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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.
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
3.
Detection of Organic Acids and pH of Fruit Vinegars Using Near-Infrared Spectroscopy and Multivariate Calibration 总被引:1,自引:0,他引:1
Near-infrared (NIR) spectroscopy was investigated to determine the acetic, tartaric, formic acids and pH of fruit vinegars.
Optimal partial least squares (PLS) models were developed with different preprocessing. Simultaneously, the performance of
least squares-support vector machine (LS-SVM) models was compared with three kinds of inputs, including wavelet transform
(WT), latent variables, and effective wavelengths (EWs). The results indicated that all LS-SVM models outperformed PLS models.
The optimal correlation coefficient (r), root mean square error of prediction and bias for validation set were 0.9997, 0.3534, and −0.0110 for acetic acid by WT-LS-SVM;
0.9985, 0.1906, and 0.0025 for tartaric acid by WT-LS-SVM; 0.9987, 0.1734, and 0.0012 for formic acid by EW-LS-SVM; and 0.9996,
0.0842, and 0.0012 for pH by WT-LS-SVM, respectively. The results indicated that NIR spectroscopy (7,800–4,000 cm−1) combined with LS-SVM could be utilized as a precision method for the determination of organic acids and pH of fruit vinegars. 相似文献
4.
研究贮藏期间损伤猕猴桃内部品质与其近红外漫反射光谱之间的关系。利用近红外光谱(12000~4000cm-1)技术和多元线性回归(multiple linear regression,MLR)、主成分回归(principal component regression,PCR)和偏最小二乘法(partial least squares,PLS)3种校正方法分别对损伤华优猕猴桃在2℃条件下贮藏4周期间的可溶性固形物含量、pH值和硬度进行定量分析;并对比吸光度原始光谱、一阶微分和二阶微分3种不同预处理方法的PLS模型校正结果。结果表明:一阶微分预处理方法时,应用PLS建立的可溶性固形物含量、pH值和硬度校正模型的效果最佳;预测集样品预测值与测量值之间的相关系数分别为0.812、0.703、0.919,预测均方根误差分别为0.749、0.153、1.700。说明应用近红外漫反射技术检测贮藏期间损伤猕猴桃的内部品质是可行的。 相似文献
5.
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
6.
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
7.
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
8.
《Journal of food engineering》2009,95(3-4):267-273
The potential of near infrared (NIR) reflectance spectroscopy over the range 780–1690 nm was investigated to measure the soluble solids content (SSC) and firmness of bell pepper fruit. Partial least squares (PLS) calibration models were constructed based on a calibration dataset which included data from two cultivars (Solution and Ferrari) and two harvest times (2005 and 2006). The effect of Savitzky–Golay second derivative preprocessing and extended multiplicative signal correction (EMSC) on the accuracy of the calibration models was investigated and the best results were obtained with the former. The SECV were equal to 5.9 N and 0.59 °Brix for firmness and SSC, respectively. When the model was applied to an external data set including data from cv. Solution and a different harvest season, the satisfactory SEP values of 4.49 N and 0.7 °Brix were obtained, but for firmness a bias of 5.6 N was observed. From these results it can be concluded that NIR spectroscopy can be used as a non-destructive technique for measuring the SSC in bell pepper, but that further research is needed to make it robust for firmness prediction. 相似文献
9.
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
10.
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
11.
草莓可溶性固形物(soluble solids content,SSC)含量是评价草莓内部品质的关键指标。为了实现对该指标的快速、无损评估,基于近红外光谱技术,构建了线性偏最小二乘(partial least squares,PLS)和非线性最小二乘支持向量机(least squares support vector machine,LS-SVM)模型,联合蒙特卡罗无信息变量消除和连续投影算法(Monte-Carlo uninformative variable elimination,successive projections algorithm,MC-UVE-SPA)从原始光谱4 254个变量中提取了27个有效变量,并构建了基于有效变量的定量分析模型。同时,考虑到草莓表面颜色的影响,基于草莓RGB图像各分量获取了颜色特征参数,进一步融合光谱和颜色特征构建了多参数融合PLS和LS-SVM模型。基于相同的校正集和预测集,比较了所有模型对草莓内部SSC的预测性能。结果表明,MC-UVESPA是一种有效的草莓光谱变量选择算法,且多参数融合非线性LS-SVM模型是草莓内部SSC定量预测的最... 相似文献
12.
