• 농업과학연구
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• 제39권1호
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• pp.111-116
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• 2012

Nondestructive evaluation of seed viability is one of the highly demanding technologies for seed production industry. Conventional seed sorting technologies, such as tetrazolium and standard germination test are destructive, time consuming, and labor intensive methods. Near infrared spectroscopy technique has shown good potential for nondestructive quality measurements for food and agricultural products. In this study, FT-NIR spectroscopy was used to classify normal and artificially aged lettuce seeds. The spectra with the range of 1100~2500 nm were scanned for lettuce seeds and analyzed using the principal component analysis(PCA) method. To classify viable seeds from nonviable seeds, a calibration modeling set was developed with a partial least square(PLS) method. The calibration model developed from PLS resulted in 98% classification accuracy with the Savitzky-Golay $1^{st}$ derivative preprocessing method. The prediction accuracy for the test data set was 93% with the MSC(Multiplicative Scatter Correction) preprocessing method. The results show that FT-NIR has good potential for discriminating non-viable lettuce seeds from viable ones.

• Journal of Biosystems Engineering
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• 제41권1호
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• pp.51-59
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• 2016

Purpose: This study examined the performance of two spectroscopy methods and multivariate classification methods to discriminate viable pepper seeds from their non-viable counterparts. Methods: A classification model for viable seeds was developed using partial least square discrimination analysis (PLS-DA) with Fourier transform near-infrared (FT-NIR) and Raman spectroscopic data in the range of $9080-4150cm^{-1}$ (1400-2400 nm) and $1800-970cm^{-1}$, respectively. The datasets were divided into 70% to calibration and 30% to validation. To reduce noise from the spectra and compare the classification results, preprocessing methods, such as mean, maximum, and range normalization, multivariate scattering correction, standard normal variate, and $1^{st}$ and $2^{nd}$ derivatives with the Savitzky-Golay algorithm were used. Results: The classification accuracies for calibration using FT-NIR and Raman spectroscopy were both 99% with first derivative, whereas the validation accuracies were 90.5% with both multivariate scattering correction and standard normal variate, and 96.4% with the raw data (non-preprocessed data). Conclusions: These results indicate that FT-NIR and Raman spectroscopy are valuable tools for a feasible classification and evaluation of viable pepper seeds by providing useful information based on PLS-DA and the threshold value.

• 원예과학기술지
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• 제30권6호
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• pp.709-717
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• 2012

To establish the standard of ripe kiwifruit sorting, near infrared (NIR) spectroscopy was performed on kiwifruit sampled from three farms. Destructive measurements of flesh firmness, soluble solids content (SSC), and acidity were performed and compared to measurement using NIR reflectance spectrums from 408 to 2,492 nm. NIR predictions of those quality factors were calculated using the modified partial least square regression method. Flesh firmness was predicted with a standard error of prediction (SEP) of 3.32 N and with a correlation coefficient ($R^2$) of 0.88. SSC was predicted with SEP of $0.49^{\circ}Brix$ and with $R^2$ of 0.98. Acidity was predicted with SEP of 0.28% and with $R^2$ of 0.91. Kiwifruit ripened at $20^{\circ}C$ for 15 days showed uneven qualities with normal distribution. Considering the SEP of each parameter, kiwifruit after ripening treatment could be non-destructively predicted their qualities and sorted by flesh firmness or soluble solids content through NIR prediction.

• 농업과학연구
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• 제39권1호
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• pp.117-123
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• 2012

Pathogenic fungi and bacteria such as Pectobacterium atrosepticum, Clavibacter michiganensis subsp. sepedonicus, Verticillium albo-atrum, and Rhizoctonia solani were the major microorganism which causes diseases in seed potato during postharvest process. Current detection method for disease-infected seed potato relies on human inspection, which is subjective, inaccurate and labor-intensive method. In this study, a reflectance spectroscopy was used to classify sound and disease-infected seed potatoes with the spectral range from 400 to 1100 nm. Partial least square discriminant analysis (PLS-DA) with various preprocessing methods was used to investigate the feasibility of classification between sound and disease-infected seed potatoes. The classification accuracy was above 97 % for discriminating disease seed potatoes from sound ones. The results show that Vis/NIR reflectance method has good potential for non-destructive sorting for disease-infected seed potatoes.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.3123-3123
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• 2001

