• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1259-1259
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• 2001

Meat becomes brown and rancid during storage in the refrigerator and display in the case. Color changes, metmyoglobin formation and lipid oxidation are the important problems in the transportation / distribution of meat and retail display. The freshness of meat is determined by the sense of vision and smell. Since conventional method determining lipid oxidation is time consuming and destructive (it needs to homogenize meat with reagents, filtrate, time for reaction and read optical density using spectroscopy), more rapid and nondestructive technical tools are desired. The objective of this work was to evaluate near-infrared spectroscopy as an analytical tool for determining meat color, metmyoglobin formation and lipid oxidation. in beef, pork and chicken. Semitendinosus and longissimus thoracis muscles from six beef steers, biceps femoris and longissimus thoracis muscles from twelve LWD crossbred pigs, and superficial pectoral muscles from twenty-four broilers were used. About a 5-cm diameter and 1-cm thick sample (20.0g) was cut from the muscle and placed on plastic foam, over-wrapped with PVC film, and displayed under flourescent lights at 4 degrees C. during 10 days for beef and pork or 4 days for chicken. The spectra was measured by NIR systems Model 5500 Spectrophotometer using fiber optic scan at range of 400 - 1100 nm. Data were recorded at 2 nm intervals and 10 scans / 10 sec were averaged for every sample. Data obtained were saved as log 1/Re, where Re is the reflectance energy, and then mathematically transformed to second derivatives to reduce effects of differences in particle size. $L^{*}$, $a^{*}$ and $b^{*}$, and metmyoglobin formation were determined by conventional spectrophotometer using the integrating sphere unit. 2-Thiobarbituric acid reactive substances (TBARS) were measured for lipid oxidation. A multiple linear regression was used to find the equation which would best fit the data. The number of wavelengths used in the equation was selected based on the fewer number compared to the increasing multiple correlation and Decreasing standard error. (omitted)

• Proceedings of the Korean Society of Crop Science Conference
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• 2005.04a
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• pp.282-283
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• 2005

• Proceedings of the Korean Society of Crop Science Conference
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• 2005.10b
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• pp.314-315
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• 2005

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1131-1131
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• 2001

To develop a nondestructive quality evaluation technique of fruits, a K-mean algorism is applied to near infrared (NIR) spectroscopy of apples. The K-mean algorism is one of neural network partition methods and the goal is to partition the set of objects O into K disjoint clusters, where K is assumed to be known a priori. The algorism introduced by Macqueen draws an initial partition of the objects at random. It then computes the cluster centroids, assigns objects to the closest of them and iterates until a local minimum is obtained. The advantage of using neural network is that the spectra at the wavelengths having absorptions against chemical bonds including C-H and O-H types can be selected directly as input data. In conventional multiple regression approaches, the first wavelength is selected manually around the absorbance wavelengths as showing a high correlation coefficient between the NIR $2^{nd}$ derivative spectrum and Brix value with a single regression. After that, the second and following wavelengths are selected statistically as the calibration equation shows a high correlation. Therefore, the second and following wavelengths are selected not in a NIR spectroscopic way but in a statistical way. In this research, the spectra at the six wavelengths including 900, 904, 914, 990, 1000 and 1016nm are selected as input data for K-mean analysis. 904nm is selected because the wavelength shows the highest correlation coefficients and is regarded as the absorbance wavelength. The others are selected because they show relatively high correlation coefficients and are revealed as the absorbance wavelengths against the chemical structures by B. G. Osborne. The experiment was performed with two phases. In first phase, a reflectance was acquired using fiber optics. The reflectance was calculated by comparing near infrared energy reflected from a Teflon sphere as a standard reference, and the $2^{nd}$ derivative spectra were used for K-mean analysis. Samples are intact 67 apples which are called Fuji and cultivated in Aomori prefecture in Japan. In second phase, the Brix values were measured with a commercially available refractometer in order to estimate the result of K-mean approach. The result shows a partition of the spectral data sets of 67 samples into eight clusters, and the apples are classified into samples having high Brix value and low Brix value. Consequently, the K-mean analysis realized the classification of apples on the basis of the Brix values.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.4114-4114
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• 2001

For the quality control of tablets several parameters have to be checked. The most important one is the content of an active ingredient which has to match a narrow range around the designated content. The only useful measurement mode is transmission which provides information of the complete tablet. A measurement in diffuse reflectance would register only the surface which is useless especially in case of a coated tablet. In this work tablets for a clinical study (placebo/verum studies) with very low concentrations of the active ingredient were measured. The concentration range was 0 to 6 mg with a total weight of the tablets of 105 mg, leading to a highest concentration of the active component of 5.7% by weight. Especially the spectroscopic distinction between the placebo and the low dosage forms with 0.25 and 0.5 mg active agent requires an extraordinarily accurate sampling technique. Using the VECTOR 22/N-T in transmission mode allows the collection of the information from the complete tablets. A quantitative PLS-model with transmission spectra from the tablets described above shows that the active substance can be predicted with a RMSECV (root mean square error of cross validation) of 0.04% absolute for this special application. The results are compared with those of measurements in diffuse reflectance using different accessories.

