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.     

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.     

Multivariate determination of free fatty acids and moisture in fish oils by partial least-squares regression and near-infrared spectroscopy

The oxidative and hydrolytic degradation of lipids in fish oil was monitored using partial least-squares (PLS) regression and near-infrared reflectance (NIR) spectroscopy. One hundred and sixty (n=160) fish oil samples from a fishmeal factory were scanned in transflectance by an NIR monochromator instrument (1100–2500 nm). Calibration models were performed for free fatty acids (FFA), moisture (M), peroxide value (PV) and anisidine value (AV). Coefficients of determination in calibration (R²) and standard errors of cross validation (SECV) were 0.96 (SECV: 0.59) and 0.94 (SECV: 0.03) for FFA and M in g/kg, respectively. The accuracy of the NIR calibration models were tested using a validation set, yielding coefficients of correlation (r) and standard errors of prediction (SEP) of 0.98 (SEP: 0.50) and 0.80 (SEP: 0.05) for FFA and M in g/kg, respectively. Poor accuracy (R²<0.80) was obtained for the NIR calibration models developed for PV and AV. The paper demonstrates that fish oil hydrolytic degradation of lipids, which seriously affect oil use and storage under industrial conditions, can be successfully monitored using PLS regression and NIR spectroscopy by the fishmeal industry.     

Quantification of glucose,fructose and sucrose in bayberry juice by NIR and PLS

Bayberry plays an important role in the nutrition and is a very important fruit-product. It has a high economic and officinal value. In this study, glucose, fructose and sucrose in bayberry juice were detected and quantified using near-infrared (NIR) spectroscopy. The HPLC method was assumed to provide the reference value of the analyte for calibration. Partial least-squares regression (PLSR) was used to construct calibration models with different pre-processing methods. The number of PLS factors was optimised. The results show PLS models are good for predicting glucose, fructose and sucrose concentrations in bayberry juice, and their prediction accuracy can be improved by using derivative process with the exception of the glucose. The best models were mostly given by the second derivative processed spectra, especially for sucrose with the determination coefficient, R² of 0.9931. This demonstrates the potential of NIR spectroscopy to quickly detect these components simultaneously in bayberry juice with the reference method of HPLC.     

Stiffness measurement of eggshell by acoustic resonance and PLS models

Non-destructive measurement of eggshell stiffness was carried out by means of acoustic resonance system. It was achieved by analysis of measured frequency response of eggshell excited with a light mechanical. Partial least squares (PLS), synergy interval PLS (si-PLS), genetic algorithm PLS (GA-PLS) and GA-siPLS algorithms were used comparatively to calibrate regression model. The performance of the final model was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. Experimental results showed that GA-PLS and GA-siPLS models got acceptable performance with fewer frequency variables. The optimal GA-PLS model was achieved with R = 0.771 and RMSEP = 3.6, and the optimal GA-siPLS model was achieved with R = 0.7591 and RMSEP = 3.55 in prediction set. It will help to build a compact and robust model serving for on-line measurement of egg’s stiffness.     

Feasibility study of quantifying and discriminating soybean oil adulteration in camellia oils by attenuated total reflectance MIR and fiber optic diffuse reflectance NIR

