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1.
Measurement of Soluble Solid Contents and pH of White Vinegars Using VIS/NIR Spectroscopy and Least Squares Support Vector Machine 总被引:1,自引:0,他引:1
Yidan Bao Fei Liu Wenwen Kong Da-Wen Sun Yong He Zhengjun Qiu 《Food and Bioprocess Technology》2014,7(1):54-61
Visible and near-infrared (VIS/NIR) spectroscopy combined with least squares support vector machine (LS-SVM) was employed to determine soluble solid contents (SSC) and pH of white vinegars. Three hundred twenty vinegar samples were distributed into a calibration set (240 samples) and a validation set (80 samples). Partial least squares (PLS) analysis was implemented for the regression model and extraction of latent variables (LVs). The selected LVs were used as LS-SVM input variables. Finally, LS-SVM models with radial basis function kernel were achieved with the comparison of PLS models. The results indicated that LS-SVM outperformed PLS models. The correlation coefficient (r), root mean square error of prediction, bias, and residual prediction deviation for the validation set were 0.988, 0.207°Brix, 0.183, and 6.4 for SSC whereas these were 0.988, 0.041, ?0.002, and 6.5 for pH, respectively. The overall results indicated that VIS/NIR spectroscopy and LS-SVM could be used as a rapid alternative method for the prediction of SSC and pH of white vinegars, and the results could be helpful for the fermentation process and quality control monitoring of white vinegar production. 相似文献
2.
Application of successive projections algorithm for variable selection to determine organic acids of plum vinegar 总被引:2,自引:0,他引:2
Visible and near infrared (Vis/NIR) spectroscopy was investigated to determine the acetic, tartaric and lactic acids of plum vinegar based on a newly proposed combination of successive projections algorithm-least squares-support vector machine (SPA-LS-SVM). SPA, compared with regression coefficients (RC), was applied to select effective wavelengths (EWs) with least collinearity and redundancies. Five concentration levels (100%, 80%, 60%, 40% and 20%) of plum vinegar were studied. Multiple linear regression (MLR) and partial least squares (PLS) models were developed for comparison. The results indicated that SPA-LS-SVM achieved the optimal performance for three acids comparing with full-spectrum PLS, SPA-MLR, SPA-PLS, RC-PLS and RC-LS-SVM. The root mean square error of prediction (RMSEP) was 0.3581, 0.0714 and 0.0201 for acetic, tartaric and lactic acids, respectively. The overall results indicated that Vis/NIR spectroscopy incorporated to SPA-LS-SVM could be applied as an alternative fast and accurate method for the determination of organic acids of plum vinegars. 相似文献
3.
Visible/near infrared spectroscopy (Vis/NIRs) technique was applied to non-destructive quantification of sugar and pH value
in yogurt. Partial least squares (PLS) analysis and least squares support vector machine (LS-SVM) were implemented for calibration
models. In this paper, three brands (Mengniu, Junyao, and Guangming) were set as the calibration, and the remaining two brands
(Yili and Shuangfeng) were used as prediction set. In the LS-SVM model, the correlation coefficient (r), root mean square error of prediction, and bias in prediction set were 0.9427, 0.2621°Brix, 1.804e−09 for soluble solids
content, and 0.9208, 0.0327, and 1.094e−09 for pH, respectively. The correlation spectra corresponding to the soluble solids
content and pH value of yogurt were also analyzed through PLS method. LS-SVM model was better than PLS models for the measurements
of soluble solids content and pH value. The results showed that the Vis/NIRs combined with LS-SVM models could predict the
soluble solids content and pH value of yogurt. 相似文献
4.
Visible/Near-Infrared Spectra for Linear and Nonlinear Calibrations: A Case to Predict Soluble Solids Contents and pH Value in Peach 总被引:2,自引:0,他引:2
Two sensitive wavelength (SWs) selection methods combined with visible/near-infrared (Vis/NIR) spectroscopy were investigated
to determine the soluble solids content (SSC) and pH value in peaches, including latent variables analysis (LVA) and independent
component analysis (ICA). A total of 100 samples were prepared for the calibration (n = 70) and prediction (n = 30) sets. Calibration models using SWs selected by LVA and ICA were developed, including linear regression of partial least
squares (PLS) analysis and nonlinear regression of least squares-support vector machine (LS-SVM). In the nonlinear models,
four SWs selected by ICA achieved the optimal ICA-LS-SVM model compared with LV-LS-SVM and both of them better than linear
model of PLS. The correlation coefficients (r
p and r
cv), root mean square error of cross validation, root mean square error of prediction, and bias by ICA-LS-SVM were 0.9537, 0.9485,
0.4231, 0.4155, and 0.0167 for SSC and 0.9638, 0.9657, 0.0472, 0.0497, and −0.0082 for pH value, respectively. The overall
results indicated that ICA was a powerful way for the selection of SWs, and Vis/NIR spectroscopy incorporated to ICA-LS-SVM
was successful for the accurate determination of SSC and pH value in peach. 相似文献
5.