• Journal of Biosystems Engineering
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• v.22 no.3
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• pp.333-342
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• 1997

The objective of this research was to develop model equations for measuring rice milling ratio by using visible / HIR spectroscopy. Twelve kinds of brown rice(n = 149) were milled to obtain various milling ratio ranged from 86% to 94%. Visible/NIR spectra were collected with a spectrophotometer with sample transport module. The reflectance and transmission spectra were measured in the range of 400~2, 500nm and 600~1, 400nm, respectively, with 2 nm intervals. Multiple linear regression(MLR), Partial least square (PLS), and Artificial neural network(ANN) were used to develop models. Model developed with reflectance spectra showed better prediction results then those with transmission spectra. The MLR model with six-wavelength obtained from first derivative spectra gave to the best results for measuring the rice milling ratio(SEP = 0.535, , $r^2$ = 0.980). The PLS model(SEP = 0.604, $r^2$= 0.976) and ANN model(SEP = 0.566, $r^2$= 0.978) also can be used to determine the rice milling ratio effectively.

• Journal of Animal Environmental Science
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• v.20 no.4
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• pp.183-188
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• 2014

Near-infrared reflectance spectroscopy (NIRS) was used to study the potential of adulteration of imported forage. Hay samples were prepared two set ; calibration set and validation one. The former were mixed 12 sets from 100% to 50% with Yangcho (Chinese leymus, leymus chinensis Trin.) and the latter were adulterated with 6 set of 8% to 38% in 5% interval. Mixed materials with Yangcho were rice straw, reed and alfalfa. Stand error of prediction (SEP) in calibration equation for alfalfa, reed and rice straw were 0.97, 0.97 and 0.99 also 0.54, 0.86 and 1.26%. Multiple correlation coefficient ($R^2$) for alfalfa, reed and rice straw were 0.99, 0.97 and 0.99. SEP in the same samples were 1.88, 2.15 and 1.49, respectively.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.45 no.5
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• pp.339-342
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• 2000

Near-infrared reflectance spectroscopy (NIRS) was used to estimate the lipid and protein contents in ground seed samples of perilla (Perilla frutescens Brit.) and peanut (Arachis hypogaea L.). A total of 46 perilla and 80 peanut calibration samples and 23 perilla and 46 pea. nut NIRS validation samples were used for NIRS equation development and validation, respectively. Validation of these NIRS equations showed a range of very low bias (-0.05 to 0.13 %) and standard error of prediction corrected for bias (0.224 to 0.803%) and very high coefficient of determination ($R^2$) (0.962 to 0.985). It was concluded that NIRS could be adapted as a mass screening method for lipid and protein contents in perilla and peanut seed.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1614-1614
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• 2001

The sweetness (。Bix) of fruit is the main quality factor contributing to the fruit taste. The brix of the apple fruit can be measured non-destructively by near infrared (NIR) spectroscopy, allowing the sweetness grading of individual apple fruit. However, the fruit quality is influenced by various factors such as growing location, producing year, variety and harvest time etc., accordingly the robust NIR calibration is required. In this experimental results are presented the influence of two variations such as growing location and producing year of apple fruit in establishing of calibrations for sweetness, and developed a stable and highly accurate calibration. Apple fruit (Fuji) was collected every year from 1995 to 1997 in 3 different growing locations (Andong, Youngchun and Chungsong) of Kyungpook in Korea. NIR reflectance spectra of apple fruit were scanned in wavelength range of 1100∼2500nm using an InfraAlyzer 500C (Bran+Luebbe) with halogen lamp and PbS detector. The multiple linear regression and stepwise was carried out between the NIR raw spectra and the brix measured by refractometer to select the best regression equations. The calibration models by each growing district were well predicted to dependent sample set, but poorly predicted to independent sample set. Combined calibration model using data of three growing districts predicted reasonable well to a population set drawn from all growing districts(SEP = 0.69%, Bias=-0.075). The calibration models by each harvest year were not transferable across harvest year, however a combined calibration model using data of three harvest years was sufficiently robust to predict each sample sets(SEP = 0.53%, Bias = 0.004).

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.3112-3112
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• 2001

Near-Infrared Spectroscopy (NIRS) was investigated aiming at the identification of falsified drugs. The identification is based on comparison of the NIR spectrum of a sample with a typical spectra of an authentic drug using multivariate modelling and classification algorithms (PCA/SIMCA). Two spectrophotometers (Brimrose - Luminar 2000 and 2030), based on acoustic-optical filter (AOTF) technology, sharing the same controlling computer, software (Brimrose - Snap 2.03) and the data acquisition electronics, were employed. The Luminar 2000 scans the range 850 1800 nm and was employed for transmitance/absorbance measurements of liquids with a transflectance optical bundle probe with total optical path of 5 mm and a circular area of 0.5 $\textrm{cm}^2$. Model 2030 scans the rage 1100 2400 nm and was employed for reflectance measurement of solids drugs. 300 spectra, acquired in about 20 s, were averaged for each sample. Chemometric treatment of the spectral data, modelling and classification were performed by using the Unscrambler 7.5 software (CAMO Norway). This package provides the Principal Component Analysis (PCA) and SIMCA algorithms, used for modelling and classification, respectively. Initially, NIRS was evaluated for spectrum acquisition of various drugs, selected in order to accomplish the diversity of physico-chemical characteristics found among commercial products. Parameters which could affect the spectra of a given drug (especially if presented as solid tablets) were investigated and the results showed that the first derivative can minimize spectral changes associated with tablet geometry, physical differences in their faces and position in relation to the probe beam. The effect of ambient humidity and temperature were also investigated. The first factor needs to be controlled for model construction because the ambient humidity can cause spectral alterations that should cause the wrong classification of a real drug if the factor is not considered by the model.