• Food Science and Biotechnology
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• v.16 no.3
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• pp.477-481
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• 2007

Fourier transform near-infrared (FT-NIR) spectroscopy and partial least squares (PLS) regression were used to predict the amylose content of polished rice. Spectral reflectance data in a wavelength range of 1,000 to 2,500 nm were obtained with a commercial spectrophotometer for 60 different varieties of Korean rice. For a comparison of this spectroscopic method to a standard chemical analysis, the amylose contents of the tested rice samples were determined by the iodine-blue colorimetric method. The highest correlation for the rice amylose ($R^2=0.94$, standard error of prediction=0.20% amylose content) was obtained when using the FT-NIR spectrum data pre-treated with normalization, the first derivative, smoothing, and scattering correction.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.3105-3105
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• 2001

Ingests and fecal contamination on a poultry carcass is a food safety hazard due to potential microbiological contamination. A visible/near-infrared (NIR) spectrometer was used to discriminate among pure ingesta and fecal material, breast skin contaminated with ingesta or fecal material and uncontaminated breast skin. Birds were fed isocaloric diets formulated with either maize, mile, or wheat and soybean meal for protein requirements. Following completion of the feeding period (14 days), the birds were humanely processed and eviscerated to obtain ingests from the crop or proventriculus and feces from the duodenum, ceca, and colon portion of the digestive tract. Pure feces and ingesta, breast skin, and contaminated breast skin were scanned from 400 to 2500 nm and analyzed from 400 to 900 nm. Principal component analysis (PCA) of reflectance spectra was used to discriminate between contaminates and uncontaminated breast skin. Results indicate that visible (400 to 760 nm) and NIR 760-900 nm spectra can detect contaminates. From PCA analysis, key wavelengths were identified for discrimination of uncontaminated skin from contaminates based the evaluation of loadings weights.

• Proceedings of the Korean Society for Food Science of Animal Resources Conference
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• 2000.11a
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• pp.1-14
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• 2000

The Near Infrared region of the energy spectrum was first discovered by Hershel in the year 1800. The principles of NIR is based on light absorption of specific organic chemical bonds. The absorption at each wavelength is measured and a spectre is obtained. The spectre is then treated mathematically and with the absorption data is converted to absolute units via a calibration. In the last two decades it has developed dramatically. With the invention of computers and the ability to treat a large amount of data in a very short time the use of NIR for many different purposes has developed very fast. During the last decade with the aid of very powerful PC's the application of NIR technology has become even more widespread. Now or days development of very robust calibrations can be done in a relatively short time with a minimum of resources. The use of Near Infrared Spectroscopy (NIR) in the Meat industry is relatively new. The first installations were taken into operation in the 80ties. The Meat Industry in often referred to as rather conservative and slow to embrace new technologies, they stay with the old and proven methods. The first NIR instruments used by the Meat Industry, and most other industries, were multipurpose build, which means that the sample presentation was not well suited to this particular application, or many other applications for that sake. As the Meat Industry grows and develops to meet the demands of the modern markets, they realise the need for better control of processes and final products. From the early 90 ties and onward the demand for 'rear time' rapid results starts growing, and some suppliers of NIR instruments (and instruments based on other technologies, like X-ray) start to develop and manufacture instrumentation dedicated to the particular needs of the Meat Industry. Today it is estimated that there are approximately 2000 rapid instruments placed in the Meat industry world-wide. By far most of these are used as at-line or laboratory installations, but the trend and need is moving towards real on-line or in-line solutions. NIR is the most cost effective and reproducible analytical procedure available for the twenty first century.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.49 no.spc1
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• pp.250-258
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• 2004

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.7
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• pp.1017-1025
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• 2000

The traditional methods for determining the quality of milk, cheese and meat are tedious and expensive, with a significant wastage of chemicals which pollute the environment. To overcome these disadvantages, the potential of near infrared spectrophotometry (NIR) for monitoring the quality of milk and meat has been evaluated by a number of researchers. While most studies indicate that NIR can be used to predict chemical composition of milk and meat, and to monitor the cutting-point during cheese manufacturing, one study demonstrated the potential of NIR to predict sensory characteristics (e.g. hardness and tenderness) of beef. These calibrations were developed on a small number of samples, limiting their value for adoption by the industries. Now that the sophisticated computer software is available, more robust calibrations need to be developed to monitor both chemical and physical characteristics of meat and meat products simultaneously.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1248-1248
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• 2001