Jiangbo Li Wenqian Huang Liping Chen Shuxiang Fan Baohua Zhang Zhiming Guo Chunjiang Zhao 《Food Analytical Methods》2014,7(9):1891-1902
Informative variable selection or wavelength selection plays an important role in the quantitative analysis of near-infrared (NIR) spectra because the modern spectroscopy instrumentations usually have a high resolution and the obtained spectral data sets may have thousands of variables and hundreds or thousands of samples. In this study, a new combination of Monte Carlo–uninformative variable elimination (MC-UVE) and successive projections algorithm (SPA; MC-UVE-SPA) was proposed to select the most effective variables. MC-UVE was firstly used to eliminate the uninformative variables in the raw spectra data. Then, SPA was applied to determine the variables with the least collinearity. A case study was done based on the NIR spectroscopy for the non-destructive determination of soluble solids content (SSC) in ‘Ya’ pear. A total of 160 samples were prepared for the calibration (n?=?120) and prediction (n?=?40) sets. Three calibration algorithms including linear regressions of partial least square regression (PLS) and multiple linear regression (MLR), and nonlinear regression of least-square support vector machine (LS-SVM) were used for model establishment by using the selected variables by SPA, UVE, MC-UVE, UVE-SPA, and MC-UVE-SPA, respectively. The results indicated that linear models such as PLS and MLR were more effective than nonlinear model such as LS-SVM in the prediction of SSC of ‘Ya’ pear. In terms of linear models, different variable selection methods can obtain a similar result with the RMSEP values range from 0.2437 to 0.2830. However, combination of MC-UVE and SPA was helpful for obtaining a more parsimonious and efficient model for predicting the SSC values in ‘Ya’ pear. Twenty-two effective variables selected by MC-UVE-SPA achieved the optimal linear MC-UVE-SPA-MLR model compared with other all developed models by balancing between model accuracy and model complexity. The coefficients of determination (r 2), root mean square error of prediction, and residual predictive deviation by MC-UVE-SPA-MLR were 0.9271, 0.2522, and 3.7037, respectively. 相似文献
13.
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
14.
Nondestructive evaluation of jujube quality by visible and near-infrared spectroscopy 总被引:2,自引:0,他引:2
This study compared prediction ability of interactance, transmission measurements of visible and near-infrared (Vis/NIR) spectroscopy in detecting the soluble solids content (SSC) of jujubes. Calibration models relating Vis/NIR spectra to SSC were developed based on partial least squares regression (PLSR) with respect to the logarithms of the reciprocal absorbance (log (1/R)), its first and second derivatives (D1log (1/R), D2log (1/R)). The PLSR models for prediction samples resulted correlation coefficients (rp) of 0.74-0.91 and root mean square error of prediction (RMSEP) of 2.018-3.200 °Brix for interactance; rp of 0.63-0.73 and RMSEP of 3.517-3.863 °Brix for transmission, respectively. The results indicate that interactance displays an obvious advantage over transmission measurement.The reflectance measurement was used to access the discrimination potential in sorting external insect-infested jujubes from intact class. Stepwise discriminant analysis (SDA) was performed to identify the effective wavelengths that best discriminated the insect-infested jujubes from intact jujubes and to derive a discriminant function in classifying the jujubes showing external infestation and those that were free of infestation. The results showed that log (1/R) had better correct classification rate than D1log (1/R), and D2log (1/R) for classifying intact, insect-infested and stem-end classes. 相似文献
15.
Variable selection for partial least squares analysis of soluble solids content in watermelon using near-infrared diffuse transmission technique 总被引:1,自引:0,他引:1
This work is focused on the variable selection in building the partial least squares (PLS) regression model of soluble solids content (SSC) that is used to evaluate quality grading of watermelon. The spectra were obtained by the near infrared (NIR) spectrometer with the device designed for on-line quality grading of watermelon and the spectra of 680–950 nm were adopted to analysis. The variable selection was based on Monte-Carlo uninformative variable elimination (MC-UVE) and genetic algorithm (GA). In comparison of the performances of the full-spectra (680–950 nm) PLS regression model and the feature wavelengths PLS regression model showed that the MC-UVE–GA–PLS model with baseline offset correction combined multiplicative scatter correction (MSC) pretreatment was much better and 14 variables in total were selected. The correlation coefficients between the predicted and actual SSC were 0.885 and 0.845, the root mean square errors were 0.562 °Brix and 0.574 °Brix for calibration and prediction set, respectively. This work can make a great contribution to the research of on-line quality grading for watermelon nondestructively. 相似文献
16.
