• Journal of Biosystems Engineering
• /
• 제42권4호
• /
• pp.293-300
• /
• 2017

Purpose: This study aims to propose a method for fast geographical origin discrimination between domestic and imported rice using a visible/near-infrared (VNIR) hyperspectral imaging technique. Methods: Hyperspectral reflectance images of South Korean and Chinese rice samples were obtained in the range of 400 nm to 1000 nm. Partial least square discriminant analysis (PLS-DA) models were developed and applied to the acquired images to determine the geographical origin of the rice samples. Results: The optimal pixel dimensions and spectral pretreatment conditions for the hyperspectral images were identified to improve the discrimination accuracy. The results revealed that the highest accuracy was achieved when the hyperspectral image's pixel dimension was $3.0mm{\times}3.0mm$. Furthermore, the geographical origin discrimination models achieved a discrimination accuracy of over 99.99% upon application of a first-order derivative, second-order derivative, maximum normalization, or baseline pretreatment. Conclusions: The results demonstrated that the VNIR hyperspectral imaging technique can be used to discriminate geographical origins of rice.

• Communications for Statistical Applications and Methods
• /
• 제22권6호
• /
• pp.575-587
• /
• 2015

The development in data collection techniques results in high dimensional data sets, where discrimination is an important and commonly encountered problem that are crucial to resolve when high dimensional data is heterogeneous (non-common variance covariance structure for classes). An example of this is to classify microbial habitat preferences based on codon/bi-codon usage. Habitat preference is important to study for evolutionary genetic relationships and may help industry produce specific enzymes. Most classification procedures assume homogeneity (common variance covariance structure for all classes), which is not guaranteed in most high dimensional data sets. We have introduced regularized elimination in partial least square coupled with QDA (rePLS-QDA) for the parsimonious variable selection and classification of high dimensional heterogeneous data sets based on recently introduced regularized elimination for variable selection in partial least square (rePLS) and heterogeneous classification procedure quadratic discriminant analysis (QDA). A comparison of proposed and existing methods is conducted over the simulated data set; in addition, the proposed procedure is implemented to classify microbial habitat preferences by their codon/bi-codon usage. Five bacterial habitats (Aquatic, Host Associated, Multiple, Specialized and Terrestrial) are modeled. The classification accuracy of each habitat is satisfactory and ranges from 89.1% to 100% on test data. Interesting codon/bi-codons usage, their mutual interactions influential for respective habitat preference are identified. The proposed method also produced results that concurred with known biological characteristics that will help researchers better understand divergence of species.

• Journal of Biosystems Engineering
• /
• 제23권6호
• /
• pp.571-582
• /
• 1998

The PLS(Partial Least Square) and ANN(Artificial Neural Network) were introduced to develop the soluble solids content prediction model of apples which is followed by making a subsequent selection of photosensor. For the optimal PLS model, number of factors needed for spectrum analysis were increased until the convergence of prediction residual error sum of squares. Analysis has shown that even part of the overall wavelength with no pretreatment may turn out better performing. The best PLS model was found in the 800 to 1,100nm wavelength region without pretreatment of second derivation, having $R^2$=0.9236, bias= -0.0198bx, SEP=0.2527bx for unknown samples. On the other hand, for the ANN model the second derivation led to higher performance. On partial range of 800 to 1,100nm wavelengh region, prediction model with second derivation for unknown samples reached $R^2$=0.9177, SEP=0.2903bx in contrast to $R^2$=0.7507, SEP =0.4622bx without pretreatment.

