• Near Infrared Analysis
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• 제1권2호
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• pp.29-33
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• 2000

The identification step of raw drug materials is an indispensible procedure in the GMP manufacturing process within the pharmaceutical industry. However, wet chemistry methods for identification of drug materials, used by the various Pharmacopeia are time-consuming and expensive steps. In this paper, near-infrared spectroscopy (NIRS) has been developed for identifying eleven drug substances including calcium pantothenate, cefaclor, cefoperazone, cephradine, dextromethorphan, ehtambutol, nicotinamide, pyrozinamide, tramadol, vitamin C, and vitamin E. Also the aim of ths work is to consturct a new algorithm for calibration model using soft independence modeling of class analogy (SIMCA) with Malinowskis Indicator Function (IND), which is used for finding the number of principal components of each class of the SIMACA model. The use of NIR technique with pattern recognition to qualify raw materials can make it possible to monitor process in real time as well as to control all procedures in the pharmaceutical industry. As the result, the samples identified of 183 different batches from 11 different compounds were separated clearly by SIMCA with 2nd derivative spectra in the NIR region of 1100∼2400 nm.

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

• 약학회지
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• 제49권1호
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• pp.74-79
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• 2005

Metabonomic analysis has been recognized as a powerful approach for characterizing metabolic changes in biofluids due to toxicity, disease process or environmental influences. To investigate the possibility of relating metabolic changes with $^{1}H-NMR$ spectra, urine samples from Sprague-Dawley rats treated with various dietary restrictions or toxic substances (nicotine) were analysed using $^{1}H-NMR$ spectroscopy and pattern recognition techniques. Dietary restrictions-given to male rats were normal diet and high fat diet and fasting. The nicotine urine samples were collected from SD rats administered with nicotine (25 mg/kg) at the various time intervals. $^{1}H-NMR$ spectra of all urine samples were acquired at 400 MHz on a VARIAN spectrometer. To establish the presence of any intrinsic class-related patterns or clusters in each NMR data, methods of PCA (principal component analysis) and soft independent modeling of class analogy (SIMCA) analysis were used, and the results from these analyses were compared to each other. In all cases of dietary conditions and nicotine treatment, SIMCA analysis gave better results for the discrimination of NMR spectra of urine samples than PCA.

• Journal of the Korean Wood Science and Technology
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• 제47권1호
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• pp.101-109
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• 2019

This paper examines the classification of five coniferous species, including larch (Larix kaempferi), red pine (Pinus densiflora), Korean pine (Pinus koraiensis), cedar (Cryptomeria japonica), and cypress (Chamaecyparis obtusa), using near-infrared (NIR) spectra. Fifty lumber samples were collected for each species. After air-drying the lumber, the NIR spectra (wavelength = 780-2500 nm) were acquired on the wide face of the lumber samples. Soft independent modeling of class analogy (SIMCA) was performed to classify the five species using their NIR spectra. Three types of spectra (raw, standard normal variated, and Savitzky-Golay $2^{nd}$ derivative) were used to compare the classification reliability of the SIMCA models. The SIMCA model based on Savitzky-Golay $2^{nd}$ derivatives preprocessing was determined as the best classification model in this study. The accuracy, minimum precision, and minimum recall of the best model (PCA models using Savitzky-Golay $2^{nd}$ derivative preprocessed spectra) were evaluated as 73.00%, 98.54% (Korean pine), and 67.50% (Korean pine), respectively.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1257-1257
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• 2001

