Non-destructive measurement of acidity of Chinese bayberry using Vis/NIRS techniques

Chinese bayberry (Myrica rubra Siebold and Zuccarini) is cultivated in southeast China for its edible fruits. In this research, the potential of using the visible/near infrared spectroscopy (Vis/NIRS) was investigated for measuring the acidity of Chinese bayberry, and the relationship was established between non-destructive Vis/NIRS measurement and the acidity of Chinese bayberry. Intact Chinese bayberry fruit was measured by reflectance Vis/NIR in 325–1075 nm range. The data set as the logarithms of the reflectance reciprocal (absorbance (log 1/R)) was analyzed in order to build the best prediction model for this characteristic, using several spectral pretreatments and multivariate calibration techniques such as partial least square regression (PLS). The model for prediction the acidity (r=0.963), standard error of prediction (SEP) 0.21 with a bias of 0.138; showed an excellent prediction performance. The Vis/NIRS technique has significantly greater accuracy for determining the acidity. This non-destructive, fast and accuracy technology can be used in food industry that would be beneficial to human health.     

Rapid Assessment of Tomato Ripeness Using Visible/Near-Infrared Spectroscopy and Machine Vision

A non-destructive assessment using visible/near-infrared spectroscopy and machine vision has been investigated for measuring tomato ripeness. Relationship between spectral wavelengths and green grayscale value was evaluated by application of chemometrics techniques based on partial least squares (PLS) regression. The tomatoes were divided randomly into two groups: 170 fruits for calibration and 71 for prediction. An accurate estimation, measured with a correlation coefficient of 0.992 and root mean square errors of prediction (RMSEP) of 9.92, was obtained when using the developed PLS model built with 550–750 nm spectral range. The accuracies of calibration and validation models based on data measured in this band were 90.93 and 90.05%. The prediction accuracy for 150 external independent samples was 90.67%. The results show that it is possible to realize detection standardization of tomato maturity based on only visible spectroscopy (550–750 nm) and machine vision technologies. This detection method does not depend on a visual grading or other maturity indices as a reference. It highlights the potential of the method to determine tomato ripeness and the optimum harvest time.     

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。     

基于NIR高光谱技术快速预测冷鲜鸡肉热杀索丝菌含量

基于NIR高光谱成像技术快速评估鸡肉热杀索丝菌含量。通过采集新鲜鸡肉高光谱图像并提取样本反射光谱信息(900~1699 nm),再采用多元散射校正(Multiplicative Scatter Correction,MSC)、基线校正(Baseline Correction,BC)和标准正态变量校正(Standard Normal Variable Correction,SNV)三种方法预处理原始光谱,分别利用偏最小二乘(Partial Least Squares,PLS)、多元线性回归(Multiple Linear Regression,MLR)挖掘光谱信息与鸡肉热杀索丝菌参考值之间的定量关系。同时采用PLS-β系数法、Stepwise算法和连续投影算法(Successive Projections Algorithm,SPA)筛选最优波长简化全波段模型(F-PLS)提高预测效率。结果显示,经BC预处理的全波段光谱(485个波长)构建的F-PLS模型预测热杀索丝菌效果较好,相关系数R_P为0.973,误差RMSEP为0.295 lg CFU/g。基于PLS-β法从BC预处理光谱中筛选出25个最优波长构建的PLS-β-PLS(R_P=0.931,RMSEP=0.434 lg CFU/g)模型预测较好。本试验表明,利用近红外高光谱成像技术可潜在实现鸡肉热杀索丝菌含量的快速评估。     

Quantification of Nitrogen Status in Rice by Least Squares Support Vector Machines and Reflectance Spectroscopy

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.     

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.     

