Detection of adulterants such as sweeteners materials in honey using near-infrared spectroscopy and chemometrics

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.     

Measurement of Soluble Solid Contents and pH of White Vinegars Using VIS/NIR Spectroscopy and Least Squares Support Vector Machine

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.     

Simultaneous measurement of total acid content and soluble salt-free solids content in Chinese vinegar using near-infrared spectroscopy

Total acid content (TAC) and soluble salt-free solids content (SSFSC) in Chinese vinegar are 2 important indicators in the assessment of its quality. This paper shows the feasibility to determine TAC and SSFSC in Chinese vinegar by near-infrared (NIR) spectroscopy. Synergy interval partial least square (Si-PLS) algorithm was performed to calibrate the regression model. The number of PLS factors and the number of intervals were optimized simultaneously by cross-validation. The performance of the model was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in the prediction set. The optimum Si-PLS model for TAC was achieved with RMSEP = 0.264 g/100 mL and R(p) = 0.9655; the optimum Si-PLS model for SSFSC was achieved with RMSEP = 1.93 g/100 mL and R(p) = 0.9302. The overall results demonstrated that NIR spectroscopy combined with Si-PLS could be utilized to determinate TAC and SSFSC in Chinese vinegar, and NIR spectroscopy has a potential to be used in vinegar industry.     

郎枣轻微损伤可见/近红外光谱分波段动态判别研究

为了实现轻微损伤郎枣的快速无损检测,以完好和轻微损伤郎枣为研究对象,动态采集其可见/近红外光谱数据。依据光谱波段定义将采集的光谱数据分为可见光(Vis)、短波近红外(SW-NIR)、长波近红外(LW-NIR)、可见/短波近红外(Vis/SW-NIR)、近红外(NIR)和可见/近红外(Vis/NIR)等6个波段,分别选取各波段最佳预处理方法。采用连续投影法(SPA)和主成分分析法(PCA)分别对各波段光谱数据降维,以全波长、SPA提取的特征波长和PCA提取的主成分作为输入,分别建立偏最小二乘回归法(PLSR)和最小二乘支持向量机(LS-SVM)模型,通过比较预测集的判别准确率,确定最佳建模方法。结果表明,PLSR模型优于LS-SVM模型,SW-NIR波段较其余5个波段有更好的判别能力,所建SW-NIR-SNV-SPA-PLSR模型判别准确率为93.3%,为最佳模型。本实验为轻微损伤郎枣的快速无损检测和相关仪器的开发提供了理论基础。     

Nondestructive estimation of maturity and textural properties on tomato ‘Momotaro’ by near infrared spectroscopy

Near infrared spectroscopy offers the possibility to classify and predict the internal quality of fruits and vegetables. The objective of this study was to evaluate the ability of near infrared spectroscopy to classify the maturity level and to predict textural properties of tomatoes variety “Momotaro”. Principal component analysis (PCA) and Soft independent modeling of class analogy (SIMCA) were used to distinguish among different maturities (mature green, pink and red). Partial least squares (PLS) regression was used to estimate textural properties, alcohol insoluble solids and soluble solids content of the tomatoes. The PCA calibration model with mean normalization pretreatment spectra of mature green tomatoes, gave the highest distinguishability (96.85%). It could classify 100.00% of red and pink tomatoes. The SIMCA model could not give better accuracy in maturity classification than individual PCA models. Among the textural parameters measured, the bioyield force from the puncture test with the near infrared (NIR) spectra (between 1100 and 1800 nm) pretreated by multiplicative scatter correction (MSC) had the highest correlation coefficient between NIR predicted and reference values (r = 0.95) and lowest standard error of prediction (SEP = 0.35 N) and bias of 0.19 N. The ratio of standard deviation of reference data of prediction set to standard error of prediction (RPD) was 2.71. In the case of Momotaro tomato, NIR spectroscopy by using PLS regression could not predict alcohol insoluble solids in fresh weight accurately but could predict soluble solids content well with r of 0.80, SEP of 0.210 %Brix and bias of 0.022 %Brix.     