Fourier transform near infrared (FT-NIR) spectroscopy was used as a rapid method to measure the $^{o}Brix$ content and to discriminate between different must samples in terms of their fee amino nitrogen (FAN) values. FT-NIR spectroscopy was also used as a rapid method to discriminate between Chardonnay wine samples in terms of the status of the male-lactic fermentation (MLF). This was done by monitoring the conversion of malic to lactic acid and thereby determining whether MLF has started, is underway or has been completed followed by classification of the samples. Furthermore, FT-NIR spectroscopy was applied as a rapid method to discriminate between table wine samples in terms of the ethyl carbamate (EC) content. EC in wine can pose a health threat and need to be monitored by determining the EC content in relation to the regulatory limits set by the authorities. For each of the above mentioned parameters, $QUANT+^{TM}$ methods were built and calibrations derived and it was found that a very strong correlation existed in the sample set for the FT-NIR spectroscopic predictions of $^{o}Brix$ (r = 0.99, SECV = 0.306), but the correlations for the FAN (r = 0.61, SECV = 272.1), malic acid (r = 0.58, SECV = 1.06), lactic acid (r = 0.51, SECV = 1.14) and EC predictions (r = 0.47, SECV = 3.67) were not as good. Soft Independent Modeling by Class Analogy (SIMCA) diagnostics and validation was applied as a sophisticated discrimination method. The must samples could be classified in terms of their FAN values when SIMCA was applied, obtaining results with recognition rates exceeding 80%. When SIMCA diagnostics and validation were applied to determine the progress of conversion of malic to lactic acid and the EC content, again results with recognition rates exceeding 80% were obtained. The evaluation of the applicability of FT-NIR spectroscopy measurement of FAN, $^{o}Brix$ values, malic acid, lactic acid and EC content in must and wine shows considerable promise. FT-NIR spectroscopy has the potential to reduce the analytical times considerably in a range of measurements commonly used during the wine making process. Where conventional FT-NIR calibrations are not effective, SIMCA methods can be used as a discriminative method for rapid classification of samples. SIMCA can replace expensive, time-consuming, quantitative analytical methods, if not completely, at least to some extent, because in many processes it is only needed to know whether a specific cut off point has been reach or not or whether a sample belongs to a certain class or not.

• Bulletin of the Korean Chemical Society
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• 제9권4호
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• pp.223-231
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• 1988

Some pattern recognition methods have been used to characterize Korean ancient porcelainsherds using their elemental composition as analyzed by instrumental neutron activation analysis. A combination of analytical data by means of statistical linear discriminant analysis(SLDA) has resulted in removal of redundant variables, optimal linear combination of meaningful variables and formulation of classification rules. The plot in the first-to-second discriminant scores has shown that the three distinct territorial regions exist among porcelainsherds of Kyungki, Chunbuk-Chungnam, and Chunnam, with respective efficiencies of 20/30, 22/27 and 14/15. Similar regions have been found to exist among punchong porcelain and ceradonsherds of Kyungki, Chungnam and Chunbuk, with respective efficiencies of 7/9, 15/16 and 6/6. Classification has been further attempted by statistical isolinear multiple component analysis(SIMCA), using the sample set selected appropriately through SLDA as training set. For this purpose, all analytical data have been used. An agreement has generally been found between two methods, i.e., SLDA and SIMCA.

• 대한화학회지
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• 제31권6호
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• pp.567-575
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• 1987

한국산 고대유리 시료 45종을 입수하여 그속에 함유된 19종의 원소(Ag, As, Br, Ce, Co, Cr, Eu, Fe, Hf, K, La, Lu, Na, Ru, Sb, Sc, Sm, Th, Zn)는 중성자방사화분석에 의하고, Pb는 원자흡수분광분석법에 의해 각각 정량하였다. 이들 20종 원소의 분석데이타를 사용하여 원소 상호간의 상관관계를 상관메트릭스법으로 검토하였다. 그리고 주성분분석법으로 각 시료의 농도분포를 평면에 나타내었으며, 측정된 제조년대 및 발굴위치가 같은 시료가 모이면 이들 시료를 SIMCA를 위한 참조시료로 삼았다. 이들 참조시료 및 시험시료를 SIMCA에 의해 분류하였더니 참조시료 전부와 시험시료중 3종이 주성분분석법에 의한 분류결과와 일치하였다.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1247-1247
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• 2001

