Nondestructive Classification Analysis of Green Coffee Beans by Using Near-Infrared Spectroscopy.

Near-infrared spectroscopy (NIRS) is a powerful tool for the nondestructive evaluation of organic materials, and it has found widespread use in a variety of industries. In the food industry, it is important to know the district in which a particular food was produced. Therefore, in this study, we focused on determining the production area (five areas and three districts) of green coffee beans using classification analysis and NIRS. Soft independent modeling of class analogy (SIMCA) was applied as the classification method. Samples of green coffee beans produced in seven locations-Cuba, Ethiopia, Indonesia (Bari, Java, and Sumatra), Tanzania, and Yemen-were analyzed. These regions were selected since green coffee beans from these locations are commonly sold in Japan supermarkets. A good classification result was obtained with SIMCA for the seven green bean samples, although some samples were partly classified into several categories. Then, the model distance values of SIMCA were calculated and compared. A few model distance values were ~10; such small values may be the reason for misclassification. However, over a 73% correct classification rate could be achieved for the different kinds of green coffee beans using NIRS.

Feasibility of near-infrared spectroscopy for on-line grading of sawn lumber.

Near-infrared (NIR) spectroscopy, coupled with multivariate analysis, has been used to evaluate the wood properties of sawn lumber of Japanese larch (Larix kaempferi), whose diffuse reflection spectra were acquired under static and moving conditions. Prediction models of the dynamic modulus of elasticity (E(fr)), the modulus of elasticity in bending tests (E(b)), the bending strength (F(b)), the wood density (DEN), and the moisture content (MC) were developed using partial least squares (PLS) analysis. For all wood properties, models obtained from data collected under the moving condition as an analogue of on-line measurement were superior to those from the static condition data. The regression coefficients for the PLS models predicting the mechanical properties in both static and moving conditions showed clear peaks at the absorption bands due to the three major polymers of wood, i.e., cellulose, hemicellulose, and lignin. NIR spectroscopy has high potential for the on-line grading of sawn lumber.

Application of time-of-flight near-infrared spectroscopy to fruits: analysis of absorption and scattering conditions of near-infrared radiation using cross-correlation of the time-resolved profile.

The absorption and scattering conditions of near-infrared radiation in a grapefruit, a popular thick-peeled fruit, were investigated by time-of-flight near-infrared spectroscopy (TOF-NIRS). The cross-correlation function was introduced to obtain fine spectroscopic information from the time-resolved profile. Variation of the optical parameters in both the time-resolved profile and the cross-correlation function showed that the NIR radiation was largely absorbed in the peel and considerably scattered in the flesh of the fruit. It also reflected the straightness of the input pulsed laser. The substantial optical path length of the grapefruit estimated from the cross-correlation function was approximately 4 to 5 times as long as the nominal optical path length (NOPL). The cross-correlation function was an effective tool to analyze the absorption/scattering conditions of NIR radiation in a sample where an unstable light source such as a Nd:YAG laser with high output energy was employed.