Spectral shift as advanced index for fruit chlorophyll breakdown

The decline of fruit chlorophyll is a valuable indicator of fruit ripeness. Fruit chlorophyll content can be nondestructively estimated by UV/VIS spectroscopy at fixed wavelengths. However, this approach cannot explain the complex changes in chlorophyll catabolism during fruit ripening. We introduce the apparent peak position of the red band chlorophyll absorption as a new qualitative spectral indicator. Climacteric fruit (apple: n?=?24, mango: n?=?38, tomato: n?=?48) were analysed at different ripeness stages. The peak position and corresponding intensity values were determined between 650 and 690 nm of nondestructively measured fruit spectra as well as of corresponding spectra of fruit extracts. In the extracts, individual contents of chlorophyll a, chlorophyll b, pheophytin a and carotenoids were analysed photometrically, using an established iterative multiple linear regression approach. Nondestructively measured peak positions shifted unimodal in all three fruit species with significant shifts between fruit ripeness classes of maximal 2.00?±?0.27 nm (mean ± standard error) in tomato and 0.57?±?0.11 nm in apple. Peak positions in extract spectra were related to varying pigment ratios (R _max?=??0.91), considering individual pigments in the pool. The peak intensities in both spectral readings, nondestructive and fruit extracts, were correlated with absolute chlorophyll contents with R _max?=??0.84 and R _max?=?1.00, respectively. The introduced spectral marker of the apparent peak position of chlorophyll absorbance bears the potential for an advanced information gain from nondestructive spectra for the determination of fruit ripeness.     

Optical path length and wavelength selection using Vis/NIR spectroscopy for olive oil's free acidity determination

Four different optical path lengths, namely 0.5, 1, 5 and 10 mm, were assayed for olive oil's free acidity determination using Vis/NIR spectroscopy. Results illustrated that the use of higher path length during spectra acquisition resulted in more accurate PLS models, especially when using solely the NIR region. The PLS model obtained with the NIR spectrum using the 10‐mm cuvette was subjected to optimisation by Monte Carlo uninformative variable elimination (MCUVE) and successive projections algorithm (SPA). Both methods drastically reduced the number of spectral variables and markedly improved the performance of the PLS model, especially the SPA‐PLS model, which achieved a SEP (0.051) quite close to SEL (0.048). Interestingly, only twelve of the eighty spectral variables selected by SPA were among the 314 variables provided by MCUVE. All in all, NIRS incorporated to MCUVE‐PLS or SPA‐PLS may be applied as an alternative method for the rapid determination of olive oil's free acidity.     

近红外高光谱联用Stepwise算法快速无接触评估冷鲜鸡肉色泽及嫩度

利用900~1700 nm近红外高光谱成像系统联用Stepwise算法快速评估鸡肉色泽和嫩度。通过采集新鲜屠宰鸡肉高光谱图像,提取试验样本感兴趣区域(Region of interests,ROI)反射光谱信息,经中值滤波平滑(Median filtering smoothing,MFS)、多元散射校正(Multiplicative scatter correction,MSC)和标准正态变量变换(Standard normal variable correction,SNV)三种预处理后,分别利用偏最小二乘(Partial Least Squares,PLS)和多元线性回归(Multiple linear regression,MLR)挖掘光谱信息与鸡肉色泽参数(L*、a*、b*)及嫩度参考值之间的定量关系。结果显示,经MFS预处理的近红外光谱(486个波长)构建的全波段PLS回归模型(F-PLS)预测L*(R_P=0.904,RMSEP=2.036)、b*(R_P=0.908,RMSEP=1.577)和嫩度(R_P=0.948,RMSEP=1.596)效果更好。为提高预测效率,采用Stepwise算法筛选最优波长优化F-PLS模型,结果显示,从SNV预处理光谱筛选的14个最优波长构建MLR回归模型预测L*值(R_P=0.894,RMSEP=2.160)效果较优,从SNV预处理光谱筛选的13最优波长构建的O-PLS回归模型预测b*值(R_P=0.877,RMSEP=1.811)效果较优,从MFS预处理光谱筛选的20个最优波长构建O-PLS回归模型预测嫩度值(R_P=0.888,RMSEP=2.408 N)效果较优。本试验表明,利用近红外高光谱成像技术结合Stepwise算法可实现鸡肉色泽参数L*、b*值以及嫩度的快速评估。     

Evaluation of Least Squares Support Vector Machine Regression and other Multivariate Calibrations in Determination of Internal Attributes of Tea Beverages

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.     

Multivariate data analysis applied to spectroscopy: Potential application to juice and fruit quality

The goal of building a multivariate calibration model is to predict a chemical or physical property from a set of predictor variables, for example the analysis of sugar concentration in fruits using near infrared (NIR) spectroscopy. Effective multivariate calibration models combined with a rapid analytical method should be able to replace laborious and costly reference methods. The quality of a calibration model primarily depends on its predictive ability. In order to build, interpret and apply NIR calibrations not only the quality of spectral data but also other properties such as effect of reference method, sample selection and interpretation of the model coefficients are also important. The objective of this short review is to highlight the different steps, methods and issues to consider when calibrations based on NIR spectra are developed for the measurement of chemical parameters in both fruits and fruit juices. The same principles described in this paper can be applied to other rapid methods like electronic noses, electronic tongues, and fluorescence spectroscopy.     