Mastitis is a major problem for the global dairy industry and causes substantial economic losses from decreasing milk production and considerable compositional changes in milk, reducing milk quality. The potential of near infrared (NIR) spectroscopy in the region from 1100 to 2500nm and chemometric method for classification to detect milk from mastitic cows was investigated. A total of 189 milk samples from 7 Holstein cows were collected for 27 days, consecutively, and analyzed for somatic cells (SCC). Three of the cows were healthy, and the rest had mastitis periods during the experiment. NIR transflectance milk spectra were obtained by the InfraAlyzer 500 spectrophotometer in the spectral range from 1100 to 2500nm. All samples were divided into calibration set and test set. Class variable was assigned for each sample as follow: healthy (class 1) and mastitic (class 2), based on milk SCC content. The classification of the samples was performed using soft independent modeling of class analogy (SIMCA) and different spectral data pretreatment. Two concentration of SCC - 200 000 cells/ml and 300 000 cells/ml, respectively, were used as thresholds fer separation of healthy and mastitis cows. The best detection accuracy was found for models, obtained using 200 000 cells/ml as threshold and smoothed absorbance data - 98.41% from samples in the calibration set and 87.30% from the samples in the independent test set were correctly classified. SIMCA results for classes, based on 300 000 cells/ml threshold, showed a little lower accuracy of classification. The analysis of changes in the loading of first PC factor for group of healthy milk and group of mastitic milk showed, that separation between classes was indirect and based on influence of mastitis on the milk components. The accuracy of mastitis detection by SIMCA method, based on NIR spectra of milk would allow health screening of cows and differentiation between healthy and mastitic milk samples. Having SIMCA models, mastitis detection would be possible by using only DIR spectra of milk, without any other analyses.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1622-1622
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• 2001

The purpose of this research was to develop a the reflection technique with near infrared (NIR) radiation for estimating physico-chemical properties in compost. The composts (cattle, pig, chicken and waste composts) were air dried and then ground to pass through a 0.5 or 2mm sieve for the physico-chemical properties and spectroscopic determinations. The physico-chemical properties of compost were shown high values ; moisture(30-60%), T-N(0.8-2.9%), organic matter(29-89%), pH(5.89-9.60) K$_2$O(0.27-5.66%), P2O$\sub$5/(0.07-2.62%), CaO(0.03-4.80%), MgO(0.09-1.56%), NaCl(0.01-1.13%), EC(1.41-13.76dS/m). Generally, we should select a simple calibration and prediction method for determining physico-chemical properties in compost under similar accuracy and precision of prediction. It should be remembered that the NIRS approach will never replace the traditional methods. However, NIRS technique may be an effective method for rapid and nondestructive measurements of a large number of compost samples. Near infrared reflectance spectra of composts was obtained by Infra Alyzer 500 scanning spectrophotometer at 2-nm intervals from 1100 to 2500nm. Multiple linear regression(MLR) or partial least square regression (PLSR) was used to evaluate a NIRS method for the rapid and nondestructive determination of physico-chemical properties and humic acid contents in composts. The standard error of prediction(SEP) for finely sized sample(<0.5mm) and coarsely sized sample(<2mm) did not show much difference. The NIR instrument of filter type showed the same accuracy of the monochromator scanning type to estimate the compost properties. The results summarized that NIR spectroscopy can be used as a routine testing method to determine quantitatively the OM, moisture, T-N, color, pH, cation content in the compost samples nondestructively.

• 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.1252-1252
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• 2001

The aim of this study was to evaluate the possibility to characterize and classify waxes applied on some type of cheeses to obtain good stability during handling and transportation. Generally, waxes are obtained from the petrochemical industry, nowadays there is the possibility to also use biodegradable waxes produced from microorganisms. Preliminary studies were carried out to optimize sample presentation in NIR analysis, such as melting conditions (influence of temperature) and coat thickness of wax. 12 waxes (biodegradable or not) were analysed by using an InfraAlyzer 500 (Bran+Luebbe). The sample size was performed cutting pieces of 1.5 cm (height) x 1.5 cm (width) x 1.5 mm (thickness), previously melted at 9$0^{\circ}C$. NIR spectra were collected at room temperature, and data were processed by Sesame Software (Bran+Luebbe) to evaluate qualitative differences among samples by cluster analysis. Waxes were gathered on the basis of their origin (petrochemical or microbial). To better understand the significance of the NIRS bands discriminating among waxes, a two-dimensional correlation with FT-IR spectra, collected by a FT-IR/ATR 420 (JASCO) instrument, was made using 2DCORR program (Galactic Industries). On the basis of its classification power, NIRS appears to be a promising tool when used in routine analysis for a qualitative control of raw materials.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.3114-3114
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• 2001

During the last years phytochemistry and phytopharmaceutical applications have developed rapidly and so there exists a high demand for faster and more efficient analysis techniques. Therefore we have established a near infrared transflectance spectroscopy (NIRS) method that allows a qualitative and quantitative determination of new polyphenolic pharmacological active leading compounds within a few seconds. As the NIR spectrometer has to be calibrated the compound of interest has at first to be characterized by using one or other a combination of chromatographic or electrophoretic separation techniques such as thin layer chromatography (TLC), high performance liquid chromatography (HPLC), capillary electrophoresis (CE), gas chromatography (GC) and capillary electrochromatography (CEC). Both structural elucidation and quantitative analysis of the phenolic compound is possible by direct coupling of the mentioned separation methods with a mass spectrometer (GC-MS, LC-MS/MS, CE-MS, CEC-MS) and a NMR spectrometer (LC-NMR). Furthermore the compound has to be isolated (NPLC, MPLC, prep. TLC, prep. HPLC) and its structure elucidated by spectroscopic techniques (UV, IR, HR-MS, NMR) and chemical synthesis. After that HPLC can be used to provide the reference data for the calibration step of the near infrared spectrometer. The NIRS calibration step is time consuming, which is compensated by short analysis times. After validation of the established NIRS method it is possible to determine the polyphenolic compound within seconds which allows to raise the efficiency in quality control and to reduce costs especially in the phytopharmaceutical industry.