A comparative study for the quantitative determination of soluble solids content,pH and firmness of pears by Vis/NIR spectroscopy 总被引:1,自引:0,他引:1
Visible and near infrared (Vis/NIR) spectroscopy was investigated to determine the soluble solids content (SSC), pH and firmness of different varieties of pears. Two-hundred forty samples (80 for each variety) were selected as sample set. Two-hundred ten pear samples (70 for each variety) were selected randomly for the calibration set, and the remaining 30 samples (10 for each variety) for the validation set. Partial least squares (PLS) and least squares-support vector machine (LS-SVM) with different spectral preprocessing techniques were implemented for calibration models. Different wavelength regions including Vis, NIR and Vis/NIR were compared. It indicated that Vis/NIR (400–1800 nm) was optimal for PLS and LS-SVM models. Then, LS-SVM models were developed with a grid search technique and RBF kernel function. All LS-SVM models outperformed PLS models. Next, effective wavelengths (EWs) were selected according to regression coefficients. The EW-LS-SVM models were developed and a good prediction precision and stability was achieved compared with PLS and LV-LS-SVM models. The correlation coefficient of prediction (rp), root mean square error of prediction (RMSEP) and bias for the best prediction by EW-LS-SVM were 0.9164, 0.2506 and −0.0476 for SSC, 0.8809, 0.0579 and −0.0025 for pH, whereas 0.8912, 0.6247 and −0.2713 for firmness, respectively. The overall results indicated that the regression coefficient was an effective way for the selection of effective wavelengths. LS-SVM was superior to the conventional linear PLS method in predicting SSC, pH and firmness in pears. Therefore, non-linear models may be a better alternative to monitor internal quality of fruits. And the EW-LS-SVM could be very helpful for development of portable instrument or real-time monitoring of the quality of pears. 相似文献
6.
Fei Liu Zonglai L. Jin Muhammad Shahbaz Naeem Tian Tian Fan Zhang Yong He Hui Fang Qingfu F. Ye Weijun J. Zhou 《Food and Bioprocess Technology》2011,4(7):1314-1321
Near-infrared (NIR) spectroscopy was investigated to determine the total amino acids (TAA) in oilseed rape (Brassica napus L.) leaves under a new herbicide—propyl 4-(2-(4,6-dimethoxypyrimidin-2-yloxy)benzylamino)benzoate (ZJ0273)—stress. In full-spectrum
partial least squares (PLS) models, direct orthogonal signal correction (DOSC) was the best preprocessing method. Successive
projections algorithm (SPA) was used to select the relevant variables. Multiple linear regression (MLR), PLS, and least squares-support
vector machine (LS-SVM) were used for calibration. The DOSC–SPA–LS-SVM model achieved the best prediction performance with
correlation coefficients r = 0.9968 and root mean squares error of prediction (RMSEP) = 0.2950 comparing all SPA–MLR, SPA–PLS, and SPA–LS-SVM models.
Some parsimonious direct functions were also developed based on the DOSC–SPA wavelength (1,340 nm) such as linear, index,
logarithmic, binominal, and exponential functions. The best performance was achieved by direct exponential function with r = 0.9968 and RMSEP = 0.2943. The overall results indicated that NIR was able to determine the TAA in herbicide-stressed oilseed
rape leaves, and the DOSC–SPA was quite helpful for the development of detection sensors and the monitoring of the growing
status and herbicide effect on field crop oilseed rape. 相似文献
7.