Near infrared (NIR) spectroscopy has become a widely used method in food and beverage analysis because of its speed, accuracy and the simplicity of sample preparation. One of the basic requirements of NIR instruments is a wide dynamic range if weak, or small, absorption changes or concentrations are to be measured. Thus the instrument must be sufficiently luminous, and efficient, to enable measurements to be made in a reasonably short time, as for some applications (e.g. sorting) short response times are essential. Diode lasers function the same way as lasers but linewidths are not as narrow as typical lasers. In this work an array of seven laser diodes (in the range of 750-1100 nm) with energy outputs of around hundred milliwatts each were combined with a fast diode array spectrometer (400-1100 nm, 1024 pixels, integration time from 3 ms) as detector. Measurements in transmission mode were performed in solutions of sugars in aqueous solutions and in deuteriumoxide. The feasibility of non-destructive measurements in transmission mode was investigated for different fruits and vegetables.

• 한국산학기술학회논문지
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• 제22권1호
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• pp.208-213
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• 2021

본 논문에서는 농산물 산지 유통 센터에서 설치되어 사용하고 있는 비파괴 과일 선별 시스템을 이용하여 정상 사과와 내부에 결함이 있는 사과를 판별하기 위한 최적 파장과 해당 스펙트럼의 특성을 구명하고자 하였다. 총 54개 사과에 대해 470 - 1150 nm의 파장 범위에서 정상 사과와 갈변 사과의 투과 스펙트럼을 획득하였다. 주성분 분석(PCA)을 활용하여 정상 사과와 내부 갈변 사과의 군집을 확인하였으며, 판별 모델의 개발과 평가를 위해 부분최소제곱회귀(PLSR) 분석을 수행하였다. PCA 분석에서는 정상 사과와 내부 갈변 사과 군집의 확연한 구분이 보여 높은 판별율의 결과를 보여주었다. PLSR 분석 결과, 전처리를 하지 않은 예측 모델의 상관계수(R)는 0.902, RMSE 값은 0.157이었으며, 전처리를 적용했을 때 예측 모델의 상관계수는 0.906, RMSE 값은 0.154이었다. 따라서, 이 PLSR 모델은 이 시스템을 활용해 내부 갈변이 있는 사과도 우수하게 판별할 수 있음을 알 수 있었다. 이와 같은 방식을 이용할 경우, 외부 결함과 더불어 내부 결함에 대한 농산물 선별과 평가에 적용될 수 있을 것으로 사료된다.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1267-1267
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• 2001

The existing fruit sorter has the method of tilting tray and extracting fruits by the action of solenoid or springs. In peaches, the most sort processing is supported by man because the sorter make fatal damage to peaches. In order to sustain commodity and quality of peach non-destructive, non-contact and real time based sorter was needed. This study was performed to develop peach sorter using near-infrared spectroscopy in real time and nondestructively. The prototype was developed to decrease internal and external damage of peach caused by the sorter, which had a way of extracting tray with it. To decrease positioning error of measuring sugar contents in peaches, fiber optic with two direction diverged was developed and attached to the prototype. The program for sorting and operating the prototype was developed using visual basic 6.0 language to measure several quality index such as chlorophyll, some defect, sugar contents. The all sorting result was saved to return farmers for being index of good quality production. Using the prototype, program and MLR(multiple linear regression) model, it was possible to estimate sugar content of peaches with the determination coefficient of 0.71 and SEC of 0.42bx using 16 wavelengths. The developed MLR model had determination coefficient of 0.69, and SEP of 0.49bx, it was better result than single point measurement of 1999's. The peach sweetness grading system based on NIR reflectance method, which consists of photodiode-array sensor, quartz-halogen lamp and fiber optic diverged two bundles for transmitting the light and detecting the reflected light, was developed and evaluated. It was possible to predict the soluble solid contents of peaches in real time and nondestructively using the system which had the accuracy of 91 percentage and the capacity of 7,200 peaches per an hour for grading 2 classes by sugar contents. Draining is one of important factors for production peaches having good qualities. The reason why one farm's product belows others could be estimated for bad draining, over-much nitrogen fertilizer, soil characteristics, etc. After this, the report saved by the peach grading system will have to be good materials to farmers for production high quality peaches. They could share the result or compare with others and diagnose their cultural practice.