Soluble solid content (SSC) in fruit is one of the most crucial internal quality factors, which could provide valuable information for commercial decision-making. Near-infrared (NIR) technique has effective potentials for determining the SSC since NIR was sensitive to the concentrations of organic materials. In this study, a novel NIR technique, long-wave near infrared (LWNIR) hyperspectral imaging with a spectral range of 930–2548 nm, was investigated for measuring the SSC in pear, which has never been examined in the past. A new combination of Monte Carlo-uninformative variable elimination (MC-UVE) and successive projections algorithm (SPA) was proposed to select most effective variables from LWNIR hyperspectral data. The selected variables were used as the inputs of partial least square (PLS) to build calibration models for determining the SSC of ‘Ya’ pear. The results indicated that calibration model built using MC-UVE-SPA-PLS on 18 effective variables achieved the optimal performance for prediction of SSC comparing with other developed PLS models (MC-UVE-PLS and SPA-PLS) by comprehensively considering the accuracy, robustness, and complexity of models. The correlation coefficients between the predicted and actual SSC were 0.88 and 0.88 and the root mean square errors were 0.49 and 0.35 °Brix for calibration and prediction set, respectively. The overall results indicated that long-wave near infrared hyperspectral imaging incorporated to MC-UVE-SPA-PLS model could be applied as an alternative, fast, accurate, and nondestructive method for the determination of SSC in pear. 相似文献
17.
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
18.
贮藏期内富士和粉红女士苹果品质的FT-NIR无损检测 总被引:2,自引:0,他引:2
为探索傅里叶近红外光谱快速无损检测贮藏期苹果品质的方法,在苹果贮藏过程中,每隔30d采集富士和粉红女士(各40个)2个苹果品种共计400个样本的近红外图谱(12000~4000cm-1),用OPUS-QUANT软件预处理光谱,用偏最小二乘法建立通用于2个品种的可滴定酸(TA)、pH值和可溶性固形物(SSC)的数学模型。结果表明:富士和粉红女士的光谱经矢量归一化预处理后,在波段7502~4247cm-1内所建立的可滴定酸模型稳定性较好,该模型校正时的相关系数(R2)和评估均方误分别为0.9231和0.0263%,预测时的相关系数R2和内部交叉验证均方根差分别为0.9071和0.0266%;在波段11995~4247cm-1内,光谱经一阶导数预处理后所建立的pH值预测模型稳定性较好,该模型校正时的R2和评估均方误分别为0.9263和0.0700,预测时的R2和内部交叉验证均方根差分别为0.9113和0.0772;近红外光谱经最大-最小归一化预处理后,在波段6102~5446cm-1所建立的SSC模型效果较好,该模型校正时的R2和评估均方误分别为0.9212和0.3570%,预测时的R2和内部交叉验证均方根差分别为0.9130和0.370%。在富士和粉红女士贮藏期品质检测过程中,建立的通用于这2个品种的TA、pH值和SSC检测的数学模型,稳定性较好,能满足品质快速无损检测的要求。 相似文献
19.
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
20.
The investigation was conducted to develop a hyperspectral imaging system in the near infrared (NIR) region (900–1700 nm) to predict the moisture content, pH and color in cooked, pre-sliced turkey hams. Hyperspectral images were acquired by scanning the ham slices (900–1700 nm) originated from different quality grade of turkey hams. Spectral data were then extracted and analyzed using partial least-squares (PLSs) regression, as a multivariate calibration method, to reduce the high dimensionality of the data and to correlate the NIR reflectance spectra with quality attributes of the samples considered. Instead of using a wide range of spectra, the number of wavebands was reduced for more stable, comprehensive and faster model in the subsequent multispectral imaging system. From this point of view, important wavelengths were selected to improve the predictive power of the calibration models as well as to simplify the model by avoiding repetition of information or redundancies. With the help of PLS regression analysis, nine wavelengths (927, 944, 1004, 1058, 1108, 1212, 1259, 1362 and 1406 nm) were selected as the optimum wavelengths for moisture prediction, eight wavelengths (927, 947, 1004, 1071, 1121, 1255, 1312 and 1641 nm) for pH prediction and nine wavelengths (914, 931, 991, 1115, 1164, 1218, 1282, 1362 and 1638 nm) were identified for color (a*) prediction. With the identified reduced number wavelengths, good coefficients of determination (R2) of 0.88, 0.81 and 0.74 with RMSECV of 2.51, 0.02 and 0.35 for moisture, pH and color, respectively, were achieved, reflecting reasonable accuracy and robustness of the models. 相似文献