Camellia oil is often the target for adulteration or mislabeling in China because of it is a high priced product with high nutritional and medical values. In this study, the use of attenuated total reflectance infrared spectroscopy (MIR-ATR) and fiber optic diffuse reflectance near infrared spectroscopy (FODR-NIR) as rapid and cost-efficient classification and quantification techniques for the authentication of camellia oils have been preliminarily investigated. MIR spectra in the range of 4000–650 cm⁻¹ and NIR spectra in the range of 10,000–4000 cm⁻¹ were recorded for pure camellia oils and camellia oil samples adulterated with varying concentrations of soybean oil (5–25% adulterations in the weight of camellia oil). Identifications is successfully made base on the slightly difference in raw spectra in the MIR ranges of 1132–885 cm⁻¹ and NIR ranges of 6200–5400 cm⁻¹ between the pure camellia oil and those adulterated with soybean oil with soft independent modeling of class analogy (SIMCA) pattern recognition technique. Such differences reflect the compositional difference between the two oils with oleic acid being the main ingredient in camellia oil and linoleic acid in the soybean oil. Furthermore, a partial least squares (PLS) model was established to predict the concentration of the adulterant. Models constructed using first derivative by combination of standard normal variate (SNV), variance scaling (VS), mean centering (MC) and Norris derivative (ND) smoothing pretreatments yielded the best prediction results With MIR techniques. The R value for PLS model is 0.994.The root mean standard error of the calibration set (RMSEC) is 0.645, the root mean standard error of prediction set (RMSEP) and the root mean standard error of cross validation (RMSECV) are 0.667 and 0.85, respectively. While with NIR techniques, NIR data without derivative gave the best quantification results. The R value for NIR PLS model is 0.992. The RMSEC, RMSEP and RMSECV are 0.70, 1.78 and 1.79, respectively. Overall, either of the spectral method is easy to perform and expedient, avoiding problems associated with sample handling and pretreatment than the conventional technique.     

Simultaneous analysis of main catechins contents in green tea (Camellia sinensis (L.)) by Fourier transform near infrared reflectance (FT-NIR) spectroscopy

This paper reported the results of simultaneous analysis of main catechins (i.e., EGC, EC, EGCG and ECG) contents in green tea by the Fourier transform near infrared reflectance (FT-NIR) spectroscopy and the multivariate calibration. Partial least squares (PLS) algorithm was conducted on the calibration of regression model. The number of PLS factors and the spectral preprocessing methods were optimised simultaneously by cross-validation in the model calibration. The performance of the final model was evaluated according to root mean square error of cross-validation (RMSECV), root mean square error of prediction (RMSEP) and correlation coefficient (R). The correlations coefficients (R) in the prediction set were achieved as follows: R = 0.9852 for EGC model, R = 0.9596 for EC model, R = 0.9760 for EGCG model and R = 0.9763 for ECG model. This work demonstrated that NIR spectroscopy with PLS algorithm could be used to analyse main catechins contents in green tea.     

Variable selection in visible and near-infrared spectra: Application to on-line determination of sugar content in pears

Informative variable (or wavelength) selection plays an important role in quantitative analysis by visible and near-infrared (Vis-NIR) spectroscopy. Four different variable selection methods, namely, stepwise multiple linear regression (SMLR), genetic algorithm-partial least squares regression (GA-PLS), interval PLS (iPLS), and successive projection algorithm-multiple linear regression combined with GA (GA-SPA-MLR), were studied to determine the sugar content of pears. The results derived by these techniques were then compared. The calibration model built using GA-SPA-MLR on 18 selected wavelengths (2% of the total number of variables) exhibited higher coefficient of determination (R²) = 0.880 and root mean square error of prediction (RMSEP) = 0.459°Brix for the validation set. Results show that the accuracy of the quantitative analysis conducted by Vis-NIR spectroscopy can be improved through appropriate wavelength selection. Despite the RMSEP value of GA-SPA-MLR was a slightly higher than that of GA-PLS, considering that this model was simpler and easier to interpret, GA-SPA-MLR can be used for industrial applications.     

近红外结合Si-ELM检测食醋品质指标

为了提高近红外光谱技术检测食醋中可溶性无盐固形物含量(SSFSC)的精度和稳定性,提出采用联合区间偏最小二乘(Si-PLS)筛选光谱特征区间,再利用极限学习机(ELM)算法建立非线性回归模型,并对该方法的优越性进行系统比较;试验通过交互验证优化模型相关参数,以预测时的相关系数(Rp)和预测均方根误差(RMSEP)作为模型的评价指标。结果表明,Si-PLS结合ELM算法(Si-ELM)所建模型最佳,预测结果:Rp=0.973 9,RMSEP=1.232g/100mL。说明利用近红外光谱技术可以快速准确检测食醋中的SSF-SC,Si-ELM的应用可以适当提高该预测模型的精度。     

Application of near infrared spectroscopy for the rapid determination of antioxidant activity of bamboo leaf extract

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 (r_pre) 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.     