Moisture content detection of maize seed based on visible/near-infrared and near-infrared hyperspectral imaging technology

To realise accurate and nondestructive detection on moisture content of maize seed based on visible/near-infrared (Vis/NIR) and near-infrared (NIR) hyperspectral imaging technology, the hyperspectral images on two sides (embryo and endosperm sides) of each maize seed of four varieties were collected. The effects of average spectra extraction regions, that is centroid region and whole seed region, and different spectral preprocessing methods, were investigated. Uninformative variable elimination (UVE) was used to extract the feature wavelengths, and the partial least squares regression (PLSR) prediction models were established. The results showed that extracting the average spectra from the centroid region did better than from the whole seed region, and S-G smoothing was prior to other preprocessing methods. The PLSR models established with NIR spectra had better performance than that with Vis/NIR spectra. The model developed for a single variety was superior to that for all varieties together.     

傅立叶近红外光谱法测定桃果中果胶含量

目的 基于傅里叶近红外光谱(Fourier transform near infrared)检测桃果中果胶含量的研究。方法 近红外光谱采集样品利用两个品种的桃,探究光谱预处理对建模的影响,建模采用偏最小二乘法(PLS)以及主成分回归(PCR)方法,模型的评价标准采用建模相关系数(R_C)、建模均方偏差(RMSE_C)、预测相关系数(R_P)、预测均方偏差(RMSE_P)。结果 两个品种的近红外光谱图和果胶含量无明显差异(P＞0.05),采用标准正态变量变换(SNV)和多元散射校正(MSC)对原始光谱的光程进行选择,所得建模结果影响基本一致,合适光谱数据格式以及平滑处理,能提高PLS和PCR模型的预测精度和稳定性。综合得出模型最佳是利用PLS方法建模并采用MSC/SNV结合一阶导数和 Savitzky-Golay (S-G)平滑对近红外光谱图进行预处理,评价参数分别为R_C=0.7795、R_P=0.7545、RMSE_C=0.0933、RMSE_P=0.0534和R_C=0.7800、R_P=0.7530、RMSE_C=0.0932、RMSE_P=0.0534。结论 该方法为利用近红外建模快速检测桃果中果胶含量提供重要依据。     

Grape and wine analysis - enhancing the power of spectroscopy with chemometrics.

Spectroscopic techniques offer the potential to simplify and reduce analytical times for a range of grape and wine analytes. It is this aspect, together with the ability to simultaneously measure several analytes, which was the impetus for developing spectroscopic methods. The Australian Wine Research Institute (AWRI) has long used spectroscopic analysis of wines in the ultraviolet (UV) and visible (Vis) wavelengths, and since 1998 has been investigating applications of spectroscopic techniques in the near infrared (NIR) and mid-infrared (MIR) wavelength regions of the electromagnetic spectrum for the rapid analysis and quality control of both grapes and wine by the Australian wine industry. This paper reviews the use of several spectroscopic techniques, including NIR, MIR, and Vis, combined with chemometrics, to assess grape and wine composition in the Australian wine industry. The achievements, current research, and proposed further applications of different spectroscopic techniques are discussed in studies into the assessments of red grape composition and of fungal diseases in grapes, monitoring phenolic compounds during red wine fermentations, quality grading of red, white and fortified wine styles, monitoring wine distillation processes, and yeast strain classification.     

Two-dimensional visible/near-infrared correlation spectroscopy study of thawing behavior of frozen chicken meats without exposure to air

The thawing behavior of frozen chicken meats without exposure to air was investigated by generalized 2D Vis/NIR correlation spectroscopy. The synchronous 2D visible correlation analysis revealed that intensities of the 435 and 555 nm bands increase, because of the relaxation of DeoxyMb and OxyMb components, whereas those of the 475 and 620 nm bands decrease as MetMb and SulfMb decompose into small molecules due to specific enzymes. The corresponding asynchronous spectra indicated that the decomposition of MetMb and SulfMb species precedes the recovery of DeoxyMb and OxyMb, and that the DeoxyMb species recovers faster than the OxyMb. Further, the asynchronous 2D NIR spectra suggested that the melting of ice crystals and the relaxation and proteolysis of proteins occurs earlier, indicating a coordination process for hydrophilic O-H and N-H groups. Moreover, strong correlation peaks correlating the bands in the visible and NIR spectral regions were observed and discussed.     

