• Korean Journal of Remote Sensing
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• v.27 no.5
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• pp.613-623
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• 2011

This study assessed the feasibility to apply Two-band and Three-band reflectance models for chlorophyll-a estimation in turbid productive waters whose scale is smaller and narrower than ocean using a high spatial resolution image. Those band ratio models were successfully applied to analyzing chlorophyll-a concentrations of ocean or coastal water using Moderate Imaging Spectroradiometer(MODIS), Sea-viewing Wide Field-fo-view Sensor(SeaWiFS), Medium Resolution Imaging Spectrometer(MERIS), etc. Two-band and Three-band models based on band ratio such as Red and NIR band were generally used for the Chl-a in turbid waters. Two-band modes using Red and NIR bands of RapidEye image showed no significant results with $R^2$ 0.38. To enhance a band ratio between absorption and reflection peak, We used red-edge band(710 nm) of RapidEye image for Twoband and Three-band models. Red-RE Two-band and Red-RE-NIR Three-band reflectance model (with cubic equation) for the RapidEye image provided significance performances with $R^2$ 0.66 and 0.73, respectively. Their performance showed the 'Approximate Prediction' with RPD, 1.39 and 1.29 and RMSE, 24.8, 22.4, respectively. Another three-band model with quadratic equation showed similar performances to Red-RE two-band model. The findings in this study demonstrated that Two-band and Three-band reflectance models using a red-edge band can approximately estimate chlorophyll-a concentrations in a turbid river water using high-resolution satellite image. In the distribution map of estimated Chl-a concentrations, three-band model with cubic equation showed lower values than twoband model. In the further works, quantification and correction of spectral interferences caused by suspended sediments and colored dissolved organic matters will improve the accuracy of chlorophyll-a estimation in turbid waters.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.581-589
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• 2021

Using visible-near infrared(Vis-NIR) spectra combined with machine learning methods, the feasibility of quick and non-destructive classification of Convolvulaceae species was studied. The main aim of this study is to classify six Convolvulaceae species in the field in different geographical regions of South Korea using a handheld spectrometer. Spectra were taken at 1.5 nm intervals from the adaxial side of the leaves in the Vis-NIR spectral region between 400 and 1,075 nm. The obtained spectra were preprocessed with three different preprocessing methods to find the best preprocessing approach with the highest classification accuracy. Preprocessed spectra of the six Convolvulaceae sp. were provided as input for the machine learning analysis. After cross-validation, the classification accuracy of various combinations of preprocessing and modeling ranged between 43.4% and 98.6%. The combination of Savitzky-Golay and Support vector machine methods showed the highest classification accuracy of 98.6% for the discrimination of Convolvulaceae sp. The growth stage of the plants, different measuring locations, and the scanning position of leaves on the plant were some of the crucial factors that affected the outcomes in this investigation. We conclude that Vis-NIR spectroscopy, coupled with suitable preprocessing and machine learning approaches, can be used in the field to effectively discriminate Convolvulaceae sp. for effective weed monitoring and management.

• Food Science and Preservation
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• v.6 no.1
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• pp.29-36
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• 1999

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.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1155-1155
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• 2001

The quality of agricultural products is very important factor for consumers. In Japan, quality is sometimes more important than cost. Usually, the quality of fresh food products is determined in terms of shape, color, size, etc. However, these indices are not always associated with taste, leaving consumers to complain. Recently, two types of the fruit quality meter (a tabletop type - K-FS200 and a portable type - K-BA100, Kubota Corp.) using NIR technology were introduced in Japan. A tabletop instrument is for post harvest use and a portable one is for precision agriculture use. The both meters use the NIR region from 600nm to 1000nm in the interactance mode to determine quality factors related to taste. The instruments can measure sugar content and acidity of such fruit as apples, tomatoes, tangerines and other fruits. The measurement is timely, nondestructive and precise. For example, the coefficient of variation (CV) is less than 6% for sugar in most fruits. The K-FS200 has been evaluated in supermarkets, grading facilities, and wholesalers in Japan. The introduction of the K-FS200) has drawn attention to taste quality and its use is becoming more popular. In addition, researchers or farmers are becoming interested in measuring product ingredient not only after harvest but also during growing in the field so that they can make intelligent judgements concerning soil amendments, such as fertilizers and water, employs the fiber probe for flexible measurement and is battery powered for field use. Design of the fruit quality meters will be discussed. Applications to fruit quality will be presented.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1529-1529
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• 2001