Quantification of Nitrogen Status in Rice by Least Squares Support Vector Machines and Reflectance Spectroscopy 总被引:3,自引:0,他引:3
The estimation of nitrogen status non-destructively in rice was performed using canopy spectral reflectance with visible and
near-infrared reflectance (Vis/NIR) spectroscopy. The canopy spectral reflectance of rice grown with different levels of nitrogen
inputs was determined at several important growth stages. This study was conducted at the experiment farm of Zhejiang University,
Hangzhou, China. The soil plant analysis development (SPAD) value was used as a reference data that indirectly reflects nitrogen
status in rice. A total of 64 rice samples were used for Vis/NIR spectroscopy at 325–1075 nm using a field spectroradiometer,
and chemometrics of partial least square (PLS) was used for regression. The correlation coefficient (r), root mean square error of prediction, and bias in prediction set by PLS were, respectively, 0.8545, 0.7628, and 0.0521
for SPAD value prediction in tillering stage, 0.9082, 0.4452, and −0.0109 in booting stage, and 0.8632, 0.7469, and 0.0324
in heading stage. Least squares support vector machine (LS-SVM) model was compared with PLS and back propagation neural network
methods. The results showed that LS-SVM was superior to the conventional linear and non-linear methods in predicting SPAD
values of rice. Independent component analysis was executed to select several sensitive wavelengths (SWs) based on loading
weights; the optimal LS-SVM model was achieved with SWs of 560, 575–580, 700, 730, and 740 nm for SPAD value prediction in
booting stage. It is concluded that Vis/NIR spectroscopy combined with LS-SVM regression method is a promising technique to
monitor nitrogen status in rice. 相似文献
8.
利用近红外光谱技术对苹果原醋中的重要指标进行定量分析,并进行模型优化以提高性能。采用遗传偏最小二乘法(GA-PLS)提取的特征波长作为最小二乘支持向量机(LS-SVM)的输入变量,先后建立苹果原醋中总酸、可溶性固形物的近红外定量模型,并与建立的偏最小二乘(PLS)模型结果进行比较。用决定系数(R2)、预测均方根误差(RMSEP)以及相对分析误差(RPD)对模型进行评价,确定最佳建模方法。结果表明,相比于PLS模型,总酸及可溶性固形物指标的LS-SVM定量模型的R2、RMSEP以及RPD值均有更好的表现,且在进行独立测试集验证时,LS-SVM模型的预测精度也明显优于PLS模型。说明遗传算法联合LS-SVM建立的定量模型有很高的准确度及稳定性,可以应用于苹果原醋总酸和可溶性固形物含量的快速检测。 相似文献
9.
The present study investigated the application of near infrared spectroscopy as a green, quick, and efficient alternative to analytical methods currently used to evaluate the quality (moisture, total sugars, acidity, soluble solids, pH and ascorbic acid) of frozen guava and passion fruit pulps. Fifty samples were analyzed by near infrared spectroscopy (NIR) and reference methods. Partial least square regression (PLSR) was used to develop calibration models to relate the NIR spectra and the reference values. Reference methods indicated adulteration by water addition in 58% of guava pulp samples and 44% of yellow passion fruit pulp samples. The PLS models produced lower values of root mean squares error of calibration (RMSEC), root mean squares error of prediction (RMSEP), and coefficient of determination above 0.7. Moisture and total sugars presented the best calibration models (RMSEP of 0.240 and 0.269, respectively, for guava pulp; RMSEP of 0.401 and 0.413, respectively, for passion fruit pulp) which enables the application of these models to determine adulteration in guava and yellow passion fruit pulp by water or sugar addition. The models constructed for calibration of quality parameters of frozen fruit pulps in this study indicate that NIR spectroscopy coupled with the multivariate calibration technique could be applied to determine the quality of guava and yellow passion fruit pulp. 相似文献
10.
This research aimed to explore the relationship between internal attributes (pH and soluble solids content) of tea beverages
and diffuse reflectance spectra. Three multivariate calibrations including least squares support vector machine regression
(LSSVR), partial least squares (PLS), and radial basis function (RBF) neural network were adopted for development of internal
attributes determination models. Ten kinds of tea beverages including green tea and black tea were selected for visible and
near infrared reflectance (Vis/NIR) spectroscopy measurement from 325 to 1,075 nm. As regard the kernel function, least squares–support
vector machine regression models were built with both linear and RBF kernel functions. Grid research and tenfold cross-validation
procedures were adopted for optimization of LSSVR parameters. The generalization ability of LSSVR models were evaluated by
adjusting the number of samples in the training set and testing set, and sensitive wavelengths that were closely correlated
with the internal attributes were explored by analyzing the regression coefficients from linear LSSVR model. Excellent LSSVR
models were built with r = 0.998, standard error of prediction (SEP) = 0.111, for pH and r = 0.997, SEP = 0.256, for soluble solids content, and it can be found that the LSSVR models outperformed the PLS and RBF
neural network models with higher accuracy and lower error. Six individual sensitive wavelengths for pH were obtained, and
the corresponding pH determination model was developed with r = 0.994, SEP = 0.173, based on these six wavelengths. The soluble solids content determination model was also developed with
r = 0.977, SEP = 0.173, based on seven individual sensitive wavelengths. The above results proved that Vis/NIR spectroscopy
could be used to measure the pH and soluble solids content in tea beverages nondestructively, and LSSVR was an effective arithmetic
for multivariate calibration regression and sensitive wavelengths selection. 相似文献
11.
