• Food Engineering Progress
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• v.14 no.1
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• pp.75-79
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• 2010

Partial least square regression (PLSR) was executed on curve data of force-deformation from back extrusion test and sensory data for commercial instant noodles. Sensory attributes considered were hardness (A), springiness (B), roughness (C), adhesiveness to teeth (D), and thickness (E). Eight and two kinds of fried and non-fried instant noodles respectively were used in the tests. Changes in weighted regression coefficients were characterized as three stages: compaction, yielding, and extrusion. Correlation coefficients appeared in the order of E>D>A>B>C, root mean square error of prediction D>C>E>B>A, and relative ability of prediction D>C>E>B>A. Overall, 'D' was the best in the correlation and prediction. 'A' with poor prediction ability but high correlation was considered good when determining the order of magnitude.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.4
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• pp.483-488
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• 2010

An online monitoring based upon sensor system is essential to assure both efficient operation and safety in the power plant. Of great importance is modeling for auto-association (AA) in online monitoring technique. The objective of auto-associative models lies in predicting true values of plant operation parameters from sensor signals transmitted. This paper presents two AA models using Support Vector Regression (SVR) and Partial Least Square Regression (PLSR). The presented models are useful, in particular, when there are many parameters to monitor in the power plant. Illustrative examples are given by using a real-world plant dataset. AA performances of SVR and PLSR are finally summarized in terms of accuracy and sensitivity. According to our results, SVR shows much higher accuracy and, however, its sensitivity is relatively degraded.

• Journal of the Korean Data and Information Science Society
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• v.25 no.5
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• pp.1151-1160
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• 2014

In DNA microarray studies, the number of genes far exceeds the number of samples and the gene expression measures are highly correlated. Partial least squares regression (PLSR) is one of the popular methods for dimensional reduction and known to be useful for the classifications of microarray data by several studies. In this study, we suggest a modified version of the partial least squares regression to analyze gene expression data with survival information. The method is designed as a new gene selection method using PLSR with an iterative procedure of imputing censored survival time. Mean square error of prediction criterion is used to determine the dimension of the model. To visualize the data, plot for variables superimposed with samples are used. The method is applied to two microarray data sets, both containing survival time. The results show that the proposed method works well for interpreting gene expression microarray data.

• YAKHAK HOEJI
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• v.47 no.5
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• pp.261-265
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• 2003

Near-infrared (NIR) system was used to determine rapidly and simply indomethacin in buffer solution for a dissolution test of tablets and capsules. Indomethacin standards were prepared ranging from 10 to 50 ppm using the mixture of phosphate buffer (pH 7.2) and water (1 : 4). The near-infrared (NIR) transmittance spectra of indomethacin standard solutions were collected by using a quartz cell in 1 mm and 2 mm pathlength. Partial least square regression (PLSR) was explored to develop calibration models over the spectral range 1100∼1700 nm. The model using 1 mm quartz cell was better than that using 2 mm quartz cell. The PLSR models developed gave standard error of prediction (SEP) of 0.858 ppm. In order to validate the developed calibration model, routine analysis was performed using another standard solutions. The NIR routine analysis showed good correlation with actual values. Standard error of prediction (SEP) is 1.414 ppm for 7 indomethacin samples in routine analysis and its error was permeable in the regulation of Korean Pharmacopoeia (VII). These results show the potential use of the real time monitoring for indomethacin during a dissolution test.

• Journal of Biosystems Engineering
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• v.33 no.4
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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.

• YAKHAK HOEJI
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• v.48 no.1
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• pp.60-64
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• 2004

Near-infrared (NIR) spectroscopy was used to determine rapidly and nondestructively the content of ambroxol in intact ambroxol tablets containing 30 mg (12.5％ m/m nominal concentration) by collecting NIR spectra in range 1100-1750 nm. The laboratory-made samples had 10.3∼15.9％ m/m nominal ambroxol concentration. The measurements were made by reflection using a fiber-optic probe and calibration was carried out by partial least square regression (PLSR) with autoscaling. Model validation was performed by randomly splitting the data set into calibration and validation data set (7 samples as a calibration data set and 5 samples as a validation data set). The developed NIR method gave results comparable to the known values of tablets in a laboratorial manufacturing Process, standard error of calibration (SEC) and standard error of prediction (SEP) being 0.49％ and 0.49％ m/m respectively. The method showed good accuracy and repeatability NIR spectroscopic determination in intact tablets allowed the potential use of real time monitoring for a running production process.

