밀병(watercore)은 과실의 저장 및 유통 산업에 있어서 큰 영향을 주므로 이를 비파괴측정할 수 있는 기술이 필요하다. 본 연구에서는 투광량을 이용한 사과의 밀병 판별 가능성과 밀병 판별에 영향을 미치는 인자들을 조사하였다. 사과의 화상데이터는 CCD 카메라를 사용하여 영상을 취득하였다. 밀병이 많이 든 사과는 밀병이 적게 든 사과보다 투광량이 더 많았으며 투광량에 의한 사과의 밀병 판별 정확도는 약 70%이었다. 사과의 과피두께, 색소층

Apple fruit grading is largely dependant on skin color degree. This work reports about the possibility of nondestructive assessment of apple fruit color using infrared(NIR) reflectance spectroscopy. NIR spectra of apple fruit were collected in wavelength range of 1100~2500nm using an InfraAlyzer 500C(Bran+Luebbe). Calibration as calculated by the standard analysis procedures MLR(multiple linear regression) and stepwise, was performed by allowing the IDAS software to select the best regression equations using raw spectra of sample. Color degree of apple skin was expressed as 2 factors, anthocyanin content by purification and a-value by colorimeter. A total of 90 fruits was used for the calibration set(54) and prediction set(36). For determining a-value, the calibration model composed 6 wavelengths(2076, 2120, 2276, 2488, 2072 and 1492nm) provided the highest accuracy : correlation coefficient is 0.913 and standard error of prediction is 4.94. But, the accuracy of prediction result for anthocyanin content determining was rather low(R of 0.761).

Using Fuji apple fruits cultivated in Kyungpook prefecture, the calibration model for firmness evaluation of fruits by near infrared(NIR) reflectance spectroscopy was developed, and the various influence factors such as instrument variety, measuring method, sample group, apple peel and selection of firmness point were investigated. Spectra of sample were recorded in wavelength range of 1100∼2500nm using NIR spectrometer (InfraAlyzer 500), and data were analyzed by stepwise multiple linear regression of IDAS program. The accuracy of calibration model was the highest when using sample group with wide range, and the firmness mean values obtained in graph by texture analyser(TA) were used as standard data. Chemometrics models were developed using a calibration set of 324 samples and an independent validation set of 216 samples to evaluate the predictive ability of the models. The correlation coefficients and standard error of prediction were 0.84 and 0.094kg, respectively. Using developed calibration model, it was possible to monitor the firmness change of fruits during storage frequently. Time, which was reached to firmness high value in graph by TA, is possible to use as new parameter for freshness of fruit surface during storage.