Kennard-Stone combined with least square support vector machine method for noncontact discriminating human blood species

Zhang, Linna; Li, Gang; Sun, Meixiu; Li, Hongxiao; Wang, Zhennan; Li, Yingxin

doi:10.1016/j.infrared.2017.08.020

Cited by 45 publications

(9 citation statements)

References 14 publications

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“…During modeling, one-third of the samples in each group were selected as the prediction set using the Kennard-Stone algorithm ( 28 ), and the remaining samples were utilized as the training set. The adopted Kennard-Stone algorithm ensures that the samples of training and test sets remain unchanged in the modeling process under the conditions of different spectral preprocessing methods and selected key variables, which is helpful to improve the stability of the model.…”

Section: Resultsmentioning

confidence: 99%

Authentication of duck blood tofu binary and ternary adulterated with cow and pig blood-based gel using Fourier transform near-infrared coupled with fast chemometrics

Han

Li²,

Aheto³

et al. 2022

Front. Nutr.

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This work aims to investigate a feasible and practical technique for the authentication of edible animal blood food (EABF) using Fourier transform near-infrared (FT-NIR) coupled with fast chemometrics. A total of 540 samples were used, including raw duck blood tofu (DBT), cow blood-based gel (CBG), pig blood-based gel (PBG), and DBT binary and ternary adulterated with CBG and PBG. The protein, fat, total sugar, and 16 kinds of amino acids were measured to validate the difference in basic organic matters among EABFs according to species. Fisher linear discriminate analysis (Fisher LDA) and extreme learning machine (ELM) were implemented comparatively to identify the adulterated EABF. To predict adulteration levels, four extreme learning machine regression (ELMR) models were constructed and optimized. Results showed that, by analyzing 27 crucial spectral variables, the ELM model provides higher accuracy of 93.89% than Fisher LDA for the independent samples. All the correlation coefficients of the optimized ELMR models’ training and prediction sets were better than 0.94, the root mean square errors were all less than 3.5%, and the residual prediction deviation and the range error ratios were all higher than 4.0 and 12.0, respectively. In conclusion, the FT-NIR paired with ELM have great potential in authenticating the EABF. This work presents amino acids content in EABFs for the first time and built tracing models for rapid authentication of DBT, which can be used to manage the EABF market, thereby preventing illegal adulteration and unfair competition.

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Section: Resultsmentioning

confidence: 99%

Authentication of duck blood tofu binary and ternary adulterated with cow and pig blood-based gel using Fourier transform near-infrared coupled with fast chemometrics

Han

Li²,

Aheto³

et al. 2022

Front. Nutr.

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“…Random selection (RS) and Kennard–Stone (KS) algorithms are the common methods used to screen training set and testing set (Mistek & Lednev, 2015; Wu et al., 2013). The discriminant model chosen by the KS algorithm often gave higher accuracies than those obtained by the RS selection, because samples are selected based on Euclidean distance or Mahalanobis distance and thus spread evenly throughout the sample space (Zhang, Li, et al., 2017). In this study, KS algorithm was used and seven samples from each batch of fermented vinegars were selected as the training set (28 samples), and the rest were used as the testing set (12 samples).…”

Section: Resultsmentioning

confidence: 99%

Characterization and identification of different Chinese fermented vinegars based on their volatile components

Liu

et al. 2021

J. Food Biochem.

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In this study, volatile components of 40 Chinese fermented vinegar samples, made from different raw materials, starters, and processing technologies, were collected from different geographic origins in China (Shanxi, Jiangsu, Sichuan, and Fujian Province) and their volatile components were analyzed by headspace-solid-phase microextraction-gas chromatography-mass spectrometry. Sixty-two aroma compounds have been identified by NIST library combined with retention index, mainly including esters, heterocyclics, acids, aldehydes, and ketones. In addition, multivariate analysis including principal component analysis and partial least squaresdiscriminant analysis (PLS-DA) were carried out to discriminate vinegars based on their composition of volatile components. For PLS-DA models, analysis of variance (ANOVA) or variable importance in the projection (VIP) value were used to select variables with the highest discriminatory power, and the Kennard-Stone algorithm was used to select the training and testing samples. The PLS-DA models (ANOVA or VIP) all provided a classification accuracy of 100% for the training set, and subsequent application of these models allowed the grouping of unknown samples (testing set) according to their characteristics (raw materials and processing technology). Practical applications Traditional Chinese vinegars have a long history but nowadays adulterations of them are becoming a problem in the market. In this study, Chinese fermented vinegars from different varieties were identified based on volatile composition. We found that starter cultures and fermentation process have the greatest influence on the volatile components of vinegars, while the influence of raw material and steaming of raw material are weaker volatile components. Then, partial least squares-discriminant analysis models, we carried out could successfully be applied to predict unknown vinegar samples based on a database of volatile components. This study provided a strategy to detect the identity of different vinegars, which can also be used to monitor the quality and safety of traditional Chinese vinegars.

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“…In this study, 600 samples were divided into a calibration set and a prediction set at a ratio of 2:1 according to the Kennard-Stone method [41]. Therefore, the calibration set included 400 samples, and the prediction set sample included 200.…”

Section: Modeling Methods and Model Evaluationmentioning

confidence: 99%

Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology

Sun

2020

Applied Sciences

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Granulation is a physiological disorder of juice sacs in citrus fruit, causing juice sacs to become hard and dry and resulting in decreased internal quality of citrus fruit. Honey pomelo is a thick-skinned citrus fruit, and it is difficult to identify the extent of granulation by observation of the outer peel and fruit shape. In this study, a rapid and non-destructive testing method using visible and near-infrared transmittance spectroscopy combined with machine vision technology was applied to identify and estimate granulation inside fruit. A total of 600 samples in different growth periods was harvested, and fruit were divided into five classes according to five granulation levels. Spectral data were obtained for two ranges of 400–1100 nm and 900–1700 nm by visible and near-infrared transmittance spectroscopy. In addition, chemometrics were used to measure the chemical changes of soluble solid content (SSC), titratable acidity (TA), and moisture content (MC) caused by different granulation levels. Machine vision technology can rapidly estimate the external characteristics of samples and measure the physical changes in mass and volume caused by different granulation levels. Compared with using a single or traditional methods, the predictive performances of multi-category classification models (PCA-SVM and PCA-GRNN) (Table A1) were significantly enhanced. In particular, the model accuracy rate (ARM) was 99% for PCA-GRNN, with classification accuracy (CA), classification sensitivity (CS), and classification specificity (CSP) of 0.9950, 0.9750, and 0.9934, respectively. The results showed that this method has great potential for the identification and estimation of granulation. Multi-source data fusion and application of a multi-category classification model with the smallest number of input layers and acceptable high predictive performances are proposed for on-line applications. This method can be effectively used on-line for the non-destructive detection of fruits with granulation.

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Kennard-Stone combined with least square support vector machine method for noncontact discriminating human blood species

Cited by 45 publications

References 14 publications

Authentication of duck blood tofu binary and ternary adulterated with cow and pig blood-based gel using Fourier transform near-infrared coupled with fast chemometrics

Authentication of duck blood tofu binary and ternary adulterated with cow and pig blood-based gel using Fourier transform near-infrared coupled with fast chemometrics

Characterization and identification of different Chinese fermented vinegars based on their volatile components

Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology

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