Quantification of total polysaccharides and triterpenoids in Ganoderma lucidum and Ganoderma atrum by near infrared spectroscopy and chemometrics

Two chemometrics, the partial least-squares (PLS) and radial basis function (RBF) network were performed to develop a quantification method for total polysaccharides and triterpenoids in Ganoderma lucidum and Ganoderma atrum from different origins based on near infrared reflectance spectroscopy (NIR). The influences of spectral window and spectral pre-treatments were initially studied in the construction of PLS model. The best result was obtained when the standard normal transformation (SNV) +1st derivative spectrum over 4100–7750 cm⁻¹ was used for the modelling. Then based on each principle, both of the two models were optimised respectively. The final results with high determination coefficient (R²) (higher than 0.973, 0.989 for PLS and RBF, respectively) and low root mean square errors of prediction (RMSEP) (low to 0.1109 and 0.01298 for polysaccharides and triterpenoids, respectively) confirm the good predictability of the two models. The overall results show that NIR spectroscopy combined with chemometrics can be efficiently utilised for accurate analysis of routine chemical compositions in G. lucidum and G. atrum.     

Feasibility study on the use of Fourier transform near-infrared spectroscopy together with chemometrics to discriminate and quantify adulteration in cocoa beans

Fourier transform near-infrared (FT-NIR) spectroscopy combined with Support Vector Machine (SVM) and synergy interval partial least square (Si-PLS) was attempted in this study for cocoa bean authentication. SVM was used to develop an identification model to discriminate between fermented cocoa beans (FC), unfermented cocoa beans (UFC) and adulterated cocoa bean (5–40 wt/wt.% content of UFC). Si-PLS model was used to quantify the addition of UFC in FC. SVM model accurately discriminated the cocoa bean samples used. After cross-validation, the optimal identification rate was 100% in both the training set and prediction set at three principal components. For quantitative analysis, Si-PLS model was evaluated according to root mean square error of prediction (RMSEP) and coefficient of correlation in prediction (R_pred). The results revealed that Si-PLS model in this work was promising. The optimal performance of Si-PLS model showed an excellent predictive potential, RMSEP = 1.68 and R_pred = 0.98 in the prediction set. The overall results indicated that FT-NIR spectroscopy together with an appropriate multivariate algorithm could be employed for rapid identification of fermented and unfermented cocoa beans as well as the quantification of UFC down to 5% in FC for quality control management.     

Quality Evaluation of Shelled and Unshelled Macadamia Nuts by Means of Near‐Infrared Spectroscopy (NIR)

The quality of shelled and unshelled macadamia nuts was assessed by means of Fourier transformed near‐infrared (FT‐NIR) spectroscopy. Shelled macadamia nuts were sorted as sound nuts; nuts infected by Ecdytolopha aurantiana and Leucopteara coffeella; and cracked nuts caused by germination. Unshelled nuts were sorted as intact nuts (<10% half nuts, 2014); half nuts (March, 2013; November, 2013); and crushed nuts (2014). Peroxide value (PV) and acidity index (AI) were determined according to AOAC. PCA‐LDA shelled macadamia nuts classification resulted in 93.2% accurate classification. PLS PV prediction model resulted in a square error of prediction (SEP) of 3.45 meq/kg, and a prediction coefficient determination value (R_p²) of 0.72. The AI PLS prediction model was better (SEP = 0.14%, R_p² = 0.80). Although adequate classification was possible (93.2%), shelled nuts must not contain live insects, therefore the classification accuracy was not satisfactory. FT‐NIR spectroscopy can be successfully used to predict PV and AI in unshelled macadamia nuts, though.     