The watercore in apple is very important factor in storage and sorting of fruit. Most consumers tend to prefer the apple included watercore in immediately after harvest, however the watercore causes fruit flesh to brown during storage and lose the worth after all. But it is practically impossible to judge to the naked eye whether an apple has watercore or not. Therefore, the rapid, accurate and non-destructive analysis method for discrimination of watercore should be settled without delay. In this study we attempted the discrimination and quantitative analysis of watercore in apple fruit using near-infrared transmittance spectroscopy ‘Fuji’ apple fruits produced in Kyungpook of Korea was used in this experiment. The watercore content in apple was evaluated by graphic treatment of culled slice sections(10 mm). NIR transmittance spectra were collected over the 500 to 1000 nm spectral region with a spectrometer (Sentronic Co., Germany). The calibration models were carried out by partial least squares (PLS) analysis between NIR spectra data of apples and chemical data of watercore content. The spectra were different in absorbance between apple included watercore and not included one. Apple included watercore had higher absorption band than sample not included one at 732 and 820 nm. The calibration model seems to be accurate to predict the watercore content in apple fruit, the correlation coefficient (R) and root mean square error of prediction (RMSEP) were 0.99 and 0.93%, respectively. This result indicates that the PLSR calibration model by using NIR transmittance spectroscopy could be used for discrimination of watercore in apple fruit.

• Journal of Biosystems Engineering
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• v.36 no.1
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• pp.33-39
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• 2011

An optical sensing approach based on diffuse reflectance has shown potential for rapid and reliable on-site estimation of soil properties. Important sensing ranges and the resulting regression models useful for soil property estimation have been reported. In this study, a similar approach was applied to investigate the potential of reflectance sensing in estimating soil properties for Korean paddy fields. Soil cores up to a 65-cm depth were collected from 42 paddy fields representing 14 distinct soil series that account for 74% of the total Korean paddy field area. These were analyzed in the laboratory for several important physical and chemical properties. Using air-dried, sieved soil samples, reflectance data were obtained from 350 to 2500 nm on a 3 nm sampling interval with a laboratory spectrometer. Calibrations were developed using partial least squares (PLS) regression, and wavelength bands important for estimating the measured soil properties were identified. PLS regression provided good estimations of Mg ($R^2$ = 0.80), Ca ($R^2$ = 0.77), and total C ($R^2$ = 0.92); fair estimations of pH, EC, $P_2O_5$, K, Na, sand, silt, and clay ($R^2$ = 0.59 to 0.72); and poor estimation of total N. Many wavelengths selected for estimation of the soil properties were identical or similar for multiple soil properties. More important wavelengths were selected in the visible-short NIR range (350-1000 nm) and the long NIR range (1800-2500 nm) than in the intermediate NIR range (1000-1800 nm). These results will be useful for design and application of in-situ close range sensors for paddy field soil properties.