Constituents of animal biofluids such as milk, blood and urine contain information specifically related to metabolic and health status of the ruminant animals. Some changes in composition of biofluids can be attributed to disease response of the animals. Mastitis is a major problem for the global dairy industry and causes substantial economic losses from decreasing milk production and reducing milk quality. The purpose of this study was to investigate potential of NIRS combined with multivariate analysis for cow's mastitis diagnosis based on NIR spectra of milk, blood and urine. A total of 112 bulk milk, urine and blood samples from 4 Holstein cows were analyzed. The milk samples were collected from morning milking. The urine samples were collected before morning milking and stored at -35$^{\circ}C$ until spectral analysis. The blood samples were collected before morning milking using a catheter inserted into the carotid vein. Heparin was added to blood samples to prevent coagulation. All milk samples were analyzed for somatic cell count (SCC). The SCC content in milk was used as indicator of mastitis and as quantitative parameter for respective urine and blood samples collected at same time. NIR spectra of blood and milk samples were obtained by InfraAlyzer 500 spectrophotometer, using a transflectance mode. NIR spectra of urine samples were obtained by NIR System 6500 spectrophotometer, using 1 mm sample thickness. 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. SIMCA was implemented to create models of the respective classes based on NIR spectra of milk, blood or urine. For the calibration set of samples, SIMCA models (model for samples from healthy cows and model for samples from mastitic cows), correctly classified from 97.33 to 98.67% of milk samples, from 97.33 to 98.61% of urine samples and from 96.00 to 94.67% of blood samples. From samples in the test set, the percent of correctly classified samples varied from 70.27 to 89.19, depending mainly on spectral data pretreatment. The best results for all data sets were obtained when first derivative spectral data pretreatment was used. The incorrect classified samples were 5 from milk samples,5 and 4 from urine and blood samples, respectively. The analysis of changes in the loading of first PC factor for group of samples from healthy cows and group of samples from mastitic cows showed, that separation between classes was indirect and based on influence of mastitis on the milk, blood and urine components. Results from the present investigation showed that the changes that occur when a cow gets mastitis influence her milk, urine and blood spectra in a specific way. SIMCA allowed extraction of available spectral information from the milk, urine and blood spectra connected with mastitis. The obtained results could be used for development of a new method for mastitis detection.

• 산업식품공학
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• 제15권2호
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• pp.136-142
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• 2011

본 연구에서는 정상 콩과 이에 혼입되는 이물질을 판별하기 위해 900 nm에서 1800 nm의 파장대역에서 단색화장치가 장착된 근적외선 분광장치를 이용하여 획득된 콩과 이물질의 반사 스펙트럼의 세기를 이용하여 각각의 판별예측모델을 개발하고 그 성능과 판별정확도를 검증해보았다. 정상콩 60 립과 이물질 60 점을 각각 2 회 반복하여 측정한 총 240 개의 반사스펙트럼에 대해서 모델 개발용인 calibration group으로 168 개를, 나머지 72 개는 개발된 모델을 예측하는 prediction group으로 나누어 사용하였다. 획득된 스펙트럼은 광원의 불안정함, 시료의 크기와 형태에서 기인되는 여러 변이들을 최소화하기 위해 다양한 수학적인 전처리를 적용하였으며 판별예측모델의 개발을 위해 PLS-DA와 SIMCA 방법을 사용하여 모델의 예측 성능과 판별율을 검토하였다. PLS-DA에서 모델 개발에 사용된 84 개의 정상 콩 스펙트럼 CLASS I은 적용된 모든 전처리에서 100%의 판별율을 보여주었으며 이물질 스펙트럼 CLASS II에서도 SNV 전처리를 제외하고는 모두 100% 이물질로 판별하여 분류하였다. 개발된 PLS-DA의 모델에 대한 prediction group의 검증에 있어서는 평균값 정규화 전처리 방법이 정상 콩과 이물질에서 100% 판별율을 보여주었다. SIMCA를 이용한 이물질 판별예측모델 개발은 PLS-DA와 비교할 때 상대적으로 저조한 판별율 결과를 나타냈으며 최대값 정규화와 일정 범위값 정규화의 전처리 방법을 적용한 모델이 평균 판별율 94.4%로 다소 양호한 결과를 보여주었다. 따라서 콩에 혼입되어 있는 이물질을 판별하는 시스템을 개발하는 데 있어서 근적외선 분광장치를 이용하여 획득한 반사도 스펙트럼은 PLS-DA로 판별예측모델을 개발하고 최적의 전처리 방법을 적용한다면 콩과 이물질의 선별시에 보다 나은 판별율을 얻을 수 있을 것이다.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.4106-4106
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• 2001

A method to discriminate human gallstones by nea. infrared(NIR) spectrometry using a soft independent modeling of class analogy (SIMCA) has been studied. The fifty NIR spectra of gallstones in the wavenumber range from 4500 to 10,000 cm$\^$-1/ were measured. The forty samples were classified to three classes, cholesterol stone, calcium bilirubinate stone and calcium carbonate stone according to the contents of major components in each gallstone. The training set which contained objects of the different known class was constructed using forty NIR spectra and the test set was made with ten different gallstone spectra. The number of important principal components(PCs) to describe the class was determined by cross validation in order to improve the decision criterion of the SIMCA for the training set. The score plots of the class training set whose objects belong to the other classes were inspected. The critical distance of each class was computed using both the Euclidean distance and the Mahalanobis distance at a proper level of significance(${\alpha}$). Two methods were compared with respect to classification and their robustness towards the number of PCs selected to describe different classes.