Method for the Discrimination of the Variety of Potatoes with Vis/NIR Spectroscopy

Potato (Solanum tuberosum L.), as one of the most important carbohydrate food crops in the China ranking the fourth after rice, wheat and maize, plays a significant role in national economy. Since there are many varieties of potato, the quality such as physical sensory property and chemical components, differ drastically with the variety of potato. Different potato varieties are suitable for different utilization. Thus, the rapid and nondestructive identification of potato cultivars plays an important role in the better use of varieties. Near infrared (NIR) spectroscopy has raised a lot of interest in the classification and identification of agricultural products because it is a rapid and non-invasive analytical technique. In this study, a rapid visible (VIS) and near infrared (NIR) spectroscopic system was explored as a tool to measure the diffuse spectroscopy of three different species of potatoes. 352 potato samples (Sample A 142, Sample B 84, Sample C 126) from different sites in Heilongjiang province of China, obtained from peddlers market, were randomly divided into two sets at random: calibration set and prediction set, with 307 samples and 45 samples respectively for each set. The potatoes in the calibration set were tested with visible-near infrared spectroscopy method. The spectral data obtained from this test were analyzed with near infrared spectral technology, along with data processing algorithm, i.e. Savitzky-Golay (S-G) smoothing and multiplicative scatter correction (MSC). The spectra data was firstly transformed by multiplicative scatter correction (MSC) to compensate for additive and/or multiplicative effects. In order to reduce the noise components from a raw spectroscopic data set, Savitzky-Golay smoothing and differentiation filter method were introduced. It was proved that, with the soothing segment size of 9, many high frequency noises components can be eliminated. Based on the following analysis with principal component analysis (PCA), partial least square (PLS) regression and back propagation artificial neural network (BP-ANN), a near infrared discrimination model was established. The results obtained from the partial least squares (PLS) analysis showed a positive cumulate reliability of more than 96% for the first four components. The clustering effect was also getting better. After that, twenty absorption peaks extracted from the first four principal components were applied as BP neural network inputswhile a three layers BP neural network [20(input) - 12(implicit) - 3 (output)] was constructed, upon which the recognition accuracy of potato varieties for those Prediction Set samples reaches 100%. As a result, the model established in this study can rapidly and accurately identify potato varieties without any destruction, which provides a new way for potato quality detection and variety identification.