• 비파괴검사학회지
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• 제34권1호
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• pp.31-37
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• 2014

본 논문에서는 가시광 및 근적외선(VIS/NIR) 분광기술을 이용하여 혈란을 비파괴적으로 검출할 수 있는 측정방법을 개발하고자 하였다. 실험에 사용된 혈란 시료는 0.1 mL, 0.07 mL, 0.04 mL, 0.01 mL의 닭의 혈을 인공적으로 노른자 주변에 주입하여 제작하였다. 분석을 위해 사용된 스펙트럼 영역은 471 ~ 1154nm 이었으며, 스펙트럼 해상도는 1.5 nm이었다. 혈란 측정을 위한 두 개 광원의 각도는 30도이었으며 광원과 시료간 거리는 100 mm로 설정하였다. 또한 광원의 열로 인해 시료가 손상되지 않도록 측정을 위한 노출시간은 30 ms으로 설정하였다. 측정된 스펙트럼에 부분최소자승 분류방법(PLS-DA)을 적용하여 선별모델을 개발하였다. 개발된 모델들로 분석한 결과 혈란의 선별 정확도는 0.1 mL, 0.07 mL 0.04 mL, 0.01 mL 혈란 시료에 대해 각각 97.9%, 97.9%, 94.8%, 86.5% 이었다. 가시광 및 근적외선 스펙트럼 기술은 0.04 mL 이상의 혈점이 포함된 혈란을 비파괴적으로 선별 할 수 있는 가능성을 보여 주었다.

• 비파괴검사학회지
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• 제32권5호
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• pp.518-525
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• 2012

본 연구에서는 초분광 반사광 영상기술을 이용하여 비파괴적으로 상추의 건전종자와 퇴화종자를 선별하는 기술을 개발하고자 하였다. 750~1000nm의 근적외선 초분광 반사광 영상의 분광데이터를 이용하여 상추의 발아종자와 불발아 종자를 판별하는 PLS-DA 모델을 개발하고 개발된 모델의 성능 평가를 실시하였다. 모델 calibration의 판별 정확도는 81.6%였으며, test의 결과는 81.2%의 판별 정확도를 보였다. 또한 개발된 PLS-DA 모델을 적용한 초분광 반사광 영상을 이용하여 대량의 불발아 종자를 동시에 영상으로 검출 가능한 영상처리 알고리즘을 개발하였다. 초분광 반사광 영상에 PLS-DA 모델이 적용된 영상을 이용한 검출 정확도는 91%로 나타났으며, 이는 초분광 반사광 영상을 이용하여 대량의 상추 종자의 비파괴 품질선별에 이용될 수 있음을 보여 주었다.

• Journal of Biosystems Engineering
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• 제38권4호
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• pp.312-317
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• 2013

Purpose: Conventional methods used to evaluate seeds viability are destructive, time consuming, and require the use of chemicals, which are not feasible to implement to process plant in seed industry. In this study, the effectiveness of Fourier transform near infrared (FT-NIR) spectroscopy to differentiate between viable and nonviable watermelon seeds was investigated. Methods: FT-NIR reflectance spectra of both viable and non-viable (aging) seeds were collected in the range of 4,000 - 10,000 $cm^{-1}$ (1,000 - 2,500 nm). To differentiate between viable and non-viable seeds, a multivariate classification model was developed with partial least square discrimination analysis (PLS-DA). Results: The calibration and validation set derived from the PLS-DA model classified viable and non-viable seeds with 100% accuracy. The beta coefficient of PLS-DA, which represented spectral difference between viable and non-viable seeds, showed that change in the chemical component of the seed membrane (such as lipids and proteins) might be responsible for the germination ability of the seeds. Conclusions: The results demonstrate the possibility of using FT-NIR spectroscopy to separate seeds based on viability, which could be used in the development of an online sorting technique.