Determination of Antioxidant Activity and Polyphenols Content in Chips by Raman and IR Spectroscopy

The application of vibrational spectroscopy for the determination of total polyphenols content, antioxidant activity, colour parameters, and fat level in chips originated from yellow-, red- and purple-fleshed potato varieties is reported. Raman, infrared (IR) and near-infrared (NIR) spectra of the laboratory-prepared chips were collected. Combining spectral data with the results of reference analyses, partial least squares regression models were built. To characterise and compare the elaborated models, the relative standard errors of prediction were calculated for calibration and validation sets. In the case of total phenolics quantification by Raman/IR/NIR techniques, these errors (%) amounted to 4.0/7.0/7.1 and 6.4/8.5/8.4 for calibration and validation samples, respectively, whereas they were 4.9/7.7/4.8 and 6.6/8.3/6.8 for antioxidant activity. The obtained results demonstrate that both infrared and Raman spectroscopy can effectively replace commonly used extraction methods. It follows that Raman spectroscopy has the highest potential to be adopted for the online potato-derived product analysis.     

Quantification of soluble solids in reconstituted açaí (Euterpe oleracea Mart.) pulp using near‐infrared spectroscopy

Açaí consumption is increasing worldwide because of the growing recognition of its nutritional and therapeutic properties. This product is classified based on its soluble solids content (SS), but the determination of SS in pulp is time consuming, tedious and not suitable for modern food processing plants. As near‐infrared (NIR) systems have been implemented to measure various quality attributes of food products, the objective of this study was to evaluate the feasibility of NIR diffuse reflectance spectroscopy to quantify the SS content of açaí pulp. Partial least squares (PLS) regression models were constructed to predict the SS. An optimum PLS model required one latent variable [principal component (PC)1 = 97%] with a root‐mean‐square error of calibration (RMSEC) of 1.06% for the calibration data set and the root‐mean‐square error of prediction (RMSEP) of 1.03% for internal cross‐validation. External validation using an independent data set showed good performance (RMSEP = 1.33% and Rp² = 0.82). NIR spectroscopy is a reliable method with which to determine SS in açaí pulp and thereby to classify açaí pulp according to established minimum quality standards.     

Preliminary study on the use of near‐infrared reflectance spectroscopy to assess nitrogen content of undried wheat plants

Near‐infrared reflectance (NIR) spectroscopy combined with chemometrics was used to assess nitrogen (N) and dry matter content (DM) and chlorophyll in whole‐wheat plant (Triticum aestivum L). Whole‐wheat plant samples (n = 245) were analysed by reference method and by visible and NIR spectroscopy, in fresh (n = 182) and dry (n = 63) presentations, respectively. Calibration equations were developed using partial least squares (PLS) and validated using full cross‐validation (leave‐one‐out method). Coefficient of determination in calibration (R²_CAL) and the standard error of cross‐validation (SECV) for N content in fresh sample presentation, after second derivative, were 0.89 (SECV: 0.64%), 0.86 (SECV: 0.66%) and 0.82 (SECV: 0.74%) using the visible + NIR, NIR and visible wavelength regions, respectively. Dry sample presentation gave better R²_CAL and SECV for N compared with fresh presentation (R²_CAL > 0.90, SECV < 0.20%) using visible + NIR. The results demonstrated that NIR is a suitable method to assess N concentration in wheat plant using fresh samples (unground and undried). Copyright © 2006 Society of Chemical Industry     

A rapid spectroscopic method for quantification of P57 in Hoodia gordonii raw material

