSS) and titratable acidity (TA). The TSS and TA were found by standard method and compared to the output of the neural network. In comparison, ANN was an excellent judge of TSS and TA. I used these same concepts to test a simulated graph of two groups (Types I and II)
NEAR INFRARED RAYS (NIR)
Near Infrared (NIR) technology is a convenient tool for not only characterization of foods, but also quality and process control. In the beginning, NIR technology was used for compositional analysis of grains, beans and seeds because of there relative low moisture. Over time, NIR was developed to be used with high moisture foods such as fruits and vegetables, and has even been expanded to be used in the medical, textile industries.
NIR spectroscopy looks as the radiation wavelength range of 800-2500 nm. Vibrations caused by overtones, or by combinations of vibrations instigate absorption within this known region. This occurs mainly with functional groups containing a hydrogen atom. These absorption spectra can then be evaluated for similarities in water, starch, and lipid content. The functional group of each component helps to define the absorption bands of that component.
Several types of analysis can be used on this data, including qualitative and quantitative analysis, depending on what you are testing. I used Principle Component Analysis (PCA) on my experiments. PCA is a statistical model that compresses correlated variables to usually one, two or possible three components.
RESEARCH
I was able to run two separate experiments, both evaluating the qualitative aspects of the mediums. In my experiment at the National Food Research Institute, four types of liquids were tested, three types of alcohol (different quality sakes) and water. Twenty samples of each, distributed in a partially random fashion, were run through the NIR system. It is interesting to note that the higher the alcohol content the more noticeable the curves within the 2250-2350 nm absorbency band. The 1900 nm absorbency band shows the water content of each sample. The control, water, having the highest band, and the low quality sake having the lowest band. The second experiment tested popular drinks in Japan for their sugar content. Milk, orange juice, cola, tea, and a sport drink were all tested against water, to compare the sugar content of each.
Principle Component Analysis (PCA) was used for qualitative analysis of the first experiment. PCA shows a clear distinction between each test group. After looking at several derivations of the PCA graphs, any outliers, points outside of the group, were shown to be samples tested near the end of the testing sequence. The thought is that a decrease in temperature over time caused these points to become outliers. Due to lack of time, the second experiment was not analyzed statistically, but looking at the absorbency curves showed a distinction. Overall, NIR is a very effective and useful method for classification of sake based on quality as shown by our preliminary tests.
INSTITUTE VISITS
The second part of my objective in Japan, was to visit research institutes and companies with interests similar to my research at home to see what technology was being used, and what research was being done.
National Food Research Institute
The Institute focuses on research of postharvest technology. This covers the preservation, processing, nutrition, safety, analysis, development, and utilization of materials. I was able to visit the nondestructive evaluation laboratory, where NIR is being used in many aspects of the food industry.
Bio-oriented Technology Research Advancement Institution (BRAIN)
BRAIN essentially promotes research in the agricultural and biotechnological fields of private enterprises. The Institute of Agricultural Machinery (IAM) was at first a government corporation but was later taken over by BRAIN. IAM still holds the majority of the responsibility in agricultural machinery research. IAM looks at the impact of cost on consumers and producers of agricultural products, and tries to invent or renovate ideas to reduce these costs. Here I was able to visit the post harvest Laboratory and see technology on nondestructive quality evaluation of fruits.
Nisshin Milling Company
Nisshin is the top four milling company in Japan. I visited their Quality Exam (QE) Center, built in November 1998. The QE Center evaluates products for safety and quality.
CONCLUSIONS
Overall, I felt that my research in Japan was a learning experience in several ways. First, I was able to learn bout Japanese research efforts, by helping with current experiments, seeing what type of research is being done, and by visiting research facilities. Second, I was able to participate in small scale research activities during my stay. Third, I was able to learn techniques and concepts being used in similar fields to my own.