• 한국컴퓨터정보학회논문지
• /
• 제25권2호
• /
• pp.49-58
• /
• 2020

본 연구는 온라인 다중 객체 추적 환경에서 모든 객체의 상태(예. 위치 및 크기) 및 identifications (IDs)를 추적하는 문제를 다룬다. 프레임들 간 검출 결과들을 연관하여 객체들의 궤도를 점진적으로 완성하는 tracking-by-detection 접근법을 기반으로 온라인 다중 객체 추적 문제를 해결하고자 한다. 정확한 온라인 연관을 수행하기 위해 이산 푸리에 변환과 부분 최소 제곱법(partial least square, PLS) 분석을 기반으로 하는 새로운 온라인 외형 학습 방법을 제안한다. 즉, 먼저 주파수 도메인에서 추적에 용이한 객체 특징량을 추출하기 위해 추적 객체에 대한 이미지를 푸리에 이미지로 변환한다. 나아가 객체간의 주파수 특징을 보다 잘 구별할 수 있도록 PLS기반 부분 공간을 학습한다. 제안된 외형 학습을 최신 신뢰도 기반 연관 기법과 결합하였고, 다중 객체 추적평가 분야에서 국제적으로 공인된 MOT 벤치마크 챌린지 데이터 셋에서 최신 다중 객체 추적 알고리즘과 비교평가를 수행하였다.

• Food Science and Biotechnology
• /
• 제18권3호
• /
• pp.724-731
• /
• 2009

Anthocyanins are considered one of the main color determinants in aged red wine. The anthocyanins in aged red wine made from 'Cabernet Gernischet' (Vitis vinifera L. cv.) grape were investigated by high performance liquid chromatography- electronic spray ionization- mass spectrometry (HPLC-ESI-MS) and their color presented in aqueous solution were evaluated using partial least square regression (PLS). The results showed that there were 37 anthocyanins identified in this wine, including 22 pyranoanthocyanins. The analysis of PLS indicated that different anthocyanins showed distinct color values: malvidin 3-O-(6-O-acetyl)-glucoside-4-vinylguaiacol (Mv3-acet-glu-vg) presented the highest color values, while malvidin 3-O-glucoside (Mv3-glu) showed least. Among the free non-acylated anthocyanins, peonidin 3-O-oglucoside (Pn3-glu) showed the highest color values; the coumarylated anthocyanins presented higher color values than their corresponding acetylated anthocyanins and parent anthocyanins; pyranoanthocyanins presented also higher color values than their original anthocyanins; the color of anthocyanins depended on their structure. This work will be helpful to reveal evolution in aged red wine.

• 응용통계연구
• /
• 제31권3호
• /
• pp.315-328
• /
• 2018

다중요인분석은 관능검사에서 주로 이용되는 분석으로 상품의 속성과 소비자들의 기호도에 대한 자료 분석에 주로 이용된다. 본 연구에서는 다중요인분석을 상품의 속성, 소비자들의 기호도 등의 자료에 이용하여 소비자들의 특성에 따라 몇 개의 군집으로 분류하고 이를 부분 최소제곱 경로모형을 이용하여 분류된 군집의 특성을 파악해 보고자 한다. 향수의 속성에 대한 자료와 소비자들이 파악한 향수의 속성, 그리고 그들의 기호도에 관한 실제 자료를 다중요인분석을 이용하여 살펴보고 이 결과를 이용하여 소비자들을 4개의 군집으로 분할한다. 분할한 군집별로 제품의 특성을 파악하고 이를 최소제곱경로모형에 적용하여 각 군집의 특성을 나타내는 잠재변수를 추정, 군집별로 소비자들이 선호하거나 기피하는 제품의 특성들, 그리고 각 군집별로 제품들을 어떻게 지각하는지 등을 파악한다. 다중요인분석을 활용한 부분 최소제곱 경로모형은 제품에 대한 특성과 소비자들의 기호도를 동시에 분석하여 이들의 관계를 규명하고 분석 결과를 제품 개발과 판매에 적용할 수 있다는 점에서 유용한 모형이라고 할 수 있다.