Shi Ji-yong Zou Xiao-bo Huang Xiao-wei Zhao Jie-wen Li Yanxiao Hao Limin Zhang Jianchun 《Food chemistry》2013
More than 3.2 million litres of vinegar is consumed every day in China. There are many types of vinegar in China. How to control the quality of vinegar is problem. Near infrared spectroscopy (NIR) transmission technique was applied to achieve this purpose. Ninety-five vinegar samples from 14 origins covering 11 provinces in China were collected. They were classified into mature vinegar, aromatic vinegar, rice vinegar, fruit vinegar, and white vinegar. Fruit vinegar and white vinegar were separated from the other traditional categories in the two-dimension principal component space of NIR after principle component analysis (PCA). Least-squares support vector machine (LS-SVM) as the pattern recognition was firstly applied to identify mature vinegar, aromatic vinegar, rice vinegar in this study. The top two principal components (PCs) were extracted as the input of LS-SVM classifiers by principal component analysis (PCA). The best experimental results were obtained using the radial basis function (RBF) LS-SVM classifier with σ = 0.8. The accuracies of identification were more than 85% for three traditional vinegar categories. Compared with the back propagation artificial neural network (BP-ANN) approach, LS-SVM algorithm showed its excellent generalisation for identification results. As total acid content (TAC) is highly connecting with the quality of vinegar, NIR was used to prediction the TAC of samples. LS-SVM was applied to building the TAC prediction model based on spectral transmission rate. Compared with partial least-square (PLS) model, LS-SVM model gave better precision and accuracy in predicting TAC. The determination coefficient for prediction (Rp) of the LS-SVM model was 0.919 and root mean square error for prediction (RMSEP) was 0.3226. This work demonstrated that near infrared spectroscopy technique coupled with LS-SVM could be used as a quality control method for vinegar. 相似文献
12.
Detection of adulterants such as sweeteners materials in honey using near-infrared spectroscopy and chemometrics 总被引:4,自引:0,他引:4
Near-infrared (NIR) spectroscopy combined with chemometrics methods has been used to detect adulteration of honey samples. The sample set contained 135 spectra of authentic (n = 68) and adulterated (n = 67) honey samples. Spectral data were compressed using wavelet transformation (WT) and principal component analysis (PCA), respectively. In this paper, five classification modeling methods including least square support vector machine (LS-SVM), support vector machine (SVM), back propagation artificial neural network (BP-ANN), linear discriminant analysis (LDA), and K-nearest neighbors (KNN) were adopted to correctly classify pure and adulterated honey samples. WT proved more effective than PCA, as a means for variables selection. Best classification models were achieved with LS-SVM. A total accuracy of 95.1% and the area under the receiver operating characteristic curves (AUC) of 0.952 for test set were obtained by LS-SVM. The results showed that WT-LS-SVM can be as a rapid screening technique for detection of this type of honey adulteration with good accuracy and better generalization. 相似文献
13.
Dimitris Alexandrakis Gerard Downey Amalia G. M. Scannell 《Food and Bioprocess Technology》2012,5(1):338-347
Near-infrared (NIR) transflectance and Fourier transform-infrared (FT-IR) attenuated total reflectance spectra of intact chicken
breast muscle packed under aerobic conditions and stored at 4° for 14 days were collected and investigated for their potential
use in rapid non-destructive detection of spoilage. Multiplicative scatter correction-transformed NIR and standard normal
variate-transformed FT-IR spectra were analysed using principal component analysis (PCA), partial least-squares discriminant
analysis (PLS2-DA) and outer product analysis (OPA). PCA and PLS2-DA regression failed to completely discriminate between
days 0 and 4 samples (total viable count (TVC) days 0 and 4 = 5.23 and 6.75 log10 cfu g−1) which had bacterial loads smaller than the accepted levels (8 log10 cfu g−1) of sensory spoilage detection but classified correctly days 8 and 14 samples (TVC days 8 and 14 = 9.61 and 10.37 log10 cfu g−1). OPA performed on both NIR and FT-IR datasets revealed several correlations that highlight the effect of proteolysis in
influencing the spectra. These correlations indicate that increase in free amino acids and peptides could be the main factor
in the discrimination of intact chicken breast muscle. This investigation suggests that NIR and FT-IR spectroscopy can become
useful, rapid, non-destructive tools for spoilage detection. 相似文献
14.