The feasibility of quantifying the perceived active ingredient (P57) in Hoodia gordonii raw material using Fourier transform near- and mid-infrared spectroscopy combined with chemometric techniques was investigated. The concentration of P57 (a triterpene glycoside) was determined in 146 plant samples with liquid chromatography coupled to mass spectrometry and these values were used to develop a calibration model based on the partial least squares projections to latent structures (PLS) and orthogonal projections to latent structures (O-PLS) regression algorithms. The performance of each calibration model was evaluated according to the root mean square error of prediction (RMSEP) and correlation coefficient (R²). The PLS model with 2nd derivative pre-processing predicted P57 content based on the FT-NIR spectra with the best accuracy and a correlation coefficient (R²) value of 0.9629 and the lowest RMSEP of 0.03%. These results demonstrated that FT-NIR spectroscopy can be used to rapidly quantify P57 in H. gordonii raw material with high accuracy.     

Simultaneous Spectrophotometric Determination of 2-Thiouracil and 2-Mercaptobenzimidazole in Animal Tissue Using Multivariate Calibration Methods: Concerns and Rapid Methods for Detection

ABSTRACT: Two multivariate calibration methods, partial least squares (PLS) and principal component regression (PCR), were applied to the spectrophotometric simultaneous determination of 2-mercaptobenzimidazole (MB) and 2-thiouracil (TU). A genetic algorithm (GA) using partial least squares was successfully utilized as a variable selection method. The concentration model was based on the absorption spectra in the range of 200 to 350 nm for 25 different mixtures of MB and TU. The calibration curve was linear across the concentration range of 1 to 10 μg mL⁻¹ and 1.5 to 15 μg mL⁻¹ for MB and TU, respectively. The values of the root mean squares error of prediction (RMSEP) were 0.3984, 0.1066, and 0.0713 for MB and 0.2010, 0.1667, and 0.1115 for TU, which were obtained using PCR, PLS, and GA-PLS, respectively. Finally, the practical applicability of the GA-PLS method was effectively evaluated by the concurrent detection of both analytes in animal tissues. It should also be mentioned that the proposed method is a simple and rapid way that requires no preliminary separation steps and can be used easily for the analysis of these compounds, especially in quality control laboratories.     

温度对黄酒酒精度和糖度近红外分析模型的影响

为研究温度对黄酒品质近红外光谱分析模型的影响,分别在5、10、15、20、25、30、35 ℃ 7 个温度条件下采集黄酒样品的可见-近红外光谱,采用偏最小二乘法建立各温度下黄酒酒精度和总糖含量定量分析模型。结果表明：温度对样品光谱存在影响,主成分分析表明不同温度下的样品有明显聚类趋势。模型精度受温度影响较大,但并无随温度变化的一致趋势。建立的混合温度模型预测相对误差较小,有实际应用潜力。     

Discrimination and classification of adulterants in maple syrup with the use of infrared spectroscopic techniques

Food adulteration is a profit‐making business for some unscrupulous manufacturers. Maple syrup is a soft target of adulterators owing to its simplicity of chemical composition. In this study the use of Fourier transform infrared (FTIR) spectroscopy and near‐infrared (NIR) spectroscopy to detect adulterants such as cane and beet invert syrups as well as cane and beet sugar solutions in maple syrup was investigated. The FTIR spectrum of adulterated samples was characterised and the regions 800–1200 cm^?1 (carbohydrates) and 1200–1800 and 2800–3200 cm^?1 (carbohydrates, carboxylic acids and amino acids) were used for detection. The region between 1100 and 1660 nm in the NIR spectrum was used for analysis. Linear discriminant analysis (LDA) and canonical variate analysis (CVA) were used for discriminant analysis, while partial least squares (PLS) and principal component regression (PCR) were used for quantitative analysis. FTIR was more accurate in predicting adulteration using two different regions (R² > 0.93 and >0.98) compared with NIR (R² > 0.93). Classification and quantification of adulterants in maple syrup show that NIR and FTIR can be used for detecting adulterants such as pure beet and cane sugar solutions, but FTIR was superior to NIR in detecting invert syrups. © 2003 Society of Chemical Industry