• Journal of Biosystems Engineering
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• v.37 no.3
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• pp.177-183
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• 2012

Purpose: In near infrared spectroscopy, interactance configuration of a light source and a spectrometer probe can provide more information regarding fruit internal attributes, compared to reflectance and transmittance configuration. However, there is no through study on the parameters of interactance measurement setup. The objective of this study was to investigate the effect of the parameters on the estimation of soluble solids content (SSC) and firmness of muskmelons. Methods: Melon samples were taken from greenhouses at three different harvesting seasons. The prediction models were developed at three distances of 2, 5, and 8 cm between the light source and the spectrometer probe, three measurement points of 2, 3, and 6 evenly distributed on each sample, and different number of fruit samples for calibration models. The performance of the models was compared. Results: In the test at the three distances, the best results were found at a 5 cm distance. The coefficient of determination ($R_{cv}{^2}$) values of the cross-validation were 0.717 (standard error of prediction, SEP=$1.16^{\circ}Brix$) and 0.504 (SEP=4.31 N) for the estimation of SSC and firmness, respectively. The minimum measurement point required to fully represent the spectral characteristics of each fruit sample was 3. The highest $R_{cv}{^2}$ values were 0.736 (SEP=$0.87^{\circ}Brix$) and 0.644 (SEP=4.16 N) for the estimation of SSC and firmness, respectively. The performance of the models began to be saturated when 60 fruit samples were used for developing calibration models. The highest $R_{cv}{^2}$ of 0.713 (SEP=$0.88^{\circ}Brix$) and 0.750 (SEP=3.30 N) for the estimation of SSC and firmness, respectively, were achieved. Conclusions: The performance of the prediction models was quite different according to the condition of interactance measurement setup. In designing a fruit grading machine with interactance configuration, the parameters for interactance measurement setup should be chosen carefully.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.6
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• pp.477-484
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• 2016

Small industrial appliances such as fan, audio, electric rice cooker mostly consist of ABS, PP, PS materials. In colored plastics, it is possible to classify by near infrared(NIR) spectroscopy, while in black plastics, it is very difficult to classify black plastic because of the characteristic of black material that absorbs the light. So the RBFNNs pattern classifier is introduced for sorting electrical and electronic waste plastics through LIBS(Laser Induced Breakdown Spectroscopy) spectrometer. At the preprocessing part, PCA(Principle Component Analysis), as a kind of dimension reduction algorithms, is used to improve processing speed as well as to extract the effective data characteristics. In the condition part, FCM(Fuzzy C-Means) clustering is exploited. In the conclusion part, the coefficients of linear function of being polynomial type are used as connection weights. PSO and 5-fold cross validation are used to improve the reliability of performance as well as to enhance classification rate. The performance of the proposed classifier is described based on both optimization and no optimization.

• Journal of Sensor Science and Technology
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• v.27 no.3
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• pp.170-174
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• 2018

In this paper, the $TiO_2$/glucose oxidase (GOD) mixture has synthesized through simple and low-cost fabrication methods. The physical properties of the mixture were proved using an FT-IR/NIR spectrometer, an X-Ray diffractometer, and a Raman spectrometer. GOD maintained its bioactivity during all fabrication process. The current characteristics of the glucose biosensor were proportional to the glucose concentration and effective surface area of square pyramid on a silicon substrate. The maximum current change was measured in a pH 7.0 buffer solution. The simple and low-cost fabrication process and surface treatment can be used widely in previous research for improvements in effective surface area.

• Food Engineering Progress
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• v.15 no.2
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• pp.136-142
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• 2011

The objective of this research was to classify intact soybeans and foreign objects using near-infrared (NIR) spectroscopy. Intact soybeans and foreign objects were scanned using a NIR spectrometer equipped with scanning monochromator. NIR spectra of intact soybeans and foreign objects in the wavelength range from 900 to 1800 nm were collected. The classification of intact soybeans and foreign objects were conducted by using partial least-square discriminant analysis (PLS-DA) and soft independent modelling of class analogy (SIMCA) multivariate methods. Various types of data pretreatments were tested to develop the classification models. Intact soybeans and foreign objects were successfully classified by the PLS-DA prediction model with mean normalization pretreatment. These results showed the potential of NIR spectroscopy combined with multivariate analysis as a method for classifying intact soybeans and foreign objects.