• 한국ITS학회 논문지
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• 제12권5호
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• pp.87-97
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• 2013

본 연구는 충청남도를 대상으로 도시권과 농어촌권을 구분하고, 고령자 및 비고령자의 여가통행시간 영향요인의 이질성을 정량적으로 규명하고자 하였다. 분석자료는 2011년 가구통행실태조사를 활용하여 도심 및 농어촌권역의 통행자특성을 추출하고, 도심 및 농어촌권의 지역경제적특성 및 교통환경적특성을 활용하여 PLS(Partial least square) 회귀모형을 구축하였다. 분석결과, 도시권과 농어촌권 고령자의 여가통행시간에 영향을 미치는 주요변수는 버스배차간격, 버스노선수, 가구원수, 가구월평균수입으로 나타났다. 비고령자의 경우에는 고령자의 중요 영향변수 이외 지역경제(GRDP, 경제활동참가율)환경과 고용형태도 여가통행시간에 영향을 미치는 중요 변수로 나타났다. 한편, 농어촌권에 거주하는 여성고령자는 남성고령자 보다 여가통행시간에 더 민감하나 비고령자그룹은 남녀별로 큰 차이가 없는 것으로 분석되었다.

• Animal Bioscience
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• 제37권5호
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• pp.918-928
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• 2024

Objective: The adulteration of raw beef (BMr) with dog meat (DMr) and pork (PMr) becomes a serious problem because it is associated with halal status, quality, and safety of meats. This research aimed to develop an effective authentication method to detect non-halal meats (dog meat and pork) in beef using metabolomics approach. Methods: Liquid chromatography-high resolution mass spectrometry (LC-HRMS) using untargeted approach combined with chemometrics was applied for analysis non-halal meats in BMr. Results: The untargeted metabolomics approach successfully identified various metabolites in BMr DMr, PMr, and their mixtures. The discrimination and classification between authentic BMr and those adulterated with DMr and PMr were successfully determined using partial least square-discriminant analysis (PLS-DA) with high accuracy. All BMr samples containing non-halal meats could be differentiated from authentic BMr. A number of discriminating metabolites with potential as biomarkers to discriminate BMr in the mixtures with DMr and PMr could be identified from the analysis of variable importance for projection value. Partial least square (PLS) and orthogonal PLS (OPLS) regression using discriminating metabolites showed high accuracy (R² >0.990) and high precision (both RMSEC and RMSEE <5%) in predicting the concentration of DMr and PMr present in beef indicating that the discriminating metabolites were good predictors. The developed untargeted LC-HRMS metabolomics and chemometrics successfully identified non-halal meats adulteration (DMr and PMr) in beef with high sensitivity up to 0.1% (w/w). Conclusion: A combination of LC-HRMS untargeted metabolomic and chemometrics promises to be an effective analytical technique for halal authenticity testing of meats. This method could be further standardized and proposed as a method for halal authentication of meats.

• 한국연초학회지
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• 제17권2호
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• pp.177-183
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• 1995

This study was conducted to develop the most precise NIR(near infrared spectrometric) calibration for rapid determination of chemical composition in ground samples of toasted burley tobacco using stepwise, stepup, principal component regression(PCR), partial least square(PLS) and modified partial least square(MPLS) calibration method. The number of wavelength(W) selected by stepup multiple linear regression using: second derivative spectra was as follows: total sugar(TS)-4 W, nicotine-9 W, total nitrogen(TN)-2 W, ash-8 W, total volatile base(TVB)-5 W, chlorine4 W, L of color-6 W, a of color-6 W and b of color-7 W. Comparing the calibration equations followed by each chemical components, the most precise calibration equation was MPLS for 75, a and b of color, PLS for nicotine, ash, TVB, chlorine and L of color and stepup for TN. The standard error of calibration(SEC) and standard error of performance(SEP) between result of near infrared analysis and standard laboratory analysis were 0.18, 0.40% for 75, 0.06, 0.08% for nicotine, 0.18, 0.16% for TN, 0.33, 0.46% for ash, 0.04, 0.03% for TVB, 0.08, 0.06% for chlorine, 0.54, 0.58 for L of color, 0.22, 0.22 for a of color and 0.27, 0.27 for b of color, respectively. The SEC and SEP of ash and TVB were within allowable error of standard laboratory analysis, nicotine, TN and chlorine were 1.2-2.0 times and 75 were 2.1-4.0 times larger than allowable error of standard laboratory analysis. The ratio of SEC and SEP to mean were 1.5, 1.6% for L of color, 3.7, 3.8% for a of color and 1.8, 1.8% for b of color, respectively. Key words : burley tobacco chemistry, near infrared spectroscopy.