基于支持向量机的食醋总酸近红外光谱建模

为了得到稳定可靠的食醋总酸光谱模型,以不同产地、不同种类的95个食醋样品为研究对象,应用基于统计学原理的最小二乘支持向量机(LS-SVM)对食醋总酸含量进行光谱分析.对预处理后的光谱进行主成分分析(PCA),以主成分信号作为输入变量建立食醋总酸含量的近红外光谱模型,并与偏最小二乘法(PLS)和向后区间偏最小二乘法(biPLS)模型进行比较.结果表明,LS-SVM模型中的校正集中的相关系数(rc)和交互验证均方根误差(RMSECV)分别达到0.9614和0.2192,预测集相关系数(rp)和预测均方根误差(RMSEP)分别达到和0.919和0.3226,均优于PLS和biPLS模型.研究表明,近红外光谱与食醋总酸含量呈非线性关系,采用LS-SVM建立的模型预测性能更好,精度更高.     

The evaluation of sugar content and firmness of non-climacteric pears based on voltammetric electronic tongue

The sugar content and firmness of non-climacteric pear of different cultivars were studied by a voltammetric electronic tongue (VE-tongue). The VE-tongue self-developed in this study comprised six working electrodes (gold, silver, platinum, palladium, tungsten, and titanium electrode), an Ag/AgCl reference electrode, and a platinum auxiliary electrode. The multi-frequency large amplitude pulse voltammetry (MLAPV) was applied to the working electrodes as the scanning potential waveform,and it consisted of four frequency segments of 1 Hz, 10 Hz, 100 Hz, and 1000 Hz. In this study, five cultivars of pear from different geographical origins were tested by VE-tongue, and the firmness and sugar content of pears were tested by the traditional methods. The characteristic data (the maximum and minimum values) obtained by VE-tongue were compressed by principal component analysis (PCA), and the principal components (PCs) were taken as the input variables of principal component regression (PCR), partial least squares regression (PLSR), and least squared-support vector machines (LS-SVMs) to predict sugar content and firmness. All the models showed good results, and LS-SVM preformed best in the prediction.     

Rapid measurement of total acid content (TAC) in vinegar using near infrared spectroscopy based on efficient variables selection algorithm and nonlinear regression tools

Total acid content (TAC) is an important index in assessing vinegar quality. This work attempted to determine TAC in vinegar using near infrared spectroscopy. We systematically studied variable selection and nonlinear regression in calibrating regression models. First, the efficient spectra intervals were selected by synergy interval PLS (Si-PLS); then, two nonlinear regression tools, which were extreme learning machine (ELM) and back propagation artificial neural network (BP-ANN), were attempted. Experiments showed that the model based on ELM and Si-PLS (Si-ELM) was superior to others, and the optimum results were achieved as follows: the root mean square error of prediction (RMSEP) was 0.2486 g/100mL, and the correlation coefficient (R(p)) was 0.9712 in the prediction set. This work demonstrated that the TAC in vinegar could be rapidly measured by NIR spectroscopy and Si-ELM algorithm showed its superiority in model calibration.     

Nondestructive measurement of soluble solid content of navel orange fruit by visible-NIR spectrometric technique with PLSR and PCA-BPNN

A relationship was established between the soluble solid content (SSC) of navel orange fruit determined by destructive measurement and visible-near infrared diffuse reflectance spectra in the wavelength range of 350-1800 nm. Multiplicative scatter correction (MSC) and standard normal variate correction (SNV) were applied to the spectra, partial least squares regression (PLSR) and back propagation neural network (BPNN) based on principal component analysis (PCA) were used to develop the models for predicting the SSC of intact navel orange fruit. Thirty-eight unknown samples were used to evaluate the performance of these models. The principal component analysis-back propagation (PCA-BPNN) method with MSC spectral pretreatment obtain the best predictive results, resulting in correlation coefficient, root mean square error of prediction (RMSEP), average difference between predicted and measured values (Bias) of 0.90, 0.68 °Brix and 0.16 °Brix, respectively. Experimental results indicate that PCA-BPNN is a suitable tool to model the non-linear complex system, with additional advantages over PLSR, and the vis/NIR spectrometric technique can be used for measuring the SSC of intact navel orange fruit, nondestructively.     