Potential of visible and near infrared spectroscopy to quantify phenol formaldehyde resin content in oriented strandboard

In this note, a preliminary test of the ability of visible and near infrared spectroscopy (NIR) coupled with multivariate statistical methods is described to predict the phenol-formaldehyde (PF) adhesive content in oriented strandboard (OSB) post manufacture. A visible-NIR-based PLS model could predict moderately well (RMSEP0=.60%) the PF loading in the face layers of pine OSB after pressing. The spectral absorbance at 614 nm was significantly different between PF loadings. NIR may provide the basis for an on-line, real-time quality control tool providing feedback information to the press.     

Rapid and non-destructive analysis of apricot fruit quality using FT-near-infrared spectroscopy

A non-destructive optical method based on near-infrared spectroscopy has been used for the evaluation of apricot fruit quality. Diffuse reflectance measurements (800–2500 nm), physical, physiological and biochemical measurements were performed individually on 877 apricot fruits from eight contrasted cultivars harvested at different ripening stages. Relationships between spectral wavelengths and quality attributes were evaluated by application of chemometric techniques based on partial least squares (PLS) on fruit set divided randomly into two groups: 598 fruits for calibration and 279 for validation. Good prediction performance was obtained for soluble solids and titratable acidity with correlation coefficients of 0.92 and 0.89 respectively and root mean square errors of prediction of 0.98% Brix and 3.62 meq 100 g⁻¹ FW respectively. For the other quality traits such as firmness, ethylene, individual sugars and organic acids, the prediction models were not satisfactorily accurate due to the high error of calibration and prediction.     

Technical note: Feasibility of near infrared transmittance spectroscopy to predict cheese ripeness

The aim of the study was to evaluate the feasibility of near infrared (NIR) transmittance spectroscopy to predict cheese ripeness using the ratio of water-soluble nitrogen (WSN) to total nitrogen (TN) as an index of cheese maturity (WSN/TN). Fifty-two Protected Designation of Origin cow milk cheeses of 5 varieties (Asiago, Grana Padano, Montasio, Parmigiano Reggiano, and Piave) and different ripening times were available for laboratory and chemometric analyses. Reference measures of WSN and TN were matched with cheese spectral information obtained from ground samples by a NIR instrument that operated in transmittance mode for wavelengths from 850 to 1,050 nm. Prediction equations for WSN and TN were developed using (1) cross-validation on the whole data set and (2) external validation on a subset of the entire data. The WSN/TN was calculated as ratio of predicted WSN to predicted TN in cross-validation. The coefficients of determination for WSN and TN were >0.85 both in cross- and external validation. The high accuracy of the prediction equations for WSN and TN could facilitate implementation of NIR transmittance spectroscopy in the dairy industry to objectively, rapidly, and accurately monitor the ripeness of cheese through WSN/TN.     

Application of Curve Fitting and Wavelength Selection Methods for Determination of Chlorogenic Acid Concentration in Coffee Aqueous Solution by Vis/NIR Spectroscopy

Coffee is considered as a functional food due to its being rich in bioactive compounds, mainly chlorogenic acid (CGA). CGA concentration in coffee aqueous solution was investigated based on visible/near-infrared (Vis/NIR) spectroscopy in this research. To enhance the spectral difference among different samples and increase the signal to noise ratio, Lorentz function curve fitting was applied to fit raw Vis/NIR spectra of samples. Then, the fitting parameters were used to correct raw full spectra. Partial least squares (PLS) regression method was used to develop calibration models of CGA concentration. Full-spectrum models were built with raw and fitting parameter-corrected spectra, respectively. Further, wavelength selection methods, such as genetic algorithms (GAs) and success projection algorithms (SPAs), were applied to eliminate redundancy information and identify relevant information from full spectra. Calibration models based on the effective wavelengths selected by GA and SPA methods were developed. The overall results showed that LFPs a/b-corrected spectra had a better performance compared with other processing methods. Performance of the selected wavelength model was better than that of the full-spectrum model. Final results indicated that the SPAs-PLS method provided a more precise prediction model of CGA concentration with R _c of 0.913 and R _cv of 0.795.     

Application of successive projections algorithm for variable selection to determine organic acids of plum vinegar

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.