• 제어로봇시스템학회논문지
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• 제11권6호
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• pp.550-557
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• 2005

The multivariate statistical analysis methods, using both multiple linear regression(MLR) and partial least square(PLS), have been applied to predict the reactor properties and the product quality of a direct esterification reactor for polyethylene terephthalate(PET) synthesis. On the basis of the set of data including the flow rate of water vapor, the flow rate of EG vapor, the concentration of acid end groups of a product and other operating conditions such as temperature, pressure, reaction times and feed monomer mole ratio, two multi-variable analysis methods have been applied. Their regression and prediction abilities also have been compared. The prediction results are critically compared with the actual plant data and the other mathematical model based results in reliability. This paper shows that PLS method approach can be used for the reasonably accurate prediction of a product quality of a direct esterification reactor in PET synthesis process.

• 한국작물학회지
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• 제43권2호
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• pp.101-104
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• 1998

This study was conducted to establish a rapid analysis method for determining protein and moisture contents of pea. Ninety and eighty pea (Pisum sativum L.) lines were analyzed to determine protein and moisture contents, respectively using near-infrared reflectance spectroscopy. Simple correlations (${\gamma}$) of protein content in a ground sample and an intact grain sample by an automatic regression method were 0.978 and 0.910, respectively. Simple correlations by partial least square regression/principal component analysis (PLS/PCA) methods were 0.982 and 0.925, respectively. Standard error of performance (SEP) in protein content was the lowest value, 0.446 in ground sample by PLS/PCA methods. Simple correlation of moisture content was the highest at 0.871 in ground samples. when using a standard regression method. Accuracy for the moisture content was slightly lower than for protein content. It was concluded that the NIRS method would be applicable only for rapid determination of protein content in pea.

• Journal of Biosystems Engineering
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• 제33권4호
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• pp.269-275
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• 2008

This study was conducted to design an impedance sensor that can measure soil water content of soils. Partial least square regression (PLSR) was applied to soil impedance data preprocessed with a smoothing method. An optimal sub-spectrum size and wavelength range were determined by comparing the coefficient of determination ($R^2$) and root mean square error (RMSE) of the PLSR models obtained using soil impedance data. various PLS analysis. Based on the PLSR analysis, it would be concluded that the optimal spectrum measurement range was $32.0{\sim}50.0\;MHz$ with the optimal sub-spectrum size of about 18.5 MHz.

• KSBB Journal
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• 제21권1호
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• pp.59-67
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• 2006

본 연구에서는 2차원 형광스펙트럼의 PCA 분석을 통하여 발효 공정을 모니터링하고 PCR과 PLS과 같은 다변량 분석기법을 이용하여 공정을 모델링하였다. 재조합 대장균 E. coli 와 효모 S.cerevisiae의 발효 공정 중에 얻어진 많은 양의 2차원 형광스펙트럼 자료는 우선 PCA를 통해 축소된다. 그리고 PCA에서 주성분점수와 적재 산점도는 발효 공정의 정성적 경향을 묘사하기 위해 사용되었다. 또한, PCR과 PLS는 2차원 형광스펙트럼의 분석을 위해 사용되었으며 PLS모델이 보정과 예측 능력에서 PCR모델보다 조금 더 우수한 성능을 나타냈다. 따라서 2차원 형광스펙트럼 자료를 이용하여 생물공정을 모델링 하고자 할 때는 PCR 방법보다는 PLS 방법을 사용하는 것이 유리할 것이다.