A comparative study for the quantitative determination of soluble solids content,pH and firmness of pears by Vis/NIR spectroscopy

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 (r_p), 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.     

Chromatographic Fingerprinting with Multivariate Data Analysis for Detection and Quantification of Apricot Kernel in Almond Powder

Adulteration of almond powder samples with apricot kernel was solved by gas chromatographic fatty acid fingerprinting combined with multivariate data analysis methods (principal component analysis (PCA), PCA-linear discriminant analysis (PCA-LDA), partial least squares (PLS), and LS support vector machine (LS-SVM). Different almond and apricot kernel samples were mixed at concentrations ranging from 10 to 90% w/w. PCA and PCA-LDA methods were applied for the classification of almonds, apricot kernels, and mixtures. PLS and LS-SVM were used for the quantification of adulteration ratios of almond. Models were developed using a training data set and evaluated using a validation data set. The root mean square error of prediction (RMSEP) and coefficient of determination (R ²) of validation data set obtained for PLS and LS-SVM were 5.01, 0.964 and 2.29, 0.995, respectively. The results showed that the methods can be applied as an effective and feasible method for testing almond adulteration.     

A feasibility study on on-line determination of rice wine composition by Vis–NIR spectroscopy and least-squares support vector machines

The feasibility of visible and near infrared (Vis–NIR) spectroscopy and least-squares support vector machines (LS-SVM) for on-line determination of rice wine composition was investigated. A circle-light fibre spectrometer system was designed to collect transreflectance spectra of rice wine samples in round brown glass bottles with the bottle sealed and the labels removed. Statistical equations were established between reference data and Vis–NIR spectra by LS-SVM. Compared to partial least squares regression (PLSR), the performance of LS-SVM was slightly better, with higher correlation coefficients for validation (r_val) of 0.915, 0.888 and 0.872, and lower root mean square error of validation (RMSEP) of 0.168 (%(V V⁻¹)), 0.146 (g L⁻¹) and 0.033 for alcohol content, titratable acidity, and pH, respectively. Based on the results, it was concluded that the Vis–NIR spectrometer system was suitable for on-line wine quality determination, and LS-SVM was a reliable multivariate method for NIR analysis.     

Infrared spectroscopy and chemometrics for the starch and protein prediction in irradiated rice

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.     

Determination of the protein content in brine from salted herring using near-infrared spectroscopy

Near-infrared reflectance (NIR) spectroscopy in the spectral range of 1000-2500 nm, was measured directly on brine from barrel salted herring, to investigate the potential of NIR as a fast method to determine the protein content. A principal component analysis performed on the NIR spectra shows two groups, separating the first 100 days of storage from the storage time exceeding 100 days. A partial least-squares regression model between selected regions of the NIR spectra and the protein content yields a correlation coefficient of 0.93 and a prediction error (RMSECV) of 0.25 g/100 g. The results clearly indicate that NIR spectroscopy has a potential as a fast and noninvasive method for assessing the protein content in brine from barrel salted herring, which again may be used as an indicator for the ripening quality of barrel salted herring.     

遗传算法联合LS-SVM的苹果原醋成分定量分析

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。     

Determination of total phenolic content and antioxidant capacity of onion (Allium cepa) and shallot (Allium oschaninii) using infrared spectroscopy

Total phenolic content (TPC) and total antioxidant capacity (TAC) of four onion varieties (red, white, yellow and sweet) and shallot from selected locations (Washington, Idaho, Oregon, Texas and Georgia) were determined using Fourier transform infrared (FT-IR) spectroscopy (4000–400 cm⁻¹). The Folin–Ciocalteu (F–C) assay was used to quantify TPC and three assays were used to determine TAC, including 2,2-diphenyl-picrylhydrazyl (DPPH) assay, Trolox equivalent antioxidant capacity (TEAC) assay and ferric reducing antioxidant power (FRAP) assay. Partial least squares regression (PLSR) with cross-validation (leave-one-out) was conducted on onion and shallot extracts (n = 200) and their corresponding F–C, DPPH, TEAC and FRAP values were employed to obtain four independent calibration models for predicting TPC and TAC for the extracts. Spectra from an extra 19 independent extracts were used as an external validation set for prediction. A correlation of r > 0.95 was obtained between FT-IR predicted and reference values (by F–C, DPPH, TEAC and FRAP assay) with standard errors of calibration (SEC) and standard errors of cross-validation (SECV) less than 2.85, 0.35 and 0.45 μmol Trolox/g FW of extracts for TEAC, FRAP and DPPH assay, respectively; and 0.36 mg gallic acid/g FW of extracts for the F–C assay. In addition, cluster analysis (principal component analysis (PCA)) and discriminant function analysis (DFA) could differentiate varieties of onions and shallot based upon infrared spectral features. Loading plots for the various chemometrics models indicated that hydroxyl and phenolic functional groups were most closely correlated with antioxidant capacity. The use of mid-infrared spectroscopy to predict the total antioxidant capacity of vegetables provides a rapid and precise alternative to traditional wet chemistry analysis.     