• Journal of the Korean Society of Food Culture
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• v.37 no.3
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• pp.239-247
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• 2022

This study evaluated the sensory characteristics of sauerkraut prepared by adding 0.5, 1.0, 1.5, 2.0, and 2.5% (w/w) sea salt to cabbage. The quantitative descriptive analysis (QDA) and acceptance test of sauerkraut were determined for each salt concentration, and the principal component analysis (PCA) and partial least square regression (PLSR) analysis were performed to confirm the correlation between each factor. Results of the QDA determined 14 descriptive terms; furthermore, brightness and yellowness of appearance and the sour, salty, and bitter flavors differed significantly according to the salt concentration. Results from the PCA explained 22.56% PC1 and 65.34% PC2 of the total variation obtained. Sauerkraut prepared using 0.5, 1.0, and 1.5% sea salt had high brightness, moistness, sour odor, green odor, sour flavor, carbonation, hardness, chewiness, and crispness, whereas sauerkraut prepared with 2.0 and 2.5% sea salt had high yellowness, glossiness, salty flavor, sweet flavor, and bitter flavor. Hierarchical cluster analysis classified the products into two clusters: sauerkraut of 0.5, 1.0, and 1.5%, and sauerkraut of 2.0 and 2.5%. Results of PLSR determined that sauerkraut of 1.0 and 1.5% were the closest to texture, taste, and overall acceptance. We, therefore, conclude that sauerkrauts prepared using 1.0 and 1.5% sea salt have excellent characteristics in appearance, taste, and texture.

• Analytical Science and Technology
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• v.15 no.2
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• pp.102-107
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• 2002

This paper describes a rapid determination of phenobarbital in intact phenobarbital tablets using partial least squares regression(PLSR) method of transmittance spectrum of near infrared (NIR) compared with the analytical data of liquid chromatograpy. The linearity, concentration range and precision of this analytical method are studied. The correlation coefficient of the calibration curve is 0.9983 and the standard error of calibration(SEC) is 0.14 %. Intra-day precision and Inter-day precision obtained in this method are CV = 0.45, CV =0.56, respectively.

• Resources Recycling
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• v.27 no.1
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• pp.84-91
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• 2018

In this study, a novel soft information based most probable classification scheme is proposed for sorting recyclable metal alloys with laser induced breakdown spectroscopy (LIBS). Regression analysis with LIBS captured spectrums for estimating concentrations of common elements can be efficient for classifying unknown arbitrary metal alloys, even when that particular alloy is not included for training. Therefore, partial least square regression (PLSR) is employed in the proposed scheme, where spectrums of the certified reference materials (CRMs) are used for training. With the PLSR model, the concentrations of the test spectrum are estimated independently and are compared to those of CRMs for finding out the most probable class. Then, joint soft information can be obtained by assuming multi-variate normal (MVN) distribution, which enables to account the probability measure or a prior information and improves classification performance. For evaluating the proposed schemes, MVN soft information is evaluated based on PLSR of LIBS captured spectrums of 9 metal CRMs, and tested for classifying unknown metal alloys. Furthermore, the likelihood is evaluated with the radar chart to effectively visualize and search the most probable class among the candidates. By the leave-one-out cross validation tests, the proposed scheme is not only showing improved classification accuracies but also helpful for adaptive post-processing to correct the mis-classifications.

• Proceedings of the PSK Conference
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• 2003.10b
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• pp.213.2-213.2
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• 2003

A simple analytical procedure using FT-NIR for the rapid determination of individual ingredients was evaluated. Direct measurements were made by reflection using a reflectance accessory, by transmittance using tablet accessory and turn table. FT-NIR spectral data were transformed to the first derivative. Partial Least Square Regression(PLSR) was applied to quantify near-infrared (NIR) spectra of 2 ingredients. These calibration models were cross-validated (leave-one-out approach). The prediction ability of the models was evaluated on ambroxol tablets and compared with the real values in manufacturing procedure. (omitted)