利用近红外光谱技术实现对白酒发酵过程中酒醅主要成分的质量控制,并进行模型优化,提高性能。采用偏最小二乘法提取的潜在变量作为最小二乘支持向量机的输入变量,先后建立了白酒酒醅中酒精度、淀粉、水分、酸度的近红外定量模型,并与经无信息变量消除法波段筛选后建立的偏最小二乘模型结果进行比较。结果表明:与偏最小二乘模型相比,4 个指标的最小二乘支持向量机定量模型的相关系数(R2)、预测均方根误差以及相对分析误差3 个评价参数均有更优表现;对未知样品进行预测时,最小二乘支持向量机模型的预测准确度明显高于偏最小二乘模型。说明最小二乘支持向量机模型的准确度、稳定性及预测性能均优于偏最小二乘法模型,为白酒酒醅的品质分析方法研究提供了新的思路。 相似文献
15.
Di WuPengcheng Nie Joel CuelloYong He Zhiping Wang Hongxi Wu 《Journal of food engineering》2011,102(3):278-286
The authentication of food products is critically important in a global economy in public-health and economic terms. The specific aims of this study were to evaluate the application of full-spectrum and NIR spectroscopy and to evaluate the adoption of PLS and LS-SVM models to accomplish a rapid and non-invasive quantification of the two common adulterants, flour and mungbean powder, in Spirulina powder. The results showed that, using all treatment sets, only the LS-SVM models were adequate in predicting either adulterant under both full spectra and NIR spectra. The use of NIR spectra would allow detection of adulterants even when masked by food dyes. Design value analysis indicated that the benefits per unit cost of applying the NIR spectra to quantify adulterants in Spirulina powder significantly exceeded that of using full spectra, and that the value of employing the LS-SVM models under NIR spectra exceeded that of using the PLS models. 相似文献
16.
Xiaoping Hu Kiyohiko Toyoda Minoru Yamanoue Ikko Ihara Kaori Nakai 《Food and Bioprocess Technology》2010,3(6):883-891
Japanese black Wagyu beef has its characteristics of fatty well-marbled texture, flavor, and tenderness which are affected
by fatty acid composition. The aim of this study was to develop an analytical method for evaluating the fatty acid profile
of Wagyu beef by Fourier transform infrared (FTIR) spectroscopy. In the current study, attenuated total reflection–FTIR (ATR-FTIR)
spectroscopy and gas chromatography (GC) were applied to the fat tissues, and the solvent-extracted fats which were sampled
from subcutaneous, inter- and intramuscular fat tissues. Results of GC analysis showed that monounsaturated fatty acids (MUFA)
content became larger in the order of intramuscular, intermuscular, and subcutaneous fats, and saturated fatty acids (SFA)
became smaller in the same order. Subcutaneous fat could be discriminated from inter- and intramuscular fats on the basis
of fatty acid composition by principal component analysis. The ATR-FTIR analysis revealed that the shift of the peak positions
of alkene C–H stretching vibration at around 3,006 cm−1 occurred depending on the unsaturation degree of fatty acids in beef fat. Partial least squares (PLS) regression analysis
with leave-one-out cross-validation was applied to the combined regions of 2,800–3,050 and 1,000–1,500 cm−1 for the fat tissues and the extracted fats. The correlation coefficients of the PLS validation models predicting the content
of the MUFA and SFA for solvent-extracted fats were higher than those for fat tissues, and the coefficients (R
2) of determination more than 0.873 were obtained for solvent-extracted fats and 0.522 for fat tissues. 相似文献
17.