• Journal of Biosystems Engineering
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• 제27권2호
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• pp.117-124
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• 2002

This research was conducted to develop a method to remove the effect of surface temperature of Shingo pears for sugar content measurement. Sugar content was measured by a near-infrared spectrum analysis technique. Reflected spectrum and sugar content of a pear were used for developing regression models. For the model development, reflected spectrums having wavelengths in the range of 654 to 1,052nm were used. To remove the effect of surface temperature, special sample preparation techniques and partial least square (PLS) regression models were proposed and tested. 71 Shingo pears stored in a cold storage, which had 2$^{\circ}C$ inside temperature, were taken out and left in a room temperature for a while. Temperature and reflected spectrum of each pear was measured. To increase the temperature distribution of samples, temperature and reflected spectrum of each pear was measured four times with one hour twenty minutes interval. During the experiment, temperature of pears increased up to 17 $^{\circ}C$. The total number of measured spectrum was 284. Three groups of spectrum data were formed according to temperature distribution. First group had surface temperature of 14$^{\circ}C$ and total number of 51. Second group consisted of the first and the fourth experiment data which contained the minimum and the maximum temperatures. Third group consisted of 155 data with normal temperature-distribution. The rest data set were used for model evaluation. Results shelved that PLS model I, which was developed by using the first data group, was inadequate for measuring sugar content of pears which had different surface temperatures from 14$^{\circ}C$. After temperature compensation, sugar content predictions became close to the measured values. Since using many data which had wide range of surface temperatures, PLS model II and III were able to predict sugar content of pears without additional temperature compensation. PLS model IV, which included the surface temperatures as an independent variable. showed slightly improved performance(R$^2$=0.73). Performance of the model could be enhanced by using samples with more wide range of temperatures and sugar contents.

• 농업과학연구
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• 제38권4호
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• pp.761-767
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• 2011

The objective of this study was to predict total bacteria count of pork meats by using the portable electronic nose systems developed throughout two stages of the prototypes. Total bacteria counts were measured for pork meats stored at $4^{\circ}C$ for 21days and compared with the signals of the electronic nose systems. PLS(Partial least square), PCR (Principal component regression), MLR (Multiple linear regression) models were developed for the prediction of total bacteria count of pork meats. The coefficient of determination ($R_p{^2}$) and root mean square error of prediction (RMSEP) for the models were 0.789 and 0.784 log CFU/g with the 1st system for the pork loin, 0.796 and 0.597 log CFU/g with the 2nd system for the pork belly, and 0.661 and 0.576 log CFU/g with the 2nd system for the pork loin respectively. The results show that the developed electronic system has potential to predict total bacteria count of pork meats.

• 원예과학기술지
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• 제32권1호
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• pp.105-114
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• 2014

본 연구에서는 UV-VIS spectrophotometer를 이용한 total carotenoids, flavonoids, phenolics 함량 데이터와 FT-IR 스펙트럼 데이터를 다변량통계분석법을 통하여 기능성 성분 함량이 높은 아프리칸 얌 고속 선발 시스템을 구축하였다. 62개 아프리칸 얌의 total carotenoids 함량은 $0.01-0.91{\mu}g{\cdot}g^{-1}$ dry wt 나타냈다. Total flavonoids와 phenolics 함량은 $12.9-229.0{\mu}g{\cdot}g^{-1}$ dry wt와 $0.29-5.2mg{\cdot}g^{-1}$ dry wt로 각각 나타났다. 아프리칸 얌은 FT-IR 스펙트럼상의 1700-1500, 1500-1300, $1,100-950cm^{-1}$, 부위에서 중요한 스펙트럼 변화가 나타났다. 이 부위는 각각 amide I과 II을 포함하는 아미노산 및 단백질계열의 화합물, phosphodiester group을 포함한 핵산 및 인지질 그리고 단당류나 복합 다당류를 포함하는 carbohydrates 계열의 화합물들의 질적, 양적 정보를 반영하는 부위이다. PCA 분석과 PLS-DA 분석에서 62개 아프리칸 얌은 유연성이 높은 종으로 3개의 그룹을 형성하였다. 아프리칸 얌의 FT-IR 스펙트럼 데이터와 UV-VIS spectrophotometer을 이용한 total carotenoids, flavonoids, phenolics 함량 데이터 간에 PLS regression 분석하였다. Total carotenoids, flavonoids, phenolics 함량 성분의 실측 값과 예측 값간에 상관계수($R^2$)가 각각 0.83, 0.86, 0.72로 나타났다. 이 결과, 아프리칸 얌으로부터 FT-IR 스펙트럼을 이용한 total carotenoids, flavonoids, phenolics 함량 예측이 가능하였다. 본 연구에서 확립된 대사체 수준에서 아프리칸 얌의 유용 기능성 성분 함량 예측 모델링을 통해 품종, 계통의 신속한 선발 수단으로 활용이 가능할 것으로 예상된다.