• 한국축산식품학회지
• /
• 제44권4호
• /
• pp.934-950
• /
• 2024

This study addresses the prevalent issue of meat species authentication and adulteration through a chemometrics-based approach, crucial for upholding public health and ensuring a fair marketplace. Volatile compounds were extracted and analyzed using headspace-solid-phase-microextraction-gas chromatography-mass spectrometry. Adulterated meat samples were effectively identified through principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). Through variable importance in projection scores and a Random Forest test, 11 key compounds, including nonanal, octanal, hexadecanal, benzaldehyde, 1-octanol, hexanoic acid, heptanoic acid, octanoic acid, and 2-acetylpyrrole for beef, and hexanal and 1-octen-3-ol for pork, were robustly identified as biomarkers. These compounds exhibited a discernible trend in adulterated samples based on adulteration ratios, evident in a heatmap. Notably, lipid degradation compounds strongly influenced meat discrimination. PCA and PLS-DA yielded significant sample separation, with the first two components capturing 80% and 72.1% of total variance, respectively. This technique could be a reliable method for detecting meat adulteration in cooked meat.

• Asian-Australasian Journal of Animal Sciences
• /
• 제33권10호
• /
• pp.1633-1641
• /
• 2020

Objective: The objective of this study was to develop a model for estimating the carcass weight of Hanwoo cattle as a function of body measurements using three different modeling approaches: i) multiple regression analysis, ii) partial least square regression analysis, and iii) a neural network. Methods: Data from a total of 134 Hanwoo cattle were obtained from the National Institute of Animal Science in South Korea. Among the 372 variables in the raw data, 20 variables related to carcass weight and body measurements were extracted to use in multiple regression, partial least square regression, and an artificial neural network to estimate the cold carcass weight of Hanwoo cattle by any of seven body measurements significantly related to carcass weight or by all 19 body measurement variables. For developing and training the model, 100 data points were used, whereas the 34 remaining data points were used to test the model estimation. Results: The R² values from testing the developed models by multiple regression, partial least square regression, and an artificial neural network with seven significant variables were 0.91, 0.91, and 0.92, respectively, whereas all the methods exhibited similar R² values of approximately 0.93 with all 19 body measurement variables. In addition, relative errors were within 4%, suggesting that the developed model was reliable in estimating Hanwoo cattle carcass weight. The neural network exhibited the highest accuracy. Conclusion: The developed model was applicable for estimating Hanwoo cattle carcass weight using body measurements. Because the procedure and required variables could differ according to the type of model, it was necessary to select the best model suitable for the system with which to calculate the model.

• 소성∙가공
• /
• 제20권6호
• /
• pp.450-455
• /
• 2011

This paper presents an inverse design methodology for the cross section geometry of mold spring with a rectangular cross section as the starting material for a coiling process. The cross-sections of mold springs are universally rectangular, as the parallel sides minimize the possibility of failure under high service loads. Pre-coiled wires are initially designed to have a trapezoidal cross section, which becomes a rectangle by the coiling process. This study demonstrates a numerical exercise to predict changes in the sectional geometry in spring manufacture and to obtain the initial cross section which becomes the exact rectangle desired from the manufacturing process. Finite element analysis was carried out to calculate the sectional changes for various mold springs. Geometrical parameters were the widths at inner and outer radii, the inner and the outer corner radii, and the height. A partial least square regression analysis was carried out to find the main contributing factors for deciding initial design values. The height and the width mainly affected various initial parameters. The initial width at the inner radius was mostly affected by various specification parameters.

• 한국항해항만학회지
• /
• 제45권2호
• /
• pp.42-53
• /
• 2021

The port space can be considered to be the space in which the characteristics of the port city are best expressed. Also, since it acts as a representative gateway along with the airport, it can have a direct impact on the image of the region and country. However, the harbor hinterland has been a refuge during the war in the past, and it has been concentrating on development related to the port industry; hence, it has a poorer residential environment. Therefore, in this study, in order to ensure equal development in space and equal access to basic urban services, urban design directions were suggested for the harbor hinterland based on the concept of an inclusive city'. To this end, through factor analysis, urban planning elements that can be applied to urban design were derived, and through PLS(Partial Least Square)regression analysis, based on the opinions of residents and experts, urban design directions for the port hinterland were presented. The study site was Gamcheon Port, one of the Busan Ports in Korea, the hinterland of Gamcheon Port was a high slope, and the residential environment was relatively poor due to the dense concentration of older residential areas.