近红外光谱技术结合支持向量机对食用醋品牌溯源的研究

研究近红外光谱技术对食用醋品牌进行快速无损溯源。收集市场上保宁、东湖、恒顺、镇江4个品种共152份具有代表性的食用醋样品,采集它们的近红外光谱数据,对原始光谱数据进行多元散射校正(multip,licative scatter corrertion,MSC)预处理,对预处理后的光谱数据利用主成分分析法(principal component analysis,PCA)进行聚类分析,根据主成分的累计贡献率选取主成分数,然后用支持向量机(support vector machine,SVM)建立预测模型,选取合适的SVM核函数,并利用粒子群优化算法(particle swarm optimization,PSO)优化模型参数。结果表明,近红外光谱技术结合支持向量机对食用醋品牌分类正确率可达100%。     

基于NIRS的食用醋品牌溯源模型的建立与优化

本文主要探讨了近红外光谱(NIRS)结合模式识别技术应用于食用醋品牌溯源研究。采集了四个品牌(四川保宁香醋、山西东湖老陈醋、镇江恒顺香醋、镇江香醋)共160组食醋样品的近红外漫反射光谱,通过主成分分析(PCA)进行光谱变量压缩及剔除8个异常样本数据后,随机选取其中的114组样品组成训练集用于建立溯源模型,剩余38组样品用作测试集进行模型验证。比较了MSC、SD、SNV等几种不同光谱预处理方法以及它们的不同组合对溯源模型的影响,同时考察了PLS-DA与SIMCA两种建模方法对模型的影响。结果表明:选择MSC与SD相结合的方法对光谱数据进行预处理,并采用SIMCA建模方法所建立的醋品牌溯源模型对四大品牌醋的正确识别率分别可达100%、100%、91.7%、90%。由此说明采用近红外光谱技术结合模式识别技术可有效实现食用醋品牌溯源的目的。     

Evaluation of monosodium glutamate,disodium inosinate and guanylate umami taste by an electronic tongue

An electronic tongue (e-Tongue) was used to evaluate umami taste and compare different flavor enhancers (concentration and manufacture). Monosodium glutamate and three kinds of disodium inosinate and guanylate (I + G) were analyzed. The obtained data were analyzed by principal component analysis (PCA). The first principal component represented the intensity of umami taste. Based on Euclidean distance between ‘I + G’ samples and MSG (0.5%) on the PCA score plot, the umami intensity of all samples was ranked. The ‘I + G’ samples produced by three manufactures were successfully discriminated on the second principal component with a high discrimination index (82.26). Besides, the drift phenomenon of sensor was discussed and finishing sensor analyses in a continuous time could help avoid the drift. This study showed that e-Tongue was useful in analyzing the umami taste and might be a complementary tool in evaluation of flavor enhancer.     

电子舌对食醋品质鉴定及区分的研究

醋是我国传统的调味品,醋中的风味是反映其质量的重要部分。为区分不同等级及不同品牌的食醋,测试了市售的同一品牌不同等级的保宁醋,及不同品牌的白醋、老陈醋和香醋对电子舌传感器的响应信号,并采用主成成分分析法(PCA)对响应信号进行分析。结果表明,电子舌传感器能明显的区别出同一品牌不同档次的保宁醋及不同类别的食醋。