Lijuan Xie Yibin Ying Hongjian Lin Ying Zhou Xiaoying Niu 《Sensing and Instrumentation for Food Quality and Safety》2008,2(2):111-115
The potential of near-infrared (NIR) transmittance spectroscopy to nondestructively detect soluble solids content (SSC) and
pH in tomato juices was investigated. A total of 200 tomato juice samples were used for NIR spectroscopy analysis at 800–2400 nm
using an FT-NIR spectrometer. Multiplicative signal correction (MSC), and the first and second derivative were applied for
pre-processing spectral data. The relationship between SSC, pH, and FT-NIR spectra of tomato juice were analyzed via partial
least-squares (PLS) regression. PLS regression models were able to predict SSC and pH in tomato juices. The r
c, RMSEC, RMSEP, and RMSECV for SSC were 0.92, 0.0703°Brix, 0.150°Brix, and 0.138°Brix, respectively, whereas those values
for pH were 0.90, 0.0333, 0.0316, and 0.0489, respectively. It is concluded that the combination of NIR transmittance spectroscopy
and PLS methods can be used to provide a technique of convenient, versatile, and rapid analysis for SSC and pH in tomato juices. 相似文献
18.
Infrared spectroscopy and chemometrics for the starch and protein prediction in irradiated rice 总被引:1,自引:0,他引:1
Infrared spectroscopy was investigated to predict components of starch and protein in rice treated with different irradiation doses based on sensitive wavelengths (SWs). Near infrared and mid-infrared regions were compared to determine which one produces the best prediction of components in rice after irradiation. Partial least-squares (PLS) analysis and least-squares-support vector machine (LS-SVM) were implemented for calibration models. The best PLS models were achieved with NIR region for starch and MIR region for protein. Moreover, independent component analysis (ICA) was executed to select several SWs based on loading weights, and the optimal LS-SVM model was achieved with SWs of 1210–1222, 1315–1330, 1575–1625, 1889–1909 and 2333–2356 nm for starch and SWs of 962–1091, 1232–1298, 1480–1497, 1584–1625 and 2373–2398 cm−1 for protein. It indicated that IR spectroscopy combined with LS-SVM could be applied as a high precision way for the determination of starch and protein in rice after irradiation. 相似文献
19.
A feasibility study on quantitative analysis of glucose and fructose in lotus root powder by FT-NIR spectroscopy and chemometrics 总被引:2,自引:0,他引:2
The feasibility of rapid analysis of glucose and fructose in lotus root powder by Fourier transform near-infrared (FT-NIR) spectroscopy was studied. Diffuse reflectance spectra were collected between 4000 and 12,432 cm−1. Calibration models established by partial least-squares regression (PLSR), interval PLS of forward (FiPLS) and backward (BiPLS), back propagation-artificial neural networks (BP-ANN) and least squares-support vector machine (LS-SVM) were compared. The optimal models for glucose and fructose were obtained by LS-SVM with the first 10 latent variables (LVs) as input. For fructose the correlation coefficients of calibration (rc) and prediction (rp), the root-mean-square errors of calibration (RMSEC) and prediction (RMSEP), and the residual predictive deviation (RPD) were 0.9827, 0.9765, 0.107%, 0.115% and 4.599, respectively. For glucose the indexes were 0.9243, 0.8286, 0.543%, 0.812% and 1.785. The results indicate that NIR spectroscopy technique with LS-SVM offers effective quantitative capability for glucose and fructose in lotus root powder. 相似文献
20.
This study was carried out to evaluate the feasibility of using near infrared (NIR) spectroscopy for determining three antioxidant activity indices of the extract of bamboo leaves (EBL), specifically 2,2-diphenyl-1-picrylhydrazyl (DPPH), ferric reducing/antioxidant power (FRAP), and 2,2′-azinobis-(3-ethylbenz-thiazoline-6-sulfonic acid) (ABTS). Four different linear and nonlinear regressions tools (i.e. partial least squares (PLS), multiple linear regression (MLR), back-propagation artificial neural network (BP-ANN), and least squares support vector machine (LS-SVM)) were systemically studied and compared in developing the model. Variable selection was first time considered in applying the NIR spectroscopic technique for the determination of antioxidant activity of food or agricultural products. On the basis of these selected optimum wavelengths, the established MLR calibration models provided the coefficients of correlation with a prediction (rpre) of 0.863, 0.910, and 0.966 for DPPH, FARP, and ABTS determinations, respectively. The overall results of this study revealed the potential for use of NIR spectroscopy as an objective and non-destructive method to inspect the antioxidant activity of EBL. 相似文献